Description These data are courtesy of Dr John Schorling. Department of Medicine, University of Virginia School...

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Description These data are courtesy of Dr John Schorling. Department of Medicine, University of Virginia School of Medicine. The data consist of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. According to Dr John Hong, Diabetes Mellitus Type II (adult onset diabetes) is associated most strongly with obesity. The waist/hip ratio may be a predictor in diabetes and heart disease. DM II is also agssociated with hypertension - they may both be part of "Syndrome X". The 403 subjects were the ones who were actually screened for diabetes. Glycosolated hemoglobin > 7.0 is usually taken as a positive diagnosis of diabetes. Data dictionary Variable Explanation (Unit) id chol stab.glu hdl ratio glyhb location subject id Total Cholesterol Stabilized Glucose High Density Lipoprotein Cholesterol/HDL Ratio Glycosolated Hemoglobin a factor with levels (Buckingham, Louisa) age (years) age gender male or female height height (inches) weight weight (pounds) frame a factor with levels (small,medium, large) bp.1s First Systolic Blood Pressure bp.ld First Diastolic Blood Pressure bp.2s Second Diastolic Blood Pressure bp.2d Second Diastolic Blood Pressure waist waist in inches hip time.ppn hip in inches Postprandial Time when Labs were Drawn in minutes a). Introduction to background and objectives. b). Introduction to data. (i.e.. interpret the variables, what is the dependent variable and independent variables). c). What is the goal of this study? d). Why regression model can be applied? e). A summary of the statistical analysis conducted (for example, assumption checking, multicollinearity checking, necessary transformation, model selection procedure (for example, stepwise procedure in R) etc.) f). Based on all the information you have from the statistical analysis, and taking into account model assumption checking and explore if transformations are needed), which model would you choose to report to the researchers and why? Explain why you chose your model and comets on whether you think the model is a good fit. You may also need to write a few sentences describing what the chose model implies about the predictors' relationships with the number of active physicians. Dtata=read.csv("your file path/diabetes.csv", header = T) Description These data are courtesy of Dr John Schorling. Department of Medicine, University of Virginia School of Medicine. The data consist of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. According to Dr John Hong, Diabetes Mellitus Type II (adult onset diabetes) is associated most strongly with obesity. The waist/hip ratio may be a predictor in diabetes and heart disease. DM II is also agssociated with hypertension - they may both be part of "Syndrome X". The 403 subjects were the ones who were actually screened for diabetes. Glycosolated hemoglobin > 7.0 is usually taken as a positive diagnosis of diabetes. Data dictionary Variable Explanation (Unit) id chol stab.glu hdl ratio glyhb location subject id Total Cholesterol Stabilized Glucose High Density Lipoprotein Cholesterol/HDL Ratio Glycosolated Hemoglobin a factor with levels (Buckingham, Louisa) age (years) age gender male or female height height (inches) weight weight (pounds) frame a factor with levels (small,medium, large) bp.1s First Systolic Blood Pressure bp.ld First Diastolic Blood Pressure bp.2s Second Diastolic Blood Pressure bp.2d Second Diastolic Blood Pressure waist waist in inches hip time.ppn hip in inches Postprandial Time when Labs were Drawn in minutes a). Introduction to background and objectives. b). Introduction to data. (i.e.. interpret the variables, what is the dependent variable and independent variables). c). What is the goal of this study? d). Why regression model can be applied? e). A summary of the statistical analysis conducted (for example, assumption checking, multicollinearity checking, necessary transformation, model selection procedure (for example, stepwise procedure in R) etc.) f). Based on all the information you have from the statistical analysis, and taking into account model assumption checking and explore if transformations are needed), which model would you choose to report to the researchers and why? Explain why you chose your model and comets on whether you think the model is a good fit. You may also need to write a few sentences describing what the chose model implies about the predictors' relationships with the number of active physicians. Dtata=read.csv("your file path/diabetes.csv", header = T)