1. BigShots, Inc. Is A Specialty E-Tailer That Operates 87 Catalog Web Sites On The Internet.

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.