Linear Regression in STATA

In STATA one can estimate a linear regression using the command regress. In this post I will present how to use the STATA function regress to run OLS on the following model

y = \alpha + \beta_{1} x_{1}

In this example, 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. For a more detailed description of the data see here.

*start with empty workspace
clear all 

*import sample data
import excel using "",first 

*estimate linear regression model 
regress weight lag_calories 


The function regress returns point estimates for \alpha and \beta_{1}. Moreover, the function returns standard error for the point estimates, along with its t-statistic, the p-value and 95% confidence intervals. The function also allows to conduct multiple regressions. That is, the function allows additional explanatory variables that can simply be added to the function as additional arguments.

This entry was posted in Computing and Others, Econometrics and tagged , . Bookmark the permalink.

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