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Book
xxv, 840 pages ; 26 cm.
  • Principles and Techniques.- Planning Experiments.- Designs With One Source of Variation.- Inferences for Contrasts and Treatment Means.- Checking Model Assumptions.- Experiments With Two Crossed Treatment Factors.- Several Crossed Treatment Factors.- Polynomial Regression.- Analysis of Covariance.- Complete Block Designs.- Incomplete Block Designs.- Designs With Two Blocking Factors.- Confounded Two-Level Factorial Experiments.- Confounding in General Factorial Experiments.- Fractional Factorial Experiments.- Response Surface Methodology.- Random Effects and Variance Components.- Nested Models.- Split-Plot Designs.
  • (source: Nielsen Book Data)9783319522487 20170821
This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter. Experimental design is an essential part of investigation and discovery in science; this book will serve as a modern and comprehensive reference to the subject.
(source: Nielsen Book Data)9783319522487 20170821
Science Library (Li and Ma)
Book
1 online resource (xii, 270 pages) : illustrations (some color)
Book
xi, 219 p. : ill. ; 25 cm
  • Simplicity of Statistical Design and Control Making a Start How Does It Work? Care Management Case: Improving Health for Thousands of People Discovery Measurement Quality Care Management Statistical Design Baseline Data Managing the Test Test Results Exploratory Analysis What Might the Results Mean? Findings Are Often Surprising Significance of the Results Implementation Implementation Troubleshooting Designed Innovation Innovation Uses More Right Brain than Left Retailing Case: New Product Sales Discovery Measurement Quality Preparing for the Test Retail Furniture Statistical Design and Its Management Exploratory Analysis and Inference What Might the Results Mean? Statistical Significance Ironing Out Some Possible Wrinkles Predicting and Delivering the Improvement Retailing Designed Innovation Case: Conclusion Statistical Control Using Statistical Control Economic Advantage Derivation Practical Use of Statistical Control Digression into Causality Concluding Scientific Work in the Care Management Case False Alarm Rate Is Neither Known Nor Useful in Statistical Control Statistical Control Terminology Statistics Breaks Down in Unstable Processes Economic Loss without Statistical Control Cost Explosion Story Unexploded Tests for Statistical Control Statistical Control Integrated with Statistical Design Managing Statistical Control Schemes Mechanics of Statistical Control Where Did Statistical Control Originate? Measurement Error and Control All Measurement Systems Are Inherently Flawed Clinical Care Case: Initial Measurement Study and Long-Term Controls Establishing a Measurement Control Scheme Statistical Design Advantages of Large Statistical Design Two-Level Designs Full Factorial Designs Fractional Factorial Designs Backpacking Case Discovery Managing the Test Measurement Quality Exploratory Analysis What Might the Initial Results Mean? Exploring Interactions Simpler Analysis Statistical Significance Solving the Puzzle Aliasing Analysis of All Pair Interactions Measurement Problem Found and Fixed after the Test Using Sales Change as the Test's Measurement Calculating Precision and Sample Size Before the Test Diagnosing Unusually High or Low Results in a Statistical Design Row Guidance on Fractional Factorial Designs Multifactorial Designs Care Management Case: More Analytical Insight Randomization Milk Story Soil Story Geometric versus Nongeometric Designs Aliasing Scheme for the Care Management Design Augmenting Multifactorials to Also Estimate Pair Interactions Testing Strategy Uniqueness and Stumbling Around Where Did Statistical Design Originate? Statistical Design and Control: A Dozen Large-Scale Case Studies Selection of Cases Simultaneous Design Solving Complex Problems Simply Simultaneous Design Idea Science Education Case Discovery Baseline Data Simultaneous Statistical Designs for Science Classes Pair Interactions across Designs and an Easier Analysis Findings Rules for Simultaneous Designs General Multichannel Optimization Case Simultaneous Design Procedure Scientific Method, Randomization, and Improvement Strategies Simplicity of the Scientific Method Scientific Method with Statistical Design and Control Randomization Distribution Randomization Device Proof Isn't in the Pudding What Science Lies beneath Implementation Being the Hardest Part? Common Improvement Strategies Randomized Control Trials (RCT) Statistical Design and Control Are for Real Problems with Everyone Contributing Managing Improvement and Innovation Organization Speed without Net Resources How to Manage Specific Improvements/Innovations Statistical Design and Control Summary Appendix: Answers to Exercises References Index.
  • (source: Nielsen Book Data)9781482233438 20160617
Competitive Innovation and Improvement: Statistical Design and Control explains how to combine two widely known statistical methods-statistical design and statistical control-in a manner that can solve any business, government, or research problem quickly with sustained results. Because the problem-solving strategy employed is pure scientific method, it makes integration into any existing problem-solving or research method quite simple. The material in the book is presented in a manner that anyone can read and immediately put to use, including executives, managers, statisticians, scientists, engineers, researchers, and all of their supervisors and employees. Organizations can apply the concepts discussed with existing staff to release latent energy rather than adding to their workload. Optional footnotes provide the opportunity for more advanced technical insight. Supplying readers with an understanding of orthogonal design, the book illustrates key ideas through large-scale case studies. The book's 12 case studies examine the coupling of statistical design with economic control across a range of industries and problem types. The book suggests the real world, rather than mathematics alone, to reveal how things work and how to make them work better. Innovation and improvement by design is explained, which will help readers open up left-brain analytics to more right-brain creativity. Although mathematics (as advanced as needed to solve the problem) is used throughout the text, it is translated into simple arithmetic without any mathematical notation. The book limits references to a few essential texts and papers that readers can refer to as they become more experienced in statistical design and control.
(source: Nielsen Book Data)9781482233438 20160617
Science Library (Li and Ma)
Book
x, 260 p. : ill. ; 23 cm.
This book is a concise and innovative book that gives a complete presentation of the design and analysis of experiments in approximately one half the space of competing books. With only the modest prerequisite of a basic (non-calculus) statistics course, this text is appropriate for the widest possible audience. Two procedures are generally used to analyze experimental design data-analysis of variance (ANOVA) and regression analysis. Because ANOVA is more intuitive, this book devotes most of its first three chapters to showing how to use ANOVA to analyze balanced (equal sample size) experimental design data. The text first discusses regression analysis at the end of Chapter 2, where regression is used to analyze data that cannot be analyzed by ANOVA: unbalanced (unequal sample size) data from two-way factorials and data from incomplete block designs. Regression is then used again in Chapter 4 to analyze data resulting from two-level fractional factorial and block confounding experiments.
