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

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

The estimation is carried out in two steps, the first step being an ordinary least squares regression. The least squares residuals are used to estimate the covariance matrix and the second step is the ...

Linear forecasting models can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables.

10.3.1 Scatterplot matrix Recall that we use SAS’s scatterplot matrix feature to quickly scan for pairs of explanatory variables that might be colinear. To do this in R we must first make sure we ...

Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.