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I purchased this book, in the Kindle edition, on the tip of my Statistics with Excel professor.Having returned to earn my Bachelor's Degree at the age of 50, I have struggled with Statistics. As in, my house is littered with scraps of paper packed with formulaic exertions; my own ver of A Attractive Mind...This book is helpful, not so much as a cover-to-cover read, but as a solid reference tool to help you through the is plainly and simply written, real to its name. I hope to use it to survive the summer, and to earn a respectable B.

If you've taken any primary intro stats course, in combination with even learning a small R- this book will be WAY too easy for you. I was hoping for modeling building, but it barely touches on that (and not until the end). If you have no understanding of R, and small statistical training, this book will probably be excellent for your purposes.

This book does not read as well as the one for macro vba. He jumps around a lot, and even references the wrong figures at times (well at least once). The step by step instructions are much better in macro vba. I thought it would be related since they are both dummies books, but the difference could not be more stark.

Je cherchais un livre pour donner des cours de statistiques avec Excel aux cadres sup dans les entreprises ayant oublié ce qu'ils avaient vu lors de leur bachelor et ce livre est très bien fait pour ceux ayant peu de temps pour créer un cours et pour des clients ne voulant travailler qu'avec des outils sans comprendre ce qu'il y a derrière avec exactitude. Evidemment le lecteur n'y trouvera pas:1. les démonstrations mathématiques2. les hypothèses détailées de fonctionnement des try d'hypothèses3. toutes les lois de probabilités et leur app (on ne trouve que les classiques qu'on voit au bac)Cela toutefois un très bon achat pour sa qualité pédagogique et pragmatique (liaisons avec Excel).

This is an perfect book. The info is presented in a logical manner and is simple to follow. I have been using power statistics and Excel for a lot of years. Yet within the first couple of chapters, I learned about some Excel capabilities that I did not know existed. The book is written with Microsoft Excel for Windows in mind. I have Excel for the Mac. There are a couple of features in Excel for Windows that are not available in Excel 2011 for the Mac or are presented in a various format. I would like to see these differences incorporated in a future edition of the book.I am using the Kindle ver of the book. It is great. I can begin the book on my computer, bring up Excel, and work them side by side. Sure beats the method I learned in school.

First of all, this is NOT a amazing book to learn R if you are a beginner. It assumes a primary knowledge of R but still goes through a lot of the basics. It does not attempt to teach a broad coverage of the language. For example, it has nothing on flow control, which makes sense since this deals with the statistical packages already in R so it doesn't cover how to actually write your own programs in R(if you wish to do that, "R For Dummies" is an perfect introduction). So, this book sticks to what it is advertised to do: statistics. Does it cover an enormous amount of topics? No, after all, statistics is very vast. It covers such things as linear models, one and two method anova and contrasts in anova, for example. It doesn't cover subjects in psychometry(which R can handle very nicely), like Cronbach's Alpha. So, this book is not encyclopedic, but you can see what it covers and what it doesn't before you buy it. No one source is going to give you everything in the R language. This book is an perfect source for what it does cover. Don't assume that just because it is part of the "For Dummies" series that it is super basic. There are a lot of detailed, intricate books in the series. The series also contains a lot of other books on R so, you might consider starting a collection!One other amazing point with this book: it is very latest so it contains the use of R Studio(as well as how to install it) which makes using R a bit more pleasant.

I read all my books on kindle application on my Samsung Tab and unfortunately the book does not present any code on the app..It does present the photos but the part where code is written appears like a blue box without any content. Did anyone face a related issue? Allow me know if a solution exists. Other content is fine, as code is the major part, Its difficult to create sense.

Code that often doesn't work and produces errors through R, clumsy prose (". . .the symbol for the mean of the sampling distribution of the mean. . ."), and a structural balance issue (the author spends lengthy amounts of time on stuff of small importance, like pages on how to plot a standard normal, including customizing dashed lines, and small time on stuff that matter, like statistical testing) mean that this is a really frustrating reading some of the other reviews, it seems a lot of people are reviewing another book, perhaps statistical ysis with Excel or something. Otherwise, it's hard for me to understand how this would have so a lot of five-star reviews. I feel duped into buying this.

My review discusses the Kindle edition only. The text makes references to "over 300 diagrams and figures." The Kindle edition does not appear to have any. Also, the text has no layout or typographic differentiation to distinguish between R code and narrative. The layout makes the text virtually unreadable. I recommend you test "R for Dummies" if you are looking for a amazing book on the Kindle.

I'll say up front: This was a tough book to rate. However, upon reflection and a careful re-reading of the introductory matter, I decided on full marks. Mr. Lewis makes no pretense this is a textbook, and I'm sure it wouldn't suffice in that role. Rather, "100 Statistical Tests in R" provides a solid reference, with a lot of worked examples, on inference procedures that a lot of scientists and researchers need. Usually they need them without all the theoretical trappings, and that's where this book excels: "Just the tests, ma'am."The presentation is spare and clean, with solid literature research. The R code appears to be up to date (at time of release, anyway), and pack references are valid. An amazingly wide range of examples is shown, from communicating with whales to stock trading, android game theory to medical concerns are not with the quality of the try procedures or the code. I tried quite a few of the procedures, and found them to be functional and clear. The organization of the tests into groups covering correlation/causation, hypothesis testing, independence, and more is useful, and clearly shown early in the volume. I especially have fun having more than one option even within a single test.I am always a bit worried about efforts to enable sophisticated yses and inference without sufficient context or help for the less-sophisticated researcher, who is tempted to use the procedure as a "black box" and then believe they know enough to decide and act. A lot of of these tests are like carving an object with a supremely sharp knife; if you're not a master carver you can lose some skin, or perhaps a finger. For those who need these tests but haven't serious grounding in their foibles, I strongly recommend developing a relationship with a practicing statistician and seeking ese concerns do not reduce the usefulness of this presentation of statistical tests. The author makes no claims about one's ability to correctly infer in a particular situation. His guidance is otherwise.If you're looking for a listing of the mathematical equations for all these tests, you'll need to look elsewhere. However, if you're in a technical, research or development setting and use R (or have access to R coders to help), I heartily recommend this book to you.

