Preface |
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v | |
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1 | (20) |
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Data, Variables, and Random Processes |
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1 | (2) |
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Basic Principles of Experimental Design |
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3 | (2) |
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5 | (2) |
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Measuring and Scaling in Statistical Medicine |
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7 | (1) |
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Experimental Design in Biotechnology |
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8 | (1) |
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Relative Importance of Effects-The Pareto Principle |
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9 | (1) |
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10 | (5) |
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A One--Way Factorial Experiment by Example |
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15 | (4) |
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19 | (2) |
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Comparison of Two Samples |
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21 | (24) |
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21 | (1) |
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Paired t-Test and Matched-Pair Design |
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22 | (3) |
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Comparison of Means in Independent Groups |
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25 | (4) |
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25 | (1) |
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Testing H0 : σ2A = σ2B = σ2 |
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25 | (1) |
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Comparison of Means in the Case of Unequal Variances |
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26 | (1) |
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Transformations of Data to Assure Homogeneity of Variances |
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27 | (1) |
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Necessary Sample Size and Power of the Test |
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27 | (1) |
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Comparison of Means without Prior Testing H0 : σ2A = σ2B; Cochran-Cox Test for Independent Groups |
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27 | (2) |
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Wilcoxon's Sign-Rank Test in the Matched-Pair Design |
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29 | (4) |
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Rank Test for Homogeneity of Wilcoxon, Mann and Whitney |
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33 | (5) |
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Comparison of Two Groups with Categorical Response |
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38 | (3) |
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McNemar's Test and Matched-Pair Design |
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38 | (1) |
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Fisher's Exact Test for Two Independent Groups |
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39 | (2) |
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41 | (4) |
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The Linear Regression Model |
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45 | (66) |
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Descriptive Linear Regression |
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45 | (2) |
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The Principle of Ordinary Least Squares |
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47 | (3) |
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Geometric Properties of Ordinary Least Squares Estimation |
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50 | (1) |
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Best Linear Unbiased Estimation |
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51 | (9) |
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52 | (1) |
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53 | (2) |
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Best Linear Unbiased Estimation |
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55 | (2) |
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57 | (3) |
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60 | (7) |
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Extreme Multicollinearity and Estimability |
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60 | (1) |
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Estimation within Extreme Multicollinearity |
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61 | (2) |
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63 | (4) |
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Classical Regression under Normal Errors |
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67 | (2) |
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Testing Linear Hypotheses |
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69 | (4) |
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Analysis of Variance and Goodness of Fit |
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73 | (10) |
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73 | (6) |
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79 | (4) |
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The General Linear Regression Model |
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83 | (3) |
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83 | (2) |
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Misspecification of the Covariance Matrix |
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85 | (1) |
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86 | (24) |
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86 | (1) |
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86 | (5) |
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Effect of a Single Observation on the Estimation of Parameters |
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91 | (5) |
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Diagnostic Plots for Testing the Model Assumptions |
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96 | (1) |
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Measures Based on the Confidence Ellipsoid |
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97 | (5) |
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102 | (2) |
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Regression Diagnostics by Animating Graphics |
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104 | (6) |
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110 | (1) |
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Single-Factor Experiments with Fixed and Random Effects |
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111 | (46) |
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Models I and II in the Analysis of Variance |
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111 | (1) |
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One-Way Classification for the Multiple Comparison of Means |
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112 | (11) |
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Representation as a Restrictive Model |
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115 | (2) |
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Decomposition of the Error Sum of Squares |
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117 | (3) |
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Estimation of σ2 by M SError |
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120 | (3) |
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Comparison of Single Means |
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123 | (9) |
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123 | (3) |
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Contrasts of the Total Response Values in the Balanced Case |
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126 | (6) |
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132 | (10) |
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132 | (1) |
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Experimentwise Comparisons |
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132 | (3) |
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Select Pairwise Comparisons |
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135 | (7) |
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Regression Analysis of Variance |
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142 | (3) |
