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Preface | |
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Acknowledgments | |
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Introduction | |
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Statistics in practice | |
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Learning statistics | |
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Foundations | |
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Identifying and summarizing data | |
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Population distributions | |
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Selecting individuals at random-probability | |
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Random sampling | |
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Central limit theorem-normal version | |
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Central limit theorem-t-version | |
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Interval estimation | |
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Hypothesis testing | |
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The rejection region method | |
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The p-value method | |
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Hypothesis test errors | |
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Random errors and prediction | |
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Chapter Summary | |
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Problems | |
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Simple linear regression | |
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Probability model for X and Y | |
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Least Squares criterion | |
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Model evaluation | |
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Regression standard error | |
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Coefficient of determination-R<sup>2</sup> | |
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Slope parameter | |
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Model assumptions | |
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Checking the model assumptions | |
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Testing the model assumptions | |
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Model interpretation | |
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Estimation and prediction | |
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Confidence interval for the population mean, E(Y) | |
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Prediction interval for an individual Y-value | |
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Chapter summary | |
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Review example | |
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Problems | |
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Multiple linear regression | |
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Probability model for (X<sub>1</sub>, X<sub>2</sub>,...) and Y | |
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Least squares criterion | |
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Model evaluation | |
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Regression standard error | |
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Coefficient of determination-R<sup>2</sup> | |
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Regression parameters-global usefulness test | |
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Regression parameters-nested model test | |
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Regression parameters-individuals tests | |
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Model assumptions | |
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Checking the model assumptions | |
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Testing the model assumptions | |
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Model interpretation | |
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Estimation and prediction | |
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Confidence interval for the population mean, E(Y) | |
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Prediction interval for an individual Y-value | |
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Chapter summary | |
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Problems | |
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Regression model building I | |
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Transformations | |
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Natural logarithm transformation for predictors | |
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Polynomial transformation for predictors | |
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Reciprocal transformation for predictors | |
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Natural logarithm transformation for the response | |
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Transformations for the response and predictors | |
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Interactions | |
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Qualitative predictors | |
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Qualitative predictors with two levels | |
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Qualitative predictors with three or more levels | |
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Chapter summary | |
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Problems | |
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Regression model building II | |
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Influential points | |
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Outliers | |
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Leverage | |
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Cook's distance | |
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Regression pitfalls | |
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Nonconstant variance | |
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Autocorrelation | |
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Multicollinearity | |
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Excluding important predictor varibales | |
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Overfitting | |
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Extrapolations | |
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Missing data | |
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Power and sample size | |
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Model building guidelines | |
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Model selection | |
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Model interpretation using graphics | |
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Chapter summary | |
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Problems | |
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Case studies | |
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Home prices | |
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Data description | |
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Exploratory data analysis | |
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Regression model building | |
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Results and conclusions | |
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Further questions | |
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Vehicle fuel efficiency | |
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Data description | |
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Exploratory data analysis | |
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Regression model building | |
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Results and conclusions | |
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Further questions | |
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Pharmaceutical patches | |
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Data description | |
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Exploratory data analysis | |
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Regression model building | |
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Model diagnostics | |
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Results and conclusions | |
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Further questions | |
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Extensions | |
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Generalized linear models | |
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Logistic regression | |
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Poisson regression | |
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Discrete choice models | |
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Multilevel models | |
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Bayesian modeling | |
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Frequentist inference | |
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Bayesian inference | |
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Computer software help | |
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Problems | |
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Critical values for t-distributions | |
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Notation and formulas | |
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Univariate data | |
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Simple linear regression | |
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Multiple linear regression | |
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Mathematics refresher | |
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The natural logarithm and exponential functions | |
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Rounding and accuracy | |
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Answers for selected problems | |
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References | |
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Glossary | |
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Index | |