UT Knoxville STAT 201 - Chapter 8 (43 pages)

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Chapter 8



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Chapter 8

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Pages:
43
School:
University of Tennessee
Course:
Stat 201 - Introduction to Statistics
Introduction to Statistics Documents

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Chapter 8 Chapter08 Presentation 1213 Linear Regression Copyright 2009 Pearson Education Inc 1 Fat Versus Protein An Example The following is a scatterplot of total fat versus protein for 30 items on the Burger King menu Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 2 Things to Look For in Scatterplots Direction Form Strength Unusual features Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 3 Correlation Conditions Before you use correlation you must check several conditions Quantitative Variables Condition Straight Enough Condition Outlier Condition Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 4 The Linear Model Remember from Algebra that a straight line can be written as y mx b In Statistics we use a slightly different notation y b0 b1 x Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 5 The Linear Model Cont The linear model that best fits our Burger King data is How well does this model fit our data Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 6 Residuals The difference between the observed value y and its associated predicted value y is called the residual residual observed predicted y y If the model fits the data well these will all be close to zero Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 7 Residuals cont The BK Broiler Chicken sandwich has x 30 grams of protein The model says it should have y 36 grams of fat In fact it has y 25 grams of fat Calculate the residual for this observation and interpret it Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc BK Broiler Chicken Sandwich 8 How Well Does Any Line Fit the Data Let s calculate ALL the residuals Can we add them up and claim if the sum is small the line fits well Chapter08 Presentation 1213 Copyright 2009 Pearson Education Inc 9 How Well Does Any Line Fit the Data How did we solve this dilemma when calculating a measure of distance from y bar i e when calculating the standard



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