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One-Factorial Models with Random Effects |
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145 | (4) |
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Rank Analysis of Variance in the Completely Randomized Design |
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149 | (5) |
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149 | (3) |
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152 | (2) |
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154 | (3) |
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157 | (22) |
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157 | (8) |
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165 | (7) |
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167 | (5) |
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Rank Variance Analysis in the Randomized Block Design |
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172 | (4) |
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172 | (3) |
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175 | (1) |
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176 | (3) |
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179 | (52) |
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Elementary Definitions and Principles |
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179 | (4) |
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Two-Factor Experiments (Fixed Effects) |
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183 | (5) |
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Two-Factor Experiments in Effect Coding |
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188 | (8) |
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Two-Factorial Experiment with Block Effects |
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196 | (3) |
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Two-Factorial Model with Fixed Effects-Confidence Intervals and Elementary Tests |
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199 | (4) |
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Two-Factorial Model with Random or Mixed Effects |
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203 | (8) |
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Model with Random Effects |
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203 | (4) |
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207 | (4) |
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211 | (4) |
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215 | (4) |
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219 | (6) |
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219 | (3) |
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222 | (3) |
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225 | (6) |
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Models for Categorical Response Variables |
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231 | (64) |
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Generalized Linear Models |
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231 | (14) |
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Extension of the Regression Model |
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231 | (2) |
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Structure of the Generalized Linear Model |
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233 | (3) |
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Score Function and Information Matrix |
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236 | (1) |
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Maximum Likelihood Estimation |
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237 | (3) |
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Testing of Hypotheses and Goodness of Fit |
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240 | (1) |
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241 | (2) |
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243 | (2) |
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245 | (9) |
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245 | (1) |
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Ways of Comparing Proportions |
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246 | (3) |
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Sampling in Two-Way Contingency Tables |
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249 | (1) |
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Likelihood Function and Maximum Likelihood Estimates |
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250 | (2) |
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Testing the Goodness of Fit |
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252 | (2) |
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Generalized Linear Model for Binary Response |
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254 | (4) |
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Logit Models and Logistic Regression |
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254 | (3) |
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257 | (1) |
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Distribution Function as a Link Function |
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258 | (1) |
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Logit Models for Categorical Data |
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258 | (2) |
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Goodness of Fit-Likelihood Ratio Test |
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260 | (1) |
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Loglinear Models for Categorical Variables |
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261 | (6) |
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Two-Way Contingency Tables |
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261 | (3) |
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Three-Way Contingency Tables |
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264 | (3) |
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The Special Case of Binary Response |
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267 | (3) |
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Coding of Categorical Explanatory Variables |
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270 | (7) |
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270 | (3) |
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Coding of Response Models |
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273 | (1) |
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Coding of Models for the Hazard Rate |
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274 | (3) |
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Extensions to Dependent Binary Variables |
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277 | (17) |
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277 | (2) |
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Modeling Approaches for Correlated Response |
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279 | (1) |
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Quasi-Likelihood Approach for Correlated Binary Response |
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280 | (1) |
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The Generalized Estimating Equation Method by Liang and Zeger |
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281 | (2) |
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Properties of the Generalized Estimating Equation Estimate βG |
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283 | (1) |
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Efficiency of the Generalized Estimating Equation and Independence Estimating Equation Methods |
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284 | (1) |
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Choice of the Quasi-Correlation Matrix Ri(α) |
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285 | (1) |
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Bivariate Binary Correlated Response Variables |
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285 | (1) |
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The Generalized Estimating Equation Method |
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286 | (2) |
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The Independence Estimating Equation Method |
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288 | (1) |
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An Example from the Field of Dentistry |
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288 | (5) |
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Full Likelihood Approach for Marginal Models |
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293 | (1) |
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294 | (1) |
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295 | (46) |
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The Fundamental Model for One Population |
