Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
Average customer rating:
- Lacking in going from abstract to application
- Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition)
- NOT USER FRIENDLY!
- Sound choice for a finite math textbook
- Many exercises in economics, life and the social sciences
|
Finite Mathematics for Business, Economics, Life Sciences and Social Sciences (11th Edition)
Raymond A Barnett ,
Michael R Ziegler , and
Karl E Byleen
Manufacturer: Prentice Hall
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The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition)
ASIN: 0132255707 |
Book Description
Designed to be accessible, this book develops a thorough, functional understanding of mathematical concepts in preparation for their application in other areas. Coverage concentrates on developing concepts and ideas followed immediately by developing computational skills and problem solving.
This book features a collection of important topics from mathematics of finance, linear algebra, linear programming, probability, and statistics, with an emphasis on cross-discipline principles and practices.
For the professional who wants to acquire essential mathematical tools for application in business, economics, and the life and social sciences.
Customer Reviews:
Lacking in going from abstract to application.......2007-09-23
This review is for the 11th edition of the book. I am using this for a business math course and overall the book is lacking. Many of the examples presented in the chapters are simplistic and have no relation to the difficulty of the application examples. It's unfortunate that the solutions manual while containing the solutions to the examples does not go into more detail in how to solve these type of problems. While it appears the authors goal is to go from abstract to application, he misses the mark. I find myself consulting external resources for almost every chapter. Not recommended.
Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition).......2005-09-17
They sent me the wrong book. I ordered the Finite Mathematics for Business Economics, Life Sciences and Social Sciences (10th Edition)by Raymond A. Barnett and received the solution manual. I feel very disapointed with this purchase.
NOT USER FRIENDLY!.......2005-09-08
I am using this textbook for my Math for Business and Economics class and it is terrible. This book is not written taking into consideration the audience "Business Majors". Concepts are better explained in the Chapter review than under the sections they are being presented. There is only 1 example for each new concept and definitions are written using one or two definitions of new concepts within it. If this book is intended to take the student from the abstract concept to the real world it sure doesn't do this. I understood the material better in my Intermediate Algebra class than I do now.
Sound choice for a finite math textbook.......2004-06-27
This is a very sound choice as a textbook for a course in finite mathematics. The coverage is appropriate, the level suitable for the non-math major, the explanations are excellent and the authors take the title seriously.
The topics are covered in the following order:
* Elementary functions and their graphs.
* The mathematics of finance.
* Matrices and systems of linear equations.
* Linear inequalities and linear programming.
* Logic, set theory and basic counting.
* Probability and probability distributions.
* Basic game and decision theory.
* Markov chains.
There are many exercises and at the end of each section there is a set of basic exercises followed by a collection of applied problems. The set of applied problems is split into three categories: business & economics, life sciences and social sciences. Since finite mathematics is often a preparation for students to work in these fields, this format is what impressed me the most. With all of these "real world" problems to work as part of their study, no student using this book could ever legitimately say that they see no purpose to their studies. Solutions to the odd-numbered problems are included.
I came into contact with this book after my choice of textbook was irrevocable. Had I seen it earlier, it would have been the one I used.
Many exercises in economics, life and the social sciences.......2004-06-26
This is a very sound choice as a textbook for a course in finite mathematics. The coverage is appropriate, the level suitable for the non-math major, the explanations are excellent and the authors take the title seriously.
The topics are covered in the following order:
* Elementary functions and their graphs.
* The mathematics of finance.
* Matrices and systems of linear equations.
* Linear inequalities and linear programming.
* Logic, set theory and basic counting.
* Probability and probability distributions.
* Basic game and decision theory.
* Markov chains.
There are many exercises and at the end of each section there is a set of basic exercises followed by a collection of applied problems. The set of applied problems is split into three categories: business & economics, life sciences and social sciences. Since finite mathematics is often a preparation for students to work in these fields, this format is what impressed me the most. With all of these "real world" problems to work as part of their study, no student using this book could ever legitimately say that they see no purpose to their studies. Solutions to the odd-numbered problems are included.
I came into contact with this book after my choice of textbook was irrevocable. Had I seen it earlier, it would have been the one I used.
Average customer rating:
- Deceptive
- An original approach. An excellent book on the subject.
- Hey, I got an A in Biostats I
- YES! I could speak and ask questions at journal club without looking like a fool.
- Excellent Statistics Book
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Intuitive Biostatistics
Harvey Motulsky
Manufacturer: Oxford University Press, USA
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Pdq Statistics (PDQ Series) Third Edition (PDQ)
ASIN: 0195086074 |
Book Description
Designed to provide a nonmathematical introduction to biostatistics for medical and health science students, graduate students in the biological sciences, physicians, and researchers, this text explains statistical principles in non-technical language and focuses on explaining the proper scientific interpretation of statistical tests rather than on the mathematical logic of the tests themselves. Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression. By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.
