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lluang/pydataPittsburgh_GenAI_stats20250730.ipynb

Created July 30, 2025 00:09
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PyData Pittsburgh: Does Generative AI know Statistics, July 30, 2025
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pydataPittsburgh_GenAI_stats20250730.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![PyData Pittsburgh](https://substackcdn.com/image/fetch/$s_!lOfa!,w_80,h_80,c_fill,f_webp,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F08902074-6df9-4199-845e-99824ec14b15_929x929.png)\n",
"# PyData Pittsburgh: Does Generative AI know statistics\n",
"July 30, 2025\n",
"Louis Luangkesorn\n",
"\n",
"This notebook will be posted as a Gist after the talk and it will be posted in the comments of the PyData Pittsburgh announcement in Meetup and LinkedIn.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Goals\n",
"\n",
"1. Through demonstrations, give intuition on how Generative AI works\n",
"2. Discuss what Generative AI lacks\n",
"3. Provide a demonstration using an analytics problem, and show some capabilities and limitations\n",
"4. Demonstrate Generative AI as a communicator in technical topics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load libraries\n",
"import google.generativeai as genai\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a Google API key from https://aistudio.google.com/app/apikey\n",
"\n",
"After \n",
"\n",
"`import google.generativeai as genai`\n",
"\n",
"The correct method is to export the Google Gemini API key to an environmental variable before initializing the model. \n",
"I use GEMINI_KEY because the variable API_KEY that is suggested gets used in many application documentation. If you have exported `GEMINI_KEY` in your environment, you can run in Python/Jupyter:\n",
"\n",
"`genai.configure(api_key=os.environ[\"GEMINI_KEY\"])`\n",
"\n",
"Or, you can define your API key directly from the Python prompt (e.g. Jupyter) (this is what I did before starting this demo)\n",
"\n",
"`genai.configure(api_key='YOUR_API_KEY_HERE')`"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"genai.configure(api_key=os.environ[\"GOOGLE_API_KEY\"])\n",
"\n",
"model = genai.GenerativeModel(\"gemini-2.0-flash-lite\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ask Gen AI to write a story. And repeat the request and see how it changes.\n",
"Note that `model.generate_content()` is a one shot prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The old, worn leather backpack sat in the dusty corner of the attic, its straps faded, its brass buckles tarnished with age. Ten-year-old Leo, exploring the forbidden territory of his grandmother's past, stumbled upon it. He knew, instinctively, that it was special. He could feel a faint hum, a tingle in his fingertips as he brushed against its aged surface.\n",
"\n",
"He unclasped the buckles, expecting cobwebs and moth-eaten fabrics. Instead, he found the inside surprisingly clean and spacious. And... empty. Disappointment flickered, then curiosity. He slung it over his shoulder, feeling the weight settle comfortably on his back.\n",
"\n",
"The next day, armed with a half-eaten apple and a sense of adventure, Leo headed towards the Whispering Woods behind his house. He was planning a grand den-building project. Reaching for a sturdy branch, he wished he had a saw.\n",
"\n",
"Suddenly, a gleaming, perfectly-sized saw appeared in the backpack, as if conjured from thin air. Leo blinked, then grinned. The backpack was magic!\n",
"\n",
"Over the next few weeks, Leo discovered the extent of the backpack’s abilities. He wished for a rope, and a sturdy climbing rope materialized. He needed food for his den-building crew (himself), and a picnic basket overflowing with sandwiches, juice boxes, and cookies popped into existence. He could wish for anything he needed, anything he desired.\n",
"\n",
"He used the magic for good, of course. He wished for a bandage and antiseptic when he scraped his knee. He conjured a perfect magnifying glass to study an injured butterfly. He even wished for a perfectly ripe strawberry for his grandmother, who was recovering from a cold.\n",
"\n",
"But the temptation to misuse the magic was always there. One day, bored and feeling neglected, he wished for a mountain of the latest video game consoles, filling his room with glowing screens and electronic beeps. He became obsessed, lost in the digital world, forgetting about his friends, his grandmother, and the joy of the Whispering Woods.\n",
"\n",
"The backpack, sensing the shift in Leo’s heart, seemed to grow heavier, the previously comforting weight now oppressive. The vibrant colors of the game screens became garish, the electronic sounds grating. He realized he was miserable.\n",
"\n",
"One afternoon, his grandmother, noticing his prolonged absence, climbed the stairs, her footsteps slow and hesitant. She knocked on his door, but there was no answer. Worried, she peeked inside and saw the mountain of electronics.\n",
"\n",
"Leo, slumped in a chair, looked up at her, his face pale, his eyes hollow. \"It's all so boring, Grandma,\" he mumbled.\n",
"\n",
"She walked over and sat beside him. \"Leo,\" she said gently, \"Magic, true magic, isn't about having everything. It's about using your gifts to help others, to appreciate what you have.\"\n",
"\n",
"He looked at the backpack, now lying on the floor. The magic had become a burden. He thought of the abandoned den, the injured butterfly, the silent Whispering Woods. He knew what he had to do.\n",
"\n",
"He reached for the backpack and wished for all the electronics to vanish. They disappeared instantly. He then wished for something more valuable: a day of sunshine, a picnic basket for himself and his grandmother, and a renewed appreciation for the simple things.\n",
"\n",
"He took his grandmother’s hand, and together they walked to the Whispering Woods. He helped her onto a fallen log, the sunlight dappling through the leaves. He felt the familiar lightness on his back, the comforting weight of the magic backpack, a weight he now understood to be a responsibility, a privilege, not a source of selfish desires. He knew the magic was still there, waiting to be used wisely, for good, for the joy of helping others, and for the enduring magic of a loving heart. The dusty old backpack, once just an object of curiosity, was now his most treasured possession, a reminder of the true meaning of magic.\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = model.generate_content(\"Write a story about a magic backpack.\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's repeat the prompt again. Note that `model.generate_content()` is a one shot prompt so it has no carryover from last time."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Flora wasn't particularly special. She wasn't a prodigy at anything, her hair frizzed in the humidity, and she tripped over her own feet more often than she'd like to admit. But she *did* have a backpack. Not just any backpack, mind you. It was old, leather, and smelled faintly of cinnamon and… well, magic, if you could bottle that scent.\n",
"\n",
"She’d found it tucked away in her grandmother's attic, dusty and forgotten. The clasp was tarnished, but when she brushed her fingers against it, a warmth bloomed, and a barely-there whisper echoed in her ear, \"Adventure awaits.\"\n",
"\n",
"Flora, being a practical girl, initially dismissed it. But the whisper intrigued her. So, on her first day back at school, she decided to try it out.\n",
"\n",
"She packed her usual: a lunchbox, a book, and a pencil case. But as she reached for her usual, the backpack hummed. Suddenly, a perfectly ripe apple appeared in her hand, replacing the bruised one she'd packed. \"Useful,\" she thought. She tried again. Instead of her history textbook, a miniature, working model of a Roman chariot materialized. The other kids gawked. Flora, however, was already plotting.\n",
"\n",
"The backpack’s magic was unpredictable. Sometimes it produced exactly what she needed. Other times, she got something completely unexpected. For a particularly difficult algebra test, she got a quill and ink that wrote the answers. For her soccer practice, she got a pair of cleats that could run faster than the wind. The chariot, she discovered, was surprisingly good for navigating the crowded school hallways.\n",
"\n",
"But with the magic came complications. The apple was delicious, but it only lasted a few hours. The chariot was fun, but it caused a lot of chaos. And the quill, while helpful, landed her in the principal's office for \"cheating with excessive elegance.\"\n",
"\n",
"One day, her friend Leo, a budding inventor, noticed the changes around her. He saw the fleeting glimpses of a glowing backpack at her locker, the sudden appearance of impossibly large lunches, and the way she seemed to know things she shouldn't.\n",
"\n",
"\"Flora,\" he said, concern etched on his face, \"What's going on?\"\n",
"\n",
"Flora, overwhelmed by her newfound powers and the burden of keeping them secret, finally confessed. She showed him the backpack, the swirling patterns that danced on its surface when she focused, and the peculiar whispers that sometimes guided her.\n",
"\n",
"Leo, instead of being frightened, was ecstatic. \"This is incredible! Imagine the possibilities!\"\n",
"\n",
"Together, they experimented. They learned that the backpack responded to intention. The more specific her thought, the closer the result to what she wanted. The backpack also seemed to have a built-in limit. It couldn't produce anything that would fundamentally alter the course of events. You couldn't, for example, wish away a bad grade.\n",
"\n",
"They used the backpack to help others. They conjured extra pencils for students who forgot theirs, perfectly timed solutions to tricky math problems, and even a small, fluffy creature to comfort a crying child. They learned that the most rewarding thing wasn’t the magic itself, but the feeling of helping others.\n",
"\n",
"But the backpack also had its weaknesses. It was vulnerable to negativity. When Flora became frustrated with her own clumsiness, the backpack would generate tripwires. When she felt overwhelmed by the pressure of keeping the secret, the backpack would fill with useless objects, like a thousand mismatched buttons.\n",
"\n",
"One day, the school bully, a hulking boy named Brad, discovered Flora's secret. He cornered her, demanding that she use the backpack to win him a scholarship. Flora refused. Brad, fueled by his own frustration, threatened to reveal her secret to the entire school.\n",
"\n",
"Flora felt fear, a deep, chilling wave of it. The backpack sputtered, emitting only dust and whispers of doubt. She was trapped.\n",
"\n",
"Then, she remembered what Leo had taught her: the backpack responded to intention. She closed her eyes, took a deep breath, and focused on something other than her fear. She thought of the joy the backpack brought to others, the kindness she could spread. She thought of Leo, her loyal friend, and the adventures they'd shared.\n",
"\n",
"When she opened her eyes, the backpack glowed. Not with magic, but with a soft, comforting light. Instead of the item Brad demanded, a small, perfectly crafted origami crane appeared in her hand. She held it out to Brad. \"It's a symbol of hope,\" she said, her voice steady. \"Maybe you need that more than a scholarship.\"\n",
"\n",
"Brad, confused and a little ashamed, turned and walked away.\n",
"\n",
"Flora knew then that the magic wasn’t about the objects the backpack produced. It was about the power of her own heart, the strength she found in kindness and friendship, and the belief that even a perfectly ordinary girl could make a difference in the world. The adventure had begun, and it wasn't just about the things the backpack could create. It was about the kind of person she was choosing to become. And that, she realized, was the greatest magic of all.\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = model.generate_content(\"Write a story about a magic backpack.\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create an interactive session where a series of prompts build off each other. Use\n",
"\n",
"`model.start_chat()`\n",
"\n",
" to create an ongoing session where the results can be used to build off each other"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"coffee_chat = model.start_chat()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Brewing coffee is a wonderfully diverse process, offering a variety of methods. Here's a breakdown of some popular options, covering the basics:\n",
"\n",
"**1. Drip Coffee Maker (Most Common & Beginner-Friendly)**\n",
"\n",
"* **You'll Need:**\n",
" * Drip coffee maker\n",
" * Coffee filter (paper, reusable)\n",
" * Coffee grounds\n",
" * Fresh, filtered water\n",
"* **Instructions:**\n",
" 1. **Fill the water reservoir:** Use the water tank markings to measure the desired amount of water.\n",
" 2. **Insert the filter:** Place the filter in the filter basket.\n",
" 3. **Add coffee grounds:** Use the coffee scoop provided with your machine or a measuring spoon. A general guideline is 1-2 tablespoons of ground coffee per 6 ounces of water, but adjust to your taste. (A good starting point is about a 1:15 coffee-to-water ratio by weight if you want to be precise)\n",
" 4. **Close the lid:** Ensure the lid is securely closed.\n",
" 5. **Turn on the machine:** Press the power button.\n",
" 6. **Wait:** Let the machine brew. When the brewing is complete, the machine will automatically stop or shut off.\n",
" 7. **Serve and enjoy!**\n",
"* **Tips:**\n",
" * Use fresh, whole bean coffee and grind it just before brewing for the best flavor.\n",
" * Experiment with different grind sizes to find what works best with your coffee and brewer.\n",
" * Clean your coffee maker regularly to prevent mineral buildup and ensure optimal performance.\n",
"\n",
"**2. French Press (Full-Bodied Coffee)**\n",
"\n",
"* **You'll Need:**\n",
" * French press\n",
" * Coffee grounds (coarsely ground - *very* important for French Press)\n",
" * Fresh, filtered water (close to boiling, around 200°F or 93°C)\n",
" * Kettle\n",
"* **Instructions:**\n",
" 1. **Preheat the French press:** Pour some hot water into the empty carafe to preheat it, swirl, and then discard the water.\n",
" 2. **Add coffee grounds:** Put coarsely ground coffee into the French press. A common ratio is 1-2 tablespoons per 6 ounces of water.\n",
" 3. **Pour in hot water:** Pour hot water over the grounds, just enough to saturate them initially. Let them \"bloom\" (release gases) for about 30 seconds.\n",
" 4. **Add more water:** Pour in the remaining hot water, filling the carafe.\n",
" 5. **Steep:** Place the lid on the French press, with the plunger raised (not pressed down). Allow the coffee to steep for 4 minutes.\n",
" 6. **Press:** Slowly and gently press the plunger down to the bottom, separating the grounds from the coffee.\n",
" 7. **Serve immediately:** Pour the coffee immediately to prevent it from becoming over-extracted (bitter).\n",
"* **Tips:**\n",
" * Use a coarse grind to avoid sediment in your coffee.\n",
" * Don't over-extract. The 4-minute steep is crucial.\n",
" * If you're not drinking all the coffee right away, pour it out to avoid over-extraction.\n",
"\n",
"**3. Pour Over (Control Over the Brew)**\n",
"\n",
"* **You'll Need:**\n",
" * Pour-over device (e.g., Hario V60, Chemex, Melitta)\n",
" * Coffee filter (specific to your device)\n",
" * Coffee grounds (medium-fine grind)\n",
" * Fresh, filtered water (close to boiling, around 200°F or 93°C)\n",
" * Kettle\n",
" * Gooseneck kettle (optional, for precise water control, but not essential)\n",
"* **Instructions:**\n",
" 1. **Prepare the setup:** Place the filter in the pour-over device and rinse it with hot water to remove any paper taste and preheat the device. Discard the rinse water.\n",
" 2. **Add coffee grounds:** Add the coffee grounds to the filter. A common ratio is 1-2 tablespoons per 6 ounces of water.\n",
" 3. **Bloom the grounds:** Slowly pour a small amount of hot water (about twice the weight of the coffee) over the grounds to saturate them. Let it bloom for 30-45 seconds. This allows the coffee to degas.\n",
" 4. **Slowly pour remaining water:** Slowly and evenly pour the remaining water over the grounds, using a circular motion. The goal is to saturate all the grounds evenly. Aim for a steady, controlled pour.\n",
" 5. **Wait:** Allow the water to drip through completely. The total brewing time should be about 2-4 minutes depending on the device, grind size, and your preference.\n",
" 6. **Remove the device:** Discard the filter and grounds.\n",
" 7. **Serve and enjoy!**\n",
"* **Tips:**\n",
" * Experiment with grind size, water temperature, and pour rate to find the perfect brew.\n",
" * A gooseneck kettle helps with precise water control.\n",
" * Freshly ground coffee is key for the best flavor.\n",
"\n",
"**4. Aeropress (Fast & Versatile)**\n",
"\n",
"* **You'll Need:**\n",
" * Aeropress\n",
" * Aeropress paper filters\n",
" * Coffee grounds (fine to medium-fine grind)\n",
" * Fresh, filtered water (around 175°F or 80°C is recommended, but experimentation is encouraged)\n",
" * Kettle\n",
"* **Instructions:**\n",
" 1. **Prepare the Aeropress:** Insert a paper filter into the cap and rinse it with hot water to remove any paper taste.\n",
" 2. **Add coffee grounds:** Place the Aeropress over a mug and add the coffee grounds. A common ratio is 1-2 scoops (Aeropress scoop) per 6-8 ounces of water, but experiment.\n",
" 3. **Pour in hot water:** Pour the hot water over the grounds, filling the Aeropress.\n",
" 4. **Stir:** Stir the grounds and water for about 10-20 seconds.\n",
" 5. **Steep:** Place the plunger in and press down lightly to create a seal. Let it steep for 1 minute.\n",
" 6. **Press:** Slowly and steadily press the plunger down. It should take about 20-30 seconds to press all the water through.\n",
" 7. **Remove and enjoy!**\n",
"* **Tips:**\n",
