Linear Regression Using Stata [Video]

More Information
Learn
  • Study the concept of linear regression
  • Understand the difference between simple linear regression and multiple linear regression
  • Discover when linear regression is used
  • Predict values
  • Understand the output produced by linear regression
About

Stata is one of the leading statistical software packages widely used in different fields. This course is divided into two parts. The first part covers the theory behind linear regression in an intuitive way, and the second part enables you to apply the theory to practical scenarios using Stata. Don’t worry if you’re not from a mathematical background; the course covers only a few equations in which addition and subtraction are used.

You’ll start by understanding what linear regression is and when it is used, and then learn the differences between simple linear regression and multiple linear regression. You’ll get to grips with the output of linear regression, test model accuracy, and assumptions. You’ll also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. As you advance, you’ll use Stata to fit multiple regression models, produce graphs that describe model fit and assumptions, and use variable specific commands that will make the output more readable. This part assumes basic knowledge of Stata.

By the end of this course, you’ll have gained all the knowledge you need to apply linear regression confidently.

All the codes and supporting files are available at - https://github.com/PacktPublishing/Linear-Regression-using-Stata

Features
  • Get to grips with the theory behind linear regression
  • Explore simple and multiple linear regression
  • Understand how and when to binary, categorical, and quadratic variables
Course Length 3 hours 22 minutes
ISBN 9781800207271
Date Of Publication 30 Mar 2020

Authors

Najib Mozahem

Najib Mozahem works as a researcher and as an assistant professor at the university level, where he teaches Quantitative Analysis. He holds a Bachelor’s degree in Computer and Communication Engineering, completed his MBA with distinction, and completed his Ph.D. in Organizational Theory where he won the best thesis prize for Ph.D. He has also received the teaching excellence award for the year 2016 – 2017. His research interests include quantitative modeling and the study of human behavior in organizations.