Statistics Is Easy 2nd Edition

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea / Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

It is not necessary to know how to do a statistical analysis to critically appraise a paper. However, it is necessary to have a grasp of the basics, of whether the right test has been used and how to interpret the resulting figures. Short, readable, and useful, this book provides the essential, basic information without becoming bogged down in the

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea / Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

Covers exploratory data analysis, 10 parametric statistical inference tests, and examples using a 7 Step method that blends estimation, analytical solutions, and computational solutions.

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then systematically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. The ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea / Bias Corrected Confidence Intervals / Pragmatic Considerations When Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References

This new edition of Medical Statistics Made Easy 2nd edition enables readers to understand the key statistical techniques used throughout the medical literature. Featuring a comprehensive updating of the 'Statistics at work' section, this new edition retains a consistent, concise, and user-friendly format. Each technique is graded for ease of use and frequency of appearance in the mainstream medical journals. Medical Statistics Made Easy 2nd edition is essential reading for anyone looking to understand: * confidence intervals and probability values * numbers needed to treat * t tests and other parametric tests * survival analysis If you need to understand the medical literature, then you need to read this book. Reviews: "This book helps medical students understand the basic concepts of medical statistics starting in a 'step-by-step approach'. The authors have designed the book assuming that the reader has no prior knowledge. It focuses on the most common statistical concepts that are likely to be faced in medical literature. All chapters are concise and simple to understand. Each chapter starts with an introduction which consists of “how important” that particular statistical concept is, using a 'star' system. A 'thumbs-up' system shows how easy the statistical concept is to understand. Both these systems indicate time-efficient learning allowing yourself to focus on areas you find most difficult. Following this, there are worked out examples with exam-tips at the end of some chapters. The last chapter, 'Statistics at Work', shows how medical statistics is put into practice using worked out examples from renowned journals. This helps in assessing the reader’s own knowledge and gives them confidence in analysis of statistics of a journal. In conclusion, we would recommend this book as an introduction into medical statistics before plunging into the deep 'statistical' waters! It gives confidence to the reader in taking up the challenge of understanding statistics and [being] able to apply knowledge in analysing medical literature." Stefanie Zhao Lin Lip & Louise Murchison, Scottish Medical Journal, June 2010 "If ever there was a book that completely lived up to its title, this is it...Perhaps above everything, it is the chapter layout and design that makes this book stand out head and shoulders above the crowd. At the beginning of each chapter two questions are posed – how important is the subject in question and how difficult is it to understand? The first is answered on the basis of how often the subject is mentioned / used in papers published in mainstream medical journals. A star rating is then given from one to five with five stars implying use in the majority of papers published. The second question is answered by means of a ‘thumbs up’ grading system. The more thumbs, the easier the concept is to understand (maximum of five). This, of course, provides a route into statistics for even the most idle of uneducated individuals! Five stars and five thumbs must surely indicate time-efficient learning! At the end of each chapter exam tips (light bulb icon!) are given – I doubt anyone could ask for more! The whole way in which the authors have written this book is commendable; the chapters are succinct, easy to follow and a pleasure to read...Is it value for money? – a definite yes even at twice the price. Of course I never exaggerate but if you breathe, you should own this book!" Ian Pearce, Urology News, June 2010

Based on Neil J. Salkind’s bestselling text, Statistics for People Who (Think They) Hate Statistics, this adapted Excel 2016 version presents an often intimidating and difficult subject in a way that is clear, informative, and personable. Researchers and students uncomfortable with the analysis portion of their work will appreciate the book′s unhurried pace and thorough, friendly presentation. Opening with an introduction to Excel 2016, including functions and formulas, this edition shows students how to install the Excel Data Analysis Tools option to access a host of useful analytical techniques and then walks them through various statistical procedures, beginning with correlations and graphical representation of data and ending with inferential techniques and analysis of variance. New to the Fourth Edition: A new chapter 20 dealing with large data sets using Excel functions and pivot tables, and illustrating how certain databases and other categories of functions and formulas can help make the data in big data sets easier to work with and the results more understandable. New chapter-ending exercises are included and contain a variety of levels of application. Additional TechTalks have been added to help students master Excel 2016. A new, chapter-ending Real World Stats feature shows readers how statistics is applied in the everyday world. Basic maths instruction and practice exercises for those who need to brush up on their math skills are included in the appendix.