(source: Nielsen Book Data)9781606499580 20160618
Science Library (Li and Ma)
Book
xvii, 730 pages : illustrations ; 27 cm
  • Preface v 1 Introduction 1 1.1 Strategy of Experimentation 1 1.2 Some Typical Applications of Experimental Design 8 1.3 Basic Principles 11 1.4 Guidelines for Designing Experiments 14 1.5 A Brief History of Statistical Design 21 1.6 Summary: Using Statistical Techniques in Experimentation 22 1.7 Problems 23 2 Simple Comparative Experiments 25 2.1 Introduction 25 2.2 Basic Statistical Concepts 27 2.3 Sampling and Sampling Distributions 30 2.4 Inferences About the Differences in Means, Randomized Designs 36 2.5 Inferences About the Differences in Means, Paired Comparison Designs 53 2.6 Inferences About the Variances of Normal Distributions 57 2.7 Problems 59 3 Experiments with a Single Factor: The Analysis of Variance 65 3.1 An Example 66 3.2 The Analysis of Variance 68 3.3 Analysis of the Fixed Effects Model 70 3.4 Model Adequacy Checking 80 3.5 Practical Interpretation of Results 89 3.6 Sample Computer Output 102 3.7 Determining Sample Size 105 3.8 Other Examples of Single-Factor Experiments 110 3.9 The Random Effects Model 116 3.10 The Regression Approach to the Analysis of Variance 125 3.11 Nonparametric Methods in the Analysis of Variance 128 3.12 Problems 130 4 Randomized Blocks, Latin Squares, and Related Designs 139 4.1 The Randomized Complete Block Design 139 4.2 The Latin Square Design 158 4.3 The Graeco-Latin Square Design 165 4.4 Balanced Incomplete Block Designs 168 4.5 Problems 177 5 Introduction to Factorial Designs 183 5.1 Basic Definitions and Principles 183 5.2 The Advantage of Factorials 186 5.3 The Two-Factor Factorial Design 187 5.4 The General Factorial Design 206 5.5 Fitting Response Curves and Surfaces 211 5.6 Blocking in a Factorial Design 219 5.7 Problems 225 6 The 2k Factorial Design 233 6.1 Introduction 233 6.2 The 22 Design 234 6.3 The 23 Design 241 6.4 The General 2k Design 253 6.5 A Single Replicate of the 2k Design 255 6.6 Additional Examples of Unreplicated 2k Design 269 6.7 2k Designs are Optimal Designs 280 6.8 The Addition of Center Points to the 2k Design 285 6.9 Why We Work with Coded Design Variables 290 6.10 Problems 292 7 Blocking and Confounding in the 2k Factorial Design 304 7.1 Introduction 304 7.2 Blocking a Replicated 2k Factorial Design 305 7.3 Confounding in the 2k Factorial Design 306 7.4 Confounding the 2k Factorial Design in Two Blocks 306 7.5 Another Illustration of Why Blocking Is Important 312 7.6 Confounding the 2k Factorial Design in Four Blocks 313 7.7 Confounding the 2k Factorial Design in 2p Blocks 315 7.8 Partial Confounding 316 7.9 Problems 319 8 Two-Level Fractional Factorial Designs 320 8.1 Introduction 320 8.2 The One-Half Fraction of the 2k Design 321 8.3 The One-Quarter Fraction of the 2k Design 333 8.4 The General 2k-p Fractional Factorial Design 340 8.5 Alias Structures in Fractional Factorials and other Designs 349 8.6 Resolution III Designs 351 8.7 Resolution IV and V Designs 366 8.8 Supersaturated Designs 374 8.9 Summary 375 8.10 Problems 376 9 Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs 394 9.1 The 3k Factorial Design 395 9.2 Confounding in the 3k Factorial Design 402 9.3 Fractional Replication of the 3k Factorial Design 408 9.4 Factorials with Mixed Levels 412 9.5 Nonregular Fractional Factorial Designs 415 9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool 431 9.7 Problems 444 10 Fitting Regression Models 449 10.1 Introduction 449 10.2 Linear Regression Models 450 10.3 Estimation of the Parameters in Linear Regression Models 451 10.4 Hypothesis Testing in Multiple Regression 462 10.5 Confidence Intervals in Multiple Regression 467 10.6 Prediction of New Response Observations 468 10.7 Regression Model Diagnostics 470 10.8 Testing for Lack of Fit 473 10.9 Problems 475 11 Response Surface Methods and Designs 478 11.1 Introduction to Response Surface Methodology 478 11.2 The Method of Steepest Ascent 480 11.3 Analysis of a Second-Order Response Surface 486 11.4 Experimental Designs for Fitting Response Surfaces 500 11.5 Experiments with Computer Models 523 11.6 Mixture Experiments 530 11.7 Evolutionary Operation 540 11.8 Problems 544 12 Robust Parameter Design and Process Robustness Studies 554 12.1 Introduction 554 12.2 Crossed Array Designs 556 12.3 Analysis of the Crossed Array Design 558 12.4 Combined Array Designs and the Response Model Approach 561 12.5 Choice of Designs 567 12.6 Problems 570 13 Experiments with Random Factors 573 13.1 Random Effects Models 573 13.2 The Two-Factor Factorial with Random Factors 574 13.3 The Two-Factor Mixed Model 581 13.4 Sample Size Determination with Random Effects 587 13.5 Rules for Expected Mean Squares 588 13.6 Approximate F Tests 592 13.7 Some Additional Topics on Estimation of Variance Components 596 13.8 Problems 601 14 Nested and Split-Plot Designs 604 14.1 The Two-Stage Nested Design 604 14.2 The General m-Stage Nested Design 614 14.3 Designs with Both Nested and Factorial Factors 616 14.4 The Split-Plot Design 621 14.5 Other Variations of the Split-Plot Design 627 14.6 Problems 637 15 Other Design and Analysis Topics 642 15.1 Nonnormal Responses and Transformations 643 15.2 Unbalanced Data in a Factorial Design 652 15.3 The Analysis of Covariance 655 15.4 Repeated Measures 675 15.5 Problems 677 Appendix 681 Table I. Cumulative Standard Normal Distribution 682 Table II. Percentage Points of the t Distribution 684 Table III. Percentage Points of the -2 Distribution 685 Table IV. Percentage Points of the F Distribution 686 Table V. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance 691 Table VI. Operating Characteristic Curves for the Random Effects Model Analysis of Variance 695 Table VII. Percentage Points of the Studentized Range Statistic 699 Table VIII. Critical Values for Dunnett's Test for Comparing Treatments with a Control 701 Table IX. Coefficients of Orthogonal Polynomials 703 Table X. Alias Relationships for 2k-p Fractional Factorial Designs with k 15 and n 64 704 Bibliography 717 Index 723.
