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Must be written by two or more authors-Some variables have various symbols in the text and certain terms such as mttbf and mtr are interchanged without proper introduction-But all in all a decent text that can be referenced when needed-
I bought this book during my masters studies, I studied all chapters, and the it is an perfect textbook.
Very amazing in depthit is from few books that really deal with system reliability in depth but need to be updated based on the fresh researches in system reliability (especial regards the repairable system)
I had a pleasure to study under Marvin Rausand in the university and there the book was used a text book for the main course on reliability theory. The effort place in to address the subjects and material covered must be appreciated. The book is somewhat massive on mathematical side but reliability analysis will in any case require some math. I liked the examples of the book and the method every concept is modeled with random e book is easier to follow if one has a amazing background in Probability and statistics. There is where I was weak so had to struggle a bit. That is why i think this is not a introductory book on the subject if someone is very fresh to reliability analysis.I use book as a standard reference and still after 5 years i search interesting stuff.
It takes a lot of time to manage material in the method this book represent the reliability to you. I think he did a amazing job in organizing concepts in this manner.
This is the most complete reliability book that I have seen. It is appropriate as both a textbook and a reference. It is well-written and simple to understand. I highly recommend this book for anybody interested in learning reliability theory.
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 Now, it's as outdated as the chapter on linearizing functions (so you can plot them on log paper.)Many of the questions in this book remind me of conversations I have with my grand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 deal with time to failure data. Much of the methodology is essentially the same. The term reliability is generally used to apply to hardware or software 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 deals 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 software from MathSoft. An ftp website is available to download data sets to use with SPlus.
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.
As a student, I search this book to provide simple ways to absorb the material and is very lightweight making it simple to transport in your bag. Some of my other multivariate books have been larger hardbound books which often stayed out the house to avoid backstrain.
I bought this book NEW for a doctoral class. It was missing pages from 238 to 263, basically all of Chapter 8. I did not realize this until midway through my class and the time period to return the book had passed. I contacted Sage Publishing about it and they said the book looked to be counterfeit. Extremely disappointed!
This book is simple to read and understand. All necessary words or concepts are highlighted in blue which is really convenient. The practice book that goes along with this is nice but I wouldn’t say it’s important since the book should be enough to support study.
I teach a graduate-level Health Services Research at a major metropolitan university and search this user's tutorial to medical literature the best I have ever read. Definitely recommending to my graduate students. The fact that everything is similar to current research in the health care industry makes this text a must have for health executives.
I've spent a lot of time with this book, reading through and working through each example. I've also found issue sets and solutions on the course www service for CMU's intermediate stats course that is taught by the author and uses this text (36-705 Intermediate Statistics). Despite what some reviewers say about this book being too dry or lacking background intuition, I still think this is a amazing book to have if you want to work through subjects in probability all the method up to statistical inference (my goal is to understand this items well enough to grok the theoretical underpinnings of machine learning). My tip for getting the most out of this book is to take it very slowly and to work your method through every example. To give you an idea of the pace: I've spent about 3 months part time working through the first 4 chapters. I also recommend cross referencing material when the examples provided are insufficient to understand the material. I've found that they are sufficient about 60-70% of the time. That is ok with me, as I don't have a hard time googling to search supporting examples or materials. At the beginning I took it particularly slow. The idea of random variables was hard to wrap my head around. It's ok though, there are a ton of resources online taking various approaches to explaining the concept. And once it clicks, it's amazing to come back to the concise theorems of probability laid out in chapter one and continue on. If the book took the time to explain the intuition behind every concept, it would be 2000 pages this book isn't magic. You won't be able to breeze through it and understand "all of statistics" in a few weeks. But it provides a comprehensive roadmap into key topics, theorems and examples—the best I've found anywhere—and when the book is lacking in explanation or examples, there are easily googleable terms to search case anyone finds it helpful, I've collected quite a few resources on studying probability and statistics here: (...)
