Strategic Analysis Paper

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?

The correlation value P is 0.357, the results indicates that the frequency distribution does was not derived from normal variables. P value is greater than alpha P > 0.05. Such case indicates that it is not statistically significant. 

2.    State the null hypothesis where age at enrollment is used to predict the time for completion of an RN to BSN program.

Age at enrollment does not predict the completion of an RN to BSN program

3.    What is b as computed by hand (or using SPSS)?

b = 0.047

4.    What is a as computed by hand (or using SPSS)? See chart in #3

A = 11.763

5.    Write the new regression equation.

Y = bx + a

Y = 0.047x + 11.76

6.    How would you characterize the magnitude of the obtained R2 value? Provide a rationale for your answer.

According to analysis the magnitude of the value of R2 is expected to be small according to description provided within the lesson

7.    How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?

Having knowledge of student’s enrollment age, gives explanation of variance in months of RN to BSN program completion, which is calculated as 1.2%?

8.    What was the correlation between the actual values and the predicted values using the new regression equation in the example?

Using new regression equation, the correlation value between actual y values and predicted y values is 0.108

9.    Write your interpretation of the results as you would in an APA-formatted journal.

A study research using linear regression with student age as the predictor and duration of months to complete the RN to BSN program we obtain values of beta as = 0.108, p = 0.651, and R2 = 1.2% The findings obtained reveal that student age at the during enrollment time does not provide accurate prediction on how long specific student take to complete the program

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

No

The results obtained when using the calculated regression equation is not accurate. It is small hence cannot provide desirable results.

Predicted value for youngest student

Y= 0.047x + 11.763 where x = 23

Y = 0.047 * 23 + 11.763

Y = 1.081 + 11.763

Y = 12.844

Predicted value for oldest student

Y = 0.047 * 51 +11.763

Y = 2.397 + 11.763

Y = 14.16

The difference for youngest and the oldest are very insignificant using the equation

Exercise 35

1.    Do the example data in Table 35-2 meet the assumptions for the Pearson χ 2 test? Provide a rationale for your answer

Yes the data meets the assumptions of Pearson χ 2 tests which includes

Data have nominal-level or are frequency data

 The sample size is adequate

Measures are independent of each other

Subject's data only fit into one category

2.    Compute the χ 2 test. What is the χ 2 value?

X2 = 11.931

3.    Is the χ 2 significant at α = 0.05? Specify how you arrived at your answer.

Yes, χ2 is significant. Since the p value is = 0.001, which is less than alpha α = 0.05

4.    If using SPSS, what is the exact likelihood of obtaining the χ2value at least as extreme as or as close to the one that was actually observed, assuming that the null hypothesis is true?

Assuming that the null hypothesis is true, the specific likelihood of obtaining a χ2 values as extreme as possible or as close as possible to the value observed. is 0.1%

5.    Using the numbers in the contingency table, calculate the percentage of antibiotic users who tested positive for candiduria.

The percentage of antibiotic users who tested positive for candiduria is given by

15 / 58 = 0.259

= 0.259 * 100 %

= 25.9 %.

6.    Using the numbers in the contingency table, calculate the percentage of non-antibiotic users who tested positive for candiduria.

Percentage of antibiotic users tested positive for candiduria is given by

0 / 39 = 0

= 0 * 100 %

= 0 %

7.    Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had a history of antibiotic use.

The percentage of veterans with candiduria who had a history of antibiotic use is given by

15 /15 = 1

= 1 * 100 %

= 100%

8.    Using the numbers in the contingency table, calculate the percentage of veterans with candiduria who had no history of antibiotic use.

The percentage of veterans with candiduria who had no history of antibiotic use given by 0 / 15 = 0

= 0 * 100 %

= 0 %

9.    Write your interpretation of the results as you would in an APA-formatted journal.

The analysis based on Pearson χ2 revealed that antibiotic users comprising of 25 % of the sample had higher rate of candiduria infections than those who did not use antibiotics comprising of 0 % of the sample, χ2(1) = 11.931, p = 0.001. Results obtained shows that antibiotic consumption increases risk for developing candiduria.

10.    Was the sample size adequate to detect differences between the two groups in this example? Provide a rationale for your answer.

Yes

The sample size was adequate for effective detecting of differences between the two groups. The significant difference was p = 0.001, the value is smaller than alpha = 0.05

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in urgent custom research papers. If you need a similar paper you can place your order from nursing school papers services.