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100 Statistical Tests

Author : Gopal K Kanji
ISBN : 141292376X
Genre : Mathematics
File Size : 83. 14 MB
Format : PDF
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This expanded and updated Third Edition of Gopal K. Kanji’s best-selling resource on statistical tests covers all the most commonly used tests with information on how to calculate and interpret results with simple datasets. Each entry begins with a short summary statement about the test’s purpose, and contains details of the test objective, the limitations (or assumptions) involved, a brief outline of the method, a worked example, and the numerical calculation. 100 Statistical Tests, Third Edition is the one indispensable guide for users of statistical materials and consumers of statistical information at all levels and across all disciplines.

Levine S Guide To Spss For Analysis Of Variance

Author : Sanford L. Braver
ISBN : 9781135674403
Genre : Psychology
File Size : 37. 56 MB
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A greatly expanded and heavily revised second edition, this popular guide provides instructions and clear examples for running analyses of variance (ANOVA) and several other related statistical tests of significance with SPSS. No other guide offers the program statements required for the more advanced tests in analysis of variance. All of the programs in the book can be run using any version of SPSS, including versions 11 and 11.5. A table at the end of the preface indicates where each type of analysis (e.g., simple comparisons) can be found for each type of design (e.g., mixed two-factor design). Providing comprehensive coverage of the basic and advanced topics in ANOVA, this is the only book available that provides extensive coverage of SPSS syntax, including the commands and subcommands that tell SPSS what to do, as well as the pull-down menu point-and-click method (PAC). Detailed explanation of the syntax, including what is necessary, desired, and optional helps ensure that users can validate the analysis being performed. The book features the output of each design along with a complete explanation of the related printout. The new edition was reorganized to provide all analysis related to one design type in the same chapter. It now features expanded coverage of analysis of covariance (ANCOVA) and mixed designs, new chapters on designs with random factors, multivariate designs, syntax used in PAC, and all new examples of output with complete explanations. The new edition is accompanied by a CD-ROM with all of the book's data sets, as well as exercises for each chapter. This book is ideal for readers familiar with the basic concepts of the ANOVA technique including both practicing researchers and data analysts, as well as advanced students learning analysis of variance.

Introductory R A Beginner S Guide To Data Visualisation Statistical Analysis And Programming In R

Author : Robert Knell
ISBN : 9780957597112
Genre :
File Size : 87. 86 MB
Format : PDF, Mobi
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R is now the most widely used statistical software in academic science and it is rapidly expanding into other fields such as finance. R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating. Introductory R is written for the novice user who knows a little about statistics but who hasn't yet got to grips with the ways of R. This new edition is completely revised and greatly expanded with new chapters on the basics of descriptive statistics and statistical testing, considerably more information on statistics and six new chapters on programming in R. Topics covered include: A walkthrough of the basics of R's command line interface Data structures including vectors, matrices and data frames R functions and how to use them Expanding your analysis and plotting capacities with add-in R packages A set of simple rules to follow to make sure you import your data properly An introduction to the script editor and advice on workflow A detailed introduction to drawing publication-standard graphs in R How to understand the help files and how to deal with some of the most common errors that you might encounter. Basic descriptive statistics The theory behind statistical testing and how to interpret the output of statistical tests Thorough coverage of the basics of data analysis in R with chapters on using chi-squared tests, t-tests, correlation analysis, regression, ANOVA and general linear models What the assumptions behind the analyses mean and how to test them using diagnostic plots Explanations of the summary tables produced for statistical analyses such as regression and ANOVA Writing your own functions in R Using table operations to manipulate matrices and data frames Using conditional statements and loops in R programmes. Writing longer R programmes. The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results.

Data Mining With R

Author : Luis Torgo
ISBN : 9781315399096
Genre : Business & Economics
File Size : 47. 6 MB
Format : PDF, Kindle
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Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Visualizing Complex Data Using R

Author : N. D. Lewis
ISBN : 1490433546
Genre :
File Size : 50. 78 MB
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Wish you had fresh ways to present data, explore relationships, visualize high volume data-sets and break free from mundane charts and diagrams? Visualizing complex relationships with ease using R begins here. In this book you will find innovative ideas to unlock the relationships in your own data and create killer visuals to help you transform your next presentation from good to great. This book is for you if you:1. Need to impress your boss with you next presentation!2. Need fresh ideas on the presentation of complex data.3. Want to boost your visual presentation toolkit. 4. Wish to capture and connect relationships in your data.5. Uncover and exploit new insights.6. Master new techniques for data presentation.7. Seek real life applications of data visualization.8. Whittle down complexity to its bare bones.9. Stimulate innovation, creativity and insight.10. Intensify knowledge discovery using visual techniques.11. Diagnose, segment, connect or clarify obtuse relationships.12. Need inspiration to ignite your own creativity.

A First Course On Time Series Analysis

Author : Falk et al.
ISBN : 9783844228458
Genre : Mathematics
File Size : 32. 28 MB
Format : PDF, Docs
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Elements from time series analysis with the statistical software package SAS

Hypothesis Testing

Author : Scott Hartshorn
ISBN : 1973181460
Genre :
File Size : 42. 65 MB
Format : PDF, ePub, Mobi
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Hypothesis Testing & Statistical Significance If you are looking for a short beginners guide packed with visual examples, this booklet is for you. Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their way into scientific and engineering tests of all kinds, from medical tests with control group and a testing group, to the analysis of how strong a newly made batch of parts is. Those same calculations are also used in investment decisions. This book goes through all the major types of statistical significance calculations, and works through an example using them, and explains when you would use that specific type instead of one of the others. Just as importantly, this book is loaded with visual examples of what exactly statistical significance is, and the book doesn't assume that you have prior in depth knowledge of statistics or that use regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way. For instance did you know that Statistical Significance Can Be Easily Understood By Rolling A Few Dice? In fact, you probably already know this key concept in statistical significance, although you might not have made the connection. The concept is this. Roll a single die. Is any number more likely to come up than another ? No, they are all equally likely. Now roll 2 dice and take their sum. Suddenly the number 7 is the most likely sum (which is why casinos win on it in craps). The probability of the outcome of any single die didn't change, but the probability of the outcome of the average of all the dice rolled became more predictable. If you keep increasing the number of dice rolled, the outcome of the average gets more and more predictable. This is the exact same effect that is at the heart of all the statistical significance equations (and is explained in more detail in the book) You Are Looking At Revision 2 Of This Book The book that you are looking at on Amazon right now is the second revision of the book. Earlier I said that you might have missed the intuitive connections to statistical significance that you already knew. Well that is because I missed them in the first release of this book. The first release included examples for the major types of statistical significance A Z-Test A 1 Sample T-Test A Paired T Test A 2 Sample T-Test with equal variance A 2 Sample T-test with unequal variance Descriptions of how to use a T-table and a Z-table And those examples were good for what they were, but were frankly not significantly different than you could find in many statistics textbooks or on Wikipedia. However this revision builds on those examples, draws connections between them, and most importantly explains concepts such as the normal curve or statistical significance in a way that will stick with you even if you don't remember the exact equation. If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Statistical Significance is fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the topic with this visual approach!

Bayes Theorem Examples A Visual Introduction For Beginners

Author : Dan Morris
ISBN : 1549761749
Genre : Bayesian statistical decision theory
File Size : 45. 15 MB
Format : PDF, Mobi
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***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples "...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem..." - Doug E. - TOP 100 REVIEWER"...For those who are predominately "Visual Learners", as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact." - Tin F. TOP 50 REVIEWER

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