This is a very amazing list of statistical tests. It provides some primary app and usage examples and then an example of how to run the try in R.I would suggest you have a solid understanding of statistics before purchasing as you will both have to know what you're looking for and the underlying concepts behind them; this is not "Statistics in R for Dummies". A knowledge of R is not required, but should of course be reccomended...You obtain a lot more than the $3 price mark out of this, if you know how to use it....

**An Introduction to Statistical Inference and Its Applications with R (Chapman & Hall/CRC Texts in Statistical Science Book 81)**[] 2020-8-25 18:49

Trosset does a amazing job introducing the student to r and eat first text for stats and r programming for stats. My only problem is with his wording/some of his problems. What the author is attempting to teach is sometimes unclear, as is what he is asking the student to do. I used this in a course and got multiple issues wrong because I didn't know what he meant/was asking/misinterpreted then question, and I am a native English speaker... hopefully this isn't harder for Esl folks.

Nice overview of available tests with short explanations and examples. This is amazing addition to other books, since it just lists all the tests and provides a kind of recipe which one is ever, the layout is horrible, looks like a ebook was just printed out. This book could be much much better and more useful if Font sizes and text layout were chosen in a more thoughtful manner.

The book serves well as a reference point and a amazing source for learning different statistical tests with their implementation in R. One needs some background in Statistics and R to benefit from most of the book; the more the better. The author has organized each section (approximately) as follows: first comes the try name, the question it answers and its purpose, next practical applications are presented, then comes implementation in R illustrated by examples along with their interpretations, all backed up by a well researched literature. Most necessary subjects are covered and when applicable alternative tests are presented. Overall, the author's explanations are clear, concise and conductive to learning. On the downside, there are few occasional typos and confusing errors (example: try 84, page 385, How to Calculate in R mistakenly repeats earlier try resettest{lmtest}, instead of white.test{tseries}). One main issue with this book is the lack of a general index at the end. This makes difficult to search tests similar to specific topics, unless you already know a test's name (for example when looking to try the usefulness of a time series in prediction of another, or in general searching for time series similar tests). The provided tests classification table (on page 13) may give a clue, but still not very useful when one is totally blind to a topic. Also, an index of R packages used in the book would have been useful. Additionally, as another reviewers pointed out, I found the font sizes and text layout on printed book somewhat inconvenient. Overall a amazing work with some room for improvement. The current price ($14.99) makes it a amazing deal!

I purchased both the digital and paper versions of this book. Our squad uses R on a everyday basis to fit both linear and logistic regression models. With the latest popularity of model risk management, formal statistical tests have become a necessity for practicing predictive modelers. The book offers a one-stop- for the "greatest hits" of formal hypothesis testing. Each try is organized as follows: what's the null hypothesis; when to use the test; applications from literature; R code (this is the selling point of the book); and academic references.

This book is a really amazing compendium of some necessary statistical tests. I found it useful because it listed a lot of I was unfamiliar with (such as Unit Root tests). I would have loved a brief mathematical synopsis which would save time digging in the literature. Also a bit more explanation of the R-output would have helped as well. Necessary also would have been one or more links to actual data sets of relevant data. If you wish to understand these tests better then you'll have to do some exploring. The Kindle Edition gets you right to each try so you can move around quickly. So, for the price, it's a amazing book.

This could have been a useful compendium of a broad range of common and not so common statistical tests. Granted, this material can be obtained for free by looking up each of the vignette at the R website for each of those tests. As you know, those vignettes vary greatly in quality. Some are amazing and clear. Others are not so amazing and not so clear. Thus, the author beautiful clear style could have added value by providing a certain consistency to the clarity of the codes important to run those ever, the author makes method too a lot of errors in R codes, tests description, try interpretation, and reference to the wrong packages for this book to earn a neutral to amazing star rating (3 or above). Earlier, I had doented some of those errors out of the first 31 tests I had studied through this book. You can see those doented errors in the following paragraphs. I found a lot of more errors as I went through all 100 tests. I am not going to doent them all for the sake of sanity within this book review. But, you obtain my point. This book was not professionally edited by someone with the adequate expertise in R to clean up this book.Within this paragraph, I doent some of the errors I noticed within the first 31 tests I studied throughout the book. In the section “How to calculate in R” when describing the Ramsey try (83) and the White Neural Network try (84) he mistakenly refers to the Harvey-Collier try (82). Those are large typos. R is confusing enough as is without being lead explicitly down the wrong path. When describing the Phillips-Ouliaris try (87), he states the try effect reject the null hypothesis of no cointegration between the variables (p-value < 0.9). That is the wrong conclusion. In this case, you should reject the null hypothesis of no cointegration when p-value < 0.05. Regarding the Elliott, Rothenberg & Stock try (89), the author now makes an error of try interpretation in the opposite direction. He states the try fails to reject the null hypothesis suggesting the presence of a unit root because the p-value > 0.01. This is far too stringent an alpha threshold level for this type of test. It also contradicts his own stated threshold throughout the book that if a p-value < 0.05 you can reject the null hypothesis. That is obviously still the case even if a p-value > 0.01. Regarding the Spearman Rank Correlation try (2), the coding of the Spearman try using the "pspearman" pack is incorrect. You have to embed the c() function a couple of times to correct the coding. Within the one sample t-test (12), where you take a single observation of a sample and compare it to an hypothetical mean, two of his examples are incorrect. Within pairwise t-test for difference in sample means, he repeats the same mistake. One of the example illustrates a paired t-test (he even names it correctly within the specific example). But, paired t-test as described is a various testing framework than pairwise. Indeed, the latter describe situations where you take two observations (pre and post) of the same sample. That's a various test: paired t-test. Within his description of two sample t-test for the difference in means (15), in one of the examples he describes a effect as having a p-value of -0.62. This is not possible. P-values are probabilities that are constrained to values between 0% and 100%. They can't be negative by definition. The Bartlett's try of sphericity (7) has an error in coding. You should remove the "ncol=3" term for the code to work. The Jennrich try (8) had incomplete coding. You need to contain the sample size of n1 and n2.

