Following Box and Cox (1964), the use of transformations in regression analysis is now common; recently there has been emphasis on diagnostic methods for transformation, much of which has involved ...

This article suggests a method for variable and transformation selection based on posterior probabilities. Our approach allows for consideration of all possible combinations of untransformed and ...

Nanjing Aobo Industrial Intelligent Technology Research Institute Co., Ltd. and Xinpobisi (Nanjing) Intelligent Technology Co., Ltd. have jointly obtained a patent titled "A Method for Predicting the ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

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, ...

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 ...