  • (source: Nielsen Book Data)9781118146927 20160608
  • Preface 1 Introduction to Designed Experiments 1.1 Strategy of Experimentation 1.2 Some Typical Applications of Experimental Design 1.3 Basic Principles 1.4 Guidelines for Designing Experiments 1.5 A Brief History of Statistical Design 1.6 Summary: Using Statistical Techniques in Experimentation 1.7 Problems 2 Basic Statistical Methods 2.1 Introduction 2.2 Basic Statistical Concepts 2.3 Sampling and Sampling Distributions 2.4 Inferences About the Differences in Means, Randomized Designs 2.5 Inferences About the Differences in Means, Paired Comparison Designs 2.6 Inferences About the Variances of Normal Distributions 2.7 Problems 3 Analysis of Variance 3.1 An Example 3.2 The Analysis of Variance 3.3 Analysis of the Fixed Effects Model 3.4 Model Adequacy Checking 3.5 Practical Interpretation of Results 3.6 Sample Computer Output 3.7 Determining Sample Size 3.8 Other Examples of Single-Factor Experiments 3.9 The Random Effects Model 3.10 The Regression Approach to the Analysis of Variance 3.11 Nonparametric Methods in the Analysis of Variance 3.12 Problems 4 Experiments with Blocking Factors 4.1 The Randomized Complete Block Design 4.2 The Latin Square Design 4.3 The Graeco-Latin Square Design 4.4 Balanced Incomplete Block Designs 4.5 Problems 5 Factorial Experiments 5.1 Basic Definitions and Principles 5.2 The Advantage of Factorials 5.3 The Two-Factor Factorial Design 5.4 The General Factorial Design 5.5 Fitting Response Curves and Surfaces 5.6 Blocking in a Factorial Design 5.7 Problems 6 Two-Level Factorial Designs 6.1 Introduction 6.2 The 22 Design 6.3 The 23 Design 6.4 The General 2k Design 6.5 A Single Replicate of the 2k Design 6.6 Additional Examples of Unreplicated 2k Design 6.7 2k Designs are Optimal Designs 6.8 The Addition of Center Points to the 2k Design 6.9 Why We Work with Coded Design Variables 6.10 Problems 7 Blocking and Confounding Systems for Two-Level Factorials 7.1 Introduction 7.2 Blocking a Replicated 2k Factorial Design 7.3 Confounding in the 2k Factorial Design 7.4 Confounding the 2k Factorial Design in Two Blocks 7.5 Another Illustration of Why Blocking Is Important 7.6 Confounding the 2k Factorial Design in Four Blocks 7.7 Confounding the 2k Factorial Design in 2p Blocks 7.8 Partial Confounding 7.9 Problems 8 Two-Level Fractional Factorial Designs 8.1 Introduction 8.2 The One-Half Fraction of the 2k Design 8.3 The One-Quarter Fraction of the 2k Design 8.4 The General 2k-p Fractional Factorial Design 8.5 Alias Structures in Fractional Factorials and other Designs 8.6 Resolution III Designs 8.7 Resolution IV and V Designs 8.8 Supersaturated Designs 8.9 Summary 8.10 Problems 9 Other Topics on Factorial and Fractional Factorial Designs 9.1 The 3k Factorial Design 9.2 Confounding in the 3k Factorial Design 9.3 Fractional Replication of the 3k Factorial Design 9.4 Factorials with Mixed Levels 9.5 Nonregular Fractional Factorial Designs 9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool 9.7 Problems 10 Regression Modeling 10.1 Introduction 10.2 Linear Regression Models 10.3 Estimation of the Parameters in Linear Regression Models 10.4 Hypothesis Testing in Multiple Regression 10.5 Confidence Intervals in Multiple Regression 10.6 Prediction of New Response Observations 10.7 Regression Model Diagnostics 10.8 Testing for Lack of Fit 10.9 Problems 11 Response Surface Methodology 11.1 Introduction to Response Surface Methodology 11.2 The Method of Steepest Ascent 11.3 Analysis of a Second-Order Response Surface 11.4 Experimental Designs for Fitting Response Surfaces 11.5 Experiments with Computer Models 11.6 Mixture Experiments 11.7 Evolutionary Operation 11.8 Problems 12 Robust Design 12.1 Introduction 12.2 Crossed Array Designs 12.3 Analysis of the Crossed Array Design 12.4 Combined Array Designs and the Response Model Approach 12.5 Choice of Designs 12.6 Problems 13 Random Effects Models 13.1 Random Effects Models 13.2 The Two-Factor Factorial with Random Factors 13.3 The Two-Factor Mixed Model 13.4 Sample Size Determination with Random Effects 13.5 Rules for Expected Mean Squares 13.6 Approximate F Tests 13.7 Some Additional Topics on Estimation of Variance Components 13.8 Problems 14 Experiments with Nested Factors and Hard-to-Change Factors 14.1 The Two-Stage Nested Design 14.2 The General m-Stage Nested Design 14.3 Designs with Both Nested and Factorial Factors 14.4 The Split-Plot Design 14.5 Other Variations of the Split-Plot Design 14.6 Problems 15 Other Topics 15.1 Nonnormal Responses and Transformations 15.2 Unbalanced Data in a Factorial Design 15.3 The Analysis of Covariance 15.4 Repeated Measures 15.5 Problems Appendix Table I. Cumulative Standard Normal Distribution Table II. Percentage Points of the t Distribution Table III. Percentage Points of the X2 Distribution Table IV. Percentage Points of the F Distribution Table V. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance Table VI. Operating Characteristic Curves for the Random Effects Model Analysis of Variance Table VII. Percentage Points of the Studentized Range Statistic Table VIII. Critical Values for Dunnett's Test for Comparing Treatments with a Control Table IX. Coefficients of Orthogonal Polynomials Table X. Alias Relationships for 2k-p Fractional Factorial Designs with k <= 15 and n <=64 Bibliography Index.
  • (source: Nielsen Book Data)9781118097939 20160614
The eighth edition of Design and Analysis of Experiments continues to provide extensive and in-depth information on engineering, business, and statistics-as well as informative ways to help readers design and analyze experiments for improving the quality, efficiency and performance of working systems. Furthermore, the text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.
(source: Nielsen Book Data)9781118146927 20160608
Science Library (Li and Ma)
Book
xi, 296 p. : ill. ; 24 cm
  • Introduction and Preliminary Results-- Theory of Linear Estimation-- Analysis of Variance-- Analysis of Covariance (ANCOVA)-- Missing and Mixed Plots-- Balanced Incomplete Block Designs-- Factorial Designs-- Elements of Modern Algebra-- Construction of Designs.
  • (source: Nielsen Book Data)9789814522533 20160612
The design of experiments holds a central place in statistics. The aim of this book is to present in a readily accessible form certain theoretical results of this vast field. This is intended as a textbook for a one-semester or two-quarter course for undergraduate seniors or first-year graduate students, or as a supplementary resource. Basic knowledge of algebra, calculus and statistical theory is required to master the techniques presented in this book. To help the reader, basic statistical tools that are needed in the book are given in a separate chapter. Mathematical results from Modern Algebra which are needed for the construction of designs are also given. Wherever possible the proofs of the theoretical results are provided.
(source: Nielsen Book Data)9789814522533 20160612
Science Library (Li and Ma)
Book
1 online resource (xxiv, 183 p.) : ill.
  • Chapter 1. Randomized Experiments 1.1 Nature and Structure of Randomized Experiments 1.2 Experimental Design and Validity of Scientific Inferences 1.3 Randomized Experiments and Methods of Randomization 1.4 Terminological and Notational Issues Chapter 2. Between-Subjects Designs Randomization 2.1 Randomization and Local Control 2.2 Completely Randomized Designs 2.3 Restrictedly Randomized Designs: Blocking 2.4 Restrictedly Randomized Designs: Stratifying 2.5 Sequential Assignment and Adaptive Randomization Methods Chapter 3. Within-Subjects Designs Randomization 3.1 Basic Assumptions and Specific Threats to Validity 3.2 Treatment Design and Methods of Randomization 3.3 Random Counterbalancing 3.4 Positional Counterbalancing 3.5 Nonrestricted Sequential Counterbalancing 3.6 Restricted Sequential Counterbalancing 3.7 Factorial Designs Chapter 4. Validity Issues, Analysis Guidelines, and Reporting Standards 4.1 Planning and Monitoring Randomized Experiments 4.2 Analyzing Randomized Experiments 4.3 Reporting Randomized Experiments Appendix 1. Random Numbers Appendix 2. Permutations, Arrangements, and Combinations Appendix 3. Latin Squares.
  • (source: Nielsen Book Data)9781452202921 20160609
This book provides a conceptual systematization and a practical tool for the randomization of between-subjects and within-subjects experimental designs in social, behavioural, and health sciences. The author adopts a pedagogical strategy that allows the reader to implement all randomization methods by relying on the materials given in the appendices and using the common features included in any word processor software. In the companion website (www.fpce.uc.pt/niips/randmethods), along with other supplementary materials, the reader can freely download IBM SPSS and R versions of SCRAED, a package that performs simple and complex random assignment in experimental design, including the 18 randomization methods presented in Chapters 2 and 3.