**An Introduction to Statistical Inference and Its Applications with R (Chapman & Hall/CRC Texts in Statistical Science Book 81)**[] 2020-8-25 18:49

Text is so hard to understand for beginners. Everything is in mathematical notations and author does not good job in explaining things, lot of short chop used for readers to figure things out. Even examples are not that good. I would not recommend this book

There are hundreds of R books, but this is the best one to address the core issue of learning to *program* in R. As reviewer Jason notes, R is used by several audiences with varying needs, but anyone who uses R for long must come to terms with learning to program it. This is the book for that.What Matloff does is to lay out the essentials of the R language (or S, if you prefer) in depth but in a readable fashion, with well-chosen examples that reinforce learning about the language itself (as opposed to focusing on statistics or data ysis).I'm a long-time (12 years) R user, which is my platform for ytics every day, and I have programmed in a dozens of languages from C to Perl. I have long missed the fact that there is nothing for R comparable to Kernighan & Ritchie ("K&R", The C Programming Language ) or related programming classics; finally there is. Matloff is not quite as attractive and elegant as K&R (and to be fair, is not in their position as the language creator) but this book has related goals and comes reasonably close.I think there are two basic audiences for this book: those who are learning R from a computer science or programming background; and statisticians and others who use the programming language and wish a thorough exposition. In my case, for instance, despite having written perhaps 100k lines of R code over the years, there remained locations where I was uneasy (e.g., exactly how do lists relate to data frames). Matloff sets it all straight, in friendly, readable fashion. Even in rudimentary chapters, I learned shortcuts and miscellaneous functions that are quite useful. The examples throughout are more "CS-like" than statistical, which is highly advantageous for this addition to the guide content, it is well-suited as a fast reference. It doesn't aim to be comprehensive from a function point of view (which is almost impossible, and what R Support is for), but it is comprehensive from a programming conceptual point of short, if you program R, and unless you're a member of R-Core, then I believe you'll have fun this, will learn something, and will refer back to it repeatedly.

I came to this book knowing next to nothing about R. I'm an experienced programmer, but my knowledge of statistics is not as deep as it should be, and e book does a amazing job at times of explaining how the different R functions work, as well as concepts such as "vectorized" functions. A bit of code is shown, and then there is a lot of explanation that describes what it does, and why. Sometimes, the phrasing could use improvement, and I found myself perhaps struggling to master a concept longer than I should have, but it was enough to obtain the job en I got about a quarter of the method through the book and hit an extended example of applying logistic regression. First, the code included a tilde operator, which had not been mentioned anywhere the book before that. Next, it called a function, glm, without explaining what it does, and it showed the results, and said, "Sure enough, we obtain a 2-by-8 matrix, with the jth column given the pair of estimated B[i] values obtained when we do a logistic regression using the jth explanatory variable."In effect, the book suddenly shifted from an explain-it-all-as-we-go text to a we-assume-you-know-statistics-as-well-as-exotic-R-operators-and-functions text. I am completely unable to understand this example until and unless I dig into both the similar concepts in statistics, and the R-related syntax. I can't blame the book too much for my lack of knowledge in statistics, but I can say that it was careful to provide explanations on some much simpler statistical concepts earlier. As far as the R syntax, I don't think there is any excuse for that. It also turns out that the caret operator in this context is not at all what a programmer would expect it to be--no coverage of that mewhat later was a very long example on a Discrete Happening Simulator. Here, as in so a lot of other places, the author likes cryptic variable names such as rw, evntty, inspt and appin. If you were to study the code long enough, you would eventually understand what all of these meant. But it's sloppy and irritating and makes the job of understanding the code much t long after this, he makes a comment on recursion that created me burst out laughing:"It's fairly abstract. I knew that the graduate student [who had asked him for tip on writing a function], as a fine mathematician, would take to recursion like a fish to water.... But a lot of programmers search it tough."What I, a mere dim-as-a-20-watt-bulb programmer, search tough, is a plethora of cryptic variable names. Recursion, not so much. I followed his example with ease. Maybe if I were a math graduate student I could understand those variables!I've also been disappointed with how small attention the book gives to the fundamental differences between some of R's "families" of functions, such as apply, lapply, sapply, and tapply, or lm and glm. There is a brief hand-waving comment and then off we go. This is unfortunate especially since, in my view, the builtin R support is often impenetrable and written more as a technical spec then a clear explanation.I have pushed on to subsequent chapters, and learned more from the book. But be forewarned that it has a tendency to shift suddenly and without warning from a from-the-ground-up perspective to a we're-all-experienced-R-users other comment, as others have noted here, the publisher really should have included data files so that readers could play along with the examples.

I'm a moderately experienced R user. I do a fair amount of data ysis and modeling and R is almost exclusively the tool I use. I am adept at data IO, plotting, fitting data, etc., however the power of R goes much beyond easy data manipulation. I found this book to be an perfect introduction to the breadth and depth of what R can do. Fresh concepts and functions are introduced by showing how they are used in easy examples, and common pitfalls and "gotchas" are anticipated and pointed should hold in mind that although R has perfect graphics capabilities via the lattice and ggplot2 libraries, only the base plotting routines are introduced here. By no means do I consider this a shortcoming for this book because there are whole books dedicated to R graphics, and this is a programming-oriented book.What this book does cover beyond the usual things you'd expect in an R book (e.g., data frames, arrays, etc.) are things like object-oriented programming, building up simulations, debugging tools and techniques, performance enhancements, interfacing to other languages, and parallel processing. The kind of things I wish to master in order to exploit the true power of R.

