Preface |
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xiii | |
Acknowledgments |
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xv | |
Introduction |
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1 | (2) |
An Overview of SigmaXL |
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3 | (1) |
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3 | (2) |
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5 | (1) |
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6 | (3) |
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9 | (34) |
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9 | (6) |
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10 | (3) |
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Program evaluation and review technique (PERT) |
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13 | (2) |
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15 | (2) |
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16 | (1) |
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16 | (1) |
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16 | (1) |
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17 | (1) |
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17 | (1) |
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17 | (1) |
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17 | (1) |
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17 | (1) |
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17 | (1) |
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17 | (1) |
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Capturing the Voice of the Customer |
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17 | (5) |
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Capturing the voice of the external customer |
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19 | (2) |
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Capturing the voice of the internal customer |
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21 | (1) |
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Capturing the voice of the customers of a project |
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21 | (1) |
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Capturing the voice of the next step in the process |
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21 | (1) |
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22 | (4) |
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24 | (2) |
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Suppliers-Input-Process-Output-Customers (SIPOC) |
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26 | (3) |
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29 | (5) |
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Assessing the cost of quality |
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29 | (1) |
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30 | (1) |
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30 | (1) |
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31 | (1) |
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31 | (1) |
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31 | (1) |
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31 | (3) |
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34 | (2) |
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Cost of Quality According to Taguchi |
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36 | (2) |
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38 | (5) |
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Force field analysis (FFA) |
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39 | (4) |
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43 | (100) |
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43 | (1) |
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43 | (1) |
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44 | (1) |
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44 | (1) |
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44 | (1) |
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44 | (22) |
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44 | (1) |
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Discrete versus continuous distributions |
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44 | (1) |
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Expected value, variance, and standard deviation of discrete distribution |
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45 | (2) |
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Discrete probability distributions |
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47 | (9) |
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Approximating binomial problems by Poisson distribution |
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56 | (1) |
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56 | (2) |
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58 | (8) |
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66 | (1) |
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Random Sampling versus Nonrandom Sampling |
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67 | (2) |
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67 | (1) |
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68 | (1) |
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68 | (1) |
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68 | (1) |
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69 | (13) |
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Sampling distribution of the mean X |
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70 | (1) |
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Estimating the population mean with large sample sizes |
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71 | (3) |
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Estimating the population mean with small sample sizes and σ unknown t-distribution |
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74 | (3) |
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77 | (3) |
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80 | (1) |
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Sample size when estimating the mean |
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81 | (1) |
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Measurement Systems Analysis |
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82 | (4) |
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86 | (14) |
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Measurement errors due to precision |
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86 | (7) |
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Variations due to accuracy |
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93 | (1) |
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94 | (2) |
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96 | (4) |
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100 | (3) |
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Assessing a Processes Ability to Meet Customers' Expectations---Process Capability Analysis |
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103 | (3) |
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Process Capabilities with Normal Data |
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106 | (1) |
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106 | (1) |
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106 | (1) |
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107 | (1) |
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107 | (14) |
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Short-term potential capabilities, Cp and Cr |
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107 | (2) |
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Long-term potential performance |
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109 | (1) |
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109 | (2) |
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Capability indices and parts per million |
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111 | (1) |
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Process capability and Z transformation |
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111 | (4) |
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115 | (1) |
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Taguchi's capability indices CPM and PPM |
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116 | (5) |
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Process Capability Analysis with Nonnormal Data |
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121 | (1) |
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Normality Assumption and Box-Cox Transformation |
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122 | (1) |
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Process Capability Using Box-Cox Transformation |
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123 | (5) |
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Process Capability Using Nonnormal Distribution |
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128 | (2) |
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130 | (11) |
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131 | (2) |
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133 | (1) |
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134 | (7) |
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141 | (2) |
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143 | (70) |
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143 | (1) |
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143 | (1) |
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144 | (2) |
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Cause-and-Effect Analysis |
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146 | (3) |
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149 | (3) |
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152 | (1) |
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152 | (2) |