This book gives an overview of classical statistics, with an introduction to more modern methods of robust estimation and machine learning. I would say the contents are more focused on practical methods, but the author is always careful to state the important theorems from the underlying mathematical foundations of each method. Most of the theorems are stated without proof, although almost each chapter is followed by a short appendix giving some more technical details. Providing a proof for each theorem would take a lot of zone and would detract from the applied aspects of this book. What I like is that each chapter has a nice list of references, so an interested reader could go on and discover each topic in more depth with all the mathematical info they e topics covered is a compromise between the practical side of classical statistics and the modern methods of machine learning. They contain convergence, the delta method, point estimation, hypothesis testing and confidence intervals, bootstrap, regression, non-parametric estimation, orthogonal functions, classification, graphical models, and monte carlo for integral evaluation. There is some bayesian estimation, but mostly the book follows a frequentist approach.I think that this book would be useful only for someone already familiar with classical statistics. It could serve as a amazing modern reference on statistics and an overview of some methods from machine learning. I do not think that this book is a amazing source for first exposure to these ideas. Someone should first go through a standard statistics book, such as for example Casella & Berger or Bickel & Doksum. Then this book could server as a "crossover" from that classical material to the modern methods of machine learning. After that the reader can go on to discover machine learning literature on their own, using this book as a ere are a little number of typos throughout the book. They pick up in chapter 22 on classification, where there are some typos in necessary equations, for example equation 22.21 on Fisher discriminant and the formula for epsilon in theorem 22.17. But overall I had a very positive experience reading this book. It helped me review some items I already learned, showed some fresh applications, and introduced some subjects which I look forward to exploring further.
I search it hard to rate the quality of the book. I am from a non-mathematical background (I got no further than calculus in college), and I've been working for three years now on building math skills, especially statistical analysis and inference. I asked a fellow employee (whom I thought I could trust) for a recommendation on a amazing book for someone with rusty math skills who is trying to learn statistics. This was his is is NOT the book for that purpose. I realized on my first perusal of the book that he was being snide and sarcastic, as I subsequently learned was his custom. This book is a reference, full of complex mathematical notation, that is perfect (so far as I can determine) for reviewing concepts you have already learned and mastered. It is the worst possible choice for someone who is just starting out on learning statistics.I can now, finally, start to dip into this book at least in places, and follow the material. So I'm glad, in the end, that I got it. It will eventually prove useful to me.
I got this book to supplement a graduate class in statistics (our main text was Casella and Berger). It is very approachable and simple to read. It didn't cover as much as our main text but every time I referred to it I understood much better.
This book is great. The overviews of the subjects are insightful, but the book can be quite confusing if you do not have a mathematical background. As a social scientist, I tried to use this book to learn statistics. I wished there were more examples.
I'm not finished with the book but search it very readable and helpful. I was a small disappointed to keep the book and message that there was a issue with its binding. It wasn't enough of a issue for me to wish to return the book, but annoying nonetheless.
The book presents an ultimate introduction to statistics with references to the literature for the interested reader. Unfortunately, there are a lot of (typographical) errors in formulae that create the text somewhat hard to read, and the erratum available online does not cover all of the errors still show in the second edition.
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 free 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.
This tome is complete, but it should be condensed. A lot of times I felt like the concepts should take only a few pages to flesh-out, but the authors seem to take pleasure in burying you with example after example and redundant exposition. Maybe some people like this form, but not me.
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.
The author(s) have a talent for using so a lot of words to say so little. A highly frustrating read. The needed readings in my college literature course were less convoluted. Easily the worst piece of academic writing I have ever read.
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.
I used this book for a course called Multivariate Data Analysis. It includes numerous examples that support the reader understand how to implement multiple methods used in multivariate statistics. This book will support you obtain up to speed with using R. It is not meant to be a comprehensive text on the topic so be sure to use with a text that is. I enjoyed his explanations of results for the methods he covers in the text. After spending hours trying to implement the code and produce the same results in the book, you can use the biographies he provides as a method for taking a sure to used the companion website as it includes the R files, exercise solutions, and datasets used throughout the book. It also includes a list of errors (there are quite a few, but that is always the case with texts on statistics).
