MGMT 630 – Midterm 1 Exam Solution
1.
A market research study is being conducted to determine if a product modification will be
well received by the public. A total of 1,000 consumers are questioned regarding this
product.
The table below provides information regarding this sample.
Positive
Neutral Negative
Reaction
Reaction Reaction
Male
240
60
100
Female
260
220
120
What is the probability that a randomly selected male would find this change unfavorable
(negative)?
Select one:
a. 0.91
=100/(240+60+100) =100/400
b. 0.25
= 0.25
c. 0.36
d. 0.28
e. None of the above is a correct equation for salary
2. A market research study is being conducted to determine if a product modification will be well
received by the public. A total of 1,000 consumers are questioned regarding this product.
The table below provides information regarding this sample.
Positive
Neutral Negative
Reaction
Reaction Reaction
Male
240
60
100
Female
260
220
120
What is the probability that a randomly selected person would be a female who had a positive
reaction?
Select one:
a. 0.26
b. 0.81
= 260/1000
c. 0.75
= 0.26
d. 0.35
e. None of the above is a correct equation for salary.
3. A market research study is being conducted to determine if a product modification will be well
received by the public. A total of 1,000 consumers are questioned regarding this product.
The table below provides information regarding this sample.
Positive
Neutral Negative
Reaction
Reaction Reaction
Male
240
60
100
Female
260
220
120
If it is known that a person had a negative reaction to the study, what is the probability that the
person is male?
Select one:
a. 0.51
b. 0.61
= 100/220
c. 0.1980
= 0.4545
d. 0.4545
e. None of the above is a correct equation for salary.
4. According to a recent survey of New Jersey households, the probability that the residents own 2
cars if annual household income is over $25,000 is 80%. Of the NJ households surveyed, 60% had
incomes over $25,000 and 70% had 2 cars. Based on the above information, what is the Probability
that the residents of a NJ household do not own 2 cars and have an income over $25,000 a year?
Select one:
a. 0.18
P(2 cars/>$25K) = 0.8; P(>$25K) = 0.60; P(2 cars) = 0.70
b. 0.12
P(2 carsC/>$25K) = 1 – P(2 cars/>$25K) = 1 – 0.8 = 0.20
c. 0.48
P(2 carsC ∩ > $25) = P(2 carsC/>$25K)P(>$25K) = 0.2(0.6) =0.12
d. 0.22
e. None of the above answers is correct.
5. Consider the following Payoff Table, Table A, with 3 decision alternatives and 3 states of nature
with the following payoff table representing profits:
States of Nature
S1
S2
S3
Decisions
D1
4
4
-2
D2
0
3
-1
D3
1
5
-3
Based on the above Table A, what is the optimal decision if the decision maker was
conservative?
Select one:
a. D1
b. D2
c. D3
d. Both D1 and D3
e. None of the above
6. Consider the following Payoff Table, Table A, with 3 decision alternatives and 3 states of nature
with the following payoff table representing profits:
States of Nature
Decisions
S1
S2
S3
D1
4
4
-2
D2
0
3
-1
D3
1
5
-3
Based on the above Table A, what is the optimal decision if the decision maker was optimistic?
Select one:
a. D1
b. D2
c. D3
d. Both D1 and D3
e. None of the above
7. Consider the following Payoff Table, Table A, with 3 decision alternatives and 3 states of nature
with the following payoff table representing profits:
States of Nature
S1
S2
S3
Decisions
D1
4
4
-2
D2
0
3
-1
D3
1
5
-3
Based on the above Table A, what is the optimal decision if the decision maker did use the minimax
regret approach?
Select one:
Regret Table
Max Regret
a. D1
0
1
1
1
b. D2
4
2
0
4
c. D3
3
0
2
3
d. Both D2 and D3
e. None of the above
8. Consider the following Payoff Table, Table A, with 3 decision alternatives and 3 states of nature
with the following payoff table representing profits:
States of Nature
Decisions
S1
S2
S3
D1
4
4
-2
D2
0
3
-1
D3
1
5
-3
Using data from the above Table A and assuming the decision maker used the following probabilities
for the 3 states: P(S1) = 0.20, P(S2) = 0.50, P(S3) = 0.3
What is the EVPI?
