Beat french with statistics book pdf free download

There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread.

It is also more commonly used nowadays. This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added. The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students hinge, H-spread, upper adjacent value. The text is internally consistent with one exception that I noted the use of the synonymous words "H-spread" and "interquartile range".

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text. The organization, in particular the ordering of the topics, is rather standard with a few exceptions.

Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r.

I have tried this approach, and I now like this approach. But it is a very challenging chapter for students. The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements. The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text.

This text is very high quality textbook in every way. Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available. Lane is the principal developer of this resource although many others have made substantial contributions.

Content Accuracy rating: 5 I did not find any errors. Clarity rating: 5 I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible. Consistency rating: 5 Even with different authors of chapter, the writing is consistent. Modularity rating: 5 The text is well organized into sections making it easy to assign individual topics and sections. Interface rating: 5 I had no problem navigating the text online.

Grammatical Errors rating: 5 The writing is grammatical correct. Cultural Relevance rating: 5 I saw no issues that would be offensive. Comments I did like this text. Content Accuracy rating: 5 I found no errors in their discussions. Clarity rating: 5 This is a Statistics text so a little dry. Consistency rating: 5 The text is consistent throughout using the same verbiage in various sections. Modularity rating: 5 The text dose lend itself to reasonable reading assignments.

Content Accuracy rating: 5 I did not find any errors or issues of inaccuracy. Clarity rating: 4 All of the key terms, formulas, and logic for statistical tests are clearly explained. Consistency rating: 5 The explanations are consistent and build from and relate to corresponding sections that are listed in each unit. Modularity rating: 5 Modularity is a strength of this text in both the PDF and interactive online format. Grammatical Errors rating: 5 The text is easy to read and free from any obvious grammatical errors.

Cultural Relevance rating: 5 Although some of the examples are outdated, I did not review any that were offensive. Comments This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Content Accuracy rating: 5 The text seems to be statistically accurate. Clarity rating: 5 It is written clearly. Consistency rating: 4 The terminology is fairly consistent.

There is room for some improvement. Modularity rating: 5 By the nature of the subject, the topics have to be presented in a sequential and coherent order. Interface rating: 5 Good interface. Grammatical Errors rating: 5 The grammar is ok. Cultural Relevance rating: 5 The book seems to be culturally neutral, and not offensive in any way. Comments I really liked the simulations that go with the book. Content Accuracy rating: 5 I found all the material accurate.

Clarity rating: 5 The book is well written for introducing beginners to statistical concepts. Consistency rating: 5 Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic. Modularity rating: 4 The book is well-written with attention to modularity where possible.

Interface rating: 5 I did not find any interface issues. Grammatical Errors rating: 5 The book is well written. In my reading I did not notice grammatical errors.

Cultural Relevance rating: 5 For this subject and in the examples given, I did not notice any cultural issues. Comments For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. Content Accuracy rating: 5 The terminology and notation used in the textbook is pretty standard. The content is accurate. Clarity rating: 4 The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion.

Consistency rating: 5 The textbook uses standard notation and terminology. Modularity rating: 4 The authors have done a good job treating each chapter as if they stand alone. Interface rating: 5 I had no problem with the interface. Grammatical Errors rating: 5 I did not see any grammatical errors. Cultural Relevance rating: 4 I am not sure how to evaluate this. Comments Overall, this textbook will cover most of the topics in a survey of statistics course.

Content Accuracy rating: 5 The content is pretty accurate, I did not find any biases or errors. Clarity rating: 4 The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. Consistency rating: 5 Consistency is good, the book has a great structure.

Modularity rating: 4 The text can be easily divided into sub-sections, some of which can be omitted if needed. Interface rating: 5 The online version has many functions and is easy to navigate.

Grammatical Errors rating: 5 No grammatical errors found. Cultural Relevance rating: 4 The text uses simple and clear language, which is helpful for non-native speakers. Comments In all, this book is a good learning experience. Content Accuracy rating: 5 I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

Clarity rating: 4 Solid. Consistency rating: 5 I did not detect any problems with inconsistent symbol use or switches in terminology. Modularity rating: 3 This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Interface rating: 5 I did not detect any problems.

Grammatical Errors rating: 5 I found no errors. This did not affect the over-all useability of the book though Cultural Relevance rating: 4 I'm not sure how well this book would work internationally as many of the examples contain domestic American references. Content Accuracy rating: 5 It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors.

Clarity rating: 5 The material is presented in a clear, concise manner. Consistency rating: 4 The structure of the text is very consistent. Modularity rating: 4 When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme.

Interface rating: 5 The learner can easily follow up the material; there is no interface problem. Grammatical Errors rating: 5 There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored. Cultural Relevance rating: 5 Not applicable for this textbook. Content Accuracy rating: 5 Content appears to be accurate throughout. Clarity rating: 5 The author does a great job of explaining nearly every new term or concept.

Consistency rating: 5 Overall consistency is good. Modularity rating: 5 The authors do a great job of partitioning the text and labeling sections with appropriate headings. Interface rating: 5 The text is free of any issues. There are no navigation problems nor any display issues. Grammatical Errors rating: 5 The text contains no grammatical errors. Cultural Relevance rating: 4 The text is not culturally insensitive or offensive in any way most of time.

Comments Overall, it's well-written and good recourse to be an introduction to statistical methods. Content Accuracy rating: 3 The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text.

Clarity rating: 5 The text is very clear and direct with the language used. Consistency rating: 5 The terminology and framework of the text is consistent.

Modularity rating: 5 The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. Interface rating: 4 Adding an option of returning to the previous page would be of great value to the reader. Grammatical Errors rating: 5 No grammatical errors were found while reviewing select pages of this text at random. Cultural Relevance rating: 3 Several examples contained data that were not formally cited. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining.

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all. Its function is something like a traditional textbook — it will provide the detail and background theory to support the School of Data courses and challenges.

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization.

Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

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In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success. The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It serves as a tutorial or guide to the Python language for a beginner audience.

If all you know about computers is how to save text files, then this is the book for you. Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.

This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end. The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples. I Dani started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form. Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.

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Also, in case you are searching for something and are not able to get through it, please do fill-up the form below and give us a chance to serve you. Till then, Happy Reading and that too free! Anusha worked for many years in the IT industry as a Project Manager, and also a senior writer and editor at FossMint. She is a huge fan of Linux and is passionate about writing Linux and technology related stuff. Follow Her On : Facebook and Instagram.

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