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This is a very solid book for learning about making sure a process is in control. While all the applications are with regards to manufacturing processes, every single thing in this book can be applied to any form of data ysis since that's really what quality control is at its heart: you measure data, you determine from said data if things are all right. There are a lot things done right with this book: the examples given are very in-depth, guaranteeing your understanding if you read them and don't just skim through the numbers to obtain the answers with the right calculations, the explanations on DMAIC and other different terms are given amazing care, and issues and questions are varied to give a solid understanding of the field. Finally, while a primary statistical background is assumed, Montgomery will walk you through exactly what you need for the rest of this book in two dedicated ever, there are several problems. First, the examples themselves are not varied at all. You'll go through quite a lot of modifications and variations of an idea (especially in the control charts chapters), but the examples just stick to the basics. Also, other examples are "hidden in the text," so they're mixed in with the paragraph style. He'll also work out more than one issue that requires use of tables outside the range of what the tables in the book provide; that might be fine at an upper level, but this is such a lower level textbook you're bound to have several students who aren't totally used to looking up values in statistical tables and using them correctly to understand where some values come from if they're not on the tables.Otherwise, the book is solid. It's not theoretical, it's not meant to be--it was amazing enough for me to purchase a copy of and hold on my shelves because of how thorough WHY things are done the method they are done is explained, and I would recommend it to anyone to hold as a reference if they do this kind of work.
I agreed with some reviewers that this book is mainly a collect of a lot of points raised over the years. For instance, for the Gauge R&R variance component ysis, there is no indication of below what percentage of the gauge variance to total variance is considered adequate or good. This maybe reflect the messiness or uselessness of the field as a whole.
Amazing quality statistics textbook, but I ended up using it primarily for the chapter issues (required by my course) and didn't do much actual reading of the textbook and was still successful in learning the material... likely due to the format of the class/ how my instructor presented the material.Equations and concepts were simple to search (straightforward formatting of the content) and I have no doubt that I could have learned solely from the book had I chosen to do so. Can definitely see myself referring back to this book for statistics/ quality "refreshers" in the future!
Very massive on the math! Since I have an engineering background, it's fine. If you wish to learn the concepts look elsewhere. In my case it was a textbook for a graduate level course and the professor supplied the extra info needed. If you're educating yourself choose another book.
Fundamentals of Quality Control and ImprovementI rented this book because my class needed me for quality control. It was not the most interesting read and unless you are going into a quality control position or aspire to search yourself in this industry I would recommend renting the book instead of purchasing it.
This is my favorite book in my little library on physics. I have read about nuclear physics in other books, but this takes the prize. Instead of flooding the student with an immense load, the focus on radiation provides a car that the student can ride with pleasure. Some surprising pieces of knowledge were read in Nuc. Rad. Interactions by Yip. The Coulomb force in a nucleus can attract at close range within some constraints from page 94. Thank you Sidney Yip for a book that will enlighten those diligent seekers of experimentally derived models.
This is a culmination of decades of lectures organized into a thoughtful, cohesive text. A must have reference for all students and practitioners of nuclear science and engineering.
I recommend this book because it includes easy concepts and pay attention to present methods and techniques for self study, calculations, probability distribution and graphs in relationship to statistical process control in any situations of manufacturing.I like to do the excercises and have a complete quizzses and final exam, from my point of view I would like you, my appeciate reader have one to read and study.Jaime Zamora vember 9, 2013
The book is basically a beautiful amazing one. But I had a couple of issues which, basically, are not the author's fault. first I just wanted a refresher on the types of, and formulas for Control Charts. The author went method beyond that; so the wordiness went too much in detail for other aspects of Total Quality Management. If this is what you need, the all means, buy it. The other issue I had was that the tables / formulae were method too small. Again, not the author's fault; that's just the method it shows up on my Fire.
Not a poor book, and one which covers the mechanics statistical process control, but the book focused method too much on how to do statistical process control, rather then the why. The end effect is that the book leaves you with a amazing collection of recipes but no true understanding of how to cook. I did like how the author focuses so much of the book on problems of collecting good, independent data samples.
