5dec2011

# 5dec2011 - CR Long C532 Regression Modeling 5 Dec 2011 Learning Objectives 1 Use residual plots to assess the multiple linear regression model

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C532: Regression Modeling 5 Dec 2011 1 Learning Objectives 1. Use residual plots to assess the multiple linear regression model assumptions 2. Detect outliers and influential observations for multiple linear regression models Multiple linear regression model assumptions : 1) i Y ~N( 2 , i ) or equivalently, i ~N( 2 , 0 ) 2) Variances are equal for each i X (i.e. 2 1 = 2 2 = … = 2 i = … = 2 ) 3) Regression function (of Y on X) is linear 4) Observations are independent Departures from the multiple linear regression model that can be studied by residuals: 1. The error terms are not normally distributed 2. The error terms do not have constant variance 3. The regression function is not linear Examine residual plots and look for model departures: 1. Graph a normal probability plot of the residuals 2. Plot the residuals against the fitted values ( 2. Variance heterogeneity: transform y 3. Nonlinearity of y on x: transform y or fit a higher order model (e.g. curvilinear)

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## This note was uploaded on 01/03/2012 for the course C 532 taught by Professor Long during the Fall '11 term at Palmer Chiropractic.

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5dec2011 - CR Long C532 Regression Modeling 5 Dec 2011 Learning Objectives 1 Use residual plots to assess the multiple linear regression model

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