Design and analysis of experiments

자료유형	단행본
서명/저자사항	Design and analysis of experiments / Douglas C. Montgomery.
개인저자	Montgomery, Douglas C.
판사항	2nd ed.
발행사항	New York : Wiley, c1984.
형태사항	xvi, 538 p. : ill. ; 24 cm.
ISBN	0471868124
일반주기	Includes index.
서지주기	Bibliography: p. 499-504.
일반주제명	Experimental design
언어	영어

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No.	등록번호	청구기호	소장처	도서상태	반납예정일	예약	매체정보
1	00020413886	001.434 84b	[신촌]도서관/인문자료실(중도2층)/	대출가능
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[영문] CONTENTS
1. Introduction = 1
1-1 The Role of Experimental Design = 1
1-2 Basic Principles = 2
1-3 Historical Perspective = 5
1-4 How to Use Statistical Techniques in Experimentation = 6
2. Simple Comparative Experiments = 9
2-1 Introduction = 9
2-2 Basic Statistical Concepts = 10
2-3 Sampling and Sampling Distributions = 13
2-4 Inferences About the Differences in Means, Randomized Designs = 21
  2-4.1 Hypothesis Testing = 21
  2-4.2 Choice of Sample Size = 24
  2-4.3 Confidence Intervals = 26
  2-4.4 The Case Where = 28
  2-4.5 The Case Where Are Known = 29
  2-4.6 Comparing a Single Mean to a Specified Value = 29
  2-4.7 Summary = 32
2-5 Inferences About the Difference in Means, Paired Comparison Designs = 32
  2-5.1 The Paired Comparison Problem = 32
  2-5.2 Advantages of the Paired Comparison Design = 35
2-6 Inferences About Variances of Normal Distributions = 36
2-7 Problems = 39
3. Experiments to Compare Several Treatments: The Analysis of Variance = 43
3-1 Introduction = 43
3-2 The One-Way Classification Analysis of Variance = 44
3-3 Analysis of the Fixed Effects Model = 45
  3-3.1 Decomposition of the Total Sum of Squares = 46
  3-3.2 Statistical Analysis = 49
  3-3.3 Estimation of the Model Parameters = 55
  3-3.4 Model Adequacy Checking: Preview = 57
  3-3.5 The Unbalanced Case = 58
3-4 Comparison of Individual Treatment Means = 59
  3-4.1 Orthogonal Contrasts = 60
  3-4.2 Scheffes Method for Comparing All Contrasts = 62
  3-4.3 Comparing Pairs of Treatment Means = 64
  3-4.4 Comparing Treatments with a Control = 70
3-5 The Random Effects Model = 71
3-6 Sample Computer Output = 78
3-7 Problems = 80
4. More About the One-Way Model = 85
4-1 Model Adequacy Checking = 85
  4-1.1 The Normality Assumption = 86
  4-1.2 Plot of Residuals in Time Sequence = 89
  4-1.3 Plot of Residuals Versus Fitted Values = 90
  4-1.4 Selecting a Variance Stabilizing Transformation = 93
  4-1.5 Plot of Residuals Versus Other Variables = 98
4-2 Choice of Sample Size = 99
  4-2.1 Operating Characteristic Curves = 99
  4-2.2 Specifying a Standard Deviation Increase = 103
  4-2.3 Confidence Interval Estimation Method = 104
4-3 Fitting Response Curves in the One-Way Model = 105
  4-3.1 General Regression Approach = 105
  4-3.2 Orthogonal Polynomials = 107
4-4 The Regression Approach to Analysis of Variance = 110
4-5 Nonparametric Methods in the Analysis of Variance = 116
  4-5.1 The Kruskal-Wallis Test = 116
  4-5.2 General Comments on the Rank Transformation = 117
4-6 Repeated Measures = 118
4-7 Problems = 121
5. Randomized Blocks, Latin Squares, and Related Designs = 123
5-1 The Randomized Complete Block Design = 123
  5-1.1 Statistical Analysis = 124
