A real estate builder wishes to determine how house size (House) is influenced b

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?

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? Non-randomness of error terms Multicolinearity Missing observations All of the above

Explanation / Answer

option B - Multicollinearity

It is caused by the inclusion of a variable which is computed from other variables(As variables which are dependent on other variable as given in question) in the data set. Multicollinearity can also result from the repetition of the same kind of variable.

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