I bought this book because it is needed for a class. I've taken other statistic and research way courses, so I already had some background. However, this book does a decent job at explaining and giving plenty of examples. The problems I have with the book is that there are some mathematical errors, they do not always explain where the values are coming from, and there are some examples that do not have an explanation of how to interpret the results. Some of the examples are kind of self-explanatory, so those ones do not cause an issue. Or at least, I think they are self-explanatory. Again, I have taken other courses for statistics and research methods. In regards to shipping, the book arrived earlier than expected, which was nice, and it was packaged in a box to create it snug. There was no visible hurt to the book upon arrival.
Only in chapter 2 and there are a lot of typos. The worst part is that there are typos on the homework issues and the solutions in the back, so how am I supposed to if I got the issue right or not?!! This is the second edition of the book, so they should have fixed all of the errors by now. The content of the book is okay though.
This boos is a master in its topic statistics, because inside found concepts and excercises, effect and charts for industrial applications in engineering and science.I own one, so I cxan reciommendate to buy for student and profesional practices of business and engineering.Jaime Z.February 6. 2014
Hastie and Tibshirani are machine learning superstars and I believe this fresh resource will play an necessary role in statistical learning just like their previous texts. The timing is excellent for a deep look at the lasso as huge data is placing stringent requirements on how enterprise data assets are being used for strategic advantage.
This is a very nice book that cleanly explains how to use graphical networks and probabilistic tools to solve practical problems. Technical stuffs are nicely written.
If you would like to practice network ysis by dynamical way, but not stagnate with statics, then this is the book for you. Alternatively, I leant a lot by following Harry Crane’s twitter feed. Highly recommend for anybody who prefer complex data ysis over classical statistics.
Though not mentioned as often as that buzz term “Big Data” it’s also the case that, just as the collection of data is expanding exponentially, so is the number of data modeling techniques. In fact, it’s growing so rapidly that it’s hard for even the practicing statistician to hold track of it of the techniques which is becoming standard practice in predictive modeling and causal inference is the lasso. Developed around the turn of the latest century, the lasso is designed to handle data generation under sparsity where sparsity can be loosely defined as having a lot of inputs with small or no result on the target r example, one could be trying to predict users’ preferences on Netflix and have each individual film as a predictor. Obviously, a lot of of the effects would be zero. Being too huge for stepwise regression, the lasso and related techniques are able to solve these issues on twenty-first century computers within mins if not seconds.And while this book caters to upper level graduate students and was far more than I required to gain an understanding of the lasso and similar techniques (such as the group lasso when one has highly correlated predictors), I strongly recommend the book to fellow applied scientists. Clearly written with ample illustrations and examples the book sets a standard for how a technical text should be ven its uncomplicated prose and the importance of the lasso to modern researchers I gave the book five stars since it is a useful tutorial to social scientists and data scientists alike. Ideally experienced in graduate school, the text is still helpful for those who, like myself, are merely trying to hold up with the recent trends in their disciplines. Highly recommended.
The statistics/machine learning community has been bombarded with so a lot of variants of LASSO, for so a lot of various types of methodology, without any general, unifying treatment of ths subject. The effect is more confusion than insight. This book fills that void, and is sure to be much cited as a reference. It will be quite useful to me.
Book is a bit intimidating when you first page through as Crane goes into detail about much of the math involved with network ysis.But, as you read you’ll search the explanations are well thought out and thorough. I took copious notes throughout my reading and I must say this is one of the best books I’ve read on the subject.I recommend this book to anyone who has a powerful surface level understanding but wants to read something that’s much more in depth.
This book is about bet on sparsity. In Machine Learning, there are plenty of approaches that might work on data of interest. The accent is on cases p>>n and one wants to obtain more interpretable models.
This is a fun book I recommend for any reader interested in learning about statistical tools for networks. Crane has an entertaining writing style that makes this one of the more enjoyable books you will search in statistics or probability.
This is a unbelievable book on optimal control especially for readers who do not have to learn the math as they go. It covers a wide range of issues in a dozens of disciplines and is an interesting book. The approach is relatively modern although a differential geometric approach is not utilized. For those wanting a beautiful complete treatment of the basics of optimal control this will be a amazing supplement to the usual suspects or even a amazing stand alone text. Although the mathematical requirements are not very great, the reader who has them firmly in hand will be the most successful.
