‘This is a highly accessible, yet an adequately sophisticated text, which can be enjoyed by students across the skills spectrum. I am confident that it will help students who suffer from maths phobia as it offers to teach statistics with anxiety-busting clarity. The book benefits from easy to follow demonstrations and illustrations of statistical methods and techniques, which are highly relevant for business and management research at the undergraduate and masters level courses.’ Professor Mustafa F. Ozbilgin, University of East Anglia, UK Are you confused about which statistical tests to use, when you should be using them and why you should be using them? This book is about helping you to choose and use the right statistical technique to analyse your data and write about your results and findings convincingly. It provides a concise and accessible guide to the essential statistical skills needed for success in your assignment, project or dissertation. Berman Brown and Saunders concentrate on particular statistical tests and their three Ws – what, why, and when. They provide you with the tools to choose the graphs and statistics that are suitable for your data, and to understand what the statistical results actually mean. In addition, the book: Explains why it is impossible to avoid using statistics in analysing data Describes the language of statistics to make it easier to understand the various terms used for statistical techniques Deals with using tables and charts to present data so that they are easy to understand Explains the statistics used to describe data used to infer differences and relationships. The book also includes a handy alphabet of statistics as well as a glossary of key statistical terms. Dealing with Statistics is key reading for students undertaking a social science or business and management assignment, project or dissertation as part of an undergraduate or Masters qualification.

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

'This is a highly accessible, yet an adequately sophisticated text, which can be enjoyed by students across the skills spectrum. I am confident that it will help students who suffer from maths phobia as it offers to teach statistics with anxiety-busting clarity. The book benefits from easy to follow demonstrations and illustrations of statistical methods and techniques, which are highly relevant for business and management research at the undergraduate and masters level courses' - Professor Mustafa F. Ozbilgin, University of East Anglia, UK. Are you confused about which statistical tests to use, when you should be using them and why you should be using them? This book is about helping you to choose and use the right statistical technique to analyse your data and write about your results and findings convincingly.It provides a concise and accessible guide to the essential statistical skills needed for success in your assignment, project or dissertation. Berman Brown and Saunders concentrate on particular statistical tests and their three W's - what, why, and when. They provide you with the tools to choose the graphs and statistics that are suitable for your data, and to understand what the statistical results actually mean.In addition, the book: explains why it is impossible to avoid using statistics in analysing data; describes the language of statistics to make it easier to understand the various terms used for statistical techniques; deals with using tables and charts to present data so that they are easy to understand; and, explains the statistics used to describe data used to infer differences and relationships. The book also includes a handy alphabet of statistics as well as a glossary of key statistical terms. "Dealing with Statistics" is key reading for students undertaking a social science or business and management assignment, project or dissertation as part of an undergraduate or Masters qualification.

Two complete eBooks for one low price! Created and compiled by the publisher, this Statistics I and Statistics II bundle brings together two math titles in one, e-only bundle. With this special bundle, you’ll get the complete text of the following two titles: Statistics For Dummies, 2nd Edition Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more. Tracks to a typical first semester statistics course Updated examples resonate with today's students Explanations mirror teaching methods and classroom protocol Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance. Statistics II For Dummies The ideal supplement and study guide for students preparing for advanced statistics. Packed with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test. About the Author Deborah Rumsey has a PhD in Statistics from The Ohio State University. Upon graduating, she joined the faculty in the Department of Statistics at Kansas State University, where she won the distinguished Presidential Teaching Award and earned tenure and promotion. She returned to Ohio State and is now a Statistics Education Specialist/Auxiliary Faculty Member for the Department of Statistics. Dr. Rumsey has served on the American Statistical Associations Statistics Education Executive Committee and is the Editor of the Teaching Bits section of the Journal of Statistics Education. She is the author of the both books in this bundle. Additionally, she has published many papers and given many professional presentations on the subject of Statistics Education. Her particular research interests are curriculum materials development, teacher training and support, and immersive learning environments.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

A New York Times bestseller "Brilliant, funny…the best math teacher you never had." —San Francisco Chronicle Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.