A FIRST COURSE IN THE DESIGN OF EXPERIMENTS A LINEAR MODELS APPROACH

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Most texts on experimental design fall into one of two distinct categories. There are theoretical works with few applications and minimal discussion on design, and there are methods books with limited or no discussion of the underlying theory. Furthermore, most of these tend to either treat the analysis of each design separately with little attempt to unify procedures, or they will integrate the analysis for the designs into one general technique. A First Course in the Design of Experiments: A Linear Models Approach stands apart. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general theory for linear models. The authors begin with a general introduction then lead students through the theoretical results, the various design models, and the analytical concepts that will enable them to analyze virtually any design. Rife with examples and exercises, the text also encourages using computers to analyze data. The authors use the SAS software package throughout the book, but also demonstrate how any regression program can be used for analysis. With its balanced presentation of theory, methods, and applications and its highly readable style, A First Course in the Design of Experiments proves ideal as a text for a beginning graduate or upper-level undergraduate course in the design and analysis of experiments.

FUNDAMENTAL CONCEPTS IN THE DESIGN OF EXPERIMENTS (5版)

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Description Fundamental Concepts in the Design of Experiments, 5/e offers comprehensive coverage of the key elements of experimental design used by applied researchers to solve problems in the field. Wide-ranging and accessible, it shows students how to use applied statistics for planning, running, and analyzing experiments. Featuring over 350 problems taken from the authors' actual industrial consulting experiences, the text gives students valuable practice with real data and problem solving. The problems emphasize the basic philosophy of design and are simple enough for students with limited mathematical back grounds to understand. The authors provide extensive coverage of the analysis of residuals, the concept of resolution in fractional replications, Plackett-Burman designs, and Taguchi techniques. SAS (Statistical Analysis System) computer programs are incorporated to facilitate analysis. Thoroughly revised and updated, this new edition includes sixty new problems, focuses more on computer use (adding computer outputs from statistical packages like Minitab, SPSS, and JMP), and emphasizes graphical procedures including residual plots and normal quantile plots. Ideal for various advanced undergraduate and graduate experimental methods courses taught in statistics, engineering, and mathematics departments, this book will also appeal to professionals and researchers doing experimental work. Features Over 350 problems based on real data from the author's consulting experiments in the industry. A breadth of realistic examples, providing a wealth of material for a wide audience. Good topical coverage at a level suitable for students who do not have extensive mathematical backgrounds. Use of SAS (statistical anlaysis system) programs to analyze results of experiments at the end of nearly all chapters. The single most unique feature is the author's breadth of coverage at a very lucid. Insightful level. This is especially helpful for students whose backgrounds in math and statistics are minimal, but are required to know this material to apply it in their field. Table of Contents Preface 1. The Experiment, the Design, and the Analysis 2. Review of Statistical Inference 3. Single-Factor Experiments with No Restrictions on Randomization 4. Single-Factor Experiments: Randomized Block and Latin Square Designs 5. Factorial Experiments 6. Fixed, Random, and Mixed Models 7. Nested and Nested-Factorial Experiments 8. Experiments of Two or More Factors: Restrictions on Randomization 9. 2f Factorial Experiments 10. 3f Factorial Experiments 11. Factorial Experiment: Split-Plot Design 12. Factorial Experiment: Confounding in Blocks 13. Fractional Replication 14. The Taguchi Approach to the Design of Experiments 15. Regression 16. Miscellaneous Topics Statistical Tables Table A Areas Under the Normal Curve Table B Student's t Distribution Table C Cumulative Chi-Square Distribution Table D Cumulative F Distribution Table E.1 Upper 5% of Studentized Range q Table E.2 Upper 1% of Studentized Range q Table F Coefficients of Orthogonal Polynomials

EXPERIMENTAL DESIGN

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Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences A practical guide to the use of basic principles of experimental design and statistical analysis in pharmacology Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences provides clear instructions on applying statistical analysis techniques to pharmacological data. Written by an experimental pharmacologist with decades of experience teaching statistics and designing preclinical experiments, this reader-friendly volume explains the variety of statistical tests that researchers require to analyze data and draw correct conclusions. Detailed, yet accessible, chapters explain how to determine the appropriate statistical tool for a particular type of data, run the statistical test, and analyze and interpret the results. By first introducing basic principles of experimental design and statistical analysis, the author then guides readers through descriptive and inferential statistics, analysis of variance, correlation and regression analysis, general linear modelling, and more. Lastly, throughout the textbook are numerous examples from molecular, cellular, in vitro, and in vivo pharmacology which highlight the importance of rigorous statistical analysis in real-world pharmacological and biomedical research. This textbook also: Describes the rigorous statistical approach needed for publication in scientific journals Covers a wide range of statistical concepts and methods, such as standard normal distribution, data confidence intervals, and post hoc and a priori analysis Discusses practical aspects of data collection, identification, and presentation Features images of the output from common statistical packages, including GraphPad Prism, Invivo Stat, MiniTab and SPSS Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences is an invaluable reference and guide for undergraduate and graduate students, post-doctoral researchers, and lecturers in pharmacology and allied subjects in the life sciences.