### Read **loggable - simple data logging and statistics** reviews, rating & opinions:

Check all loggable - simple data logging and statistics reviews below or publish your opinion.
100 Reviews Found

**Sort by: Most Accurate (default) | Newest | Top Rated**

Granted, all books of this nature are obsolete to some degree when published, but in this case, it is too much to accept. My version, just received, is the 2nd release of the 2nd edition, dated November 15th, ere is no mention of the strong datatable package, which, to me., is the most necessary latest development since purrr, dplyr, and of course, the tidyverse. R programing with datatable a quantum jump in efficiency. The datatable pack long predates the release of this r this reason alone, I would recommend waiting for the next update.

I have the first edition of this book, so was excited to keep the second edition. The second edition follows the tone and structure of the first edition, with updates throughout. I especially appreciate the explanation of what you are doing, versus just blindly following instructions or copying code (hello, tensorflow cookbook). If I wanted to just copy random code I'd go to stackoverflow. The point of a book like this is to provide a more in-depth explanation, and this certainly delivers. It's simple to overlook R in favor of the highly accessible python notebooks and the like, but R is such a strong statistical tool, once you learn it, you'll love it!!

Im fairly fresh to R/data science myself so I can say I was reading this book as basically a beginner.I think it's beautiful simple to understand and does a amazing job of explaining structural functions and foundational concepts. It could be a amazing base for learning primary R functions and concepts but I don't think this book should be your only source. The data exploration section (among others), for example is very, very brief and a fast Google find led me to a lot of more useful functions and most programming books they're amazing resources, but having multiple resources is always better.I'd still recommend if you're a beginner though!Definitely test to used since these books can be so expensive.Hope this review helps!:)

There is a whole sub-culture of meal porn, the lavish coffee table books that set out to educate you to an entire culture through its food. I once saw a library of several ese are page flippers – doesn’t that look marvelous, we’ll have to test it next time we search halibut cheeks. The sad fact, though, is that any of these books are lucky if the owner actually uses one or two of its recipes and exalted if a recipe becomes a dinner party e first edition of R Cookbook by Paul Teetor, published in 2011, was, in analogy, related in that it provided flavors with amazing recipes, a lot of of which proved useful for specific e second edition, just out with J.D. Long as co-author retraces and updates much of the content of the first edition, brings RStudio and tidy tools to bear, and subverts the dominant paradigm. You flip through it and it first appears to be the cookbook equivalent of granny’s index card recipe box. But it starts off with enough fresh that you stop flipping pages and begin took me two days to read the 554 pages. I had the support of several airplane hours, but it would have been a page turner anyway. What captivates is the rigorous R guide cleverly hidden in asides, call-out boxes and brief explanations of why a particular code chunk does one thing when you might reasonably expect my childhood, Donald Duck had three nephews, Huey, Dewey and Louie who got their uncle out of more than one jam by consulting The Junior Woodchuck Manual, which somehow contained the respond to any question that could possibly arise. The R Cookbook, 2ND Ed. comes close to that standard for the beginner to intermediate user who is completely innocent, on the one hand, or who has been covertly using functions without a well-founded understanding of their requirements and would be an unconventional choice, perhaps, but this is the book that I would choose for the text if I were teaching a course in introductory R. Why? Because this book tells you what R does, not what it is. Any student coming to it with even an imprecise notion of f(x) = y is going to be able to follow along to the point, perhaps, of wanting to write her own package. The text doesn’t cover this, but points you in the right direction, and suggests source(“good_stuff.R”) as an interim other words, its approach is like R’s, functional, not imperative, definitional not procedural. In my view, the implications in the R vs. Python battles are obvious. In one model, Professor Higgens tells Eliza bring me my slippers. In the other, he first has to declare a topic (the slippers), a possessive (my slippers), object (grammatical, me) and then, with the verb, provide detailed instructions on where they are to be found, how brought into possession and the means by which they are to be transported to their meone once said something to the result that assembly language uses humans as pre-processors. I’m afraid that is real of a lot of of its do-this, then do-that r now, their defense is the superiority of compiled programs in speed on the metal and the much more expensive wetware. And they are right for the one-off case. But as cases proliferate or become abstracted, wetware costs go up faster than throughput will not happen while I’m still sentient enough to follow, but a compiled R or an R to Haskell API does seem to provide the best of both worlds. Place the wetware where it belongs, in defining the issue and the appropriate solution and leave the optimization to languages that are amazing at implementing functional logic.