Customer Reviews:
Deceptive.......2007-08-10
If you think you can learn Statistics intuitively and without mathematics or in otherwords the easy way, I have an intuitive Brain Surgery book for sale.
An original approach. An excellent book on the subject........2007-06-13
The majority of reviewers really liked this book. I can see why, I did too. The author uses a unique approach to teaching statistics that is focused on calculating and explaining Confidence Intervals (the minimum and maximum value you expect an outcome to be given a confidence level typically 95%) rather than P values (probability outcome is due to chance). He also uses common sense and clearly distinguishes between what is statistically significant and what is "significant." Thus, he translates well statistical mumbo jumbo into plain English. He tells you what you should care about and look for.
He shares with you all the statistical flaws that clinical studies may have including testing multiple hypothesis to come up with just a single statistically meaningful one, using large samples to prove something trivial, using small samples that raises uncertainty level, etc...
His section on Bayesian Logic is excellent. His table on what test or methodology to use given the shape of the data and objective you have is worth the price of the book alone. That's one of the clearest taxonomy of statistical methods I have seen anywhere.
Some knowledgeable reviewers have picked up a few errors the author made. I stumbled upon a couple while attempting to replicate the calculation of a few examples. I emailed the author and each time within an hour he either clarified the calculation or corrected the typo that was present in the book. Given his prompt answers, I can't ding him for the couple of typos I caught.
Although the author presents this book as an introductory one, I recommend the reader acquires a good foundation in basic statistics before studying this book. Forgotten Statistics would fit that bill. Indeed, `Intuitive Biostatistics' covers a huge amount of ground. It is far more than an introductory text. It covers material that is pretty advanced including nonparametric hypothesis tests, non linear regression, logistic regression, Bayesian analysis, etc... If it is the first time you come across that stuff you'd be well served having a solid stats foundation. Given that, this book has a lot to offer. I'll keep it as a great reference for years.
Hey, I got an A in Biostats I.......2007-01-04
I am not a high faltuin' math person, the calculus I went through in undergrad was a struggle and I remember very little. I am a chemist by training, currently seeking my PhD in Public Health while working full time. What that came down to was little to no time to doof around with a muddled textbook or an equally muddled professor or a non-English speaking Teacher's Assistant.
I have no intention of becoming a biostatistician or an epidemiologist, I am interested in policy.
So coming from that perspective, as a student, this book was an absolute God-send.
Not only did I get an A in the class, but I feel like I have a sturdy foundation for my future coursework and career. I will not be intimidated by numbers or jargon because Dr. Motulsky made it all as straightforward and clear as possible, and I recall even laughing a few times.
Overall, if you are in school, facing a biostatics class with extreme trepidation, buy this book as a supplement. Look up the topics in the index as you go and you will have more than the $40 worth of "eureka" moments.
YES! I could speak and ask questions at journal club without looking like a fool........2006-09-25
Helped me from looking a fool during residency. Thank you Harvey!
Excellent Statistics Book.......2006-03-13
Intuitive Biostatistics takes a confidence interval approach that should be required reading for all persons interested in statistics. His discussion of basic statistical concepts in the context of interpreting lab test results is clear and informative and introduces the reader to Bayesian concepts that are presented very simply.
Book Description
Meta-analysis, decision analysis, and cost-effectiveness analysis are the cornerstones of evidence-based medicine. These related quantitative methods have become essential tools in the formulation of clinical and public policy based on the synthesis of evidence. All three methods are taught with increasing frequency in medical schools and schools of public health and in health policy courses at the undergraduate and graduate level. This book is a lucid introduction, and will serve the needs of students taking introductory courses that cover these topics. It will also be useful to clinicians and policymakers who need to understand the quantitative underpinnings of the methods in order to best apply the information that derives from them. The second edition of this popular book adds new material on cumulative meta-analysis as a method to explore heterogeneity. The coverage of cost-effectiveness analysis has been brought into close alignment with recommendations of the U.S. Public Health Panel on Cost-Effectiveness Analysis in Health and Medicine. Many of the examples have been replaced with more current examples, and all of the material has been updated to reflect recent advances in the methods and the emergence of consensus about some previously controversial issues. analysis. These three closely related methods have become even more important for synthesizing research since the first edition was published in 1994. And they have gained legitimacy as tools for guiding health policy.
Customer Reviews:
Best guide for analysis.......2006-12-20
There are several ways to do analysis of the health care industry but this book covers the big three. Cost Effectiveness is the most useful from a business standpoint. This book covers the math and theory behind each of these methods and gives strong arguments for how to write in each of them. As a health and pharmaceutical economist I found this book to be invaluable. It is written very clearly and helps sort through many of the issues especially meta analysis. If you are starting out in the health analyst field this is a must read and a book you will want to have handy at all times.
