This groundbreaking book shows how apply modern resampling techniques to mathematical statistics. The book includes permutation tests and bootstrap methods and classical inference methods. Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The use of R throughout the book underscores the significance of resampling since its implementation is fast enough to be both convenient and explanatory. While computer clock speeds have leveled off, new multi-core computers are well suited for parallel applications like resampling. The book contains examples, figures, exercise sets, case studies, and helpful remarks. An author-maintained web site is available.

Laura Chihara, PhD, is professor of Mathematics at Carleton College. She has extensive experience teaching mathemetical statistics and applied regression analysis. She ha supervised undergraduates working on statistics projects for local business and organizations such as Targer Corporation and the Minnesota Pollution Control Agency. Dr. Chihara has experiance with S and R from her work at Insightful Corporation (formeely MathSoft) and in statistical consulting. Tim Hesterberg, PhD, is Senior Ads Quality Statistician at Google. He was a senior research scientist for Insightful Corporation and led the development of S_ Resample and other S+ and R software. Dr. Hesterberg has published numerous articles in the areas of boorstrap and related resampling techniques, Monte Carlo simulation methodology modern regression, tectonic deformation estimation, and electri demand forecasting.

Preface	p. xiii
Data and Case Studies	p. 1
Case Study: Flight Delays	p. 1
Case Study: Birth Weights of Babies	p. 2
Case Study: Verizon Repair Times	p. 3
Sampling	p. 3
Parameters and Statistics	p. 5
Case Study: General Social Survey	p. 5
Sample Surveys	p. 6
Case Study: Beer and Hot Wings	p. 8
Case Study: Black Spruce Seedlings	p. 8
Studies	p. 8
Exercises	p. 10
Exploratory Data Analysis	p. 13
Basic Plots	p. 13
Numeric Summaries	p. 16
Center	p. 17
Spread	p. 18
Shape	p. 19
Boxplots	p. 19
Quantiles and Normal Quantile Plots	p. 20
Empirical Cumulative Distribution Functions	p. 24
Scatter Plots	p. 26
Skewness and Kurtosis	p. 28
Exercises	p. 30
Hypothesis Testing	p. 35
Introduction to Hypothesis Testing	p. 35
Hypotheses	p. 36
Permutation Tests	p. 38
Implementation Issues	p. 42
One-Sided and Two-Sided Tests	p. 47
Other Statistics	p. 48
Assumptions	p. 51
Contingency Tables	p. 52
Permutation Test for Independence	p. 54
Chi-Square Reference Distribution	p. 57
Chi-Square Test of Independence	p. 58
Test of Homogeneity	p. 61
Goodness-of-Fit: All Parameters Known	p. 63
Goodness-of-Fit: Some Parameters Estimated	p. 66
Exercises	p. 68
Sampling Distributions	p. 77
Sampling Distributions	p. 77
Calculating Sampling Distributions	p. 82
The Central Limit Theorem	p. 84
CLT for Binomial Data	p. 87
Continuity Correction for Discrete Random Variables	p. 89
Accuracy of the Central Limit Theorem	p. 90
CLT for Sampling Without Replacement	p. 91
Exercises	p. 92
The Bootstrap	p. 99
Introduction to the Bootstrap	p. 99
The Plug-In Principle	p. 106
Estimating the Population Distribution	p. 107
How Useful Is the Bootstrap Distribution?	p. 109
Bootstrap Percentile Intervals	p. 113
Two Sample Bootstrap	p. 114
The Two Independent Populations Assumption	p. 119
Other Statistics	p. 120
Bias	p. 122
Monte Carlo Sampling: The "Second Bootstrap Principle"	p. 125
Accuracy of Bootstrap Distributions	p. 125
Samnle Mean: Large Sample Size	p. 126
Sample Mean: Small Sample Size	p. 127
Sample Median	p. 127
How Many Bootstrap Samples are Needed?	p. 129
Exercises	p. 129
