UNIVARIATE VS. BIVARIATE ANALYSES AND REGRESSION

UNIVARIATE VS. BIVARIATE ANALYSES AND REGRESSION
Assignment
Interpret the two models that appear below and address the following additional questions as they pertain to each:
Diabetes (1 unit) = 1.3 + 2.4 (BMI) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05 What about confounding? Which of the variables are potential confounders? Compare and contrast matching on potential confounders versus including them in a regression model. Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05 What about confounding? Which of the variables are potential confounders? Compare and contrast matching on potential confounders versus including them in a regression model. Length: At least 2 pages in length, APA format, scholarly sources, use subheadings. Required Reading: Barrat, H. & Kirwan, M. (2009) Confounding, interactions, methods for assessment of effect modification. Health Knowledge. Retrieved from http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/confounding-interactions-methods DeLong, E., Li, L., & Cook, A., (2014). Pairing matching vs.stratification in cluster Randomized trial. NIH Collaboratory LaMorte, W.W. & Sullivan, L. (2016). Confounding and effect measure modification. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM5.html MarinStatsLectures. (2018). One Way ANOVA (Analysis of Variance): Introduction | Statistics Tutorial #25. https://www.youtube.com/watch?v=_VFLX7xJuqk Public Health Action Support Team (PHAST). (2020). Role of chance, bias and confounding in epidemiological studies. https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/chance-bias-confounding Wunsch, G. (2007). Confounding and control. Demographic Research 16(4). Retrieved from http://www.demographic-research.org/Volumes/Vol16/4/16-4.pdf

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