The variance inflation factor can be used to reduce multicollinearity by . eliminating variables for a multiple regression model b. decreasing homoscedasticity c. evaluating the distribution of residuals d. testing the null hypothesis that all regression coefficients equal zero

a. eliminating variables for a multiple regression model

Step-by-step explanation:

Variance inflation factor (VIF) could be described as a measure to the amount of multi-collinearity in a set of multiple regression variables. In mathematics, the variance inflation factor for a regression model variable will be equal  to the ratio of the overall model variance to the variance of a model that includes only that single independent variable. Accordingly, this ratio is evaluated  for every independent variable and the high factor shows the associated independent variable which is highly collinear to the other variables which can be included in the model.

A multiple regression can be utilized in the case of when there is a need to test the effect of multiple variables on a particular result. As a result, the dependent variable is the outcome which is being acted upon by the independent variables, that are the inputs into the model. Multi-collinearity appears in the case of when either there is a linear relationship, or correlation, between one or more of the independent variables or inputs and it makes the establishment of a problem in the multiple regression due to since the inputs are all influencing each other, they are not actually independent.

show us the graphs dude

step-by-step explanation: