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I hated this textbook with every fiber of my being. The authors do a not good job at explaining statistical methods. They use a lot of fluff words to seemingly obtain a word count. They beat around the bush when trying to create a point that it distracts the reader from learning the material. Much too wordy.

The purpose of this book was supposed to serve very broad groups of people: students, statisticians and engineers. Unfortunately, I found this book not quite suitable in engineering practice. From practical point of view, when dealing with reliability estimations, one has to connect mathematical theory with real-life data. It appears that to accomplish this task it is important to understand some primary statistical ideas, plus specifics of the topic under consideration. Sometimes common sense knowledge can come in handy. Strangely enough but a lot of fundamental principles are in fact surprisingly simple, elegant and thus beautiful. What is missing in the book is the lack of clear explanations of fundamental statistical concepts that certainly can be presented in a complicated form but in reality they are not. On the other side, the book could serve as a solid textbook to students, statisticians and mathematicians.

This book is amazing iff:1) You have fun doing math by hand on paperOr2) You travel back in time to a point where computers don't existOr3) You have lots of time to reinvent the wheelOr4) You plow a field with an ox and feel the old ways are the bestOr5) You're Amish or a LudditeFor the rest of us, don't bother. This book wold have been something unique 40 years ago. Now, it's as outdated as the chapter on linearizing functions (so you can plot them on log paper.)Many of the questions in this book remind me of conversations I have with my grand mother about the "old days". I think my grandmother is the target audience.

This book might be of use to someone who's interested in all of the math behind reliability estimation, but it's of small practical use to anyone who doesn't have a year to study it. It's full of small gems like: "This [equation] can be used to parametrically adjust the nonparametric estimate of probability plot shown in...". You obtain equation after equation with no explicit method to obtain from the abstract math to the plots and conclusions you really t for anyone who needs a quick, practical tutorial to reliability analysis.

Reliability and survival analysis both with time to failure data. Much of the methodology is essentially the same. The term reliability is generally used to apply to hardware or whereas survival analysis is a term for biological systems such as animals or humans. This book contains the standard nonparametric and parametric methods for estimating reliability functions and parameters. It contains system reliability and repairable systems and with latest developments with repairable systems including Nelson's mean cumulative function. A couple of years ago I asked Wayne Nelson if and when he might revise his famous text "Applied Life Data Analysis". He said he did not plan to do it because Meeker and Escobar had just finished a work that would be as amazing as any revision he might wish to produce. Other subjects contain failure time regression models including the famous Cox proportional hazards model and accelerated life try models. It also contains modern subjects such as bootstrap confidence intervals (both semi-parametric and nonparametric) for reliability parameters. The book is comprehensive and up-to-date. It also contains discussion of Bayesian methods. Some case studies are also included. The only subjects it misses are reliability growth and warranty and service contracts. These subjects are covered in the latest book by Blischke and Murthy "Reliability Modeling, Prediciton, and Optimization" also published by John Wiley and Sons, merical examples are done using the SPlus from MathSoft. An ftp website is available to data sets to use with SPlus.

The book is perfect for the training of quality specialists on multivariate statistical methods. Quality assurance projects can use the considered methods as strong tools in any industrial environment. The theory of selected multivariate statistical methods, relative examples and case studies create the book a valuable practical tutorial and a reference source on the topic. Clear and effective presentation of the material is determined by the content, writing style and the whole organization of the book.

Covers the important topics, but lacks sufficient examples demonstrating the primary concepts in each chapter. When introducing fresh concepts, tends to cover too a lot of edge cases (eg delving into all the assumptions and tests for such assumptions that must be met to use a certain technique such as regression) as opposed to starting with simplified cases and then covering more complex cases. Also, a lot of examples assume one has SPSS software. The kindle ver does not have page numbers corresponding to the printed version, which makes things difficult for students using this ver as professors often give assignments to read certain pages.

