2 comments about Introduction to Statistics for Forensic Scientists.

1. It is a very useful an helpful book... everyone involved in teaching may find it an excellent guide to help and involve students of forensics into the effective use of statistics.



2. After so many courses and operational practice of statistics applied to forensic sciences, I finally found the relevant book to not only summarize all my data around, give some intelligibility to my process, but even have an "all of a sudden" revelation about this field. The structure adopted in this book by Dr LUCY, a well recognized forensic statistician, allows the reader to learn, check and understand back to basics statistics and jumped into the relevant subjective ones for forensic science.
   Unfortunately, my plain satisfaction is largely overshadowed by too many written mistakes (even detectable by the French reader I am...), some typewriter errors (which could be largely misleading for reader having no statistician skill to be able to correct) and visibly an absence of referee lecture. Else how to explain references to previous pages with only ... (three dots) or permanent mistakes when referring to appendices ?
   The critic is not only addressed to Dr Lucy, but also to the editor (Wiley), who didn't accustomize myself to such a poor formalist scientific book, which cost is irrelevant with the imperfections.
   Would any of the responsible have a look to this comment, I am ready to send him page per page the detected errors, and would be glad to get a new edition, which would replace my all over the text corrected one.
   
   Dr CRISPINO, Forensic Scientist

1 comments about Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis).

1. Searching available data to determine insights and information is fundamental to EDA. When EDA was first being developed in the 1970's the avaialble computers were small and weak. As a result techniques to utilize small data sets with pencil and paper were developed. These proved that the concept had value in certain situations where the investigator did not have a preconceived notion of what he was seeking.
   
   MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran.
   
   It's a natural then to tie EDA to MATLAB. With the interactive and visualiztion capabilities of MATLAB large amounts of data can be analyzed rapidly in many ways. This book ties the concept to the software. It has a good introduction to EDA, and then illustrates several applications where MATLAB provides the analysis of data to produce unexpected results.

No comments about Evaluating Clinical Research: All that glitters is not gold.

2 comments about Methods in Observational Epidemiology.

1. I think this book,Methods in Observational Epidemiology, might be very good reference to many scientists, especially biostatistist or epidemiologists.



2. I drudged my way through the early chapters of this book. Most all-star topics in epidemiology are reliably laid out there. Unfortunately, the book is boring as hell. The one time I tried to use one of examples given in the book to calculate something (power or sample size?) I got hopelessly lost - the layout does not allow formulas to stand out. I found class notes and handouts much more useful than this book.

1 comments about News and Numbers: A Guide to Reporting Statistical Claims and Controversies in Health and Other Fields.

1. This book is a great resource for journalists who cover health issues and report on research studies and statistics. It is also a useful tool for classroom instruction.

No comments about A Pocket Guide to Epidemiology.

2 comments about Evolution and the Theory of Games.

1. All you want to know about theory of games



2. With this book, John Maynard Smith initiated a major strand of modern theoretical biology! The book does not require sophisticated mathematical preparation, but it operates on a consistently high level of analytical rigor. It is also very nicely written, with lots of biological examples.

5 comments about Recursive Partitioning and Applications (Statistics for Biology and Health).

1. Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.



2. Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.



3. Zhang and Singer have done a splendid job of explaining recursive partitioning, a topic that should be of great interest to anyone who wants to make sense of data in which there are many potentially important variables contributing to some outcome or variable of interest. One should not be put off by the "... in the Health Sciences" part of the book's title; the potential audience of readers who can benefit from reading it is much greater than this implies (I'm an ecologist, for example). Why? First, because the topics covered have wide applicability in many fields; and second, because the writing is exceptionally clear and easy to follow. If you are able to use a typical introductory text on multiple regression, for example, you should have no difficulty getting a lot out of Zhang and Singer. If you are able to handle a mathematically rigorous approach to statistics but are new to the topics covered here, this book will provide an excellent starting place before you jump into the many references to the recent literature provided by the authors.



4. Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning. There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.



5. Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning.
   
   There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.
   
   It is a little more difficult to read then CART. CART was motivated by biomedical problems but the book covered other applications in business and pattern recognition as well. This texts puts an emphasis on the important medical applications.

No comments about Understanding Health Care Outcomes Research.

No comments about The Statistical Analysis of Functional MRI Data (Statistics for Biology and Health).