Anyone seeking to learn R faces two major challenges: (1) learning how to swim in the sea of information: R packages, books, websites, blog posts, notice boards etc. that threatens to drown a newbie and (2) and coming to grips with the structure, syntax and features of the language itself. Having some idea of what one wants to do with R is clearly an necessary first step that will set the path of learning. R, an begin source computer language, is the premier software system for statistical computing. Not only can any statistical idea be expressed in R, it is likely that someone in the begin source community has already written a function to accomplish or at least facilitate any statistical ysis a working statistician or data scientist might be contemplating.R functions are organized into libraries or packages that usually relate to some particular statistical task. Assuming something like an average of 20 functions per package, the 3400 available contributed packages[1] offer over 68,000 routines to read in data, manipulate it yze it and visualize the results. No one could possibly become familiar with all of these. But, because R is an interpreted (instant feedback) language that encourages experimentation, some serious, sophisticated statistical yses can be accomplished by stringing together the appropriate functions into a script. If interest in R is to only perform some particular ysis then a beginner’s best bet might be to select one of 100 or so books or blogs on doing statistics with R that provides relevant sample code and chop and paste to obtain a workable script. There is no shame in this. That is why all the begin source authors went to the problem of packaging up their ever, if a person really wants to be able to speak the R language and become a competent R programmer then, at the show time, one can search no better tutorial than Norman Matloff’s The Art of R Programming. Professor Matloff is a statistician and a computer scientist with a considerable amount of teaching experience. His book is no mere programming reference guide. It is a carefully crafted sequence of lessons that begin at the beginning and work up to some fairly advanced subjects including a lucid acc of object-oriented programming in R, a presentation of the rudiments of TCP/IP operations and a discussion of R programming for the internet, examples of parallel programming with R, and a discussion spanning several chapters of how to write production-level R code that contains methods and tip on debugging R code, writing efficient R code, and interfacing R with other languages. Other distinguishing features of the book are brief examples showcasing a huge number of functions (including rare gems such as D() for symbolic differentiation) that indicate the power and scope of R, and over thirty “Extended Examples” each of which is a credible study in writing careful, professional code. The most captivating aspect of the book, however, is Matloff’s thoughtful manner of exposition. R’s rich, compact syntax can be challenging the first time around. Matloff knows where the difficulties are. His presentations of R’s different features and functions start from a point of view that anticipates obstacles that likely to confound someone going down the R path for the first time and tutorials the novice around them. I expect that The Art of R Programming will appeal to diverse audience of aspiring R programmers.

I like this book !I have no "R" background and have been learning R from watching youtube videos as well as stack overflow. I came across this book and decided to give it a try. I must say its beautiful good. I definitely like the detailed examples of codes and love the method it flows. I bought the paperback ver because I like holding a book but you could just as easily buy Kindle ver so you can copy paste the code. I actually wanted to stay away from copy/pasting to force myself to obtain in the habit of actually "Coding" but either method the book is a phenomenal resource. Thanks.

I love this book! Finally, a book about R Programming. And, not the ersatz ver that we obtain in the statistics books. Here is an R book devoted exclusively to how to program in R. OK, all that items about data structures, that they publish in the R statistics books is amazing as far as it goes. But, here is a book that is focused on R programming/scripting, and R as a computer language.OK, I am biased, since I create my living as a software engineer. But, it is refreshing to have a book that is focused on the programming, and not have to dig it out of or read between the lines. Highly recommended, if you need to do any programming in R, write R scripts, automate some R processes, etc.

Some really helpful stuff about overall scripting in R. Not helpful if you are looking for "how do I do this in R" (that's "The R Book"). Even if it has what you want, it can be difficult to search (not well organized nor indexed). But definitely worth a read if you write a lot of long involved scripts for how to do them more efficiently.

Matloff provides an perfect starting point for learning R. He presents just enough detail to enable a reader to learn but not enough so that the reader gets bogged down. For instance, I initially started with Crawley's The R Book but lost interest before I got an overview of the language. After reading Matloff, however, Crawley filled in a lot of the detail that Matloff didn't attempt to address. This book provides an overview of the language at just the right level to master it. The order of presentation is appropriate; the examples are interesting and pertinent (loved the Chinese dialect and textual examples); errors are extremely rare. My one complaint would be no exercises to practice with, but these aren't part of the author's mandate. There's dozens of books on various disciplines that do have exercises once you've learned the primary language (e.g., the Use R! series). Lastly, this book doesn't require more than a primary knowledge of statistics for understanding of the material or examples. I highly recommend it as a starter book for R learners at all levels.

"The Art of R Programming" takes an interesting approach to teaching R: it focuses primarily on the programming aspects of the language (as evidenced by the book title). Most books on the topic that I've read focus more on the ytics/data side of things, but what makes this book special is that it explores more of the computer science-y aspects of R. I would highly recommend.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

This is a unbelievable book written by luminaries in the field. While it is not for casual consumption, it is a relatively approachable review of the state of the art for people who do not have the hardcore math required for The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). This book is the text for the free Winter 2014 MOOC run out of Stanford called StatLearning (sorry Amazon will not let me to contain the website). Find for the class and you can watch Drs. Hastie and Tibshirani teach the material in this book.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

For those with a few solid courses in statistics, this is the kind of book that can patch holes and lead to a solid foundation important to dive into machine learning and data science. I would strongly recommend it as both a desk reference and (re) training e kind of book that I would recommend anyone know back to back before considering themselves "seasoned".A few negatives, the writing isn't perfect, especially in the examples. R has also evolved since publication. It is better to think of the code snippets as the general idea, and possibly seek outside support if you have problem implementing or even interpreting some of them.I can see how the method code is displayed could be a disservice to beginners, which is unfortunate, as otherwise the book is both accessible and comprehensive.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103)**[] 2020-7-12 18:37

