# Linear Regression Using Stata [Video]

 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 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. Donâ€™t worry if youâ€™re not from a mathematical background; the course covers only a few equations in which addition and subtraction are used. Youâ€™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. Youâ€™ll get to grips with the output of linear regression, test model accuracy, and assumptions. Youâ€™ll also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. As you advance, youâ€™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, youâ€™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 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 3 hours 22 minutes 9781800207271 30 Mar 2020
 Introduction Simple linear regression The slope R-squared The p-value Model fit The residuals
 Multiple linear regression The slopes R-squared The p-value Model fit and residuals
 Binary variables Categrical variables Quadratic variables
 Prediction Normality of residuals Independence of residuals Constant variance Multicolinearity Outliers Influencial observations Selection algorithms
 Introduction Simple linear regression Model fit Multiple linear regression Model fit Binary variables Categrical variables Quadratic variables
 Prediction Normality of residuals Independence of residuals and Constant variance Multicolinearity Outliers Influencial observations Selection algorithms
 Introduction One independent variable Two independent variables Three independent variables Quadratic variables
 Introduction The project