Answer true or false to each of the following statements and explain your answers. a. Provided we do not extrapolate, multicollinearity does not affect the ability of a regression equation to predict the response variable. b. If x1 and x2 are predictor variables in a regression equation, the variable x3 = x1 + x2 can be added to the regression equation without affecting the VIF of x1 or x2. c. The severity of multicollinearity can be reduced by removing one or more highly intercorrelated predictor variables from the regression equation.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Answer true or false to each of the following statements and explain your answers.
a. Provided we do not extrapolate, multicollinearity does not affect the ability of a regression equation to predict the response variable.
b. If x1 and x2 are predictor variables in a regression equation, the variable x3 = x1 + x2 can be added to the regression equation without affecting the VIF of x1 or x2.
c. The severity of multicollinearity can be reduced by removing one or more highly intercorrelated predictor variables from the regression equation.
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