Analysis of Messy Data: Volume 1

Analysis of Messy Data: Volume 1 : Designed Experiments

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A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researchers analyze the kinds of data sets encountered in the real world. Written by two long-time researchers and professors, this second edition has been fully updated to reflect the many developments that have occurred since the original publication. New to the Second Edition * Several modern suggestions for multiple comparison procedures * Additional examples of split-plot designs and repeated measures designs * The use of SAS-GLM to analyze an effects model * The use of SAS-MIXED to analyze data in random effects experiments, mixed model experiments, and repeated measures experiments The book explores various techniques for multiple comparison procedures, random effects models, mixed models, split-plot experiments, and repeated measures designs. The authors implement the techniques using several statistical software packages and emphasize the distinction between design structure and the structure of treatments. They introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. Bringing a classic work up to date, this edition will continue to show readers how to effectively analyze real-world, nonstandard data more

Product details

  • Hardback | 674 pages
  • 184 x 258 x 38mm | 1,399.98g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 2nd Revised edition
  • 100 black & white illustrations
  • 1584883340
  • 9781584883340
  • 1,389,451

Table of contents

The Simplest Case: One-Way Treatment Structure in a Completely Randomized Design Structure with Homogeneous Errors Model Definitions and Assumptions Parameter Estimation Inferences on Linear Combinations-Tests and Confidence Intervals Example-Tasks and Pulse Rate Simultaneous Tests on Several Linear Combinations Example-Tasks and Pulse Rate (Continued) Testing the Equality of all Means Example-Tasks and Pulse Rate (Continued) General Method for Comparing Two Models-The Principle of Conditional Error Example-Tasks and Pulse Rate (Continued) Computer Analyses One-Way Treatment Structure in a Completely Randomized Design Structure with Heterogeneous Errors Model Definitions and Assumptions Parameter Estimation Tests for Homogeneity of Variances Example-Drugs and Errors Inferences on Linear Combinations Example-Drugs and Errors (Continued) General Satterthwaite Approximation for Degrees of Freedom Comparing All Means Simultaneous Inference Procedures and Multiple Comparisons Error Rates Recommendations Least Significant Difference Fisher's LSD Procedure Bonferroni's Method Scheffe's Procedure Tukey-Kramer Method Simulation Methods Sidak Procedure Example-Pairwise Comparisons Dunnett's Procedure Example-Comparing with a Control Multivariate t Example-Linearly Independent Comparisons Sequential Rejective Methods Example-Linearly Dependent Comparisons Multiple Range Tests Waller-Duncan Procedure Example-Multiple Range for Pairwise Comparisons A Caution Basics for Designing Experiments Introducing Basic Ideas Structures of a Designed Experiment Examples of Different Designed Experiments Multilevel Designs: Split-Plots, Strip-Plots, Repeated Measures, and Combinations Identifying Sizes of Experimental Units-Four Basic Design Structures Hierarchical Design: A Multilevel Design Structure Split-Plot Design Structures: Two-Level Design Structures Strip-Plot Design Structures: A Nonhierarchical Multilevel Design Repeated Measures Designs Designs Involving Nested Factors Matrix Form of the Model Basic Notation Least Squares Estimation Estimability and Connected Designs Testing Hypotheses about Linear Model Parameters Population Marginal Means Balanced Two-Way Treatment Structures Model Definition and Assumptions Parameter Estimation Interactions and Their Importance Main Effects Computer Analyses Case Study: Complete Analyses of Balanced Two-Way Experiments Contrasts of Main Effect Means Contrasts of Interaction Effects Paint-Paving Example Analyzing Quantitative Treatment Factors Multiple Comparisons Using the Means Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers Model Definitions and Assumptions Parameter