How do you interpret multicollinearity in SPSS?
Test muticollinearity as a basis the VIF value of multicollinearity test results using SPSS. If the VIF value lies between 1-10, then there is no multicollinearity. If the VIF <1 or> 10, then there is multicollinearity.
What does it mean if residuals are not normally distributed?
When the residuals are not normally distributed, then the hypothesis that they are a random dataset, takes the value NO. This means that in that case your (regression) model does not explain all trends in the dataset. Thus, your predictors technically mean different things at different levels of the dependent variable.
How do you interpret multicollinearity results?
“ VIF score of an independent variable represents how well the variable is explained by other independent variables. So, the closer the R^2 value to 1, the higher the value of VIF and the higher the multicollinearity with the particular independent variable.
What happens if VIF is high?
Variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. A high VIF indicates that the associated independent variable is highly collinear with the other variables in the model.
How do you confirm homoscedasticity?
Homoscedasticity means that the residuals have constant variance no matter the level of the dependent variable. How can it be verified? To verify homoscedasticity, one may look at the residual plot and verify that the variance of the error terms is constant across the values of the dependent variable.
How do you test data for homoscedasticity?
The general rule of thumb1 is: If the ratio of the largest variance to the smallest variance is 1.5 or below, the data is homoscedastic.
How do you use stepwise method?
How Stepwise Regression Works
- Start the test with all available predictor variables (the “Backward: method), deleting one variable at a time as the regression model progresses.
- Start the test with no predictor variables (the “Forward” method), adding one at a time as the regression model progresses.