(source: Nielsen Book Data)9781452202921 20160609
Book
xvii, 656 p. : ill. ; 26 cm.
  • Preface. 1. Introduction. 2. Simple Comparative Experiments. 3. Experiments with a Single Factor: The Analysis of Variance. 4. Randomized Blocks, Latin Squares, and Related Designs. 5. Introduction to Factorial Designs. 6. The 2k Factorial Design. 7. Blocking and Confounding in the 2k Factorial Design. 8. Two-Level Fractional Factorial Designs. 9. Three-Level and Mixed-Level Factorial and Fractional Factorial Designs. 10. Fitting Regression Models. 11. Response Surface Methods and Designs. 12. Robust Parameter Design and Process Robustness Studies. 13. Experiments with Random Factors. 14. Nested and Split-Plot Designs. 15. Other Design and Analysis Topics. Bibliography. Appendix. Table I. Cumulative Standard Normal Distribution. Table II. Percentage Points of the t Distribution. Table III. Percentage Points of the x2 Distribution. Table IV. Percentage Points of the F Distribution. Table V. Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance. Table VI. Operating Characteristic Curves for the Random Effects Model Analysis of Variance. Table VII. Percentage Points of the Studentized Range Statistic. Table VIII. Critical Values for Dunnett's Test for Comparing Treatments with a Control. Table IX. Coefficients of Orthogonal Polynomials. Table X. Alias Relationships for 2k-p Fractional Factorial Designs with k 15 and n. Index.
  • (source: Nielsen Book Data)9780470398821 20160527
This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and, SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.
(source: Nielsen Book Data)9780470398821 20160527
Science Library (Li and Ma)
Book
xxix, 716 p. : ill. ; 25 cm.
  • Preface to the Second Edition. Preface to the First Edition. Suggestions of Topics for Instructors. List of Experiments and Data Sets. 1 Basic Concepts for Experimental Design and Introductory Regression Analysis. 1.1 Introduction and Historical Perspective. 1.2 A Systematic Approach to the Planning and Implementation of Experiments. 1.3 Fundamental Principles: Replication, Randomization, and Blocking. 1.4 Simple Linear Regression. 1.5 Testing of Hypothesis and Interval Estimation. 1.6 Multiple Linear Regression. 1.7 Variable Selection in Regression Analysis. 1.8 Analysis of Air Pollution Data. 1.9 Practical Summary. 2 Experiments with a Single Factor. 2.1 One-Way Layout. 2.2 Multiple Comparisons. 2.3 Quantitative Factors and Orthogonal Polynomials. 2.4 Expected Mean Squares and Sample Size Determination. 2.5 One-Way Random Effects Model. 2.6 Residual Analysis: Assessment of Model Assumptions. 2.7 Practical Summary. 3 Experiments with More Than One Factor. 3.1 Paired Comparison Designs. 3.2 Randomized Block Designs. 3.3 Two-Way Layout: Factors With Fixed Levels. 3.4 Two-Way Layout: Factors With Random Levels. 3.5 Multi-Way Layouts. 3.6 Latin Square Designs: Two Blocking Variables. 3.7 Graeco-Latin Square Designs. 3.8 Balanced Incomplete Block Designs. 3.9 Split-Plot Designs. 3.10 Analysis of Covariance: Incorporating Auxiliary Information. 3.11 Transformation of the Response. 3.12 Practical Summary. 4 Full Factorial Experiments at Two Levels. 4.1 An Epitaxial Layer Growth Experiment. 4.2 Full Factorial Designs at Two Levels: A General Discussion. 4.3 Factorial Effects and Plots. 4.4 Using Regression to Compute Factorial Effects. 4.5 ANOVA Treatment of Factorial Effects. 4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity. 4.7 Comparisons with the "One-Factor-at-a-Time" Approach. 4.8 Normal and Half-Normal Plots for Judging Effect Significance. 4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates. 4.10 Nominal-the-Best Problem and Quadratic Loss Function. 4.11 Use of Log Sample Variance for Dispersion Analysis. 4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment. 4.13 Test of Variance Homogeneity and Pooled Estimate of Variance. 4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments with Variance Estimates. 4.15 Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks. 4.16 Practical Summary. 5 Fractional Factorial Experiments at Two Levels. 5.1 A Leaf Spring Experiment. 5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration. 5.3 Analysis of Fractional Factorial Experiments. 5.4 Techniques for Resolving the Ambiguities in Aliased Effects. 5.5 Selection of 2k-p Designs Using Minimum Aberration and Related Criteria. 5.6 Blocking in Fractional Factorial Designs. 5.7 Practical Summary. 6 Full Factorial and Fractional Factorial Experiments at Three Levels. 6.1 A Seat-Belt Experiment. 6.2 Larger-the-Better and Smaller-the-Better Problems. 6.3 3k Full Factorial Designs. 6.4 3k-p Fractional Factorial Designs. 6.5 Simple Analysis Methods: Plots and Analysis of Variance. 6.6 An Alternative Analysis Method. 6.7 Analysis Strategies for Multiple Responses I: Out-of-Spec Probabilities. 6.8 Blocking in 3k and 3k-p Designs. 6.9 Practical Summary. 7 Other Design and Analysis Techniques for Experiments at More Than Two Levels. 7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design. 7.2 Method of Replacement and Construction of 2m4n Designs. 7.3 Minimum Aberration 2m4n Designs with n = 1, 2. 7.4 An Analysis Strategy for 2m4n Experiments. 7.5 Analysis of the Router Bit Experiment. 7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design. 7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels. 7.8 rk-p Fractional Factorial Designs for any Prime Number r. 7.9 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling. 7.10 Practical Summary. 8 Nonregular Designs: Construction and Properties. 8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing. 8.2 Some Advantages of Nonregular Designs Over the 2k-p and 3k-p Series of Designs. 8.3 A Lemma on Orthogonal Arrays. 8.4 Plackett-Burman Designs and Hall's Designs. 8.5 A Collection of Useful Mixed-Level Orthogonal Arrays. 8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices. 8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement. 8.8 Orthogonal Main-Effect Plans Through Collapsing Factors. 8.9 Practical Summary. 9 Experiments with Complex Aliasing. 9.1 Partial Aliasing of Effects and the Alias Matrix. 9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis. 9.3 Simplification of Complex Aliasing via Effect Sparsity. 9.4 An Analysis Strategy for Designs with Complex Aliasing. 9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing. 9.6 Supersaturated Designs: Design Construction and Analysis. 9.7 Practical Summary. 10 Response Surface Methodology. 10.1 A Ranitidine Separation Experiment. 10.2 Sequential Nature of Response Surface Methodology. 10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search. 10.4 Analysis of Second-Order Response Surfaces. 10.5 Analysis of the Ranitidine Experiment. 10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions. 10.7 Central Composite Designs. 10.8 Box-Behnken Designs and Uniform Shell Designs. 10.9 Practical Summary. 11 Introduction to Robust Parameter Design. 11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments. 11.2 Strategies for Reducing Variation. 11.3 Noise (Hard-to-Control) Factors. 11.4 Variation Reduction Through Robust Parameter Design. 