Understanding residuals

In this section, we’ll explore the concept of residuals in depth, focusing on their role in linear regression and their importance in assessing the accuracy and quality of our models. We’ll illustrate the significance of residuals through various examples, ensuring you have a comprehensive understanding of this critical aspect of regression analysis.

Residuals are the differences between the actual observed values (data points) and the values predicted by the regression model (the line of best fit). In simple terms, residuals represent the errors in our model – how far off our predictions are from reality. By analyzing the residuals, we can evaluate the performance of our regression model and identify potential areas for improvement.

The formula to calculate the residual for a specific data point is as follows:

residual = observed value − predicted value

Let’s explore the concept of residuals through an example...