A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). 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 ran the multiple regression. 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) missing observations
B) normality of residuals
C) collinearity
D) randomness of error terms

Question

Quizplus · Accepted Answer

The real estate builder should be particularly concerned with collinearity because education is known to be a factor that influences both family income and family size, which are two of the independent variables in the multiple regression analysis. This means that there may be high correlation between the independent variables, leading to unstable and unreliable estimates of the regression coefficients. Therefore, collinearity needs to be assessed and addressed in the analysis.

A Real Estate Builder Wishes to Determine How House Size