A real estate developer 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 metres,income is measured in thousands of dollars and education is in years.The developer randomly selected 50 families and developed a 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 developer be particularly concerned with when analysing the multiple regression model?
A) Normality of residuals
B) Collinearity
C) Missing observations
D) Randomness of error terms

Question

Quizplus · Accepted Answer

The developer should particularly be concerned with collinearity, which refers to high correlation among the independent variables. In this case, education may be highly correlated with both income and family size, which can cause issues with interpretability and accuracy of the regression coefficients. As such, collinearity can lead to difficulties in understanding each independent variable's unique contribution to the dependent variable. Normality of residuals, missing observations, and randomness of error terms are also important considerations in the analysis, but collinearity is the most pressing concern in this case.

A Real Estate Developer Wishes to Determine How House Size