# What does regress do in Stata?

## What does regress do in Stata?

Stata’s rreg command implements a version of robust regression. It first runs the OLS regression , gets the Cook’s D for each observation, and then drops any observation with Cook’s distance greater than 1. Then iteration process begins in which weights are calculated based on absolute residuals.

## What are the four assumptions of linear regression?

The four assumptions on linear regression. It is clear that the four assumptions of a linear regression model are: Linearity, Independence of error, Homoscedasticity and Normality of error distribution.

What does linear regression tell us?

Linear regression is used to determine trends in economic data. For example, one may take different figures of GDP growth over time and plot them on a line in order to determine whether the general trend is upward or downward.

What is simple linear regression is and how it works?

A sneak peek into what Linear Regression is and how it works. Linear regression is a simple machine learning method that you can use to predict an observations of value based on the relationship between the target variable and the independent linearly related numeric predictive features.

### Why use multiple regression analysis?

Purpose of multiple regression. Multiple regression analysis is used to examine the relationship between one numerical variable, called a criterion, and a set of other variables, called predictors. In addition, multiple regression analysis is used to investigate the correlation between two variables after controlling another covariate.

### What is an example of multiple regression analysis?

Multiple regression analysis is used to predict the value of a variable (dependent) using two or more variables (independent variables). Multiple regression analysis is an extension of linear regression analysis that uses one predictor to predict the value of a dependent variable. An example of a linear regression model is Y=b 0 + b 1X.

What is a multiple regression model?

Multiple linear regression model is the most popular type of linear regression analysis. It is used to show the relationship between one dependent variable and two or more independent variables.