Week 11: Homework, Model Building

Quiz Instructions

Question 1
The following is the quadratic regression equation for a sample of :
(1) predict Y for . (Keep integer)
(2) Suppose that the computed test statistic for the quadratic regression coefficient is 1.95. At the 0.05 level of significance, is there evidence that the quadratic model is better than the linear model?

(Put “Y” for “Yes” or “N” for “N”)

Question 2

The logarithm transformation can be used

  • to overcome violations to the autocorrelation assumption.
  • to test for possible violations to the autocorrelation assumption.
  • to change a nonlinear model into a linear model.
  • to change a linear independent variable into a nonlinear independent variable.

Question 3

Which of the following is NOT an approach for handling nonlinear relationship between Y and X?

  • Logarithmic transformation
  • Square-root transformation
  • Quadratic regression model
  • Variance inflationary factor

Question 4

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 constructed the 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 builder be particularly concerned with when analyzing the multiple regression model?

 

  • Randomness of error terms
  • Collinearity
  • Normality of residuals
  • Missing observations

Question 5

A regression diagnostic tool used to study the possible effects of collinearity is

  • the slope.
  • the Y-intercept.
  • the VIF.

Question 6

The Variance Inflationary Factor (VIF) measures the

  • correlation of the X variables with the Y variable.
  • correlation of the X variables with each other.
  • contribution of each X variable with the Y variable after all other X variables are included in the model.
  • standard deviation of the slope.

Question 7

An independent variable X is considered highly correlated with the other independent variables if

  • VIF<5
  • VIF>5
  • VIF>1
  • VIF>4

Question 8

Which of the following is used to find a “best” (most appropriate) model?

  • statistic or adjusted R square
  • Standard error of the estimate
  • VIF

Question 9

The statistic is used

  • to determine if there is a problem of collinearity.
  • if the variances of the error terms are all the same in a regression model.
  • to choose the best model.
  • to determine if there is an irregular component in a time series.

Question 10
Using the best-subsets approach to model building, models are being considered when their