<P>2005最新出版</P>
<P><FONT face=新細明體><B>Prefac</B>e <B>xii</B>i <br><br></FONT>
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<P><FONT face=新細明體>1 <B>Scatterplot</B>s <B>an</B>d <B>Regressio</B>n 1 <br><br><br>
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<P><FONT face=新細明體>1.1 Scatterplots, 1 <br><br><br>
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<P><FONT face=新細明體>1.2 Mean Functions, 9 <br><br><br>
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<P><FONT face=新細明體>1.3 Variance Functions, 11 <br><br><br>
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<P><FONT face=新細明體>1.4 Summary Graph, 11 <br><br><br>
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<P><FONT face=新細明體>1.5 Tools for Looking at Scatterplots, 12 <br><br><br>
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<P><FONT face=新細明體>1.5.1 Size, 13 <br><br><br>
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<P><FONT face=新細明體>1.5.2 Transformations, 14 <br><br><br>
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<P><FONT face=新細明體>1.5.3 Smoothers for the Mean Function, 14 <br><br><br>
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<P><FONT face=新細明體>1.6 Scatterplot Matrices, 15<br>Problems, 17<br><br><br><br><br>
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<P><FONT face=新細明體>2 <B>Simpl</B>e <B>Linea</B>r <B>Regressio</B>n <B>1</B>9 <br><br><br>
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<P><FONT face=新細明體>2.1 Ordinary Least Squares Estimation, 21 <br><br><br>
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<P><FONT face=新細明體>2.2 Least Squares Criterion, 23 <br><br><br>
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<P><FONT face=新細明體>2.3 Estimating σ <SUP>2</SUP>,25 <br><br><br>
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<P><FONT face=新細明體>2.4 Properties of Least Squares Estimates, 26 <br><br><br>
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<P><FONT face=新細明體>2.5 Estimated Variances, 27 <br><br><br>
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<P><FONT face=新細明體>2.6 Comparing Models: The Analysis of Variance, 28 <br><br><br>
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<P><FONT face=新細明體>2.6.1 The F -Test for Regression, 30 <br><br><br>
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<P><FONT face=新細明體>2.6.2 Interpreting <I>p</I>-values, 31 <br><br><br>
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<P><FONT face=新細明體>2.6.3 Power of Tests, 31 <br><br><br>
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<P><FONT face=新細明體>2.7 The Coef</FONT>fi<FONT face=新細明體>cient of Determination, <I>R</I><SUP>2</SUP>,31 <br><br><br>
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<P><FONT face=新細明體>2.8 Con</FONT>fi<FONT face=新細明體>dence Intervals and Tests, 32 <br><br><br>
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<P><FONT face=新細明體>2.8.1 The Intercept, 32 <br><br><br>
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<P><FONT face=新細明體>2.8.2 Slope, 33 <br><br><br>
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<P><FONT face=新細明體>2.8.3 Prediction, 34 <br><br><br>
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<P><FONT face=新細明體>2.8.4 Fitted Values, 35 <br><br><br>
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<P><FONT face=新細明體>2.9 The Residuals, 36<br>Problems, 38<br><br><br><br><br>
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<P><FONT face=新細明體>3 <B>Multipl</B>e <B>Regressio</B>n <B>4</B>7 <br><br><br>
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<P><FONT face=新細明體>3.1 Adding a Term to a Simple Linear Regression Model, 47 <br><br><br>
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<P><FONT face=新細明體>3.1.1 Explaining Variability, 49 <br><br><br>
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<P><FONT face=新細明體>3.1.2 Added-Variable Plots, 49 <br><br><br>
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<P><FONT face=新細明體>3.2 The Multiple Linear Regression Model, 50 <br><br><br>
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<P><FONT face=新細明體>3.3 Terms and Predictors, 51 <br><br><br>
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<P><FONT face=新細明體>3.4 Ordinary Least Squares, 54 <br><br><br>
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<P><FONT face=新細明體>3.4.1 Data and Matrix Notation, 54 <br><br><br>
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<P><FONT face=新細明體>3.4.2 Variance-Covariance Matrix of <B>e</B>,56 <br><br><br>
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<P><FONT face=新細明體>3.4.3 Ordinary Least Squares Estimators, 56 <br><br><br>
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<P><FONT face=新細明體>3.4.4 Properties of the Estimates, 57 <br><br><br>
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<P><FONT face=新細明體>3.4.5 Simple Regression in Matrix Terms, 58 <br><br><br>
