(Ebook) Handbook of statistical distributions with applications 1st Edition by Kalimuthu Krishnamoorthy ISBN 9781498741491 1498741495

(Ebook) Handbook of statistical distributions with applications 1st Edition by Kalimuthu Krishnamoorthy ISBN 9781498741491 1498741495

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ISBN 10: 1498741495
ISBN 13: 9781498741491
Author: Kalimuthu Krishnamoorthy

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 (http://www.crcpress.com) 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

(Ebook) Handbook of statistical distributions with applications 1st Edition Table of contents:

0.1 Introduction
0.2 Contents of StatCalc
1.1 Random Variables and Expectations
1.2.2 Moments
1.2.3 Measures of Variability
1.2.5 Other Measures
1.3 Some Functions Relevant to Reliability
1.4 Model Fitting
1.4.2 The Chi-Square Goodness-of-Fit Test
1.5.1 Moment Estimation
Some Terminologies
Errors and Powers
Tolerance Intervals
The Accept/Reject Method
1.8 Some Special Functions
2.1 Description
2.2 Moments
3.1 Description
3.2 Moments
3.3 Computing Table Values
Illustrative Examples
3.4.2 Power of the Exact Test
3.5.1 An Exact Confidence Interval
3.5.2 Computing Exact Limits and Sample Size Calculation
3.6.1 An Unconditional Test
3.6.2 Power of the Unconditional Test
3.7 Fisher's Exact Test
3.7.1 Calculation of p-Values
3.7.2 Exact Powers
3.8.2 Relation to Other Distributions
3.9 Random Number Generation
3.10 Computation of Probabilities
4.1 Description
4.2 Moments
4.3 Computing Table Values
Illustrative Examples
4.4 Point Estimation
4.5.1 An Exact Test
4.5.2 Power of the Exact Test
4.6.1 Confidence Intervals
Expected Length
4.7.1 The Test
4.7.2 Power Calculation
4.8.3 Approximations
4.9 Random Number Generation
4.10 Computation of Probabilities
5.1 Description
5.2 Moments
5.3 Computing Table Values
5.5.1 An Exact Test
5.5.2 Powers of the Exact Test
5.6.1 An Exact Confidence Interval
5.7.1 A Conditional Test
5.7.2 Powers of the Conditional Test
5.9 A Test for the Difference between Two Means
5.9.1 An Unconditional Test
5.9.2 Powers of the Unconditional Test
5.10 Model Fitting with Examples
5.11.2 Relation to Other Distributions
5.12 Random Number Generation
5.13 Computation of Probabilities
6.1 Description
6.3 Computing Table Values
6.4 Properties and Results
6.5 Random Number Generation
7.1 Description
7.2 Moments
Illustrative Examples
7.5 A Test for the Proportion
7.7.1 Properties
7.8 Random Number Generation
7.9 A Computational Method for Probabilities
8.1 Description
8.3 Computing Table Values
8.4.2 Interval Estimation
8.6 Random Number Generation
8.7 A Computational Algorithm for Probabilities
9.1 Description
9.3 Inferences
9.5 Random Number Generation
10.1 Description
An Example for Model Checking
10.3 Computing Table Values
Some Illustrative Examples
10.4.1 Point Estimation
Power Computation
10.4.3 Interval Estimation for the Mean
Some Illustrative Examples
Test about a Normal Variance
Confidence Interval for a Normal Variance
10.5 Two-Sample Inference
Interval Estimation for the Ratio of Variances
10.5.2 Inference for the Difference between Two Means when the Variances are Equal
Power of the Two-Sample t-test
Interval Estimation of u1 -- u2
10.5.3 Inference for the Difference between Two Means
10.6.1 Two-Sided Tolerance Intervals
10.6.2 One-Sided Tolerance Limits
Applications
10.6.3 Equal-Tail Tolerance Intervals
10.6.4 Simultaneous Hypothesis Testing for Quantiles
10.6.5 Tolerance Limits for One-Way Random Effects Model
10.7 Properties and Results
10.8 Relation to Other Distributions
10.9 Random Number Generation
10.10 Computing the Distribution Function
11.1 Description
11.2 Moments
11.4 Applications
11.5.1 Properties
11.5.2 Relation to Other Distributions
11.5.3 Approximations
11.7 Computing the Distribution Function
12.1 Description
12.3 Computing Table Values