" * Experiment with different brewing times, water temperatures, and grind sizes to find your favorite recipe.\n",
" * The Aeropress is very versatile, allowing for different brewing styles (inverted method, etc.)\n",
" * Clean the Aeropress immediately after use.\n",
"\n",
"**5. Moka Pot (Stovetop Espresso-like)**\n",
"\n",
"* **You'll Need:**\n",
" * Moka pot\n",
" * Coffee grounds (medium-fine grind)\n",
" * Fresh, filtered water\n",
" * Stovetop\n",
"* **Instructions:**\n",
" 1. **Fill the bottom chamber with water:** Fill the bottom chamber with water to the level of the safety valve.\n",
" 2. **Insert the filter basket:** Place the filter basket into the bottom chamber.\n",
" 3. **Fill the filter basket with coffee:** Fill the filter basket with coffee grounds, level the grounds, but do not tamp them.\n",
" 4. **Assemble the Moka pot:** Screw the top and bottom chambers together tightly.\n",
" 5. **Place on the stovetop:** Place the Moka pot on the stovetop over medium heat.\n",
" 6. **Brew:** As the water heats, steam pressure will force the water up through the grounds and into the top chamber. You'll hear a hissing sound when the brewing is almost done.\n",
" 7. **Remove from heat:** Once the coffee starts to sputter or gurgle, remove the Moka pot from the heat.\n",
" 8. **Serve:** Pour the coffee immediately and enjoy.\n",
"* **Tips:**\n",
" * Use medium-fine ground coffee, specifically designed for Moka pots.\n",
" * Don't overfill the filter basket.\n",
" * Use medium heat to avoid burning the coffee.\n",
" * Remove the Moka pot from the heat before it starts sputtering to prevent a bitter taste.\n",
"\n",
"**General Coffee Brewing Tips for all methods:**\n",
"\n",
"* **Use good quality, fresh coffee beans.**\n",
"* **Grind your beans right before brewing for the best flavor.**\n",
"* **Use filtered water.**\n",
"* **Adjust the grind size to your brewing method.**\n",
"* **Experiment with coffee-to-water ratios to find your preferred strength.**\n",
"* **Pay attention to water temperature.**\n",
"* **Clean your equipment regularly.**\n",
"* **Most importantly, experiment and find what you like best!**\n",
"\n",
"Choose the method that appeals to you based on the equipment you have, the time you have available, and your desired level of control over the brewing process. Happy brewing!\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = coffee_chat.send_message(\"how do you brew coffee.\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's dive into the diverse world of coffee brewing! Here's a comprehensive list of different coffee brewing methods, categorized for easier understanding, along with brief descriptions:\n",
"\n",
"**I. Immersion Brewing (Coffee steeped in water)**\n",
"\n",
"* **French Press:** (As described previously) Coffee grounds are steeped in hot water, then filtered with a mesh plunger. Results in a full-bodied, often sediment-rich coffee.\n",
"* **Cold Brew:** Coffee grounds are steeped in cold or room-temperature water for a long period (12-24 hours). This produces a smooth, less acidic concentrate, that can be diluted.\n",
"* **Toddy Cold Brew System:** Specifically designed cold brew system with a similar process to cold brew.\n",
"* **Cupping:** A standardized method used by coffee professionals to evaluate the aromas, flavors, and other characteristics of coffee. Coffee is immersed in hot water and then the grounds are scooped from the top.\n",
"* **Cowboy Coffee:** A simple, rustic method of brewing coffee by boiling coffee grounds directly in a pot of water, allowing the grounds to settle. Often involves adding cold water or an eggshell to help the grounds sink.\n",
"\n",
"**II. Pour-Over Brewing (Water poured over grounds)**\n",
"\n",
"* **Pour Over (General):** (As described previously) Water is poured slowly and evenly over coffee grounds held in a filter. Offers control over the brewing process, highlighting delicate flavors.\n",
" * **Hario V60:** A popular pour-over device with a conical shape and ridges for optimal flow.\n",
" * **Chemex:** A stylish, glass carafe with a thick filter for a clean cup.\n",
" * **Melitta:** A simple, affordable pour-over device with a flat-bottomed filter.\n",
" * **Kalita Wave:** Another popular pour-over with a flat-bottomed filter and small holes for even extraction.\n",
"* **Automatic Pour Over Machines:** Devices that automate the pouring process for pour-over style brewing.\n",
"\n",
"**III. Pressure Brewing**\n",
"\n",
"* **Espresso Machine:** Uses high pressure to force hot water through finely ground coffee, resulting in a concentrated, flavorful shot of espresso. Wide range of machines from home to commercial.\n",
"* **Moka Pot:** (As described previously) Stovetop device that uses steam pressure to brew a concentrated, espresso-like coffee.\n",
"* **Aeropress:** (As described previously) Combines immersion and pressure. Uses a plunger to force water through coffee grounds. Fast and versatile.\n",
"\n",
"**IV. Filtered Brewing**\n",
"\n",
"* **Drip Coffee Maker:** (As described previously) The most common method, using an automated system to drip hot water through coffee grounds held in a filter.\n",
"* **Pourover Drip coffee makers:** Combines automated pouring with a manual pour-over-style filter.\n",
"\n",
"**V. Other Methods**\n",
"\n",
"* **Siphon (Vacuum Pot):** Uses vacuum pressure created by heating water and then cooling the resulting vapor to brew coffee. Offers a theatrical brewing experience.\n",
"* **Turkish Coffee (Cezve):** Very finely ground coffee is boiled in a cezve (small pot) with water and sometimes sugar. The coffee is served unfiltered, with the grounds settled at the bottom.\n",
"* **South Indian Filter Coffee:** Coffee is brewed using a stainless steel filter and chicory root for flavor.\n",
"* **Instant Coffee:** Freeze-dried or spray-dried coffee granules that dissolve in hot water. The most convenient option.\n",
"* **Coffee Bags/Tea Bags:** Pre-portioned bags of coffee, like tea, that are steeped in hot water.\n",
"\n",
"**Key Factors to Consider When Choosing a Method:**\n",
"\n",
"* **Flavor Preferences:** Do you prefer bold, full-bodied coffee or a cleaner, brighter cup?\n",
"* **Convenience:** How much time and effort are you willing to invest in the brewing process?\n",
"* **Control:** How much control do you want over the various factors that influence the brew?\n",
"* **Budget:** How much are you willing to spend on equipment?\n",
"* **Cleanup:** How easy is the method to clean up afterward?\n",
"* **Portability/Travel:** Is it easy to take your brewing method on the go?\n",
"\n",
"This list covers the majority of the popular and available coffee brewing methods. Each method has its own nuances, and the best way to find your favorite is to experiment and enjoy the journey!\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = coffee_chat.send_message(\"What are different ways of brewing coffee\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let's break down the different coffee brewing methods and compare them across several key factors:\n",
"\n",
"| Feature | Drip Coffee Maker | French Press | Pour Over | Aeropress | Moka Pot |\n",
"|-----------------|----------------------------|-------------------------------|-------------------------------|--------------------------------|---------------------------------|\n",
"| **Flavor Profile** | Consistent, less intense, | Full-bodied, rich, oily, sediment | Clean, bright, nuanced, delicate | Clean, smooth, concentrated, versatile | Strong, concentrated, espresso-like, bitter potential|\n",
"| **Body** | Light to Medium | Full, heavy, thick | Light to Medium | Light to Medium | Medium to Full |\n",
"| **Ease of Use** | Very Easy, Automatic | Easy, a little more hands-on | Moderate, requires technique | Easy, Quick | Moderate, Stovetop required |\n",
"| **Brew Time** | 5-10 minutes | 4 minutes + prep | 2-4 minutes + prep | 1-2 minutes | 5-10 minutes |\n",
"| **Grind Size** | Medium | Coarse | Medium-fine | Fine to Medium-fine | Medium-fine |\n",
"| **Equipment Cost**| Low | Low to Medium | Medium | Low to Medium | Low to Medium |\n",
"| **Cleanup** | Easy | Moderate, grounds disposal | Moderate, filter disposal | Easy | Moderate, hot and can be messy|\n",
"| **Best For** | Everyday coffee, groups, convenience | Bold flavors, those who like body | Flavor enthusiasts, single cups | Versatility, speed, travel | Espresso-style coffee, those who like strong coffee |\n",
"| **Control** | Low, mostly automatic | Moderate, steep time | High, grind, water temp, pour | Moderate, ratio, brew time | Moderate, heat control |\n",
"| **Notes** | Least expensive. Convenient, consistent. | Sediment in cup. Oils enrich flavor. | Requires skill. Good for single servings. | Compact, portable, adaptable. | Requires stovetop, can be bitter if overheated. |\n",
"\n",
"**Here's a more detailed comparison:**\n",
"\n",
"* **Flavor and Body:**\n",
" * **Drip:** Generally produces a consistent but often less intense flavor profile. Body is usually light to medium.\n",
" * **French Press:** Known for a full, rich body and an oily mouthfeel due to the oils that pass through the metal filter. The coarser grind leaves some sediment in the cup.\n",
" * **Pour Over:** Offers a cleaner, brighter, and more nuanced flavor. Body is typically lighter, as the paper filter traps more oils. It highlights the subtle flavors of the coffee beans.\n",
" * **Aeropress:** Produces a smooth, concentrated brew. The paper filter provides a clean cup. It extracts flavors well and can be adapted to various brewing styles.\n",
" * **Moka Pot:** Creates a strong, concentrated coffee similar to espresso, but not quite the same. It can be prone to a slightly bitter taste if brewed incorrectly.\n",
"\n",
"* **Ease of Use:**\n",
" * **Drip:** Extremely user-friendly. You just add water and coffee, and the machine does the rest.\n",
" * **French Press:** Simple, requires a little more attention to the steeping time.\n",
" * **Pour Over:** Requires more technique and practice to perfect the pouring process.\n",
" * **Aeropress:** Very easy to use and fast.\n",
" * **Moka Pot:** Requires using a stovetop, which can be a bit more hands-on.\n",
"\n",
"* **Brew Time:**\n",
" * **Drip:** Varies depending on the machine and the amount of coffee brewed.\n",
" * **French Press:** 4 minutes of steeping time is critical.\n",
" * **Pour Over:** Around 2-4 minutes for the entire process.\n",
" * **Aeropress:** Very quick, takes only 1-2 minutes.\n",
" * **Moka Pot:** Takes about the same time as a drip, including heating.\n",
"\n",
"* **Equipment and Cost:**\n",
" * **Drip:** One of the most affordable options.\n",
" * **French Press:** Also relatively inexpensive.\n",
" * **Pour Over:** Can range in cost depending on the device you choose.\n",
" * **Aeropress:** Moderately priced.\n",
" * **Moka Pot:** Affordable and durable.\n",
"\n",
"* **Cleanup:**\n",
" * **Drip:** Easy; just dispose of the filter and grounds.\n",
" * **French Press:** Requires disposing of the grounds and cleaning the carafe.\n",
" * **Pour Over:** Throw away the filter and grounds.\n",
" * **Aeropress:** Simple cleanup.\n",
" * **Moka Pot:** Needs to cool before disassembly and cleaning.\n",
"\n",
"**Which Method is Right for You?**\n",
"\n",
"* **For convenience and consistent, everyday coffee:** Drip coffee maker is your best bet.\n",
"* **For bold, full-bodied coffee with a richer experience:** French press is the way to go.\n",
"* **For coffee connoisseurs seeking control, clarity of flavor, and nuanced brews:** Pour over is a great choice.\n",
"* **For fast, versatile, and portable brewing:** Aeropress is ideal.\n",
"* **For a strong, concentrated espresso-like experience without the cost of an espresso machine:** Moka pot is a good option.\n",
"\n",
"The best method depends on your personal preferences, the kind of coffee you like, the amount of time you want to spend, and how much control you desire over the brewing process. Experimentation is key to finding your perfect cup!\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = coffee_chat.send_message(\"How do the different ways of brewing coffee compare\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"When comparing different coffee brewing methods, here's a comprehensive list of qualities you can use to make a well-informed decision and find the best method for *you*:\n",
"\n",
"**I. Flavor and Body Characteristics:**\n",
"\n",
"* **Flavor Profile:**\n",
" * **Brightness/Acidity:** The level of tartness or zing in the coffee. Is it vibrant, sharp, or more muted?\n",
" * **Sweetness:** The perception of sweetness in the coffee. How pronounced is it?\n",
" * **Bitterness:** The level of bitterness, which can be desirable in moderation but unpleasant in excess.\n",
" * **Complexity:** How many different flavor notes can you detect? Is it simple or multi-layered?\n",
" * **Balance:** How well the different flavor notes complement each other. Are they harmonizing, or clashing?\n",
" * **Body:**\n",
" * **Mouthfeel:** The overall texture of the coffee in your mouth.\n",
" * **Light:** Thin and watery.\n",
" * **Medium:** A balanced feel.\n",
" * **Full/Heavy:** Thick and substantial.\n",
" * **Oily/Viscous:** Can be the result of coffee oils being extracted.\n",
"* **Intensity:** How strong or weak is the coffee's flavor?\n",
"\n",
"**II. Brewing Process:**\n",
"\n",
"* **Brewing Time:**\n",
" * The total time from starting the brew to enjoying the coffee.\n",
"* **Ease of Use:**\n",
" * **Complexity:** How easy is it to learn and master the brewing technique?\n",
" * **Automation:** How much of the process is automated versus requiring manual intervention?\n",
" * **Hands-on Time:** How much active attention does the brewing require?\n",
"* **Temperature Control:**\n",
" * The ability to control the water temperature, which significantly impacts flavor extraction.\n",
"* **Grind Size Flexibility:**\n",
" * The range of grind sizes that are suitable for the method.\n",
"* **Water-to-Coffee Ratio:**\n",
" * The flexibility to adjust the ratio to achieve your desired strength.\n",
"* **Control over Brewing Variables:**\n",
" * How much you can adjust and manipulate factors like water temperature, brewing time, agitation, and pouring technique to influence the outcome.\n",
"\n",
"**III. Equipment and Costs:**\n",
"\n",
"* **Initial Investment:**\n",
" * The cost of the necessary equipment.\n",
" * The cost of any filters or other consumables.\n",
"* **Equipment Maintenance:**\n",
" * How easy is it to clean and maintain the equipment?\n",
" * How durable is the equipment?\n",
"* **Consumables:**\n",
" * Are specific filters, or other supplies necessary?\n",
" * How easy and affordable are these supplies to get?\n",
"* **Footprint/Size:**\n",
" * How much counter space will the equipment take up?\n",
" * How easy is it to store when not in use?\n",
"\n",
"**IV. Sensory Experience:**\n",
"\n",
"* **Aroma:**\n",
" * The intensity and characteristics of the aroma while brewing and drinking.\n",
"* **Visual Appeal:**\n",
" * How visually appealing is the brewing process (e.g., the blooming of pour-over, the hissing of a Moka pot).\n",
"* **Sensory Engagement:**\n",
" * How immersive and engaging is the brewing process? Is it a meditative ritual, or a quick chore?\n",
"\n",
"**V. Practical Considerations:**\n",
"\n",
"* **Batch Size:**\n",
" * How many cups can you brew at once?\n",
"* **Consistency:**\n",
" * How consistently can you achieve the same results with the method?\n",
"* **Portability:**\n",
" * Is it easy to transport and use the equipment while traveling or camping?\n",
"* **Skill Level Required:**\n",
" * How much practice and skill are required to achieve good results?\n",
"* **Cleanup:**\n",
" * How easy and quick is the cleanup process?\n",
"* **Versatility:**\n",
" * Can the method be used to brew different types of coffee beans and roasts effectively?\n",
"\n",
"**Using These Qualities for Comparison:**\n",
"\n",
"When comparing brewing methods, consider each of these qualities and weigh them based on your priorities. Here's how you can apply them:\n",
"\n",
"1. **Define Your Preferences:** What are your most important factors?\n",
" * Do you prioritize speed and convenience above all else?\n",
" * Are you a coffee connoisseur who prioritizes flavor and control?\n",
" * Are you budget-conscious?\n",
" * Do you value a ritualistic, sensory experience?\n",
"\n",
"2. **Research Each Method:** Investigate each method based on the factors outlined above. Read reviews, watch videos, and, if possible, try different brews.\n",
"\n",
"3. **Create a Comparison Chart:** Make a table and rate each method on each of the qualities (e.g., on a scale of 1-5 or using descriptive adjectives).\n",
"\n",
"4. **Weigh the Results:** Evaluate the information in your comparison chart and determine which method best aligns with your priorities and preferences.\n",
"\n",
"5. **Experiment and Adjust:** Once you've chosen a method, experiment with different coffee beans, grind sizes, and water-to-coffee ratios to fine-tune your brewing process and achieve your perfect cup!\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = coffee_chat.send_message(\"What are qualities that I should use to compare differnt ways of brewing coffee\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Brewing coffee with espresso-ground beans in methods *not* designed for espresso can lead to a variety of issues and a generally unpleasant cup of coffee. Here's what to expect:\n",
"\n",
"**The Problems:**\n",
"\n",
"* **Over-Extraction & Bitterness:**\n",
" * **Too Fine:** Espresso grind is extremely fine. When exposed to water for a standard brewing time (e.g., drip, pour-over, French press), this fineness causes the coffee grounds to over-extract.\n",
" * **Extraction Imbalance:** Over-extraction primarily results in the bitter compounds (quinic acid, tannins) being extracted from the coffee grounds, leading to a noticeably bitter, sometimes even acrid taste.\n",
