| Preface |
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ix | |
| Acknowledgments |
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xi | |
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1 | (16) |
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1 | (1) |
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What is design of experiments (DOE)? |
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2 | (1) |
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Why design of experiments or statistically designed experiments? |
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3 | (1) |
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Three approaches to design of experiments---classical, Taguchi and Shainin |
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4 | (3) |
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Classical design of experiments |
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4 | (1) |
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5 | (1) |
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6 | (1) |
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Benefits of Taguchi DOE in manufacturing |
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7 | (6) |
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Problems and gaps in the state of the art |
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13 | (4) |
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15 | (1) |
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15 | (2) |
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The Taguchi approach to quality improvement |
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17 | (30) |
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Taguchi's definition of quality |
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17 | (1) |
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18 | (2) |
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20 | (3) |
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20 | (1) |
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The standard deviation, ``SD'' |
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21 | (1) |
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The mean deviation, ``MD'' |
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22 | (1) |
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Variation and its influence on quality |
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23 | (1) |
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Traditional and Taguchi's approach to quality loss functions |
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24 | (5) |
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Determination of manufacturing tolerances |
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29 | (2) |
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31 | (4) |
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Smaller-the-better quality characteristics |
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31 | (2) |
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Larger-the-better quality characteristics |
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33 | (2) |
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An example of Taguchi's loss function analysis |
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35 | (2) |
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Taguchi's seven points of achieving quality |
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37 | (2) |
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Taguchi's quality engineering system |
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39 | (8) |
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On-line quality control system |
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40 | (2) |
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Off-line quality control system |
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42 | (3) |
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45 | (1) |
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45 | (2) |
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The Taguchi approach to industrial experimentation |
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47 | (26) |
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Traditional approach to experimentation |
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47 | (4) |
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What are orthogonal arrays? |
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51 | (2) |
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The role of orthogonal arrays |
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53 | (3) |
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56 | (7) |
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63 | (3) |
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Randomization in industrial designed experiments |
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66 | (1) |
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Selecting a standard OA for two-level factors |
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67 | (6) |
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70 | (1) |
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71 | (2) |
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Assignment of factor and interaction effects to an OA |
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73 | (10) |
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73 | (1) |
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How to assign factor effects to an OA |
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74 | (9) |
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79 | (2) |
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81 | (2) |
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Classification of factors and choice of quality characteristics |
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83 | (20) |
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Classification of factors in Taguchi's experimental design methodology |
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83 | (6) |
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83 | (3) |
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86 | (2) |
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88 | (1) |
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The role and contribution of noise factors in industrial experiments |
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89 | (1) |
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Design for robustness---the key to improve product and process quality |
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90 | (1) |
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Treating noise factors incorrectly |
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91 | (1) |
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Taguchi's product array approach to experimentation |
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92 | (1) |
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Choice of quality characteristics for industrial experiments |
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93 | (10) |
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Examples of quality characteristics or responses |
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95 | (1) |
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Multiple quality characteristics or responses |
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96 | (4) |
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Quality characteristics for industrial experiments |
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100 | (1) |
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100 | (1) |
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100 | (3) |
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A strategic methodology for Taguchi design of experiments |
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103 | (14) |
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103 | (1) |
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104 | (11) |
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115 | (2) |
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115 | (1) |
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116 | (1) |
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117 | (18) |
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117 | (1) |
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Tools for the development of the problem classification framework |
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118 | (3) |
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Understanding and analysing the process |
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119 | (1) |
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Identification and investigation of the problem |
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119 | (1) |
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Prioritization of problem causes |
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120 | (1) |
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121 | (1) |
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Problem classification framework |
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121 | (2) |
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Generic problem source (GPS) |
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123 | (3) |
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Problem selection framework (PSF) |
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126 | (6) |
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132 | (3) |
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132 | (1) |
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133 | (2) |
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Metrology considerations for industrial experimentation |
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135 | (16) |
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135 | (1) |
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136 | (1) |
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Direct method of measurement |
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136 | (1) |
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Indirect method of measurement |
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136 | (1) |
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Comparison method of measurement |
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136 | (1) |
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Types of errors in measurements |
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136 | (1) |
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137 | (1) |
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137 | (1) |
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137 | (1) |
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Properties of a good measurement system |
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138 | (1) |
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The role of measurements in industrial experiments |
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139 | (1) |
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Gauge repeatability and reproducibility |
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140 | (1) |
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Planning gauge R&R studies |
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141 | (7) |
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Procedure for conducting a gauge R&R study |
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142 | (1) |
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Statistical control charts for analysing the measurement process variation |
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142 | (3) |
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Analysis of results from R&R studies |
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145 | (3) |
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Sampling variation in measurement system analysis |
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148 | (1) |
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Environmental considerations for measurements |
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149 | (2) |
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150 | (1) |
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150 | (1) |
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Analysis and interpretations of data from Taguchi experiments |
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151 | (44) |
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151 | (1) |
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Main and interaction effects |
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152 | (10) |
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Determination of the statistical significance of the main and interaction effects |
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162 | (10) |
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Signal-to-Noise ratio (SNR) |
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172 | (2) |
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Relationship between the SNR and quality loss function (QLF) |
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174 | (1) |
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When and how to use the SNR analysis |
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175 | (3) |
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ANOVA for the signal-to-noise ratio |
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178 | (2) |
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Determination of optimal process parameter settings |
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180 | (2) |
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Estimation of the response at the optimal condition |
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182 | (2) |
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Confidence interval for the estimated value |
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184 | (2) |
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Confirmation run or experiment |
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186 | (1) |
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187 | (1) |
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188 | (7) |
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189 | (4) |
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193 | (2) |
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195 | (30) |
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195 | (1) |
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196 | (29) |
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Optimization of the life of a critical component in a hydraulic valve |
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196 | (8) |
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Optimization of welding on cast iron using Taguchi methods |
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204 | (7) |
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Reducing variability in transformer inductance through Taguchi methods |
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211 | (7) |
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Optimization of machine performance using Taguchi methods |
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218 | (7) |
| Appendices |
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225 | (14) |
| Glossary |
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239 | (8) |
| Index |
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247 | |