Julia presents various ways to carry out linear 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 latex 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 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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