TLDR: To be successful with this book you need a primary understanding of statistics, and the willingness to work through issues solving methodically. In return you will obtain a solid understanding of R that is at an advanced beginner level.I teach an intro stats class, but to Honors students. So every semester I have a fairly huge handful who wish to go beyond the Excel and online sources we use in that beginning class to do more complex work. Sometimes this is just because they are interested, but often it is because they are working in labs and nobody knows how to do the stat. Excel does not chop it past a primary ANOVA so they come looking for more power (get it? Stats joke?) SPSS and SATS are just crazy expensive, and I see the writing on the wall that is moving toward begin source, like R so I test to encourage them in that direction. I do not teach R in the class and so I need a sources the students can mainly use to learn on their own with a small support during office hours. I have been loaning out R for Dummies by de Vries and Learning R by Cotton out, I have the earlier R cookbook by Teetor alone but that book seemed only comprehensible to my really hard core STEM students and was getting a small long in the tooth. So I jumped at reviewing this version.I am still going to stick with loaning out R for Dummies to my softer science and non science students with less math and programming experiences. It holds their hands a small more and walks through RSudio more slowly. And does not throw around terms like functions and PERL and matrix so casually. But this book is amazing for my STEM students with a small bit of determination and logic skills. I like the structure better than the previous book, it focuses more on actually understanding what you are doing which in the long term is more useful than a bunch of tricks. Working through this book will effect in a beautiful solid understanding of the basics in R and a bit past that as is book mostly assumes you understand the underlying statistics, which is fine, I doubt those who do not are going to be venturing into programming in R.

This is a very cool and clever “cookbook” that outlines how to use a tool that is and also super helpful for me when it comes to data analysis that I use for forecasting and budgeting. I had no idea this existed but I’m so thankful there is a book that breaks it down in easy-to-learn chunks not unlike a meal cookbook. A serious is that the is accessible to all. I’ve got a LOT more to learn but I am very impressed and excited about what I’ve added to my repertoire thus far!

This is one of the most user-friendly statistics books I have ever purchased. The info and concepts are simple to understand with multiple examples. The specific topic in each chapter is highlighted for simple locating. There are extra tables in the back that can be used to solve problems. I highly recommend this book.

I have been in the development field for several years and I have worked with plenty of languages and seen plenty of books on the topic of development. O’Reilly books are usually among the best books available for a given subject, though sometimes the organization is lacking and the writing less than good. I have a couple of their “cookbook” books and they follow a consistent formula – show a issue to be solved, provide a solution to said problem, and then follow up with a discussion of the solution. I have seen some variations – try results, presentation of other alternative solutions, analysis of performance, etc. The R Cookbook follows the primary cookbook formula and presents a broad picture of the language in bite-sized chunks.I will state up front that I have not previously worked with the R language and this book is my very first exposure to it. I did not know what to expect and came at this with the experience of a seasoned developer learning a brand-new language. I will also state that I am writing this review for the book, not the is book covers a lot of ground. It presents the basics of the language, I/O, graphics, data structures, probability and statistics and a whole lot more. Where it gets interesting, though, is in the manner in which all of this material is presented. Starting from the very beginning, the authors show primary examples to solve problems, but in the they do so and the scope in which they cover the issue zone they’re actually teaching the language from end to end. There are advanced topics, such as time series analysis and all the subfunctions that fall under it, but these are presented later, after the basics of the language have been revealed. Even these examples are kept short and simple to understand. The language coverage is not exhaustive, but it is enough to give the reader a powerful e writing itself is very approachable. The authors did a amazing job of not talking down to the reader and not keeping the writing so lighthearted as to be silly. Instead, this book seems to be written on a high-school reading level and the reader should not feel talked down to or in over their head.I am still working through this book but it is an interesting approach to an even more interesting programming language. The cookbook format is used to amazing result in this book to do more than just solve a few issues but to teach a language well enough to be productive.

