Handbook of Statistical Distributions with Applications (Google eBook)

Front Cover
CRC Press, Jun 19, 2006 - Mathematics - 376 pages
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In the area of applied statistics, scientists use statistical distributions to model a wide range of practical problems, from modeling the size grade distribution of onions to modeling global positioning data. To apply these probability models successfully, practitioners and researchers must have a thorough understanding of the theory as well as a familiarity with the practical situations. The Handbook of Statistical Distributions with Applications is the first reference to combine popular probability distribution models, formulas, applications, and software to assist you in computing probabilities, percentiles, moments, and other statistics.

Presenting both common and specialized probability distribution models, as well as providing applications with practical examples, this handbook offers comprehensive coverage of plots of probability density functions, methods of computing probability and percentiles, algorithms for random number generation, and inference, including point estimation, hypothesis tests, and sample size determination. The book discusses specialized distributions, some nonparametric distributions, tolerance factors for a multivariate normal distribution, and the distribution of the sample correlation coefficient, among others.

Developed by the author, the StatCal software (available for download at www.crcpress.com), along with the text, offers a useful reference for computing various table values. By using the software, you can compute probabilities, parameters, and moments; find exact tests; and obtain exact confidence intervals for distributions, such as binomial, hypergeometric, Poisson, negative binomial, normal, lognormal, inverse Gaussian, and correlation coefficient.

In the applied statistics world, the Handbook of Statistical Distributions with Applications is now the reference for examining distribution functions - including univariate, bivariate normal, and multivariate - their definitions, their use in statistical inference, and their algorithms for random number generation.
  

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Contents

Preliminaries
9
Discrete Uniform Distribution
29
Binomial Distribution
31
Hypergeometric Distribution
51
Poisson Distribution
71
Geometric Distribution
93
Negative Binomial Distribution
97
Logarithmic Series Distribution
107
Logistic Distribution
241
Lognormal Distribution
247
Pareto Distribution
257
Weibull Distribution
263
Extreme Value Distribution
269
Cauchy Distribution
275
Inverse Gaussian Distribution
281
Rayleigh Distribution
289

Continuous Uniform Distribution
115
Normal Distribution
119
ChiSquare Distribution
155
F Distribution
163
Students t Distribution
171
Exponential Distribution
179
Gamma Distribution
185
Beta Distribution
195
Noncentral Chisquare Distribution
207
Noncentral F Distribution
217
Noncentral t Distribution
225
Laplace Distribution
233
Bivariate Normal Distribution
293
Distribution of Runs
307
Sign Test and Confidence Interval for the Median
311
Wilcoxon SignedRank Test
315
Wilcoxon RankSum Test
319
Nonparametric Tolerance Interval
323
Tolerance Factors for a Multivariate Normal Population
325
Distribution of the Sample Multiple Correlation Coefficient
329
References
335
Index
345
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Page 343 - Estimation of the Mean of a Multivariate Normal Distribution," The Annals of Statistics 9, pp.

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