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Data-Centric Statistical Inference Using R and Tidyverse

Conditions for Inference for Regression-II

Explore the critical conditions necessary for valid regression inference, such as normality and equal variance of residuals. Understand how to assess these with graphical methods, the importance of each condition, and the implications when they are not fully met. This lesson guides you through interpreting residual analysis and recognizing potential model shortcomings to improve regression analysis outcomes.

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Normality of residuals

The third condition is that the residuals should follow a normal distribution. Furthermore, the center of this distribution should be 0. In other words, sometimes the regression model will make positive errors — y−y^>0y−\hat{y} >0. Other times, the regression model will make equally negative errors — y−y^<0y−\hat{y} <0 ...