Is Your Regression Misbehaving? Unraveling the Mystery of Non-Normal Residuals !
Residuals Not Being Normal in Regression Analysis:
In regression analysis, residuals represent the differences between observed values (actual data points) and predicted values (values estimated by the regression model). Ideally, these residuals should be normally distributed, which implies that the model's assumptions are met and predictions are unbiased. However, if the residuals are not normal, it suggests potential issues with the model.
Implications of Non-Normal Residuals:
Biased Predictions: If residuals have a systematic pattern (e.g., consistently over-predicting or under-predicting), the model may be biased.
Incorrect Confidence Intervals: Confidence intervals and hypothesis tests rely on the assumption of normally distributed residuals. Non-normal residuals can lead to incorrect conclusions.
Inefficient Estimates: Non-normal residuals can reduce the efficiency of regression estimates, affecting the precision and reliability of the model.