I just finished a graduate level statistics course that needed this textbook. I received a 99% in the course, but this book did nothing to support me. The chapters begin out okay in the beginning, defining terms and stating how to obtain where you wish to go. It rapidly deteriorates from there, and begins explaining further in depth with complicated phrases and terms beyond my limited comprehension. It also only explains how to calculate things utilizing a computer program, telling you how to enter the numbers and it will calculate it for you. I don't have the program it speaks of. I passed this course on google and a prayer. Thanks for nothing Munro.

I would not this book from them. It is a created of various material than everyone else's book in my class and it is falling apart. When I write in the book and erase, the letters erase with it. WHAT? Seriously. I $50 for this book for it to latest and I've had it for maybe 3 weeks. Are you kidding me. Never again.

Covers the important topics, but lacks sufficient examples demonstrating the primary concepts in each chapter. When introducing fresh concepts, tends to cover too a lot of edge cases (eg delving into all the assumptions and tests for such assumptions that must be met to use a certain technique such as regression) as opposed to starting with simplified cases and then covering more complex cases. Also, a lot of examples assume one has SPSS software. The kindle ver does not have page numbers corresponding to the printed version, which makes things difficult for students using this ver as professors often give assignments to read certain pages.

This is not a review of the seller, but the book itself. It was a requirement for my course. I absolutely dread chapters in this book. Figures are seemingly created up at some points, drawn in from previous chapters but no explanation of why they are used or where they were retrieved from. All of my cohort reports the same complaints. I attempt to use youtube guides and the Green and Salkind text rather than this if possible.

it's a stats book, decent to understand, but a lot of errors that were not caught in editing, some that affected the learning of critical concepts. the book cover was nearly chop off the book, so I had to remove the scotch tape someone used and replace it with packing tape to hold the book from falling completely apart during the semester. otherwise the book was in decent shape.

I just finished a graduate level statistics course that needed this textbook. I received a 99% in the course, but this book did nothing to support me. The chapters begin out okay in the beginning, defining terms and stating how to obtain where you wish to go. It rapidly deteriorates from there, and begins explaining further in depth with complicated phrases and terms beyond my limited comprehension. It also only explains how to calculate things utilizing a computer program, telling you how to enter the numbers and it will calculate it for you. I don't have the program it speaks of. I passed this course on google and a prayer. Thanks for nothing Munro.

The content in this book is, for the most part, well designed. There are some issues that have no mention or no examples and those can be difficult to figure out. However, my complaint is mostly towards the quality. My book is literally falling apart. There are pages and pages coming out and I do handle it carefully. I'm not sure if I just got unlucky, but this has been very frustrating because this book wasn't THAT and I thought it would be a nice future resource after my course.

This is not a review of the seller, but the book itself. It was a requirement for my course. I absolutely dread chapters in this book. Figures are seemingly created up at some points, drawn in from previous chapters but no explanation of why they are used or where they were retrieved from. All of my cohort reports the same complaints. I attempt to use youtube guides and the Green and Salkind text rather than this if possible.

it's a stats book, decent to understand, but a lot of errors that were not caught in editing, some that affected the learning of critical concepts. the book cover was nearly chop off the book, so I had to remove the scotch tape someone used and replace it with packing tape to hold the book from falling completely apart during the semester. otherwise the book was in decent shape.

This book happened me really understand statistics on a much deeper and fundamental level that my college stats professor did. It has equation summaries that gives you all of the equations you need and is packed full of examples and statistical true globe analysis. Would highly recommend, especially if your professor leaves you feeling lost. Helped me obtain an A in statistics.

