Reference no: EM131627784
Assignment
Discussion Question 1
Explain when a z-test would be appropriate over a t-test.
Discussion Question 2
Researchers routinely choose an alpha level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower alpha level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)?
Discussion Question 3
How would you explain the analysis of variance, assuming that your audience has not had a statistics class before?
Discussion Question 4
What is an interaction? Describe an example and identify the variables within your population (work, social, academic, etc.) for which you might expect interactions?
Discussion Question 5
Describe the error in the conclusion. Given: There is a linear correlation between the number of cigarettes smoked and the pulse rate. As the number of cigarettes increases the pulse rate increases. Conclusion: Cigarettes cause the pulse rate to increase.
Discussion Question 6
Now that you are familiar with the basic concepts of statistics, what are some examples of when you have seen or heard statistics used inappropriately?
EXERCISE 1
Calculating Simple Linear Regression
1. If you have access to SPSS, compute the Shapiro-Wilk test of normality for the variable age (as demonstrated in Exercise 26). If you do not have access to SPSS, plot the frequency distributions by hand. What do the results indicate?
2. State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.
3. What is b as computed by hand (or using SPSS)?
4. What is a as computed by hand (or using SPSS)?
5. Write the new regression equation
6. How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.
7. How much variance in months to RN to BSN program completion is explained by knowing the student's enrollment age?
8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?
9. Write your interpretation of the results as you would in an APA-formatted journal.
10. Given the results of your analyses, would you use the calculated regression equation to predict future students' program completion time by using enrollment age as x? Provide a rationale for your answer.
Exercise 2
1. Do the example data meet the assumptions for the paired samples t -test? Provide a rationale for your answer.
2. If calculating by hand, draw the frequency distributions of the two variables. What are the shapes of the distributions? If using SPSS, what are the results of the Shapiro-Wilk tests of normality for the two variables?
3. What are the means for the baseline and post-treatment affective distress scores, respectively?
4. What is the paired samples t -test value?
5. Is the t -test significant at α = 0.05? Specify how you arrived at your answer.
6. If using SPSS, what is the exact likelihood of obtaining a t- test value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?
7. On average, did the affective distress scores improve or deteriorate over time? Provide a rationale for your answer.
8. Write your interpretation of the results as you would in an APA-formatted journal.
9. What do the results indicate regarding the impact of the rehabilitation on emotional distress levels?
10. What are the weaknesses of the design in this example?
EXERCISE 3
Calculating Pearson Chi-Square
1. Do the example data in Table 35-2 meet the assumptions for the Pearson χ2 test? Provide a rationale for your answer.
2. Compute the χ2 test. What is the χ2 value?
3. Is the χ2 significant at α =0.05? Specify how you arrived at your answer.
4. If using SPSS, what is the exact likelihood of obtaining the χ2 value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true
5. Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.
6. Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested negative for candiduria
7. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.
8. Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.
9. Write your interpretation of the results as you would in an APA-formatted journal.
10. Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.