Ulteriori informazioni
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.
Sommario
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
Info autore
BrianS. Yandell
Riassunto
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
Testo aggiuntivo
"…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
Relazione
"...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
