PRACTICE FINAL EXAM QMB 3200 QUESTIONS AND ANSWERS (GRADED A)
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QMB 3200
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QMB 3200
PRACTICE FINAL EXAM QMB 3200 QUESTIONS AND ANSWERS (GRADED A)PRACTICE FINAL EXAM QMB 3200 QUESTIONS AND ANSWERS (GRADED A)PRACTICE FINAL EXAM QMB 3200 QUESTIONS AND ANSWERS (GRADED A)PRACTICE FINAL EXAM QMB 3200 QUESTIONS AND ANSWERS (GRADED A)
1. What is the experimental unit for this analysis?
...
PRACTICE FINAL EXAM QMB 3200
QUESTIONS AND ANSWERS (GRADED
A)
1. What is the experimental unit for this analysis?
A. Miles per hour
B. A road
C. A new car
D. Elapsed time
E. A second - ANSWER-C.
2. Fill in the blank: "At alpha=.05, there is between maximum speed and acceleration
time."
A. sufficient evidence of a negative linear relationship
B. insufficient evidence of a negative linear relationship
C. sufficient evidence of a positive linear relationship
D. sufficient evidence of a downward curvilinear relationship
E. sufficient evidence of an upward curvilinear relationship - ANSWER-A.
3. Approximately what percentage of the sample variation in acceleration time can be
explained by the simple linear model?
A. 70% B. 0% C. -17% D. 8% E. 113% - ANSWER-A.
4. Complete the sentence: "About 95% of the sampled new cars have acceleration
times that
A. fall within 1.13 seconds of their predicted values
B. equal .696 second
C. fall outside 1.28 miles per hour of their true values
D. fall within 2.26 seconds of their predicted values
E. fall between 0 and 2.26 miles per hour - ANSWER-D.
5. A 95% confidence interval for beta1 is (-.09, -.07). Practically interpret this interval.
Each answer begins with "We are 95% confident that ...".
A. acceleration time will fall between .07 and .09 second.
B. acceleration time will decrease between .07 and .09 second.
C. for every 1 second increase in acceleration time, maximum speed will decrease
between .07 and .09 mile per hour.
, D. for a new car with a maximum speed of 1 mile per hour, acceleration time will fall
between .07 and .09 second.
E. for every 1 mile per hour increase in maximum speed, acceleration time will
decrease between .07 and .09 second. - ANSWER-E.
6. Interpret the 95% confidence interval (6.78, 7.23) shown on the printout. Each answer
begins with "We are 95% confident that ...".
A. the mean acceleration time for all new cars falls between 6.78 and 7.23 seconds.
B. the slope of the simple linear regression line is positive.
C. the increase in acceleration time for every 1 mile per hour increase in maximum
speed falls between 6.78 and 7.23 seconds.
D. the mean acceleration time for all new cars with a maximum speed of 140 miles per
hour falls between 6.78 and 7.23 seconds.
E. the acceleration time for a new car with a maximum speed of 140 miles per hour falls
between 6.78 and 7.23 seconds. - ANSWER-D.
7. Give a theoretical interpretation of the phrase "95% confident" in the question above.
A. In repeated sampling, 95% of all similarly constructed intervals will equal (6.78,
7.23).
B. If we repeatedly sample from the population of new cars and compute a similar
interval for each sample, 95% of all intervals constructed would capture the true mean
acceleration time.
C. We are 95% confident that the interval (6.78, 7.23) will capture the true mean
acceleration time.
D. 95% of the acceleration times in the sample will fall within the interval (6.78, 7.23);
5% will fall outside the interval.
E. If we repeatedly sample from the population of new cars and compute a sample
mean acceleration time for each, 95% of the sample means will fall within the interval
constructed. - ANSWER-B.
8. Examine the stem-and-leaf plot of the standardized residuals. What can you infer
from this plot?
A. Due to the cone shape, the assumption of constant error variance appears to be
violated.
B. Due to the curvilinear pattern, the model appears to be misspecified (i.e., we need to
add curvature to the model).
C. The assumption of independent errors appears to be violated.
D. The assumption of parsimony appears to be violated.
E. Although the distribution of errors is slightly skewed to the high side, the regression
inferences are still valid due to robustness. - ANSWER-E.
9. How many y-outliers can you detect in the regression analysis?
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