Reference no: EM132650420

Objective 1: To assess relationship of body mass index (BMI) and relevant variables using dataset (Homework_EX_1). Data were collected on 2763female to assess their risk of metabolic conditions.The variable Age, smoking, race, exercise, physical activity, alcohol, glucose, statins anddiabetes are included in the given dataset.

Q1. First, determine the relationship between body mass index, BMI (dependent variable) and Blood glucose, glucose only (independent variable).
a) What is the null and alternative hypotheses?
b) Comment on the fit of the model? How much variability does it explains?
c) Is there a significant relationship between these two variables?
d) Interpret the blood glucose coefficient?
e) Are the assumptions of the linear regression model satisfied?
Q2. First, determine the relationship between body mass index, BMI(dependent variable) and diabetes only (independent variable).
a) Is there a significant relationship between these two variables?
b) Interpret the diabetes coefficient?

Q3. Next, determine the relationship between body mass index, BMI(dependent variable) and glucose (independent variable) whileincludingAge, diabetes, smoking, alcohol or physical activityvariables in the model.
a) Fit regression model for body mass index using entermethod for all these variables. If Age, diabetes, smoking, alcohol or physical activity are significant variables to keep in the model. If yes, then please specific the nature of this relationship i.e. confounder or effect modifier and also include interaction term, if appropriate.
b) Now, fit regression model for body mass index using different selection methods (stepwise, backward, forward) and compare their results?
c) Comment on the fit of the final chosen modelusing different selection methods? How much variability does it explains?
d) Is there significant relationship between these independent and the dependent variable? Which variables are significant and interpret these coefficients?
e) Are the assumptions of the linear regression model satisfied for your final chosen model?

Q4. Now, Compare the fit of the first model in Q1and the final model in Q3? Does the inclusion of these variablesimprove the model? Should all of them be included in the model? Explain your reasoning?

Objective 2: To assess relationship of diabetes and relevant variables using dataset (Homework_EX_1). Data were collected on 2763female to assess their risk of metabolic conditions. The variable Age, smoking, race, exercise, physical activity, alcohol, glucose, statins and body mass index are included in the given dataset.

Q5. To assess relationship between diabetes as dependent variable and other variables as independent variable.
a) Run a regression model to test for association between diabetes andblood glucose. Does this suggest that blood glucose is associated with diabetes? How do you interpret the effect size and 95% CI? Now, also includerace variable in the model, does this suggest that race is associated with diabetes? How do you interpret the effect size and 95% CI of variable race when adjusted for blood glucose level?

b) Run a regression model to test for association between diabetes and independent variables (body mass index and blood glucose) and also their interaction term (body mass index and blood glucose). Is the interaction term significant? How do you interpret the interaction co-efficient effect sizes and their 95% CI?

c) Run a regression model to test for association between diabetes andstatins. Does this suggest that statins are associated with diabetes? How do you interpret the effect size and 95% CI?

d) Run a regression model to test for association between diabetes and exercise. Does this suggest that blood glucose is associated with diabetes? How do you interpret the effect size and 95% CI?

e) Run a regression model to test for association between diabetes and Smoking. Does this suggest that smoking is associated with diabetes? How do you interpret the effect size and 95% CI?

f) Run a regression model to test for association between diabetes and alcohol. Does this suggest that alcohol is associated with having diabetes? How do you interpret the effect size and 95% CI?

Q6. Use forward LR method and enter method to assess relationship between diabetes as a dependent variable and all other co-variates (Age, blood glucose, race, smoking, alcohol, exercise, physical activity, and body mass index). Also include in the models to assess the following three mentioned interaction terms; body mass index and blood glucose, alcohol and smoking and finally body mass index and physical activity. Which variables and their interaction terms are statistically associated with diabetes and only include Interaction terms which are appropriate? Choose your final model between enter and forward LR method and then perform model diagnostics. How do you interpret the effect size and 95% CI of your final model?

Attachment:- Homework - Objective.rar