The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform. The logical places to look would be the front offices of major league teams, and the dugouts, perhaps even in the minds of the players themselves.
Lewis mines all these possibilities—his intimate and original portraits of big league ballplayers are alone worth the price of admission—but the real jackpot is a cache of numbers—numbers! What these geek numbers show—no, prove—is that the traditional yardsticks of success for players and teams are fatally flawed. Even the box score misleads us by ignoring the crucial importance of the humble base-on-balls. This information has been around for years, and nobody inside Major League Baseball paid it any mind. There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications.
But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level.
If You're an Educator
The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study. Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.
Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis.
Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information. This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts.
Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
Drawing upon over 40 years of experience, the authors of Statistics, 10th Edition provide business professionals with a clear and methodical approach to essential statistical procedures. The text clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. It features an emphasis on expressions involving sums of squares and degrees of freedom as well as a strong stress on the importance of variability. This accessible approach will help business professionals tackle such perennially mystifying topics as the standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and estimates of effect size.
But statistical analysis is tricky to get right, even for the best and brightest of us. 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. This is the hardcover format of Statistics For Dummies, 2nd Edition. The fun and easy way to get down to business with statistics Stymied by statistics? 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.
Introductory Mathematics and Statistics for Islamic Finance, + Website
Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts. Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises.
If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book. Aimed at high school and college students who need to take statistics to fulfill a degree requirement, this book follows a standard statistics curriculum with topics that include frequency distributions, probability, binomial distribution, poisson distribution, normal distribution, hypothesis testing, simple regression analysis, and more.
Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day. Describing data; Data distributions; Variation of study results: The Cartoon Guide to Statistics covers all the central ideas of modern statistics: Never again will you order the Poisson Distribution in a French restaurant!
How can we catch schools that cheat on standardized tests? 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 , 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.
Head First Statistics brings this typically dry subject to life, teaching you everything you want and need to know about statistics through engaging, interactive, and thought-provoking material, full of puzzles, stories, quizzes, visual aids, and real-world examples. There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience.
Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data.
The text provides complete examples with the R programming language and BUGS software both freeware , and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs. The textbook bridges the students from their undergraduate training into modern Bayesian methods.
This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis.
Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication.
A glossary of statistical terms and symbols is also included. Renowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques. A-B , Last Name: I-J , History , Last Name: Titles Appear On 1 List Each.
A Course in Probability Theory. Big Data Made Simple. The Remarkable Story of Risk. Analysis and Adjustment of Survey Measurements. Things That Gain from Disorder. Applied Multivariate Statistical Analysis. Applied Statistics and Probability for Engineers. Baseball Between the Numbers: Business Statistics — 5th Edition-. Models, Reasoning, and Inference. Cause and Correlation in Biology: Contributions to a General Asymptotik Statistical Theory. Optimal Control, Statistics, and Path Planning.
Data Analysis with Open Source Tools. A Model Comparison Approach. Practical Machine Learning Tools and Techniques. Next, there is and wide-ranging description of the theory of chromatic polynomials. The last section discusses symmetry and regularity properties.
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Biggs makes important connections with other branches of algebraic combinatorics and group theory. Anthony wrote a book titled Computational Learning Theory: Both Biggs and Anthony focused on the necessary background material from logic , probability , and complex theory.
This book is an introduction to computational learning.
The Best Statistics Books Of All-Time -
The chip-firing game has been around for less than 20 years. It has become an important part of the study of structural combinatorics.
The set of configurations that are stable and recurrent for this game can be given the structure of an abelian group. In addition, the order of the group is equal to the tree number of the graph. For other published work on the history of mathematics, please see. From Wikipedia, the free encyclopedia. For the Welsh rugby player, see Norman Biggs. London School of Economics.
Retrieved 29 April Discrete Mathematics Second ed.
Retrieved 15 April Journal of the British Society for the History of Mathematics. Journal of Algebraic Combinatorics: Retrieved 10 May