Question 2
What is the crude mortality rate?
AgeGroup 
Population 
Number of Deaths 

<30 
15,000 
20 

3065 
17,000 
55 

>65 
6,000 
155 

a. 
230 

b. 
6.1 per 1,000 

c. 
8.6 per 1,000 

d. 
6.1 per 10,000 

Question 3
The agespecific death rate for the over65 age group is
AgeGroup 
Population 
Number of Deaths 

<30 
15,000 
20 

3065 
17,000 
55 

>65 
6,000 
155 

a. 
155 

b. 
25.8 per 1,000 

c. 
1.55 per 10,000 

d. 
25.8 per 10,000 

Question 4
Calculate the relative risk of stroke of male smokers to male nonsmokers
Stroke 

Smokers 
Yes 
No 
Total 

Yes 
171 
3,264 
3,435 

No 
117 
4,320 
4,437 

Total 
288 
7,584 
7,872 

a. 
1.54 

b. 
1.88 

c. 
2.08 

d. 
None of the above is correct 

Question 5
Calculate the odds ratio of having a stroke in men who smoke to those who do not smoke
Stroke 

Smokers 
Yes 
No 
Total 

Yes 
171 
3,264 
3,435 

No 
117 
4,320 
4,437 

Total 
288 
7,584 
7,872 

a. 
1.93 

b. 
1.88 

c. 
1.78 

d. 
1.34 

Question 6
Is the following interpretation of the odds ratio true or false?
The odds of having a stoke are 1.93 times higher in men who smoke than in men who do not smoke
Stroke 

Smokers 
Yes 
No 
Total 
Yes 
171 
3,264 
3,435 
No 
117 
4,320 
4,437 
Total 
288 
7,584 
7,872 
True
False
Question 7
A new type of test, Generation A, was given to 500 individuals with suspected diabetes, of whom 320 were actually found to have diabetes. The results of the examination are presented in the following table:
Generation A Result 

Diabetes 

Test Result 
Present 
Absent 
Positive 
300 
50 
Negative 
20 
130 
Compute the sensitivity and specificity of the findings shown for Test A.
a. 
Sensitivity = 93.7%, Specificity = 72.2% 

b. 
Sensitivity = 96.7%, Specificity = 70.2% 

c. 
Sensitivity = 95.7%, Specificity = 76.2% 

d. 
Sensitivity = 91.7%, Specificity = 78.2% 
Question 8
A new type of test, Generation A, was given to 500 individuals with suspected diabetes, of whom 320 were actually found to have diabetes. The results of the examination are presented in the following table:
Generation A Result 

Diabetes 

Test Result 
Present 
Absent 
Positive 
300 
50 
Negative 
20 
130 
Compute the positive and negative predictive values of the findings shown for the test.
a. 
Positive Predictive value = 82.3% , Negative Predictive Value = 87.5% 

b. 
Positive Predictive value = 85.7% , Negative Predictive Value = 86.7% 

c. 
Positive Predictive value = 80.1% , Negative Predictive Value = 82.2% 

d. 
Positive Predictive value = 77.3% , Negative Predictive Value = 79.3% 
Question 9
From the following scatter plot, we can say that between y and x there is _______
a. 
Perfect positive correlation 

b. 
Virtually no correlation 

c. 
Positive correlation 

d. 
Negative correlation 
7 points
Question 10
A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.
Variable 
Description 
Y 
number of days absent last fiscal year 
x1 
commuting distance (in miles) 
x2 
employee’s age (in years) 
x3 
length of employment at PPP (in years) 
Coefficients 
Standard Error 
t Statistic 
pvalue 

