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1 | (12) |
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1.1 What Is Experimental Design? |
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1 | (1) |
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1.2 Applications of Experimental Design |
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2 | (1) |
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1.3 Old Philosophy of Quality |
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3 | (1) |
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1.4 New Philosophy of Quality |
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4 | (2) |
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6 | (1) |
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1.6 Experimentation Steps |
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7 | (2) |
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1.7 Goals and Outline of the Design of Experiments Concepts |
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9 | (1) |
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9 | (1) |
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10 | (3) |
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2. Designing and Conducting the Experiment |
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13 | (42) |
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13 | (1) |
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2.2 One-Factor-at-a-Time Approach |
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13 | (3) |
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2.3 Two-Level Factorial Designs |
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16 | (36) |
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A. Two-Level Full Factorial Designs |
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17 | (8) |
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B. Fractional Factorial Designs of Resolution III |
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25 | (6) |
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C. Plackett-Burman (PB) Designs |
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31 | (5) |
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D. Fractional Factorial Designs of Resolution IV |
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36 | (8) |
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E. Fractional Factorial Designs of Resolution V |
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44 | (8) |
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52 | (1) |
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52 | (3) |
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3. Optimization of the Location Parameter |
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55 | (26) |
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55 | (1) |
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3.2 Guidelines For Location Optimization |
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56 | (1) |
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3.3 Replicated Experimental Runs |
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57 | (10) |
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A. Maximizing the Location Parameter |
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57 | (4) |
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61 | (1) |
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62 | (5) |
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3.4 An Alternative Approach to the Pareto Chart |
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67 | (11) |
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78 | (2) |
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80 | (1) |
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4. Minimization of the Dispersion |
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81 | (16) |
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81 | (2) |
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4.2 Dispersion Minimization for Replicated Study |
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83 | (6) |
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4.3 Dispersion Minimization for Unreplicated Study |
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89 | (7) |
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96 | (1) |
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96 | (1) |
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5. Taguchi's Approach to the Design of Experiments |
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97 | (24) |
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97 | (1) |
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98 | (1) |
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99 | (2) |
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5.4 Signal-to-Noise Ratio |
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101 | (5) |
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101 | (2) |
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103 | (1) |
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104 | (2) |
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5.5 Applications of Taguchi's Approach to Robust Designs |
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106 | (13) |
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A. Analysis: Large-Is-the-Best |
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106 | (3) |
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B. Analysis: Small-Is-the-Best |
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109 | (4) |
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C. Analysis: Nominal-Is-the-Best |
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113 | (6) |
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5.6 Comments on the Taguchi Method |
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119 | (1) |
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119 | (1) |
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120 | (1) |
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6. Statistical Optimization of the Location Parameter |
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121 | (22) |
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121 | (2) |
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6.2 Replicated Two-Level Full Factorial Design |
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123 | (5) |
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6.3 Unreplicated Two-Level Full Factorial Design |
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128 | (5) |
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6.4 Two-Level Fractional Factorial Design |
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133 | (5) |
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138 | (3) |
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141 | (2) |
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7. Statistical Minimization of the Dispersion Parameter |
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143 | (12) |
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143 | (1) |
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143 | (7) |
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A. Analysis of Variance Techniques |
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143 | (6) |
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B. Normal Probability Plot of Effects |
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149 | (1) |
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150 | (4) |
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154 | (1) |
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154 | (1) |
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8. Validity of the Prediction Equation |
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155 | (16) |
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155 | (1) |
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155 | (8) |
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8.3 Adjusted Coefficient of Determination |
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163 | (1) |
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8.4 F Test for Lack of Fit |
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164 | (3) |
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8.5 Analysis Recommendation |
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167 | (2) |
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169 | (1) |
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169 | (2) |
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9. Three-Level Factorial Designs |
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171 | (22) |
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171 | (1) |
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9.2 Three-Level Full Factorial Design |
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171 | (3) |
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174 | (3) |
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9.4 Central Composite Designs |
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177 | (11) |
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A. Rotatable Central Composite Design |
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178 | (9) |
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B. Face Centered Central Composite Design |
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187 | (1) |
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9.5 Three-Level Taguchi Designs |
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188 | (2) |
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190 | (1) |
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191 | (2) |
10. Second-Order Analysis |
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193 | (22) |
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193 | (1) |
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10.2 Second-Order Model in Matrix Terms |
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193 | (2) |
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10.3 Estimation of the Second-Order Model Parameters |
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195 | (1) |
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10.4 Estimation of the First-Order Model Parameters |
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196 | (2) |
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10.5 Fitting a Second-Order Model |
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198 | (3) |
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10.6 Inferences about Regression Parameters |
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201 | (1) |
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10.7 Confidence Limits for Predicted Values |
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202 | (3) |
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10.8 Validity of the Prediction Equation |
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205 | (1) |
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10.9 Quadratic Optimization |
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205 | (7) |
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206 | (1) |
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206 | (6) |
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212 | (1) |
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213 | (2) |
Appendices |
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Appendix 1 Two-Level Fractional Factorial Designs |
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215 | (4) |
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Appendix 2 Plackett-Burman Designs |
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219 | (2) |
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Appendix 3 Taguchi Designs |
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221 | (4) |
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Appendix 4 Standardized Normal Distribution |
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225 | (2) |
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Appendix 5 Percentiles of the t Distribution |
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227 | (2) |
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Appendix 6 Percentiles of the F Distribution |
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229 | (10) |
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Appendix 7 Some Useful Box-Behnken Designs |
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239 | (2) |
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Appendix 8 Matrix Algebra |
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241 | (8) |
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241 | (2) |
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8.2 Matrix Addition and Subtraction |
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243 | (1) |
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8.3 Matrix Multiplication |
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243 | (1) |
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8.4 Special Types of Matrices |
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244 | (1) |
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245 | (4) |
Index |
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249 | |