ChE253K Lecture08 -- Evaluating Lin Regn.Rev03

ChE253K Lecture08 -- Evaluating Lin Regn.Rev03 - Class...

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1 ChE 253K Lecture 08 Class Business JMP for HW03 Lecture 07 Upgraded Class Schedule MT 1: March 2. MT 2: April 8
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2 ChE 253K Lecture 08 Limits On vs. Off The Line Mean Point on the line Predict Point off the line off the line Prediction Limits on the line Confidence Limits
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3 ChE 253K Lecture 08 CL’s Of Points On And Off The Line Point on the line (mean point at Xi): Points off the line (limits of prediction) : ( 29 2 2 1 i i i e xx X X bX a bX t s n S α - + = + ± + g ( 29 2 2 1 1 i i i e xx X X bX a bX t s n S - + = + ± + + g
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4 ChE 253K Lecture 08 How To Evaluate Regression Results Lecture 8 -- Descriptive Statistics
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5 ChE 253K Lecture 08 Outline Of This Lecture Assumptions of linear regression Regression evaluation plots Scatter plots Confidence envelope Residuals plot Residuals ANOVA R 2
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6 ChE 253K Lecture 08 Readings Re: This Lecture Assumptions Text Section 11.2 Confidence Limits Text Section 11.2 Residuals Plots Text Section 11.5 Text Section 11.6
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7 ChE 253K Lecture 08 Assumptions Of Least Squares Errors in the independent variable (X) are negligible compared to the errors in dependent variable (Y) The errors or std deviation of Y is the same at all Y The Y values at X(i) are normally distributed about their mean Linear: Y = a + bX 1 1 ( ) n i j i j i Yi Y X n = =
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8 ChE 253K Lecture 08 Why Fit By Least Squares? Given data that fit its assumptions, least
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ChE253K Lecture08 -- Evaluating Lin Regn.Rev03 - Class...

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