The R language provides everything you need to do statistical work, but its structure can be difficult to master. Each recipe addresses a specific issue and contains a discussion that explains the solution and provides insight into how it works. Make vectors, handle variables, and perform primary functions; Simplify data input and output; Tackle data structures such as matrices, lists, factors, and data frames; Work with probability, probability distributions, and random variables;

A lot of of us are afraid of statistics as a field that is obscure; with a jargon that not only we cannot understand but it is seemingly boring and amazing only for sleepless nights.I was very surprised with this book; Polit not only created statistics understandable, but she uses plenty of examples to drive home the point. Even if your course does not require this specific book, obtain it anyway: it will create a globe of difference.

This was a needed textbook for a graduate level nursing statistics course. The text's chapters are organized and provide a comprehensive overview of the various statistical tests. All provided examples are pulled directly from nursing research and are understandable. I found the end of chapter exercises to be helpful but unfortunately, explanations or rational to the answers were not provided in the explanation. While this textbook contributed to my understanding of course content I didn't search it to be a particularly amazing "reference book" and I don't imagine I'll refer to it again now that the course is over.

**Statistics Made Simple for School Leaders: Data-Driven Decision Making (Scarecrow Education Book)**[] 2019-12-18 20:47

The info is fine and well explained, but my copy was missing several pages of text. Nearly every second page in chapters 2 and 3 were blank--and should not have been. For a 30.00+ book, this is absolutely unacceptable. Do not this book.

**Statistics Made Simple for School Leaders: Data-Driven Decision Making (Scarecrow Education Book)**[] 2019-12-18 20:47

Data and evidence are parts of education's reality, whether you like it or not. This book is simple to process and an enjoyable read whilst giving you the nuts and bolts of data analysis and inference.

**Statistics for Beginners: Fundamentals of Probability and Statistics for Data Science and Business Applications, Made Easy for You**[] 2020-1-15 19:8

I emphatically suggest this book for your business based library. The point by point models and straightforward language truly helped me and it created measurements, a troublesome topic to grasp and vanquish, so natural for me. On the off possibility that you need to begin settling on extreme business choices depending on figures and numbers, this total apprentice's manual for factual science will carry extraordinary preferred position to you.

**Statistics for Beginners: Fundamentals of Probability and Statistics for Data Science and Business Applications, Made Easy for You**[] 2020-1-15 19:8

I strongly recommend this book for your business based library. The detailed examples and simple to understand language really helped me and it created statistics, a difficult topic to comprehend and conquer, so simple for me. If you wish to start making ultimate business decisions relying on figures and numbers, this complete beginner's tutorial to statistical science will bring amazing advantage to you.

**Statistics for Beginners: Fundamentals of Probability and Statistics for Data Science and Business Applications, Made Easy for You**[] 2020-1-15 19:8

Having read a couple of introductory books to statistics, it was refreshing to search a book for beginners that is simple and clear to follow without the author trying too hard to create statistics so-called “fun” & “entertaining”. For me, the historical context added to this book was very insightful and for the most part, the author explains how the different concepts tie in with one another. The further resources section could be more extensive – it seemed more like an afterthought – but all in all an perfect introduction to inferential statistics.

This book does a not good job explaining the content. Some concepts are presented with pages of examples and others obtain a one-paragraph explanation & then moves on. I spent so much time on the internet trying to inderstand what the book was saying (or not saying) that I ended up buying a second book to support me understand the concepts I was trying to learn with this book. The chapters are stuffed full of practice questions, but it does not explain the answers for the practice questions. If you obtain a question wrong, you are left on your own to figure out why & how they arrived at the correct answer. The tone of the book is rather condescending.

Amazing one, being in a globe where technology rules, we don't realize what is going on in the background. I mean sure you have conspiracy theorists that believe Alexa works for the CIA and such. But in reality everything we do is being monitored in some method or function. This book is an eye-opener as to some of the ways our info is taken from us and being used. Some of the ways featured in this book I had absolutely no clue that was happening. I mean we have all noticed that if we look something up on Google, that following that Fb starts to use ads targeting something we are already interested in. This is a amazing book to read if you wish to know how and why your info is taken and used. Thankful! very nice one !

