Preface |
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1 | (12) |
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1.1 Why Study Statistics? |
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1 | (1) |
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2 | (1) |
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1.3 Statistics and Engineering |
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3 | (1) |
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1.4 The Role of the Scientist and Engineer in Quality Improvement |
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3 | (1) |
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1.5 A Case Study: Visually Inspecting Data to Improve Product Quality |
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4 | (2) |
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1.6 Two Basic Concepts-Population and Sample |
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6 | (7) |
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10 | (1) |
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10 | (3) |
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13 | (40) |
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2.1 Pareto Diagrams and Dot Diagrams |
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13 | (3) |
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2.2 Frequency Distributions |
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16 | (3) |
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2.3 Graphs of Frequency Distributions |
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19 | (3) |
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2.4 Stem-and-leaf Displays |
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22 | (6) |
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28 | (5) |
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2.6 Quartiles and Percentiles |
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33 | (5) |
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2.7 The Calculation of -x and s |
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38 | (7) |
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2.8 A Case Study: Problems with Aggregating Data |
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45 | (8) |
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48 | (1) |
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48 | (5) |
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53 | (48) |
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3.1 Sample Spaces and Events |
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53 | (5) |
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58 | (7) |
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65 | (2) |
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3.4 The Axioms of Probability |
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67 | (3) |
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3.5 Some Elementary Theorems |
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70 | (8) |
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3.6 Conditional Probability |
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78 | (7) |
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85 | (6) |
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3.8 Mathematical Expectation and Decision Making |
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91 | (10) |
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96 | (1) |
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97 | (4) |
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4 Probability Distributions |
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101 | (45) |
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101 | (3) |
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4.2 The Binomial Distribution |
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104 | (6) |
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4.3 The Hypergeometric Distribution |
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110 | (5) |
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4.4 The Mean and the Variance of a Probability Distribution |
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115 | (7) |
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122 | (5) |
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4.6 The Poisson Approximation to the Binomial Distribution |
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127 | (3) |
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130 | (3) |
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4.8 The Geometric Distribution |
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133 | (3) |
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4.9 The Multinomial Distribution |
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136 | (2) |
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138 | (8) |
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141 | (1) |
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142 | (4) |
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146 | (57) |
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5.1 Continuous Random Variables |
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146 | (7) |
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5.2 The Normal Distribution |
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153 | (7) |
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5.3 The Normal Approximation to the Binomial Distribution |
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160 | (5) |
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5.4 Other Probability Densities |
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165 | (1) |
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5.5 The Uniform Distribution |
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165 | (1) |
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5.6 The Log-Normal Distribution |
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166 | (3) |
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5.7 The Gamma Distribution |
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169 | (2) |
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5.8 The Beta Distribution |
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171 | (2) |
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5.9 The Weibull Distribution |
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173 | (3) |
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5.10 Joint Distributions-Discrete and Continuous |
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176 | (14) |
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5.11 Checking if the Data Are Normal |
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190 | (3) |
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5.12 Transforming Observations to Near Normality |
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193 | (2) |
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195 | (4) |
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199 | (1) |
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200 | (3) |
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203 | (23) |
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6.1 Populations and Samples |
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203 | (4) |
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6.2 The Sampling Distribution of the Mean (σ known) |
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207 | (9) |
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6.3 The Sampling Distribution of the Mean (σ unknown) |
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216 | (2) |
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6.4 The Sampling Distribution of the Variance |
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218 | (8) |
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222 | (1) |
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223 | (3) |
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7 Inferences Concerning Means |
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226 | (55) |
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226 | (5) |
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231 | (7) |
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238 | (2) |
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7.4 Null Hypotheses and Tests of Hypotheses |
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240 | (6) |
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7.5 Hypotheses Concerning One Mean |
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246 | (5) |
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7.6 The Relation Between Tests and Confidence Intervals |
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251 | (1) |
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7.7 Operating Characteristic Curves |
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252 | (8) |
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7.8 Inference Concerning Two Means |
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260 | (12) |
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7.9 Design Issues-Randomization and Pairing |
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272 | (9) |
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274 | (2) |
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276 | (5) |