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154 | (5) |
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156 | (1) |
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157 | (1) |
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158 | (1) |
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158 | (1) |
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Unnecessary transportation |
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158 | (1) |
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159 | (1) |
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159 | (1) |
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Lean Approach to Waste Reduction |
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159 | (2) |
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161 | (4) |
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162 | (2) |
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Batch versus one-piece flow |
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164 | (1) |
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Data Gathering and Process Improvement |
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165 | (3) |
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166 | (1) |
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How to map your value stream |
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167 | (1) |
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Failure Mode and Effect Analysis |
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168 | (14) |
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170 | (1) |
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171 | (3) |
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174 | (3) |
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177 | (5) |
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Nonparametric Hypothesis Testing |
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182 | (16) |
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182 | (3) |
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Contingency analysis---Chi square test of independence |
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185 | (2) |
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187 | (8) |
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195 | (1) |
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196 | (2) |
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198 | (6) |
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200 | (4) |
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204 | (9) |
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Simple linear regression (or first-order linear model) |
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206 | (4) |
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Multiple regression analysis |
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210 | (3) |
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213 | (70) |
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213 | (2) |
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215 | (19) |
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Main effect and interaction effect |
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216 | (1) |
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217 | (1) |
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2 2 Two factors and two levels |
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218 | (4) |
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222 | (1) |
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222 | (3) |
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225 | (1) |
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225 | (9) |
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234 | (34) |
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236 | (1) |
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236 | (1) |
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2k Two levels with more than 2 factors |
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237 | (2) |
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Main effects for 2 3---two levels with three factors |
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239 | (13) |
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252 | (2) |
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254 | (2) |
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2k-1 Fractional factorial design |
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256 | (2) |
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23-1 Fractional factorial design |
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258 | (2) |
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260 | (1) |
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260 | (8) |
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The Theory of Constraints |
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268 | (5) |
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The process throughput is tied to the bottleneck |
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271 | (2) |
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273 | (1) |
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273 | (1) |
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274 | (1) |
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The Goldratt Reality Trees |
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275 | (8) |
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276 | (2) |
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278 | (2) |
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280 | (3) |
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283 | (50) |
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Statistical Process Control (SPC) |
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283 | (12) |
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Variation Is the Root Cause of Defects |
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284 | (1) |
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Assignable (or special) causes of variation |
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285 | (1) |
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Common (or chance) causes of variation |
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286 | (1) |
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How to build a control chart |
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287 | (1) |
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288 | (1) |
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Probability for misinterpreting control charts |
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289 | (1) |
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289 | (2) |
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291 | (2) |
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How to determine if the process is out of control---WECO rules |
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293 | (2) |
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Categories of Control Charts |
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295 | (14) |
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295 | (1) |
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The mean and range charts---X and R charts |
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296 | (1) |
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Calculating the sample statistics to be plotted |
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296 | (1) |
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Calculating the center line and control limits |
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297 | (1) |
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Control limits for X chart |
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298 | (1) |
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Standard-error-based X chart |
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298 | (1) |
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Mean-range-based X control charts |
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299 | (3) |
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Control limits for R chart |
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302 | (3) |
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The mean and standard deviation charts X and s charts |
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305 | (4) |
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Individual Values Control Charts |
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309 | (1) |
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Individual Moving Range Charts |
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310 | (1) |
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311 | (2) |
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Monitoring Shifts in the Process Mean |
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313 | (4) |
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314 | (3) |
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317 | (2) |
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Exponentially Weighted Moving Average |
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319 | (3) |
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322 | (11) |
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324 | (3) |
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327 | (2) |
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329 | (1) |
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330 | (3) |
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333 | (14) |
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334 | (5) |
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339 | (4) |
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343 | (1) |
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344 | (1) |
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345 | (1) |
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346 | (1) |
Index |
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347 | |