Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

The problem of assessing influence and detecting influential cases in multiple linear regression with incomplete data is considered. A case is said to be influential if appreciable changes in fitted ...

Julie Barber, Simon Thompson, Multiple regression of cost data: use of generalised linear models, Journal of Health Services Research & Policy, Vol. 9, No. 4 (October 2004), pp. 197-204 ...

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

DTSA 5011 Modern Regression Analysis in R DTSA 5011 Modern Regression Analysis in R Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, ...

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, ...

Multiple regression models with survey data Regression becomes a more useful tool when researchers want to look at multiple factors simultaneously. If we want to know whether the racial divide ...