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EXST 7015  Statistical Inference II, Fall 2011
Lab 3: Simple Linear Regression: Regression Diagnostics
OBJECTIVES
Simple linear regression (SLR) is a common analysis procedure, used to describe the significant
relationship a researcher presumes to exist between two variables: the dependent (or response)
variable, and the independent (or explanatory) variable. In previous labs, SLR was performed to
fit a straight line model relating two variables. We learned how to interpret parameter estimates
and R^2, and how to test specific hypothesis of SLR such as slope test and joint tests. We are
also getting familiar with how to evaluate the assumptions of SLR by using residual and
normality test.
You might realize that a single observation that is substantially different from all other
observations can make a large difference in the results of your regression analysis. If a single
observation (or small group of observations) substantially changes your results, you would want
to know about this and investigate further. In this lab exercise, we will use appropriate regression
diagnostics to detect outliers (or unusual observations) besides evaluation of assumption. It is,
however, very important to emphasize that simply discarding observations that appears to be
outliers is not good statistical practice.
LABORATORY INSTRUCTIONS
Housekeeping Statements
dm
'log; clear; output; clear'
;
options
nodate
nocenter
pageno =
1
ls
=
78
ps
=
53
;
title1
'EXST7015 lab 2, Name, Section#'
;
ods rtf
file = ‘c:/temp/lab2.rtf’;
ods html file = ‘c:/temp/lab2.html’;
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This note was uploaded on 12/29/2011 for the course EXST 7015 taught by Professor Wang,j during the Fall '08 term at LSU.
 Fall '08
 Wang,J

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