Answer:
This statement is true because if F statistics is significant, then the entire multiple regression model is useful for the prediction of y.
Step-by-step explanation:
Solution:
In a multiple linear regression analysis, the statistical model utility is determined by the value of R2 and ∂2, while the Global F test p-value determines the practical model.
This statement is true because if F statistics is significant, then the entire multiple regression model is useful for the prediction of y.
The F test is a statistical test that is very flexible. F test can evaluate multiple model terms.
The F test indicates whether a linear regression model provides a better fit to data than a model that contains no independent variable.
The F test for overall significance has two hypotheses:
1- Null hypothesis:
The fit of the intercept only model and your model are equal.
2- Alternative hypothesis:
The fit of the intercept-only model is significantly reduced compared to your model.
Compare p-value for the F test,
If the p-value is less than the significance level, the sample data provide sufficient evidence that the regression model fits the data better with no independent variable.