This book is a very amazing overview of Six Sigma. It includes 43 Chapters of material and has a amazing set of Appendixes. A word of warning, this book is a cross between reference and text book. If you do not have a little working relation with statistics you could obtain lost. Also there are examples of computer outputs that are a bit confusing (e.g. in Chapter 12 there is an example of a computer print out of Analysis of Variance(ANOVA), but ANOVA is not covered until Chapter 24). Overall this is a amazing refernce book but be aware the issues at the end of the chapter do NOT have the answers in the back of the book

The book is decent for what it covers, but it lacked discussion of MANOVA, MANCOVA, factorial MANOVA, and factorial MANCOVA. These are useful quantitative statistical methods to consider when dealing with with experiments that have (a) multiple categorical independent variables; and (b) multiple quantitative dependent variables.If the authors could contain MANOVA and MANCOVA in the next edition, this would create their book more complete and I would consider buying it (as opposed to merely borrowing it from the library).

**Statistical Methods in Epidemiology (Monographs in Epidemiology and Biostatistics)**[] 2020-1-22 1:14

The authors are reputable epidemiologists who have written a concise and clear elementary treatment of the statistical methods that are used in epidemiology. The book is very practical and contains some major true data sets. The authors spend a lot of time covering random sampling, the relative risk and the odds ratio and provide enough exercises at the end of the chapters to engage the students. This is a amazing text for a first course or for self-study. It also is a fine reference book for statisticians and practitioners.

**Statistical Methods in Epidemiology (Monographs in Epidemiology and Biostatistics)**[] 2020-1-22 1:14

What we like. Comprehensive, concise, and clear. Fills the VOID left by other epidemiology analysis texts and the statistics not calculated by SPSS and SAS. Our constant companion in each epidemiology research task. What we do not like. More info on regression analysis in the next edition please.

I saw the book out of cross-discipline curiosity: my statistical background is of econometrics variety, and I was wondering what sort of statistics meteorologists do. In the event, this was not a right choice: the book is an introduction to statistical methods, and at this level, things do not obtain too specialized. All the same, I quite liked the book as a nicely written and very substantial statistics textbook - the chapters on frequency-domain analysis and PCA, CCA, etc. are some of the highlights - so enthusiastic kudos to the author.

This book is obtuse, unhelpful, and takes pleasure in overly complicating primary statistical concepts. This book skimps on the answers to the practice problems, and the link provided advertising "expanded answers to selected exercise" is broken.I search myself referring back to a stats text I used in a previous course or to youtube videos to clarify concepts this book has butchered.Word of caution: if you have to take a course that requires this text, be prepared to seek out supplemental study materials.

The Six Sigma methodology for using statistical methods to drive continuous improvement is fairly easy in concept, but is burdened by the onerous and obscure research important to understand the toolkit and know which tool to apply when. At GE, Six Sigma training entails two full weeks of study, two large binders of reference material, then the completion of at least two projects under the watchful eyes of the Six Sigma Black Belt community. Even after all this, real comprehension remains rtunately, "Implementing Six Sigma" serves as a handy reference work to support tutorial us through the tangled thickets of Six Sigma. Aside from providing numerous examples of how and when to use each of the tools in the Six Sigma toolkit, the work also frankly discusses why the methodology succeeds or fails (a lot of which has to do with the method it is implemented), and gives a amazing general overview of the principles behind the e one negative about the work is the lack of a comprehensive summary. There is a smorgasbord of tools to use; when on earth is someone going to produce a handy 2-page tutorial to what to use when? The book is also sadly a bit light on Design for Six Sigma principles; let's face it---quality gurus tend to focus exclusively on product improvement and not on business process improvement, yet the driving force behind hidden factory costs is the inefficiency of business processes themselves. This is a glaring omission in any work which calls itself comprehensive in ill, this is a reference work which belongs on the shelf of anyone interested in Six Sigma or in the improvement of manufacturing processes. Despite the hefty tag, it truly is the best single-volume reference to Six Sigma available.

The book arrived in very amazing conditions, like brand new. The book its very insightful and can support you to understand six sigma. It could be a tad boring if you are not currently implementing some of the techniques that are explained. It is best to read the book if you are in a similar field, such as manufacturing, quality control, engineering etc.