This book proved to be very useful in understanding the topic of orientation kinematics and DCM matrix. It helped me write a small guide on my blog (google: starlino dcm ) that include some notes and my own view on the subject. This book is really simple to follow, just create sure you read the introduction that covers the notation conventions.
As in matrix and linear algebra, partial derivatives, numerical methods, dynamics, kinematics and of course vector ysis w/lots of trig. One might think that one of the best latest texts ever written on inverse kinematics would be ideal for those of us designing joints both for animation and robotics, but a BIG dose of engineering kinematics is assumed to start with. The authors say "three years of undergrad" would do it-- maybe at MIT, but a LOT of this material is graduate level in UK and US y animators are not engineers, but are interested in the math behind kinematics. There are few books as up to date at this text in that field. But when you see that quaternion multiplication is "explained" in terms of matrix multiplication and linear algebra, it becomes clear that you need a amazing grounding in linear and matrix algebra before tackling this volume.Even the control feedback sections assume you've had at least one or two courses in feedback theory and math. The authors describe this as an "intermediate" text; however, given the paucity of other texts on kinematics in general (at least up to date texts), I'd disagree and call this is is not to knock the outstanding quality of the material, just to warn you that if you're into self study, you might be wasting your cash when you search the material level assumes a lot of engineering background. NOT a beginning text, sadly, as there are few amazing ones up to date on kinematics. If you spend your days with Maya and other programs skinning figures, or designing robot joints, and are willing to spend a lot of time on the prep math-- there is no better text available. But it is NOT an introductory text by any means. After all, moving joints around in 3 and 4D IS ytic geometry in motion, and PDE's are abundant in that field.
This book is clearly written by an expert for an expert. It may be a amazing reference for those familiar with the material who simply need a reference. For students interested in learning the material it is presented at a very high level which is difficult to follow.
One of the early books on Optimal Control and Optimal Estimation Theory and still very relevant, and perhaps still the most respected graduate-level text there is. Tremendous depth of coverage, and contains a nice selection of aerospace issues very useful for bench-marking your own software implementation against, especially if you are a researcher involved in the field of computational optimal ever, here is the caveat: the text is *not* an simple read, and could be off-putting for beginners. This text was chosen by my graduate instructor during my MS, and it was terribly frustrating to learn from. Fortunately, I ended up consulting Stengel's Optimal Control and Estimation, and Kirk's Optimal Control Theory, both very accessible. Computational Optimal Control eventually ended up being my dissertation topic, and once I started exploring Optimal Control Theory in greater depth did I realize what a unbelievable piece of work this text is!However, I do reiterate that students would do well to use this only as a reference, and not the main text when getting introduced to Optimal Control for the first time.
Preface: Dear student, you need a pre-existing degree in Robotics professional experience to be able to grasp what this book is going to be teaching you. What can I say, this was a college level book that was written beyond the scope of a student.
One of the best books I've come across on the subjects. Beautiful much split in two halves: the first part is Euler-Lagrange and deterministic control and the second half is random processes, filtering, and stochastic control. Covers everything extremely elegantly, including the connection to HBJ, bang-bang control, Pontryagin's principle, Kalman filters, etc.
I have a love and hate relationship with this book. At times, the author does an awesome job of breaking things down to their most primary level and covering a fair amount of content; yet, at other times he brushes over some material so briefly that you literally have lots of questions about what you just read and no answers provided anywhere in the textbook. Two thumbs up to the author for making himself available for clarifications. Myself and another student had a question regards rotation about global vs local in a certain instance, due to some differences in the info provided in the book and by our professor -- Dr. Jazar was very fast to answer to my email (I think a few days or less). In the end, I think it's a fairly decent text if you approach it with the mindset of knowing that it's an academic text. I've never kept a textbook used by my community college or my university, because I've always regarded them as worthless, but this book is an exception. While there are some quirks, I think it's worthy of remaining in my library, as it does do a fairly decent job of introducing one to the field of applied robotics. If it's a needed text for you, due to the class you'r taking, you won't have a choice but to obtain this book. On the other hand, if you're just looking for a textbook to learn about robotics on your own, I suggest doing a bit of a research, since I believe I've seen some better books out there (certainly don't rule this out, though... it's just that for its price, I don't know if you're willing to deal with the academic nature of the book).