Select one:
EVPI = EVwPI - max{EMV}
a. 1.2
= (0.2(4) + 0.5(5)-0.3(1)) – 2.2
b. 2.6
= 0.8
c. 0.67
d. 0.80
e. None of the above
9. The sharing of patient records is a controversial issue in health care. A survey of 500
respondents asked whether they objected to their records being shared by insurance companies, by
pharmacies, and by medical researchers. The results are summarized on the following table:
Organization
Object to
Insurance
Record Sharing Companies
Pharmacies
Medical
Researchers
Yes
410
295
335
No
90
205
165
Based on the data, of those who don’t mind sharing their record, about what percentage said so with
regard to sharing their record with the pharmacies?
Select one:
a. 46.0
= (205/500)100
b. 41.0
= 41%
c. 59.0
d. 44.5
e. None of the above
10. The following data gives the number of occupied rooms on hotel check-ins for a 6-month
period:
July:
10
October:
20
August:
15
November:
18
September:
12
December:
24
With alpha (a) = 0.2, what is the simple exponential smoothing forecast for October?
Select one:
a. 12.6
b. 11.2
F1 = 10; A1 = 10
F2 = F1 + 0.2(A1- F1) = 10 +0.2(10 -10) = 10
c. 14.1
F3 = F2 + 0.2(A2 –F2) = 10 + 0.2(15-10) =11
d. 18.0
F4 = F3 + 0.2(A3 – F3) = 11 + 0.2(12-11) = 11.2
e. None of the above
11. Table B
Please use this table to answer questions 11 to 15
It is believed that, the average numbers of hours spent studying per day (HOURS) during
undergraduate education should have a positive linear relationship with the starting salary
(SALARY), measured in thousands of dollars per month) after graduation. Given below is the Excel
output from regressing starting salary on number of hours spent studying per day for a sample of 51
students .
NOTE: Some of the numbers in the output are purposely omitted (marked with xxx).
Regression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square 0.7801
Standard Error
Observations
1.3704
51
ANOVA
df
Regressions 1
SS
MS
335.0472
Residual
xx
xxxx
Total
50
427.0798
Coefficients Standard
Error
Intercept -1.8940
Hours
0.9795
335.0473
F
178.3859
Significance
F
xxx
1.8782
tStat
P-value
Lower
95%
Upper
95%
0.4018
-4.7134
2.051E-05 -2.7015
-1.0865
0.0733
13.3561
5.944E-18 0.8321
1.1269
Referring to Table B, the estimated average change in salary (in $1000s) as a result of spending
an extra HOURS studying per day is (please select the correct answer):
Select one:
a. -1.8940
b. 0.7845
c. 0.9795
d. 335.0473
e. None of the above is correct
12. Table B
It is believed that, the average numbers of hours spent studying per day (HOURS) during
undergraduate education should have a positive linear relationship with the starting salary
(SALARY), measured in thousands of dollars per month) after graduation. Given below is the Excel
output from regressing starting salary on number of hours spent studying per day for a sample of 51
students .
NOTE: Some of the numbers in the output are purposely omitted (marked with xxx).
Regression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square 0.7801
Standard Error
Observations
1.3704
51
ANOVA
df
Regressions 1
SS
MS
335.0472
Residual
xx
xxxx
Total
50
427.0798
Coefficients Standard
Error
Intercept -1.8940
Hours
0.9795
335.0473
F
178.3859
Significance
F
xxx
1.8782
tStat
P-value
Lower
95%
Upper
95%
0.4018
-4.7134
2.051E-05 -2.7015
-1.0865
0.0733
13.3561
5.944E-18 0.8321
1.1269
Referring to Table B, the value of the measured t-test statistic to test whether average SALARY
depends linearly on HOURS is
Select one:
a. 4.7134
b. 1.8940
c. 0.9795
d. 13.3561
e. None of the above.
