Chapter 11--Regression and Correlation Methods

So it is of interest to determine exercise a modiable

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: iction (Forecasting) Dummy Variables Confounding: Example Glucose levels above 125 mg/dL are diagnostic of diabetes, while levels in the range 100-125 mg/dL signal increased risk of progression to this serious and increasingly widespread condition. So it is of interest to determine exercise, a modiﬁable lifestyle factor, would help people reduce their glucose level and thus avoid diabetes. An observational study of women participants was conducted to see whether exercise might help to prevent progression to diabetes among women at risk. The data was sent to you by email this morning. Chapter 11: Regression and Correlation Methods Stat 491: Biostatistics Introduction Least Square Estimates of the Parameters Inference about the Parameters Prediction Assessing Adequacy of Fit Correlation Multiple Regression Introduction Inferences in Multiple Regression Tests for Subset of Regression Coeﬃcients Prediction (Forecasting) Dummy Variables 80 1.0 Confounding: Example Cont’d... q 75 50 q q q 0.8 q q q q q q q q q q 70 q q q q q q q q Use of Alcohol 65 20 50 0.2 55 30 0.4 60 BMI Age 40 q q q q q q q q q q q q q 0.6 q q q q 45 q q 0.0 q 0 1 0 Exercise Chapter 11: Regression and Correlation Methods 1 Exercise 0 1 Exercise Stat 491: Biostatistics Introduction Least Square Estimates of the Parameters Inference about the Parameters Prediction Assessing Adequacy of Fit Correlation Multiple Regression Introduction Inferences in Multiple Regression Tests for Subset of Regression Coeﬃcients Prediction (Forecasting) Dummy Variables Confounding: Example Cont’d... Fit a simple linear regression (SLR) of glucose on exercise and interpret the results. Note that analysis should exclude diabetic participants. Age, alcohol use and body mass index were also measured. Fit a multiple linear regression (MLR) of Glucose on exercise, age, alcohol use and BMI, and interpret the results. Compare the estimated regression coeﬃcient of exercise in the SLR and and the partial regression coeﬃcient of exercise in MLR. Chapter 11: Regression and Correlation Methods Stat 491: Biostatistics Introduction Least Square Estimates of the Parameters Inference about the Parameters Prediction Assessing Adequacy of Fit Cor...
View Full Document

This note was uploaded on 02/03/2014 for the course STAT 491 taught by Professor Solomonharrar during the Fall '12 term at Montana.

Ask a homework question - tutors are online