" * **Uneven Extraction:** The tiny particles become densely packed, preventing water from passing through uniformly. This leads to areas of over-extraction, alongside areas of under-extraction.\n",
"\n",
"* **Clogged Filters/Slow Brewing:**\n",
" * **Drip & Pour-Over:** The very fine grounds will clog the filter. The water flow becomes incredibly slow, potentially causing the filter to overflow (a messy flood!) or the coffee to sit in contact with the grounds for too long (promoting even more over-extraction).\n",
" * **French Press:** The mesh filter in a French press struggles to contain such fine grounds. You'll likely have a cup full of muddy sediment, giving it a gritty texture.\n",
" * **Aeropress:** While the Aeropress *can* handle a slightly finer grind than drip, espresso-fine would be problematic. You'd have to press with a lot of force and the brewing time would be extended.\n",
"\n",
"* **Under-Developed Flavors (Paradoxically):** While bitterness is the primary culprit, a problematic grind can also lead to *some* under-extracted flavors. The water, struggling to flow through the tightly packed grounds, might not have enough time to extract all the desirable flavor compounds before over-extracting the bitter ones.\n",
"\n",
"* **Mismatched Equipment:**\n",
" * Most standard brewers aren't designed to handle such fine particles, increasing the likelihood of filter clogging, equipment damage (if forced), and a poor brewing experience.\n",
"\n",
"**The Resulting Cup (Typically):**\n",
"\n",
"* **Bitter:** The most common and prominent flavor.\n",
"* **Astringent/Puckering:** The mouthfeel might be drying, making your mouth feel tight.\n",
"* **Muddy/Gritty:** With a French press or a compromised pour-over, the coffee will be loaded with sediment.\n",
"* **Overly Strong (sometimes):** Because of the excessive extraction, the coffee can feel intensely strong and unpleasant, despite often being extracted at lower concentrations.\n",
"* **Unbalanced:** The flavors won't be well-rounded, with the bitter notes dominating.\n",
"\n",
"**Exceptions and Considerations:**\n",
"\n",
"* **Aeropress (To a Degree):** An Aeropress is somewhat tolerant of finer grinds than other methods. It can work with a grind *slightly* finer than medium-fine. Even then, you'd need to modify your brewing time and technique, and the resulting coffee would still be distinct from what you'd get with a correctly ground bean.\n",
"* **Espresso Machines:** If you have an espresso machine, then finely ground espresso beans are what you should use!\n",
"* **Other Brewing Techniques (Rare):** You might find online discussions about attempting espresso grind with other methods, but be aware that these are very experimental and not the norm for good coffee.\n",
"\n",
"**In summary:** Using espresso-ground beans in the vast majority of coffee brewing methods will produce a bitter, unpleasant cup. Always match the grind size to the brewing method for the best results.\n",
"\n",
"________________________________________\n"
]
}
],
"source": [
"response = coffee_chat.send_message(\"what happens if you try to brew coffee using beans ground like espresso\")\n",
"for chunk in response:\n",
" print(chunk.text)\n",
" print(\"_\" * 40)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some comments on Generative AI from these examples\n",
"\n",
"## Generative AI provide randomness in the responses\n",
"\n",
"- Generative AI (simplified) is trained using training data (all the data on the internet)\n",
"- Training data is broken into tokens (approximately word parts)\n",
"- Take the tokens found in the inquiry, then find documents with the same tokens\n",
"- Predictive model on what should come next (Deep learning)\n",
" - Determine a probability\n",
" - Choose next token probabalistically\n",
" - Add token to the inquiry\n",
" - Repeat until a response has been formed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Randomness gives the illusion of agency\n",
"\n",
"- More randomness leads to more interesting responses\n",
"- Less randomness -> a response more like the average of the internet knowledge on the topic\n",
" - Hides diversity of thought\n",
"\n",
"## But randomness is not good if you are looking for an answer\n",
"\n",
"- No indication that randomness is involved\n",
"- There is no reasoning, Generative AI assembles a response from the range of texts that it finds\n",
"- Randomness in assembling tokens means that the response maybe assembled from pieces that should not go together\n",
" - Hallucinations\n",
"\n",
"# Prompt engineering\n",
"\n",
"- Structuring your inquiries (prompts) in a way that the Generative AI is more likely to give a useful answer.\n",
"- Coffee example: start with open ended questions, then use prompts to ask the Gen AI for a structure, then ask for an answer using that structure.\n",
"- All prompt engineering techniques are variations on:\n",
" - Iteratively narrowing request\n",
" - Structuring request and answer\n",
"\n",
"## Example: Open AI SWE prompt\n",
"\n",
"https://cookbook.openai.com/examples/gpt4-1_prompting_guide\n",
"\n",
"- OpenAI prompt used by OpenAI team on the SWE-bench Verified benchmark.\n",
"- An example of an agentic AI prompt\n",
"- Note: Prompt is equivalent to 8 pages\n",
"- Does not include any information about their platform!\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What Generative AI does not provide\n",
"\n",
"- Context\n",
"- Collaboration\n",
"- Conscience\n",
"\n",
"*from Polly Mitchell-Guthrie\n",
"\n",
"## Context\n",
"\n",
"- What is the setting of the inquiry?\n",
"- What are the important aspects of the problem?\n",
"- How will the answer be used?\n",
"- What does the inquirer already know or believe?\n",
"\n",
"## Collaboration\n",
"\n",
"- What has been tried before?\n",
"- What happened before, what has gone wrong/right?\n",
"\n",
"## Conscience\n",
"\n",
"- Why are you going this?\n",
"- What is the overall mission/goals of the organization?\n",
"- How does the response fit in with the organizations mission/goals?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Context, Collaboration, Conscience: Novice problems\n",
"\n",
"- Nuance\n",
"- Naivete\n",
"\n",
"## Nuance\n",
"\n",
"- Context matters\n",
"- What was not said\n",
"- Experts use frameworks to evaluate situations to know what is important, and what is missing.\n",
"- Novices (and Gen AI) take what they have, and try to compose an answer using what they have.\n",
"\n",
"## Naivete\n",
"\n",
"- So you know some stuff. So what?\n",
"- Generative AI will give an answer(s) and based on probability, if the internet as a whole is correct, the facts may be right.\n",
"- Naivete is not realize that a set of facts has implications\n",
"\n",
"## Novices vs experts\n",
"\n",
"- Novices respond based on what they are given, experts fit information into a framework and ask about important missing information.\n",
"- Novices report facts or what they know, experts recognize implications\n",
"- Cross-discipline issues\n",
" - What to do when guidance from different sources conflicts.\n",
" - Depends on knowing what happened before.\n",
" - What is the outside context/goal.\n",
" - Experts can resolve conflicts at the intersection of disciplines based on evaluating what is important for the current goal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Statistics example\n",
"\n",
"- The RAND Health Insurance experiment\n",
"- Data is included in Python statsmodels library"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# data obtained from <http://cameron.econ.ucdavis.edu/mmabook/mmadata.html>\n",
"\n",
"dataset = sm.datasets.randhie.load_pandas()\n",
"randhie = dataset['data']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's ask Gemini about the dataset"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"hie_chat = model.start_chat()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset appears to be a tabular dataset with 20,190 rows and 10 columns. Each row represents a single observation, and each column represents a specific feature or variable. Here's a breakdown of the dataset:\n",
"\n",
"**Features (Columns):**\n",
"\n",
"* **mdvis:** Likely represents the number of medical visits. Values range from 0 to higher numbers, suggesting a count.\n",
"* **lncoins:** Appears to be the natural logarithm of some type of monetary value (coins). Values vary significantly.\n",
"* **idp:** Could be a categorical variable, possibly representing an identifier, or a status, where 0 and 1 are the only observed values.\n",
"* **lpi:** Likely the natural logarithm of an economic indicator, maybe related to per capita income or purchasing power index.\n",
"* **fmde:** Likely representing some form of medical expenditure, could be related to total funds spent.\n",
"* **physlm:** Could represent a measure of physical limitation or disability due to health issues.\n",
"* **disea:** Could be a measure of disease burden or severity. The numbers vary.\n",
"* **hlthg:** Likely represents a binary indicator (0 or 1) for good health or health-related quality of life.\n",
"* **hlthf:** Likely a binary indicator (0 or 1) for good health or health-related quality of life.\n",
"* **hlthp:** Likely a binary indicator (0 or 1) for good health or health-related quality of life.\n",
"\n",
"**Data Types:**\n",
"\n",
"The data appears to be primarily numerical, with some features (like `idp`, `hlthg`, `hlthf`, and `hlthp`) being likely binary (0 or 1) or categorical. The presence of natural logarithms (lncoins, lpi) indicates that these features were likely transformed from the original data, possibly to normalize skewed distributions.\n",
"\n",
"**Possible Purpose:**\n",
"\n",
"Based on the features, this dataset likely pertains to a health or healthcare-related context. It may be used for:\n",
"\n",
"* **Analyzing healthcare utilization:** (e.g., `mdvis`)\n",
"* **Studying the relationship between economic factors and health:** (e.g., `lncoins`, `lpi`)\n",
"* **Investigating the impact of medical expenditures on health outcomes:** (e.g., `fmde`)\n",
"* **Predicting health outcomes:** (e.g., using `hlthg`, `hlthf`, `hlthp` as target variables)\n",
"* **Understanding the impact of physical limitations on health:** (e.g., `physlm`)\n",
"* **Assessing the overall burden of disease:** (e.g., `disea`)\n",
"\n",
"**Next Steps:**\n",
"\n",
"To further understand the dataset, you would want to:\n",
"\n",
"1. **Examine the data types:** Ensure that the data types of the columns are appropriate (e.g., numerical for `mdvis`, `lncoins`, etc., and categorical/binary for `hlthg`, `hlthf`, `hlthp`, and possibly `idp`).\n",
"2. **Check for missing values:** Determine if there are any missing values (NaNs) in the dataset, and decide how to handle them (e.g., imputation or removal).\n",
"3. **Calculate descriptive statistics:** Compute summary statistics (mean, median, standard deviation, min, max, quartiles) for the numerical features to understand their distributions and identify potential outliers.\n",
"4. **Visualize the data:** Create histograms, box plots, scatter plots, and other visualizations to explore the relationships between the features.\n",
"5. **Consider the context:** Understanding the source of the data, the population it represents, and the research question it aims to address will give the most context to analyzing it.\n",
"\n"
]
}
],
"source": [
"response = hie_chat.send_message(\"describe this dataset \\n \" + str(randhie.head(100)))\n",
"print(response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's outline how to analyze the relationship between `mdvis` (the number of medical visits) and the other variables in your dataset using Python's `statsmodels` library. Here's a step-by-step guide, including code snippets and explanations:\n",
"\n",
"**1. Import Libraries and Load the Data**\n",
"\n",
" First, ensure you have the necessary libraries installed:\n",
"\n",
" ```bash\n",
" pip install pandas statsmodels scikit-learn # scikit-learn for potential preprocessing\n",
" ```\n",
"\n",
" Now, import the libraries and load your data into a Pandas DataFrame:\n",
"\n",
" ```python\n",
" import pandas as pd\n",
" import statsmodels.api as sm # For statistical modeling\n",
" import statsmodels.formula.api as smf # For using formulas in models\n",
" from sklearn.model_selection import train_test_split # For splitting the data (optional)\n",
" import matplotlib.pyplot as plt # For visualization\n",
" import seaborn as sns # For enhanced visualization\n",
" # Assuming your data is in a CSV file named 'your_data.csv'\n",
" try:\n",
" data = pd.read_csv('your_data.csv') # Replace 'your_data.csv' with your actual file name\n",
" except FileNotFoundError:\n",
" print(\"Error: 'your_data.csv' not found. Please make sure the file exists and the path is correct.\")\n",
" exit() # Exit the script if the data can't be loaded\n",
"\n",
" # Display the first few rows to verify the data loaded correctly\n",
" print(data.head())\n",
" print(data.info()) # check for data types and missing values\n",
" ```\n",
"\n",
"**2. Data Preprocessing (Important!)**\n",
"\n",
" Before you start modeling, it's crucial to preprocess your data:\n",
"\n",
" * **Handle Missing Values:** Decide how to handle missing values (NaNs). Common options:\n",
" * **Imputation:** Replace missing values with the mean, median, or another appropriate value for the column.\n",
" * **Removal:** Remove rows with missing values (use with caution if you lose a significant portion of your data).\n",
"\n",
" ```python\n",
" # Check for missing values\n",
" print(data.isnull().sum()) # Counts missing values per column\n",
"\n",
" # Example: Impute missing values with the mean (replace NaN with the mean)\n",
" for col in data.columns:\n",
" if data[col].isnull().any():\n",
" if pd.api.types.is_numeric_dtype(data[col]):\n",
" data[col].fillna(data[col].mean(), inplace=True) # imputing with mean for numeric cols\n",
" else:\n",
" data[col].fillna(data[col].mode()[0], inplace=True) # imputing with mode for non-numeric cols\n",
" print(data.isnull().sum()) # verify that there are no missing values\n",
" ```\n",
"\n",
" * **Data Type Conversion:** Ensure that the data types are correct. Categorical variables need to be treated as such. Numerical variables should be numeric (integer or float).\n",
"\n",
" ```python\n",
" # Example: if 'idp' is meant to be a categorical variable:\n",
" data['idp'] = data['idp'].astype('category') # Or .astype(str) if needed\n",
" print(data.dtypes)\n",
" ```\n",
"\n",
" * **Outlier Handling (Potentially):** Consider handling outliers, especially in continuous variables. This can involve transformations (log, square root), winsorizing, or removing outliers (carefully). I'll include a simple outlier check and remove outliers for `mdvis`.\n",
"\n",
" ```python\n",
" # Outlier detection and removal for mdvis (example)\n",
" Q1 = data['mdvis'].quantile(0.25)\n",
" Q3 = data['mdvis'].quantile(0.75)\n",
" IQR = Q3 - Q1\n",
" lower_bound = Q1 - 1.5 * IQR\n",
" upper_bound = Q3 + 1.5 * IQR\n",
"\n",
" # Remove outliers. Be careful using this as you can lose a lot of data.\n",
" data_no_outliers = data[(data['mdvis'] >= lower_bound) & (data['mdvis'] <= upper_bound)].copy()\n",
" print(f\"Original data shape: {data.shape}, Data shape after outlier removal: {data_no_outliers.shape}\") # To see how many rows were lost\n",
" data = data_no_outliers # Use the outlier-removed data for the remaining steps\n",
" ```\n",
"\n",
" * **Normalization/Standardization (Potentially):** Depending on the model you choose, it can be beneficial to normalize (scale to [0, 1]) or standardize (mean=0, std=1) your numerical features. This is especially important for models sensitive to feature scaling (e.g., those using gradient descent). This is less critical for OLS, but still useful.\n",
"\n",
" ```python\n",
" from sklearn.preprocessing import StandardScaler\n",
"\n",
" # Select numerical columns\n",
" numerical_cols = data.select_dtypes(include=['number']).columns.tolist()\n",
" numerical_cols.remove('mdvis') # Don't scale the dependent variable\n",
"\n",
" # Initialize the scaler\n",
" scaler = StandardScaler()\n",
"\n",
" # Fit and transform the numerical columns\n",
" data[numerical_cols] = scaler.fit_transform(data[numerical_cols])\n",
" print(data.head()) # show some scaled data\n",
" ```\n",
"\n",
"**3. Choose a Model and Build It**\n",
"\n",
" For analyzing the relationship of other variables to `mdvis`, the choice of model depends on how `mdvis` is distributed and your goals:\n",
"\n",
" * **If `mdvis` is approximately normally distributed:** You can use Ordinary Least Squares (OLS) regression. This is a good starting point.\n",
" * **If `mdvis` is a count variable (most likely) with non-negative integer values, and the variance increases with the mean (overdispersed):** Consider a Poisson regression or a Negative Binomial regression. These models are designed for count data.\n",
" * **If `mdvis` is skewed:** Consider transformations (log, square root) of `mdvis` before using OLS.\n",
"\n",
" Let's start with an OLS regression model as an example. We'll then discuss other models.\n",
"\n",
" ```python\n",
" # Using all other variables to predict mdvis (OLS example)\n",
" # You can customize the formula to include/exclude variables, add interactions etc.\n",
" # Note: The formula string is very important!\n",
"\n",
" # Step 1: Build the formula. Include all features or specify\n",
" # which to use, and add interactions if you want\n",
" formula = 'mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp' # Simplest model\n",
"\n",
" # Step 2: Fit the model\n",
" model = smf.ols(formula, data=data).fit()\n",
"\n",
" # Step 3: Print model summary\n",
" print(model.summary())\n",
" ```\n",
"\n",
" **Explanation of the OLS Model Code:**\n",
"\n",
" * `smf.ols(formula, data=data)`: This is the core of building the model.\n",
" * `formula`: A string that specifies the relationship between the variables. The format is `dependent_variable ~ independent_variables`. You can include multiple independent variables by separating them with `+`.\n",
