Question

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?

Question 25 options:

	Collinearity
	Normality of residuals
	Randomness of error terms
	Missing observations

Answer 1

We consider the problem of regression when study variable depends on more than one explanatory or
independent variables, called as multiple linear regression model.

Multiple regression is the model that shows the linear relation between the dependent variable and two or more independent variables.

Here we see that education influences an individual’s annual income and the combined these influence the family size.It means that the variable are correlated .

Thus in this case real estate builder concerned collinearity

when analyzing the multiple regression model.

Thus option (a) is correct answer.