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295 | (3) |
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The Repeated Measures Model for Two Populations |
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298 | (3) |
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Univariate and Multivariate Analysis |
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301 | (5) |
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The Univariate One-Sample Case |
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301 | (1) |
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The Multivariate One-Sample Case |
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301 | (5) |
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The Univariate Two-Sample Case |
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306 | (1) |
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The Multivariate Two-Sample Case |
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307 | (1) |
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308 | (1) |
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Univariate Analysis of Variance in the Repeated Measures Model |
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309 | (15) |
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Testing of Hypotheses in the Case of Compound Symmetry |
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309 | (2) |
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Testing of Hypotheses in the Case of Sphericity |
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311 | (4) |
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The Problem of Nonsphericity |
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315 | (1) |
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Application of Univariate Modified Approaches in the Case of Nonsphericity |
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316 | (1) |
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317 | (1) |
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318 | (6) |
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Multivariate Rank Tests in the Repeated Measures Model |
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324 | (5) |
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Categorical Regression for the Repeated Binary Response Data |
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329 | (10) |
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Logit Models for the Repeated Binary Response for the Comparison of Therapies |
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329 | (1) |
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First-Order Markov Chain Models |
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330 | (2) |
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Multinomial Sampling and Loglinear Models for a Global Comparison of Therapies |
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332 | (7) |
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339 | (2) |
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341 | (44) |
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341 | (1) |
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Linear Model and Notations |
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342 | (1) |
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2 x 2 Cross-Over (Classical Approach) |
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343 | (25) |
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344 | (4) |
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348 | (4) |
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Residual Analysis and Plotting the Data |
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352 | (4) |
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Alternative Parametrizations in 2 x 2 Cross-Over |
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356 | (12) |
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Cross-Over Analysis Using Rank Tests |
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368 | (1) |
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2 x 2 Cross-Over and Categorical (Binary) Response |
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368 | (16) |
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368 | (4) |
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Loglinear and Logit Models |
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372 | (12) |
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384 | (1) |
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Statistical Analysis of Incomplete Data |
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385 | (30) |
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385 | (5) |
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Missing Data in the Response |
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390 | (3) |
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Least Squares Analysis for Complete Data |
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390 | (1) |
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Least Squares Analysis for Filled-Up Data |
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391 | (1) |
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Analysis of Covariance-Bartlett's Method |
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392 | (1) |
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Missing Values in the X-Matrix |
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393 | (7) |
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Missing Values and Loss of Efficiency |
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394 | (3) |
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Standard Methods for Incomplete X-Matrices |
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397 | (3) |
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Adjusting for Missing Data in 2 x 2 Cross-Over Designs |
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400 | (7) |
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400 | (2) |
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Maximum Likelihood Estimator (Rao, 1956) |
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402 | (1) |
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403 | (4) |
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407 | (5) |
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407 | (1) |
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Maximum Likelihood Estimation in the Complete Data Case |
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408 | (1) |
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409 | (1) |
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410 | (2) |
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412 | (3) |
A Matrix Algebra |
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415 | (38) |
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415 | (3) |
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418 | (1) |
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418 | (2) |
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420 | (1) |
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421 | (1) |
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422 | (1) |
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422 | (1) |
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Eigenvalues and Eigenvectors |
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423 | (2) |
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Decomposition of Matrices |
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425 | (2) |
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Definite Matrices and Quadratic Forms |
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427 | (6) |
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433 | (1) |
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434 | (8) |
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442 | (1) |
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Functions of Normally Distributed Variables |
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443 | (3) |
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Differentiation of Scalar Functions of Matrices |
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446 | (3) |
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Miscellaneous Results, Stochastic Convergence |
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449 | (4) |
B Theoretical Proofs |
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453 | (26) |
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The Linear Regression Model |
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453 | (22) |
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Single-Factor Experiments with Fixed and Random Effects |
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475 | (4) |
C Distributions and Tables |
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479 | (8) |
References |
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487 | (10) |
Index |
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497 | |