  5-1.2 Model Adequacy Checking = 136
  5-1.3 Estimating Missing Values = 139
  5-1.4 Estimating Model Parameters and the General Regression Significance Test = 141
  5-1.5 Sample Computer Output = 144
5-2 The Latin Square Design = 146
5-3 The Graeco-Latin Square Design = 156
5-4 Problems = 160
6. Incomplete Block Designs = 165
6-1 Introduction = 165
6-2 Balanced Incomplete Block Designs = 165
  6-2.1 Statistical Analysis = 166
  6-2.2 Least Squares Estimation of the Parameters = 173
6-3 Recovery of Interblock Information in the Balanced Incomplete Block Design = 174
6-4 Partially Balanced Incomplete Block Designs = 177
6-5 Youden Squares = 180
6-6 Lattice Designs = 183
6-7 Problems = 184
7. Introduction to Factorial Designs = 189
7-1 Basic Definitions and Principles = 189
7-2 The Advantage of Factorials = 192
7-3 The Two-Factor Factorial Design = 192
  7-3.1 Statistical Analysis of the Fixed Effects Model = 194
  7-3.2 Model Adequacy Checking = 201
  7-3.3 Estimating the Model Parameters = 206
  7-3.4 Choice of Sample Size = 208
  7-3.5 The Assumption of No Interaction in a Two-Factor Model = 210
  7-3.6 One Observation per Cell = 211
7-4 Random and Mixed Models = 215
  7-4.1 The Random Effects Model = 215
  7-4.2 Mixed Models = 218
  7-4.3 Choice of Sample Size = 223
7-5 The General Factorial Design = 223
7-6 Fitting Response Curves and Surfaces = 229
7-7 Dealing with Unbalanced Data = 236
  7-7.1 Proportional Data: An Easy Case = 237
  7-7.2 Approximate Methods = 238
  7-7.3 The Exact Method = 240
7-8 Problems = 241
8. Rules for Sums of Squares and Expected Mean Squares = 247
8-1 Rules for Sums of Squares = 247
8-2 Rules for Expected Mean Squares = 250
8-3 Approximate FTests = 254
8-4 Problems = 259
9. <TEX>$$2^k$$</TEX> and <TEX>$$3^k$$</TEX> Factorial Designs = 261
9-1 Introduction = 261
9-2 Analysis of the <TEX>$$2^k$$</TEX> Factorial Design = 261
  9-2.1 The <TEX>$$2^2$$</TEX> Design = 262
  9-2.2 The <TEX>$$2^3$$</TEX> Design = 266
  9-2.3 The General <TEX>$$2^k$$</TEX> Design = 271
  9-2.4 A Single Replicate of the <TEX>$$2^k$$</TEX> Design = 273
  9-2.5 Yates' Algorithm for the <TEX>$$2^k$$</TEX> Design = 280
9-3 Analysis of the <TEX>$$3^k$$</TEX> Factorial Design = 281
  9-3.1 Notation for the <TEX>$$3^k$$</TEX> Series = 281
  9-3.2 The <TEX>$$3^2$$</TEX> Design = 281
  9-3.3 The <TEX>$$3^3$$</TEX> Design = 284
  9-3.4 The General <TEX>$$3^k$$</TEX> Design = 289
  9-3.5 Yates' Algorithm for the <TEX>$$3^k$$</TEX> Design = 290
9-4 Problems = 292
10. Confounding = 299
10-1 Introduction = 299
10-2 Confounding in the <TEX>$$2^k$$</TEX> Factorial Design = 299
  10-2.1 The <TEX>$$2^k$$</TEX> Factorial Design in Two Blocks = 300
  10-2.2 The <TEX>$$2^k$$</TEX> Factorial Design in Four Blocks = 306
  10-2.3 The <TEX>$$2^k$$</TEX> Factorial Design in <TEX>$$2^p$$</TEX> Blocks = 308
10-3 Confounding in the <TEX>$$3^k$$</TEX> Factorial Design = 311
  10-3.1 The <TEX>$$3^k$$</TEX> Factorial Design in Three Blocks = 311
  10-3.2 The <TEX>$$3^k$$</TEX> Factorial Design in Nine Blocks = 315
  10-3.3 The <TEX>$$3^k$$</TEX> Factorial Design in <TEX>$$3^p$$</TEX> Blocks = 317
10-4 Partial Confounding = 317
10-5 Other Confounding Systems = 321
10-6 Problems = 323