12.4.2 Relation to Other Distributions
12.4.3 Series Expansions
12.5 Random Number Generation
12.6 A Computational Method for Probabilities
13.1 Description
13.2 Moments
13.4 Distribution of the Maximum of Several /t/ Variables
Simultaneous Confidence Intervals for the Treatment Means
13.4.3 An Example
13.5.2 Relation to Other Distributions
13.5.3 Series Expansions for Cumulative Probability
13.7 A Computational Method for Probabilities
14.1 Description
14.3 Computing Table Values
Confidence Intervals
14.5.2 Relation to Other Distributions
14.6 Random Number Generation
15.1 Description
15.2 Moments
15.3 Computing Table Values
15.4 Applications with Some Examples
15.5.1 Maximum Likelihood Estimators
15.5.3 Interval Estimation
15.6 Properties and Results
15.7 Random Number Generation
15.8 A Computational Method for Probabilities
16.1 Description
16.2 Moments
16.3 Computing Table Values
16.5 Applications with an Example
16.6.1 An Identity and Recurrence Relations
16.6.2 Relation to Other Distributions
16.7 Random Number Generation
16.8 Evaluating the Distribution Function
17.1 Description
17.3 Computing Table Values
17.4 Applications
17.5.3 Approximations to Percentiles
17.7 Evaluating the Distribution Function
18.1 Description
18.4 Applications
18.5.1 Properties
18.6 Random Number Generation
18.7 Evaluating the Distribution Function
19.1 Description
19.2 Moments
19.4 Applications
19.5.1 Properties
19.7 Evaluating the Distribution Function
20.1 Description
20.2 Moments
20.4.1 Maximum Likelihood Estimators
20.5 Applications
20.6 Relation to Other Distributions
20.7 Random Number Generation
21.1 Description
21.2 Moments
21.3 Computing Table Values
21.5 Applications
21.7 Random Number Generation
22.1 Description
22.2 Moments
22.3 Computing Table Values
22.5 Confidence Interval and Test for the Mean
22.6 Inferences for the Difference between Two Means
Illustrative Examples
22.7 Inferences for the Ratio of Two Means
22.9 Properties and Results
22.11 Computation of Probabilities and Percentiles
23.1 Description
23.2 Moments
23.4 Inferences
23.5 Applications
23.8 Computation of Probabilities and Percentiles
24.1 Description
24.2 Moments
24.4 Applications
24.5 Point Estimation
24.8 Computation of Probabilities and Percentiles
25.1 Description
25.2 Moments
25.4 Maximum Likelihood Estimators
25.5 Applications
25.8 Computation of Probabilities and Percentiles
26.1 Description
26.3 Computing Table Values
26.4.1 Estimation Based on Sample Quantiles
26.6 Properties and Results
26.8 Computation of Probabilities and Percentiles
27.1 Description
27.2 Moments
27.4 One-Sample Inference
27.4.2 Confidence Interval for the Mean
27.5.1 Inferences for the Difference between Two Means
27.6 Random Number Generation
27.7 Computational Methods for Probabilities and Percentiles
28.1 Description
28.3 Computing Table Values
28.5 Relation to Other Distributions
28.6 Random Number Generation
29.1 Description
29.2 Computing Table Values
29.3 An Example
29.4 Inferences on Correlation Coefficients
An Exact Test
A Generalized Variable Test
An Exact Confidence Interval
The Generalized Confidence Interval for p
An Asymptotic Approach
Generalized Test and Confidence Limits for p1 -- p2
29.6 Random Number Generation
29.7 A Computational Algorithm for Probabilities
30.1 Description
Moments
30.3 Examples
31.1 Hypothesis Test for the Median
31.3 Computing Table Values
31.4 An Example
32.1 Description
32.2 Moments and an Approximation
32.4 An Example
33.1 Description
33.3 Mann-Whitney U Statistic
33.5 An Example
34.1 Description
34.3 An Example
35.1 Description
35.3 Examples
36.1 Description
36.3.1 Point Estimation
36.3.3 Hypothesis Testing
36.6 A Computational Method for Probabilities
36.7 Computing Table Values

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Tags: Kalimuthu Krishnamoorthy, statistical distributions, applications