I don't like to write long reviews, so allow me give you a fast rundown : 1) Treatment of subjects is very shallow, it is not meant as an anchor textbook, but then why buy this along with a solid text? 2) The key pieces of code are highlighted in gray! 3) It is not one for getting into any of the , consider your motives in purchasing, and if you need depth or an anchor text, this is not for you!
The only amazing thing about this book is that it covers a wide range of topics, touching upon nearly all the multivariate methods. Author starts every chapter with a brief history of prominent mathematicians who have contributed to the relevant topic, which is an interesting introduction. However, after that, the book is very poorly organized and full of missing links. Sometimes, it is unclear what is being talked about, which data set is being used, or which packages are needed. The descriptions jumps from one package/data to another without providing any context. It is a complete hotch potch. In my assessment, this is not a textbook (meant for independent study), but a hastily place together compilation of lecture notes, which can only be understood if you are taking the author's class.
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 cheap and I thought it would be a nice future resource after my course.
I would not buy 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 paid $50 for this book for it to latest and I've had it for maybe 3 weeks. Are you kidding me. Never again.
Let's face it.. you're not buying this book because you wish to, you're buying it because you have to. There's nothing unique about it.. you'll just barely obtain through the class and be excited for the day you can give the book away.
Very well written. Instead of stuffing data down your throat in an effort to obtain you to comprehend statistical concepts, the author makes a smooth and effective presentation as she makes deep concepts digestible. She has done an exceptional job.
All of the pages seem to be there, I've spotted no tears so ere is, however, some highlighting- which is versus what the product description said. Somewhat annoying, but not a major concern.On the other hand, it came 1 week earlier than expected so plus points for that.
It's a really informative book. It isn't boring either which is awesome since its a textbook. This book has really helped me understand a lot of various methods and terms that come from the Research Methods class. So I feel as if it's helped me gain a better understanding of the field thus far and I plan on keeping it even though I rented it. Mainly because its a really amazing resource and I also highlighted very well in it. I'm sure it'll be of amazing use in the future in my career
This book gives a useful foundation in GIS with examples. If you're a beginner and you're wanting to begin with the basics, this is the book to go with. It will give you a foundation in GIS AND statistics. The only downside is that it does not come with a GIS program key (in other words, you cannot obtain a temporary ArcGIS license or anything like that).
I bought a fresh book but when started reading i realize in Chap 2 that the book is supposed to be delivered with a CD that include data for practical exercises. Unfortunately this was not part of the pack I received!
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.
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.
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.
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.
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 have not found much use for this text book, but it’s a amazing review of undergrad statistics. Math is either black or white, either you know it or you don’t. So if math is not your powerful point, this book might be some use to you in the long run.
I am using this book for an online stats course. I have to say, and a lot of classmates agree, that this is not an simple textbook to understand. Everyone knows that with most online courses, you need to teach yourself thru reading. This DOES NOT create it easier.
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.
TOO MANY is book has enough errors to pay one's tuition. I'm just not sure of content being studied because it might be "right" now, and wrong after instructor's review. I also want the explanations were done for EXCEL not SPSS.
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 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.
Even though I've been meditating for 45 years, there are aspects to this book on the spiritual life that go method beyond anything I'd considered. And at the same time, the book is excellent for beginners. Simon's stories are funny and wise. His categories of Educator, Warrior, Merchant, etc. typify our Inner core nature, our natural bent. And the Feng Shui aspects were interesting - (I was checking my South wall to create sure there are no mirrors!) He also covers meal and life force. So nothing is missing here! in the quest for overall Spiritual development. The tone of the book is warm, insightful, and inviting.
At 140 pages this is purposely shorter than Simon's previous books, as he explains in the introduction. Straightforward and sometimes funny- contains 'spiritual jokes,' anecdotes from his guru, and other private stories that are as interesting as the main material. My favorite ideas: "The All-Saints Diet," and "The 7 for 11 Dharma Method". Overall, highly recommended!
A unbelievable book that gives step by step guidance in developing our dharma and spirit. If you can't immediately do the more intense tasks, Simon gives you alternate ways to ease 'up to' the more intense ones. I have so a lot of highlights in the book, that I know I will use it as reference for years to come.