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Probability Distributions With Truncated, Log and Bivariate Extensions [Hardcover]

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  • Category: Books (Mathematics)
  • Author:  Thomopoulos, Nick T.
  • Author:  Thomopoulos, Nick T.
  • ISBN-10:  3319760416
  • ISBN-10:  3319760416
  • ISBN-13:  9783319760414
  • ISBN-13:  9783319760414
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Apr-2018
  • Pub Date:  01-Apr-2018
  • SKU:  3319760416-11-SPRI
  • SKU:  3319760416-11-SPRI
  • Item ID: 101247488
  • List Price: $129.99
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jul 04 to Jul 06
  • Notes: Brand New Book. Order Now.
This volume presents a concise and practical overview of statistical methods and tables not readily available in other publications.  It begins with a review of the commonly used continuous and discrete probability distributions. Several useful distributions that are not so common and less understood are described with examples and applications in full detail: discrete normal, left-partial, right-partial, left-truncated normal, right-truncated normal, lognormal, bivariate normal, and bivariate lognormal. Table values are provided with examples that enable researchers to easily apply the distributions to real applications and sample data. The left- and right-truncated normal distributions offer a wide variety of shapes in contrast to the symmetrically shaped normal distribution, and a newly developed spread ratio enables analysts to determine which of the three distributions best fits a particular set of sample data. The book will be highly useful to anyone who does statistical and probability analysis. This includes scientists, economists, management scientists, market researchers, engineers, mathematicians, and students in many disciplines.

 
1. Continuous Distributions
1.1 Introduction
1.2 Sample Data Statistics
1.3 Notation
1.4 Parameter Estimating Methods
1.5 Transforming Variables
Transform Data to (0,1)
Transform Data to (x e0)
1.6 Continuous Random Variables
1.7 Continuous Uniform 
Coefficient of Variation
Parameter Estimates
1.8 Exponential 
1.9 Erlang  
Parameter Estimates
1.10 Gamma  
Parameter Estimates