MIDTERM EXAM 2
1.
2.
3.
Y
4.
1.
A measure of the degree of relatedness of two variables is _______.
A.
regression
B.
correlation
C.
residual
D.
least squares analysis
If a researcher wants to conduct a test about the differences in the means for more than two
independent populations, she can use _______.
A.
the related samples t-test
B.
analysis of variance
C.
a confidence interval
D.
the multiple population t-test
Determining the table value for the F-distribution is different from finding values in the tdistribution tables because the F-table requires _____ value/s for degrees of freedom.
A.
one
B.
two
C.
three
D.
more than three
Consider the following scatter plot and regression line. At x = 65, the residual (error term) is
___________.
600
500
400
300
200
100
0
0
10
20
30
40
50
60
70
X
5.
6.
A.
positive
B.
zero
C.
negative
D.
imaginary
Suppose the mean squares for treatment in a one-way ANOVA are 24.4 and the mean squares
for error are 9.8. There were four treatments and 7 subjects received each treatment (for a total
of 28). The calculated value of F is _______.
A.
9.8
B.
34.2
C.
2.49
D.
14.6
Powell’s Pharmacy Ltd operates a regional chain of 120 pharmacies. Each pharmacy’s floor plan
includes a greeting card section which is relatively isolated. Sandra Ronaki, Marketing Manager,
feels that the level of lighting in the greeting card section may affect sales in this area. She chooses
three levels of lighting (soft, medium and bright) and randomly assigns six pharmacies to each
lighting level. Analysis of Sandra’s data yielded the following ANOVA table.
Source of Variation
Treatment
Error
Total
SS
49411.11
35529.17
84940.28
df
2
15
17
MS
24705.56
2368.611
F
10.4304
Using  = 0.05, the appropriate decision is _____________.
A.
reject the null hypothesis  1 =  2 =  3
B.
reject the null hypothesis  1 ≠  2 ≠  3
C.
do not reject the null hypothesis  1   2   3
D.
do not reject the null hypothesis  1   2   3
Data from a completely randomised design are shown in the following table.
7.
1
27
26
23
24
Treatment Level
2
26
22
21
23
3
27
29
27
26
For a one-way ANOVA, the Total Sum of Squares (SST) is ________.
A.
36.17
B.
28.75
C.
64.92
D.
18.03
Data from a completely randomised design are shown in the following table.
8.
Treatment Level
1
2
27
26
26
22
23
21
24
23
3
27
29
27
26
For a one-way ANOVA using  = 0.05, the critical F-value is ________.
A.
3.86
B.
3.59
C.
19.38
D.
4.26
9. The following scatter plot indicates _______.
800
Y
600
400
200
0
0
A.
B.
C.
D.
20
40
X
perfect positive correlation
virtually no correlation
positive correlation
negative correlation
60
80
10. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Regression
Residual
Total
Coefficients Standard Error t Statistic p-value
616.6849
154.5534 3.990108 0.000947
–3.33833
2.333548 –1.43058 0.170675
1.780075
0.335605 5.30407 5.83E-05
df
2
15
17
SS
MS
F
p-value
121783 60891.48 14.76117 0.000286
61876.68 4125.112
183659.6
For x1 = 60 and x2 = 200, the predicted value of y is ____________.
A.
1,173.00
B.
772.40
C.
460.97
D.
615.13
11. Restaurateur, Daniel Valentine, is evaluating two sites, Port Douglas and Mission Beach, for his
next restaurant. He wants to prove that Port Douglas residents (population 1) dine out more often
than Mission Beach residents (population 2). Denny plans to test this hypothesis using a random
sample of 81 families from each town. His alternate hypothesis is __________.
A.
12 < 22
B.
1– 2  0
C.
p1 – p2 = 0
D.
1 – 2 = 0
12. For a data set the regression equation is y = 21 – 3x. The correlation coefficient for this data _______.
A.
must be 0
B.
is negative
C.
must be 1
D.
is positive
13. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Regression
Residual
Total
Coefficients Standard Error
752.0833
336.3158
11.87375
5.32047
1.908183
0.662742
df
2
12
14
t Statistic p-value
2.236241 0.042132
2.231711 0.042493
2.879226 0.01213
SS
MS
F
p-value
203693.3 101846.7 6.745406 0.010884
181184.1 15098.67
384877.4
These results indicate ____________.
