Basic Principles and Applications of Probability Theory by
The book is an introduction to modern probability theory written by one of the famous experts in this area. Readers will learn about the basic concepts of probability and its applications, preparing them for more advanced and specialized works.
Chance and Chaos by
How do scientists look at chance, or randomness, and chaos in physical systems? In answering this question for a general audience, Ruelle writes in the best French tradition: he has produced an authoritative and elegant book--a model of clarity, succinctness, and a humor bordering at times on the sardonic.
Fundamental Probability: a computational approach by
Probability is a vital measure in numerous disciplines, from bioinformatics and econometrics to finance/insurance and computer science. Developed from a successful course, Fundamental Probability provides an engaging and hands-on introduction to this important topic. Whilst the theory is explored in detail, this book also emphasises practical applications, with the presentation of a large variety of examples and exercises, along with generous use of computational tools. Based on international teaching experience with students of statistics, mathematics, finance and econometrics, the book: Presents new, innovative material alongside the classic theory. Goes beyond standard presentations by carefully introducing and discussing more complex subject matter, including a richer use of combinatorics, runs and occupancy distributions, various multivariate sampling schemes, fat-tailed distributions, and several basic concepts used in finance. Emphasises computational matters and programming methods via generous use of examples in MATLAB. Includes a large, self-contained Calculus/Analysis appendix with derivations of all required tools, such as Leibniz' rule, exchange of derivative and integral, Fubini's theorem, and univariate and multivariate Taylor series. Presents over 150 end-of-chapter exercises, graded in terms of their difficulty, and accompanied by a full set of solutions online. This book is intended as an introduction to the theory of probability for students in biology, mathematics, statistics, economics, engineering, finance, and computer science who possess the prerequisite knowledge of basic calculus and linear algebra.
Introduction to Probability Theory and Statistical Inference by
Discusses probability theory and to many methods used in problems of statistical inference. The Third Edition features material on descriptive statistics. Cramer-Rao bounds for variance of estimators, two-sample inference procedures, bivariate normal probability law, F-Distribution, and the analysis of variance and non-parametric procedures. Contains numerous practical examples and exercises.
Introduction to Probability with Mathematica by
Updated to conform to Mathematica ¿ 7.0, Introduction to Probability with Mathematica ¿, Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanying CD-ROM offers instructors the option of creating class notes, demonstrations, and projects. New to the Second Edition Expanded section on Markov chains that includes a study of absorbing chains New sections on order statistics, transformations of multivariate normal random variables, and Brownian motion More example data of the normal distribution More attention on conditional expectation, which has become significant in financial mathematics Additional problems from Actuarial Exam P New appendix that gives a basic introduction to Mathematica New examples, exercises, and data sets, particularly on the bivariate normal distribution New visualization and animation features from Mathematica 7.0 Updated Mathematica notebooks on the CD-ROM (Go to Downloads/Updates tab for link to CD files.) After covering topics in discrete probability, the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions, including normal, bivariate normal, gamma, and chi-square distributions. The author goes on to examine the history of probability, the laws of large numbers, and the central limit theorem. The final chapter explores stochastic processes and applications, ideal for students in operations research and finance.
Probability Theory: A Comprehensive Course by
This second edition of the popular textbook contains a comprehensive course in modern probability theory, covering a wide variety of topics which are not usually found in introductory textbooks, including: * limit theorems for sums of random variables * martingales * percolation * Markov chains and electrical networks * construction of stochastic processes * Poisson point process and infinite divisibility * large deviation principles and statistical physics * Brownian motion * stochastic integral and stochastic differential equations. The theory is developed rigorously and in a self-contained way, with the chapters on measure theory interlaced with the probabilistic chapters in order to display the power of the abstract concepts in probability theory. This second edition has been carefully extended and includes many new features. It contains updated figures (over 50), computer simulations and some difficult proofs have been made more accessible. A wealth of examples and more than 270 exercises as well as biographic details of key mathematicians support and enliven the presentation. It will be of use to students and researchers in mathematics and statistics in physics, computer science, economics and biology.
Probability Theory: The Logic of Science by
The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.
Basics of Modern Mathematical Statistics by
​The complexity of today's statistical data calls for modern mathematical tools. Many fields of science make use of mathematical statistics and require continuous updating on statistical technologies. Practice makes perfect, since mastering the tools makes them applicable. Our book of exercises and solutions offers a wide range of applications and numerical solutions based on R. In modern mathematical statistics, the purpose is to provide statistics students with a number of basic exercises and also an understanding of how the theory can be applied to real-world problems. The application aspect is also quite important, as most previous exercise books are mostly on theoretical derivations. Also we add some problems from topics often encountered in recent research papers. The book was written for statistics students with one or two years of coursework in mathematical statistics and probability, professors who hold courses in mathematical statistics, and researchers in other fields who would like to do some exercises on math statistics.
Basic Statistics: understanding conventional methods and modern insights by
This introductory statistics textbook for non-statisticians covers basic principles, concepts, and methods routinely used in applied research. What sets this text apart is the incorporation of the many advances and insights from the last half century when explaining basic principles. These advances provide a foundation for vastly improving our ability to detect and describe differences among groups and associations among variables and provide a deeper and more accurate sense of when basic methods perform well and when they fail. Assuming no prior training, Wilcox introduces students to basic principles and concepts in a simple manner that makes these advances and insights, as well as standard ideas and methods, easy to understand and appreciate.
