# 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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