Linear regression can be used to analyze risk. For example, a health insurance company might conduct a linear regression plotting number of claims per customer against age and discover that older ...

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

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software ...

Homoskedastic refers to a condition in which the variance of the error term in a regression model is constant. Learn more about its importance and how it is used.

Multiple linear regression is a classical statistics technique that predicts a single numeric value from two or more numeric predictor variables, for example, predicting income from age and height.

In our example of simple linear regression 1, we saw how one continuous variable (weight) could be predicted on the basis of another continuous variable (height).

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 developments in linear regression methodology that have taken place during the 25-year history of Technometrics are summarized. Major topics covered are variable selection, biased estimation, ...

This paper reviews three basic methods of non-linear least squares estimation and the best known modifications to the popular Gauss-Newton procedure. With regard to the Gauss-Newton procedure the ...