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Goodness-of-fit-techniques (Statistics: a Series of Textbooks and Monographs, Vol. 68)From Dekker
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- Sales Rank: #1712408 in Books
- Published on: 1986-06-02
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 6.50" w x 1.50" l, 1.83 pounds
- Binding: Hardcover
- 576 pages
Review
. . .a comprehensive book, well presented and clearly written.
---The New Zealand Statistician
. . .Chapters contain some theory but they are mainly directed toward application with some numerical illustrations which generally use simulated sets to be found in the Appendix. . .The level of exposition is generally clear and direct. . .the book should prove useful to practising statisticians.
---Short Book Reviews (International Statistical Institute)
. . .the authors have carried out the helpful task of bringing together into a useful reference volume a large amount of material on an interesting topic which has previously been scattered throughout the literature.
---Royal Statistical Society
Most helpful customer reviews
3 of 3 people found the following review helpful.
Completely useless
By V. Isoz
I bought this book expecting having mathematical and algorithms details on how tables were computed and in reality this book contains only a compilation of well know old articles for 200$ !!! It could have contain mathematical proofs for this high price but there is even one proof...!!! I'm very disappointed, the price that does not justify the content (it must cost 10$ not more).
1 of 2 people found the following review helpful.
How to identify best non-normal model for a dataset
By Norm Bird
This book provides the information necessary to calculate the Anderson-Darling statistic and P-value for many distributions (Normal, Weibull, Gamma, etc.). Calculation of these values is key in determining the best model to use to represent collected data.
This book also provides useful information and background on graphical techniques necessary to complement numerical techniques to determine best model to represent a dataset.
I was able to use the information in this book to develop a simple Excel spreadsheet that can take a dataset and identify the best model to use for further statistical analysis. Like any other piece of software, as I use it I identify it's weaknesses. For instance, many times people will take data from machine A and machine B and pool that data. Data from machine A alone could be clearly normal, same for machine B, but the pooled data could clearly be bimodal and non-normal due to the offset in mean from machine A to B. I realize I hadn't accounted for this inthe automation even though it was easy to spot in the probability plots, and have since added logic to deal with this.
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