# 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 Distributions
using GLM
using DataFrames
using DataArrays
using Taro
Taro.init()

# get data
path = "https://economictheoryblog.files.wordpress.com/2016/08/data.xlsx"
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),