Regression Model Diagnostics - Articles

Articles - Regression Model Diagnostics

After building a linear regression model (Chapter @ref(linear-regression)), you need to make some diagnostics to detect potential problems in the data.

In this part, you will learn:

Linear regression assumptions and diagnostics (Chapter @ref(regression-assumptions-and-diagnostics))
Potential problems when computing a linear regression model, including:
- non-linear relationship between the outcome and the predictors (Chapter @ref(polynomial-and-spline-regression))
- Multicollinearity (Chapter @ref(multicollinearity))
- Confounding variables (Chapter @ref(confounding-variables))

The Book:

Machine Learning Essentials: Practical Guide in R

Linear Regression Assumptions and Diagnostics in R: Essentials

By kassambara, The 11/03/2018 in Regression Model Diagnostics

Linear regression (Chapter @ref(linear-regression)) makes several assumptions about the data at hand. This chapter describes regression assumptions and provides built-in plots for regression... [Read more]

Multicollinearity Essentials and VIF in R

By kassambara, The 11/03/2018 in Regression Model Diagnostics

In multiple regression (Chapter @ref(linear-regression)), two or more predictor variables might be correlated with each other. This situation is referred as collinearity. There is an extreme... [Read more]

Confounding Variable Essentials

By kassambara, The 11/03/2018 in Regression Model Diagnostics

A Confounding variable is an important variable that should be included in the predictive model but you omit it.Naive interpretation of such models can lead to invalid conclusions. For... [Read more]