1.
BigShots, Inc. is a specialty e-tailer 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 e-tailer 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
|
p-value
|
|
Intercept
|
752.0833
|
336.3158
|
2.236241
|
0.042132
|
|
x1
|
11.87375
|
5.32047
|
2.231711
|
0.042493
|
|
x2
|
1.908183
|
0.662742
|
2.879226
|
0.01213
|
|
Source
|
df
|
SS
|
MS
|
F
|
p-value
|
|
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
|
p-value
|
|
Intercept
|
616.6849
|
154.5534
|
3.990108
|
0.000947
|
|
x1
|
-3.33833
|
2.333548
|
-1.43058
|
0.170675
|
|
x2
|
1.780075
|
0.335605
|
5.30407
|
5.83E-05
|
|
Source
|
df
|
SS
|
MS
|
F
|
p-value
|
|
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 H0: 1 = 2 = 0, the critical F value is ____.
9.
A multiple regression analysis produced the following tables.
|
Predictor
|
Coefficients
|
Standard Error
|
tStatistic
|
p-value
|
|
Intercept
|
624.5369
|
78.49712
|
7.956176
|
6.88E-06
|
|
x1
|
8.569122
|
1.652255
|
5.186319
|
0.000301
|
|
x2
|
4.736515
|
0.699194
|
6.774248
|
3.06E-05
|
|
Source
|
df
|
SS
|
MS
|
F
|
p-value
|
|
Regression
|
2
|
1660914
|
830457.1
|
58.31956
|
1.4E-06
|
|
Residual
|
11
|
156637.5
|
14239.77
|
|
|
|
Total
|
13
|
1817552
|
|
|
|
| |
|
|
|
|
|
|
The adjusted R2 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
|
p-value
|
|
Intercept
|
19.68247
|
10.01176
|
1.965934
|
0.077667
|
|
x1 (income)
|
1.735272
|
0.174564
|
9.940612
|
1.68E-06
|
|
x2 (neighborhood)
|
49.12456
|
7.655776
|
6.416667
|
7.67E-05
|
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
|
p-value
|
|
|
|
|
|
|
|
Intercept
|
1411.876
|
762.1533
|
1.852483
|
0.074919
|
|
x1
|
35.18215
|
96.8433
|
0.363289
|
0.719218
|
|
x12
|
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
|
p-value
|
|
Intercept
|
19.68247
|
10.01176
|
1.965934
|
0.077667
|
|
X1 (income)
|
1.735272
|
0.174564
|
9.940612
|
1.68E-06
|
|
X2 (neighborhood)
|
49.12456
|
7.655776
|
6.416667
|
7.67E-05
|
Abby’s model is ________________.
14.
An “all possible regressions” search of a data set containing 9 independent variables will produce ______ regressions.