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8 Inferences Concerning Variances |
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281 | (11) |
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8.1 The Estimation of Variances |
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281 | (3) |
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8.2 Hypotheses Concerning One Variance |
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284 | (2) |
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8.3 Hypotheses Concerning Two Variances |
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286 | (6) |
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290 | (1) |
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290 | (2) |
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9 Inferences Concerning Proportions |
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292 | (28) |
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9.1 Estimation of Proportions |
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292 | (6) |
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9.2 Hypotheses Concerning One Proportion |
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298 | (2) |
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9.3 Hypotheses Concerning Several Proportions |
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300 | (8) |
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9.4 Analysis of r x c Tables |
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308 | (3) |
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311 | (9) |
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316 | (1) |
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317 | (3) |
10 Nonparametric Tests |
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320 | (17) |
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320 | (1) |
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321 | (1) |
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322 | (6) |
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328 | (3) |
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10.5 The Kolmogorov-Smirnov and Anderson-Darling Tests |
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331 | (13) |
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334 | (1) |
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335 | (2) |
11.1 The Method of Least Squares |
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337 | (60) |
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11.2 Inferences Based on the Least Squares Estimators |
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344 | (14) |
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11.3 Curvilinear Regression |
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358 | (5) |
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363 | (4) |
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11.5 Checking the Adequacy of the Model |
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367 | (7) |
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374 | (13) |
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11.7 Multiple Linear Regression (Matrix Notation) |
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387 | (10) |
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391 | (1) |
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392 | (5) |
12 Analysis of Variance |
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397 | (53) |
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12.1 Some General Principles |
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397 | (3) |
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12.2 Completely Randomized Designs |
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400 | (17) |
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12.3 Randomized-Block Designs |
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417 | (8) |
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12.4 Multiple Comparisons |
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425 | (6) |
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12.5 Some Further Experimental Designs |
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431 | (8) |
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12.6 Analysis of Covariance |
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439 | (11) |
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445 | (1) |
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445 | (5) |
13 Factorial Experimentation |
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450 | (60) |
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13.1 Two-Factor Experiments |
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450 | (9) |
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13.2 Multifactor Experiments |
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459 | (11) |
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13.3 2n Factorial Experiments |
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470 | (8) |
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13.4 The Graphic Presentation of 2² and 2³ Experiments |
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478 | (16) |
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13.5 Confounding in a 2n Factorial Experiment |
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494 | (4) |
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13.6 Fractional Replication |
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498 | (12) |
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504 | (1) |
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504 | (6) |
14 The Statistical Content of Quality-Improvement Programs |
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510 | (39) |
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14.1 Quality-Improvement Programs |
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510 | (3) |
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14.2 Starting a Quality-Improvement Program |
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513 | (2) |
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14.3 Experimental Designs for Quality |
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515 | (3) |
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518 | (2) |
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14.5 Control Charts for Measurements |
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520 | (6) |
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14.6 Control Charts for Attributes |
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526 | (6) |
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532 | (3) |
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535 | (14) |
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544 | (1) |
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545 | (4) |
15 Application to Reliability and Life Testing |
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549 | (24) |
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549 | (3) |
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15.2 Failure-Time Distribution |
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552 | (2) |
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15.3 The Exponential Model in Reliability |
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554 | (4) |
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15.4 The Exponential Model in Life Testing |
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558 | (4) |
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15.5 The Weibull Model in Life Testing |
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562 | (47) |
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568 | (1) |
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568 | (5) |
Appendix A Bibliography |
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573 | (2) |
Appendix B Statistical Tables |
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575 | (34) |
Appendix C Using the R Software Program |
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609 | (6) |
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609 | (1) |
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609 | (1) |
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610 | (1) |
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611 | (1) |
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Probability Distributions |
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611 | (1) |
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Normal Probability Calculations |
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611 | (1) |
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612 | (1) |
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Confidence Intervals and Tests of Means |
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612 | (1) |
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Inference About Proportions |
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612 | (1) |
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613 | (1) |
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One-way Analysis of Variance (ANOVA) |
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613 | (2) |
Appendix D Answers to Odd-Numbered Exercises |
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615 | (20) |
Index |
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635 | |