This is a unbelievable book written by luminaries in the field. While it is not for casual consumption, it is a relatively approachable review of the state of the art for people who do not have the hardcore math required for The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). This book is the text for the free Winter 2014 MOOC run out of Stanford called StatLearning (sorry Amazon will not let me to contain the website). Find for the class and you can watch Drs. Hastie and Tibshirani teach the material in this book.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103)**[] 2020-7-12 18:37

The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the latest two authors of this one (Elements of Statistical Learning), which was/is a small massive on the theoretical side (at least for practitioners without a powerful mathematical background). The authors create no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an perfect introduction to Learning due to the ability of the authors to strike a excellent balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter L is an perfect choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who wish to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in massive mathematical theory, a amazing method to kick-off corporate Learning units, or as an aid to support statisticians and learners communicate better.A required and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.____________________________________________UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web website (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Awesome opportunity! Enjoy!____________________________________________UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics Book 103)**[] 2020-7-12 18:37

ISLR is possibly the best book I've ever encountered on the topic of statistical learning. Throughout my studies, I spent countless days reading more complicated theoretical texts, but few have stuck, and I've never fully understood how to translate the theory to code (specifically, in R). This is probably because my background in statistics was limited to a single prerequisite undergraduate course. I have been waiting a decade to search a book like this, containing primary theory (with plenty of figures), some math, code snippets and reproducible examples. If you are completing a graduate degree in any field where data is collected en masse, I *strongly* recommend this book. I think graduate advisors should create it needed reading in the first year of graduate school for students who will be performing computational data LR teaches primary regression techniques for prediction and classification and formally explains sampling methods (cross-validation, bootstrapping). The figures are amazing and the code examples create it very simple to apply the lessons in your own studies. This book formalizes what took me years to learn by diffusion watching seminars and reading papers and blogs. 5 stars!Bear in mind that this book is not a solid introduction to the R programming language. To learn R, study online courses, the "swirl" package. Once you've mastered the basics, look for the book "Advanced R".

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

In 2009, Stanford Statistics professors Hastie/Tibshirani/Friedman wrote 'The Elements of Statistical Learning', a book that demands a Master's or Doctoral level knowledge of Mathematical Statistics. Years ago, as a part of earning my MS Mathematics, I passed a doctoral-level qualifying examination in Mathematical Statistics. But that was years ago and I required a friendly refresher before reading 'Elements', which is gathering dust on my shelf.Well, I'm lucky (and probably so are you) because in 2013 Stanford Statistics professors James/Witten/Hastie/Tibshirani wrote this simpler 'An Introduction to Statistical Learning' that requires only a Bachelor's degree in Mathematics or Statistics. If you have that math grounding, then this is a unbelievable book to begin your Statistical Learning. The book offers a clear app of Mathematical Statistics and the programming language R to Statistical Learning. At the end of each chapter, the authors provide 10-15 questions to try whether you've digested the y a few times have I required to review my Hogg/Craig 'Introduction to Mathematical Statistics'. If you wish an perfect book on Mathematical Statistics to prepare you for both 'Introduction to Statistical Learning' and 'The Elements of Statistical Learning', buy the 7th edition of 'Introduction to Mathematical Statistics' by Hogg/McKean/Craig, which is typically used for a year-long (2 semesters) class for 1st or 2nd year graduate students in Mathematics or Statistics. In fact, you could simply bone up on Hogg/McKean/Craig, skip 'Introduction to Statistical Learning', and go straight to the more challenging 'Elements of Statistical Learning'. I wanted to digest some Statistical Learning asap and probably so will you. Enjoy.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

I think this textbook does well with providing primary intuitions of algorithms to those who do not have a powerful math background, but I don't appreciate the quality of the R code. (My criticism has nothing with avoiding modern paradigms, such as the tidyverse.)As one example, I have established as a private practice that I will never use the subset argument of lm(), even though it is used throughout this entire text. Why is this? I was curious one day, and decided to compare subsetting the data argument, vs putting the indices inside the subset turns out that in both cases, I obtained a various result. (See StackOverflow, with q/46939063/ appended to the link.) After asking around on Cross Validated as well (q/309931 appended to the URL of Cross Validated), I concluded that using the subset argument of lm() was poor , in prediction, this problem doesn't occur. But if you're planning on using lm() to interpret parameter estimates, don't follow this textbook's advice.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

I love this book. I read it all, did the labs and thought through the exercises (i.e., I didn't do every one of them); and I will likely end up re-reading guidance provided on some of the subjects several times. By "Goldilocks", I mean this book provides a level of explanation, mathematical basis and practical consideration that is "just right" for where I sit on the continuum of data science practitioners. As an expert yst, I need to understand which concepts, models and algorithms are applicable given the business objective and constraints (e.g. available data, time frame and forum in which to provide insight). I also need to understand what assumptions I am making when I choose an approach, as well as the tradeoffs. We know there are always tradeoffs!That is the level of understanding this book provides for all approaches covered. Where the authors provide the mathematical basis for an approach, they specify the model; and they stop short of *proving* the model. This, for me, is ideal.About a year or so back, I began reading Elements of Statistical Learning, and I could not support but think "Do I really need to know 'this' at 'that' level to accomplish my goals?" When I discovered this book, Introduction to Statistical Learning (thanks to Amazon recommendations), I knew I'd found Goldilocks :-)

This book will not support you understand the ESL book (Elements of Statistical Learning).If you are already programming ML a lot and you wish to step up your ML math but search ESL too hard because it is not self-contained and uses too much graduate stats terminology then do not fall for the reviewers that recommend reading ISL (Introduction to Statistical Learning) instead. ISL does not include explanations missing from ESL. In fact, it does not explain math at all, but instead, it gives a very broad overview of statistical methods that overlap with en who is this book for? This book is for someone who juuust started learning ML, like completed the coursera ML course or started using Python e book is well-written though. It is not self-contained because it does not explain math but merely gives a minimum intuition behind it.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

