One of the more challenging aspects of data analysis is determining which statistical tests to run (given the circumstances) and performing the statistical software steps correctly. There are several types of decision trees you can use to select a statistical test, but we will look at just one type in this assignment.
At the most fundamental level, statistical tests are usually chosen according to:
- The nature of the data you have collected to answer the research question in your study (nominal, ordinal, or interval/ratio).
- The number of samples being analyzed for a given variable (often described by groupings).
- What you wish the test to do (find differences between samples/groups, explore relationships between variables, make predictions using different variables).
Before choosing a test for interval/ratio data, there is one final characteristic of the data that must be determined, which is whether the data is “normally” distributed. If the data distribution violates the assumption of normality, a nonparametric equivalent test must be selected for the analysis.
There are many other issues that can influence the analytical technique (sample size, variability of the data, inter-relatedness of the variables, et cetera), but these challenges are for another time, another course.
Preparation
You are encouraged to review the t-test and ANOVA materials from previous weeks. Then, examine How to Choose a Statistical Test and the test-selection tutorials linked in the Resources to determine which statistical test is most likely to be appropriate for your data type.
Instructions
Use the Framingham study data set to perform and interpret statistical tests that answer the following research questions. Then, provide a written analysis of your results.
Q1- Smoking and total cholesterol: First, test the normal distribution assumption and select the appropriate statistical analysis path. Next, compare smokers and nonsmokers (variable: cursmoke1) in the Framingham study to determine whether there was a significant difference in baseline cholesterol levels (variable: totchol1).
Q2- BMI categories and baseline glucose levels: First, without testing for the assumption of a normal distribution of data, test for the equality of variances and select the appropriate statistical analysis path. Then, use the categorical variable BMI, which has four BMI categories (variable:
bmiCat1) to compare baseline glucose levels (variable: glucose1) to determine if there is



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