Estimation	p. 135
Maximum Likelihood Estimation	p. 135
Maximum Likelihood for Discrete Distributions	p. 136
Maximum Likelihood for Continuous Distributions	p. 139
Maximum Likelihood for Multiple Parameters	p. 143
Method of Moments	p. 146
Properties of Estimators	p. 148
Unbiasedness	p. 148
Efficiency	p. 151
Mean Square Error	p. 155
Consistency	p. 157
Transformation Invariance	p. 160
Exercises	p. 161
Classical Inference: Confidence Intervals	p. 167
Confidence Intervals for Means	p. 167
Confidence Intervals for a Mean, $$$ Known	p. 167
Confidence Intervals for a Mean, $$$ Unknown	p. 172
Confidence Intervals for a Difference in Means	p. 178
Confidence Intervals in General	p. 183
Location and Scale Parameters	p. 186
One-Sided Confidence Intervals	p. 189
Confidence Intervals for Proportions	p. 191
The Agresti-Coull Interval for a Proportion	p. 193
Confidence Interval for the Difference of Proportions	p. 194
Bootstrap t Confidence Intervals	p. 195
Comparing Bootstrap t and Formula t Confidence Intervals	p. 200
Exercises	p. 200
Classical Inference: Hypothesis Testing	p. 211
Hypothesis Tests for Means and Proportions	p. 211
One Population	p. 211
Comparing Two Populations	p. 215
Type I and Type II Errors	p. 221
Type I Errors	p. 221
Type II Errors and Power	p. 226
More on Testing	p. 231
On Significance	p. 231
Adjustments for Multiple Testing	p. 232
P-values Versus Critical Regions	p. 233
Likelihood Ratio Tests	p. 234
Simple Hypotheses and the Neyman-Pearson Lemma	p. 234
Generalized Likelihood Ratio Tests	p. 237
Exercises	p. 239
Regression	p. 247
Covariance	p. 247
Correlation	p. 251
Least-Squares Regression	p. 254
Regression Toward the Mean	p. 258
Variation	p. 259
Diagnostics	p. 261
Multiple Regression	p. 265
The Simple Linear Model	p. 266
Inference for ¿ and ß	p. 270
Inference for the Response	p. 273
Comments About Assumptions for the Linear Model	p. 277
Resampling Correlation and Regression	p. 279
Permutation Tests	p. 282
Bootstrap Case Study: Bushmeat	p. 283
Logistic Regression	p. 286
Inference for Logistic Regression	p. 291
Exercises	p. 294
Bayesian Methods	p. 301
Bayes' Theorem	p. 302
Binomial Data, Discrete Prior Distributions	p. 302
Binomial Data, Continuous Prior Distributions	p. 309
Continuous Data	p. 316
Sequential Data	p. 319
Exercises	p. 322
Additional Topics	p. 327
Smoothed Bootstrap	p. 327
Kernel Density Estimate	p. 328
Parametric Bootstrap	p. 331
The Delta Method	p. 335
Stratified Sampling	p. 339
Computational Issues in Bayesian Analysis	p. 340
Monte Carlo Integration	p. 341
Importance Sampling	p. 346
Ratio Estimate for Importance Sampling	p. 352
Importance Sampling in Bayesian Applications	p. 355
Exercises	p. 359
Review of Probability	p. 363
Basic Probability	p. 363
Mean and Variance	p. 364
The Mean of a Sample of Random Variables	p. 366
The Law of Averages	p. 367
The Normal Distribution	p. 368
Sums of Normal Random Variables	p. 369
Higher Moments and the Moment Generating Function	p. 370
Probability Distributions	p. 373
The Bernoulli and Binomial Distributions	p. 373
The Multinomial Distribution	p. 374
The Geometric Distribution	p. 376
The Negative Binomial Distribution	p. 377
The Hypergeometric Distribution	p. 378
The Poisson Distribution	p. 379
The Uniform Distribution	p. 381
The Exponential Distribution	p. 381
The Gamma Distribution	p. 382
The Chi-Square Distribution	p. 385
The Student's t Distribution	p. 388
The Beta Distribution	p. 390
The F Distribution	p. 391
Exercises	p. 393
Distributions Quick Reference	p. 395
Solutions to Odd-Numbered Exercises	p. 399
Bibliography	p. 407
Index	p. 413
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