11. Fractional Factorial Designs = 325
11-1 Introduction = 325
11-2 Fractional Replication of the <TEX>$$2^k$$</TEX> Factorial Design = 325
  11-2.1 The One-Half Fraction of the <TEX>$$2^k$$</TEX> Design = 326
  11-2.2 The One-Quarter Fraction of the <TEX>$$2^k$$</TEX> Design = 335
  11-2.3 The General <TEX>$$2^{k-p}$$</TEX> Fractional Factorial Design = 337
  11-2.4 Designs of Resolution Ⅲ = 340
  11-2.5 Designs of Resolution Ⅳ and Ⅴ = 347
11-3 Fractional Replication of the <TEX>$$3^k$$</TEX> Factorial Design = 349
  11-3.1 The One-Third Fraction of the <TEX>$$3^k$$</TEX> Design = 349
  11-3.2 Other <TEX>$$3^{k-p}$$</TEX> Fractional Factorial Designs = 352
11-4 Problems = 354
12. Nested or Hierarchial Designs = 357
12-1 Introduction = 357
12-2 The Two-Stage Nested Design = 358
  12-2.1 Statistical Analysis = 358
  12-2.2 Estimation of the Model Parameters = 363
  12-2.3 Diagnostic Checking = 366
12-3 The General m-Stage Nested Design = 368
12-4 Designs with Nested and Crossed Factors = 370
12-5 Problems = 374
13. Multifactor Experiments with Randomization Restrictions = 379
13-1 Randomized Blocks and Latin Squares as Multifactor Designs = 379
13-2 The Split-Plot Design = 386
13-3 The Split-Split Plot Design = 391
13-4 Problems = 395
14. Regression Analysis = 399
14-1 Introduction = 399
14-2 Simple Linear Regression = 400
14-3 Hypothesis Testing in Simple Linear Regression = 407
14-4 Interval Estimation in Simple Linear Regression = 410
14-5 Model Adequacy Checking = 414
  14-5.1 Residual Analysis = 414
  14-5.2 The Lack-of-Fit Test = 416
  14-5.3 The Coefficient of Determination = 419
14-6 Multiple Linear Regression = 420
14-7 Hypothesis Testing in Multiple Linear Regression = 429
14-8 Other Linear Regression Models = 435
14-9 Sample Computer Printout = 438
14-10 Problems = 440
15. Response Surface Methodology = 445
15-1 Introduction = 445
15-2 The Method of Steepest Ascent = 447
15-3 Analysis of Quadratic Models = 453
15-4 Response Surface Designs = 460
  15-4.1 Designs for Fitting the First-Order Model = 460
  15-4.2 Designs for Fitting the Second-Order Model = 462
15-5 Evolutionary Operation = 463
15-6 Problems = 470
16. Analysis of Covariance = 475
16-1 Introduction = 475
16-2 One-Way Classification with a Single Covariate = 476
16-3 Development by the General Regression Significance Test = 490
16-4 Other Covariance Models = 493
16-5 Problems = 495
Bibliography = 499
Appendix = 505
Table Ⅰ. Cumulative Standard Normal Distribution = 506
Table Ⅱ. Percentage Points of the t Distribution = 508
Table Ⅲ. Percentage Points of the <TEX>$$X^2$$</TEX> Distribution = 509
Table Ⅳ. Percentage Points of the F Distribution = 510
Table Ⅴ. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance = 515
Table Ⅵ. Operating Characteristic Curves for the Random Effects Model Analysis of Variance = 519
Table Ⅶ. Significant Ranges for Duncan's Multiple Range Test = 523
Table Ⅷ. Percentage Points of the Studentized Range Statistic = 525
Table Ⅸ. Critical Values for Dunnett's Test for Comparing Treatments with a Control = 527
Table Ⅹ. Coefficients of Orthogonal Polynomials = 531
Table XI. Random Numbers = 532
Index = 535

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