**Statistical Methods in Epidemiology (Monographs in Epidemiology and Biostatistics)**[] 2020-1-22 1:14

This book remains one of the best primary Epidemiology Methods books on the planet. It is a no nonsense book that gives the proper statistic with its confidence interval or statistical test, often with its derivation. Its index is highly useable: if i need to know how to calculate a confidence interval on an an SMR, I can just look up SMR and there will be an entry for Confidence Interval for SMR. This is a book that should remain of the shelf of every practicing epidemiologist who needs to look items up. A word of caution: there is NO philosophy (eg on selection bias) in this book so in that sense it not really a stand-alone Epi text-book, but an extremely useful supplement to be used along with a talkative text or your own coursepack.

Worst book ever! Full of mathematical mistakes and ambivalent procedures. It was not useful at all for my grad school Six Sigma class! There are better options out there to fully learn the concepts and procedures of such an import topic.I couldn’t even it back, ‘cause no “buy book back” supplier would wish it back, not even Amazon, where I bought it from)! So I ended up just giving away to a classmate who is about to take this course!

This books is not only simple to read and understand but it will support you make training for others in your company by providing practical examples, and solid, logical will support you understand WHAT Six Sigma is, as well as HOW to implement it in your company - be it a manufacturing zone or service area. This is what I personally found most helpful. In a services business like ours, practical examples of the applied methodology are Breyfogle's book, as far as I am concerned, is the "college textbook" ver for the corporate workplace. An perfect resource, and one that should be used again and again.

I have to say this is the most detailed book ever written on six sigma. I have read other books on ss - as i'm preparing for my ASQ ss green belt - but havent found them to be more in depth than this, especially on the technical side. I would recommend this book for all ss practitioners and who need to have a reference tutorial book on their desktops!I want the newer edition would include less typos and to be more clear. More examples are needed, and lots of case studies as wellAll in all, its an perfect book!

I usually do not write negative comments because I tend to accept things the method they are and, rather than criticizing, I test to grow up to the tasks. But in this case, I must caution everybody who is considering buying this book: this is far THE WORST statistics text EVER!!! Just stay away from it. Besides being absolutely confusing and not explaining anything at all, it uses an unintelligible language, and you will search VERY MISLEADING errors on every single page. Literally, even the solutions at the end of the book are full of mistakes which is rather unfortunate if your aim is to check the correctness of your work. But you do not need to obtain to the end of the book. Errors are all over the put in it. Truly, this is a pathetic intent to write a stats book. I am sorry for the authors. They may be unbelievable statisticians but are the worst teacher-authors I have ever met.

At 700+ pages, this is the equivalant of a college level text book on applying Six Sigma methods. I suggest it be used as a reference tutorial in conjunction with a formal six sigma training program, (The author contains this book in his Six Sigma training seminars) as it is an perfect tools and reference book. The book also gives a amazing introduction and walk through on how a six sigma program is typically implemented. The appendix includes an perfect glossary of six sigma terms and e only criticism I have is that some of the explanations on relatively easy concepts (Cause & Result diagrams, FMEA, etc.) are a small more verbose than they need to be.

I've read a lot of books on Six Sigma analytics and consider Forrest Breyfogles' "Implementing Six Sigma" to be the best all-around Six Sigma book available. It is 1,200 pages of Six Sigma program management and applied statistics presented in incremental stages of complexity across a lot of various industries with working examples of production and other business issues to solve. Breyfogle understands that Six Sigma is more than an analytical and program management toolkit, it's a philosophy and an organizational mindset and this is reflected in his writing. If you wish or need a single tome to be the central mainstay of your Six Sigma reference library - this is it.

This book is THE reference for the intersection of statistics and meteorology. Each edition improves and expounds upon the last. As a researcher who does a lot of work in weather forecast verification, this book gets pulled from my shelf more than any other. Dan's writing style is compact; it's got most everything you need, small you don'@#$%!&[email protected] a suitable reference for an undergrad or graduate course in statistical meteorology, but it's also highly recommended to most any researcher in atmospheric science as a handy reference.