Literally one of the worst textbooks I've ever attempted to read. It has virtually no detail just generalizes everything. On top of that, the homework in each section is nicely broken into 4 questions per question. So, when assigned 50 questions you end up actually doing over 200. I felt like I was slamming my head versus a wall while trying to use this book to learn statistics. If I could give a - star review I would.

This book is an eye-opener as to some of the ways our info is taken from us and being used. Some of the ways featured in this book I had absolutely no clue that was happening. I mean we have all noticed that if we look something up on Google, that following that Fb starts to use ads targeting something we are already interested in. This is a amazing book to read if you wish to know how and why your info is taken and used.

I mean, it's statistics and data, so it is hard to obtain really excited about it. I have a decent background knowledge in stats, but I still found some of the verbiage hard to follow. A more clearly defined vocabulary would help... but that's a issue in ever stat book I've ever seen. I really did love that it used real-world healthcare scenarios to teach. That created the concepts more accessible to me. I passed with a amazing score, so it's not impossible, just be very careful about vocabulary when answering the questions.

Does not break issues down step by step adequately. Often presumes you remember "which end is up." (You won't, if your class moves as quick as mine just did.) Pages in the loose-leaf edition tear right out, which is not good. Suggest reinforcers for famous pages - you'll need them to obtain through the chapter reviews. Also suggest page flags and tequila.

**Random Data: Analysis and Measurement Procedures (Wiley Series in Probability and Statistics Book 729)**[] 2020-3-31 18:42

The digital ver is not formatted properly, missing links, equations, incorrect reference numbers. The worst part is there are no page numbers in the document nor in the Kindle application configurator. Do not obtain this.

**An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report**[] 2019-12-18 20:47

This book provides an perfect step-by-step introduction to statistics and Stata. It covers the basics of the research process, data collection, sampling, questionnaire design, and writing research (with a amazing overview of how to do research projects). There is a separate chapter on writing research papers, which rare in this kind of book. Notably, it also contains APA is book could be covered two semester stats course. Ideally, it would be used in an intermediate-advanced undergraduate course or a lower level graduate class. It addresses both methods and stats. It could be also used with a methods class doing a quantitative research project. Scholars might search it helpful for brushing e text addresses how various analyses are used in various fields, descriptive stats, hypothesis testing, covers key topics, and expected tables. It was pleasantly surprising to see regression diagnostics, logit/probit, and regression analysis with categorical dependent variables e text uses clear straightforward language and contains no massive math review (which often turns people away). There are news articles pop outs, framed in the current context but also relying on media that students are likely to encounter regularly. Easy definitions are provided for complicated terminology, no sidebar boxes for definitions are necessary. There are a lot of subsections to thoroughly breakdown topics. Commands appear in bold throughout the text and in the index. Each chapter also contains a summary of commands. The book contains a glossary, name index, and topic index. There is also an appendix of stata commands.

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)**[] 2020-1-31 3:0

Math books, at least data science texts, can usually be divided into those which are simple to read but include small technical rigor and those which are written with a scientific approach to methodology but are so equation dense that it’s hard to imagine them being read outside an advanced academic rtunately, The Elements of Statistical Learning proves the exception. The text is full with the equations important to root the methodology without engaging the reader with long proofs that would tax those of us employing these techniques in the business e visual aspects of the text seem to have been written with John Tukey or Edward Tufte in mind. Though their frequent use makes the book some seven hundred pages long, reading and comprehension is created much easier.And, though it’s been almost ten years since the book was published, the techniques described remain, for the most part, at the cutting edge of data science.I was told by some other analysts I know that this was their bible for data science. I was somewhat skeptical of this kind of hyperbole but was pleasantly surprised that the book matched these high expectations. If you have an undergraduate degree in a mathematically similar discipline, The Elements of Statistical Learning will prove to be an invaluable reference to understand the rapidly advancing avalanche of data mining techniques.