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<P><FONT face=新細明體>3.5 The Analysis of Variance, 61 <br><br><br>
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<P><FONT face=新細明體>3.5.1 The Coef</FONT>fi<FONT face=新細明體>cient of Determination, 62 <br><br><br>
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<P><FONT face=新細明體>3.5.2 Hypotheses Concerning One of the Terms, 62 <br><br><br>
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<P><FONT face=新細明體>3.5.3 Relationship to the <I>t</I>-Statistic, 63 <br><br><br>
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<P><FONT face=新細明體>3.5.4 <I>t</I>-Tests and Added-Variable Plots, 63 <br><br><br>
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<P><FONT face=新細明體>3.5.5 Other Tests of Hypotheses, 64 <br><br><br>
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<P><FONT face=新細明體>3.5.6 Sequential Analysis of Variance Tables, 64 <br><br><br>
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<P><FONT face=新細明體>3.6 Predictions and Fitted Values, 65<br>Problems, 65<br><br><br><br><br>
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<P><FONT face=新細明體><B>4 Drawing Conclusions 69 </B>
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<P><FONT face=新細明體>4.1 Understanding Parameter Estimates, 69 <br><br><br>
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<P><FONT face=新細明體>4.1.1 Rate of Change, 69 <br><br><br>
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<P><FONT face=新細明體>4.1.2 Signs of Estimates, 70 <br><br><br>
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<P><FONT face=新細明體>4.1.3 Interpretation Depends on Other Terms in the Mean Function, 70 <br><br><br>
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<P><FONT face=新細明體>4.1.4 Rank De</FONT>fi<FONT face=新細明體>cient and Over-Parameterized Mean<br>Functions, 73<br><br><br><br><br>
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<P><FONT face=新細明體>4.1.5 Tests, 74 <br><br><br>
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<P><FONT face=新細明體>4.1.6 Dropping Terms, 74 <br><br><br>
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<P><FONT face=新細明體>4.1.7 Logarithms, 76 <br><br><br>
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<P><FONT face=新細明體>4.2 Experimentation Versus Observation, 77 4.3 Sampling from a Normal Population, 80 <br><br><br>
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<P><FONT face=新細明體>4.4 More on R<SUP>2</SUP>,81 <br><br><br>
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<P><FONT face=新細明體>4.4.1 Simple Linear Regression and <I>R</I><SUP>2</SUP>,83 <br><br><br>
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<P><FONT face=新細明體>4.4.2 Multiple Linear Regression, 84 <br><br><br>
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<P><FONT face=新細明體>4.4.3 Regression through the Origin, 84 <br><br><br>
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<P><FONT face=新細明體>4.5 Missing Data, 84 <br><br><br>
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<P><FONT face=新細明體>4.5.1 Missing at Random, 84 <br><br><br>
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<P><FONT face=新細明體>4.5.2 Alternatives, 85 <br><br><br>
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<P><FONT face=新細明體>4.6 Computationally Intensive Methods, 87 <br><br><br>
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<P><FONT face=新細明體>4.6.1 Regression Inference without Normality, 87 <br><br><br>
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<P><FONT face=新細明體>4.6.2 Nonlinear Functions of Parameters, 89 <br><br><br>
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<P><FONT face=新細明體>4.6.3 Predictors Measured with Error, 90<br>Problems, 92<br><br><br><br><br>
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<P><FONT face=新細明體>5 <B>Weights</B>, <B>Lac</B>k <B>o</B>f <B>Fit</B>, <B>an</B>d <B>Mor</B>e <B>9</B>6 <br><br><br>
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<P><FONT face=新細明體>5.1 Weighted Least Squares, 96 <br><br><br>
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<P><FONT face=新細明體>5.1.1 Applications of Weighted Least Squares, 98 <br><br><br>
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<P><FONT face=新細明體>5.1.2 Additional Comments, 99 <br><br><br>
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<P><FONT face=新細明體>5.2 Testing for Lack of Fit, Variance Known, 100 <br><br><br>
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<P><FONT face=新細明體>5.3 Testing for Lack of Fit, Variance Unknown, 102 <br><br><br>
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<P><FONT face=新細明體>5.4 General F Testing, 105 <br><br><br>
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<P><FONT face=新細明體>5.4.1 Non-null Distributions, 107 <br><br><br>