11.5 Experimentation and Modeling Strategies I: Cross Array. 11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling. 11.7 Cross Arrays: Estimation Capacity and Optimal Selection. 11.8 Choosing Between Cross Arrays and Single Arrays. 11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization. 11.10 Further Topics. 11.11 Practical Summary. 12 Robust Parameter Design for Signal-Response Systems. 12.1 An Injection Molding Experiment. 12.2 Signal-Response Systems and their Classification. 12.3 Performance Measures for Parameter Design Optimization. 12.4 Modeling and Analysis Strategies. 12.5 Analysis of the Injection Molding Experiment. 12.6 Choice of Experimental Plans. 12.7 Practical Summary. 13 Experiments for Improving Reliability. 13.1 Experiments with Failure Time Data. 13.2 Regression Model for Failure Time Data. 13.3 A Likelihood Approach for Handling Failure Time Data with Censoring. 13.4 Design-Dependent Model Selection Strategies. 13.5 A Bayesian Approach to Estimation and Model Selection for Failure Time Data. 13.6 Analysis of Reliability Experiments with Failure Time Data. 13.7 Other Types of Reliability Data. 13.8 Practical Summary. 14 Analysis of Experiments with Nonnormal Data. 14.1 A Wave Soldering Experiment with Count Data. 14.2 Generalized Linear Models. 14.3 Likelihood-Based Analysis of Generalized Linear Models. 14.4 Likelihood-Based Analysis of the Wave Soldering Experiment. 14.5 Bayesian Analysis of Generalized Linear Models. 14.6 Bayesian Analysis of the Wave Soldering Experiment. 14.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data. 14.8 Modeling and Analysis for Ordinal Data. 14.9 Analysis of Foam Molding Experiment. 14.10 Scoring: A Simple Method for Analyzing Ordinal Data. 14.11 Practical Summary. Appendix A Upper Tail Probabilities of the Standard Normal Distribution. Appendix B Upper Percentiles of the t Distribution. Appendix C Upper Percentiles of the chi2 Distribution. Appendix D Upper Percentiles of the F Distribution. Appendix E Upper Percentiles of the Studentized Range Distribution. Appendix F Upper Percentiles of the Studentized Maximum Modulus Distribution. Appendix G Coefficients of Orthogonal Contrast Vectors. Appendix H Critical Values for Lenth's Method. Author Index. Subject Index.
  • (source: Nielsen Book Data)9780471699460 20160528
Praise for the First Edition: 'If you ...want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library' - "Journal of the American Statistical Association". Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, "Experiments, Second Edition" introduces some of the newest discoveries - and sheds further light on existing ones-on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: expected mean squares and sample size determination; one-way and two-way ANOVA with random effects; split-plot designs; ANOVA treatment of factorial effects; and, response surface modeling for related factors. Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. "Experiments, Second Edition" is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.
(source: Nielsen Book Data)9780471699460 20160528
Science Library (Li and Ma)
Book
xxiv, 679 p. : ill. ; 25 cm.
  • 1. Introduction. 1.1 Observational Studies and Experiments. 1.2 Brief Historical Remarks. 1.3 Basic Terminology and Concepts of Experimentation. 1.4 Basic Principles of Experimentation. 2. Review of Elementary Statistics. 2.1 Experiments for a Single Treatment. 2.2 Experiments for Comparing Two Treatments. 2.3 Linear Regression. 2.4 Chapter Summary. Exercises. 3. Single Factor Experiments: Completely Randomized Designs. 3.1 Summary Statistics and Graphical Displays. 3.2 Model. 3.3 Statistical Analysis. 3.4 Model Diagnostics. 3.5 Data Transformations. 3.6 Power of the F-test and Sample Size Determination. 3.7 Quantitative Treatment Factors. 3.8 One-Way Analysis of Covariance. 3.9 Chapter Notes. 3.10 Chapter Summary. Exercises. 4. Single Factor Experiments: Multiple Comparison and Selection Procedures. 4.1 Basic Concepts of Multiple Comparisons. 4.2 Pairwise Comparisons. 4.3 Comparisons with a Control. 4.4 General Contrasts. 4.5 Ranking and Selection Procedures. 4.6 Chapter Summary. Exercises. 5. Randomized Block Designs and Extensions. 5.1 Randomized Block (RB) Designs. 5.2 Balanced Incomplete Block (BIB) Designs. 5.3 Youden Square (YSQ) Designs. 5.4 Latin Square (LSQ) Designs. 5.5 Chapter Notes. 5.6 Chapter Summary. Exercises. 6. General Factorial Experiments. 6.1 Factorial vs. One-Factor-at-a-Time Experiments. 6.2 Balanced Two-Way Layouts. 6.3 Unbalanced Two-Way Layouts. 6.4 Chapter Notes. 6.5 Chapter Summary. Exercises. 7. Two-Level Factorial Experiments. 7.1 Estimation of Main Effects and Interactions. 7.2 Statistical Analysis. 7.3 Single Replicate Case. 7.4 Factorial Designs in Incomplete Blocks: Confounding of Effects. 7.5 Chapter Notes. 7.6 Chapter Summary. Exercises. 8. Two-Level Fractional Factorial Experiments . 8.1 Two-Level Fractional Factorial Experiments. 8.2 Plackett-Burman Designs. 8.3 Hadamard Designs. 8.4 Supersaturated Designs. 8.5 Orthogonal Arrays. 8.6 Sequential Assemblies of Fractional Factorials. 8.7 Chapter Summary. Exercises. 9. Three-Level and Mixed-Level Factorial Designs. 9.1 Three-Level Full Factorial Designs. 9.2 Three-Level Fractional Factorial Designs. 9.3 Mixed-Level Factorial Designs. 9.4 Chapter Notes. 9.5 Chapter Summary. Exercises. 10. Experiments for Response Optimization. 10.1 Response Surface Methodology. 10.2 Mixture. 10.3 The Taguchi Method of Quality Improvement. 10.4 Chapter Summary. Exercises. 11. Random and Mixed Crossed Factors Designs. 11.1 One-Way Layouts. 11.2 Two-Way Layouts. 11.3 Three-Way Layouts. 11.4 Chapter Notes. 11.5 Chapter Summary. Exercises. 12. Nested, Crossed-Nested and Split Plot Designs. 12.1 Two-Stage Nested Designs. 12.2 Three-Stage Nested Designs. 12.3 Crossed and Nested Designs. 12.4 Split Plot Designs. 12.5 Chapter Notes. 12.6 Chapter Summary. Exercises. 13. Repeated Measures Designs. 13.1 Repeated Measures Designs: Univariate Approach. 13.2 Repeated Measures Designs: Multivariate Approach. 13.3 Chapter Notes. 13.4 Chapter Summary. Exercises. 14. Linear Models with Fixed Effects. 14.1 Basic Linear Model and Least Squares Estimation. 14.2 Confidence Intervals and Hypothesis Testing. 14.3 Power of the F-Test. 14.4 Chapter Notes. 14.5 Chapter Summary. Exercises. A. Vector-Valued Random Variables and Some Distribution Theory. A.1 Mean Vector and Covariance Matrix of a Random Vector. A.2 Covariance Matrix of a Linear Transformation of a Random Vector. A.3 Multivariate Normal Distribution. A.4 Chi-Square, F and t-Distributions. A.5 Distributions of Quadratic Forms. A.6 Multivariate t-Distribution. A.7 Multivariate Normal Sampling Distribution Theory. B. Case Studies. B.1 Case Study 1: Effects of Field Strength and Flip Angle on MRI Contrast. B.1.1 Background. B.1.2 Design. B.1.3 Data Analysis. B.1.4 Results. B.2 Case Study 2: Growing Stem Cells for Bone Implants. B.2.1 Background. B.2.2 Design. B.2.3 Data Analysis. B.2.4 Results. B.3 Case Study 3: Router Bit Experiment. B.3.1 Background. B.3.2 Design. B.3.3 Data Analysis. B.3.4 Results.
