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Question
The following ANOVA table is from a multiple regression analysis.| Source | df | SS | MS | F | p |
| Regression | 3 | 1500 | |||
| Error | 26 | ||||
| Total | 2300 |
The observed F value is __________.
a) 16.25
b) 30.77
c) 500
d) 0.049
e) 0.039
Answer
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Related questions
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8
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7 Block 2
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Q:
For the following ANOVA table, the critical value of the studentized range distribution using a= 0.05 is ______ Source of Variation
SS
df
MS
F Treatment
36.17
2
18.08
5.66 Error
28.75
9
3.19 Total
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Q:
Data from a completely randomized design are shown in the following table. Treatment Level 1
2
3 27
26
27 26
22
29 23
21
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23
26 For a one-way ANOVA, the Error Sum of Squares (SSE) is ________.
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Q:
Data from a completely randomized design are shown in the following table. Treatment Level 1
2
3 27
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27 26
22
29 23
21
27 24
23
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384.3333
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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. Source of Variation
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68102.33
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Q:
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A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant (Kingsland, and Yorktown), and production shift (day, and evening). In this model, "shift" is ______.
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Q:
The mean square error (MSerr) is calculated by dividing the sum of squares error (SSerr) by the number of error degrees of freedom (dferr).
Q:
Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients
Standard Error
t Statistic
p-value Intercept
-0.14156
0.292143
-0.48455
0.653331 x
0.105195
0.013231
7.950352
0.001356 Source
df
SS
MS
F Se = 0.237 Regression
1
3.550325
3.550325
63.20809 r2 = 0.940483 Residual
4
0.224675
0.056169 Total
5
3.775 For a city with a population of 500,000, Annie's model predicts annual sales of ________________.
a) $70,780
b) $5,259
c) $170,780
d) $52,597
e) $152,597
Q:
Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients
Standard Error
t Statistic
p-value Intercept
-0.14156
0.292143
-0.48455
0.653331 x
0.105195
0.013231
7.950352
0.001356 Source
df
SS
MS
F Se = 0.237 Regression
1
3.550325
3.550325
63.20809 r2 = 0.940483 Residual
4
0.224675
0.056169 Total
5
3.775 Annie's sample size is __________.
a) 2
b) 4
c) 6
d) 8
e) 10
Q:
Annie Mikhail, market analyst for a national company specializing in historic city tours, is analyzing the relationship between the sales revenue from historic city tours and the size of the city. She gathers data from six cities in which the tours are offered. Annie's dependent variable is annual sales revenues and her independent variable is the city population. Regression analysis of the data yielded the following tables. Coefficients
Standard Error
t Statistic
p-value Intercept
-0.14156
0.292143
-0.48455
0.653331 x
0.105195
0.013231
7.950352
0.001356 Source
df
SS
MS
F Se = 0.237 Regression
1
3.550325
3.550325
63.20809 r2 = 0.940483 Residual
4
0.224675
0.056169 Total
5
3.775 Annie's regression model can be written as: __________.
a) y = 7.950352 - 0.48455x
b) y = -0.48455 + 7.950352x
c) y = -0.14156 + 0.105195x
d) y = 0.105195 - 0.14156x
e) y = 0.105195 + 0.14156x
Q:
A manager wants to predict the cost (y) of travel for salespeople based on the number of days (x) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip took 4 days, the predicted cost of the trip is _____________.
a) 480
b) 880
c) 524
d) 2080
e) 1080
Q:
In the regression equation, y=2.164+1.3657x n=6, the mean of x is 8.667, Sxx=89.333 and Se=3.44. A 95% prediction interval for y when x=8 is _________
a) (9.13, 17.05)
b) (2.75, 23.43)
c) (10.31, 15.86)
d) (3.56, 22.62)
e) (12.09, 14.09)
Q:
A researcher has developed a regression model from fourteen pairs of data points. He wants to test if the slope is significantly different from zero. He uses a two"‘ tailed test and a = 0.01. The critical table t value is _______.
a) 2.650
b) 3.012
c) 3.055
d) 2.718
e) 2.168
Q:
In a regression analysis if SST = 200 and SSR = 200, r 2 = _________.
a) 0.25
b) 0.75
c) 0.00
d) 1.00
e) -1.00
Q:
The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the _______.
a) sum of squares error
b) coefficient of correlation
c) coefficient of determination
d) covariance
e) regression sum of squares
Q:
The numerical value of the coefficient of determination must be _______.
a) between -1 and +1
b) between -1 and 0
c) between 0 and 1
d) equal to SSE/(n-2)
e) between -100 and +100
Q:
A standard deviation of the error of the regression model is called the _______.
a) coefficient of determination
b) sum of squares of error
c) standard error of the estimate
d) R-squared
e) coefficient of correlation
Q:
The following residuals plot indicates _______________. a) a nonlinear relation
b) a nonconstant error variance
c) the simple regression assumptions are met
d) the sample is biased
e) the sample is random