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What is included with this book?
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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