A Dictionary of Statistics by
This wide-ranging, jargon-free dictionary contains over 2,000 entries on all aspects of statistics, including terms used in computing, mathematics, and probability. It also includes biographical information on over 200 key figures in the field and coverage of statistical journals andsocieties. While embracing the whole multi-disciplinary spectrum of this complex subject, information is presented in a clear and practical manner. This revised and updated edition features recommended web links for many entries, accessible via the Dictionary of Statistics website, which providevaluable extra information.Entries are generously illustrated with useful figures and diagrams, and include worked examples where applicable. Appendices include a historical calendar of important statistical events, lists of statistical and mathematical notation, and statistical tables. An invaluable dictionary for statisticsstudents and professionals from a wide range of disciplines, including economics, politics, market research, medicine, psychology, pharmaceuticals, and mathematics. Also provides a clear introduction to the subject for the general reader.
Introduction to Statistical Inference by
This excellent text emphasizes the inferential and decision-making aspects of statistics. The first chapter is mainly concerned with the elements of the calculus of probability. The second chapter contains the essential statistical techniques of summarizing the data in a sample prior to making inferences about the population. Additional chapters cover the general properties of distributions, testing hypotheses, and more.
Making sense of data : a practical guide to exploratory data analysis and data mining by
A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: * Problem definitions * Data preparation * Data visualization * Data mining * Statistics * Grouping methods * Predictive modeling * Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
Mathematical Statistics: exercises and solutions by
The exercises are grouped into seven chapters with titles matching those in the author's Mathematical Statistics. Can also be used as a stand-alone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book. Suitable for self-study for a statistics Ph.D. qualifying exam.
A Modern Introduction to Probability and Statistics: Understanding Why and How by
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included - this is a modern method missing in many other books
Statistical Methods, Experimental Design, and Scientific Inference by
R.A. Fisher has had more influence on the development of statistical theory and practice than any other twentieth-century statistician. His writings (both in paper and book form) have proved to be as relevant to present-day statisticians as they were when first published.This book brings together as a single volume three of Fisher's most influential textbooks: Statistical Methods for Research Workers, The Design of Experiments , and Statistical Methods and Scientific Inference . In this new edition Frank Yates has provided a foreword which sheds fresh light onFisher's thinking and on the writing and reception of each of the books. He discusses some of the key issues tackled in the three books and reflects on how the ideas expressed have come to permeate modern statistical practice.
A Concise Guide to Market Research: the process, data, and methods using IBM SPSS statistics by
This book offers an easily accessible and comprehensive guide to the entire market research process, from asking market research questions to collecting and analyzing data by means of quantitative methods. It is intended for all readers who wish to know more about the market research process, data management, and the most commonly used methods in market research. The book helps readers perform analyses, interpret the results, and make sound statistical decisions using IBM SPSS Statistics. Hypothesis tests, ANOVA, regression analysis, principal component analysis, factor analysis, and cluster analysis, as well as essential descriptive statistics, are covered in detail. Highly engaging and hands-on, the book includes many practical examples, tips, and suggestions that help readers apply and interpret the data analysis methods discussed. The new edition uses IBM SPSS version 25 and offers the following new features: A single case and dataset used throughout the book to facilitate learning New material on survey design and all data analysis methods to reflect the latest advances concerning each topic Improved use of educational elements, such as learning objectives, keywords, self-assessment tests, case studies, and much more A glossary that includes definitions of all the keywords and other descriptions of selected topics Links to additional material and videos via the Springer Multimedia App
How to use SPSS: a step-by-step guide to analysis and interpretation by
How to Use SPSS® is designed with the novice computer user in mind and for people who have no previous experience of using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report. The book begins with the basics, such as starting SPSS, defining variables, and entering and saving data. It covers all major statistical techniques typically taught in beginning statistics classes, such as descriptive statistics, graphing data, prediction and association, parametric inferential statistics, nonparametric inferential statistics and statistics for test construction. More than 250 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. The book includes a glossary of statistical terms and practice exercises. A complete set of online resources including video tutorials and output files for students, and PowerPoint slides and test bank questions for instructors, make How to Use SPSS® the definitive, field-tested resource for learning SPSS. New to this edition: Fully updated to SPSS 24 and IBM SPSS Statistics Cloud New chapter on ANOVA New material on inter-rater reliability New material on syntax Additional coverage of data entry and management
Medical Statistics: a guide to SPSS, data analysis, and critical appraisal by
Medical Statistics provides the necessary statistical tools to enable researchers to undertake and understand evidence-based clinical research. It is a practical guide to conducting statistical research and interpreting statistics in the context of how the participants were recruited, how the study was designed, what types of variables were used, what effect size was found, and what the P values mean. It guides researchers through the process of selecting the correct statistics and show how to best report results for presentation and publication. Clear and concise explanations, combined with plenty of examples and tabulated explanations are based on the authors' popular medical statistics courses. The table of contents is divided into sections according to whether data are continuous or categorical in nature as this distinction is fundamental to selecting the correct statistics. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. The chapters conclude with critical appraisal guidelines to help researchers review the reporting of results from each type of statistical test. This new edition includes a new chapter on repeated measures and mixed models and a helpful glossary of terms provides an easy reference that applies to all chapters.
A Simple Guide to IBM SPSS Statistics - Version 23. 0 by
Completely up to date and extremely student friendly, A SIMPLE GUIDE TO IBM SPSS: FOR VERSION 23.0, Fourteenth Edition, equips you with everything you need to know about the newest version of SPSS® for Windows® so you can effectively use the program in your statistics class. The guide's straightforward style frees you to concentrate on learning basic statistical concepts, while still developing familiarity with SPSS®. Its clear, step-by-step instruction quickly gets you up to speed, enabling you to confidently use SPSS® to do homework problems and conduct statistical analyses for research projects.
SPSS Statistics for Data Analysis and Visualization by
Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.