MATH 325 DeVry Week 4 iLab
MATH 325 DeVry Week 4 iLab
MATH 325 DeVry Week 4 iLab
Become familiar with Minitab menus and commands related to performing t tests.
Use Minitab to obtain an independent sample t test.
Use Minitab to obtain a paired sample t test.
Read the information in the lab on independent sample t tests and go through the Minitab tutorials on independent sample t tests and paired sample t tests.
Analyze the Beck Depression Inventory Posttest given after 4 weeks of treatment for depression patients. Use an independent sample t test based on the patients’ prescribed treatment: placebo or Lexapro.
Analyze diabetes patients’ pretest and posttest scores using a paired sample t test.
Using Minitab Statistics to calculate the various T-Tests
The steps required for completing the deliverables for this assignment (screen shots that correspond to these instructions can be found immediately following them).
Complete the questions below and paste the answers from Minitab below each question (type your answers to the questions where noted). Therefore, your response to the lab will be this ONE document submitted to the Dropbox.
Context (remember that statistics are far more than numbers or values – you need to know the context to perform a good analysis!).
Independent Samples: Patients diagnosed with depression according to the Beck Depression Inventory are randomly assigned to receive a Placebo vs. Lexapro for their treatment. Reassessment occurs at four weeks.
Note: Moderate Depression is indicated by a score of 21 or more when using the Beck Depression Inventory. Patients scoring 30 or more would not qualify for this study as they would need more intense treatment and would not be given a placebo.
Paired Sample T-Test: Assess the effectiveness of the hospital’s diabetic education program by comparing pre teaching and post teaching test scores for new diabetics on disease management.
Note: These patients cannot be discharged until they pass their posttest and insurance typically will not pay for the hospital stay if they leave against medical advice (AMA).
View the Minitab tutorial on T-Tests. The 2-Sample tutorial can be found by going to the Help menu in Minitab, selecting Tutorials then selecting 2-Sample t.
Read through Uses, Data and How To in the Tutorial window.
Note: The data files referenced in the tutorial are available in DocSharingfile (Minitab_Sample DataSets_HelpMenu). I suggest you print out the steps needed to perform the deliverables for the lab and as these items/steps come up in the tutorials, also use the HealthCareData.mpj data set to work along at that point.
For a specific example, choose Stat, Basic Statistics and then 2-Sample t. In the dialog box that pops ups, select Help.
You will want to do the same with the Paired-t in both the Tutorial (Help, Tutorial) and the Help in the Paired-t itself (Stat, Basic Statistics, Paired-t).
Independent Samples t-Test
1. Open the HealthCareData.mpj file using Minitab.
2. Will the rectangular nature of the Minitab data set require us to create a new data file for this analysis due to all the missing values at the end of Second_BDI and BDI_treatment? If you are not sure, try it both ways, observe any differences, then take the appropriate action and continue to step 3.
3. From Menus, select Stat, Basic Statistics and then 2-Sample t.
4. Select Second_BDI for the Samples and BDI_treatment for the Subscripts. Make sure to select Assume equal variances.
5. Click OK to perform the t-Test and view the results in the output window. Review the results. This is the point at which you perform a contextual analysis of the output.
6. Think about it: Were all the assumptions for a t-Test with independent samples met? What did the t-Test show? Are the results significant? What conclusions would you draw?
1. If necessary open the HealthCareData.mpj file using Minitab.
2. Will the rectangular nature of the Minitab data set require us to create a new data file for this analysis due to all the missing values at the end of Second_BDI and BDI_treatment? If you are not sure, try it both ways, observe any differences, then take the appropriate actions and continue to step 3.
3. From Menus, select Stat, Basic Statistics and then Paired-t.
4. Select two variables: Diab_Pretest and Diab_Posttest.
Click OK then review the results. This is where you begin the contextual analysis.
5. Think about it: Patients need to score 95% or better to be discharged. Was the teaching program effective? While the test may have indicated the means of the two groups were indeed significantly different, is this all the hospital needs to assess their teaching effectiveness? What other follow up analysis would you recommend?
6. Deliverable: Save this document and submit it as Week_4_i-Lab_YourNameHere.docx to the Dropbox.
The Independent Samples t-Test procedure compares means for two groups of cases. Ideally, for this test, the subjects should be randomly assigned to two groups, so that any difference in response is due to the treatment (or lack of treatment) and not to other factors. This is not the case if you compare average income for males and females. A person is not randomly assigned to be a male or female. In such situations, you should ensure that differences in other factors are not masking or enhancing a significant difference in means. Differences in average income may be influenced by factors such as education (and not by sex alone).
Example. Patients with high blood pressure are randomly assigned to a placebo group and a treatment group. The placebo subjects receive an inactive pill, and the treatment subjects receive a new drug that is expected to lower blood pressure. After the subjects are treated for two months, the 2-Sample t-Test is used to compare the average blood pressures for the placebo group and the treatment group. Each patient is measured once and belongs to one group.
Note: Patients with high blood pressure are not normally left untreated – this is an ethical question that arises all the time in healthcare studies. Could one ethically conduct such a study? Would such a study receive approval from the administration and medical boards? What legal issues would need to be addressed?
Statistics. For each variable: sample size, mean, standard deviation, and standard error of the mean. For the difference in means: mean, standard error, and confidence interval (you can specify the confidence level). Tests: Levene’s test for equality of variances, and both pooled-variances and separate-variances t-Tests for equality of means.
The Independent Samples t-Test is appropriate whenever two means drawn from independent samples are to be compared. The variable used to form the groups may already exist; however, a cut point on a continuous variable can be provided to dynamically create the groups during the analysis.
As with all t-Tests, the Independent Samples t-Test assumes that each sample mean comes from a population that is reasonably normally distributed, especially with respect to skewness. Test variables with extreme or outlying values should be carefully checked; boxplots can be used for this.