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<P><FONT face=新細明體>5.4.2 Additional Comments, 108 <br><br><br>
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<P><FONT face=新細明體>5.5 Joint Con</FONT>fi<FONT face=新細明體>dence Regions, 108<br>Problems, 110<br><br><br><br><br>
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<P><FONT face=新細明體>6 <B>Polynomial</B>s <B>an</B>d <B>Factor</B>s <B>11</B>5 <br><br><br>
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<P><FONT face=新細明體>6.1 Polynomial Regression, 115 <br><br><br>
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<P><FONT face=新細明體>6.1.1 Polynomials with Several Predictors, 117 <br><br><br>
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<P><FONT face=新細明體>6.1.2 Using the Delta Method to Estimate a Minimum or a Maximum, 120 <br><br><br>
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<P><FONT face=新細明體>6.1.3 Fractional Polynomials, 122 <br><br><br>
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<P><FONT face=新細明體>6.2 Factors, 122 <br><br><br>
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<P><FONT face=新細明體>6.2.1 No Other Predictors, 123 <br><br><br>
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<P><FONT face=新細明體>6.2.2 Adding a Predictor: Comparing Regression Lines, 126 <br><br><br>
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<P><FONT face=新細明體>6.2.3 Additional Comments, 129 <br><br><br>
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<P><FONT face=新細明體>6.3 Many Factors, 130 <br><br><br>
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<P><FONT face=新細明體>6.4 Partial One-Dimensional Mean Functions, 131 <br><br><br>
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<P><FONT face=新細明體>6.5 Random Coef</FONT>fi<FONT face=新細明體>cient Models, 134<br>Problems, 137<br><br><br><br><br>
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<P><FONT face=新細明體><B>7 Transformations 147 </B>
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<P><FONT face=新細明體>7.1 Transformations and Scatterplots, 147 <br><br><br>
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<P><FONT face=新細明體>7.1.1 Power Transformations, 148 <br><br><br>
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<P><FONT face=新細明體>7.1.2 Transforming Only the Predictor Variable, 150 <br><br><br>
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<P><FONT face=新細明體>7.1.3 Transforming the Response Only, 152 <br><br><br>
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<P><FONT face=新細明體>7.1.4 The Box and Cox Method, 153 <br><br><br>
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<P><FONT face=新細明體>7.2 Transformations and Scatterplot Matrices, 153 <br><br><br>
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<P><FONT face=新細明體>7.2.1 The 1D Estimation Result and Linearly Related Predictors, 156 <br><br><br>
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<P><FONT face=新細明體>7.2.2 Automatic Choice of Transformation of Predictors, 157 <br><br><br>
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<P><FONT face=新細明體>7.3 Transforming the Response, 159 <br><br><br>
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<P><FONT face=新細明體>7.4 Transformations of Nonpositive Variables, 160<br>Problems, 161<br><br><br><br><br>
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<P><FONT face=新細明體>8 <B>Regressio</B>n <B>Diagnostics</B>: <B>Residual</B>s <B>16</B>7 <br><br><br>
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<P><FONT face=新細明體>8.1 The Residuals, 167 <br><br><br>
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<P><FONT face=新細明體>8.1.1 Difference Between <B>e</B></FONT>ˆ<FONT face=新細明體>and <B>e</B>, 168 <br><br><br>
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<P><FONT face=新細明體>8.1.2 The Hat Matrix, 169 <br><br><br>
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<P><FONT face=新細明體>8.1.3 Residuals and the Hat Matrix with Weights, 170 <br><br><br>
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<P><FONT face=新細明體>8.1.4 The Residuals When the Model Is Correct, 171 <br><br><br>
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<P><FONT face=新細明體>8.1.5 The Residuals When the Model Is Not Correct, 171 <br><br><br>
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<P><FONT face=新細明體>8.1.6 Fuel Consumption Data, 173 <br><br><br>
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<P><FONT face=新細明體>8.2 Testing for Curvature, 176 <br><br><br>
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<P><FONT face=新細明體>8.3 Nonconstant Variance, 177 <br><br><br>
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<P><FONT face=新細明體>8.3.1 Variance Stabilizing Transformations, 179 <br><br><br>
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<P><FONT face=新細明體>8.3.2 A Diagnostic for Nonconstant Variance, 180 <br><br><br>
