The writer is very fast, professional and responded to the review request fast also. Thank you.
Book/movie review
22/08/2022
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.
The writer is very fast, professional and responded to the review request fast also. Thank you.
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.
Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
Read moreThanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.
Read moreYour email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.
Read moreBy sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.
Read more