  • (source: Nielsen Book Data)9780471750437 20160528
This is an indispensable guide to understanding and designing modern experiments. The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. "Statistical Analysis of Designed Experiments" provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. There is an appendix that features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab[registered] software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, "Statistical Analysis of Designed Experiments" is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.
(source: Nielsen Book Data)9780471750437 20160528
Science Library (Li and Ma)

11. Statistical design [2008]

Book
xxiii, 307 p. : ill. ; cm.
  • Basics.- Completely randomized designs.- Complete block designs.- Interlude: assessing the effects of blocking.- Split plot designs.- Confounding in blocks.
  • (source: Nielsen Book Data)9780387759647 20160528
Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks.
(source: Nielsen Book Data)9780387759654 20160527
Book
xxiii, 307 p. : ill. ; 25 cm.
  • Basics.- Completely randomized designs.- Complete block designs.- Interlude: assessing the effects of blocking.- Split plot designs.- Confounding in blocks.
  • (source: Nielsen Book Data)9780387759647 20160528
Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks.
(source: Nielsen Book Data)9780387759654 20160527
dx.doi.org SpringerLink
Book
xiv, 693 p. : ill. ; 25 cm.
  • Preface.Introduction.The Completely Randomized Design.Linear Models for Designed Experiments.Testing Hypotheses and Determining Sample Size.Methods of Reducing Unexplained Variation.Latin Squares.Split-Plot and Related Designs.Incomplete Block Designs.Repeated Teatments Designs.Factorial Experiments, the 2n System.Factorial Experiments, the 3n System.Analysis of Experiments Without Designed Error Terms.Confounding Effects with Blocks.Fractional Factorial Experiments.Response Surface Designs.Plackett-Burmann Hadamard Plans.The General Pn and Nonstandard Factorials.Factorial Experiments with Quantitative Factors.Plans for Which Run Order is Important.Supersaturated Plans.Sequences of Fractions of Factorials.Multi-Stage xperiments.Orthogonal Arrays and Related Structures.Factorial Plans Derived via Orthogonal Arrays.Experiments on the Computer.
  • (source: Nielsen Book Data)9780471213956 20160528
At once a comprehensive handbook for the active researcher and a thorough introduction for the advanced student, this reference provides: Coverage of wide range of applications, including agricultural sciences, animal and biomedi-cal sciences, and industrial and engineering studies Information on new developments in the design of fractional factorials with non-prime numbers of levels in mixed-level fractional factorials Detailed information on the construction of plans and the relationships among categories of designs Thorough discussion of balanced, partially balanced, lattice, cyclic and alpha-designs Accommodations for how to evaluate the power and efficiency of designs that are not perfectly balanced Unified and simplified presentation of general forms for estimation and hypothesis testing.
(source: Nielsen Book Data)9780471213956 20160528
dx.doi.org Wiley Online Library
SAL3 (off-campus storage)
Book
xxi, 623 p. : ill. ; 23 cm.
  • 1. Experiments and Generalized Causal Inference 2. Statistical Conclusion Validity and Internal Validity 3. Construct Validity and External Validity 4. Quasi-Experimental Designs That Either Lack a Control Group or Lack Pretest Observations on the Outcome 5. Quasi-Experimental Designs That Use Both Control Groups and Pretests 6. Quasi-Experimentation: Interrupted Time Series Designs 7. Regression Discontinuity Designs 8. Randomized Experiments: Rationale, Designs, and Conditions Conducive to Doing Them 9. Practical Problems 1: Ethics, Participant Recruitment, and Random Assignment 10. Practical Problems 2: Treatment Implementation and Attrition 11. Generalized Causal Inference: A Grounded Theory 12. Generalized Causal Inference: Methods for Single Studies 13. Generalized Causal Inference: Methods for Multiple Studies 14. A Critical Assessment of Our Assumptions.
  • (source: Nielsen Book Data)9780395615560 20160618
This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation:.
(source: Nielsen Book Data)9780395615560 20160618
Business Library
ACCT-610-01
Book
xvi, 480 p. : ill. ; 25 cm.
  • 1. Introduction to Experimental Design. What Is Experimentation. A Growing Interest in Experimental Design. The Six Steps of Experimental Design. Experimental Design Applications in Management. Closure. 2. One-Factor Designs and the Analysis of Variance. Corporate Environmental Behavior at Clean Air Co. (Case Illustration). One-Factor Designs. Analysis of (the) Variance (ANOVA). Example (Using EXCEL, SPSS). Forming the F-Statistic: Logic and "Derivation". A Larger Scale Example (Using JMP). Corporate Environmental Behavior at Clean Air Electric Co.-Revisited. A Comment. Exercises. 3. Some Further Issues in One-Factor Designs and Anova. Introduction. Basic Assumptions of ANOVA. Kruskal-Walls Test. Review of Hypothesis Testing. Power of the F-Test. Confidence Intervals. Exercises. 4. Multiple Comparison Testing. The Qualities of a Superior Hotel (Case Illustration). Logic of Multiple Comparison Testing. Type I Errors in Multiple Comparison Testing. Principal Example. Pairwise Comparisons. Fisher's Least Significance Difference Test. Tukey's Honestly Significance Difference Test. Neuman-Keuls Test. Two Other Test Comparing All Pairs of Column Means. Dunnett Test. Post Hoc/Exploratory Comparisons-The Scheffe Test. 5. Orthogonality, Orthogonal Decomposition, and Its Role in Modern Experimental Design. Planning Travel Packages at Joyful Voyages, Inc. (Case Illustration). Introduction. Forming an Orthogonal Matrix. First Example-Portfolio Rating. Drug Example. Amended Drug Example. An Example Using SPSS. Planning Travel Packages at Joyful Voyages, Inc. - Revisited. Exercises. 6. Two Factor Cross-Classification Designs. Planning Travel Packages at Joyful voyages, Inc., A Second Look (Case Illustration). Introduction to Studying Two Factors. Designs with Replication. The Model. Parameter Estimates. Interaction Effects. Example analysis Using Excel. Example Analysis Using SPSS. A larger Example-First United Federal Bank of Boston (Using JMP). Fixed Levels VS. Random Levels. An Interesting Application of "Two Factors With Replication" Model. Two Factors With No Replication (And No Interaction). Example Analysis Using Excel. Example Analysis Using SPSS. Blocking. Friedman Non-parametric Test. Planning Travel Packages at Joyful Voyages, Inc., A Second Look-Revisited. Exercises. 7. Nested (Hierarchical) Designs. Shaving Cream Efficiency at American Razor Corporation (Case Illustration). Introduction to Nested Designs. The Model. A Numerical Example. Software/Professor Example Using JMP. A Larger Scale Example (First United Bank of Boston-using JMP). Discussion. Shaving Cream Efficiency at American Razor Corporation-Revisited. Exercises. 8. Designs with Three or More Factors-Latin Squares Designs. Maximizing Profits at Nature's Land Farms (Case Illustration). Introduction to Multi-Factor Designs. Latin Square Model and ANOVA. Example Latin-Square Analysis. Latin-Square Example Using SPSS. Latin-Square Example Using JMP. Graeco-Latin Square Designs. Other Designs With Three or More Factors. Maximizing Profits at Nature's Land Farms-Revisited. Exercises. 