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 ...

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

This task includes performing a linear regression analysis to predict the variable oxygen from the explanatory variables age, runtime, and runpulse. Additionally, the task requests confidence ...

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 ...

Course Topics 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 ...

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.