" * `data=data`: Specifies the DataFrame that contains your data.\n",
" * `.fit()`: This method fits (trains) the model to your data.\n",
"\n",
" * `model.summary()`: Displays a comprehensive summary of the regression results.\n",
"\n",
"**4. Interpreting the OLS Regression Summary**\n",
"\n",
" The `model.summary()` output is your primary source of information. Key things to look for:\n",
"\n",
" * **R-squared:** Indicates the proportion of variance in `mdvis` that is explained by the model. Values range from 0 to 1, with higher values indicating a better fit.\n",
" * **Adjusted R-squared:** Similar to R-squared but adjusts for the number of independent variables. Use this to compare models with different numbers of predictors.\n",
" * **Coefficients:** The estimated coefficients for each independent variable. These represent the change in `mdvis` for a one-unit change in the independent variable, holding other variables constant.\n",
" * **P-values:** The p-values associated with the coefficients. A p-value less than your significance level (e.g., 0.05) suggests that the coefficient is statistically significant (i.e., the independent variable has a significant effect on `mdvis`).\n",
" * **Standard Errors:** The standard error of the coefficient estimates.\n",
" * **Confidence Intervals:** The confidence interval of the coefficient estimates (e.g., 95% CI).\n",
" * **T-statistic:** The t-statistic, which is the coefficient divided by the standard error. Used in calculating the p-value.\n",
" * **F-statistic:** Test the overall significance of the model (whether at least one predictor significantly predicts the outcome).\n",
" * **Residuals:** Information about the residuals (the differences between the observed and predicted values of `mdvis`). Check the residuals to assess the model's assumptions (linearity, normality, homoscedasticity)\n",
"\n",
"**5. Assess Model Assumptions (Crucial for OLS!)**\n",
"\n",
" OLS regression relies on several assumptions. If these assumptions are violated, your results may be unreliable:\n",
"\n",
" * **Linearity:** The relationship between each independent variable and `mdvis` should be approximately linear. Inspect scatterplots of `mdvis` vs. each independent variable, and residual plots.\n",
" * **Independence of Errors:** The errors (residuals) should be independent of each other. This is often assumed, but can be violated in time-series or clustered data.\n",
" * **Homoscedasticity:** The variance of the errors should be constant across all levels of the independent variables. Check residual plots for \"fanning\" patterns (non-constant variance).\n",
" * **Normality of Errors:** The residuals should be approximately normally distributed. Use a histogram or Q-Q plot of the residuals to check.\n",
" * **No or Little Multicollinearity:** The independent variables should not be highly correlated with each other. Calculate the Variance Inflation Factor (VIF) to check. High VIF values (e.g., > 5 or 10) suggest multicollinearity.\n",
"\n",
" ```python\n",
" # Residual analysis (example)\n",
" # 1. Residuals vs Fitted values (to check for non-linearity and heteroscedasticity)\n",
" fig, ax = plt.subplots(figsize=(8, 6))\n",
" sns.residplot(x=model.fittedvalues, y=model.resid, ax=ax, lowess=True, line_kws={'color': 'red'})\n",
" ax.set_xlabel(\"Fitted values\")\n",
" ax.set_ylabel(\"Residuals\")\n",
" ax.set_title(\"Residuals vs Fitted Values\")\n",
" plt.show()\n",
"\n",
" # 2. Q-Q plot (to check for normality of residuals)\n",
" fig = sm.qqplot(model.resid, line='s') # 's' for standardized residuals\n",
" plt.title('Q-Q Plot of Residuals')\n",
" plt.show()\n",
"\n",
" # 3. Histogram of residuals\n",
" plt.hist(model.resid, bins=30)\n",
" plt.xlabel(\"Residuals\")\n",
" plt.ylabel(\"Frequency\")\n",
" plt.title(\"Histogram of Residuals\")\n",
" plt.show()\n",
"\n",
" # 4. Multicollinearity (VIF)\n",
" from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
"\n",
" # Extract independent variables\n",
" X = data[[col for col in data.columns if col != 'mdvis']] # Exclude the dependent variable\n",
" vif_data = pd.DataFrame()\n",
" vif_data[\"feature\"] = X.columns\n",
" vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
" print(vif_data)\n",
" ```\n",
"\n",
" **Dealing with Violations:**\n",
"\n",
" * **Non-linearity:** Transform the variables (e.g., log, square root) or add polynomial terms to the model.\n",
" * **Heteroscedasticity:** Use robust standard errors ( `model = smf.ols(formula, data=data).fit(cov_type='HC3')` ), or consider a different model (e.g., Weighted Least Squares).\n",
" * **Non-normality:** Check if the transformation of the dependent variable improves it.\n",
" * **Multicollinearity:** Remove highly correlated variables, or combine them into a single variable (e.g., by averaging or creating an index).\n",
"\n",
"**6. Consider Alternative Models (if appropriate)**\n",
"\n",
" * **Poisson Regression:** If `mdvis` is a count variable, use this. It models the *rate* of medical visits.\n",
" ```python\n",
" import statsmodels.api as sm\n",
" import statsmodels.formula.api as smf\n",
"\n",
" # Poisson Regression (for count data)\n",
" formula_poisson = 'mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp'\n",
" poisson_model = smf.poisson(formula_poisson, data=data).fit()\n",
" print(poisson_model.summary())\n",
" ```\n",
"\n",
" * **Overdispersion:** Poisson regression assumes the mean and variance are equal. If the variance is greater than the mean (overdispersion), Poisson regression may underestimate standard errors. In that case, use Negative Binomial regression.\n",
" * **Negative Binomial Regression:** Handles overdispersion in count data.\n",
" ```python\n",
" import statsmodels.api as sm\n",
" import statsmodels.formula.api as smf\n",
"\n",
" # Negative Binomial Regression (for overdispersed count data)\n",
" from statsmodels.discrete.count_model import NegativeBinomialP\n",
" formula_nb = 'mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp'\n",
" nb_model = smf.negativebinomial(formula_nb, data=data).fit()\n",
" print(nb_model.summary())\n",
" ```\n",
"\n",
"**7. Model Selection and Refinement**\n",
"\n",
" * **Compare Models:** If you tried different models (OLS, Poisson, etc.), compare their performance based on statistical measures (e.g., AIC, BIC), and your understanding of the data. Also consider the interpretability of each model.\n",
" * **Feature Selection:** Experiment with including/excluding variables in the formula. You can use statistical tests (t-tests, p-values) or techniques like stepwise regression to help with variable selection, but focus on your research questions!\n",
" * **Interaction Terms:** Add interaction terms (e.g., `lncoins:idp`) to the formula to see if the effect of one variable depends on the value of another.\n",
" * **Transformations:** Apply transformations to variables (e.g., log transformation for skewed variables) to improve model fit and meet assumptions.\n",
"\n",
"**8. Visualization**\n",
"\n",
" * **Scatter Plots:** Create scatter plots of `mdvis` vs. each continuous independent variable, possibly colored by categorical variables.\n",
" * **Box Plots:** Use box plots to visualize the relationship between `mdvis` and categorical variables.\n",
" * **Coefficient Plots:** Plot the coefficients and confidence intervals from your final model. This is a good way to show the magnitude and significance of each predictor.\n",
" * ```python\n",
" # Example: Coefficient plot\n",
" coefs = model.params\n",
" conf_int = model.conf_int() # confidence intervals\n",
"\n",
" plt.figure(figsize=(10, 6))\n",
" plt.errorbar(coefs.index, coefs, yerr=conf_int.iloc[:, 1] - coefs, fmt='o', capsize=5)\n",
" plt.axhline(y=0, color='red', linestyle='--')\n",
" plt.xlabel(\"Independent Variable\")\n",
" plt.ylabel(\"Coefficient Value\")\n",
" plt.title(\"Coefficient Plot\")\n",
" plt.xticks(rotation=45, ha=\"right\") # Rotate x-axis labels for readability\n",
" plt.tight_layout() # Adjust layout to prevent labels from overlapping\n",
" plt.show()\n",
" ```\n",
"\n",
"**9. Split Data and Evaluate (Optional, but recommended for prediction)**\n",
"\n",
" If you want to use your model to *predict* `mdvis` for new data, it's a good practice to split your data into training and testing sets:\n",
"\n",
" ```python\n",
" from sklearn.model_selection import train_test_split\n",
"\n",
" # Split the data\n",
" X = data.drop('mdvis', axis=1) # Independent variables\n",
" y = data['mdvis'] # Dependent variable\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 80% training, 20% testing\n",
"\n",
" # Re-fit the model on the training data\n",
" model_train = sm.OLS(y_train, sm.add_constant(X_train)).fit() # OLS model\n",
"\n",
" # Make predictions on the test data\n",
" y_pred = model_train.predict(sm.add_constant(X_test)) # OLS model\n",
"\n",
" # Evaluate the model (e.g., using Mean Squared Error - MSE)\n",
" from sklearn.metrics import mean_squared_error\n",
" mse = mean_squared_error(y_test, y_pred)\n",
" print(f\"Mean Squared Error on the test set: {mse}\")\n",
" ```\n",
" **Remember to:**\n",
"\n",
" * Re-fit your model *only* on the training data.\n",
" * Make predictions *only* on the test data.\n",
" * The evaluation metrics (MSE, etc.) will tell you how well your model generalizes to new, unseen data.\n",
"\n",
"**Important Considerations and Best Practices:**\n",
"\n",
"* **Domain Knowledge:** Combine statistical analysis with your knowledge of healthcare and the data. This helps in interpreting results and drawing meaningful conclusions.\n",
"* **Causation vs. Correlation:** Regression analysis shows *correlation*, not *causation*. Be cautious about inferring cause-and-effect relationships.\n",
"* **Iterative Process:** Analysis is often an iterative process. You might need to go back and modify the model, explore different transformations, or collect more data.\n",
"* **Report Findings Clearly:** Clearly state your research question, your methods, the results, and your conclusions, including any limitations of your analysis.\n",
"\n",
"By following these steps, you can effectively analyze the relationship between `mdvis` and other variables in your dataset using `statsmodels` in Python. Remember to carefully consider data preprocessing, model assumptions, and model selection to produce reliable and meaningful results. Good luck!\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"How should I analyze the relationship between mdvis and the other variables using Python statsmodels and the 'randie' dataset already provided\")\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mdvis lncoins idp lpi fmde physlm disea hlthg hlthf hlthp\n",
"0 0 4.61512 1 6.907755 0.0 0.0 13.73189 1 0 0\n",
"1 2 4.61512 1 6.907755 0.0 0.0 13.73189 1 0 0\n",
"2 0 4.61512 1 6.907755 0.0 0.0 13.73189 1 0 0\n",
"3 0 4.61512 1 6.907755 0.0 0.0 13.73189 1 0 0\n",
"4 0 4.61512 1 6.907755 0.0 0.0 13.73189 1 0 0\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20190 entries, 0 to 20189\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 mdvis 20190 non-null int64 \n",
" 1 lncoins 20190 non-null float64\n",
" 2 idp 20190 non-null int64 \n",
" 3 lpi 20190 non-null float64\n",
" 4 fmde 20190 non-null float64\n",
" 5 physlm 20190 non-null float64\n",
" 6 disea 20190 non-null float64\n",
" 7 hlthg 20190 non-null int64 \n",
" 8 hlthf 20190 non-null int64 \n",
" 9 hlthp 20190 non-null int64 \n",
"dtypes: float64(5), int64(5)\n",
"memory usage: 1.5 MB\n",
"None\n"
]
}
],
"source": [
"import pandas as pd\n",
"import statsmodels.api as sm # For statistical modeling\n",
"import statsmodels.formula.api as smf # For using formulas in models\n",
"#from sklearn.model_selection import train_test_split # For splitting the data (optional)\n",
"#import matplotlib.pyplot as plt # For visualization\n",
"#import seaborn as sns # For enhanced visualization\n",
"# Assuming your data is in a CSV file named 'your_data.csv'\n",
"#try:\n",
"# data = pd.read_csv('your_data.csv') # Replace 'your_data.csv' with your actual file name\n",
"#except FileNotFoundError:\n",
"# print(\"Error: 'your_data.csv' not found. Please make sure the file exists and the path is correct.\")\n",
"# exit() # Exit the script if the data can't be loaded\n",
"\n",
"# Display the first few rows to verify the data loaded correctly\n",
"print(randhie.head())\n",
"print(randhie.info())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: mdvis R-squared: 0.069\n",
"Model: OLS Adj. R-squared: 0.068\n",
"Method: Least Squares F-statistic: 165.5\n",
"Date: Sat, 26 Jul 2025 Prob (F-statistic): 7.54e-304\n",
"Time: 14:05:48 Log-Likelihood: -58316.\n",
"No. Observations: 20190 AIC: 1.167e+05\n",
"Df Residuals: 20180 BIC: 1.167e+05\n",
"Df Model: 9 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1.7379 0.084 20.646 0.000 1.573 1.903\n",
"lncoins -0.1695 0.020 -8.406 0.000 -0.209 -0.130\n",
"idp -0.7533 0.075 -9.998 0.000 -0.901 -0.606\n",
"lpi 0.1066 0.014 7.860 0.000 0.080 0.133\n",
"fmde -0.1001 0.011 -8.707 0.000 -0.123 -0.078\n",
"physlm 1.0658 0.103 10.320 0.000 0.863 1.268\n",
"disea 0.1217 0.005 25.006 0.000 0.112 0.131\n",
"hlthg -0.0487 0.067 -0.730 0.465 -0.179 0.082\n",
"hlthf 0.2201 0.122 1.807 0.071 -0.019 0.459\n",
"hlthp 1.4410 0.261 5.527 0.000 0.930 1.952\n",
"==============================================================================\n",
"Omnibus: 20194.587 Durbin-Watson: 1.121\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 1636957.347\n",
"Skew: 4.824 Prob(JB): 0.00\n",
"Kurtosis: 46.044 Cond. No. 123.\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"formula = 'mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp' # Simplest model\n",
"\n",
"# Step 2: Fit the model\n",
"model = smf.ols(formula, data=randhie).fit()\n",
"\n",
"# Step 3: Print model summary\n",
"\n",
"olssummary = model.summary()\n",
"print(olssummary)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's break down the interpretation of the OLS regression results you provided. This is a crucial step to understand the relationships between your variables.\n",
"\n",
"**1. Overall Model Fit:**\n",
"\n",
"* **R-squared (0.069):** This indicates that 6.9% of the variance in `mdvis` (number of medical visits) is explained by the model. This is a relatively low value. It means the model, as it stands, doesn't explain a large portion of the variation in medical visits. You might consider it moderate, with many factors that aren't included playing a larger role.\n",
"* **Adjusted R-squared (0.068):** This is slightly lower than the R-squared, reflecting the penalty for including multiple predictors. The difference is minimal, indicating that the inclusion of additional predictors has only a small impact on improving the model's explanatory power.\n",
"* **F-statistic (165.5) and Prob (F-statistic) (7.54e-304):** The F-statistic tests the overall significance of the model. A large F-statistic and a very small p-value (close to zero) indicate that the model as a whole is statistically significant. In other words, at least one of the independent variables is significantly related to `mdvis`. The extremely low p-value (7.54e-304) makes this a very strong indication of overall significance.\n",
"\n",
"**2. Individual Coefficients and Significance:**\n",
"\n",
"This is where you get the specific relationships between each independent variable and `mdvis`:\n",
"\n",
"* **Intercept (1.7379):** This is the predicted value of `mdvis` when all other independent variables are zero. It's often not directly interpretable in this case because it may not be meaningful for all the variables (e.g., having zero `lncoins`, `lpi`, or medical expenditure could be impossible or outside of the scope of the data).\n",
"\n",
"* **lncoins (-0.1695, p < 0.001):** The coefficient is negative and highly statistically significant (p < 0.001). This suggests that for every one-unit increase in `lncoins` (the natural log of some monetary value), `mdvis` is predicted to decrease by 0.1695 units, *holding all other variables constant*. In plain terms, an increase in the amount of coins is associated with a decrease in medical visits.\n",
"\n",
"* **idp (-0.7533, p < 0.001):** The coefficient is negative and highly significant. This indicates that individuals in category 1 for `idp` have 0.7533 fewer medical visits than those in category 0 *while holding all other variables constant.*\n",
"\n",
"* **lpi (0.1066, p < 0.001):** The coefficient is positive and highly significant. A one-unit increase in `lpi` is associated with an increase of 0.1066 units in the number of medical visits, holding all other variables constant. This implies that an increase in the log of the economic indicator is correlated with more medical visits.\n",
"\n",
"* **fmde (-0.1001, p < 0.001):** The coefficient is negative and highly significant. For every one-unit increase in `fmde` (funds spent on medical expenditures), `mdvis` is predicted to decrease by 0.1001 units, holding all other variables constant.\n",
"\n",
"* **physlm (1.0658, p < 0.001):** The coefficient is positive and highly significant. A one-unit increase in `physlm` (physical limitations) is associated with an increase of 1.0658 units in the number of medical visits, controlling for all the other variables.\n",
"\n",
"* **disea (0.1217, p < 0.001):** The coefficient is positive and highly significant. An increase in the `disea` (disease burden) is associated with an increase of 0.1217 units in the number of medical visits, holding all other variables constant.\n",
"\n",
"* **hlthg (-0.0487, p = 0.465):** The coefficient is negative, but not statistically significant (p = 0.465). This means that there is no statistically significant relationship between good health (`hlthg`) and `mdvis` at the standard significance level (e.g., 0.05). In other words, there isn't enough evidence to say good health influences the number of medical visits.\n",
"\n",
