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

Our dependent variable will be my weekly average weight, the explanatory variable represents the sum of calories that I burned during the previous week, and variable 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 = "https://economictheoryblog.files.wordpress.com/2016/08/data.xlsx"
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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if anyone knows how to do this..please contact me

hi jonathan, I just updated the post. It should work now. Let me know if you still encounter any problems. Best, ad