Details

Common Errors in Statistics (and How to Avoid Them)


Common Errors in Statistics (and How to Avoid Them)


3. Aufl.

von: Phillip I. Good, James W. Hardin

45,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 06.10.2009
ISBN/EAN: 9780470473917
Sprache: englisch
Anzahl Seiten: 304

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Beschreibungen

<b>Praise for the <i>Second Edition</i></b> <p>"All statistics students and teachers will find in this book a friendly and intelligentguide to . . . applied statistics in practice."<br /> —<b><i>Journal of Applied Statistics</i></b></p> <p>". . . a very engaging and valuable book for all who use statistics in any setting."<br /> —<b><i>CHOICE</i></b></p> <p>". . . a concise guide to the basics of statistics, replete with examples . . . a valuablereference for more advanced statisticians as well."<br /> —<b><i>MAA Reviews</i></b></p> <p>Now in its <i>Third Edition</i>, the highly readable <i>Common Errors in Statistics (and How to Avoid Them)</i> continues to serve as a thorough and straightforward discussion of basic statistical methods, presentations, approaches, and modeling techniques. Further enriched with new examples and counterexamples from the latest research as well as added coverage of relevant topics, this new edition of the benchmark book addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research.</p> <p>The <i>Third Edition</i> has been considerably expanded and revised to include:</p> <ul> <li>A new chapter on data quality assessment</li> <li> <p>A new chapter on correlated data</p> </li> <li> <p>An expanded chapter on data analysis covering categorical and ordinal data, continuous measurements, and time-to-event data, including sections on factorial and crossover designs</p> </li> <li> <p>Revamped exercises with a stronger emphasis on solutions</p> </li> <li> <p>An extended chapter on report preparation</p> </li> <li> <p>New sections on factor analysis as well as Poisson and negative binomial regression</p> </li> </ul> <p>Providing valuable, up-to-date information in the same user-friendly format as its predecessor, <i>Common Errors in Statistics (and How to Avoid Them)</i>, Third Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.</p>
<b>PREFACE xi</b> <p><b>PART I FOUNDATIONS 1</b></p> <p><b>1 Sources of Error 3</b></p> <p>Prescription, 4</p> <p>Fundamental Concepts, 5</p> <p>Ad Hoc, Post Hoc Hypotheses, 7</p> <p>To Learn More, 11</p> <p><b>2 Hypotheses: The Why of Your Research 13</b></p> <p>Prescription, 13</p> <p>What is a Hypothesis?, 14</p> <p>Found Data, 16</p> <p>Null Hypothesis, 16</p> <p>Neyman–Pearson Theory, 17</p> <p>Deduction and Induction, 21</p> <p>Losses, 22</p> <p>Decisions, 23</p> <p>To Learn More, 25</p> <p><b>3 Collecting Data 27</b></p> <p>Preparation, 27</p> <p>Response Variables, 28</p> <p>Determining Sample Size, 32</p> <p>Sequential Sampling, 36</p> <p>One-Tail or Two?, 37</p> <p>Fundamental Assumptions, 40</p> <p>Experimental Design, 41</p> <p>Four Guidelines, 43</p> <p>Are Experiments Really Necessary?, 46</p> <p>To Learn More, 47</p> <p><b>PART II STATISTICAL ANALYSIS 49</b></p> <p><b>4 Data Quality Assessment 51</b></p> <p>Objectives, 52</p> <p>Review the Sampling Design, 52</p> <p>Data Review, 53</p> <p>The Four-Plot, 55</p> <p>To Learn More, 55</p> <p><b>5 Estimation 57</b></p> <p>Prevention, 57</p> <p>Desirable and Not-So-Desirable Estimators, 57</p> <p>Interval Estimates, 61</p> <p>Improved Results, 65</p> <p>Summary, 66</p> <p>To Learn More, 66</p> <p><b>6 Testing Hypotheses: Choosing a Test Statistic 67</b></p> <p>First Steps, 68</p> <p>Test Assumptions, 70</p> <p>Binomial Trials, 71</p> <p>Categorical Data, 72</p> <p>Time-to-Event Data (Survival Analysis), 73</p> <p>Comparing the Means of Two Sets of Measurements, 76</p> <p>Comparing Variances, 85</p> <p>Comparing the Means of k Samples, 89</p> <p>Subjective Data, 91</p> <p>Independence Versus Correlation, 91</p> <p>Higher-Order Experimental Designs, 92</p> <p>Inferior Tests, 96</p> <p>Multiple Tests, 97</p> <p>Before You Draw Conclusions, 97</p> <p>Summary, 99</p> <p>To Learn More, 99</p> <p><b>7 Miscellaneous Statistical Procedures 101</b></p> <p>Bootstrap, 102</p> <p>Bayesian Methodology, 103</p> <p>Meta-Analysis, 110</p> <p>Permutation Tests, 112</p> <p>To Learn More, 113</p> <p><b>PART III REPORTS 115</b></p> <p><b>8 Reporting Your Results 117</b></p> <p>Fundamentals, 117</p> <p>Descriptive Statistics, 122</p> <p>Standard Error, 127</p> <p>p-Values, 130</p> <p>Confidence Intervals, 131</p> <p>Recognizing and Reporting Biases, 133</p> <p>Reporting Power, 135</p> <p>Drawing Conclusions, 135</p> <p>Summary, 136</p> <p>To Learn More, 136</p> <p><b>9 Interpreting Reports 139</b></p> <p>With a Grain of Salt, 139</p> <p>The Analysis, 141</p> <p>Rates and Percentages, 145</p> <p>Interpreting Computer Printouts, 146</p> <p>To