No comments about Applied Data Analytic Technique for Turning Points Research (Multivariate Applications Series).

No comments about Roots to Research.

2 comments about Operations Research: Applications and Algorithms.

1. Love the text. Has a lot of basic information starting from basic algebra. Any one who is interested to learn Non-linear programming needs to have this book. Great content.



2. This is a grat book, it covers a broad area of operations research. It is equally good for students, professors and profesionals. Very good explanations and applied exercises from real life.
   Definetly a must buy.

3 comments about Introduction to Metric and Topological Spaces.

1. A lot of books on topology assume some basic knowledge of real analysis, which can throw a lot of readers off. This book starts from the very beginning, and thus is truly a great introduction. Each section has some good exercises, with even a few pointers at the back of the book for the more challenging ones. It starts with topological aspects, and then refers to them in the case of metric spaces (amongst many others), which is a much better approach than most other books, as the reader doesn't take the details of the specific to the general. A great little book, which is a must for most advanced maths Analysis courses.



2. I enjoyed reading this book because of its clarity, conciseness, and nice way of relating topological and metric spaces. This book is ideal for the student who is learning about these subjects for the first time, whether or not they intend to do more advanced work on the subject. The reader who wants to go on and learn about more advanced topics, should consult Munkres's book.



3. I purchased Introduction to Metric and Topological Spaces two years ago. I was unprepared for its rigor. I am not a mathematics major, but I enjoy reading mathematics. My background includes calculus, linear algebra, differential equations, and other applied mathematics, but I have not had a course in real analysis. W. A. Sutherland intended this text as the next step after analysis.

   After a brief foray, I retreated, placed Sutherland back on my bookshelf, and attacked some marginally easier introductory texts: Metric Spaces by Victor Bryant, Introduction to Topology by Bert Mendelson, and most recently, several chapters in Introduction to Analysis by Maxwell Rosenlicht. I periodically return to W. A. Sutherland's text to measure my understanding. I am now working on chapter five, Compact Spaces.

   I doubt that Introduction to Metric and Topological Spaces would be foreboding to students that are familiar with real analysis. Sutherland understands that the abstractness and generalization can be difficult and shows concern with motivating the student. He repeatedly attempts to illustrate the value of generalization, especially in the study of continuity.

   Sutherland often uses a lengthy series of examples of increasing difficulty to illustrate abstract concepts. In his discussion of metric spaces, we begin with Euclidian n-space metrics, and move on to discrete metric spaces, function spaces, and even Hilbert sequence spaces. He introduces open sets and topological spaces in a similar fashion.

   The author occasionally suggests that the student might wish to make a geometrical diagram to help clarify some subtle point, but Sutherland includes few geometrical drawings in his text. His focus is clearly on proofs using the axioms of metric spaces and topological spaces.

   Sutherland highlights sections that either require more knowledge of abstract algebra, or for other reasons are thought to be more severe.

   Despite Sutherland's use of Introduction in the title, I suggest that any reader considering independent study might defer tackling Introduction to Metric and Topological Spaces until after completing a more basic text. Possibly a better title might be A Second Introduction to Metric and Topological Spaces.

No comments about Math Fair Projects And Research Activities: A Comprehensive Guide For Teachers And Students (Kids' Stuff).

1 comments about A Course in Group Theory (Oxford Science Publications).

1. The book covers all the elements of group theory,
   with a great deal of care. Each concept introduced
   is supported with at least some examples, difficult
   concepts with many. The book is rigorous without
   being pedantic. The last chapter, which I could not
   resist skipping to, contains a survey of the description
   of finite groups with some historical notes and indications
   of where the reader might go next. I am looking for
   other books by the same author. It is obfious from
   this book that the author is an excellent teacher.

2 comments about The Handbook of Research Synthesis.

1. For anyone interested in doing research synthesis, meta-analysis, or even a literature review, this is a "must have" in your collection. Various researchers present quite helpful information to save time, sweat, and tears in the process



2. This book is the best overall work on meta-analysis and research synthesis that has appeared in the past decade.All of its chapters are clearly written, and the data sets that are provided to illustrate the various techniques are extremely helpful. A lot of work went into the development of this book, and it shows.

No comments about Writing Math Research Papers: A Guide for Students and Instructors.

3 comments about Design and Analysis of Experiments (Springer Texts in Statistics).

1. detail and specific.
   It is appropriate for graduate students.



2. This book is one of the best Exp.Design ever!It demonstrates the sas code in each design thus makes it easy to go through.



3. The previous user has made pen markings allover the book.
   Apart from that the book is in O K condition.

3 comments about Fundamental Concepts in the Design of Experiments.

1. After using this book for a class, we all agreed that this book was not the best for those of us who are just learning the concepts in analyzing variance. The author tends to assume the basics are "hard-coded" into the students head. Fair enough. His next step, however, is not to discuss the theory/concept. Rather, it is to give an example of what he is talking about, hoping you will grasp the concept. It would very helpful to have the concepts explained and then the examples given. In addition, the notation (subscripts) are not always explained, nor are the formulas obvious/straightforward. Finally, the book has editing problems.

   A better editor and supplementary manuals (step-by-step explanation of concepts and formulas, SPSS instruction, etc.) would be a big help.

   My recommendation would be to find another book from which you will learn statistics.




2. This book is written for people who already know the theory of statistics and want to do statistic consulting.

   The author begins with the basics of design of experiments: experiment, design and analysis. Then a brief (lovely) review of statistical inference follows; including: Estimation, test of hypothesis, power function and some easy applications.

   In the following chapters almost all statistical methods are presented; among others: single factor experiments, randomized block and latin square, factorial experiments, nested, experiments of two or more factors, 2^f -, 3^f factorials, split plot design, Taguchi, regression and finally miscellaneous topics including covariance analysis, response-surface experimentation and more.

   After each chapter there are problems and answers to odd-numbered problems can be found at the end of the book. Included is a practical summery with all methods presented in one table. Additionally you find a glossary of terms used in statisics, statistical tables and an index.

   The examples in the book are analysed using SAS. Knowing that S-Plus is much easier to handle (and knowing that SAS is frequently used in the industry), this is very useful.

   The mathematics used is easy, but - as mentioned in the preface - the fundamental concepts of statistical inference must be known.




3. This book is very valuable for those actively engaged in the conduct of experiments, either operational or developmental in nature. It does require someone with a background in statistical methods using analysis of variance. The user needs to have a good understanding of statistical inference. There are many good working models of various analytic procedures provided.