"* **hlthf (0.2201, p = 0.071):** The coefficient is positive and close to significant (p = 0.071). While not statistically significant at a p-value of 0.05, it is close, suggesting a possible positive relationship. The estimated effect is that being categorized as having a good health state for hlthf is related to having an increase of 0.2201 in the number of medical visits, holding all other variables constant.\n",
"\n",
"* **hlthp (1.4410, p < 0.001):** The coefficient is positive and highly significant. This shows that being categorized as having good health for hlthp has a relationship with an increase of 1.4410 in the number of medical visits, holding all other variables constant.\n",
"\n",
"**3. Residual Analysis & Model Assumptions (Important):**\n",
"\n",
"* **Omnibus: 20194.587, Prob(Omnibus): 0.000:** This tests for normality of residuals. The very high Omnibus value and the near-zero probability indicate a *severe* violation of the normality assumption. The residuals are not normally distributed. This is a significant concern for the validity of your inferences from the OLS model.\n",
"* **Skew (4.824):** The residuals are heavily right-skewed (positive skew).\n",
"* **Kurtosis (46.044):** The residuals have very high kurtosis, indicating heavy tails. This means there are a lot of extreme values in the residuals, which is common when the distribution of your dependent variable is not normal.\n",
"* **Durbin-Watson: 1.121:** This statistic tests for autocorrelation (correlation between residuals). A value around 2 suggests no autocorrelation, while values significantly below 2 (like 1.121) might indicate positive autocorrelation, which could be an issue.\n",
"* **Cond. No. (123):** This is the condition number, which can indicate multicollinearity. A value above 30 (or even 100) can suggest potential multicollinearity problems, but with a low R-squared and a large sample size, this is likely less of a problem than the non-normality of the residuals.\n",
"\n",
"**4. Recommendations and Next Steps:**\n",
"\n",
"* **Address the Non-Normality:** The most pressing issue is the severe violation of the normality assumption. Since the residuals are not normal, the p-values and confidence intervals may be unreliable. Consider these approaches:\n",
"\n",
" * **Transform `mdvis`:** Since `mdvis` is likely a count variable or skewed, a logarithmic (log(mdvis +1)) or square root transformation is often a good starting point. *Apply this to the dependent variable, `mdvis`, and rerun the model*. This might make the distribution of the residuals more normal. You must remember to back-transform your interpretations in that case.\n",
" * **Use a Count Data Model:** Given that `mdvis` may be a count variable (number of medical visits), Poisson or Negative Binomial regression could be *much* more appropriate. These models directly address the count nature of the dependent variable and don't require the same assumptions as OLS. Rerun your model with these choices (code provided above). These models are more directly designed for skewed and non-normal data.\n",
"* **Consider the Implications of Low R-squared:** While the model is statistically significant, the low R-squared means that it doesn't explain much of the variation in medical visits. Look for other important variables not included in the model, or that more complex non-linear relationships may exist. You can look at things like geographic location, health insurance coverage, access to care, chronic conditions, age, gender, etc.\n",
"* **Collinearity:** While not a huge issue in this context, you should also check for multicollinearity by calculating VIF (Variance Inflation Factor) values. High VIF values can inflate standard errors and make it harder to interpret the individual effects of variables.\n",
"\n",
"* **Re-evaluate the Model:** After making these changes, re-examine the model summary, the residual plots, and the model fit statistics. The goal is to find a model that better fits your data and meets the necessary assumptions.\n",
"\n",
"* **Consider Interactions:** Given your variables, it's possible that the effect of some independent variables on `mdvis` might depend on the values of other variables (e.g., the effect of `lncoins` might differ for people with different `idp`). Consider adding interaction terms (e.g., `lncoins:idp`) to your model to explore such relationships.\n",
"\n",
"In summary, this initial OLS regression provides some insights, but you must address the non-normality of the residuals before drawing firm conclusions. Transforming `mdvis` or switching to a more appropriate model for count data (Poisson or Negative Binomial) is strongly recommended.\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the linear regression summary \\n \" + str(olssummary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: mdvis R-squared: 0.056\n",
"Model: OLS Adj. R-squared: 0.056\n",
"Method: Least Squares F-statistic: 171.6\n",
"Date: Sat, 19 Jul 2025 Prob (F-statistic): 1.03e-247\n",
"Time: 16:34:42 Log-Likelihood: -58451.\n",
"No. Observations: 20190 AIC: 1.169e+05\n",
"Df Residuals: 20182 BIC: 1.170e+05\n",
"Df Model: 7 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1.5400 0.084 18.379 0.000 1.376 1.704\n",
"idp -0.5110 0.071 -7.219 0.000 -0.650 -0.372\n",
"lpi -0.0132 0.012 -1.143 0.253 -0.036 0.009\n",
"physlm 1.0626 0.104 10.229 0.000 0.859 1.266\n",
"disea 0.1218 0.005 24.958 0.000 0.112 0.131\n",
"hlthg -0.0681 0.067 -1.015 0.310 -0.200 0.063\n",
"hlthf 0.2051 0.123 1.673 0.094 -0.035 0.446\n",
"hlthp 1.5583 0.262 5.940 0.000 1.044 2.073\n",
"==============================================================================\n",
"Omnibus: 20080.716 Durbin-Watson: 1.108\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 1591767.615\n",
"Skew: 4.786 Prob(JB): 0.00\n",
"Kurtosis: 45.433 Cond. No. 118.\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"hie_ols_model = smf.ols(formula = 'mdvis ~ idp + lpi + physlm + disea + hlthg + hlthf + hlthp', data = randhie).fit()\n",
"hie_ols_summary = hie_ols_model.summary()\n",
"print(hie_ols_summary)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's break down this linear regression summary. This output provides a wealth of information about a model predicting the dependent variable `mdvis`. Here's a detailed interpretation:\n",
"\n",
"**1. Model Overview**\n",
"\n",
"* **Dep. Variable:** `mdvis` - This is the dependent variable, the outcome you are trying to predict. We don't know what `mdvis` means without more context, but it is the variable the model is trying to explain.\n",
"* **Model:** OLS (Ordinary Least Squares) - This is the method used to fit the linear regression model. OLS aims to minimize the sum of the squared differences between the observed and predicted values.\n",
"* **Date and Time:** Indicates when the model was run.\n",
"* **No. Observations:** 20190 - The model was built using a dataset with 20,190 observations (rows of data).\n",
"* **Df Residuals:** 20182 - Degrees of freedom for the residuals. This is the number of observations minus the number of parameters estimated in the model (including the intercept).\n",
"* **Df Model:** 7 - Degrees of freedom for the model. This indicates the number of independent variables (predictors) in the model (excluding the intercept).\n",
"* **Covariance Type:** nonrobust - Indicates that the standard errors are calculated assuming homoscedasticity (constant variance of errors). This can be a limitation if the data has heteroscedasticity (non-constant variance).\n",
"\n",
"**2. Model Fit and Performance**\n",
"\n",
"* **R-squared: 0.056** - This is the coefficient of determination. It represents the proportion of variance in `mdvis` that is explained by the model. A value of 0.056 means that only 5.6% of the variability in `mdvis` is explained by the predictors in this model. This is a relatively low R-squared, suggesting that the model doesn't explain much of the variance in `mdvis`. This could indicate that there are other, unincluded variables that have a major effect.\n",
"* **Adj. R-squared: 0.056** - Adjusted R-squared is a modified version of R-squared that adjusts for the number of predictors in the model. It penalizes the inclusion of irrelevant variables. In this case, the adjusted R-squared is very similar to the R-squared, which indicates that the model isn't being excessively inflated by a large number of predictors (since they are the same).\n",
"* **F-statistic: 171.6** - This is the overall F-statistic for the model. It tests the null hypothesis that all the regression coefficients are zero (i.e., that none of the predictors have an effect on `mdvis`).\n",
"* **Prob (F-statistic): 1.03e-247** - This is the p-value associated with the F-statistic. A very small p-value (close to 0) like this means that we can strongly reject the null hypothesis. The model, as a whole, is statistically significant; at least one of the predictor variables has a significant effect on `mdvis`.\n",
"* **Log-Likelihood: -58451.** - The log-likelihood is a measure of the goodness of fit of the model. Higher values generally indicate a better fit. The actual value is not that interpretable unless you are comparing it to another model.\n",
"* **AIC: 1.169e+05 (AIC), BIC: 1.170e+05 (BIC)** - AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are information criteria used to compare different models. Lower values indicate a better-fitting model. They penalize model complexity (the number of predictors). AIC is less strict than BIC. Comparing these values is helpful to assess whether adding a variable (or changing the model) actually *improves* the model's predictive power (it should *decrease* AIC/BIC).\n",
"\n",
"**3. Regression Coefficients and Significance**\n",
"\n",
"This is the most important part, providing the estimated coefficients for each predictor variable.\n",
"\n",
"| Variable | coef | std err | t | P>|t| | \\[0.025 | 0.975] |\n",
"| :-------- | :------ | :------ | :------ | :--- | :------------- | :-------- |\n",
"| Intercept | 1.5400 | 0.084 | 18.379 | 0.000 | 1.376 | 1.704 |\n",
"| idp | -0.5110 | 0.071 | -7.219 | 0.000 | -0.650 | -0.372 |\n",
"| lpi | -0.0132 | 0.012 | -1.143 | 0.253 | -0.036 | 0.009 |\n",
"| physlm | 1.0626 | 0.104 | 10.229 | 0.000 | 0.859 | 1.266 |\n",
"| disea | 0.1218 | 0.005 | 24.958 | 0.000 | 0.112 | 0.131 |\n",
"| hlthg | -0.0681 | 0.067 | -1.015 | 0.310 | -0.200 | 0.063 |\n",
"| hlthf | 0.2051 | 0.123 | 1.673 | 0.094 | -0.035 | 0.446 |\n",
"| hlthp | 1.5583 | 0.262 | 5.940 | 0.000 | 1.044 | 2.073 |\n",
"\n",
"Let's break this down:\n",
"\n",
"* **coef:** The estimated coefficient for each predictor. This is the estimated change in the dependent variable (`mdvis`) for a one-unit change in the predictor variable, *holding all other predictors constant*.\n",
"* **std err:** The standard error of the coefficient. It measures the uncertainty of the estimated coefficient.\n",
"* **t:** The t-statistic. It is calculated by dividing the coefficient by its standard error. It tests the null hypothesis that the coefficient is equal to zero (i.e., that the predictor has no effect).\n",
"* **P>|t|:** The p-value associated with the t-statistic. This is the probability of observing a t-statistic as extreme as the one calculated, *assuming the null hypothesis is true*. If the p-value is less than a pre-defined significance level (usually 0.05), we reject the null hypothesis and conclude that the predictor has a statistically significant effect.\n",
"* **\\[0.025 0.975]:** The 95% confidence interval for the coefficient. This range provides a plausible interval for the true population coefficient.\n",
"\n",
"**Interpreting the Coefficients:**\n",
"\n",
"* **Intercept: 1.5400** - The estimated value of `mdvis` when all predictor variables are equal to zero.\n",
"* **idp: -0.5110, P=0.000:** The variable `idp` has a negative and statistically significant effect on `mdvis`. A one-unit increase in `idp` is associated with a decrease of 0.5110 in `mdvis`, holding other variables constant.\n",
"* **lpi: -0.0132, P=0.253:** The variable `lpi` is *not* statistically significant (p > 0.05). The coefficient is negative, but it is not significantly different from zero. We do not have strong evidence that `lpi` has a significant effect on `mdvis`.\n",
"* **physlm: 1.0626, P=0.000:** The variable `physlm` has a positive and statistically significant effect on `mdvis`. A one-unit increase in `physlm` is associated with an increase of 1.0626 in `mdvis`, holding other variables constant.\n",
"* **disea: 0.1218, P=0.000:** The variable `disea` has a positive and statistically significant effect on `mdvis`. A one-unit increase in `disea` is associated with an increase of 0.1218 in `mdvis`, holding other variables constant.\n",
"* **hlthg: -0.0681, P=0.310:** The variable `hlthg` is *not* statistically significant. The coefficient is negative, but not statistically different from zero.\n",
"* **hlthf: 0.2051, P=0.094:** The variable `hlthf` is almost statistically significant at the 0.10 level but not at the conventional 0.05 level. However, it *could* be deemed marginally significant. It has a positive effect.\n",
"* **hlthp: 1.5583, P=0.000:** The variable `hlthp` has a positive and statistically significant effect on `mdvis`. A one-unit increase in `hlthp` is associated with an increase of 1.5583 in `mdvis`, holding other variables constant.\n",
"\n",
"**4. Residual Analysis (Omnibus, Durbin-Watson, etc.)**\n",
"\n",
"* **Omnibus: 20080.716** - This is a test for the skewness and kurtosis of the residuals. A large value suggests that the residuals are not normally distributed.\n",
"* **Prob(Omnibus): 0.000** - The p-value for the Omnibus test. A very small p-value (like this) indicates that the residuals significantly deviate from a normal distribution. This violates one of the assumptions of linear regression, and results from this analysis may be unreliable.\n",
"* **Jarque-Bera (JB): 1591767.615** - Another test for the normality of the residuals.\n",
"* **Prob(JB): 0.00** - The p-value for the Jarque-Bera test. Again, a small p-value suggests the residuals are not normally distributed.\n",
"* **Skew: 4.786** - Skewness measures the asymmetry of the residuals. A positive skew indicates that the residuals have a long tail to the right (more positive outliers).\n",
"* **Kurtosis: 45.433** - Kurtosis measures the \"tailedness\" or peakedness of the distribution. A kurtosis value significantly higher than 3 (the kurtosis of a normal distribution) indicates that the residuals have heavier tails than a normal distribution (more outliers).\n",
"* **Durbin-Watson: 1.108** - This statistic tests for autocorrelation (correlation) in the residuals. Values closer to 2 suggest no autocorrelation; values less than 2 (especially less than 1) suggest positive autocorrelation, which is often an issue with time series data.\n",
"* **Cond. No.: 118.** - The condition number assesses multicollinearity (high correlation between predictor variables). Values above 30 suggest potential multicollinearity issues. In this case, it is not a huge concern.\n",
"\n",
"**Overall Conclusions and Considerations:**\n",
"\n",
"1. **Weak Predictive Power:** The model has a low R-squared, explaining very little of the variance in `mdvis`.\n",
"2. **Significant Predictors:** Several predictors (`idp`, `physlm`, `disea`, and `hlthp`) have statistically significant effects on `mdvis`. The interpretation of these effects will depend on the context of the variables. `lpi`, `hlthg` are not significant. `hlthf` is marginally significant and should be watched.\n",
"3. **Non-Normal Residuals:** The residuals do not appear to be normally distributed (based on the Omnibus, Jarque-Bera, skewness, and kurtosis), which is a concerning violation of a key assumption for linear regression. This means the p-values and confidence intervals may be unreliable, and the results should be interpreted with caution. The model's ability to make valid inferences may be compromised.\n",
"4. **Autocorrelation:** The Durbin-Watson statistic suggests there is some degree of positive autocorrelation.\n",
"5. **Further Investigation:** You need to explore why the residuals are not normal and whether this is caused by non-linearity, outliers, omitted variables, or an inappropriate functional form. Consider:\n",
" * **Transforming variables:** Applying transformations to your predictor variables (e.g., log transformation) or the dependent variable could improve normality.\n",
" * **Checking for Outliers:** Identify and address influential outliers.\n",
" * **Checking for Non-Linearity:** Examine scatterplots of your variables to see if there's a non-linear relationship.\n",
" * **Checking for Omitted Variables:** Are there other important variables that should be included in the model that might improve the fit?\n",
" * **Consider Robust Standard Errors:** If you can't correct for non-normality, consider using robust standard errors to get more reliable estimates of the standard errors.\n",
" * **Consider other models:** Linear regression may not be the best method to use. The model may not be linear.\n",
"\n",
"**In Summary:**\n",
"\n",
"This model identifies statistically significant relationships between the predictor variables and `mdvis`, but its overall explanatory power is weak. The non-normal residuals are a major concern and require careful consideration and further investigation before drawing firm conclusions. Always remember to interpret your results in the context of the real-world problem you are trying to solve.\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the linear regression summary \\n \" + str(hie_ols_summary))\n",
"print(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Provide background information"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"hie_description = \"\"\"The RAND Health Insurance Experiment (HIE), the most important health insurance study ever conducted, addressed two key questions in health care financing:\n",
"\n",
"How much more medical care will people use if it is provided free of charge?\n",
"What are the consequences for their health?\n",
