190075767 - FIFTH ED I T I 0 N A Second Course in Statistics Regression Analysis WILLIAM MENDENHALL University of Florida TERRY SINCICH University

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FIFT H E D I T I 0 N A Second Course in Statistics: Regression Analysis WILLIA M MENDENHAL L University of Florida TERR Y SINCIC H University of South Florida PRENTIC E HAL L Upper Saddle River, New Jersey 07458
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Contents Preface xii CHAPTER! A Review of Basic Concepts (Optional) 1 1.1 Statistics and Data 2 1.2 Populations, Samples, and Random Sampling 6 1.3 Describing Data Sets Graphically 10 1.4 Describing Data Sets Numerically 19 1.5 The Normal Probability Distribution 29 1.6 Sampling Distributions and the Central Limit Theorem 35 1.7 Estimating a Population Mean 39 1.8 Testing a Hypothesis About a Population Mean 51 1.9 Inferences About the Difference Between Two Population Means 59 1.10 Comparing Two Population Variances 72 CHAPTER L Introduction to Regression Analysis 90 2.1 Modeling a Response 91 2.2 Overview of Regression Analysis 93 2.3 Regression Applications 96 2.4 Collecting the Data for Regression 97 3 CHAPTER 0 Simple Linear Regression 101 3.1 Introduction 102 3.2 The Straight-Line Probabilistic Model 102 3.3 Fitting the Model: The Method of Least Squares 105 3.4 Model Assumptions 115 3.5 An Estimator of cr 2 117 3.6 Assessing the Utility of the Model: Making Inferences About the Slope ^ 120 3.7 The Coefficient of Correlation 127 3.8 The Coefficient of Determination 133 3.9 Using the Model for Estimation and Prediction 139 3.10 Simple Linear Regression: An Example Using the Computer 146 3.11 Regression Through the Origin (Optional) 153 3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis 162
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Vi CONTENTS CHAPTER 4 Multiple Regression 172 4.1 The General Linear Model 173 4.2 Model Assumptions 174 4.3 Fitting the Model: The Method of Least Squares 175 4.4 Estimation of a 2 , the Variance of e 178 4.5 Inferences About the /3 Parameters 180 4.6 The Multiple Coefficient of Determination, R 2 191 4.7 Testing the Utility of a Model: The Analysis of Variance F Test 193 4.8 Using the Model for Estimation and Prediction 204 4.9 Other Linear Models 211 4.10 A Test for Comparing Nested Models 233 4.11 Stepwise Regression 242 4.12 Other Variable Selection Techniques (Optional) 252 4.13 Multiple Regression: A Complete Example 257 4.14 A Summary of the Steps to Follow in a Multiple Regression Analysis 262 H A P T E R 5 Model Building 273 5.1 Introduction: Why Model Building Is Important 274 5.2 The Two Types of Independent Variables: Quantitative and Qualitative 275 5.3 Models with a Single Quantitative Independent Variable 277 5.4 First-Order Models with Two or More Quantitative Independent Variables 285
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This note was uploaded on 02/06/2012 for the course STAT 401 taught by Professor Shelley during the Spring '08 term at Iowa State.

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190075767 - FIFTH ED I T I 0 N A Second Course in Statistics Regression Analysis WILLIAM MENDENHALL University of Florida TERRY SINCICH University

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