# Lecture6 - Lecture 6 Material Covered in This Lecture...

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Lecture 6 Material Covered in This Lecture: Chapter 2, Section 2.4: Cautions about Correlation and Regression L Residual plot A residual is the difference between an observed value of the response variable and the value predicted by the regression line. That is Residual = observed y – predicted y = y - y ˆ An important fact about residuals: The mean of the least-squares residuals is always 0. Example (Example 2.15 continued): The regression line is fat = 3.505-0.00344x(EA) The residuals are 0.37 -0.70 0.10 …… -0.03 The sum of these residual is 0, so is the mean.

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Residualplot: How to make residual plot with minitab: While you are still in the Regression Dialog Box and before you move on to obtain the regression analysis, you can store and plot the residuals: Click on Storage > Residuals and click on OK. Back in the Regression Dialog Box, click on Graphs.
Residual plots help us assess the fit of a regression line: If the regression line catches the overall pattern of the data, there should be

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## This note was uploaded on 07/25/2008 for the course STT 421 taught by Professor Nane during the Summer '08 term at Michigan State University.

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Lecture6 - Lecture 6 Material Covered in This Lecture...

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