# prelim 3 sheet - Chapter 30: Multiple Regression Linearity...

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Chapter 30: Multiple Regression Linearity Assumption- Straight enough condition- plot the residuals against the predicted values and check for patterns, especially for bends or other nonlinearities. The residuals should appear to have no pattern with respect to the predicted values. Independence Assumption- The data should arise from a random sample or randomized experiment. We also check displays of the regression residuals for evidence of patterns, trends, or clumping, any of which would suggest a failure of independence. The residuals should appear to be randomly scattered and show no patterns or clumps when plotted against the predicted values. Equal Variance Assumption- Does the scatterplot thicken? The spread of the residuals should be uniform when plotted against any of the x’s or against the predicted values. Normality Assumption- we assume that the errors around the idealized regression model at any specified values of the x-variables follow a normal model. What can go wrong?

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## This note was uploaded on 02/27/2008 for the course ILRST 2120 taught by Professor Vellemanp during the Fall '06 term at Cornell University (Engineering School).

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prelim 3 sheet - Chapter 30: Multiple Regression Linearity...

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