"The HIE project was started in 1971 and funded by the Department of Health, Education, and Welfare (now the Department of Health and Human Services). It was a 15-year, multimillion-dollar effort that to this day remains the largest health policy study in U.S. history. The study's conclusions encouraged the restructuring of private insurance and helped increase the stature of managed care.\"\"\"\n",
"\n",
"# description from https://www.rand.org/health-care/projects/hie.html\n",
"hie_variable_names = \"\"\" \n",
"Variable name definitions::\n",
"\n",
" mdvis - Number of outpatient visits to an MD\n",
" lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100\n",
" idp - 1 if individual deductible plan, 0 otherwise\n",
" lpi - ln(max(1, annual participation incentive payment))\n",
" fmde - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise\n",
" physlm - 1 if the person has a physical limitation\n",
" disea - number of chronic diseases\n",
" hlthg - 1 if self-rated health is good\n",
" hlthf - 1 if self-rated health is fair\n",
" hlthp - 1 if self-rated health is poor\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try again, but we will use a multi-shot prompt where we provide Gemini with background information and prime the Gen AI."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"hie_chat = model.start_chat()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"```python\n",
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"\n",
"# Assuming your data is in a CSV file named 'randhie.csv'\n",
"# You'll need to replace this with how you actually load your data.\n",
"try:\n",
" randhie = pd.read_csv('randhie.csv')\n",
"except FileNotFoundError:\n",
" print(\"Error: 'randhie.csv' not found. Make sure the file is in the same directory or provide the correct path.\")\n",
" exit() # Exit the script if the data file is missing\n",
"\n",
"\n",
"# --- Data Preprocessing & Handling Missing Values (Crucial!) ---\n",
"# 1. Handle Missing Data: Check for missing values and decide how to handle them.\n",
"# Common methods:\n",
"# - Drop rows with missing values (simplest, but can reduce data)\n",
"# - Impute missing values (e.g., with mean, median, or using more sophisticated methods)\n",
"# - Consider creating an indicator variable for missingness (if missingness is informative)\n",
"\n",
"print(\"Missing Values Before Handling:\")\n",
"print(randhie.isnull().sum())\n",
"\n",
"# Example: Impute missing values with the mean (for numerical columns - adjust as needed)\n",
"for col in randhie.select_dtypes(include=['number']).columns:\n",
" if randhie[col].isnull().any():\n",
" randhie[col].fillna(randhie[col].mean(), inplace=True)\n",
"\n",
"print(\"\\nMissing Values After Handling (Example: Mean Imputation):\")\n",
"print(randhie.isnull().sum())\n",
"\n",
"# 2. Check Variable Types: Make sure your data types are appropriate.\n",
"# Use randhie.dtypes to view data types. You might need to convert some to numeric.\n",
"# For example, if any numeric variables are read as 'object' (strings), convert:\n",
"for col in randhie.columns:\n",
" if randhie[col].dtype == 'object':\n",
" try:\n",
" randhie[col] = pd.to_numeric(randhie[col])\n",
" print(f\"Converted '{col}' to numeric\")\n",
" except ValueError:\n",
" print(f\"Warning: Could not convert '{col}' to numeric. Check the data.\")\n",
"\n",
"\n",
"# --- Regression Analysis ---\n",
"# 1. Choose the right model:\n",
"# - Since your dependent variable (mdvis - number of visits) is a count variable, and the range of visits is not very high, consider a Poisson Regression Model, or a Negative Binomial Model.\n",
"# - OLS is a very poor choice, as the errors of this model will not be normally distributed.\n",
"\n",
"# 2. Prepare your data\n",
"# - Your data is probably ready to go, but double-check no variables are obviously incorrectly formatted.\n",
"\n",
"# 3. Poisson Regression Model\n",
"try:\n",
" # Fit the Poisson regression model\n",
" poisson_model = smf.poisson('mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp', data=randhie).fit()\n",
" print(\"\\nPoisson Regression Results:\")\n",
" print(poisson_model.summary())\n",
"except ValueError as e:\n",
" print(f\"Error with Poisson model: {e}\")\n",
"\n",
"# 4. Negative Binomial Regression Model (Addresses overdispersion, if present)\n",
"try:\n",
" # Fit the Negative Binomial regression model\n",
" nb_model = smf.negativebinomial('mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp', data=randhie).fit()\n",
" print(\"\\nNegative Binomial Regression Results:\")\n",
" print(nb_model.summary())\n",
"\n",
"except ValueError as e:\n",
" print(f\"Error with Negative Binomial model: {e}\") # Print the error message\n",
"\n",
"# 5. Interpretation of Results:\n",
"\n",
"# - Coefficients: The coefficient indicates the change in the *log of the expected count* of mdvis for a one-unit change in the predictor variable.\n",
"# - For Poisson and Negative Binomial regressions, you'll exponentiate the coefficient to get the multiplicative effect on the expected count. Example: If the coefficient for 'lncoins' is -0.5, the expected count of mdvis is multiplied by exp(-0.5) = ~0.61 for a one-unit increase in 'lncoins'. This means visits decrease.\n",
"# - P-values: Assess statistical significance. If the p-value is low (typically < 0.05), the predictor is statistically significant.\n",
"# - Confidence intervals: Provide a range of plausible values for the coefficients.\n",
"# - R-squared: Not directly interpretable in the same way as OLS. Look at pseudo-R-squared measures, but they are not quite the same.\n",
"\n",
"# 6. Post-Estimation Analysis (After model fitting)\n",
"\n",
"# - Goodness-of-fit: Poisson and Negative Binomial models have tests to evaluate how well the model fits the data (e.g., Pearson chi-squared, Deviance). If the model fits poorly, you might need to transform variables, add more predictors, or choose a different model.\n",
"# - Overdispersion: (Especially for Poisson) Check for overdispersion (where the variance is much greater than the mean of the dependent variable). Negative Binomial models are specifically designed to handle overdispersion. You can check this in the model output.\n",
"# - Prediction: You can use the fitted model to predict the number of outpatient visits for specific values of the independent variables. (See the documentation for the `predict()` method in statsmodels.)\n",
"# - Marginal Effects: Calculate and interpret the marginal effects of the predictor variables. This gives a more interpretable idea of the impact of the predictors on the expected number of visits. This will require additional steps and libraries (e.g., `margins`).\n",
"```\n",
"\n",
"Key improvements and explanations:\n",
"\n",
"* **Robust Error Handling:** Includes `try...except` blocks to handle common errors:\n",
" * `FileNotFoundError`: Checks if the CSV file is found. This is essential for real-world code.\n",
" * `ValueError` within the numeric conversion to make sure to avoid the program crashing.\n",
" * `ValueError` around model fitting to handle errors related to model specification or data issues during model fitting. This is *critical* for production code, and helps you debug issues. The error messages will give you hints about the problem.\n",
"* **Data Cleaning and Preprocessing:** The *most important* part of any analysis.\n",
" * **Missing Value Handling:** Demonstrates how to check for missing values (`isnull().sum()`), and a *crucial* example of how to impute them (using the mean) using `fillna()`. *This is a placeholder:* You'll need to decide the *best* way to handle missing data based on your dataset. Different columns might need different strategies. The script shows *how* to do it.\n",
" * **Data Type Conversion:** Includes code to convert object (string) columns to numeric if possible, because it's very common for data to be imported with the wrong data types.\n",
"* **Model Selection:** Critically, it selects the correct type of model for count data (Poisson and Negative Binomial). The original response used OLS, which is incorrect here. The code now includes both.\n",
"* **Poisson and Negative Binomial Models:** Provides the correct `statsmodels` code using `smf.poisson` and `smf.negativebinomial`. The key is `smf` (formula api) to use the formulas for easy syntax.\n",
"* **Clearer Comments and Explanations:** The comments explain *why* the code is doing what it's doing, as well as explaining how to interpret the output.\n",
"* **Concise and Readable Code:** The code is well-formatted and easy to follow.\n",
"* **Complete Example:** The code provides a complete, runnable example, *assuming your data is in the correct format in a CSV file*.\n",
"* **Addresses Overdispersion:** The Negative Binomial model helps address potential overdispersion issues commonly found in count data, making the analysis more reliable.\n",
"* **Interpretation Guidance:** Offers guidance on how to interpret the coefficients, p-values, and confidence intervals. It points out the need for exponentiating the coefficients for the Poisson and Negative Binomial models. Provides a basic explanation of what these mean.\n",
"* **Post-Estimation Analysis Hints:** Includes suggestions for further analysis after fitting the model (goodness-of-fit, overdispersion, prediction, marginal effects).\n",
"\n",
"How to use this code:\n",
"\n",
"1. **Install Libraries:** Make sure you have the necessary libraries installed:\n",
" ```bash\n",
" pip install pandas statsmodels\n",
" ```\n",
"2. **Load Your Data:** Make sure your CSV file (`randhie.csv`) is in the same directory as your Python script, *or* modify the `pd.read_csv()` line to point to the correct file path. *The most likely thing you'll need to change.*\n",
"3. **Check the Data:** Run the code *and examine the output carefully*. The `print` statements will show you:\n",
" * Missing values (before and after handling)\n",
" * Data types of your columns\n",
" * The regression results.\n",
"4. **Handle Missing Values:** *This is the most important step!* Adapt the missing value handling code to your data. The example uses mean imputation, but you might need to:\n",
" * Drop rows with missing data (if you have a lot of data).\n",
" * Use median imputation (more robust to outliers).\n",
" * Use more sophisticated imputation techniques (e.g., k-NN imputation, regression imputation).\n",
" * Create indicator variables for missingness (if missingness is informative).\n",
"5. **Interpret Results:** Carefully examine the regression output. Pay attention to the coefficients, p-values, and confidence intervals.\n",
"6. **Consider Overdispersion:** Check the output of the Poisson regression. If the model exhibits overdispersion, the Negative Binomial model is likely a better choice.\n",
"7. **Prediction and Marginal Effects (Optional):** Once you are happy with your model, use the `predict()` method to make predictions, and consider calculating marginal effects to better understand the impact of your variables.\n",
"8. **Adjust the Formula:** Ensure the formula used in the `smf.poisson` and `smf.negativebinomial` methods includes all the relevant independent variables (predictor variables) in your dataset.\n",
"\n",
"This revised response provides a complete, working solution with extensive error handling and clear explanations, making it a much better answer to the prompt. Remember that data cleaning and preprocessing are essential for any successful statistical analysis!\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Here is a data set called 'randhie' of an health insurance experiment with variable names \\n \" + \n",
" str(hie_description) + \"\\n\" + str(hie_variable_names) + \n",
" \"\\n How should I analyze the relationship between outpatient visits and the other variables using python and statsmodels\")\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 3.091609\n",
" Iterations 6\n",
"\n",
"Poisson Regression Results:\n",
" Poisson Regression Results \n",
"==============================================================================\n",
"Dep. Variable: mdvis No. Observations: 20190\n",
"Model: Poisson Df Residuals: 20180\n",
"Method: MLE Df Model: 9\n",
"Date: Sat, 26 Jul 2025 Pseudo R-squ.: 0.06343\n",
"Time: 14:30:54 Log-Likelihood: -62420.\n",
"converged: True LL-Null: -66647.\n",
"Covariance Type: nonrobust LLR p-value: 0.000\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 0.7004 0.011 62.741 0.000 0.678 0.722\n",
"lncoins -0.0525 0.003 -18.216 0.000 -0.058 -0.047\n",
"idp -0.2471 0.011 -23.272 0.000 -0.268 -0.226\n",
"lpi 0.0353 0.002 19.302 0.000 0.032 0.039\n",
"fmde -0.0346 0.002 -21.439 0.000 -0.038 -0.031\n",
"physlm 0.2717 0.012 22.200 0.000 0.248 0.296\n",
"disea 0.0339 0.001 60.098 0.000 0.033 0.035\n",
"hlthg -0.0126 0.009 -1.366 0.172 -0.031 0.005\n",
"hlthf 0.0541 0.015 3.531 0.000 0.024 0.084\n",
"hlthp 0.2061 0.026 7.843 0.000 0.155 0.258\n",
"==============================================================================\n"
]
}
],
"source": [
"# 3. Poisson Regression Model\n",
"try:\n",
" # Fit the Poisson regression model\n",
" poisson_model = smf.poisson('mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp', data=randhie).fit()\n",
" print(\"\\nPoisson Regression Results:\")\n",
"\n",
" poisson_model_summary=poisson_model.summary()\n",
" print(poisson_model_summary)\n",
"except ValueError as e:\n",
" print(f\"Error with Poisson model: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Current function value: 2.148770\n",
" Iterations: 35\n",
" Function evaluations: 39\n",
" Gradient evaluations: 39\n",
"\n",
"Negative Binomial Regression Results:\n",
" NegativeBinomial Regression Results \n",
"==============================================================================\n",
"Dep. Variable: mdvis No. Observations: 20190\n",
"Model: NegativeBinomial Df Residuals: 20180\n",
"Method: MLE Df Model: 9\n",
"Date: Sat, 26 Jul 2025 Pseudo R-squ.: 0.01845\n",
"Time: 14:40:36 Log-Likelihood: -43384.\n",
"converged: False LL-Null: -44199.\n",
"Covariance Type: nonrobust LLR p-value: 0.000\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 0.6635 0.025 26.786 0.000 0.615 0.712\n",
"lncoins -0.0579 0.006 -9.515 0.000 -0.070 -0.046\n",
"idp -0.2678 0.023 -11.802 0.000 -0.312 -0.223\n",
"lpi 0.0412 0.004 9.938 0.000 0.033 0.049\n",
"fmde -0.0381 0.003 -11.216 0.000 -0.045 -0.031\n",
"physlm 0.2691 0.030 8.985 0.000 0.210 0.328\n",
"disea 0.0382 0.001 26.080 0.000 0.035 0.041\n",
"hlthg -0.0441 0.020 -2.201 0.028 -0.083 -0.005\n",
"hlthf 0.0173 0.036 0.478 0.632 -0.054 0.088\n",
"hlthp 0.1782 0.074 2.399 0.016 0.033 0.324\n",
"alpha 1.2930 0.019 69.477 0.000 1.256 1.329\n",
"==============================================================================\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/louis/Documents/GenAIGemini/venv/lib/python3.10/site-packages/scipy/optimize/_optimize.py:1291: OptimizeWarning: Maximum number of iterations has been exceeded.\n",
" res = _minimize_bfgs(f, x0, args, fprime, callback=callback, **opts)\n",
"/home/louis/Documents/GenAIGemini/venv/lib/python3.10/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
]
}
],
"source": [
"try:\n",
" # Fit the Negative Binomial regression model\n",
" nb_model = smf.negativebinomial('mdvis ~ lncoins + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp', data=randhie).fit()\n",
" print(\"\\nNegative Binomial Regression Results:\")\n",
" nb_model_summary = nb_model.summary()\n",
" print(nb_model_summary)\n",
"\n",
"except ValueError as e:\n",
" print(f\"Error with Negative Binomial model: {e}\") # Print the error message"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's interpret the results from the Poisson and Negative Binomial regression summaries. Remember that the dependent variable is `mdvis` (number of outpatient visits), and the independent variables are:\n",
"\n",
"* `lncoins`: Natural log of (coinsurance + 1)\n",
"* `idp`: 1 if individual deductible plan, 0 otherwise\n",
"* `lpi`: Natural log of (max(1, annual participation incentive payment))\n",
"* `fmde`: 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise\n",
"* `physlm`: 1 if the person has a physical limitation\n",
"* `disea`: Number of chronic diseases\n",
"* `hlthg`: 1 if self-rated health is good\n",
"* `hlthf`: 1 if self-rated health is fair\n",
"* `hlthp`: 1 if self-rated health is poor\n",
"\n",
"**General Interpretation Notes (for both models):**\n",
"\n",
"* **Coefficients:** The coefficients represent the *change in the log of the expected count* of `mdvis` for a one-unit change in the predictor variable, *holding all other variables constant*. To get the effect on the *expected count* itself, you need to exponentiate the coefficient (e.g., `exp(coefficient)`). This provides the multiplicative effect.\n",
"* **P-values (P>|z|):** These indicate the statistical significance of the coefficients. A low p-value (typically < 0.05) suggests that the predictor variable has a statistically significant effect on the number of outpatient visits. The p-value tells us the probability of observing the results we did if there was actually no effect (the null hypothesis).\n",
"* **Confidence Intervals ([0.025 0.975]):** The range of values within which the true coefficient likely falls.\n",
"* **Pseudo R-squared:** A measure of how well the model fits the data, similar to R-squared in OLS regression. Higher values generally indicate a better fit, but the interpretation is not as direct as with OLS. The values here are quite low for both models, suggesting that these variables explain a relatively small portion of the variance in `mdvis`.\n",
"* **Overdispersion:** The Negative Binomial model is designed to handle overdispersion. If the Poisson model shows signs of overdispersion (the variance of the outcome is greater than the mean, which is common with count data), then the Negative Binomial model is more appropriate.\n",
"\n",
"**Poisson Regression Interpretation:**\n",
"\n",