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)**[] 2020-1-31 3:0

My background in statistics, statistical mechanics, and stochastic theory is old, but I'm not a zero at it. This is an unfriendly book. Some of the derivations are things you would see on the blackboard of an advanced course in statistics, not machine learning, and take careful notes of. I emailed one one of the authors for suggestions of a companion text, but received no e tragedy is that the material is well selected, and obviously essential for work in the field. It is just very poorly supported by off-handed, sketchy derivations that resemble inside jokes more than e book brags of color illustrations. I would have preferred didactic the pdf from the Stanford site. If you search a companion text, only then the hard copy.

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)**[] 2020-1-31 3:0

ESL is certainly a challenging read in the sense that you need to be comfortable with the subjects of linear algebra, statistical theory, and the basics of probability theory. I would highly recommend supplementing this text with "A Solution Manual and Notes for: The Elements of Statistical Learning by Jerome Friedman, Trevor Hastie, and Robert Tibshirani" (Weatherwax and Epstein) as they do a nice job supplying derivations and explanations to several of the equations presented ere are certain regions of this book that are more mathematically intensive than others. Overall, I would say this book provides a solid overview of a lot of of the necessary fundamental subjects of statistical inference and machine learning. Deep neural networks are only given a single and rather light chapter so this book is not the best resource in that e organization and presentation of the material is very pleasing and logical. I read the book cover-to-cover, but the authors provide some guidance on their recommendation for what parts of the book should be read first before skipping around.I would highly recommend this book if you are involved in the fields of machine learning and artificial intelligence, but be prepared to wrestle with the material in to understand it fully.

**Head First Data Analysis: A Learner's Guide To Big Numbers, Statistics, And Good Decisions**[] 2020-1-31 3:44

Various issues need various methods to be solved properly. This book takes different examples and lets the reader work through the problems. It is actually fun to read this book. Very well explained. Of course, not all the issues worked 100%, but I have not read a book with examples and issues that all work. Especially, some of my R did not work too well. Other than that it is a amazing book, and a amazing method to learn about data analysis.

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

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

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

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

My background in statistics, statistical mechanics, and stochastic theory is old, but I'm not a zero at it. This is an unfriendly book. Some of the derivations are things you would see on the blackboard of an advanced course in statistics, not machine learning, and take careful notes of. I emailed one one of the authors for suggestions of a companion text, but received no e tragedy is that the material is well selected, and obviously essential for work in the field. It is just very poorly supported by off-handed, sketchy derivations that resemble inside jokes more than e book brags of color illustrations. I would have preferred didactic the pdf from the Stanford site. If you search a companion text, only then the hard copy.

I have advanced degree in statistics, but my knowledge is getting old so I picked this book up for an update. The book is quick paced, covers a lot of areas. A lot of of the derivations are place in exercises to save space, so it is hard to follow. My suggestion is if you have the time, test to work out the exercise after each chapter, it will support you understand the content so the first time I received the book, it was missing a lot of pages. Check your fresh book to see if there are pages missing.

**Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics**[] 2020-1-22 1:2

This book is a must if you invest or need to dispassionately evaluate a lot of kinds of evidence used in studies involving mathematics including graphs. If you take nothing else from it, the explanation of regression to the mean is more than worth the of the book. After reading it on loan from my local library and even before returning it, I realized I would wish to refer to it again and again and purchased it.I studied primary statistics and statistics for social sciences in college a lot of years ago. I want this book had been available then. While my grades would have been better for it, more than that, my understanding would have certainly been enhanced.

I'm a student at the same university Dr. Terrell teaches at (though, I never had him as a professor) and I have to say that Dr. Terrell spells it out CLEARLY in this book. Amazing book for someone looking to dive into statistics or obtain a refresher. Bravo Dr. Terrell! You heard the frustrated students call for a amazing statistics textbook and you delivered! :)

**An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report**[] 2019-12-18 20:47

I now have about 12 of the most recently published STATA (and one MATA) books out there. I really appreciate the author's clear writing style and explanations around each section. Also the book uniquely provides how you would show a summary of the results of each technique to laypeople and then to statistical experts (peer reviews). The summary of when to use each type of statistical study, analysis, regression, etc. is also very nicely done. I would recommend this book and Mitchell's "A visual tutorial to STATA graphics" which is a must for STATA users.

