This textbook presents of the main approaches to and ideas of mathematical statistics. It collects the basic mathematical ideas and tools needed as a basis for more serious study or even independent research in statistics. The majority of existing textbooks in mathematical statistics follow the classical asymptotic framework. Yet, as modern statistics has changed rapidly in recent years, new methods and approaches have appeared. The emphasis is on finite sample behavior, large parameter dimensions, and model misspecifications. The present book provides a fully self-contained introduction to the world of modern mathematical statistics, collecting the basic knowledge, concepts and findings needed for doing further research in the modern theoretical and applied statistics.
This book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.
This invaluable text allows science students to extend necessary skills and techniques, with the topics being developed through examples in science which are easily understood by students from a range of disciplines. The introductory approach eases students into the subject, progressing to cover topics relevant to first and second year study and support data analysis for final year projects.
This book helps students understand the relationship between statistics and the world, bringing life to the theory and methods. Contains an unprecedented amount of real and interesting data with more than 1,585 exercises and hundreds of examples.
An accessible text that explains fundamental concepts in business statistics that are often obscured by formulae and mathematical notation A Guide to Business Statistics offers a practical approach to statistics that covers the fundamental concepts in business and economics. The concepts are introduced through examples, most of the mathematical formulae and notation appears in technical appendices at the end of each chapter. Introduces the concepts and techniques through concise and intuitive examples. Features coverage of sampling techniques, descriptive statistics, probability, sampling distributions, confidence intervals, hypothesis tests, and regression .
The field of statistics focuses on drawing conclusions from data by modeling and analyzing the data using probabilistic models. The authors of this introductory text describe three key concepts from statistics--estimators, tests, and confidence regions--which they demonstrate and apply in an extensive variety of examples and case studies. An entire chapter covers regression models, including linear regression and analysis of variance. This book, designed for students, assumes a basic knowledge of probability theory, calculus, and linear algebra.
An Introduction to Probability and Mathematical Statistics provides information pertinent to the fundamental aspects of probability and mathematical statistics. This book covers a variety of topics, including random variables, probability distributions, discrete distributions, and point estimation.
A well-balanced introduction to probability theory and mathematical statistics. Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals. Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout. This book is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering.
This book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises--with selected "hints"--scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications This is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.
Croucher demonstrates the relevance of mathematics and statistics in making decisions in day-to-day commercial situations. Numerous contemporary examples and pictorial presentations of data are provided to illustrate real world application of statistics.
Introductory Statistics, 9th Edition is written for a one or two semester first course in applied statistics and is intended for students who do not have a strong background in mathematics. The only prerequisite is knowledge of elementary algebra. Introductory Statistics is known for its realistic examples and exercises, clarity and brevity of presentation, and soundness of pedagogical approach.
This textbook is designed to give an engaging introduction to statistics and the art of data analysis. The unique scope includes, but also goes beyond, classical methodology associated with the normal distribution. What if the normal model is not valid for a particular data set? This cutting-edge approach provides the alternatives. It is an introduction to the world and possibilities of statistics that uses exercises, computer analyses, and simulations throughout the core lessons. These elementary statistical methods are intuitive. Counting and ranking features prominently in the text. Nonparametric methods, for instance, are often based on counts and ranks and are very easy to integrate into an introductory course. The ease of computation with advanced calculators and statistical software, both of which factor into this text, allows important techniques to be introduced earlier in the study of statistics. This book's novel scope also includes measuring symmetry with Walsh averages, finding a nonparametric regression line, jackknifing, and bootstrapping. Concepts and techniques are explored through practical problems. Quantitative reasoning is at the core of so many professions and academic disciplines, and this book opens the door to the most modern possibilities.
Making Sense of Statistics is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning. It focuses on presenting the why as well as the how of statistical concepts, rather than computations and formulae, so is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. Once students understand and feel comfortable with the statistics that meet these criteria, they should find it easy to master additional statistical concepts.
Explores mathematical statistics in its entirety--from the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions.
This text focuses on the most widely used applications of mathematical methods, including those related to probability and statistics. The 4-part treatment begins with algebra and analytic geometry and proceeds to an exploration of the calculus of algebraic functions and transcendental functions and applications. 1985 edition. Includes 310 figures and 18 tables.
Coverage of all fundamentals in easy-to-understand methodology.Clarity in the presentation of concepts.Review questions, true/false, and fill in the blanks for each chapter.Over 230 fully solved problems with step-by-step solutions.Over 520 additional practice problems with answers. The text covers all major aspects of engineering statistics, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples and curve fitting, correlation, regression, chi-square tests, and analysis of variance. The book continues to maintain a student-friendly approach and numerical problem solving orientation. Presentations are limited to very basic topics to serve as an introduction to advance topics in those areas of discipline. The purpose of the book is to present the principles and concepts of Probability and Statistics as relevant to student learning.
Provides students 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 students 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.
A knowledge of statistical interpretation is vital for many careers and students. Idiot's Guides: Statistics explains the fundamental tenets in language anyone can understand. Content includes:
- Calculating descriptive statistics, Measures of central tendency: mean, median, and mode, Probability, Variance analysis, Inferential statistics, Hypothesis testing and Organizing data into statistical charts and tables.
This text offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data.
This introductory text gives students the tools to see a bigger picture and make informed choices. It is filled with ideas and strategies that are practical and helps students see that statistics is connected, not only to individual concepts, but also with the world at large. MyStatLab™ not included but is a recommended/mandatory component of the course.
Prepare Your Students for Statistical Work in the Real WorldStatistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statist
Statistics for the Terrified offers a clear and concise introduction to statistics. Perfect as a brief core or supplementary text for undergraduate courses in statistics and research methods, this Sixth Edition is also an ideal refresher for graduate students who have already taken a statistics course. Designed for students who may struggle with mathematical concepts, its informal and highly engaging narrative includes self-help strategies, numerous concrete examples, and a great deal of humor to encourage students from all backgrounds to the study of statistics.
Statistics is confusing and many students and professionals find that existing books and web resources don't give them an intuitive understanding of confusing statistical concepts. This book is: * Easy to Understand: Uses unique "graphics that teach" such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance "rememberability." * Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam. * Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments.
Introductory comprehensive text covers all the traditional topics in a first-semester course. * Divided into 67 short sections, this book makes the topics easy to digest. Students regularly get positive reinforcement as they check their mastery with exercises at the end of each section. * Each exercise is based on a humorous riddle.
The book is intended as a quick source of reference and as an aide-memoir for students taking A-level, undergraduate or postgraduate statistics courses. It includes numerous examples, helping instructors on such courses by providing their students with small data sets with which to work.