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

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

The literature of regression analysis with missing values of the independent variables is reviewed. Six classes of procedures are distinguished: complete case analysis, available case methods, least ...

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, ...

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 ...

Conclusions: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified ...

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 ...