This book will not support you understand the ESL book (Elements of Statistical Learning).If you are already programming ML a lot and you wish to step up your ML math but search ESL too hard because it is not self-contained and uses too much graduate stats terminology then do not fall for the reviewers that recommend reading ISL (Introduction to Statistical Learning) instead. ISL does not include explanations missing from ESL. In fact, it does not explain math at all, but instead, it gives a very broad overview of statistical methods that overlap with en who is this book for? This book is for someone who juuust started learning ML, like completed the coursera ML course or started using Python e book is well-written though. It is not self-contained because it does not explain math but merely gives a minimum intuition behind it.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

ISLR is possibly the best book I've ever encountered on the topic of statistical learning. Throughout my studies, I spent countless days reading more complicated theoretical texts, but few have stuck, and I've never fully understood how to translate the theory to code (specifically, in R). This is probably because my background in statistics was limited to a single prerequisite undergraduate course. I have been waiting a decade to search a book like this, containing primary theory (with plenty of figures), some math, code snippets and reproducible examples. If you are completing a graduate degree in any field where data is collected en masse, I *strongly* recommend this book. I think graduate advisors should create it needed reading in the first year of graduate school for students who will be performing computational data LR teaches primary regression techniques for prediction and classification and formally explains sampling methods (cross-validation, bootstrapping). The figures are amazing and the code examples create it very simple to apply the lessons in your own studies. This book formalizes what took me years to learn by diffusion watching seminars and reading papers and blogs. 5 stars!Bear in mind that this book is not a solid introduction to the R programming language. To learn R, study online courses, the "swirl" package. Once you've mastered the basics, look for the book "Advanced R".

I think this textbook does well with providing primary intuitions of algorithms to those who do not have a powerful math background, but I don't appreciate the quality of the R code. (My criticism has nothing with avoiding modern paradigms, such as the tidyverse.)As one example, I have established as a private practice that I will never use the subset argument of lm(), even though it is used throughout this entire text. Why is this? I was curious one day, and decided to compare subsetting the data argument, vs putting the indices inside the subset turns out that in both cases, I obtained a various result. (See StackOverflow, with q/46939063/ appended to the link.) After asking around on Cross Validated as well (q/309931 appended to the URL of Cross Validated), I concluded that using the subset argument of lm() was poor , in prediction, this problem doesn't occur. But if you're planning on using lm() to interpret parameter estimates, don't follow this textbook's advice.

This is a unbelievable book for an intro to the globe of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots create the understanding even easier. I would like to recommend to anyone who wish to enter the globe of statistical ever, from a graduate level student, I would say this book is more suitable for a undergrad stat or similar field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any true globe problem, even though they talk about cross validation or something a small bit involved, practitioners may either came across so much issues in statistical ysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying various algorithms to search lowest MSE may not be suitable for some ill, this is a unbelievable book for two cases:1. If you have some background in theoretical or mathematical statistics and wish to gain some knowledge of applied methods, this book will be unbelievable for you to search applications with your theoretical knowledge;2. If you have few knowledge about rigorous statistics, but wish to enter the globe of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as r myself, this books is more like a ticket. I have the ticket of a attractive state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the attractive scenes of the park. The map described on the ticket is only contained the main street of the park. If you wish to check more attractive scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while.

For those with a few solid courses in statistics, this is the kind of book that can patch holes and lead to a solid foundation important to dive into machine learning and data science. I would strongly recommend it as both a desk reference and (re) training e kind of book that I would recommend anyone know back to back before considering themselves "seasoned".A few negatives, the writing isn't perfect, especially in the examples. R has also evolved since publication. It is better to think of the code snippets as the general idea, and possibly seek outside support if you have problem implementing or even interpreting some of them.I can see how the method code is displayed could be a disservice to beginners, which is unfortunate, as otherwise the book is both accessible and comprehensive.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

This is a unbelievable book for an intro to the globe of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots create the understanding even easier. I would like to recommend to anyone who wish to enter the globe of statistical ever, from a graduate level student, I would say this book is more suitable for a undergrad stat or similar field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any true globe problem, even though they talk about cross validation or something a small bit involved, practitioners may either came across so much issues in statistical ysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying various algorithms to search lowest MSE may not be suitable for some ill, this is a unbelievable book for two cases:1. If you have some background in theoretical or mathematical statistics and wish to gain some knowledge of applied methods, this book will be unbelievable for you to search applications with your theoretical knowledge;2. If you have few knowledge about rigorous statistics, but wish to enter the globe of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as r myself, this books is more like a ticket. I have the ticket of a attractive state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the attractive scenes of the park. The map described on the ticket is only contained the main street of the park. If you wish to check more attractive scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

Amazing book! I don't have math/statistics/computer programming background. I have been supplementing it with my Statistics course with R. This book is very thorough in explaining the concepts. It has outstanding explanations of "how" and "why". For example, it explains in amazing detail the summary output in R, what to expect from a amazing model vs. a poor model and why. I've searched for these explanation on-line and the book blows them all. Explanations in the book are life based and not theoretical, which I usually lack from the classes and dry stats books. After reading this book it finally comes together for me. Now I don't punch R code blindly, I know what to expect from the output. What is also nice, is that the book does not overload you with formulas and proofs that mean nothing for a non-math major. Very glad I invested this cash to buy it.

