1. bigshots, inc. is a specialty e-tailer that operates 87

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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