# weighted least squares, extract coef, and std of error term

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## weighted least squares, extract coef, and std of error term

 suppose I have variables y, x, and weights w, eg x = randn(100) σ = 0.1 w = rand(length(x)) y = x + σ*randn(length(x))./w and I want to estimate 1. the coefficient and the intercept (something around 0, and 1) 2. and some simple estimate for the standard deviation of the error term (eg something around 0.1). What's the recommended way of doing this in Julia? GLM allows me to fit the model, but I could not figure out how to do it with weights, and I have a hard time extracting residuals etc to calculate an estimator for sigma (maybe I am doing it wrong, but residuals is not supported for the fit object). Julia 0.5-rc2, using latest DataFrames, last released GLM. To be clear, this does what I want: function myfit(y, X, w)     W = Diagonal(sqrt(w))     WX = W*X     Wy = W*y     β = WX \ Wy     ϵ = Wy-WX*β     (β, sqrt(dot(ϵ, ϵ)/(size(X, 1)-size(X, 2)))) end myfit(y, hcat(ones(length(x)), x),  w) but I want to learn the "standard" way of doing this. Best, Tamas -- You received this message because you are subscribed to the Google Groups "julia-stats" group. To unsubscribe from this group and stop receiving emails from it, send an email to [hidden email]. For more options, visit https://groups.google.com/d/optout.