Statistics Assignment

1. What would be the appropriate statistical procedure to test the following hypothesis: “Triglyceride values are a good predictor of weight in obese adults.”

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2. What is (are) the function(s) of parametric statistical procedures?

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3. What is Type I Error?

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4. What are the assumptions underlying the use of parametric, statistical procedures?

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5. If a critical value is greater than the test statistic, would you accept or reject the null hypothesis?

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6. Under what circumstance(s) is it appropriate to use a 2-tailed test of significance?

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7. What is the appropriate statistical procedure to use when your interest is in detecting a bivariate, curvilinear association?

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8. For a study comparing outcomes under alternate treatment conditions, when the null hypothesis is rejected, the researcher concludes that a difference among groups exists.

_____True

_____False

9. A researcher, for reasons passing understanding, wishes to assess the association between gender and total cholesterol values. What would be the appropriate statistical procedure?

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10. An HIV educator wishes to determine whether the method of delivering teaching influences adherence with antiretroviral therapy. She decides to measure adherence as viral load (a ratio measure). She teaches one group using lecture-discussion techniques. She adapts the information for access on the internet and gives another group the information using this medium. For yet another group, she decides to give a CD Rom for home study and then meets with individuals to answer any questions. She obtains viral loads for all clients for comparison. What procedure will determine the significance of any differences?

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Items 11-15 relate to the following study results:

Study A Study B Study C

(2 = 1.683 F = 7.357 r = .83

df = 4 df = 3/203 df = 98

p > .05 p < .05 p < .01

11. What statistical procedure was used to analyze data in study B?

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12. How many groups were compared in study B?

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13. How many subjects were enrolled in Study C?

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14. Which study demonstrated the greatest level of statistical significance?

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15. In which study is the likelihood of Type I error greatest?

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Items 16, 17 and 18 relate to the following:

In a regression analysis, a nurse researcher found a correlation of .82 between pain relief scores and satisfaction with nursing care. She also calculated the following for her regression analysis:

Pain Relief (x): Mean = 58 sd = 3.9

Satisfaction (y): Mean = 42 sd = 4.4

slope = 1.56

y intercept = - 3.53

16. What will be the predicted satisfaction score (expressed as a point estimate) for a patient with a pain relief score of 62?

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17. What is the standard error of estimate when predicting satisfaction from knowledge of pain relief score?

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18. What would be the interval estimate for satisfaction for the patient in problem 16?

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Items 19-21: A nurse researcher is investigating the effect of timing of standard pain control interventions on severity of pain in adolescents with sickle-cell disease. She establishes three treatment protocols: 1) initiation of pain control immediately upon the presence of prodromal sign (an “aura” signaling the imminent onset of pain); 2) initiation of pain control one hour after the onset of pain; and 3) initiation of pain control only at the points where non-steroidal anti-inflammatories and guided imagery are no longer effective in keeping pain bearable. She conducted a one-way ANOVA to analyze her data and the following table summarizes her findings:

Source df SS MSS F p

Among 2 75536.2 37768.1 5.159 <.05

Within 27 197660.3 7320.8

Total 29 273196.5

On the basis of these data alone, she drew the following conclusions. For each conclusion, indicate whether you feel the conclusion is justified or unjustified.

19. Severity of pain is influenced by the timing of pain interventions in sickle-cell crises.

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20 Immediate intervention is better than either slightly delayed intervention or initiation at crisis stage.

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21. She has more than 99% confidence in her conclusion that severity of pain is influenced by timing.

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Items 22-23 relate to the following study results:

Study A Study B

r = .64 r = .77

df = 18 df = 121

p<.05 p<.01

22. In using the data from study A to make predictions, what percent of the time would you expect predictions to be exactly correct?

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23. Which study would have the smallest margin of error in predicting one variable from knowledge of the other?

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Items 24 and 25 relate to the following SPSS output. A researcher is interested in characteristics of HIV+ and HIV- adolescents interviewed 166 young adults about their experiences during adolescence. He wished to know, among other things, if there were significant differences in the ages at which HIV+ and HIV- young adults became sexually active. The following is the printout of this analysis:

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HIV Status N Mean sd Stnd. Error

Age at Positive 57 13.2 2.96576 .39282

first sexual

experience Negative 109 15.1 2.57286 .24644

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Independent Samples Test

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Levene’s Test for

Equality of Variances

F Sig

Age at Equal variance assumed 1.313 .254

first sexual

experience Equal variance not assumed

t-test for Equality of Means

t df sig. mean difference

Age at Equal variance assumed -2.870 164 .005 -1.9

first sexual

experience Equal variance not assumed -2.745 99.66 .007 -1.9

24. Were there significant differences between the groups. Give the relevant stastical data to support your answer?

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25. What is the confidence interval associated with your answer to item # 24?

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Each of the questions on the following pages is a calculation problem worth 10 points. Partial credit will be awarded if I can following your procedures and determine that errors are arithmetic rather than conceptual. It would be wise, therefore, to clearly indicate your worksteps on each problem.

26. For the following data set, calculate the oneway ANOVA and test for significance at the .05 level.

Group 1 Group 2 Group 3 Group 4

x x2 x x2 x x2 x x2

7 49 10 100 12 144 16 256

8 64 12 144 14 196 15 225

7 49 13 169 13 169 18 324

9 81 13 169 11 121 17 289

9 81 14 196 13 169 20 400

11 121 15 225 15 225 21 441

10 100 14 196 13 169 22 484

61 545 91 1,199 91 1,193 129 2,419

27. Calculate the (2 for the following 3 X 2 table and test for significance at the .01 level.

Group 1 Group 2 Group 3

Positive

Outcome 9 12 8 29

Negative

Outcome 5 16 4 25

14 28 12 54

28. For the following group data, calculate a t-test and test for significance at the .05 level, 2-tailed level of significance.

Treatment Group Control Group

Mean = 68.4 Mean = 52.2

sd = 5.6 sd = 6.0

n = 42 n = 46

29. For the following paired observations, calculate the Pearson product-moment correlation coefficient and test for significance at the .01 level.

x x2 y y2 xy

17 289 23 529 391

17 289 19 361 323

18 324 20 400 360

19 361 17 289 323

21 441 15 225 315

22 484 19 361 418

21 441 20 400 420

23 529 19 361 437

22 484 20 400 440

18 324 16 256 288

198 3,966 188 3,582 3,715

30. For the following data regarding paired rank orders for a sample, calculate the correlation coefficient and test for significance at the .05 level.

Subject # Rank 1 Rank 2

1 2 1

2 1 2.5

3 3 2.5

4 5 4

5 6 5

6 4 8

7 7 6

8 9 7

9 8 10

10 10 9