Practical Data Analysis for Designed Experiments
Published on 22. November 2017
440 pages
978-1-351-42299-4 (ISBN)
Description
Placing data in the context of the scientific discovery of knowledge through experimentation, Practical Data Analysis for Designed Experiments examines issues of comparing groups and sorting out factor effects and the consequences of imbalance and nesting, then works through more practical applications of the theory. Written in a modern and accessible manner, this book is a useful blend of theory and methods. Exercises included in the text are based on real experiments and real data.
Reviews / Votes
"...the book should be useful for statisticians who are starting out as consultants...also contains much good practical advice based on the writer's experience as a teacher and statistical advisor."-M. Talbot, Biometrics, December 1998
"...gives a generally lucid and well thought out introduction to the use of data driven approaches for statistical data analysis...the explanations are clear, without being obscured by too much mathematical detail...an excellent basis for a statistics course with an applied orientation, and most institutions that teach statistics or analyse data will probably want a library copy."
-S.N. Wood,Biometrics,December 1998
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
File size
16,13 MB
ISBN-13
978-1-351-42299-4 (9781351422994)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Brian S. Yandell
Practical Data Analysis for Designed Experiments
Book
01/1997
Chapman & Hall/CRC
€85.59
Shipment within 10-20 days
Person
Brian S. Yandell
Content
Preface
Part A: Placing Data in Context
Practical Data Analysis
Effect of Factors
Nature of Data
Summary Tables
Plots for Statistics
Computing
Interpretation
Problems
Collaboration in Science
Asking Questions
Learning from Plots
Mechanics of a Consulting Session
Philosophy and Ethics
Intelligence, Culture and Learning
Writing
Problems
Experimental Design
Types of Studies
Designed Experiments
Design Structure
Treatment Structure
Designs in This Book
Problems
Part B: Working with Groups of Data
Group Summaries
Graphical Summaries
Estimates of Means and Variance
Assumptions and Pivot Statistics
Interval Estimates of Means
Testing Hypotheses about Means
Formal Inference on the Variance
Problems
Comparing Several Means
Linear Contrasts of Means
Overall Test of Difference
Partitioning Sums of Squares
Expected Mean Squares
Power and Sample Size
Problems
Multiple Comparisons of Means
Experiment- and Comparison-Wise Error Rates
Comparisons Based on F-Tests
Comparisons Based on Range of Means
Comparisons of Comparisons
Problems
Part C: Sorting Out Effects with Data
Factorial Designs
Cell Means Models
Effects Models
Estimable Functions
Linear Constraints
General Form of Estimable Functions
Problems
Balanced Experiments
Additive Models
Full Models with Two Factors
Interaction Plots
Higher Orders Models
Problems
Model Selection
Pooling Interactions
Selected the "Best" Model
Model Selection Criteria
One Observation per Cell
Tukey's Test for Interaction
Problems
Part D: Dealing with Imbalance
Unbalanced Experiments
Unequal Samples
Additive Model
Types I, II, III and IV
Problems
Missing Cells
What Are Missing Cells?
Connected Cells and Incomplete Designs
Type IV Comparisons
Latin Square Designs
Fractional Factorial Designs
Problems
Linear
Part A: Placing Data in Context
Practical Data Analysis
Effect of Factors
Nature of Data
Summary Tables
Plots for Statistics
Computing
Interpretation
Problems
Collaboration in Science
Asking Questions
Learning from Plots
Mechanics of a Consulting Session
Philosophy and Ethics
Intelligence, Culture and Learning
Writing
Problems
Experimental Design
Types of Studies
Designed Experiments
Design Structure
Treatment Structure
Designs in This Book
Problems
Part B: Working with Groups of Data
Group Summaries
Graphical Summaries
Estimates of Means and Variance
Assumptions and Pivot Statistics
Interval Estimates of Means
Testing Hypotheses about Means
Formal Inference on the Variance
Problems
Comparing Several Means
Linear Contrasts of Means
Overall Test of Difference
Partitioning Sums of Squares
Expected Mean Squares
Power and Sample Size
Problems
Multiple Comparisons of Means
Experiment- and Comparison-Wise Error Rates
Comparisons Based on F-Tests
Comparisons Based on Range of Means
Comparisons of Comparisons
Problems
Part C: Sorting Out Effects with Data
Factorial Designs
Cell Means Models
Effects Models
Estimable Functions
Linear Constraints
General Form of Estimable Functions
Problems
Balanced Experiments
Additive Models
Full Models with Two Factors
Interaction Plots
Higher Orders Models
Problems
Model Selection
Pooling Interactions
Selected the "Best" Model
Model Selection Criteria
One Observation per Cell
Tukey's Test for Interaction
Problems
Part D: Dealing with Imbalance
Unbalanced Experiments
Unequal Samples
Additive Model
Types I, II, III and IV
Problems
Missing Cells
What Are Missing Cells?
Connected Cells and Incomplete Designs
Type IV Comparisons
Latin Square Designs
Fractional Factorial Designs
Problems
Linear
