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.
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