Use the Pima and German Diabetes datasets • Perform frequency analysis to clean the data. • Look for unrealistic glucose and other values (HINT: a glucose of 0 (zero) or insulin of 0 (zero) is a dead person or a bad values) • BEFORE merging, create a vari
Use the Pima and German Diabetes datasets • Perform frequency analysis to clean the data. • Look for unrealistic glucose and other values (HINT: a glucose of 0 (zero) or insulin of 0 (zero) is a dead person or a bad values) • BEFORE merging, create a variable “Country” and let Pima = USA, and German = Germany • Merge Pima and German Diabetes datasets. • Perform PROC UNIVARIATE and assess normality of glucose and age, weight, and BMI • Use PROC CORR to evaluate the correlation between age, height, weight, BMI, glucose and pregnancies. Which variables are most highly intercorrelated? • Perform PROC TTEST and determine if there are differences between diabetes and non-diabetes in German and then Pima populations in glucose, age, insulin, number of pregnancies. • Perform PROC REG to evaluate the association of age, glucose and BMI, controlling for age in 2 groups (Do PROC REG for Pima and German separately and then combined). Are there any differences between Pima and German regressions by PROC REG? • Are there significant differences in age, glucose, BMI, skinfold thickness, and insulin measures between Pima and German diabetes patients? How will you test these differences? Why did you choose this method?
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