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())