Intercept 
6.594146 
3.273005 
2.014707 
0.047671 
x1 
0.18019 
0.141949 
1.26939 
0.208391 
x2 
0.268156 
0.260643 
1.028828 
0.307005 
x3 
2.31068 
0.962056 
2.40182 
0.018896 
R=0.498191 
R2=0.248194 
Adj R2=0.192089 
se = 3.553858 
n = 73 
What is the regression equation based on this analysis?
a. 
Y = 0.18 x1 + 0.27 x2 â€“0.51 x3 

b. 
Y = 6.59 â€“ 0.18 x1 + 0.27 x2 

c. 
Y = 6.59 â€“ 0.18 x1 + 0.27 x2 â€“ 2.31×3 

d. 
None of the above 
Question 11
A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.
Variable 
Description 
Y 
number of days absent last fiscal year 
x1 
commuting distance (in miles) 
x2 
employee’s age (in years) 
x3 
length of employment at PPP (in years) 
Coefficients 
Standard Error 
t Statistic 
pvalue 

Intercept 
6.594146 
3.273005 
2.014707 
0.047671 
x1 
0.18019 
0.141949 
1.26939 
0.208391 
x2 
0.268156 
0.260643 
1.028828 
0.307005 
x3 
2.31068 
0.962056 
2.40182 
0.018896 
R=0.498191 
R2=0.248194 
Adj R2=0.192089 
se = 3.553858 
n = 73 
Which of the following interpretations is correct?
a. 
For every additional year in the employee’s age, the average number of absent days in the last year significantly (pvalue<0.05) increases by 0.27 days. 

b. 
For every additional year in employeeâ€™s length of employment, the average number of absent days in the last year significantly (pvalue<0.05) decreases by 0.51 days. 

c. 
None of the above is correct. 
6 points
Question 12
A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.
Variable 
Description 
Y 
number of days absent last fiscal year 
x1 
commuting distance (in miles) 
x2 
employee’s age (in years) 
x3 
length of employment at PPP (in years) 
Coefficients 
Standard Error 
t Statistic 
pvalue 

Intercept 
6.594146 
3.273005 
2.014707 
0.047671 
x1 
0.18019 
0.141949 
1.26939 
0.208391 
x2 
0.268156 
0.260643 
1.028828 
0.307005 
x3 
2.31068 
0.962056 
2.40182 
0.018896 
R=0.498191 
R2=0.248194 
Adj R2=0.192089 
se = 3.553858 
n = 73 
Which of the following statements is correct about the R2?
a. 
The adjusted R2 value is 0.25. This means that the model explains around 25% of the variation in the average number of days absent in the last year. 

b. 
The adjusted R2 value is approximately 0.19. This means that the model explains around 19% of the variation in the average number of days absent in the last year. 

c. 
The adjusted R2 value is 0.50. This means that the model explains around 50% of the variation in the average number of days absent in the last year. 

d. 
None of the above is correct. 
Question 13
The following graph of a timeseries data suggests a _______________ trend.
a. 
linear 

b. 
quadratic 

c. 
cosine 

d. 
tangential 
7 points
Question 14
Fitting a linear trend to 36 monthly data points (January 2000 = 1, February 2000 =2, March 2000 = 3, etc.) produced the following tables.
Coefficients 
Standard Error 
t Statistic 
pvalue 

Intercept 
222.379 
67.35824 
3.301438 
0.002221 
x 
9.009066 
3.17471 
2.83776 
0.00751 
df 
SS 
MS 
F 
pvalue 

Regression 
1 
315319.3 
315319.3 
8.052885 
0.007607 
Residual 
34 
1331306 
39156.07 

Total 
35 
1646626 
The projected trend value for January 2003 is ________.
a. 
231.39 

b. 
555.71 

c. 
339.50 

d. 
447.76 
6 points
Question 15
Using a threemonth moving average, the forecast value for November in the following time series is ____________.
July 
5 

Aug 
11 

Sept 
13 

Oct 
6 

a. 
11.60 

b. 
10.00 

c. 
9.67 

d. 
8.60 

6 points
Question 16
When forecasting with exponential smoothing, data from previous periods is _________.
a. 
given equal importance 

b. 
given exponentially increasing importance 

c. 
ignored 

d. 
given exponentially decreasing importance 
Question 17
A time series with forecast values and error terms is presented in the following table. The mean absolute deviation (MAD) for this forecast is ___________.