This practical tool for statisticians offers techniques and methods for effectively analyzing non-standard or messy data sets that arise from experimental design situations. The volume focuses on the analysis of variance techniques, covering the more basic ones in early chapters, including one-and two-way analyses of variance and multiple-comparison procedures. It also provides a unique approach to experimental design, which emphasizes the distinction between design structure and the structure of treatments. The middle portion of the book deals with unbalanced data in two-way structures. Here, the book describes and uses different linear models, the so-called means model, and the effects model, with some treatment of higher-order structures. The book then moves on to random and mixed models, stressing the estimation of, and inference about, variance components. The final chapters focus on more complex structures, including designs with several sizes of experimental units, such as split-plot designs and repeated-measure designs.
Throughout, the book introduces each topic with several examples, follows up with a more theoretical discussion, and concludes with a case study using actual data. Its overriding emphasis on practical implementation extends to computers, with several available statistical packages, including SAS, BMD and SPSS.
This practical tool for statisticians offers techniques and methods for effectively analyzing non-standard or messy data sets that arise from experimental design situations. The volume focuses on the analysis of variance techniques, covering the more basic ones in early chapters, including one-and two-way analyses of variance and multiple-comparison procedures. It also provides a unique approach to experimental design, which emphasizes the distinction between design structure and the structure of treatments. The middle portion of the book deals with unbalanced data in two-way structures. Here, the book describes and uses different linear models, the so-called means model, and the effects model, with some treatment of higher-order structures. The book then moves on to random and mixed models, stressing the estimation of, and inference about, variance components. The final chapters focus on more complex structures, including designs with several sizes of experimental units, such as split-plot designs and repeated-measure designs.
Throughout, the book introduces each topic with several examples, follows up with a more theoretical discussion, and concludes with a case study using actual data. Its overriding emphasis on practical implementation extends to computers, with several available statistical packages, including SAS, BMD and SPSS.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Kluwer Academic Publishers Group
Zielgruppe
Editions-Typ
Illustrationen
Maße
Höhe: 152 mm
Breite: 235 mm
Gewicht
ISBN-13
978-0-442-01309-7 (9780442013097)
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Schweitzer Klassifikation
The simplest case: One-way treatment structure in a completely randomized design structure with homogenous errors. One-way treatment structure in a completely randomized design structure with heterogenous errors. Simultaneous inference procedures and multiple comparisons. Basics of experimental design. Experimental designs involving several sizes of experimental units. Matrix form of the model. Balanced two-way treatment structures. Case study: Complete analyses of balanced two-way experiments. Using the means model to analyze balanced two-way treatment structures with unequal subclass numbers. Using the effects model to analyze balanced two-way treatment structures with unequal subclass numbers. Analyzing large balanced two-way experiments with unequal subclass numbers. Case study: Balanced two-way treatment structure with unequal subclass numbers. Using the means model to analyze two-way treatment structures with missing treatment combinations. Using the effects model to analyze two-way treatment structures with missing treatment combinations. Case study: Two-way treatment structure with missing treatment combinations. Analyzing three-way and higher-order treatment structures. Case study: Three-way treatment structures with many missing treatment combinations. Random models and variance components. Methods for estimating variance components. Methods for making inference about variance components. Case study: Analysis of a random model. Analysis of mixed models. Two case studies of mixed models. Methods for analyzing balanced split-plot designs. Strip-plot experimental designs. Analysis of repeated measure designs for which the usual assumptions do not hold. Analyzing split-plot and certain repeated measure experiments with unbalanced and missing data. Computing the variances of contrasts for repeated measures and split-plot designs by using Hartley's method of synthesis. Analysis of repeated measure experiments by using multivariate methods. Analysis of crossover designs.
