Question

A real estate builder wishes to determine how house size is influenced by family income, family size, and education of the head of the household. House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and constructed the multiple regression model. The business literature involving human capital shows that education influences an individual's annual income. Combined, these may influence family size. With this in mind, what should the real estate builder be particularly concerned with when analyzing the multiple regression model?
a. Collinearity.

b. Missing observations.

c. Randomness of error terms.

d. Normality of residual.

Answer 1

Answer:

a. Collinearity.

Explanation:

Collinearity is a statistical term that describes a situation in which some of the independent variables are highly correlated.

This is shown when the variables to determine house sizes, is family income which is correlated with the education level, and while we combined both the factors we have another correlation with the family size.

Hence, this phenomena where two or more predictors in a regression model are moderately or highly correlated is known as COLLINEARITY.

Therefore, what should the real estate builder be particularly concerned with when analyzing the multiple regression model is COLLINEARITY.