This is a amazing book for those who need to learn how to use SPSS to run statistical tests. The data sets and examples are simple to follow. I used this book as a tutorial for completing the data analysis for my master's thesis. Now I am working on my doctorate and I often refer back to this book when I need a fast review. I highly recommend this book.

**Introduction to Statistical Methods for Clinical Trials (Chapman & Hall/CRC Texts in Statistical Science)**[] 2020-7-26 19:26

The author are very accomplished statisticians with a lot of years of clinical experience and research. DeMets along with Gordon Lan is popular for the alpha spending function approach that allowed added flexibility to group sequential trials. In addition to authoring several chapters of the book, Cook and Demets edited the book and invited other prominent researchers to contribute to the chapters. The other contributors are Robin Bechhofer, T. Charles Casper, Richard Chappell, Jens Eickhoff, Jan Feyzi, Marian Fisher, Kyungmann Kim, Rebecca Koscik, Mary Lindstrom, and Ellen e book covers a wide dozens of subjects and starts from the basics. But although some people equate introductory in a title to mean elementary that would be a wrong conclusion in this case. A lot of of the subjects are advanced and involve state-of-the-art methodology. The zone of adaptive designs is, for example, a very hot subject these days and is the topic of a amazing of e chapters are very well written and contain most of the crucial subjects that come up in design and development. For example, in the first chapter randomization is discussed in detail as are problems of organization, ethical issues, the reasons why randomized clinical trials are necessary and some regulatory apter 2 covers issue definition, composite outcomes and the use of surrogate endpoints. Chapter 3 covers design for all phases of clinical trials and contains sections on early phase trials, phase III trials and the phase IV postmarketing trials. Methodology contains non-inferiority, screening , prevention, therapeutic and adaptive apter 4 with the necessary problem of sample size determination primarily using frequentist approaches. This chapter contains the sticky problems of how to with clustered data, survival data and censoring due to loss to follow-up and non-adherence to the is is followed by complete chapters on randomization including response-adaptive randomization, data collection and data quality control, survival analysis. longitudinal data, quality of life data and instrument development, data monitoring and interim analysis, a chapter dealing with missing data, subgroup analysis, multiple testing and ways to avoid bias. The final chapter with the very necessary practical problems on how to close out a and prepare and report results.I like this book both as a possible introductory text and as a reference for clinical statisticians. The appendix provide sophisticated methods of inference including Brownian motion, info theory, asymptotic theory and the delta only criticism of the book is lack of discussion of software. Statistical pakages are crucial to the analysis of clinical trials with SAS being the most frequently used. Also there are now a number of fine packages for sample size determiniation and the design of group sequential trials. In this regard Demets and Lan have their own product and Cytel has East which is now entering the zone of adaptive design as is AddPlan by Wassmer and the pack produced by Tag Chang. So for a practical text on clinical trials the absence of coverage of the available along with recommendations of what to use and how to use it is the one glaring omission of the book.I especially recommend this book because from the methodologic viewpoint there is no other book with more depth or broader coverage. Longitudinal analysis and repeated measure designs are very necessary in clinical trials but are not often covered in introductory biostatistics courses. Chapter 8 covers random effects models, population-average, and subject-specific models and different sophisticated estimation techniques including restricted maximum likelihood estimation, two-stage estimation and generalized estimating equations.

**Introduction to Statistical Methods for Clinical Trials (Chapman & Hall/CRC Texts in Statistical Science)**[] 2020-7-26 19:26

It may have been an introduction, but I did not search it to be elementary. I was hired at a fresh job and first introduced to clinical trials. I got this book hoping it would support me out. I am sure it has a amazing information, but it was a lot of info and most of it went over my head. I want they would have given more formulas for sample size calculations rather than theoretical discussions. The book is too wordy--meaning there is a lot of text with very few formulas or workable examples.Overall, it is a beautiful amazing book, but I would not recommend it if you have small or no background in clinical trials (even though it says "introduction" in the title). It does have amazing subjects to obtain you thinking, but you might have to search other sources to really understand the material.

