Introduction to Design and Analysis of Experiments
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Introduction to Design and Analysis of Experiments explains how to choose sound and suitable design structures and engages students in understanding the interpretive and constructive natures of data analysis and experimental design. Cobb's approach allows students to build a deep understanding of statistical concepts over time as they analyze and design experiments. The field of statistics is presented as a matrix, rather than a hierarchy, of related concepts. Developed over years of classroom use, this text can be used as an introduction to statistics emphasizing experimental design or as an elementary graduate survey course. Widely praised for its exceptional range of intelligent and creative exercises, and for its large number of examples and data sets, Introduction to Design and Analysis of Experiments--now offered in a convenient paperback format--helps students increase their understanding of the material as they come to see the connections between diverse statistical concepts that arise from the experiments around which the text is built.
List price: $169.95
Copyright year: 1998
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/10/2008
Size: 7.00" wide x 9.50" long x 1.00" tall
To the Instructor Sample Exam Questions To the Student Acknowledgments 1.Introduction To Experimental Design 1 1. The Challenge of planning a good experiment 1 The content of an experiment: three decisions 2 Three sources of variability: one we want and two we don't 4 Three kinds of variability: one we want, one we can live with, and one that threatens disaster. 6 Chance error and bias compared 10 Design in a nutshell: Isolate the effects of interest-control what you can, and randomize the rest. 13 2. Three basic principles and four experimental designs 13 Random assignment and the one-way randomized basic factorial (RBF) design. 14 Blocking, and the one-way complete block (CB) design. 16 Factorial crossing, and the two-way factorial (BF) design. 19 Kelly's experiment: a split plot / repeated measures design, or SP/RM. 21 3. The factor structure of the four experimental designs 22 Factor structure of the one-way basic factorial (BF) design 24 Factor structure of the one-way complete block (CB) design 25 Factor structure of the two-way basic factorial (BF) design 26 Factor structure of the split plot / repeated measures (SP/RM) design. 27 Appendix: Other ways to guard against bias. 30 2. Informal Analysis and Checking Assumptions 38 1. What analysis of variance does 39 2. The Six Fisher Assumptions 42 Two assumptions unknown true values 42 Four assumptions residual errors 43 3.Informal analysis, part 1: parallel dot graphs and choosing a scale 45 Averages and parallel dot graphs 46 Choosing a new scale 48 4. Informal analysis, part 2: interaction graph for the log concentrations 52 Interaction graphs