Description
Question 2
What is the crude mortality rate?
Age-Group | Population | Number of Deaths | |||
<30 | 15,000 | 20 | |||
30-65 | 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 age-specific death rate for the over-65 age group is
Age-Group | Population | Number of Deaths | |||
<30 | 15,000 | 20 | |||
30-65 | 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 | p-value | |
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 –0.51 x3 | |
b. | Y = 6.59 – 0.18 x1 + 0.27 x2 | |
c. | Y = 6.59 – 0.18 x1 + 0.27 x2 – 2.31x3 | |
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 | p-value | |
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 (p-value<0.05) increases by 0.27 days. | |
b. | For every additional year in employee’s length of employment, the average number of absent days in the last year significantly (p-value<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 | p-value | |
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 time-series 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 | p-value | |
Intercept | 222.379 | 67.35824 | 3.301438 | 0.002221 |
x | 9.009066 | 3.17471 | 2.83776 | 0.00751 |
df | SS | MS | F | p-value | |
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 three-month 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 ___________.