Julia presents various ways to carry out linear regressions. In this previous post, I explained how to run linear regression in Julia using the function *linreg().* Unfortunately, *linreg() *is deprecated and no longer exists in Julia v1.0.

In this post I will present how to use the native function of Julia to run OLS on the following model

An alternative way to run a linear regression is to use the *lm()* function of the GLM package. In case you are interested in running a regression based on the GLM package, you can check out this post. It describes how to conduct a multiple regression in Julia and uses the *lm()* function provided by the GLM package.

In this example, our dependent variable will be my weekly average weight, the explanatory variable represents the sum of calories that I burned during the previous week. For a more detailed description of the data see here.

#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")
using DataFrames
data = DataFrame(data)
deleterows!(data,findall(ismissing,data[:,1]))
deleterows!(data,findall(ismissing,data[:,2]))
y = convert(Array{Float64,1},data[:,1])
x = convert(Array{Float64,1},data[:,2])
reverse([x ones(length(x))]\y)

The function *reverse()* returns point estimates for and . Unfortunately, the function does not return standard error for the point estimates. In order to obtain standard errors for the estimated coefficients, one can use the *lm()* function form the GLM package. The GLM package of Julia does provide a more flexible environment. You can find a working example of how to conduct multiple regression in Julia using the GLM package here.

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