**Statistical Methods for Practice and Research: A Guide to Data Analysis Using SPSS (Response Books)**[] 2019-12-23 19:43

I teach Marketing Research at the college level and use this as a supplement to the is a amazing book for primary SPSS usage. It will not teach statistics or go into the advanced methods but will serve as a reference to those that know their stuff.I would recommend this book to anyone that needs a constant in their library, to remind them of the basics when they need some help.

**Numerical and Statistical Methods for Bioengineering: Applications in MATLAB (Cambridge Texts in Biomedical Engineering)**[] 2020-1-19 23:54

The examples in the book are extremely straight forward, and the MATLAB codes included in the book are step by step, and explained throughout. I hadn't used MATLAB for about 2 years prior to this, and the book is so well organized by the chapters, that it is simple to pick it up. I mean it is beautiful hard not to understand a lot in the book as most of it is explained in basic, simple, and simple terminology. I think this is the best book in this zone to learn from.

**Statistical Methods for Reliability Data (Wiley Series in Probability and Statistics)**[] 2020-2-6 18:58

The purpose of this book was supposed to serve very broad groups of people: students, statisticians and engineers. Unfortunately, I found this book not quite suitable in engineering practice. From practical point of view, when dealing with reliability estimations, one has to connect mathematical theory with real-life data. It appears that to accomplish this task it is important to understand some primary statistical ideas, plus specifics of the topic under consideration. Sometimes common sense knowledge can come in handy. Strangely enough but a lot of fundamental principles are in fact surprisingly simple, elegant and thus beautiful. What is missing in the book is the lack of clear explanations of fundamental statistical concepts that certainly can be presented in a complicated form but in reality they are not. On the other side, the book could serve as a solid textbook to students, statisticians and mathematicians.

**Statistical Design and Analysis of Clinical Trials: Principles and Methods (Chapman & Hall/CRC Biostatistics Series)**[] 2020-6-30 19:42

I have become very familiar with this book as part of my clinical trials education and training. I believe it stands out as an exceptional tutorial to the Design and Analysis of Clinical Trials for several reasons:1) Clear and Concise Info – Shih and Aisner do a unbelievable job of condensing the wealth of published material about clinical trials into easily digestible chapters that each have clear learning objectives.2) Statistical Rigor and Ingenuity – Learning from this book, I felt I really developed a solid foundation of statistical concepts to apply towards clinical trials. These concepts were straightforward and were explained thoroughly. My favorite part was that I also developed a proficiency in several “lesser-known” but very valuable statistical methods and practices which have impressed my colleagues and I believe will benefit my career.3) Purpose-Driven Issues – This book contains end-of-chapter questions for each of its subjects. I HIGHLY recommend you complete these problems. For students and specialists alike, they really do a amazing job of illustrating how each chapter’s concepts can be practically applied to clinical development and analysis.4) Programming Practice and App – Most of the topics covered in this book have examples linked to modern statistical programs including R and SAS. As someone who started this book with only minor experience using these programs, I felt that this practice really helped me better understand the capabilities of these software. It also did a amazing job demonstrating how to apply statistical theory to build programs that could be applied to design and analyze clinical summary, I recommend this book as both a amazing tool for clinical education and as a must-have reference for specialists looking to improve their clinical skill-set. I know it is a book that I will hold handy for a long, long time.

Useful review?

Statistical Methods[] 2020-9-15 19:57Amazing book!

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Useful review?

Statistical Methods[] 2020-9-15 19:57Got this as more of a reference tool to refresh myself on some stats. Amazing lay out and not too difficult to understand. Some books seem to create analysis more difficult that what it is by overexplaining the topics. This covers most basic/intermediate subjects fully and in an understandable way. For the and content this was a amazing buy!

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Useful review?

Statistical Methods[] 2020-9-15 19:57It's amazing as it should be.

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