In 2009, Stanford Statistics professors Hastie/Tibshirani/Friedman wrote 'The Elements of Statistical Learning', a book that demands a Master's or Doctoral level knowledge of Mathematical Statistics. Years ago, as a part of earning my MS Mathematics, I passed a doctoral-level qualifying examination in Mathematical Statistics. But that was years ago and I required a friendly refresher before reading 'Elements', which is gathering dust on my shelf.Well, I'm lucky (and probably so are you) because in 2013 Stanford Statistics professors James/Witten/Hastie/Tibshirani wrote this simpler 'An Introduction to Statistical Learning' that requires only a Bachelor's degree in Mathematics or Statistics. If you have that math grounding, then this is a unbelievable book to begin your Statistical Learning. The book offers a clear app of Mathematical Statistics and the programming language R to Statistical Learning. At the end of each chapter, the authors provide 10-15 questions to try whether you've digested the y a few times have I required to review my Hogg/Craig 'Introduction to Mathematical Statistics'. If you wish an perfect book on Mathematical Statistics to prepare you for both 'Introduction to Statistical Learning' and 'The Elements of Statistical Learning', buy the 7th edition of 'Introduction to Mathematical Statistics' by Hogg/McKean/Craig, which is typically used for a year-long (2 semesters) class for 1st or 2nd year graduate students in Mathematics or Statistics. In fact, you could simply bone up on Hogg/McKean/Craig, skip 'Introduction to Statistical Learning', and go straight to the more challenging 'Elements of Statistical Learning'. I wanted to digest some Statistical Learning asap and probably so will you. Enjoy.

Amazing book! I don't have math/statistics/computer programming background. I have been supplementing it with my Statistics course with R. This book is very thorough in explaining the concepts. It has outstanding explanations of "how" and "why". For example, it explains in amazing detail the summary output in R, what to expect from a amazing model vs. a poor model and why. I've searched for these explanation on-line and the book blows them all. Explanations in the book are life based and not theoretical, which I usually lack from the classes and dry stats books. After reading this book it finally comes together for me. Now I don't punch R code blindly, I know what to expect from the output. What is also nice, is that the book does not overload you with formulas and proofs that mean nothing for a non-math major. Very glad I invested this cash to buy it.

tl;dr if you're a professor, don't use this book for a first regression course—use it for a statistical programming course or to supplement a second course in applied regression course uses this as the basic text. It doesn't work for that.Faraway filled the textbook with code, and added some exposition that breaks up the code. He omits proofs, his explanations aren't always clear, and there are plenty of times where he has you download a random R pack to solve something. (The classic "just accept it" response from professors who don't wish to spend time explaining.) Midway through the semester, I can code well, but I don't always understand what I'm 's the problem: Faraway trades intuition and exposition for code.If you're already familiar with linear regression, the theory behind it, and its limitations, this book will probably work for you. I wouldn't recommend it if you aren't familiar with R—and you should definitely stay away if you've never programmed before—but it's amazing if you need to learn how to apply what you've learned in an earlier regression course.BUT, Faraway can throw code at you and not explain what the code does, or why it does what it does. That can create the exercises difficult. (You'll see things like: "this pack uses a genetic algorithm to search the best matches and..." OK. No I'm really confused.) Sometimes, to solve the chapter exercises, you just need to change the data set and run code in the chapter. Other times you need to substantially modify the code; if Faraway doesn't explain the code well, that can be a major , use it if you've programmed R before and you understand more than just the gist of linear regression. Otherwise a more traditional, theory-based textbook will serve you better.

**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**[] 2020-2-2 17:30

The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the latest two authors of this one (Elements of Statistical Learning), which was/is a small massive on the theoretical side (at least for practitioners without a powerful mathematical background). The authors create no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an perfect introduction to Learning due to the ability of the authors to strike a excellent balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter L is an perfect choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who wish to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in massive mathematical theory, a amazing method to kick-off corporate Learning units, or as an aid to support statisticians and learners communicate better.A required and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.____________________________________________UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web website (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Awesome opportunity! Enjoy!____________________________________________UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.

I love this book. I read it all, did the labs and thought through the exercises (i.e., I didn't do every one of them); and I will likely end up re-reading guidance provided on some of the subjects several times. By "Goldilocks", I mean this book provides a level of explanation, mathematical basis and practical consideration that is "just right" for where I sit on the continuum of data science practitioners. As an expert yst, I need to understand which concepts, models and algorithms are applicable given the business objective and constraints (e.g. available data, time frame and forum in which to provide insight). I also need to understand what assumptions I am making when I choose an approach, as well as the tradeoffs. We know there are always tradeoffs!That is the level of understanding this book provides for all approaches covered. Where the authors provide the mathematical basis for an approach, they specify the model; and they stop short of *proving* the model. This, for me, is ideal.About a year or so back, I began reading Elements of Statistical Learning, and I could not support but think "Do I really need to know 'this' at 'that' level to accomplish my goals?" When I discovered this book, Introduction to Statistical Learning (thanks to Amazon recommendations), I knew I'd found Goldilocks :-)

This is an perfect text on linear regression techniques. Rather than teaching R step by step, Dr. Faraway jumps right in with ysis of specific data sets, listing the R commands required to generate the given output. An hour or so after getting the book, I downloaded R and the free pack from Dr. Faraway containing all of the data sets used in the book (I followed the directions in the very short Appendix A), and I, too, was trying out the given commands on the data sets referred to in the exercises for the first chapter.Dr. Faraway is particularly amazing in his discussions of interpreting the output from linear regression problems. A standout chapter is the one on using regression for modelling vs. using regression for prediction. I also learned a lot from the chapter on principal components, a subject I remember covering in Grad school, but which I confess I didn't really understand at the time. Dr. Faraway's explanation of the procedure is excellent, and he uses an example in which it is possible to explain what the selected components represent in terms of the original problem, but he points out that this is not always possible; sometimes you just have to be content with accurate predictions, but no ideas as to what the principal components represent. I want I had been told this the first time I learned the procedure.If I like the book so much, why only four stars? Well, I do have a few minor quibbles. I would have liked an index of R commands, so that if you remember a command, but can't remember the correct syntax for using the command, you could search the page on which it first appeared. I would also have liked an index of data sets, so that I could quickly search every exercise set that referenced the teengamb data set, say. But these are minor complaints.I reviewed a lot of material I already knew, and learned quite a few things I did not know. And, I went from never having used R before, to being able to use it for some rather complicated yses. I do not teach a course at a level appropriate for the use of this text, but if I did, I would certainly consider using commended.

