Preface |
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xi | |
1 Variation |
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1 | (28) |
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1 | (2) |
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3 | (1) |
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1.3 Summarizing Your Data |
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4 | (3) |
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4 | (3) |
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1.4 Reporting Your Results |
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7 | (4) |
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8 | (2) |
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10 | (1) |
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11 | (1) |
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.5.1 Depicting Categorical Data |
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12 | (1) |
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1.6 Displaying Multiple Variables |
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12 | (2) |
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1.6.1 From Observations to Questions |
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14 | (1) |
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14 | (7) |
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1.7.1 Which Measure of Location? |
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15 | (4) |
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19 | (2) |
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1.8 Samples and Populations |
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21 | (4) |
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1.8.1 Drawing a Random Sample |
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22 | (1) |
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1.8.2 Using R to Draw a Sample |
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23 | (2) |
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1.8.3 Ensuring the Sample Is Representative |
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25 | (1) |
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1.9 Variation-Within and Between |
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25 | (2) |
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27 | (2) |
2 Probability |
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29 | (23) |
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29 | (4) |
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2.1.1 Events and Outcomes |
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31 | (1) |
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31 | (2) |
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33 | (10) |
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2.2.1 Permutations and Rearrangements |
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35 | (3) |
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2.2.2 Back to the Binomial |
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38 | (1) |
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38 | (1) |
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2.2.4 Properties of the Binomial |
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39 | (4) |
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43 | (1) |
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2.3 Conditional Probability |
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43 | (4) |
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2.3.1 Market Basket Analysis |
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45 | (1) |
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46 | (1) |
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47 | (2) |
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2.5 Applications to Genetics |
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49 | (1) |
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50 | (2) |
3 Distributions |
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52 | (24) |
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3.1 Distribution of Values |
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52 | (3) |
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3.1.1 Cumulative Distribution Function |
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53 | (1) |
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3.1.2 Empirical Distribution Function |
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54 | (1) |
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3.2 Discrete Distributions |
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55 | (3) |
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3.3 Poisson: Events Rare in Time and Space |
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58 | (2) |
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3.3.1 Applying the Poisson |
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58 | (1) |
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3.3.2 Comparing Empirical and Theoretical Poisson Distributions |
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59 | (1) |
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3.4 Continuous Distributions |
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60 | (4) |
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3.4.1 The Exponential Distribution |
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61 | (1) |
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3.4.2 Normal Distribution |
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62 | (2) |
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3.4.3 Mixtures of Normal Distributions |
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64 | (1) |
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3.5 Properties of Independent Observations |
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64 | (2) |
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66 | (5) |
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3.6.1 Analyzing the Experiment |
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67 | (2) |
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3.6.2 Two Types of Errors |
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69 | (2) |
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3.7 Estimating Effect Size |
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71 | (3) |
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3.7.1 Additional Applications |
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71 | (2) |
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3.7.2 Using Confidence Intervals to Test Hypotheses |
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73 | (1) |
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74 | (2) |
4 Testing Hypotheses |
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76 | (20) |
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76 | (4) |
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4.1.1 Percentile Bootstrap |
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76 | (1) |
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4.1.2 Parametric Bootstrap |
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77 | (1) |
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78 | (2) |
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4.2 Comparing Two Samples |
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80 | (7) |
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4.2.1 Comparing Two Poisson Distributions |
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80 | (1) |
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4.2.2 What Should We Measure? |
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80 | (1) |
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4.2.3 Permutation Monte Carlo |
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81 | (2) |
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4.2.4 One- versus Two-Sided Tests |
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83 | (1) |
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4.2.5 Bias-Corrected Nonparametric Bootstrap |
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83 | (3) |
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86 | (1) |
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4.3 Which Test Should We Us? |
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87 | (7) |
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4.3.1 p-Values and Significance Levels |
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87 | (1) |
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88 | (1) |
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89 | (1) |
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4.3.4 Power of a Test Procedure |
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90 | (2) |
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4.3.5 Testing for Correlation |
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92 | (2) |
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94 | (2) |
5 Designing an Experiment or Survey |
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96 | (33) |
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97 | (3) |
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5.1.1 Crafting an Experiment |
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98 | (2) |
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5.2 Designing an Experiment or Survey |
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100 | (12) |
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100 | (1) |
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5.2.2 Sample from the Right Population |
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101 | (2) |
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5.2.3 Coping with Variation |
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103 | (1) |
