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 7 options: Randomness of error terms Collinearity Missing observations Normality of residuals

Multicollinearity

Step-by-step explanation:

Multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. Also here we can see that variables to determine house sizes is family income which is correlated with the education level further combining both the factor we have another correlation with the family size, this phenomena where two or more predictors in a regression model are moderately or highly correlated is known as multicollinearity.

The basic problem is multicollinearity results in unstable parameter estimates which makes it very difficult to assess the effect of independent variables on dependent variables.

if the probability is 1/12, 75 people will receive a $25 gift card

step-by-step explanation:

yes   because as the functions are graphed they eventually will intersect unless otherwise

step-by-step explanation: