Learn how to compare and select the best regression model for your data analysis, using some basic criteria and steps. Find out how to define your goal, check your data, and evaluate different models.

Learn what dummy variables are, how to create them, how to interpret them, and how to avoid dummy variable trap in regression models.

You can use any number of MODEL statements to estimate different regression models or estimate the same model using different options. See Example 20.1 in the section "Examples." ...

In general, linear regression is used to model the relationship between a continuous variable and other explanatory variables, which can be either continuous or categorical. When applying this ...

An example would be structural failure of mechanical heart valves 8 and sudden cardiac death in patients with hypertrophic cardiomyopathy. 6 In such situations, use of standard regression methods to ...

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is ...

Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

Example 8.13: Parameter Estimation for a Regression Model with ARMA Errors Nonlinear estimation algorithms are required for obtaining estimates of the parameters of a regression model with innovations ...

Predicting the Future The most common use of regression in business is to predict events that have yet to occur. Demand analysis, for example, predicts how many units consumers will purchase.