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104 | (1) |
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5.2.5 The Experimental Unit |
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105 | (1) |
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5.2.6 Formulate Your Hypotheses |
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106 | (1) |
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5.2.7 What Are You Going to Measure |
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107 | (1) |
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5.2.8 Random Representative Samples |
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108 | (1) |
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5.2.9 Treatment Allocation |
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109 | (1) |
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5.2.10 Choosing a Random Sample |
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110 | (1) |
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5.2.11 Ensuring Your Observations Are Independent |
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111 | (1) |
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112 | (14) |
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5.3.1 Samples of Fixed Size |
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113 | (7) |
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114 | (3) |
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117 | (2) |
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119 | (1) |
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5.3.2 Sequential Sampling |
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120 | (10) |
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Stein's Two-Stage Sampling Procedure |
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120 | (1) |
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121 | (5) |
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126 | (1) |
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126 | (1) |
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126 | (3) |
6 Analyzing Complex Experiments |
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129 | (26) |
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6.1 Changes Measured in Percentages |
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129 | (1) |
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6.2 Comparing More Than Two Samples |
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130 | (7) |
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6.2.1 Programming the Multisample Comparison in R |
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131 | (2) |
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6.2.2 Reusing Your R Functions |
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133 | (1) |
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6.2.3 What Is the Alternative? |
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133 | (1) |
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6.2.4 Testing for a Dose Response or Other Ordered Alternative |
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134 | (3) |
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137 | (2) |
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139 | (6) |
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6.4.1 One-Sided Fisher's Exact Test |
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142 | (1) |
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143 | (1) |
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144 | (1) |
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6.5 Multivariate Analysis |
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145 | (9) |
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6.5.1 Manipulating Multivariate Data in R |
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146 | (1) |
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6.5.2 Pesarin-Fisher Omnibus Statistic |
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147 | (2) |
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6.5.3 Programming Guidelines |
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149 | (5) |
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154 | (1) |
7 Developing Models |
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155 | (37) |
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155 | (3) |
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156 | (2) |
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158 | (1) |
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158 | (3) |
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160 | (1) |
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7.3 Fitting a Regression Equation |
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161 | (8) |
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7.3.1 Ordinary Least Squares |
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161 | (4) |
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165 | (1) |
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7.3.2 Least Absolute Deviation Regression |
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165 | (1) |
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7.3.3 Errors-in-Variables Regression |
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166 | (2) |
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168 | (1) |
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7.4 Problems with Regression |
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169 | (12) |
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7.4.1 Goodness of Fit Versus Prediction |
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170 | (1) |
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170 | (2) |
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Measures of Predictive Success |
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172 | (1) |
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7.4.3 Multivariable Regression |
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172 | (9) |
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181 | (2) |
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183 | (3) |
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7.6.1 Independent Verification |
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184 | (1) |
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7.6.2 Splitting the Sample |
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184 | (2) |
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7.6.3 Cross-validation with the Bootstrap |
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186 | (1) |
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7.7 Classification and Regression Trees (CART) |
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186 | (4) |
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190 | (2) |
8 Reporting Your Findings |
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192 | (16) |
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193 | (3) |
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8.2 Text, Table, or Graph? |
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196 | (1) |
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8.3 Summarizing Your Results |
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197 | (5) |
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8.3.1 Center of the Distribution |
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199 | (2) |
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201 | (1) |
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8.4 Reporting Analysis Results |
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202 | (3) |
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8.4.1 p-Values or Confidence Intervals? |
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203 | (2) |
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8.5 Exceptions Are the Real Story |
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205 | (2) |
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205 | (1) |
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205 | (1) |
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206 | (1) |
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8.5.4 Recognize and Report Biases |
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206 | (1) |
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207 | (1) |
9 Problem Solving |
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208 | (10) |
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208 | (5) |
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9.2 Solving Practical Problems |
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213 | (5) |
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9.2.1 The Data's Provenance |
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213 | (1) |
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213 | (1) |
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9.2.3 Validate the Data Collection Methods |
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214 | (1) |
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9.2.4 Formulate Hypotheses |
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215 | (1) |
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9.2.5 Choosing a Statistical Methodology |
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215 | (1) |
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9.2.6 Be Aware of What You Don't Know |
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216 | (1) |
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9.2.7 Qualify Your Conclusions |
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216 | (2) |
Appendix: S-PLUS |
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218 | (7) |
Index to R Commands |
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225 | (2) |
Index |
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227 | |