'Residual plot' presentation slideshows

Review

Review. A quick review of each chapter. Chapter 1. One-Variable Statistics. How old is our teacher?. N=82. Describe the distribution. CUSS: C enter(mean and median), U nusual features(are there outliers?), S hape(skewed left,symmetric , skewed right), S pread(max to min).

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Analysis of Variance & One Factor Designs

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Simple Linear Regression: An Introduction

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Model Checking

Model Checking. Using residuals to check the validity of the linear regression model assumptions. The simple linear regression model. The mean of the responses, E(Y i ), is a l inear function of the x i . The errors, ? i , and hence the responses Y i , are i ndependent .

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DSC: Data Analysis

DSC: Data Analysis. Michael Blaber Professor of Biomedical Sciences College of Medicine Florida State University. 2. Outline. Thermodynamic parameters. Model properties and assumptions. Error. Some examples. Consider protein as the biomolecule of interest. Thermodynamic parameters.

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Stat 112: Lecture 13 Notes

Stat 112: Lecture 13 Notes. Finish Chapter 5: Review Predictions in Log-Log Transformation. Polynomials and Transformations in Multiple Regression Start Chapter 6: Checking Assumptions of Multiple Regression and Remedies for the Assumptions.

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Stat 112: Lecture 10 Notes

Stat 112: Lecture 10 Notes. Fitting Curvilinear Relationships Polynomial Regression (Ch. 5.2.1) Transformations (Ch. 5.2.2-5.2.4) Schedule: Homework 3 due on Thursday. Quiz 2 next. Curvilinear Relationship.

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EARTHQUAKE SCIENCE-ENGINEERING INTERFACE: STRUCTURAL ENGINEERING RESEARCH PERSPECTIVE Allin Cornell Stanford University

EARTHQUAKE SCIENCE-ENGINEERING INTERFACE: STRUCTURAL ENGINEERING RESEARCH PERSPECTIVE Allin Cornell Stanford University SCEC WORKSHOP Oakland, CA October, 2003. Objective. ? C = mean annual rate of State C , e.g., collapse

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Chapter 12: More About Regression

Chapter 12: More About Regression. Section 12.1 Inference for Linear Regression. Introduction

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ALR 8.3 & 9.7

ALR 8.3 & 9.7 . Alicia Hymel. Problem 8.3

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Everyday is a new beginning in life. Every moment is a time for self vigilance.

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Stat 112: Lecture 7 Notes

Stat 112: Lecture 7 Notes. Homework 2: Due next Thursday The Multiple Linear Regression model (Chapter 4.1) Inferences from multiple regression analysis (Chapter 4.2). Interpretation of Regression Coefficients. Gas mileage regression from Car89.JMP. Partial Slopes vs. Marginal Slopes.

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Lecture 19

Lecture 19. Transformations, Predictions after Transformations Other diagnostic tools: Residual plot for nonconstant variance, histogram to check normality assumption and residual plots for special situations. Regression Diagnostics.

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Linear Regression t-Tests

Linear Regression t-Tests. Cardiovascular fitness among skiers. Cardiovascular fitness is measured by the time required to run to exhaustion on a treadmill. In the following study, cardiovascular fitness is compared to performance in a 20-km ski race.

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A Clear and Present Danger to the Caribbean: Climate Change or Climate Alarmism? Reynold J. Stone Department of Food P

A Clear and Present Danger to the Caribbean: Climate Change or Climate Alarmism? Reynold J. Stone Department of Food Production. Warming Trend. Percent of Days Tmax > 90 th Percentile. Percent of Days Tmax > 90 th Percentile.

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Chapter 3 Review Two Variable Statistics

Chapter 3 Review Two Variable Statistics. Veronica Wright Christy Treekhem River Brooks. The Big Idea.

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AP Statistics Section 3.2 B Residuals

AP Statistics Section 3.2 B Residuals.

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Transformations

Transformations. Process. Make scatterplot of ( x,y ) Check for linearity Look at residual plot (x, resid ) Check for random scattering of points Look at r 2 Higher r 2 ? Better model Begin transformations. Linear Transformations. LinReg (log x, y) LinReg (x, log y)

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