This book does a very amazing job of showing every step involved with a lot of examples. It tends to present numerical examples and is a small light on theory, but this is an applied robotics book so no fault there. It makes a amazing text to have in conjunction with other robotics books.
Perfect book - Very thorough and extremely didactic ! I know of no other text that clearly explains the app of Bayesian a priori and posteriori probabilities to iteratively improve the estimate of reliability model parameters !
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 ysis 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 ysis.I use book as a standard reference and still after 5 years i search interesting stuff.
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.
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)
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.
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 had to buy this for class, but I did not like it. The book is very thin and just doesn't have a lot of content. It's laughable that they charge over $100 for this thing. Definitely rent it if you must have it for school; otherwise, obtain a more comprehensive book - it wouldn't cost any more than this one does. I've read several Quality books for school over the years, and this one is definitely the sparsest.
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.
While it has all the content that it says it does, the author is too wrapped up in giving examples and specific applications of the theorems that he fails to create the theorems and reasoning for them immediately obvious. What he should have done is: X -> Why X works -> example for X. Instead, he goes: Example for X -> an overly short description of can still learn from it, but it's more useful for the practice issues than actually learning the material.
The first few chapters of this textbook are somewhat reasonable, but as the text progresses it devolves into incoherent e textbook seems to be decent for those with a powerful background in statistics trying to review past material, but for someone without a powerful background in the field, it falls e text is written in such a method that it appears to be a person's notes, with quite a bit of critical info omitted that would otherwise provide the reader with a thorough understanding of how to apply the rhaps paired with a decent professor to explain the material, this textbook might actually be decent, but a textbook should be designed to stand on its own, and this textbook didn't.
Examples in the chapters don't explain each step of the solution. They don't provide the reader with an exact issue they're trying to solve. It simply starts with equations, then ends with equations, and doesn't provide the reader any sense of what is going on or why. Had to search an online ver of the course, which provided much better explanations of the book and the concepts within.
We're over halfway through the semester and have gone through about half the book so far, so I guess I can drop a beautiful informed review: it's not ere have been several times throughout the weeks where I have tried to refer to the contents of the chapter for clarification only to see that it was pointless. You're bombarded with a chunk of text and complicated-looking equations in beautiful much every chapter, even the simpler sections, for no amazing reason. When I refer to my notes, almost a near transcript what the professor has said in class with my own additions, I am able to understand this material better than the book could ever hope to explain.If you are assigned this book, pray that you have a decent to amazing professor.About the only positive I can really consider is that most of the exercise issues seem decent, but that's not a whole lot of support if you don't know how to do them in the first place. The tables and charts, especially those in the front and back of the book, are also nice but that's the sort of standard features we should expect from such an expensive t this on rental. Even if you're a general math or statistics major, there are likely better textbooks out there for you.
I can't speak much for the informative content of the book, since I have a various probability book that I like better. However, for my class, homework is assigned from the end of the section exercises, and while everything ELSE in this global edition of this textbook seems to be the same, some of the end of section exercises are different. Not by much... for example, section 1.3 # 4... The normal edition has you picking two cards and the global edition has you picking three... The first two cards you choose are the same, but that doesn't really matter, because I can tell you that graders don't care if you give them correct answers to the wrong questions.If you will be assigned issues from this book as homework, save yourself the cash and hassle and obtain the normal hardcover book.Textbook companies disgust me. Yes, I'm talking about you, Pearson.
This text, while nearly identical in instruction to "Probability and Statistical Inference, Ninth Edition" has slightly various problems. If you are looking for a textbook to follow along with course content, I'd recommend it, but if you're needing to turn in homework from the book, unfortunately, this is not a amazing solution.
This book is fantastic! It is the fourth edition of the famous textbook on biopharmaceutics. The authors have maintained the perfect standards of previous editions, by clearly explaining the concepts while keeping mathematical derivations to a minimum. The "practical focus" sections present the app of pharmacokinetics in a clinical setting. A fresh chapter on targeted delivery and newer biotech is very timely and relevant. Overall, this is an perfect book on biopharmaceutics and pharmacokinetics. It is recommended not only for the newcomer, but serves as a valuable reference in any library.