9. Two-Level Factorial Designs. Pricing A Supplemental Medical/Health Benefit Offer at Healthmark Insurance. Co. (Case Illustration). Introduction. Two Factor Experiments. Remarks on Effects and Interactions. Symbolism, Notation, and Language. Table of Signs. Four Examples. Modern Notation and Yates's Order. Three Factors, Each at Two Levels. Example-Response Rate. Number and Kinds of Effects. Yates's Forward Algorithm. A Note on Replicated 2k Experiments. SPSS Example. JMP Example. Main Effects in the Face of Large Interaction Effects. Levels of Factors. Factorial Designs vs. Designs Varying Factors One-at-a-Time. Factors Not Studied. Errors in Estimates in 2k Factorial Designs. A Comment on Testing the Effects in 2k Designs. Pricing a Supplemental Medical/Health Benefit Offer at Healthmark Insurance. Co. -Revisited. Exercises. 10. Confounding/Blocking in 2k Deigns. Pricing a Supplemental Medical/Health Benefit Offer at Healthmark Insurance Company-Phase II (Case Illustration). Introduction. Simple Confounding. Partial Confounding. Multiple Confounding. Determining the Blocks. Number of Blocks and Confounded Effects. A Comment on Calculating Effects. Pricing A Supplemental Medical/Health Benefit Offer at Healthmark Insurance Company-Phase II-Revisited. Appendix. Exercises. 11. Two-Level Fractional Factoral Designs. Managerial Decision-Making at FoodMart Supermarkets (Case Illustration). Introduction. 2k-P Designs. Four Factor, Half-Replicate Example. Five Factor, Half-Replicate Example. Yates's Algorithm Revisited. Quarter Replicate Designs: A 25-2 Example. Orthogonality Revisited. Ad Agency Example. SPSS Example. Power And Minimum Detectable Effects in 2k-P Designs. Managerial Decision-Making at FoodMart Supermarkets-Revisited. Exercises. Appendix-Selection of a "Workable" Set of Dead Letters. 12. Designs with Factors at Three Levels. Optimal Frequency and Size of Print Ads for MegaStroe Electronics, Inc. (Case Illustration). Introduction. Design with One Factor at Three Levels. Design with Two Factors, Each at Three Levels. Illustrative Example. SPSS Example. JMP Example. One Benefit of Recognizing Non-Linearity. Three Levels vs. Two Levels. Optimal Frequency and Size of Print Ads for MegaStore Electronics, Inc.- Revisited. Unequally-Spaced Levels. A Comment. Exercises. 13. Introduction to Taguchi Methods. New Product Development at HighTech Corporation. (Case Illustration.). Introduction. Taguchi's Quality Philosophy and "Loss Function". Control of the Variability of Performance. Taguchi Methods-Designing Fractional Factorial Designs. Experiments Without Interactions. Experiments Involving Interactions. Taguchi's L16. Experiments Involving Nonlinearities/Factors with Three Levels. A Final Illustrative Example. Confirmation. Economic Evaluation of Proposed Solution. A Final Word on Taguchi Methods. New Product Development at High Tech Corporation - Revisited. Exercises. 14. Introduction to Response Surface Methodology. Determining an Optimal Product Price Warranty Length, and Promotional Expense at Luna Electronics, Inc. (Case Illustration). Introduction. The Underlying Philosophy of RSM. Methods of Steepest Ascent. Method of Local Exploration. Central Composite Designs. Box-Behnken Designs. Post-Experimental Methodology. Summation to This Point. Real World Example. NASA Example Using JMP. Follow-up Use of Solver to Explore a Response Surface. Determining and Optimal Product Price, Warranty Length, and Promotional Expense at Luna Electronics, Inc. -Revisited. Concluding Remark. Exercises. 15. Literature on Experimental Design, and Discussion of Some Topics Not Covered in this Text. Introduction. Literature Discussion. Discussion of Some Topics Not Covered in This Text. References. Statistical Tables. Index.
  • (source: Nielsen Book Data)9780534358228 20160528
Based on decades of teaching, consulting, and industrial experience in the field of design and analysis of experiments, the authors provide an intuitive understanding of the principles of experimental design and analysis. The emphasis is on the application of experimental design concepts in such traditional management and industrial engineering areas such as marketing, operations, management information systems, organizational behavior, and others. The authors also apply this material to such non-profit areas as education, health care, and government. Using popular analytical tools such as SPSS, JMP, and Microsoft Excel, Berger and Maurer emphasize the modern application of experimental design to real problems.
(source: Nielsen Book Data)9780534358228 20160528
Engineering Library (Terman)
Book
xiii, 244 p. : ill. ; 24 cm.
  • Introduction.- Advanced Topics in Optimal Design.- Compound Symmetric Error Structure.- Optimal Designs in the Presence of Random Block Effects.- Constrained Split-Plot Designs.- Optimal Split-Plot Designs in the Presence of Hard-to-Change Factors.- Optimal Split-Plot Designs.- Summary and Future Research.
  • (source: Nielsen Book Data)9780387955155 20160528
The optimal design approach is applied to two common types of experiments, blocked and split-plot experiments. Blocked experiments are needed when not all the experimental observations can be carried out under homogenous circumstances, for example, when more than one batch of material is required or when the experiment takes up more than one day. Split-plot experiments are used when it is impractical to change the levels of some of the experimental factors.
(source: Nielsen Book Data)9780387955155 20160528
SAL3 (off-campus storage)
Book
xv, 500 p. : ill. ; 25 cm.
  • Introduction.- Comparison of Two Samples.- The Linear Regression Model.- Single-Factor Experiments with Fixed and Random Effects.- More Restrictive Designs.- Multifactor Experiments.- Repeated Measures Model.- Cross-Over Design.- Statistical Analysis of Incomplete Data.- Models for Categorical Response.
  • (source: Nielsen Book Data)9780387987897 20160528
The book is unique in the sense that it starts off with relatively simple statistical concepts and ideas found in most introductory statistical textbooks and afterwards, it proceeds to cover more and more materials useful for undergraduates and graduate students in statistics and biostatistics.
(source: Nielsen Book Data)9780387987897 20160528
SAL3 (off-campus storage)
Book
xi, 164 p. ; 24 cm.
  • Scope of the Monograph * Optimal Regression Designs in Symmetric Domains * Optimal Regression Designs in Asymmetric Factor Domains * Optimal Regression Designs for Covariates' Models with Structured Intercept Parameter * Stochastic Distance Optimality * Designs in the Presence of Trends * Additional Selected Topics.
  • (source: Nielsen Book Data)9780387953489 20160528
This book is a follow up of "Theory of Optimal Designs" by K.R. Shah and Bikas K. Sinha published in this series. Unlike the previous book, this one covers a wide range of topics in both discrete and continuous optimal designs. The topics discussed include designs for regression models, covariates models, models with trend effects, and models with competition effects. An extensive study on a new optimality criterion is also presented. The prerequisites are a basic course in the design and analysis of experiments and some familiarity with the concepts of optimality criteria. Erkki P. Liski is Professor of Statistics at the University of Tampere in Finland. He has been head of various research projects on theory and applications of statistics with the Academy of Finland. Nripes K. Mandal is Professor of Statistics at Calcutta University in India. He is closely associated with all academic activities of the Calcutta Statistical Association. Kirti R. Shah is Professor of Statistics at the University of Waterloo in Canada. He is currently the president of the International Indian Statistical Association. Bikas K. Sinha is Professor of Statistics at the Indian Statistical Institute in Calcutta, India. He has served on the editorial board of a number of international statistical journals.