An excellent introductory text.......2002-02-05
Meta-analysis, as both an applied and theoretical science, continues to develop from its rather humble beginnings. Since most physicians lack a strong background in quantitative science, rigorous statistical approaches to clinical decision making have been slow to gain widespread support. Despite its limitations, meta-analysis is proving to be an important vehicle for making sense of the increasingly overwhelming amount of published data in the biomedical sciences. Petitti's book goes a long way in demystifying meta-analysis for the rank and file and also serves as an excellent introduction to the field for the more statistically literate.
Most of my own work is in meta-analysis and I bought the book exclusively for this content and not information on decision analysis and cost-effectiveness analysis. I do not mean to minimize the importance of the latter but rather point out my own narrow interest in the Petitti text.
The book is well written and concise. Petitti "cuts to the chase" explaining the theory underlying meta-analytic methods and provides many useful examples from the medical literature. The text is easy to follow, understandable and well balanced. A thorough reading should leave the reader well prepared for further exploration of applied meta-analysis as well as capable of articulating its strengths, weaknesses and future direction.
Overall, Petitti's text is an excellent place to start for those interested in learning the fundamentals of "research synthesis". The book is also an extremely handy references for the more experienced practitioner. I highly recommend it.
clearest yet description this topic.......1997-07-08
In a forest of mirky and arcane descriptions of a field that has come to dominate medical research and policy making, this book stands out for its clarity. Dr. Petitti speaks a language that normal people can understand. She avoids jargon and frequently uses quantitative examples that make it easy to plug in your own problems and directly apply her lessons to your tasks. This is easily the best book in print on this topic
Book Description
Analysis and Management of Animal Populations deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations.
Key Features
*Integrates population modeling, parameter estimation and decision-theoretic approaches to management in a single, cohesive framework
* Provides authoritative, state-of-the-art descriptions of quantitative approaches to modeling, estimation and decision-making
* Emphasizes the role of mathematical modeling in the conduct of science and management
* Utilizes a unifying biological context, consistent mathematical notation, and numerous biological examples
Customer Reviews:
Didn't have book, Issued VERY fast refund.......2005-09-23
They didn't have the book even though it was posted. However they issued a very promt refund without any problems.
Good reference book.......2003-04-03
This book provides a good summary of methods and techniques that are available for wildlife studies. It is a good starting point for graduate students and researchers who would like to get a broad overview, but for more details on particular types of analysis, other resources are needed. For someone who has never been exposed to population biology, the expansive breadth of the book may be somewhat overwhelming.
Book Description
The most important techniques available for longitudinal data analysis are discussed in this book. The discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalized estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. This practical guide is especially suitable for non-statisticians and all those undertaking medical research or epidemiological studies.
Customer Reviews:
GREAT book! .......2004-12-15
This book is really useful and handy. It is very well written and easy to read. As the name stated, it provides very practical guides for those who don't have strong background in Statistics but are dealing with longitudinal data. It is written in an example guided format. The outputs from the analysis and guidelines on how to interpret them step by step are included. There is no heavy Statistical notation and you don't need to translate Statistics into English. At the end of the book, there are chapters of how to handle missing data and softwares used in longitudinal data analysis. This book is probably too boring if you are a hardcore Statistician.
Book Description
Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.
Customer Reviews:
Great reference.......2007-09-06
A great reference and a good introduction to many important concepts in sequence analysis. However, if you don't have a reasonable grounding in math you may struggle with the terse notation.
Borodovsky's companion book is an excellent partner for this book. Get both.
One of the best available.......2007-08-17
Although this book is based primarily on work that was completed in 1998, and therefore somewhat out of date, it is the best book I have found for teaching bioinformatics. I selected this as the best of the available books on the subject for use in my bioinformatics and numerical methods course which is to be taught in the fall of 2007 at Univ. of Conn. This course is an upper division undergraduate and first year graduate course. That is roughly the level of this text and the comparative advantage of this book is the excellent presentation and thorough discussion of the algorithms. A student armed with Matlab or MathScriptor can take this book and start writing algorithms for sequence alignment and Hidden Markov Method (HMM) analysis after only the first three or four chapters. This book is in its 11th printing and is nearly error free (I found only a few in the figures). This book is strongly recommended for both students and researchers, particularly those interested in protein alignment, phylogenic analysis or an introduction to Hidden Markov Methods.
Biological Sequence Analysis.......2006-03-07
This is a very good book. I got it for a class and it is very helpful and insightful.
