Biostatistics for the Biological and Health Sciences with Statdisk
Biostatistics for the Biological and Health Sciences is the result of collaboration between the author of the #1 statistics book in the country and an expert in the biological sciences field. The major objective of this book is to provide a thorough, yet engaging introduction to statistics for students and professors in the biological, life, and health sciences. This text reflects the important features of a modern introductory statistics course and includes an abundance of real data and biological applications, and a variety of pedagogical components to help students succeed in their study of biological statistics. MARKET: It is the ideal introduction to statistics for students and professors in the biological, life, and health sciences.
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Statistical Inference
 Used Book in Good Condition
This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for firstyear graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
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Practical and Easy to read.,
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Fantastic Stats Book!,
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Biostatistics for the Biological and Health Sciences with Statdisk,
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Good, but with many shortcomings. Too specialized, and improperly named,
The book’s strengths are selfevident. The exposition of probability theory is excellent, and presented with an eye towards its use in statistics. The mathematical aspects of this book are clean and thorough, and the omissions of certain difficult proofs enhance rather than detract from the book’s quality. But as one progresses further in this text, there are many shortcomings. The order in which topics are presented doesn’t always seem natural to me.
My main criticism of this book is that it presents a narrow view of what statistics is, and as such I think it is misnamed; “Statistical Inference” encompasses much more than what this book covers. This book is really about “classical” statistics and it does not acknowledge or integrate more modern ways of looking at things, even when they could be presented at an elementary level. The Bayesian paradigm is hardly mentioned, nonparametric approaches are hardly mentioned, and decision theory is ignored. As such, I don’t see how it offers any improvement over older texts, such as Hogg and Craig.
My second criticism of this book is that it is divorced from applications; there is almost no data presented in the text or problems. Discussion of modeling is almost completely absent, and the material on distributions in chapter 3 doesn’t probe very far into the particular reasons why certain distributions arise in certain situations. This remark leads into my next criticism: the book emphasizes symbolic manipulations and ignores the deeper meaning of the mathematics. I think that an understanding of the meaning is critical if one is to find useful applications of the material.
This book is clearly more suited to certain learning styles than others. People who find manipulations of equations and formulas natural will find the proofs natural and the exercises helpful. But people interested in the ideas behind the equations will find this book lacking. The proofs are clean and easy to follow but many give little insight into the meaning of the theorems. While the motivated reader can find meaning (sometimes with considerable effort), this book’s approach isn’t particularly pedagogical. The exercises are numerous and challenging, but the challenge is technical rather than deep–most exercises require a clever or lucky manipulation, and occasionally drawnout calculations, and as other reviewers have pointed out, the authors do not do a good job of creating a gradient of problems of different difficulty levels. Many of the problems in advanced chapters can be solved mechanically (even though they are not easy) without really understanding the implications and meaning of the results. A few of the problems in advanced chapters require truly tedious and lengthy calculations that, in my opinion, are a total waste of a students’ time.
I understand why people use this text as a textbook, but in my opinion it needs to be supplemented by something else, either by teacher who focuses on the “why” and the deeper meaning, or, preferably, by other books that do so. This book will advance a students’ understanding of certain topics but it will do little to help the students connect that knowledge with applications or other related theoretical areas. Instructors should be cautious when assigning exercises from this book–there are many excellent exercises but the level of difficulty (as well as the amount students can learn from a given exercise) is highly inconsistent. In many ways, I think this book is supplemented or complemented by the text by A.H. Welsh, a book whose weak points are more than covered by this Casella & Berger text. Another book that is a better alternative is “All of Statistics” by Larry Wasserman; his book is less thorough, but more balanced in terms of perspective, and more focused on helping the reader to learn and understand the underlying ideas. As a more advanced and more philosophical text, and to cover decision theory and Bayesian methods in more depth, I would recommend “Statistical Decision Theory and Bayesian Analysis” by J.O. Berger.
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good text for first graduate course in statistics,
When I was a graduate student we used Ferguson and Cox and Hinkley and we also used Lehmann’s book for hypothesis testing. This book starts with basic probability and goes on to cover all the bases. It has everything one needs in a modern text on mathematical statistics. I have seen it referenced very often in statistics articles and I decided that I had to get a copy for myself in spite of the high price. i think this should be one of the preferred texts for the first year PhD course in mathematical statistics. It certainly requires a full year of calculus as would any good math stat book but the level is even higher than that and that also should be expected by the students.
Typically first year PhD students in statistics would take this course concurrently with a course in advanced probability that includes measure theory. So the measure theory knowledge gained by the student in the probability course will and should be needed for the latter chapters of this math stat course.
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Excellent on introduction to univariate statistics,
Comparing with many badly written mathematical books by famous names that gave me terrible experiences, I strongly recommend this book. As I was enjoying reading of this book, my memory constantly went back to the difficult time I had experienced when I tried so hard on Royden’s “Real Analysis” or M. Artin’s “Algebra”. These two are classical math textbooks that are appraised by the majority of mathematicians. But from my observation, quite a few students hate these two books to some extreme, because they are so hard to follow unless you read other textbooks. In my opinion, these “bad” textbooks are good only for those who have already mastered the contents (for example, professors who have been teaching this subject for their entire lives). After completely understood the topics, I found these two books are quite useful as reference books. But still I do not think these two books are good to begin with if the reader knows little about the subjects in the books. As contrary, CasellaBerger’s book is very good for entrylevel students. Good knowledge in calculus is sufficient for you to easily follow the topics. Moreover, the content of this book is not simple; it contains almost all aspects of univariate statistics. (many poor calculus books are written in such a way that in order to please the students, the author intentionally omitted some important subjects and/or reduced the level of the contents. By doing so, the author became famous and the book went to bestselling. The students, without any working, are happy to wrongly believe that they know everything while they don’t). “Statistical Inference” is good only because it is carefully written. CasellaBerger are not only outstanding researchers, they are also excellent educators. They know students, they know at what point students would encounter what difficulty and at this point, you definitely will find an appropriate example to help you out. The sharp contrasts between “Statistical Inference” and many “bad” textbooks in mathematics convince me that mathematicians are trying to make our lives more miserable (and this is one of the reasons I lost my interests in mathematics, though I have been good at math) while statisticians are trying to make our lives easier.
At the same time of going through “Statistical Inference”, I was also reading Richard Durrett’s “Probability: theory and examples”, a widely used typical textbook in probability for first year PhD student. Compared with majority entrylevel PhDs in statistics, my background in mathematics (Lebesgue Measure, Integration and Differentiation) is no weaker, yet I experienced the same hard time as I did in some other math classes. My blame can only go to the bad written textbook, I have to read other textbook to understand the topics, and this is not good for a notstupid and hard working student. I am always curious that among all the textbooks available, why mathematicians prefer the textbooks that will give students more hard time. For the same topic, using different approaches, students will have different feelings, why can’t the professor pick up the more friendly written books for the sake of student’s easy understanding and their continuing interests in the area?
My belief was strengthened after completing the reading of CasellaBerger’s “Statistical Inference” and R. Durrett’s “Probability”, that one must keep away from mathematicians as far as possible since your life will be tough if you are close to them. And as for myself, I won’t do research in probability since the book “Probability” gave me the impression that more mathematicians are involved in the area of probability theory. I’ll go with Casella & Berger, concentrate on the filed of statistical inferences since scientists in this particular field are trying to make our lives better and easier.
In conclusion, if you need to learn statistics while having no specific back ground, I strongly recommend Casella Berger’s “Statistical Inference”..
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