Common Errors in Statistics (and How to Avoid Them) / Edition 4

Common Errors in Statistics (and How to Avoid Them) / Edition 4

4.3 3
by Phillip I. Good, James W. Hardin
     
 

The Fourth Edition of this tried-and-true book elaborates on many key topics such as epidemiological studies, distribution of data; baseline data incorporation; case control studies; simulations; statistical theory publication; biplots; instrumental variables; ecological regression; result reporting, survival analysis; etc. Including new modifications and figures,

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Overview

The Fourth Edition of this tried-and-true book elaborates on many key topics such as epidemiological studies, distribution of data; baseline data incorporation; case control studies; simulations; statistical theory publication; biplots; instrumental variables; ecological regression; result reporting, survival analysis; etc. Including new modifications and figures, the book also covers such topics as research plan creation; data collection; hypothesis formulation and testing; coefficient estimates; sample size specifications; assumption checking; p-values interpretations and confidence intervals; counts and correlated data; model building and testing; Bayes' Theorem; bootstrap and permutation tests; and more.

Product Details

ISBN-13:
9781118294390
Publisher:
Wiley
Publication date:
07/31/2012
Pages:
352
Sales rank:
1,384,624
Product dimensions:
14.00(w) x 8.90(h) x 0.70(d)

Related Subjects

Meet the Author

PHILLIP I. GOOD, PhD, is Operations Manager of Information Research, a consulting firm specializing in statistical solutions for private and public organizations and has published eighteen books.

JAMES W. HARDIN, PhD, is Associate Research Professor in the Department of Epidemiology and Biostatistics at the University of South Carolina.

Table of Contents

Preface xi

Part I Foundations 1

1 Sources of Error 3

Prescription 4

Fundamental Concepts 5

Ad Hoc, Post Hoc Hypotheses 7

To Learn More 11

2 Hypotheses: The Why of Your Research 13

Prescription 13

What is a Hypothesis? 14

Found Data 16

Null Hypothesis 16

Neyman-Pearson Theory, 17

Deduction and Induction 21

Losses 22

Decisions 23

To Learn More 25

3 Collecting Data 27

Preparation 27

Response Variables 28

Determining Sample Size 32

Sequential Sampling 36

One-Tail or Two? 37

Fundamental Assumptions 40

Experimental Design 41

Four Guidelines 43

Are Experiments Really Necessary? 46

To Learn More 47

Part II Statistical Analysis 49

4 Data Quality Assessment 51

Objectives 52

Review the Sampling Design 52

Data Review 53

The Four-Plot 55

To Learn More 55

5 Estimation 57

Prevention 57

Desirable and Not-So-Desirable Estimators 57

Interval Estimates 61

Improved Results 65

Summary 66

To Learn More 66

6 Testing Hypotheses: Choosing a Test Statistic 67

First Steps 68

Test Assumptions 70

Binomial Trials 71

Categorical Data 72

Time-to-Event Data (Survival Analysis) 73

Comparing the Means of Two Sets of Measurements 76

Comparing Variances 85

Comparing the Means of k Samples 89

Subjective Data 91

Independence Versus Correlation 91

Higher-Order Experimental Designs 92

Inferior Tests 96

Multiple Tests 97

Before You Draw Conclusions 97

Summary 99

To Learn More 99

7 Miscellaneous Statistical Procedures 101

Bootstrap 102

Bayesian Methodology 103

Meta-Analysis 110

Permutation Tests 112

To Learn More 113

Part III Reports 115

8 Reporting Your Results 117

Fundamentals117

Descriptive Statistics 122

Standard Error 127

p-Values 130

Confidence Intervals 131

Recognizing and Reporting Biases 133

Reporting Power 135

Drawing Conclusions 135

Summary 136

To Learn More 136

9 Interpreting Reports 139

With a Grain of Salt 139

The Analysis 141

Rates and Percentages 145

Interpreting Computer Printouts 146

To Learn More 146

10 Graphics 149

The Soccer Data 150

Five Rules for Avoiding Bad Graphics 150

One Rule for Correct Usage of Three-Dimensional Graphics 159

The Misunderstood and Maligned Pie Chart 161

Two Rules for Effective Display of Subgroup Information 162

Two Rules for Text Elements in Graphics 166

Multidimensional Displays 167

Choosing Graphical Displays 170

Summary 172

To Learn More 172

Part IV Building a model 175

11 Univariate Regression 177

Model Selection 178

Stratification 183

Estimating Coefficients 185

Further Considerations 187

Summary 191

To Learn More 192

12 Alternate Methods of Regression 193

Linear Versus Non-Linear Regression 194

Least Absolute Deviation Regression 194

Errors-in-Variables Regression 196

Quantile Regression 199

The Ecological Fallacy 201

Nonsense Regression 202

Summary 202

To Learn More 203

13 Multivariable Regression 205

Caveats 205

Correcting for Confounding Variables 207

Keep It Simple 207

Dynamic Models 208

Factor Analysis 208

Reporting Your Results 209

A Conjecture 211

Decision Trees 211

Building a Successful Model 214

To Learn More 215

14 Modeling Correlated Data 217

Common Sources of Error 218

Panel Data 218

Fixed-and Random-Effects Models 219

Population-Averaged GEEs 219

Quick Reference for Popular Panel Estimators 221

To Learn More 223

15 Validation 225

Objectives 225

Methods of Validation 226

Measures of Predictive Success 229

Long-Term Stability 231

To Learn More 231

Glossary, Grouped by Related but Distinct Terms 233

Bibliography 237

Author Index 259

Subject Index 267

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