13. Table B
It is believed that, the average numbers of hours spent studying per day (HOURS) during
undergraduate education should have a positive linear relationship with the starting salary
(SALARY), measured in thousands of dollars per month) after graduation. Given below is the Excel
output from regressing starting salary on number of hours spent studying per day for a sample of 51
students .
NOTE: Some of the numbers in the output are purposely omitted (marked with xxx).
Regression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square 0.7801
Standard Error
Observations
1.3704
51
ANOVA
df
Regressions 1
SS
MS
335.0472
Residual
xx
xxxx
Total
50
427.0798
Coefficients Standard
Error
Intercept -1.8940
Hours
0.9795
335.0473
F
178.3859
Significance
F
xxx
1.8782
tStat
P-value
Lower
95%
Upper
95%
0.4018
-4.7134
2.051E-05 -2.7015
-1.0865
0.0733
13.3561
5.944E-18 0.8321
1.1269
Referring to Table B, the p-value of the measure f-test statistic to test whether HOURS affects
SALARY is (please select the correct answer)
Select one:
a. (5.944E-18)/2
b. 5.944E-18
c. (2.051E-05)/2
d. 2.051E-05
e. None of the above
14. Table B
It is believed that, the average numbers of hours spent studying per day (HOURS) during
undergraduate education should have a positive linear relationship with the starting salary
(SALARY), measured in thousands of dollars per month) after graduation. Given below is the Excel
output from regressing starting salary on number of hours spent studying per day for a sample of 51
students .
NOTE: Some of the numbers in the output are purposely omitted (marked with xxx).
Regression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square 0.7801
Standard Error
Observations
1.3704
51
ANOVA
df
Regressions 1
SS
MS
335.0472
Residual
xx
xxxx
Total
50
427.0798
Hours
0.9795
178.3859
Significance
F
xxx
1.8782
Coefficients Standard
Error
Intercept -1.8940
335.0473
F
tStat
P-value
Lower
95%
Upper
95%
0.4018
-4.7134
2.051E-05 -2.7015
-1.0865
0.0733
13.3561
5.944E-18 0.8321
1.1269
Referring to Table B, the sum of squares (SSE) of the above regression is (please select the
correct answer):
Select one:
a. 1.878215
SSE = SST - SSR
b. 92.0325465
= 427.0798 – 335.0472
c. 335.047257
= 92.0326
d. 427.079804
e. None of the above
15. Table B
It is believed that, the average numbers of hours spent studying per day (HOURS) during
undergraduate education should have a positive linear relationship with the starting salary
(SALARY), measured in thousands of dollars per month) after graduation. Given below is the Excel
output from regressing starting salary on number of hours spent studying per day for a sample of 51
students .
NOTE: Some of the numbers in the output are purposely omitted (marked with xxx).
Regression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square 0.7801
Standard Error
Observations
1.3704
51
ANOVA
df
Regressions 1
SS
MS
335.0472
Residual
xx
xxxx
Total
50
427.0798
Coefficients Standard
Error
Intercept -1.8940
Hours
0.9795
335.0473
F
178.3859
Significance
F
xxx
1.8782
tStat
P-value
Lower
95%
Upper
95%
0.4018
-4.7134
2.051E-05 -2.7015
-1.0865
0.0733
13.3561
5.944E-18 0.8321
1.1269
Referring to Table B, the linear regression equation what can be used to forecast average SALARY
based on HOURS studying is (please select the correct answer):
Select one:
a. SALARY = 1.3704 + 09795 HOURS
b. SALARY = 1.8782 + 178.3859 HOURS
c. SALARY = -1.8940 + 0.9795 HOURS
d. SALARY = -1.8940 + 0.8857 HOURS
e. None of the above is a correct equation for salary.