Preface | p. xi |

Acknowledgments | p. xv |

Introduction | p. xvii |

Statistics in practice | p. xvii |

Learning statistics | p. xix |

Foundations | p. 1 |

Identifying and summarizing data | p. 1 |

Population distributions | p. 5 |

Selecting individuals at random-probability | p. 9 |

Random sampling | p. 11 |

Central limit theorem-normal version | p. 12 |

Central limit theorem-t-version | p. 14 |

Interval estimation | p. 15 |

Hypothesis testing | p. 19 |

The rejection region method | p. 19 |

The p-value method | p. 21 |

Hypothesis test errors | p. 24 |

Random errors and prediction | p. 25 |

Chapter Summary | p. 28 |

Problems | p. 29 |

Simple linear regression | p. 35 |

Probability model for X and Y | p. 35 |

Least Squares criterion | p. 40 |

Model evaluation | p. 45 |

Regression standard error | p. 46 |

Coefficient of determination-R^{2} | p. 48 |

Slope parameter | p. 52 |

Model assumptions | p. 59 |

Checking the model assumptions | p. 61 |

Testing the model assumptions | p. 66 |

Model interpretation | p. 66 |

Estimation and prediction | p. 68 |

Confidence interval for the population mean, E(Y) | p. 68 |

Prediction interval for an individual Y-value | p. 69 |

Chapter summary | p. 72 |

Review example | p. 74 |

Problems | p. 78 |

Multiple linear regression | p. 83 |

Probability model for (X_{1}, X_{2},...) and Y | p. 83 |

Least squares criterion | p. 87 |

Model evaluation | p. 92 |

Regression standard error | p. 92 |

Coefficient of determination-R^{2} | p. 94 |

Regression parameters-global usefulness test | p. 101 |

Regression parameters-nested model test | p. 104 |

Regression parameters-individuals tests | p. 109 |

Model assumptions | p. 118 |

Checking the model assumptions | p. 119 |

Testing the model assumptions | p. 123 |

Model interpretation | p. 124 |

Estimation and prediction | p. 126 |

Confidence interval for the population mean, E(Y) | p. 126 |

Prediction interval for an individual Y-value | p. 127 |

Chapter summary | p. 130 |

Problems | p. 132 |

Regression model building I | p. 137 |

Transformations | p. 138 |

Natural logarithm transformation for predictors | p. 138 |

Polynomial transformation for predictors | p. 144 |

Reciprocal transformation for predictors | p. 147 |

Natural logarithm transformation for the response | p. 151 |

Transformations for the response and predictors | p. 155 |

Interactions | p. 159 |

Qualitative predictors | p. 166 |

Qualitative predictors with two levels | p. 167 |

Qualitative predictors with three or more levels | p. 174 |

Chapter summary | p. 182 |

Problems | p. 184 |

Regression model building II | p. 189 |

Influential points | p. 189 |

Outliers | p. 189 |

Leverage | p. 194 |

Cook's distance | p. 196 |

Regression pitfalls | p. 199 |

Nonconstant variance | p. 199 |

Autocorrelation | p. 202 |

Multicollinearity | p. 206 |

Excluding important predictor varibales | p. 209 |

Overfitting | p. 212 |

Extrapolations | p. 213 |

Missing data | p. 215 |

Power and sample size | p. 217 |

Model building guidelines | p. 218 |

Model selection | p. 221 |

Model interpretation using graphics | p. 224 |

Chapter summary | p. 231 |

Problems | p. 234 |

Case studies | p. 243 |

Home prices | p. 243 |

Data description | p. 243 |

Exploratory data analysis | p. 245 |

Regression model building | p. 246 |

Results and conclusions | p. 247 |

Further questions | p. 252 |

Vehicle fuel efficiency | p. 253 |

Data description | p. 253 |

Exploratory data analysis | p. 253 |

Regression model building | p. 255 |

Results and conclusions | p. 256 |

Further questions | p. 261 |

Pharmaceutical patches | p. 261 |

Data description | p. 261 |

Exploratory data analysis | p. 261 |

Regression model building | p. 263 |

Model diagnostics | p. 263 |

Results and conclusions | p. 264 |

Further questions | p. 266 |

Extensions | p. 267 |

Generalized linear models | p. 268 |

Logistic regression | p. 268 |

Poisson regression | p. 273 |

Discrete choice models | p. 275 |

Multilevel models | p. 278 |

Bayesian modeling | p. 281 |

Frequentist inference | p. 281 |

Bayesian inference | p. 281 |

Computer software help | p. 285 |

Problems | p. 287 |

Critical values for t-distributions | p. 289 |

Notation and formulas | p. 293 |

Univariate data | p. 293 |

Simple linear regression | p. 294 |

Multiple linear regression | p. 295 |

Mathematics refresher | p. 297 |

The natural logarithm and exponential functions | p. 297 |

Rounding and accuracy | p. 298 |

Answers for selected problems | p. 299 |

References | p. 309 |

Glossary | p. 315 |

Index | p. 321 |

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