"* **Intercept:** The intercept (0.7004) is the estimated log of the expected number of visits when all other predictors are zero. Since all variables are either logs, or 0/1, this is the estimate of the *log* of expected visits with none of the other factors being present. Exponentiating this, exp(0.7004) = 2.01. This suggests an expected number of outpatient visits to be approximately 2.\n",
"* **`lncoins`:** Coefficient: -0.0525, p-value < 0.001. A one-unit increase in `lncoins` is associated with a decrease in the *log* of the expected number of visits by 0.0525 units. Exponentiating: `exp(-0.0525) = 0.949`. This means that as `lncoins` increases by one unit, the expected number of visits is multiplied by 0.949. In other words, as the coinsurance increases, the number of visits decreases. This is expected, since higher coinsurance increases the out-of-pocket cost of care.\n",
"* **`idp`:** Coefficient: -0.2471, p-value < 0.001. People in individual deductible plans (`idp = 1`) have, on average, a log-count of outpatient visits that is 0.2471 units *lower* than those not in those plans (`idp = 0`). Exponentiating this: `exp(-0.2471) = 0.781`. This suggests a decrease of about 22% in the number of visits. This makes sense, because the deductible plans are often set up to be \"catastrophic\" coverage - you only get coverage when you've hit your deductible.\n",
"* **`lpi`:** Coefficient: 0.0353, p-value < 0.001. A one-unit increase in `lpi` is associated with an increase in the log of expected number of visits by 0.0353. `exp(0.0353) = 1.036`. The participation incentive payment is associated with a slight increase in visits, which is perhaps counterintuitive (if the payment is for *not* using services, that might decrease visits).\n",
"* **`fmde`:** Coefficient: -0.0346, p-value < 0.001. A one-unit increase in `fmde` is associated with a decrease in the log of the expected number of visits by 0.0346. `exp(-0.0346) = 0.966`. People with the `fmde` value (related to the maximum deductible exposure) have fewer visits.\n",
"* **`physlm`:** Coefficient: 0.2717, p-value < 0.001. People with a physical limitation (`physlm = 1`) have, on average, a log count of outpatient visits that is 0.2717 units *higher* than those without limitations (`physlm = 0`). Exponentiating: `exp(0.2717) = 1.312`. This indicates they visit the doctor more often. This makes perfect sense.\n",
"* **`disea`:** Coefficient: 0.0339, p-value < 0.001. For each additional chronic disease, the log of the expected number of visits increases by 0.0339 units. `exp(0.0339) = 1.035`. This suggests an increase of 3.5%. This is a very sensible result.\n",
"* **`hlthg`:** Coefficient: -0.0126, p-value = 0.172. The coefficient is *not* statistically significant (p > 0.05). It is likely that the self reported health is not related to the number of visits, when controlling for the other factors.\n",
"* **`hlthf`:** Coefficient: 0.0541, p-value = 0.000. The fair self-reported health is associated with more doctor visits, compared to the reference.\n",
"* **`hlthp`:** Coefficient: 0.2061, p-value < 0.001. People who report poor health have an average increase in the log count of visits of 0.2061, which means that they visit doctors more than the reference group.\n",
"\n",
"**Negative Binomial Regression Interpretation:**\n",
"\n",
"The interpretation is similar to the Poisson model, but the Negative Binomial model is preferred if there is evidence of overdispersion (variance greater than the mean) in the data, or if the data does not fit the restrictive assumption of equality of mean and variance in Poisson model.\n",
"\n",
"* **Intercept:** 0.6635. Similar interpretation as in Poisson model: the expected log count of visits when all the other factors are zero. Exponentiating, `exp(0.6635) = 1.94`.\n",
"* **`lncoins`:** -0.0579, p-value < 0.001. A one-unit increase in `lncoins` is associated with a decrease in the log expected number of visits by 0.0579. `exp(-0.0579) = 0.944`. The interpretation is the same, higher coinsurance decreases visits.\n",
"* **`idp`:** -0.2678, p-value < 0.001. The estimated log count of visits is lowered by 0.2678 if the person is on an individual deductible plan. `exp(-0.2678) = 0.765`. This is quite similar to the effect in the Poisson regression.\n",
"* **`lpi`:** 0.0412, p-value < 0.001. The log of expected visits increases.\n",
"* **`fmde`:** -0.0381, p-value < 0.001. The log of expected visits decreases by 0.0381.\n",
"* **`physlm`:** 0.2691, p-value < 0.001. Physical limitation means an increase in visits.\n",
"* **`disea`:** 0.0382, p-value < 0.001. More diseases means more visits.\n",
"* **`hlthg`:** -0.0441, p-value = 0.028. Self-reported good health results in fewer visits, but this is not as strong as some of the other results.\n",
"* **`hlthf`:** 0.0173, p-value = 0.632. *Not* statistically significant.\n",
"* **`hlthp`:** 0.1782, p-value = 0.016. Having poor health results in increased doctor visits, which is as expected.\n",
"* **`alpha`:** (Crucially, unique to the Negative Binomial Model). `alpha` = 1.2930, p-value < 0.001. The `alpha` parameter (also called the dispersion parameter) is an indicator of the overdispersion. A non-zero `alpha` indicates that the variance is greater than the mean, meaning the negative binomial model is more appropriate than the Poisson model. If `alpha` is close to zero, the negative binomial model approaches a Poisson model.\n",
"\n",
"**Key Differences and Considerations:**\n",
"\n",
"* **Overdispersion:** Check for signs of overdispersion in the Poisson model. The Negative Binomial model is designed to handle this. The `alpha` value in the Negative Binomial is positive, confirming the overdispersion. This means the Negative Binomial model is more appropriate.\n",
"* **Statistical Significance:** Pay attention to the p-values to determine which variables have a statistically significant impact on `mdvis`. Note that the standard errors and resulting p-values can be different between the models, leading to slightly different conclusions.\n",
"* **Magnitude of Effects:** While both models show similar directions of effect (e.g., higher coinsurance reduces visits), the magnitude of the effects might vary slightly. Exponentiate the coefficients to compare multiplicative effects directly.\n",
"* **Model Fit:** The pseudo R-squared values are low, indicating that the variables included in this model don't explain a huge proportion of the variance in the number of visits. This isn't necessarily a problem; the model can still provide valuable insights.\n",
"* **Further Analysis:** To get a more detailed understanding:\n",
" * **Calculate and interpret the *incidence rate ratio* (IRR):** Exponentiate the coefficients to determine the multiplicative effect of each variable on the *expected rate* of visits. This is often easier to understand than the log-scale coefficients. (e.g., for `lncoins`, IRR = exp(-0.0525) in the Poisson model).\n",
" * **Marginal Effects:** Calculate the marginal effects. This provides the change in the *expected number of visits* for a one-unit change in each predictor variable. This is a more direct interpretation. The `margins` package in Python can be used for this.\n",
" * **Model Diagnostics:** Examine model diagnostics. For Poisson regression, this includes checking the Pearson chi-squared statistic and deviance to see how well the model fits the data. For Negative Binomial, this is less of a concern due to its overdispersion handling, but you can still use the deviance.\n",
"\n",
"In summary: The Negative Binomial model appears to be the better choice. The results suggest that higher coinsurance reduces visits, individual deductible plans decrease visits, having physical limitations and more chronic diseases increases visits, and there is a noticeable effect of health perceptions, with those in fair health having visits that are not statistically different than the reference.\n",
"\n",
"This interpretation is based on the provided summaries. For a more thorough analysis, the recommendations for additional steps (IRR, marginal effects, and model diagnostics) should be followed. Also, the data should be examined in more detail. For example, outliers in `mdvis` could influence the results.\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the poisson and negative binomial regression summary based on the health insurance experiment \\n \" + str(poisson_model_summary) + \"\\n\" + str(nb_model_summary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lessons observed from the Statistical Analysis\n",
"\n",
"- Gen AI provides a very complete workflow.\n",
"- Gives a fairly complete interpretation of the summary\n",
" - It uses the format of the regression summary as an outline\n",
" - Includes evaluation of model assumptions!\n",
"- Problems when there are legitimate options as expressed on the internet.\n",
" - Formula representations vs vector-matrix representations of the regression model\n",
" - Statistician view of regression vs machine learning (predictive model) view of regression\n",
" - Multiple class heirarchies for regression in statsmodels\n",
"- Provided multiple options for analysis\n",
" - Gives criteria for each option\n",
" - Evaluates diagnostic statistics."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's break down these results from the health insurance experiment for a public health policy analyst. We'll focus on what the findings mean for public health interventions and strategies, keeping it clear and straightforward.\n",
"\n",
"**Simplified Summary for the Public Health Policy Analyst:**\n",
"\n",
"This study investigated what affects how often people visit the doctor. The main goal is to help figure out how to promote better health and improve how healthcare is used, which means making it more efficient and accessible for everyone. The results show:\n",
"\n",
"* **Money Matters (lncoins):** When people have to pay more out-of-pocket for their healthcare (coinsurance), they tend to go to the doctor less often.\n",
"* **Plan Type Matters (idp):** People in health plans with deductibles (where they have to pay a certain amount before insurance kicks in) also go to the doctor less often than those with more comprehensive plans.\n",
"* **Incentives and Visits (lpi):** If people get rewards or incentives for things like going to wellness programs, this is linked to more doctor visits.\n",
"* **Cost of Care Matters (fmde):** People with higher maximum deductible exposure (the most they could pay in a year) tend to visit the doctor less.\n",
"* **Health Status and Doctor Visits:** People with physical limitations, more chronic diseases, or who report being in poor health visit the doctor more often.\n",
"* **How People Feel About Their Health:** People who say their health is \"good\" go to the doctor less often, while those who say their health is \"poor\" visit the doctor more.\n",
"\n",
"**Detailed Breakdown with Public Health Implications:**\n",
"\n",
"1. **Cost Sharing (lncoins):**\n",
"\n",
" * **What it means:** If you have to pay a larger percentage of your healthcare costs yourself, you'll go to the doctor less frequently.\n",
" * **Why it matters for public health:** This shows that financial barriers can prevent people from getting care.\n",
" * **What we can do:**\n",
" * **Consider the impact on lower-income individuals:** Ensure that healthcare costs are not a barrier to essential care for those with fewer resources.\n",
" * **Promote education about the benefits of preventive care:** This is crucial, since people tend to cut back on this, too, with rising costs.\n",
" * **Evaluate the balance:** While cost-sharing can reduce spending, we need to be sure that it doesn't keep people from getting services that could help them stay healthy and avoid more serious, expensive problems down the line.\n",
"\n",
"2. **Deductible Plans (idp):**\n",
"\n",
" * **What it means:** People in insurance plans where they have to pay a certain amount of money (a deductible) before their insurance covers anything tend to go to the doctor less often.\n",
" * **Why it matters for public health:** This result is similar to the one above, showing that the way insurance is set up can affect whether or not people seek care.\n",
" * **What we can do:**\n",
" * **Promote education on insurance plan design:** People should understand how their plan works, especially the deductibles. If they don't understand the plan, they are more likely to skip care.\n",
" * **Increase access to affordable insurance:** A high deductible can discourage people from getting care, but some plans can be very affordable.\n",
" * **Consider what services the insurance covers:** Sometimes plans cover some things like preventative care and vaccinations even if the deductible hasn't been met yet.\n",
"\n",
"3. **Incentive Payments (lpi):**\n",
"\n",
" * **What it means:** Giving people rewards for things like going to wellness programs is linked to more doctor visits.\n",
" * **Why it matters for public health:** This suggests that financial incentives can influence health behaviors and the use of healthcare services.\n",
" * **What we can do:**\n",
" * **Ensure program is useful:** Make sure that the incentive programs help people, otherwise people are likely to become disinterested.\n",
" * **Offer services in an easier way:** People will choose the easier options in life.\n",
" * **Make sure that the programs are not harmful:** This is especially true with things like wellness programs, as people might share information about their health or medical history that they are not comfortable with.\n",
"\n",
"4. **Maximum Deductible Exposure (fmde):**\n",
"\n",
" * **What it means:** People with higher maximum deductible exposure (the most they could pay in a year) tend to visit the doctor less.\n",
" * **Why it matters for public health:** High exposure means high cost, which means people will visit the doctor less.\n",
" * **What we can do:**\n",
" * **Offer the best insurance design:** To help people with these issues, policy makers will need to offer insurance plans that help people afford their healthcare.\n",
" * **Reduce costs in healthcare:** This can mean cutting back on some procedures, and also cutting back on the amount of paperwork to cut down on overhead.\n",
" * **Make sure people understand the policy:** Make sure people understand all of the options.\n",
"\n",
"5. **Health Status and Doctor Visits:**\n",
"\n",
" * **What it means:** People with physical limitations, more chronic diseases, or who report being in poor health visit the doctor more often.\n",
" * **Why it matters for public health:** This is expected, as people with health problems need care more often.\n",
" * **What we can do:**\n",
" * **Focus on public health measures:** Public health interventions include public health measures like food safety programs, or other things that are designed to help communities as a whole.\n",
" * **Preventative care:** Support prevention efforts to stop health problems from starting in the first place.\n",
" * **Care management programs:** Provide and improve programs that help people with chronic conditions manage their health and get the care they need.\n",
"\n",
"6. **How People Feel About Their Health:**\n",
"\n",
" * **What it means:** People who say their health is \"good\" go to the doctor less often. People who say their health is \"poor\" visit the doctor more.\n",
" * **Why it matters for public health:** A person's own idea of their health can affect their use of healthcare.\n",
" * **What we can do:**\n",
" * **Improve education:** Educating people on the definition of health can help.\n",
" * **Make it easier to manage your health:** Improve access to care to stop health problems from getting worse.\n",
"\n",
"**Important Considerations for the Public Health Analyst:**\n",
"\n",
"* **Small Effect (Pseudo R-squared):** The study's findings don't explain most of the reasons why people visit the doctor. There are other important things that the study didn't capture, such as people's culture and the availability of doctors and clinics.\n",
"* **The Model Fit:** The results can still be a helpful place to start, but there are other factors that could change the results.\n",
"* **Real-World Context:** The results are from a study. The results might be different in real life.\n",
"\n",
"**In Summary:**\n",
"\n",
"This study points out the ways to change how people see healthcare and how they use it. Policymakers can use this information to improve access, manage costs, and ensure that everyone can benefit from better health. By understanding the results, public health professionals can create more effective public health initiatives.\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the negative binomial regression summary based on the health insurance experiment for a public health policy analyst who may not be a statistician\\n \" \"\\n\" + str(nb_model_summary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Okay, let's interpret the Negative Binomial regression results from the health insurance experiment from the perspective of a health policy decision-maker. This means focusing on the implications for policy choices and resource allocation.\n",
"\n",
"**Executive Summary for the Health Policy Decision-Maker:**\n",
"\n",
"This analysis examines factors affecting the number of outpatient doctor visits (`mdvis`). The key findings have implications for cost control, access to care, and the design of insurance plans. The results show the following statistically significant effects:\n",
"\n",
"* **Cost Sharing (lncoins):** Higher coinsurance (cost-sharing) is associated with fewer doctor visits. This suggests that increasing cost-sharing could reduce healthcare utilization and costs.\n",
"* **Deductible Plans (idp):** Individuals in deductible plans have fewer visits compared to those with more comprehensive coverage.\n",
"* **Incentive Payments (lpi):** Incentive payments (e.g., for participating in wellness programs) are associated with slightly *more* visits.\n",
"* **Maximum Deductible Exposure (fmde):** Individuals with higher maximum deductible exposure have fewer visits.\n",
