Assignment: Assessment
The testing of assumptions, recognition of limitations, and proper use of diagnostics are all necessary elements in the use of multiple linear regression for public health research. All of these elements allow biostatisticians to better assess the results of multiple linear regression models.
For this Assignment, you test that assumptions for multiple linear regression have been met, use SPSS to create a multiple linear regression, evaluate results to determine whether the model is appropriate, and finally interpret the relationships uncovered through this statistical test between the independent and dependent variables. Use the Week 4 Dataset (SPSS document) from the Learning Resources area to complete this assignment.
1. Explain the assumptions of Linearity, Sampling independence, Normality, and Homoscedasticity (or equal variance). (30 points)
1. How would you test whether these have been met? (Note: for the exam you do not need to test these assumptions)
2. Using SPSS, test the assumption of Linearity between the independent and dependent variables.
3. Using SPSS, test the assumption of Normality for the dependent variable.
2. Conduct a multiple linear regression using SPSS. Provide relevant SPSS output and assess the statistical significance of the effects of mother’s Age, BMI, and Coffee (Cups per Day) on Birth weight. (30 points)
3. Explain the practical implications of your finding. Include a reference to the R square of the model in your discussion. (20 points)
4. Discuss whether or not there is interaction (effect modification) first between Age and BMI and second between BMI and Coffee. (20 points)
Daniel, WW & Cross, CL. (2013). Biostatistics: A Foundation for Analysis in the Health Sciences. Hoboken, NJ: Wiley
Krantz, M. J., Coronel, S. M., Whitley, E. M., Dale, R., Yost, J., & Estacio, R. O. (2013). Effectiveness of a community health worker cardiovascular risk reduction program in public health and health care settings. American Journal of Public Health, 103(1), e19–e27.
Weil, M., Bressler, J., Parsons, P., Bolla, K., Glass, T., & Schwartz, B. (2005). Blood mercury levels and neurobehavioral function. JAMA, 293(15), 1875–1882
Williamson, D. F., Madans, J., Anda, R. F., Kleinman, J. C., Giovino, G. A., & Byers, T. (1991). Smoking cessation and severity of weight gain in a national cohort. New England Journal of Medicine, 324(11), 739–745.
Huff, D., & Geis, I. (1954). How to lie with statistics. New York, NY: Norton.

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