Question

1. Imagine you fit a regression model to a dataset and find that R‐squared = 0.69. Is this a good regression model or not? If you cannot tell, what additional information do you need? Explain.

2. Research and then explain the “regression fallacy”. Provide at least one example.

Answer 1

here R square value is 0.69 simply says your predictive model is able to explain 69% of the data points effectively.

An ideal range of R square value is between 0.60 to 0.90 this means the predictive model is able to explain a good amount of varaince in the data ad can be taken into consideration for testing and accuracy calculation on test data .

R square < 0.5 means tending towards Underfitting of the model .and

R Square > 0.9 means tending towards Overfitting of the model .

## Regression fallacy : it is occurs when one mistakes regresssion to the mean , which is a statistical phenomenon , for a casual relationship .

Exalple , if a tall father were to conclude that his tall wife committed adultery becuase their children were shorter , he would be committing the regression fallacy

( there are several example would be regression fallacy )