**Handbook of Markov Chain Monte Carlo (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)**[] 2020-8-17 18:55

I read chapters 1 -3 of this book. I found chapter 1 to be an informative overview of MCMC. Chapter 3 provides a very useful discussion of reverse jump MCMC. All three chapter are well written.

It was a very amazing book for some primary changes. It doesn't adequately cover the multiple changes I would consider common. Hard to create all the adjustments required for our little frames, huge cup sizes, sway back etc. I know you can't have every scenario, but I felt it was a small abbreviated for the multiple adjustments.

Like: terse and to the point. Small dawdling in exposition.Dislike: terse and to the point. Sometimes a small dawdling is amazing (or great).Bought for a semester of Stat Mech. Supplementing with owned-books and some internet searches (MIT OCW - written by Kardar himself, sometimes; etc.). Would buy again if I had to.

I'm really enjoying the book so far. The quality of the content is 4 to 5 stars. However, I feel really ripped off as to the price and length. This book is 200 something pages. And the author released another 200 page book along side it. Both 80 dollars. Together, these two books would create a legitimate 80 dollar book. But 160 dollars is a rip off to be e book should be "Statistical Physics" and part one should be "Statistical Physics of Particles" and part two should be "Statistical Physics of Fields."

This book is so beautiful. I think it's one of the clearest exposition on statistical mechanics. The derivations follow a bit of the Landau [email protected]#$%z style, being very systematic and rigorous at the same time, and very economical on word count. It's also very concise, I would say if you like the Landau series, you'll like this. I also like the emphasis on probability. I search a lot of statistical mechanics can just be derived from info theory, which unifies the whole subject, and Kardar uses that fact to create the whole treatment very ware, however, the issues are very hard, and require complete mastery of the material to solve (some require an ingenious trick).

Perhaps I am a bit biased as I took Mehran Kardar's statistical mechanics class, but this is the best graduate-level statistical mechanics textbook I have looked at (including Pathria, Huang, and Landau). In the tradition of Landau's perfect mechanics textbook, Kardar is a master of statistical physics who starts with only primary assumptions about the nature of the physical laws in each chapter, and derives unbelievable results elucidating the nature of statistical physics. The meat of the textbook is less than 200 pages and contains all of the primary results of thermodynamics, a section on probability, an introduction to kinetic theory, and the bulk of classical and quantum statistical mechanics; brevity is the soul of wit, as they say. A few locations could have used a small more elaboration (the derivation of the Boltzmann equation seemed to skip a few necessary steps in implementing the streaming collision terms, and a better explanation for the basics of diagrammatical techniques would have been nice), but none of the other books I have looked at even broached these subjects in any depth. Unlike Landau's perfect statistical physics book, very small assumed knowledge is needed to follow this textbook; obviously, skill in elementary algebra, calculus, differential equations, and a bit of Hamiltonian mechanics and a few very primary results of quantum mechanics are prerequisites. Recommended!

In the seventies, Nancy Zieman showed a series on her PBS show. My parents were getting ready to go to Europe on a much hoped for vacation. But, my mom was rather on the huge size. She'd gone to several stores looking for outfits that she could wear and feel comfortable in. No , she brought out her tapes of Nancy's shows and after viewing them, I was able to alter patterns and construct several outfits and blouses Mom could wear and feel presentable in.Her outfits were color coordinated of natural fibers, so she could mix and match on her 6 week tour. She got several compliments from other ladies on the tour; she did look good, ever, Mom stored her VHS tapes in the attic and they didn't survive the heat. When I was faced with a related problem...Well, it's been about 40 years since using those techniques and I didn't feel confident enough cutting into cotton, linen and silk before referring to those unbelievable techniques.Well, the book is exactly what is , I'll look amazing and feel comfortable watching my daughter obtain married and won't embarrass her on their wedding cruise.

I absolutely love this book and method. It clarified the adjustment process to use on patterns to create clothes fit me better. I've since used the pivot way on a blouse and it fits me beautifully! I also used her slide method, in conjunction changes advised from with "Pants for True People," on a pair of shorts that created the pattern adjustment simple and flawless. I would highly recommend purchasing this book if your have a body shape that is not the "standard" shape. I can't wait to create more attractive clothes that actually fit me comfortably!

I've been sewing for over 50 years and I've gotten plumper since I started so I need to be able to alter patterns to fit better. I went to a class for a system to alter patterns but by the time I would have bought all the tools and patterns it would have been over $150. This book covers a system that is very related to the expensive one and I know that Nancy Zieman is a very amazing seamstress. I look forward to making some pajama bottoms for my husband that will fit without being baggy and misshapen.

This book is an excellent, well written text which I hope will remain in print a long time. It is just the sort of simple to understand, well illustrated book which should continue to teach this primary art of pattern fitting to fresh generations of clothing designers. In it she gave step by step instructions with just enough simplicity for the learner and just enough complexity for the pro.I was saddened to hear that Nancy had recently passed. Her family has my condolences and the assurance that her audience and readers loved her.

Useful review?

Statistical ysis of Network Data with R (Use R! Book 65)[] 2020-1-29 2:38Simple to comprehend, concise code, and attractive printing.

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Useful review?

Statistical ysis of Network Data with R (Use R! Book 65)[] 2020-1-29 2:38Simple to understand and very comprehensive!

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Useful review?

Statistical ysis of Network Data with R (Use R! Book 65)[] 2020-1-29 2:38This is a amazing introduction to the 'igraph' pack for R. Someone who is just a beginner in using R can probably use this book, but it's probably best to have already been using it for some time. The 'igraph' pack is beautiful extensive, but this book will give you enough tools to discover what else the pack can also covers the basics of doing network yses. It doesn't go very deeply into any topics, really (nor does it have proofs or much theory), so it is best used in conjunction with (or perhaps after reading) Kolaczyk's other text ( ). I used this after having used the other text.

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