A.
none of the predictor variables are significant at the 5% level
B.
each predictor variable is significant at the 5% level
C.
x1 is the only predictor variable significant at the 5% level
D.
x2 is the only predictor variable significant at the 5% level
14. Restaurateur, Daniel Valentine, is evaluating two sites, Port Douglas and Mission Beach, for his
next restaurant. He wants to prove that Port Douglas residents (population 1) dine out more often
than Mission Beach residents (population 2). Denny commissions a market survey to test this
hypothesis. The market researcher used a random sample of 64 families from each town, and
reported the following: x 1 = 15 times per month and x 2 = 14 times per month. Assume that 1
= 2 and 2 = 3. With  = .01, the observed z-value is _________________.
A.
2.22
B.
12.81
C.
4.92
D.
3.58
15. The following ANOVA table is from a multiple regression analysis with n = 35 and four independent
variables.
Source
Regression
Error
Total
df
SS
700
MS
F
p
1000
The MSE value is __________.
A.
8.57
B.
8.82
C.
10.00
D.
75.00
16. Auckland First Bank’s policy requires consistent, standardised training of employees at all branches.
Consequently, David Marshall, Human Resources Manager, is planning a survey of mean
employee training time in the Southern region (population 1) and the Northern region (population
2). His null hypothesis is ___________.
A.
1 – 2 = 0
B.
1 – 2 < 0
C.
1 – 2 ≠ 0
D.
1 – 2 > 0
17. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Regression
Residual
Total
Coefficients Standard Error t Statistic p-value
616.6849
154.5534 3.990108 0.000947
–3.33833
2.333548 –1.43058 0.170675
1.780075
0.335605 5.30407 5.83E-05
df
2
15
17
SS
MS
F
p-value
121783 60891.48 14.76117 0.000286
61876.68 4125.112
183659.6
The regression equation for this analysis is ____________.
A.
y = 616.6849 + 3.33833 x1 + 1.780075 x2
B.
y = 154.5535 – 1.43058 x1 + 5.30407 x2
C.
y = 616.6849 – 3.33833 x1 + 1.780075 x2
D.
y = 154.5535 + 2.333548 x1 + 0.335605 x2
18. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Coefficients Standard Error t Statistic p-value
616.6849
154.5534 3.990108 0.000947
–3.33833
2.333548 –1.43058 0.170675
1.780075
0.335605 5.30407 5.83E-05
Regression
Residual
Total
df
2
15
17
SS
MS
F
p-value
121783 60891.48 14.76117 0.000286
61876.68 4125.112
183659.6
Using  = 0.05 to test the null hypothesis H0: 1 = 0, the critical t-value is ____.
19. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Regression
Residual
Total
Coefficients Standard Error t Statistic p-value
616.6849
154.5534 3.990108 0.000947
–3.33833
2.333548 –1.43058 0.170675
1.780075
0.335605 5.30407 5.83E-05
df
2
15
17
SS
MS
F
p-value
121783 60891.48 14.76117 0.000286
61876.68 4125.112
183659.6
These results indicate ____________.
A.
none of the predictor variables are significant at the 5% level
B.
each predictor variable is significant at the 5% level
C.
x1 is the only predictor variable significant at the 5% level
D.
x2 is the only predictor variable significant at the 5% level
20. Multiple regression analysis produced the following tables.
Intercept
x1
x2
Regression
Residual
Total
Coefficients Standard Error t Statistic p-value
616.6849
154.5534 3.990108 0.000947
–3.33833
2.333548 –1.43058 0.170675
1.780075
0.335605 5.30407 5.83E-05
df
2
15
17
SS
MS
F
p-value
121783 60891.48 14.76117 0.000286
61876.68 4125.112
183659.6
For x1 = 60 and x2 = 200, the predicted value of y is ____________.
A.
1,173.00
B.
772.40
C.
460.97
D.
615.13