Estimation Testing whether All Means Are Equal Interaction and Main Effect Hypotheses Population Marginal Means Simultaneous Inferences and Multiple Comparisons Using the Effects Model to Analyze Balanced Two-Way Treatment Structures with Unequal Subclass Numbers Model Definition Parameter Estimates and Type I Analysis Using Estimable Functions in SAS Types I-IV Hypotheses Using Types I-IV Estimable Functions in SAS-GLM Population Marginal Means and Least Squares Means Computer Analyses Analyzing Large Balanced Two-Way Experiments Having Unequal Subclass Numbers Feasibility Problems Method of Unweighted Means Simultaneous Inference and Multiple Comparisons An Example of the Method of Unweighted Means Computer Analyses Case Study: Balanced Two-Way Treatment Structure with Unequal Subclass Numbers Fat-Surfactant Example Using the Means Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations Parameter Estimation Hypothesis Testing and Confidence Intervals Computer Analyses Using the Effects Model to Analyze Two-Way Treatment Structures with Missing Treatment Combinations Type I and II Hypotheses Type III Hypotheses Type IV Hypotheses Population Marginal Means and Least Squares Means Case Study: Two-Way Treatment Structure with Missing Treatment Combinations Case Study Analyzing Three-Way and Higher-Order Treatment Structures General Strategy Balanced and Unbalanced Experiments Type I and II Analyses Case Study: Three-Way Treatment Structure with Many Missing Treatment Combinations Nutrition Scores Example An SAS-GLM Analysis A Complete Analysis Random Effects Models and Variance Components Introduction General Random Effects Model in Matrix Notation Computing Expected Mean Squares Methods for Estimating Variance Components Method of Moments Maximum Likelihood Estimators Restricted or Residual Maximum Likelihood Estimation MIVQUE Method Estimating Variance Components Using JMP(R) Methods for Making Inferences about Variance Components Testing Hypotheses Constructing Confidence Intervals Simulation Study Case Study: Analysis of a Random Effects Model Data Set Estimation Model Building Reduced Model Confidence Intervals Computations Using JMP(R) Analysis of Mixed Models Introduction to Mixed Models Analysis of the Random Effects Part of the Mixed Model Analysis of the Fixed Effects Part of the Model Best Linear Unbiased Prediction Mixed Model Equations Case Studies of a Mixed Model Unbalanced Two-Way Mixed Model JMP(R) Analysis of the Unbalanced Two-Way Data Set Methods for Analyzing Split-Plot Type Designs Introduction Model Definition and Parameter Estimation Standard Errors for Comparisons among Means A General Method for Computing Standard Errors of Differences of Means Comparison via General Contrasts Additional Examples Sample Size and Power Considerations Computations Using JMP(R) Methods for Analyzing Strip-Plot Type Designs Description of the Strip-Plot Design and Model Techniques for Making Inferences Example: Nitrogen by Irrigation Example: Strip-Plot with Split-Plot 1 Example: Strip-Plot with Split-Plot 2 Strip-Plot with Split-Plot 3 Split-Plot with Strip-Plot 4 Strip-Strip-Plot Design with Analysis via JMP(R)7 Methods for Analyzing Repeated Measures Experiments Model Specifications and Ideal Conditions The Split-Plot in Time Analyses Data Analyses Using the SAS-MIXED Procedure Analysis of Repeated Measures Experiments When the Ideal Conditions Are Not Satisfied Introduction MANOVA Methods p-Value Adjustment Methods Mixed Model Methods Case Studies: Complex Examples Having Repeated Measures Complex Comfort Experiment Family Attitudes Experiment Multilocation Experiment Analysis of Crossover Designs Definitions, Assumptions, and Models Two Period/Two Treatment Designs Crossover Designs with More Than Two Periods Crossover Designs with More Than Two Treatments Analysis of Nested Designs Definitions, Assumptions, and Models Parameter Estimation Testing Hypotheses and Confidence Interval Construction Analysis Using JMP(R) Appendix Index Concluding Remarks, Exercises, and References appear at the end of each more

About Dallas E. Johnson

Kansas State University, Manhattan, Kansas, USAshow more

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