By Carl J. Huberty, Stephen Olejnik(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)
A whole creation to discriminant analysis--extensively revised, extended, and updated
This Second Edition of the vintage ebook, Applied Discriminant Analysis, displays and references present utilization with its new identify, Applied MANOVA and Discriminant Analysis. completely up to date and revised, this publication is still crucial for any researcher or scholar wanting to profit to talk, learn, and write approximately discriminant research in addition to advance a philosophy of empirical study and knowledge research. Its thorough advent to the appliance of discriminant research is unprecedented.
providing the main updated computing device purposes, references, phrases, and real-life learn examples, the Second Edition additionally contains new discussions of MANOVA, descriptive discriminant research, and predictive discriminant research. more moderen SAS macros are incorporated, and graphical software program with facts units and courses are supplied at the book's comparable website.
The publication features:
- Detailed discussions of multivariate research of variance and covariance
- An elevated variety of bankruptcy routines besides chosen solutions
- Analyses of knowledge received through a repeated measures layout
- A new bankruptcy on analyses regarding predictive discriminant research
- Basic SPSS(r) and SAS(r) desktop syntax and output built-in during the booklet
Applied MANOVA and Discriminant Analysis permits the reader to observe numerous varieties of learn questions utilizing MANOVA and discriminant research; to profit the that means of this field's suggestions and phrases; and as a way to layout a learn that makes use of discriminant research via issues reminiscent of one-factor MANOVA/DDA, assessing and describing MANOVA results, and deleting and ordering variables.Content:
Chapter 1 Discriminant research in learn (pages 3–14):
Chapter 2 Preliminaries (pages 15–32):
Chapter three workforce Separation (pages 35–59):
Chapter four Assessing MANOVA results (pages 61–79):
Chapter five Describing MANOVA results (pages 81–102):
Chapter 6 Deleting and Ordering Variables (pages 103–116):
Chapter 7 Reporting DDA effects (pages 117–127):
Chapter eight Factorial MANOVA (pages 131–162):
Chapter nine research of Covariance (pages 163–192):
Chapter 10 Repeated?Measures research (pages 193–225):
Chapter eleven Mixed?Model research (pages 227–251):
Chapter 12 category fundamentals (pages 255–267):
Chapter thirteen Multivariate basic ideas (pages 269–284):
Chapter 14 class effects (pages 285–293):
Chapter 15 Hit fee Estimation (pages 295–314):
Chapter sixteen Effectiveness of class ideas (pages 315–333):
Chapter 17 Deleting and Ordering Predictors (pages 335–347):
Chapter 18 Two?Group category (pages 349–360):
Chapter 19 Nonnormal ideas (pages 361–374):
Chapter 20 Reporting PDA effects (pages 375–383):
Chapter 21 PDA?Related Analyses (pages 385–389):
Chapter 22 matters in PDA and DDA (pages 393–400):
Chapter 23 difficulties in PDA and DDA (pages 401–410):
Read or Download Applied MANOVA and Discriminant Analysis, Second Edition PDF
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Additional info for Applied MANOVA and Discriminant Analysis, Second Edition
It is assumed that the reader is familiar with the use of univariate analysis of variance (ANOVA) to study the effects of one or more grouping variables on a single outcome variable. Having familiarity with univariate ANOVA, the reader undoubtedly can think of research situations involving one factor (or grouping variable) and involving multiple factors with a single outcome variable. To come up with MANOVA situations, then, one need only think of multiple outcome variables for one-factor or multiple-factor designs.
54 A square matrix is one with the number of rows equal to the number of columns. An example of a symmetric square matrix is the covariance matrix, Sj , where the elements on the main diagonal represent the variances of the variables under investigation, and the off-diagonal elements represent the covariances. For the TA group (j = 1) the covariance matrix is written as: S1 = Cov1 (Y1 Y2 ) . 679 . 784 2 The covariance matrix, Sj , is described as a symmetric matrix because the entries are reflected about the main diagonal.
Specifically, we will be seeking the vector solution of the messy matrix equation (B − λI) (m × m) · b (m × 1) = 0, (m × 1) where b is a (column) vector of m weights and 0 is a vector of m zeros. It turns out that there is one vector solution bm corresponding to each of the m eigenvalues of B—some vector solutions may be the trivial zero vector. The vector bm is called the eigenvector (or characteristic vector) of the matrix B associated with the eigenvalue λv (v = 1, 2, . . , m). The computation of eigenvalues and eigenvectors in nearly all discriminant analyses is quite complicated, indeed.
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