Linear fit on x and y, in y = mx + b form

fit.jl

function fit(x,y)
	x̄ = mean(x)
	ȳ = mean(y)

	# Calculation of fit parameters
	m = sum([(xi - x̄) * yi for (xi, yi) ∈ zip(x, y)]) / sum([(xi - x̄)^2 for xi ∈ x])
	b = ȳ - m*x̄

	# Calculation of standard error
	D = sum([(xi - x̄)^2 for xi ∈ x])
	F = sum([(yi - ȳ)^2 for yi ∈ y])
	E = sum([(xi - x̄) * yi for (xi, yi) ∈ zip(x, y)])

	n = length(x)
	Δm² = (1 / (n - 2)) * ((F*D - E^2) / D^2)
	Δb² = (1 / (n - 2)) * (D / n + x̄^2) * ((F*D - E^2) / D^2)

	Δm = sqrt(Δm²)
	Δb = sqrt(Δb²)

	Dict([:m => m, :Δm => Δm, :b => b, :Δb => b])
end