MATH 325 DeVry Week 6 iLab

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MATH 325 DeVry Week 6 iLab

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MATH 325 DeVry Week 6 iLab


MATH 325 DeVry Week 6 iLab

MATH 325 DeVry Week 6 iLab

Using Minitab to conduct ANOVA procedures

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!).

Study: A nurse practitioner is studying the effect of blood sugar (glucose) control, which involves collecting the average daily AC and QHS (fasting) blood sugar levels of the patients to determine if there is relationship between these and the patients’ Hemoglobin A1C level. She hypothesizes that good blood sugar control will result in ideal Hemoglobin A1C levels and inadequate control of the patients’ blood sugar will result in high Hemoglobin A1C levels.

She also tracks other factors that may contribute to the patients’ control of their blood sugar such as carbohydrate intake, age, frequency of glucose checks, and insurance coverage of diabetic supplies.

Hemoglobin: Ideal Hemoglobin A1C levels for patients are 6 or 7, a value of 8 or 9 merits concern, values 10 and up are considered severely uncontrolled, while values less than 6 are rare in diabetic patients. 4 and 5 can be found normally in patients that are not diabetic.

Blood Sugar: Glucose levels under 70 are considered low, between 70 and 110 is considered normal, 111 to 170 is considered moderately high, and values above 170 are considered high. There is some debate on the cut points, however, these are the values used to categorize glucose levels in this study.

Glucose _Range: This is a categorical variable describing the group into which the patient’s glucose level places them: low, normal, moderately high, and high.

Glucose_Group: This is a numeric variable containing the same information as the Glucose_Range, however, the numeric value assigned to each group can be used in analysis that requires a ratio or interval level of measurement. Low is assigned a 1, Normal is assigned a 2, Moderately High is assigned a 3, and High is assigned a 4.

Carbohydrates: Diabetic patients try to consume 14 servings of carbohydrates daily where each serving is approximately 15 grams. This study tracks the average grams of carbohydrates consumed on a daily basis by these patients.

Age_Range: This is a Categorical Variable where each patient is classified by age: under 10, 11 – 16, 17 – 25, 26 – 40, 41 – 60, and over 60.

Age_Group: This is a numeric variable that contains the same information as the Age_Range, however, the numeric value assigned to each group can be used in analysis that requires a ratio or interval level of measurement. Each patient is classified by age: under 10 is assigned a 0, 11 – 16 is assigned a 1, 17 – 25 is assigned a 2, 26 – 40 is assigned a 3, 41 – 60 is assigned a 4, and over 60 is assigned a 5.

Insurance: This is a categorical variable that describes if the patient’s insurance covers diabetic supplies. Yes/No.

Insurance_Group: This is a numeric variable that describes the same information as the Insurance variable; however, the numeric value assigned to each group can be used in analysis that requires a ratio or interval level of measurement. Yes is assigned a 1 and No is assigned a 0.

Frequency: This numeric variable describes how many daily checks of their glucose level are typically performed on a given day for each diabetic patient.

View the Minitab tutorial on ANOVA. The ANOVA tutorial can be found by going to the Help menu in Minitab, selecting ANOVA and then selecting One-Way ANOVA.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, ANOVA and then One-Way. In the dialog box that pops ups, select Help.

With the One-Way ANOVA procedure, you will be able to: ·

  • Validate the assumption of variance equality ·
  • Obtain the ANOVA table and results ·
  • Visually inspect the group means ·
  • Perform custom contrasts, tailored to your specific hypotheses ·
  • Compare each mean to every other mean, assuming variance equality or not ·
  • Perform two types of robust analysis of variance

One of the first steps in the performance of an analysis of variance (ANOVA) is to validate the assumptions necessary for use of the test. Then we perform the analysis.

Examine descriptive statistics for each of the independent variable’s groups and the Levene statistic to assess if the variances are equal. The F statistic used in the ANOVA test can be robust to unequal variances if the samples sizes are approximately equal. However, our first step is always to test the equality of the variances.

It is also advisable to perform a plot of the means to get a visual indication of where you may expect to find similarities and differences among the groups.

We will use Minitab and request the descriptive statistics, the Levene statistic and a plot of the Means as well as calculate the F-Statistic with its significance.

After the initial analysis, we will then perform an analysis using contrasts to target specific groups indicated by the data:

Is there truly a difference between the low and normal groups?

Is there a difference between the moderately high and high groups?

Is there a difference between the normal and moderately high groups?

To obtain a One-Way ANOVA using Minitab

  1. Open the HealthCareData.mpj file using Minitab.
  2. First, we want to test the equality of the variances. From the menus, select Stat, ANOVA, Test for Equal Variances Choose Hemoglobin for the Response and Glucose_Group.
  3. Click OK to examine the output and perform an initial analysis of what you see.
  4. To obtain the One-Way ANOVA, select Stat, ANOVA, One-Way. Choose Hemoglobin for the Response and Glucose_Group for the Factor.
  5. Click Graphs and then select Boxplots of data.
  6. Click OK, OK and examine the output. Now we begin the contextual analysis.
  7. Think about it: Were the assumptions for ANOVA met? Can we proceed even if they aren’t? Under what circumstances? What did the data and tests show us about the two variables and the groups involved? Did we need the Levene test?
  8. Deliverable: Save this document and submit it as Week_6_i-Lab_YourNameHere.docx to the Dropbox.