Chp5-lecture_notes_correlation_residual_analysis

Chp5-lecture_notes_correlation_residual_analysis - . . . ....

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Module 5 Correlation and Residual Analysis Assessing the ‘Goodness’ of Regression Models Objectives: Consider two quantitative variables, you should be able to • assess the ‘goodness’ of a linear regression model • calculate and interpret the correlation coefficient and coefficient of determination • perform residual analysis We can almost always construct the Least Square Regression Equation Two Questions: Is this approach appropriate? How good are the predictions?
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Measures of ‘Goodness’ • Subjective – Graphical Examination • Objective – Coefficient of Determination, (denoted by R 2 )
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Scatterplot of Residuals vs X Residual values should be centered about zero display a random pattern (see page 750) Linear Relationships . X Residual . .
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Unformatted text preview: . . . . . . . . . . . . . . . . Nonlinear Relationships . X . . . . . . . . . . . . . . . . . . Linear Relationships (nonconstant variances) . X Residual . . . . . . . . . . . . . . . . . . . . . . . Numerical Summaries for ‘goodness’ of linear models Interpretation: R 2 near zero – poor predictions R 2 near one – useful predictions Coefficient of Determination – R 2 Interpretation See Page 725 - 726 Key Concepts: • parameter vs statistic • Residual analysis • Coefficient of Determination • Linear Correlation Coefficient Assignment: • Read Chapter 14 sections 3-4 • Reproduce text examples using Minitab • Analyze the Anscombe Data • Check WebCT Assignment Folder THE END...
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Chp5-lecture_notes_correlation_residual_analysis - . . . ....

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