Introduction

As you know, testing assumptions helps ensure the validity and reliability of our statistical model. When we perform multiple regression, we make several key assumptions about our data and the relationships between variables.

These assumptions include the normality of residuals, homoscedasticity (constant variance), and independence of errors. If these assumptions are violated, our regression results may be biased or unreliable, leading to incorrect conclusions.

Understanding and testing these assumptions allows researchers to:

• Validate the accuracy of their regression models
• Identify potential problems in their data or analysis
• Make necessary adjustments or transformations when assumptions are violated
• Draw more reliable conclusions from their statistical analyses

In the following sections, we'll explore each of these assumptions in detail and learn how to test them effectively.

Content map

To learn more about testing assumptions, read each of the following sections:

Normality of Residuals

Homoscedasticity

Independence of Errors

This is the end of the topic ‘Multiple Regression’.

Next topic in the module:

Logistic Regression