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<P><FONT face=新細明體>8.3.3 Additional Comments, 185 <br><br><br>
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<P><FONT face=新細明體>8.4 Graphs for Model Assessment, 185 <br><br><br>
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<P><FONT face=新細明體>8.4.1 Checking Mean Functions, 186 <br><br><br>
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<P><FONT face=新細明體>8.4.2 Checking Variance Functions, 189<br>Problems, 191<br><br><br><br><br>
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<P><FONT face=新細明體>9 <B>Outlier</B>s <B>an</B>d <B>In</B></FONT><B>fl</B><FONT face=新細明體><B>uenc</B>e <B>19</B>4 <br><br><br>
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<P><FONT face=新細明體>9.1 Outliers, 194 <br><br><br>
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<P><FONT face=新細明體>9.1.1 An Outlier Test, 194 <br><br><br>
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<P><FONT face=新細明體>9.1.2 Weighted Least Squares, 196 <br><br><br>
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<P><FONT face=新細明體>9.1.3 Signi</FONT>fi<FONT face=新細明體>cance Levels for the Outlier Test, 196 <br><br><br>
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<P><FONT face=新細明體>9.1.4 Additional Comments, 197 <br><br><br>
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<P><FONT face=新細明體>9.2 In</FONT>fl<FONT face=新細明體>uence of Cases, 198 <br><br><br>
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<P><FONT face=新細明體>9.2.1 Cook</FONT>’<FONT face=新細明體>s Distance, 198 9.2.2 Magnitude of Di , 199 <br><br><br>
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<P><FONT face=新細明體>9.2.3 Computing Di , 200 <br><br><br>
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<P><FONT face=新細明體>9.2.4 Other Measures of In</FONT>fl<FONT face=新細明體>uence, 203 <br><br><br>
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<P><FONT face=新細明體>9.3 Normality Assumption, 204<br>Problems, 206<br><br><br><br><br>
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<P><FONT face=新細明體><B>10 Variable Selection 211 </B>
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<P><FONT face=新細明體>10.1 The Active Terms, 211 <br><br><br>
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<P><FONT face=新細明體>10.1.1 Collinearity, 214 <br><br><br>
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<P><FONT face=新細明體>10.1.2 Collinearity and Variances, 216 <br><br><br>
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<P><FONT face=新細明體>10.2 Variable Selection, 217 <br><br><br>
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<P><FONT face=新細明體>10.2.1 Information Criteria, 217 <br><br><br>
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<P><FONT face=新細明體>10.2.2 Computationally Intensive Criteria, 220 <br><br><br>
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<P><FONT face=新細明體>10.2.3 Using Subject-Matter Knowledge, 220 <br><br><br>
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<P><FONT face=新細明體>10.3 Computational Methods, 221 <br><br><br>
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<P><FONT face=新細明體>10.3.1 Subset Selection Overstates Signi</FONT>fi<FONT face=新細明體>cance, 225 <br><br><br>
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<P><FONT face=新細明體>10.4 Windmills, 226 <br><br><br>
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<P><FONT face=新細明體>10.4.1 Six Mean Functions, 226 <br><br><br>
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<P><FONT face=新細明體>10.4.2 A Computationally Intensive Approach, 228<br>Problems, 230<br><br><br><br><br>
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<P><FONT face=新細明體><B>11 Nonlinear Regression 233 </B>
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<P><FONT face=新細明體>11.1 Estimation for Nonlinear Mean Functions, 234 <br><br><br>
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<P><FONT face=新細明體>11.2 Inference Assuming Large Samples, 237 <br><br><br>
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<P><FONT face=新細明體>11.3 Bootstrap Inference, 244 <br><br><br>
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<P><FONT face=新細明體>11.4 References, 248<br>Problems, 248<br><br><br><br><br>
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<P><FONT face=新細明體><B>12 Logistic Regression 251 </B>
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<P><FONT face=新細明體>12.1 Binomial Regression, 253 <br><br><br>
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<P><FONT face=新細明體>12.1.1 Mean Functions for Binomial Regression, 254 <br><br><br>
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<P><FONT face=新細明體>12.2 Fitting Logistic Regression, 255 <br><br><br>
