Multiple Regression in Julia

Julia presents various ways to carry out multiple regressions. One easy way is to use the lm() function of the GLM package. In this post I will present how to use the lm() and run OLS on the following model

y = \alpha + \beta_{1} x_{1} + \beta_{2} x_{2} + \beta_{3} x_{3}

Our dependent variable y will be my weekly average weight, the explanatory variable x_{1} represents the sum of calories that I burned during the previous week, and variable x_{2} is a binary variable that takes a value of 1 in case I was cycling the week earlier and 0 otherwise. For a more detailed description of the data see here.

# load a couple of packages
using DataFrames
using XLSX

# get data
path = ""
data = XLSX.readdata(download(path), "data", "A1:C357")
data_df = convert(DataFrame,data[2:end,:])
names!(data_df, Symbol.(data[1,:]))
data_df[:,:weight] = convert(Vector{Float64}, data_df[:,:weight])
data_df[:,:lag_calories] = convert(Vector{Float64}, data_df[:,:lag_calories])
data_df[:,:lag_cycling] = convert(Vector{Float64}, data_df[:,:lag_cycling])

# estimate the linear regression model
glm(@formula(weight ~ lag_calories + lag_cycling),
        data, Normal(), IdentityLink())


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