We calculate the f statistic for the overall model using the residuals squares of null model and that of predicted model divided by their respected degrees of freedom right sir? just clarifying
Thank you for the article. One correction: A p-value of 0.6 means that there is a 60% probability of observing the data (or something more extreme) if the null hypothesis is true. It does not mean there is a 60% chance that the feature "X" has no effect on "Y."
In the omnibus instance, 0.001 suggests that we would have only a 0.1% probability of observing such data if the residuals were indeed normally distributed. Therefore, we would likely conclude that the residuals are not normally distributed.
We calculate the f statistic for the overall model using the residuals squares of null model and that of predicted model divided by their respected degrees of freedom right sir? just clarifying
Thank you for the article. One correction: A p-value of 0.6 means that there is a 60% probability of observing the data (or something more extreme) if the null hypothesis is true. It does not mean there is a 60% chance that the feature "X" has no effect on "Y."
In the omnibus instance, 0.001 suggests that we would have only a 0.1% probability of observing such data if the residuals were indeed normally distributed. Therefore, we would likely conclude that the residuals are not normally distributed.