(source: Nielsen Book Data)9780387953489 20160528
SAL3 (off-campus storage)
Book
xii, 684 p. : ill. ; 26 cm.
  • Simple Comparative Experiments.Experiments with a Single Factor: The Analysis of Variance.Randomized Blocks, Latin Squares, and Related Designs.Introduction to Factorial Designs.The 2 k Factorial Design.Blocking and Confounding in the 2 k Factorial Design.Two-Level Fractional Factorial Designs.Three-Level and Mixed-Level Factorial and Fractional Factorial Designs.Fitting Regression Models.Response Surface Methods and Other Approaches to Process Optimization.Experiments with Random Factors.Nested and Split-Plot Designs.Other Design and Analysis Topics.Bibliography.Appendices.Index.
  • (source: Nielsen Book Data)9780471316497 20160527
Learn How to Achieve Optimal Industrial Experimentation Through four editions, Douglas Montgomery has provided statisticians, engineers, scientists, and managers with the most effective approach for learning how to design, conduct, and analyze experiments that optimize performance in products and processes. Now, in this fully revised and enhanced Fifth Edition, Montgomery has improved his best-selling text by focusing even more sharply on factorial and fractional factorial design and presenting new analysis techniques (including the generalized linear model). There is also expanded coverage of experiments with random factors, response surface methods, experiments with mixtures, and methods for process robustness studies. The book also illustrates two of today's most powerful software tools for experimental design: Design-Expert and Minitab. Throughout the text, You'll find output from these two programs, along with detailed discussion on how computers are currently used in the analysis and design of experiments. You'll also learn how to use statistically designed experiments to: Obtain information for characterization and optimization of systems Improve manufacturing processes Design and develop new processes and products Evaluate material alternatives in product design Improve the field performance, reliability, and manufacturing aspects of products Learn how to conduct experiments effectively and efficiently Other important textbook features: Student version of Design-Expert software is available. Web site (www wiley.com/college/montgomery) offers supplemental text material for each chapter, a sample syllabus, and sample student projects from the author's Design of Experiments course at Arizona State University.
(source: Nielsen Book Data)9780471316497 20160527
Science Library (Li and Ma)
Book
205 p. : ill. ; 24 cm.
  • INTRODUCTION Clinical Applications of Pretest-Posttest Data Why use the Pretest Data Graphical Presentation of Pretest-Posttest Data How to Analyze Pretest-Postest Data: Possible Solutions A Note on SAS Notation Focus of the Book MEASUREMENT CONCEPTS What is Validity? What is Reliability? What is Regression Towards the Mean? Why is Regression Towards the Mean Important? Dealing with Regression Towards the Mean and How to Take Advantage of Test-Retest Reliability What is Pretest Sensitization? Controlling for Pretest Sensitization with Factorial Designs Alternative Methods for Controlling for Pretest Sensitization DIFFERENCE SCORES Definition and Assumptions Case 1: The Absence of a Treatment Intervention Between Measurement of the Pretest and Posttest Scores Case 2: The Application of a Treatment Intervention Between Measurement of the Pretest and Posttest Scores Nonparametric Alternative to Case 1 or Case 2 Case 3: Two Groups with Different Treatment Interventions Between Measurement of Pretest and Posttest Scores Case 4: More than Two Groups with Different Treatment Interventions Between Measurement of Pretest and Posttest Scores Unreliability of Difference Scores Testing the Distribution of Change and Relative change Scores Effect of Regression Towards the Mean on Difference Scores RELATIVE CHANGE FUNCTIONS Definitions and Assumptions Statistical Analyses with Change Scores Change Scores and Regression Towards the Mean Difference Scores or Relative change Scores? Other Relative change Functions Distribution of Relative change Scores ANALYSIS OF COVARIANCE Definitions and Assumptions Parametric ANCOVA ANCOVA with Difference Scores as the Dependent Variable ANCOVA using Percent change as the Dependent Variable Assumptions of the ANCOVA Violation of Homogeneity of Within-Groups Regression Coefficients Error-in-Variables ANCOVA Other Violations Effect of Outliers and Influential Observations Nonrandom Assignment of Subject to Treatment Groups BLOCKING TECHNIQUES Using Stratification to Control for the Pretest Post-Hoc Stratification REPEATED MEASURES ANALYSIS OF VARIANCE Using Repeated Measures ANOVA for Analysis of Pretest-Posttest Data Regression Towards the Mean with Multiple Posttest Measurements Using Repeated Measures ANOVA for Analysis of Pretest-Posttest Data with Multiple Posttest Measurements Analysis of Repeated Measures using Summary Measures CHOOSING A STATISTICAL TEST Choosing a Test Based on how the Data will be Presented Generation of Bivariate, Normally Distributed Data with a Specified Covariance Structure Monte Carlo Simulation when the Assumptions of the Statistical Test are Met Monte Carlo simulation when Systematic Bias Affects the Pretest and Posttest Equally Monte Carlo Simulation when the variance of the Posttest Scores does not Equal the Variance of the Pretest Scores Monte Carlo Simulation when Subjects are Grouped A Priori based on Pretest Score Monte Carlo Simulation when the Marginal Distribution of the Pretest and Posttest Scores is Non-Normal RANDOMIZATION TESTS Permutation Tests and Randomization Tests Randomization Tests and Pretest-Posttest Data Analysis of Covariance Resampling within Block or Time Periods Resampling with Missing Data SPECIAL TOPICS: EQUALITY OF VARIANCE Methods and Procedures APPENDIX: SAS Code.
  • (source: Nielsen Book Data)9781584881735 20160528
How do you analyze pretest-posttest data? Difference scores? Percent change scores? ANOVA? In medical, psychological, sociological, and educational studies, researchers often design experiments in which they collect baseline (pretest) data prior to randomization. However, they often find it difficult to decide which method of statistical analysis is most appropriate to use. Until now, consulting the available literature would prove a long and arduous task, with papers sparsely scattered throughout journals and textbook references few and far between."Analysis of Pretest-Posttest Designs" brings welcome relief from this conundrum. This one-stop reference - written specifically for researchers - answers the questions and helps clear the confusion about analyzing pretest-posttest data. Keeping derivations to a minimum and offering real life examples from a range of disciplines, the author gathers and elucidates the concepts and techniques most useful for studies incorporating baseline data.This title helps to understand the pros and cons of different methods - ANOVA, ANCOVA, percent change, difference scores, and more. You can learn to choose the most appropriate statistical test - numerous Monte Carlo simulations compare the various tests and help you select the one best suited to your data. You can tackle more difficult analyses - the extensive SAS code included saves you programming time and effort. Requiring just a basic background in statistics and experimental design, this book incorporates most, if not all of the reference material that deals with pretest-posttest data. If you use baseline data in your studies, "Analysis of Pretest-Posttest Designs" will save you time, increase your understanding, and ultimately improve the interpretation and analysis of your data.
(source: Nielsen Book Data)9781584881735 20160528
Science Library (Li and Ma)

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