1. bigshots, inc. is a specialty etailer that operates 87
1.
BigShots, Inc. is a specialty etailer that operates 87 catalog Web sites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a Web site may affect its sales. He chooses three levels of design style (neon, old world, and sophisticated) and randomly assigns six catalog Web sites to each design style. Analysis of Kevin’s data yielded the following ANOVA table.
Using = 0.05, the calculated F value is __________.
2.
BigShots, Inc. is a specialty etailer that operates 87 catalog Web sites on the Internet. Kevin Conn, Sales Director, feels that the style (color scheme, graphics, fonts, etc.) of a Web site may affect its sales. He chooses three levels of design style (neon, old world, and sophisticated) and randomly assigns six catalog Web sites to each design style. Analysis of Kevin’s data yielded the following ANOVA table.
Using = 0.05, the critical F value is __________.
3.
For the following ANOVA table, the df Treatment value is __________.
4.
Cindy Ho, VP of Finance at Discrete Components, Inc. (DCI), theorizes that the discount level offered to credit customers affects the average collection period on credit sales. Accordingly, she has designed an experiment to test her theory using four sales discount rates (0%, 2%, 4%, and 6%) by randomly assigning five customers to each sales discount rate. Cindy’s null hypothesis is __________.
5.
Suppose a researcher sets up a completely randomized design in which there are four different treatments and a total of 32 measurements in the study. For alpha = .05, the critical table F value is __________.
6.
A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
tStatistic 
pvalue 

Intercept 
752.0833 
336.3158 
2.236241 
0.042132 

x_{1} 
11.87375 
5.32047 
2.231711 
0.042493 

x_{2} 
1.908183 
0.662742 
2.879226 
0.01213 

Source 
df 
SS 
MS 
F 
pvalue 

Regression 
2 
203693.3 
101846.7 
6.745406 
0.010884 

Residual 
12 
181184.1 
15098.67 



Total 
14 
384877.4 




The regression equation for this analysis is ____________.
7.
The following ANOVA table is from a multiple regression analysis.
Source 
df 
SS 
MS 
F 
p 
Regression 
5 
2000 



Error 
25 




Total 

2500 



The MSE value is __________.
8.
A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
tStatistic 
pvalue 

Intercept 
616.6849 
154.5534 
3.990108 
0.000947 

x_{1} 
3.33833 
2.333548 
1.43058 
0.170675 

x_{2} 
1.780075 
0.335605 
5.30407 
5.83E05 

Source 
df 
SS 
MS 
F 
pvalue 

Regression 
2 
121783 
60891.48 
14.76117 
0.000286 

Residual 
15 
61876.68 
4125.112 



Total 
17 
183659.6 




Using a = 0.01 to test the null hypothesis H_{0}: _{1} = _{2} = 0, the critical F value is ____.
9.
A multiple regression analysis produced the following tables.
Predictor 
Coefficients 
Standard Error 
tStatistic 
pvalue 

Intercept 
624.5369 
78.49712 
7.956176 
6.88E06 

x_{1} 
8.569122 
1.652255 
5.186319 
0.000301 

x_{2} 
4.736515 
0.699194 
6.774248 
3.06E05 

Source 
df 
SS 
MS 
F 
pvalue 

Regression 
2 
1660914 
830457.1 
58.31956 
1.4E06 

Residual 
11 
156637.5 
14239.77 



Total 
13 
1817552 




The adjusted R^{2} is ____________.
10.
Yvonne Yang, VP of Finance at Discrete Components, Inc. (DCI), wants a regression model which predicts the average collection period on credit sales. Her data set includes two qualitative variables: sales discount rates (0%, 2%, 4%, and 6%), and total assets of credit customers (small, medium, and large). The number of dummy variables needed for “sales discount rate” in Yvonne’s regression model is ________.
11.
Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby’s dependent variable is monthly household expenditures on groceries (in $’s), and her independent variables are annual household income (in $1,000’s) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Coefficients 
Standard Error 
tStatistic 
pvalue 
Intercept 
19.68247 
10.01176 
1.965934 
0.077667 
x_{1} (income) 
1.735272 
0.174564 
9.940612 
1.68E06 
x_{2} (neighborhood) 
49.12456 
7.655776 
6.416667 
7.67E05 
For a suburban household with $70,000 annual income, Abby’s model predicts monthly grocery expenditure of ________________.
12.
A multiple regression analysis produced the following tables.

Coefficients 
Standard Error 
tStatistic 
pvalue 





Intercept 
1411.876 
762.1533 
1.852483 
0.074919 
x_{1} 
35.18215 
96.8433 
0.363289 
0.719218 
x_{1}^{2} 
7.721648 
3.007943 
2.567086 
0.016115 

df 
SS 
MS 
F 
Regression 
2 
58567032 
29283516 
57.34861 
Residual 
25 
12765573 
510622.9 

Total 
27 
71332605 


The regression equation for this analysis is ____________.
13.
Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is analyzing household budget data collected by her firm. Abby’s dependent variable is monthly household expenditures on groceries (in $’s), and her independent variables are annual household income (in $1,000’s) and household neighborhood (0 = suburban, 1 = rural). Regression analysis of the data yielded the following table.

Coefficients 
Standard Error 
t Statistic 
pvalue 
Intercept 
19.68247 
10.01176 
1.965934 
0.077667 
X_{1} (income) 
1.735272 
0.174564 
9.940612 
1.68E06 
X_{2} (neighborhood) 
49.12456 
7.655776 
6.416667 
7.67E05 
Abby’s model is ________________.
14.
An “all possible regressions” search of a data set containing 9 independent variables will produce ______ regressions.