This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
<i>Experimental Design and Statistics for Psychology: A First Course</i> is a concise, straighforward and accessible introduction to the design of psychology experiments and the statistical tests used to make sense of their results.<br><ul><br><li>Makes abundant use of charts, diagrams and figures. <br><li>Assumes no prior knowledge of statistics. <br><li>Invaluable to all psychology students needing a firm grasp of the basics, but tackling of some of the topic’s more complex, controversial issues will also fire the imagination of more ambitious students. <br><li>Covers different aspects of experimental design, including dependent versus independent variables, levels of treatment, experimental control, random versus systematic errors, and within versus between subjects design. <br><li>Provides detailed instructions on how to perform statistical tests with SPSS.</li></ul><br><p>Downloadable instructor resources to supplement and support your lectures can be found at www.blackwellpublishing.com/sani and include sample chapters, test questions, SPSS data sets, and figures and tables from the book.
It is well known that contemporary mathematics includes many disci- plines. Among them the most important are: set theory, algebra, topology, geometry, functional analysis, probability theory, the theory of differential equations and some others. Furthermore, every mathematical discipline consists of several large sections in which specific problems are investigated and the corresponding technique is developed. For example, in general topology we have the following extensive chap- ters: the theory of compact extensions of topological spaces, the theory of continuous mappings, cardinal-valued characteristics of topological spaces, the theory of set-valued (multi-valued) mappings, etc. Modern algebra is featured by the following domains: linear algebra, group theory, the theory of rings, universal algebras, lattice theory, category theory, and so on. Concerning modern probability theory, we can easily see that the clas- sification of its domains is much more extensive: measure theory on ab- stract spaces, Borel and cylindrical measures in infinite-dimensional vector spaces, classical limit theorems, ergodic theory, general stochastic processes, Markov processes, stochastical equations, mathematical statistics, informa- tion theory and many others.
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