Hi, I’m pretty new to Data Science. I have some Data with measurement errors and I want to fit a linear model. using DataFrames using GLM using StatsPlots df = DataFrame(x = [0.0, 0.0669873, 0.25, 0.5, 0.75, 0.933013, 1.0], y = [0.223, 0.291, 0.393, 0.549, 0.73, 0.85, 0.896], u_y = [0.023, 0.024…

As indicated in GLM.jl’s doc above, glm does not handle inverse-variance weighting. For this purpose, you may use LsqFit.jl. using DataFrames, LsqFit, Printf, Plots; gr() df = DataFrame(x = [0.0, 0.0669873, 0.25, 0.5, 0.75, 0.933013, 1.0], y = [0.223, 0.291, 0.393, 0.549, 0.73, 0.85, 0.89…

Weighted linear regression with confidence interval fitted to error bars

General Usage

nilshg May 8, 2021, 6:47am 2

Your question is hopefully answered here Plot the confidence interval for a model fit

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Weighted linear regression with confidence interval fitted to error bars

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