Learn More, 146</p> <p><b>10 Graphics 149</b></p> <p>The Soccer Data, 150</p> <p>Five Rules for Avoiding Bad Graphics, 150</p> <p>One Rule for Correct Usage of Three-Dimensional Graphics, 159</p> <p>The Misunderstood and Maligned Pie Chart, 161</p> <p>Two Rules for Effective Display of Subgroup Information, 162</p> <p>Two Rules for Text Elements in Graphics, 166</p> <p>Multidimensional Displays, 167</p> <p>Choosing Graphical Displays, 170</p> <p>Summary, 172</p> <p>To Learn More, 172</p> <p><b>PART IV BUILDING A MODEL 175</b></p> <p><b>11 Univariate Regression 177</b></p> <p>Model Selection, 178</p> <p>Stratification, 183</p> <p>Estimating Coefficients, 185</p> <p>Further Considerations, 187</p> <p>Summary, 191</p> <p>To Learn More, 192</p> <p><b>12 Alternate Methods of Regression 193</b></p> <p>Linear Versus Non-Linear Regression, 194</p> <p>Least Absolute Deviation Regression, 194</p> <p>Errors-in-Variables Regression, 196</p> <p>Quantile Regression, 199</p> <p>The Ecological Fallacy, 201</p> <p>Nonsense Regression, 202</p> <p>Summary, 202</p> <p>To Learn More, 203</p> <p><b>13 Multivariable Regression 205</b></p> <p>Caveats, 205</p> <p>Correcting for Confounding Variables, 207</p> <p>Keep It Simple, 207</p> <p>Dynamic Models, 208</p> <p>Factor Analysis, 208</p> <p>Reporting Your Results, 209</p> <p>A Conjecture, 211</p> <p>Decision Trees, 211</p> <p>Building a Successful Model, 214</p> <p>To Learn More, 215</p> <p><b>14 Modeling Correlated Data 217</b></p> <p>Common Sources of Error, 218</p> <p>Panel Data, 218</p> <p>Fixed- and Random-Effects Models, 219</p> <p>Population-Averaged GEEs, 219</p> <p>Quick Reference for Popular Panel Estimators, 221</p> <p>To Learn More, 223</p> <p><b>15 Validation 225</b></p> <p>Objectives, 225</p> <p>Methods of Validation, 226</p> <p>Measures of Predictive Success, 229</p> <p>Long-Term Stability, 231</p> <p>To Learn More, 231</p> <p><b>GLOSSARY, GROUPED BY RELATED BUT DISTINCT TERMS 233</b></p> <p><b>BIBLIOGRAPHY 237</b></p> <p><b>AUTHOR INDEX 259</b></p> <p><b>SUBJECT INDEX 267</b></p>
"The new edition incorporates more graphics and examples using more recent data. … Good's advice is usually wise, and always worth considering. Recommended as stimulating reading for the statistical sophisticate." (<i>Journal of Biopharmaceutical Statistics</i>, January 2010)
<b>PHILLIP I. GOOD, PhD,</b> is Operations Manager of Statcourse.com, a consulting firm specializing in statistical solutions for industry. He has published more than thirty scholarly works, more than six hundred popular articles, and twenty-one books, including <i>Introduction to Statistics Through Resampling Methods and R/S-PLUS and Introduction to Statistics Through Resampling Methods and Microsoft Office Excel,</i> both published by Wiley. <p><b>JAMES W. HARDIN, PhD</b>, is Research Associate Professor and Director of the Biostatistics Collaborative Unit at the University of South Carolina.</p>
<b>Praise for the <i>Second Edition</i></b> <p>"All statistics students and teachers will find in this book a friendly and intelligentguide to . . . applied statistics in practice."<br /> —<b><i>Journal of Applied Statistics</i></b></p> <p>". . . a very engaging and valuable book for all who use statistics in any setting."<br /> —<b><i>CHOICE</i></b></p> <p>". . . a concise guide to the basics of statistics, replete with examples . . . a valuablereference for more advanced statisticians as well."<br /> —<b><i>MAA Reviews</i></b></p> <p>Now in its <i>Third Edition</i>, the highly readable <i>Common Errors in Statistics (and How to Avoid Them)</i> continues to serve as a thorough and straightforward discussion of basic statistical methods, presentations, approaches, and modeling techniques. Further enriched with new examples and counterexamples from the latest research as well as added coverage of relevant topics, this new edition of the benchmark book addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research.</p> <p>The <i>Third Edition</i> has been considerably expanded and revised to include:</p> <ul> <li>A new chapter on data quality assessment</li> <li> <p>A new chapter on correlated data</p> </li> <li> <p>An expanded chapter on data analysis covering categorical and ordinal data, continuous measurements, and time-to-event data, including sections on factorial and crossover designs</p> </li> <li> <p>Revamped exercises with a stronger emphasis on solutions</p> </li> <li> <p>An extended chapter on report preparation</p> </li> <li> <p>New sections on factor analysis as well as Poisson and negative binomial regression</p> </li> </ul> <p>Providing valuable, up-to-date information in the same user-friendly format as its predecessor, <i>Common Errors in Statistics (and How to Avoid Them)</i>, Third Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.</p>

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