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 Distributions using GLM using DataFrames using DataArrays # load Taro - Pkg to read Excel Data using Taro Taro.init() # get data path = "https://economictheoryblog.files.wordpress.com/2016/08/data.xlsx" data = Taro.readxl(download(path), "data", "A1:C357") data = deleterows!(data,find(isna(data[:,1])|isna(data[:,2]))) data[:,1] = convert(DataArrays.DataArray{Float64,1},data[:,1]) data[:,2] = convert(DataArrays.DataArray{Float64,1},data[:,2]) data[:,3] = convert(DataArrays.DataArray{Float64,1},data[:,3]) # estimate the linear regression model glm(@formula(weight ~ lag_calories + lag_cycling), data, Normal(), IdentityLink())

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