**An Introduction to Statistics and Data Analysis Using Stata®: From Research Design to Final Report**[] 2019-12-18 20:47

Very well organized, simple to understand and laid out so that it's not overwhelming. There's plenty of examples specifically for STRATA in the book, and code is blocked off in gray and very simple to read( and use).It is well written and presented in a very logical method that slowly builds on previous ere's a generous use of charts, diagram, and other visual learning aids that break the text this book up and hold it from being dry or boring.

**Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics**[] 2020-1-22 1:2

This makes statistics fun & you learn so much, especially how you are being fooled everyday. I bought 3 more copies & sent them to friends, some of them MBA's & big-time stock buyers & they too have learned a lot.

**Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics**[] 2020-1-22 1:2

This would create a amazing supplemental book for science or math class for a high school student. It presents an opportunity to clear up misconceptions early and encourages healthy skepticism. I especially liked the discussions about randomness.

Awesome book. It covers all the necessary aspects that you need know as a physician wanting to perform statistical tests using SPSS. One of the amazing aspects about this book is that it gives you practice datasets in which you could double check if you did the analysis correctly. Also, it explains the assumptions of each statistical try and explain when should it be used.

Awesome book. It covers all the necessary aspects that you need know as a physician wanting to perform statistical tests using SPSS. One of the amazing aspects about this book is that it gives you practice datasets in which you could double check if you did the analysis correctly. Also, it explains the assumptions of each statistical try and explain when should it be used.

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

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

If you don't have a background in math or statistics, I would recommend "An Introduction to Statistical Learning: With Applications in R" instead, but if you wish a more rigorous book on machine learning, this is the the book for addition the bound copy, I have the PDF from Hastie's website on my kindle, but for a reference as amazing as this one, having the bound ver is absolutely worth the money. When I'm reading a math text, I tend to have one finger in a previous section, so I can refer back to definitions when I need to, and one finger in the index, which just isn't possible with the PDF / Kindle edition.

**Head First Data Analysis: A Learner's Guide To Big Numbers, Statistics, And Good Decisions**[] 2020-1-31 3:44

I have long thought that the most effective teaching way is to use a "present a problem" approach and allow the reader's curiosity drive the is book uses this 's enjoyable and has helped me understand the basics of data analysis quickly.

I have been using The Elements of Statistical Learning for years, so it is finally time to test and review e Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you obtain amazing derivations of famous methods such as help vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on).In addition to having perfect and correct mathematical derivations of necessary algorithms The Elements of Statistical Learning is fairly special in that it actually uses the math to accomplish huge things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the different regression methods. In such a standard formulation two regression methods are various if they have superficially various steps or if various citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each way and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of various methods. This hard use of isomorphism allows awesome results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking various models from the same data) and section 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems).The largest problem is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to let the authors to organize a lot of methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific problem that is making inference difficult you may search the solution in this book. This is amazing for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite fresh idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through ly- don't the kindle version, but the print book. This book is satisfying deep reading and you will wish the advantages of the printed page (and Amazon's problems in conversion are certainly not the authors' fault).

Very comprehensive, sufficiently technical to obtain most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book).The authors are the true thing (Tibshirani is the one behind the LASSO regularization technique).Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs.I own two copies of this edition, one for the office, one for my house, and the authors generously provide the PDF for travelers like me.

Useful review?

Loggable - Simple Data Logging and Statistics[App] 2019-10-9 21:38Could track and graph all the times I got ghosted. 11/10 would sob at the exponential curve again.

0

Useful review?

Loggable - Simple Data Logging and Statistics[App] 2019-10-9 21:38Exceptional . . . I was looking for an application like this for a while . . .!

0

Useful review?

Loggable - Simple Data Logging and Statistics[App] 2019-10-9 21:38The ad is poor and intrusive leading me to uninstall, this application doesn't have much to but the interface design and performance is 5/5

0

## Add your opinion on

loggable - simple data logging and statisticsor scroll down to read more reviews ↓