To transform the predictors in the model, we use a change-point model, or "change-point transformation," which can yield more interpretable models and transformations than the standard Box-Tidwell ...

In a transformation model h(Y) = X'β + ε for some smooth and usually monotone function h, we are often interested in the direction of β without knowing the exact form of h. We consider a projection of ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Keywords: statistical model selection, assumptions, linear regression, ANOVA, machine learning, homoscedasticity, normality, independence, data transformation, non-parametric tests, robust ...

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, ...

A behind-the-scenes blog about research methods at Pew Research Center. For our latest findings, visit pewresearch.org. Many of Pew Research Center’s survey analyses show relationships between two ...

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...

Simply collecting data is not enough. You can fill spreadsheets with data, but it's useless if you can't act on it. Regression is one of the most powerful statistical tools for finding relationships ...

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand for its product or variables that describe the economic climate. There ...