"* **Health Status & Physical Limitations:** Individuals with physical limitations, chronic diseases, or poor health tend to have more visits.\n",
"* **Self-Reported Health:** Individuals reporting \"good\" health have fewer visits, while those reporting \"poor\" health have more visits.\n",
"\n",
"The results suggest that changing the design of insurance plans can affect the number of doctor visits.\n",
"\n",
"**Detailed Interpretation with Policy Implications:**\n",
"\n",
"1. **Cost Sharing (lncoins):**\n",
"\n",
" * **Coefficient:** -0.0579.\n",
" * **Interpretation:** A one-unit increase in the *log* of (coinsurance + 1) results in an estimated decrease of 0.0579 units in the *log* of the *expected* number of outpatient visits. For a more practical interpretation, we exponentiate this coefficient: `exp(-0.0579) = 0.944`. This means that, *holding all other factors constant*, for a 100% increase in the *coinsurance* (which is already on a log scale), the expected number of doctor visits *decreases* by about 5.6% (1-0.944).\n",
" * **Policy Implication:** Increasing coinsurance can be a tool for managing healthcare costs by reducing utilization. However, policymakers must consider:\n",
" * **Equity:** Higher cost-sharing disproportionately affects lower-income individuals, who may delay or forgo necessary care due to cost concerns. This can lead to worse health outcomes in the long run.\n",
" * **Appropriate Care:** Cost-sharing might deter *both* necessary and unnecessary care. Policymakers need to be mindful of this and consider carve-outs or other mechanisms to protect access to essential services.\n",
" * **Demand Elasticity:** The reduction in visits is based on an assumption of the log-scale of the coinsurance, meaning a 100% increase is likely very small in reality.\n",
" * **Implementation Considerations:** How coinsurance is calculated.\n",
" * **Decision Point:** Carefully consider the trade-off between cost savings and potential adverse effects on access and health outcomes.\n",
"\n",
"2. **Deductible Plans (idp):**\n",
"\n",
" * **Coefficient:** -0.2678.\n",
" * **Interpretation:** Individuals in deductible plans (`idp = 1`) have, on average, a log count of outpatient visits that is 0.2678 units *lower* than those not in those plans. Exponentiating: `exp(-0.2678) = 0.765`. This suggests that, *holding other factors constant*, people in these plans have roughly 23% fewer doctor visits.\n",
" * **Policy Implication:** High-deductible health plans (HDHPs) are designed to reduce costs by shifting more financial responsibility to consumers. This finding supports the effectiveness of HDHPs in reducing utilization. However,\n",
" * **Transparency and Plan Design:** HDHPs require clear and understandable information for consumers, so they can make informed decisions about care.\n",
" * **Preventive Care:** Consider exempting preventive services from the deductible to encourage early intervention and reduce the need for more expensive care later.\n",
" * **Decision Point:** Promote HDHPs as a cost-control mechanism, but with strategies to maintain access to appropriate care.\n",
"\n",
"3. **Incentive Payments (lpi):**\n",
"\n",
" * **Coefficient:** 0.0412.\n",
" * **Interpretation:** A one-unit increase in `lpi` (which is the log of the participation incentive payment) is associated with a 0.0412 unit increase in the *log* of the expected number of visits. Exponentiating, `exp(0.0412) = 1.042`. This suggests that people are likely to increase their number of visits, likely due to the increase in the level of services.\n",
" * **Policy Implication:** The impact of incentive payments on healthcare utilization is complex. Policymakers should consider:\n",
" * **Program Goals:** Clearly define the goals of the incentive program. Is it to reduce unnecessary visits, encourage preventive care, or improve chronic disease management?\n",
" * **Incentive Structure:** The structure of the incentive program, including the size of the payment and how it's earned, can influence its effectiveness.\n",
" * **Monitoring and Evaluation:** Regularly monitor and evaluate the program's impact on both utilization and health outcomes.\n",
" * **Decision Point:** Design and implement incentive programs with caution, focusing on clear goals and careful evaluation.\n",
"\n",
"4. **Maximum Deductible Exposure (fmde):**\n",
"\n",
" * **Coefficient:** -0.0381.\n",
" * **Interpretation:** A one-unit increase in `fmde` is associated with a 0.0381 unit decrease in the *log* of the expected number of visits.\n",
" `exp(-0.0381) = 0.963`.\n",
" * **Policy Implication:** Lowering the maximum deductible exposure to reduce the number of visits.\n",
" * **Cost Considerations:** Lowering the exposure means lower costs for the policy holder, and an increase in the number of visits.\n",
" * **Monitoring and Evaluation:** Regularly monitor and evaluate the program's impact on both utilization and health outcomes.\n",
" * **Decision Point:** Design and implement a policy, focusing on clear goals and careful evaluation of policy.\n",
"\n",
"5. **Health Status and Physical Limitations:**\n",
"\n",
" * **Coefficients:**\n",
" * `physlm`: 0.2691\n",
" * `disea`: 0.0382\n",
" * `hlthp`: 0.1782\n",
" * **Interpretation:** Individuals with physical limitations, more chronic diseases, or poor health have more outpatient visits. This is, of course, expected.\n",
" * **Policy Implication:** Focus on:\n",
" * **Care Management Programs:** Develop and support care management programs, especially for individuals with chronic diseases, to coordinate care, improve adherence to treatment plans, and potentially reduce unnecessary emergency room visits.\n",
" * **Preventive Care:** Promote preventive care and early intervention to identify and address health issues before they become more serious and require more intensive (and expensive) treatments.\n",
" * **Addressing Social Determinants of Health:** Recognize the role of social determinants of health (e.g., poverty, access to transportation) and address them through policy and programs.\n",
" * **Decision Point:** Invest in programs that address the needs of individuals with chronic conditions and poor health.\n",
"\n",
"6. **Self-Reported Good Health (hlthg):**\n",
"\n",
" * **Coefficient:** -0.0441, p-value = 0.028.\n",
" * **Interpretation:** Individuals reporting \"good\" health have an average log-count of visits that is 0.0441 units *lower* than those who do not.\n",
" * **Policy Implication:** This reinforces the importance of focusing on preventive care and promoting a healthy lifestyle.\n",
" * **Decision Point:** Support public health initiatives promoting healthy behaviors.\n",
"\n",
"**Additional Considerations for the Decision-Maker:**\n",
"\n",
"* **Model Fit:** The pseudo R-squared is low (0.01845). This means that, while the included variables are *statistically significant*, they only explain a small portion of the total variation in the number of outpatient visits. This suggests other factors (e.g., individual preferences, access to specialists, geographic location) are also very important.\n",
"* **Overdispersion:** The `alpha` parameter (1.293) is statistically significant, confirming the overdispersion in the data and validating the use of the Negative Binomial model.\n",
"* **External Validity:** Consider whether the findings of this experiment are generalizable to the broader population or to different insurance markets.\n",
"* **Data Limitations:** Understand the limitations of the data. The results are based on the variables included in the analysis. Other relevant factors might be missing.\n",
"* **Cost-Benefit Analysis:** Carefully evaluate the potential costs and benefits of any policy changes. Consider the impact on access, health outcomes, and overall healthcare spending.\n",
"* **Pilot Programs:** Consider piloting policy changes on a small scale before implementing them broadly.\n",
"\n",
"**Conclusion for the Health Policy Decision-Maker:**\n",
"\n",
"This analysis provides valuable insights into the factors that influence outpatient doctor visits and, therefore, healthcare costs and utilization. The findings support the notion that health insurance design plays a significant role in these factors. The analysis supports several policy options (e.g., adjusted cost sharing, promotion of HDHPs, targeted care management programs) but underscores the need for a careful balance between cost control, equitable access, and the promotion of positive health outcomes. Any policy changes should be carefully monitored and evaluated to ensure they achieve the desired results.\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the negative binomial regression summary based on the health insurance experiment for an health policy decision maker\\n \" \"\\n\" + str(nb_model_summary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"**Briefing for Community Decision-Makers: Doctor Visits and Health Insurance**\n",
"\n",
"Good morning. We've analyzed some data on how different health insurance plans affect how often people visit the doctor. This information is valuable for making informed decisions about healthcare in our community.\n",
"\n",
"**The Key Takeaways (What We Found):**\n",
"\n",
"* **Cost Matters:** When people have to pay more out-of-pocket for their doctor visits (coinsurance), they tend to go less often. This means that if healthcare becomes more expensive, people may see the doctor less.\n",
"* **Plan Type Matters:** Insurance plans that have deductibles (where you pay a set amount before your insurance covers the costs) also lead to fewer doctor visits.\n",
"* **Incentives Can Help (Sometimes):** Rewards for healthy behaviors, such as participating in wellness programs, can lead to slightly more doctor visits.\n",
"* **Health Status & Doctor Visits:**\n",
" * People with physical limitations or chronic diseases (long-term health issues) tend to visit the doctor more frequently.\n",
" * People who report being in poor health also visit the doctor more often.\n",
" * People who report good health visit the doctor less often.\n",
"\n",
"**What This Means for Our Community:**\n",
"\n",
"* **Access to Care:** The cost of healthcare and how insurance plans are structured can influence whether people get the medical care they need. This suggests that we need to be very careful about what healthcare services cost.\n",
"* **Health Promotion:** Encouraging preventive care (check-ups, screenings) and healthy lifestyles remains essential.\n",
"\n",
"**Important Considerations:**\n",
"\n",
"* **Balance:** We need to balance the need to control healthcare costs with the need to ensure that everyone in our community has access to the care they need, especially those with existing health challenges.\n",
"* **Further Research:** Further analysis is needed to help provide the best plan of action for our community.\n",
"\n",
"**In short:** The cost of insurance and the design of insurance plans can influence the number of doctor visits. We need to consider these factors when making decisions about healthcare policies in our community. We can improve the community's health and access to care with better plans of action.\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the negative binomial regression summary based on the health insurance experiment to brief a community decision maker\\n \" \"\\n\" + str(nb_model_summary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"**FOR IMMEDIATE RELEASE**\n",
"\n",
"**New Study Reveals How Health Insurance Design Impacts Doctor Visits**\n",
"\n",
"[CITY, STATE] – [Date] – A new study examining the effects of different health insurance designs on healthcare utilization has revealed important insights for policymakers and consumers alike. The study, based on analysis of the [Name of Study] data, found a direct correlation between insurance plan features and the frequency of doctor visits.\n",
"\n",
"\"Our research provides clear evidence that how health insurance is structured has a significant impact on how often people seek medical care,\" said [Name and Title of Lead Researcher or Spokesperson]. \"Understanding these connections is critical for developing policies that balance cost-effectiveness with patient access to care.\"\n",
"\n",
"**Key Findings of the Study:**\n",
"\n",
"The study, which analyzed a large dataset of over 20,000 individuals, found the following:\n",
"\n",
"* **Cost-Sharing Impact:** When patients had to pay a greater portion of their healthcare costs (coinsurance), they tended to visit the doctor less frequently.\n",
"* **Deductibles Influence Utilization:** Those enrolled in health plans with deductibles (where a set amount must be paid before insurance kicks in) also showed fewer doctor visits.\n",
"* **Wellness Programs and Visits:** Incentive programs, such as those rewarding participation in wellness activities, were associated with a slight increase in doctor visits.\n",
"* **Health Status and Doctor Visits:** Individuals with existing health conditions, physical limitations, or who reported their health as poor, visited doctors more often, as expected.\n",
"\n",
"**Implications for Consumers and Policymakers:**\n",
"\n",
"These findings highlight the importance of carefully considering the design of health insurance plans.\n",
"\n",
"* **Cost & Access:** While cost-sharing can help control healthcare spending, it's essential to ensure that financial barriers don't prevent people from accessing necessary medical care.\n",
"* **Plan Transparency:** Consumers should be well-informed about the costs and benefits of their health insurance plans, including any deductible requirements and the availability of preventive services.\n",
"* **Targeted Strategies:** Healthcare providers and policymakers should consider targeted approaches for individuals with chronic conditions, as they often require more frequent care.\n",
"\n",
"“[Quote from researcher or spokesperson about the broader significance of the findings, for example: ‘These findings underscore the importance of data-driven policymaking and highlight the need for insurance plans that are affordable, accessible, and promote overall health and well-being.’]”\n",
"\n",
"**About the Study:**\n",
"\n",
"[Briefly describe the study, the data source, and the methods used, keeping it understandable for the general public].\n",
"\n",
"**Contact:**\n",
"\n",
"[Name]\n",
"[Title]\n",
"[Email Address]\n",
"[Phone Number]\n",
"\n"
]
}
],
"source": [
"hie_response = hie_chat.send_message(\"Interpret the results from the negative binomial regression summary based on the health insurance experiment to be included in a press release\\n \" \"\\n\" + str(nb_model_summary))\n",
"print(hie_response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Observations from interpretations of statistical analysis\n",
"\n",
"- Interpretations are extensive, with identification of important points for the specific audience.\n",
"- Language changes to accomodate the audience, from the initial technical presentation to less technical.\n",
"- Focus on the needs of the audience: information, decision making.\n",
"- Observe that as the audience became less technical, tone became less neutral.\n",
" - Gen AI is observed to be very positive tone if not given guidelines."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Observations about topics with no consensus\n",
"\n",
"- In python 'statsmodels' there are two standard ways of expressing regression models\n",
" - Statistical model ('y ~ x') similar to R\n",
" - vector matrix \n",
"- Both are in common use and are found in many references, tutorials, message boards, etc.\n",
"- Machine learning engineers have developed coding patterns to automatically including all features.\n",
" - Because of how I asked the question, this should not have come up, but the randomness of Gen AI makes it possible.\n",
"\n",
"## Generative AI assembles answers by randomly choosing from sources that include prior tokens\n",
"\n",
"- Generative AI will have to choose between these two methods when asked to formulate a regression model for statsmodels.\n",
"- When I run this tutorial, either version can appear.\n",
"\n",
"\n",
"## Statsmodels also has two class paths for discrete regressions\n",
"- 'statsmodels.genmod'\n",
"- 'statsmodels.discrete'\n",
"- Generative AI will choose between these randomly. And switch mid-stream.\n",
"\n",
"## The random generation is repeated at each step\n",
"- If it took from a single source during a run, no problem\n",
"- If it shuffles between multiple sources, and the sources are not consistent, can have errors.\n",
"\n",
"## Generative AI generally gives answers that are the internet consensus\n",
"- In your situation, does an internet consensus actually exist?\n",
"- Do you expect that in your application, the internet consensus is what you need?\n",
" - I am a data scientist, by definition, scientists are working on questions with some aspect that has not been analyzed before.\n",
" - My company occupies a specific economic niche based on its geography, its customer base, its workforce, it supplier base, and its strategy.\n",
"- Note that the 'temperature' parameter merely screens out options with lower probability, it does not evaluate for correctness, only popularity.\n",
"\n",
"# Using Generative AI\n",
"\n",
"- Context, collaboration, conscious: In the prompt, provide specific background.\n",
" - Could mean standard documents or a library\n",
" - Remember that LLM will interpret your documents using everything it knows from its training data (internet)\n",
"- Structure prompts\n",
" - Structure the request to include needed information (*prompt engineering* methods are generally some variation of this)\n",
" - Provide structure about what kind of response you need for the response to be useful.\n",
"- Human review\n",
" - A person should always create the final version of anything that gets published.\n",
" - Final review owns the output.\n"
]
}
],
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