MATH 325 Week 7 Lab 6 Using Microsoft Excel to Solve Nonparametric Problems

MATH325 Lab 6 Using Microsoft Excel to Solve Nonparametric Problems The steps required for completing the deliverables for this assignment, including screen shots that correspond to these instructions, are outlined below. Complete the questions below and paste the answers from Excel below each question (type your answers to the questions where noted). Therefore, your response to the lab will be this ONE submitted document. Context: Remember that statistics are far more than numbers or values – you need to know the context to perform a good analysis! Chi-Square Test The Chi-Square test procedure tabulates a variable into categories and computes a square statistic. This goodness-of-fit test compares the observed and expected frequencies in each category to test that all categories contain the same proportion of values or test that each category contains a user-specified proportion of values. Example. The Chi-Square test could be used to determine whether a bag of jelly beans contains equal proportions of blue, brown, green, orange, red, and yellow candies. You could also test to see whether a bag of jelly beans contains 5% blue, 30% brown, 10% green, 20% orange, 15% red, and 15% yellow candies. Statistics. Mean, standard deviation, minimum, maximum, and quartiles. The number and the percentage of non-missing and missing cases; the number of cases observed and expected for each category; residuals; and the chi-square statistic. Data. Use ordered or unordered numeric categorical variables (ordinal or nominal levels of measurement). Assumptions. Nonparametric tests do not require assumptions about the shape of the underlying distribution. The data are assumed to be a random sample. The expected frequencies for each category should be at least 1. No more than 20% of the categories should have expected frequencies of less than 5. Study: A nurse practitioner is studying the effect of blood sugar (glucose) control, which involves collecting the average daily AC & QHS (fasting) blood sugar levels of the patients to determine if there is a 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 diabetes 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