For this assignment complete the exercises below. Show enough of your work so that your instructor can follow your logic. #2 (Pg 545) The owner of the Original Italian Pizza restaurant chain would...

For this assignment complete the exercises below. Show enough of your work so that
your instructor can follow your logic.
#2 (Pg 545) The owner of the Original Italian Pizza restaurant chain would like to
predict the sales of his specialty, deep-dish pizza. He has gathered data on the
monthly sales of deep-dish pizzas at his restaurants and observations on other
potentially relevant variables for each of his 15 outlets in central Indiana. These data
are provided in the file P10_04.xlsx.
a. Estimate a multiple regression model between the quantity sold (Y ) and the
explanatory variables in columns C–E.
Model Summary
b

Model R R Square
Adjusted R
Square
Std. Error of the
Estimate Durbin-Watson
1 .975
a
.950 .936 3333.092 1.542
a. Predictors: (Constant), Disposable Income per Household, Average Price, Monthly
Advertising Expenditures
b. Dependent Variable: Quantity Sold
ANOVA
b

Model Sum of Squares df Mean Square F Sig.
1 Regression 2.305E9 3 7.685E8 69.175 .000
a

Residual 1.222E8 11 1.111E7
Total 2.428E9 14
a. Predictors: (Constant), Disposable Income per Household, Average Price, Monthly Advertising
Expenditures
b. Dependent Variable: Quantity Sold
Coefficients
a

Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -33301.728 17898.414 -1.861 .090
Average Price -4041.534 1040.640 -.300 -3.884 .003 .765 1.306
Monthly Advertising
Expenditures
1.454 .152 .774 9.593 .000 .703 1.423
Disposable Income
per Household
1.528 .513 .251 2.979 .013 .643 1.554
a. Dependent Variable: Quantity Sold
b. Is there evidence of any violations of the key assumptions of regression analysis?




The histogram and normal probability plot suggests that the assumption of normality
is valid.
The VIF of each of the independent variables are nearly one which indicate that there
is no evidence of multicollinearity.
The durbin-watson statistic is 1.542.
If we choose α=0.05, then the critical values corresponding to n=15 and three
regressors as dL=0.82 and dU=1.75.
∵d=1.08 and dLdU
∴The test is inconclusive.
The scatter plot of residual against dependent variable shows no specific pattern and
this indicates of no heteroskedasticity i.e. errors are homoskedatic as assumed.
c. Which of the variables in this equation have regression coefficients that are
statistically different from zero at the 5% significance level?
The p-values of the estimated regression coefficients of all the independent variables
are less than 0.05. This suggests that all the regression coefficients are statistically
different from zero.
#18 (Pg 555) The Undergraduate Data sheet of the file P10_21.xlsx contains
information on 101 undergraduate business programs in the U.S., including various
rankings by Business Week. Use multiple regression to explore the relationship
between the median starting salary and the following set of potential explanatory
variables: annual cost, full-time enrollment, faculty-student ratio, average SAT score,
and average ACT score. Which explanatory variables should be included in a final
version of this regression equations? Justify your choices. Is multicollinearity a
problem? Why or Why not?
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .786
a
.617 .597 3408.211
a. Predictors: (Constant), Average ACT Score, Fulltime enrollment,
Annual Cost, Faculty Student Ratio, Average SAT Score
ANOVA
b

Model Sum of Squares df Mean Square F Sig.
1 Regression 1.799E9 5 3.599E8 30.980 .000
a

Residual 1.115E9 96 1.162E7
Total 2.914E9 101
a. Predictors: (Constant), Average ACT Score, Fulltime enrollment, Annual Cost, Faculty Student
Ratio, Average SAT Score
b. Dependent Variable: Median Starting Salary
Coefficients
a

Model...