Truly an Excellent Book.......2006-02-18
I will agree and submit: this is an invaluable introduction to the field of bioinformatics. With introductions to everything from sequence analysis to hidden markov models and even a primer on grammars, this is a useful introduction both to biological applications for computer scientists *as well as* computational methods for biologists.
I am in a joint graduate-level biology/computer science class and we are using this book as a foundation to bring both groups up to speed and it seems to be working out nicely.
However, one criticism is that sometimes Durbin et al jump into subjects without an adequate introduction or with one that is overcomplexified. In other words, they sometimes break Einstein's the rule of "make everything as simple as possible but not simpler". Durbin et al do not always make things as simple as possible. And it is annoying when they do not. Especially when I see them confusing the bejebus out of the biology people over computer science concepts that are really not that complicated through overly technical jargon.
But this is rare and they provide many insightful diagrams to clear up their algorithms as well as lucid ways to introduce biological concepts. Sometimes the introduction of an algorithm/theory *and* a biological concept molds together beautifully such that the reader is simultaneously being infused with both. An example of this phenomenon is their dual introduction to CpG islands and markov models.
Excellent book ... a little boring to read ..........2005-09-30
I bought "Biological Sequence Analysis" for my introductory bioinformatics course. AS the course covers almost everything mentioned in the book I have (almost) finished reading and studying it.
I find this book an excellent textbook but wouldn't consider it a classic. There are some important topics missing or some topics are just briefly touched upon. (e.g. heuristic pairwaise alignment) Maybe it's just because of my theoretical background, but I find that the book does a poor job in explaining/proving the intuition behind certain aspects of the algorithms (e.d. why does a convex gap penalty lead to a different complexity than a strictly increasing gap penalty ...) . On the other hand, the probabilistic foundations of the different techniques is well written.
My final remark is that the book is not fun to read at all. The authors have made no effort to spice up the content with some historical background, some explanations of how the theory fits in the bigger picture ...
Summarized: an excellent textbook for anyone taking a course in bioinformatics but do not use this book to wet your appetite for the field ...
Book Description
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.
This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.
The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.
The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.
Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.
Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.
Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).
Customer Reviews:
Most Elegant Account of Bioinformatics.......2004-11-27
I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes. But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).
Average customer rating:
- Outstanding
- The best second book of statistics for biologists.
- The best advanced statistics book for biologists
|
Primer of Applied Regression & Analysis of Variance
Stanton A. Glantz , and
Bryan K. Slinker
Manufacturer: McGraw-Hill Medical
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ASIN: 0071360867 |
Book Description
Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).
Customer Reviews:
Outstanding.......2006-01-31
I looked at several options for a regression textbook that would be both understandable and relatively complete for my introduction to the topic. This book won hands down. The authors keep it simple and use a wide variety of examples to get the point across. I felt that the sections on logistic and Cox regression could have been a bit better, but these subjects are best learned by dedicated textbooks such as Hosmer and Lemeshow and Collett.
I think that this may be the best introductory regression book out there.
The best second book of statistics for biologists........2000-11-13
Once you've learned the basic principles of statistics, how can a biologist learn more advanced techniques? Many books focus on math rather than on understanding concepts. Other books are too narrow -- discussing only a single method. And books that focus on multiple regression and ANOVA tend to have examples from psychology and social sciences. Glantz and Slinker do a great job of explaining the principles of multiple regression, analysis of variance, and analysis of covariance. The focus is not on mathematical proofs, but rather on making sense of the results in the context of biological and medical research.
This book also has excellent chapters on linear regression, nonlinear regression (curve fitting) and logistic and proportional hazards regression (regression when the outcome is an either-or binary variable).
New to the second edition are a chapter on analysis of covariance, more extensive discussions of multiple comparisons methods, and a discussion of Cox proportional hazards regression for analyses of survival data.
The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book for biologists to learn about various kinds of ANOVAS and regressions.
The best advanced statistics book for biologists.......1998-05-30
Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book to learn about various kinds of ANOVAS and regressions.
Book Description
Are there geographic clusters of disease cases, or hotspots of crime? Can the geography of air quality be matched to where people hospitalized for respiratory complaints actually live? Spatial data is data about the world where the attribute of interest and its location on the earth's surface are recorded. This comprehensive overview of the subject shows how the above questions can be tackled. It is written for students and researchers in geography, economics, social science, the environmental sciences and statistics.
Books:
- Self-Organizing Maps
- Sex, Evolution and Behavior
- Spatial Analysis: A Guide for Ecologists
- Stalemate (Eve Duncan Forensics Thrillers)
- Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health)
- Statistics For People Who (Think They) Hate Statistics
- Stories from Around the World (New Windmill)
- Surgical Pathology Dissection: An Illustrated Guide
- Survival of the Sickest: A Medical Maverick Discovers Why We Need Disease
- The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science (James H. Silberman Books)
Books Index
Books Home
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