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<P><FONT face=新細明體>12.2.1 One-Predictor Example, 255 <br><br><br>
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<P><FONT face=新細明體>12.2.2 Many Terms, 256 <br><br><br>
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<P><FONT face=新細明體>12.2.3 Deviance, 260 <br><br><br>
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<P><FONT face=新細明體>12.2.4 Goodness-of-Fit Tests, 261 <br><br><br>
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<P><FONT face=新細明體>12.3 Binomial Random Variables, 263 <br><br><br>
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<P><FONT face=新細明體>12.3.1 Maximum Likelihood Estimation, 263 <br><br><br>
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<P><FONT face=新細明體>12.3.2 The Log-Likelihood for Logistic Regression, 264 <br><br><br>
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<P><FONT face=新細明體>12.4 Generalized Linear Models, 265<br>Problems, 266<br><br><br><br><br>
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<P><FONT face=新細明體><B>Appendix 270 </B>
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<P><FONT face=新細明體>A.1 Web Site, 270 <br><br><br>
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<P><FONT face=新細明體>A.2 Means and Variances of Random Variables, 270 <br><br><br>
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<P><FONT face=新細明體>A.2.1 E Notation, 270 <br><br><br>
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<P><FONT face=新細明體>A.2.2 Var Notation, 271 <br><br><br>
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<P><FONT face=新細明體>A.2.3 Cov Notation, 271 <br><br><br>
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<P><FONT face=新細明體>A.2.4 Conditional Moments, 272 <br><br><br>
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<P><FONT face=新細明體>A.3 Least Squares for Simple Regression, 273 <br><br><br>
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<P><FONT face=新細明體>A.4 Means and Variances of Least Squares Estimates, 273 <br><br><br>
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<P><FONT face=新細明體>A.5 Estimating E<I>(</I>Y |<I>X</I>) Using a Smoother, 275 <br><br><br>
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<P><FONT face=新細明體>A.6 A Brief Introduction to Matrices and Vectors, 278 <br><br><br>
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<P><FONT face=新細明體>A.6.1 Addition and Subtraction, 279 <br><br><br>
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<P><FONT face=新細明體>A.6.2 Multiplication by a Scalar, 280 <br><br><br>
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<P><FONT face=新細明體>A.6.3 Matrix Multiplication, 280 <br><br><br>
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<P><FONT face=新細明體>A.6.4 Transpose of a Matrix, 281 <br><br><br>
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<P><FONT face=新細明體>A.6.5 Inverse of a Matrix, 281 <br><br><br>
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<P><FONT face=新細明體>A.6.6 Orthogonality, 282 <br><br><br>
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<P><FONT face=新細明體>A.6.7 Linear Dependence and Rank of a Matrix, 283 <br><br><br>
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<P><FONT face=新細明體>A.7 Random Vectors, 283 <br><br><br>
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<P><FONT face=新細明體>A.8 Least Squares Using Matrices, 284 <br><br><br>
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<P><FONT face=新細明體>A.8.1 Properties of Estimates, 285 <br><br><br>
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<P><FONT face=新細明體>A.8.2 The Residual Sum of Squares, 285 <br><br><br>
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<P><FONT face=新細明體>A.8.3 Estimate of Variance, 286 <br><br><br>
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<P><FONT face=新細明體>A.9 The QR Factorization, 286 <br><br><br>
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<P><FONT face=新細明體>A.10 Maximum Likelihood Estimates, 287 <br><br><br>
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<P><FONT face=新細明體>A.11 The Box-Cox Method for Transformations, 289 <br><br><br>
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<P><FONT face=新細明體>A.11.1 Univariate Case, 289 <br><br><br>
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<P><FONT face=新細明體>A.11.2 Multivariate Case, 290 <br><br><br>
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<P><FONT face=新細明體>A.12 Case Deletion in Linear Regression, 291 <br><br><br>
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<P><FONT face=新細明體><B>References 293 </B>
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<P><FONT face=新細明體><B>Author Index 301 </B>
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<P><B><FONT face=新細明體>Subject Index 305 </FONT></B>
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[此贴子已经被作者于2006-2-24 8:28:06编辑过]