Using the Framingham Heart Study dataset provided, perform the ANOVA multivariable linear regression analysis using BMI as a continuous variable. Before conducting the analysis, be sure that all participants have complete data on all analysis variables.
Describe how each characteristic is related to BMI. Are crude and multivariable effects similar? What might explain or account for any differences?
H0 The BMI is not related to the patient characteristics in the Framingham Heart Study. (Null Hypothesis)
H1 The BMI is related to the patient characteristics in the Framingham Heart Study. (Alternative Hypothesis)
Upload both Excel sheet into R Studio. (Refer to Chapters 7 & 12 in Introductory Statistics with R or pages 111–122 in EXCEL Statistics a Quick Guide). Exclude participants with missing data on analysis variables (age, sex, systolic blood pressure, total serum cholesterol, current smoker, and diabetes = cleaning the data). Conduct the simple linear regression (ANOVA) by using the Excel Regression tool in the Data Analysis Toolpak.
Remember SEX is coded 1=male and 2=female.
Present your findings in a Word document by copying and pasting the ANOVA table into the document. Your paper must be written with a title page, an introduction, a discussion where you interpret the meaning of the ANOVA test, and a conclusion should be included. Your submission should be 2–3 pages to discuss and display your findings.
Provide support for your statements with in–text citations from a minimum of two scholarly, peer–reviewed articles. One of these sources may be from the class readings, textbook, or lectures, but the others must be external. The Saudi Digital Library is a good place to find these sources and should be your primary resource for conducting research.
Follow APA and Saudi Electronic University writing standards.
Review the grading rubric to see how you will be graded for this assignment.
You are strongly encouraged to submit all assignments to the TurnItIn Originality Check prior to submitting them to your instructor for grading.
Exclude participants with missing data on analysis variables (age, sex, systolic blood pressure, total serum cholesterol, current smoker, and diabetes = cleaning the data).
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