Develop a Classical Linear Regression Model with Python (II): Model Diagnostics

Model Diagnostics are an integral part of the model development process, which help us judge whether a model is good or bad

Part I: Model Estimation

Part II: Model Diagnostics

Part III: Model Improvement

Part IV: Model Evaluation

In the previous post, we have created a classical statistic multiple linear regression model. In this article, we will evaluate the model assumptions and check if the model has problems. It is an integral part of the model development process, and the model is usually unbelievable without a reasonable diagnostics evaluation process. In this article, we will see the practical main process of model diagnostics rather than deeply dig into the theory behind.

1. Model result table

In the last post, we have generated a model result table. I paste this table again as follows so that you need not go backward and forward.