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Watch a video introduction here. Statistics Through Applications (STA) is the only text written specifically for high school statistics course. Designed to be read, the book takes a data analysis approach that emphasizes conceptual understanding over computation, while recognizing that some computation is necessary. The focus is on the statistical thinking behind data gathering and interpretation. The high school statistics course is often the first applied math course students take. STA engages students in learning how statisticians contribute to our understanding of the world and helps students to become more discerning consumers of the statistics they encounter in ads, economic reports, political campaigns, and elsewhere. New and improved! STA 2e features expanded coverage of probability, a reorganized presentation of data analysis, a new color design and much more. Please see the posted sample chapter or request a copy today to see for yourself.

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Genre: Mathematics
Author: Daren S. Starnes
Publisher: Macmillan
Release: 2009-12-25
File: 600 Pages
ISBN-13: 9781429219747

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The NCTM urges the study of statistics in high school, and many schools have designed courses based on these NCTM recommendations. Now from the authors of the overwhelming favorite for the AP Statistics course (The Practice of Statistics) comes the first text written exclusively for a non-AP level high introduction to statistics for high school students. Statistics Through Applications is the ideal alternative for juniors and seniors not going into high level courses such as calculus, but who are interested in an introduction to the important topics of statistics. The book's data analysis approach moves students away from monotonous computation to focus on the statistical thinking behind data gathering and interpretation. This approach more accurately reflects the way working statisticians contribute to our understanding of the world. It also helps students be more discerning consumers of statistics, teaching them to look closely at what the numbers from surveys, election polls, and medical studies are really saying. State Correlationshttp://www.bfwpub.com/pdfs/yatessta/state.htm

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Genre: Computers
Author: Dan Yates
Publisher: Macmillan
Release: 2004-03
File: 600 Pages
ISBN-13: 0716747723

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Integrating the theory and practice of statistics through a series of case studies, each lab introduces a problem, provides some scientific background, suggests investigations for the data, and provides a summary of the theory used in each case. Aimed at upper-division students.

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Genre: Mathematics
Author: Deborah Nolan
Publisher: Springer Science & Business Media
Release: 2006-05-02
File: 283 Pages
ISBN-13: 9780387227436

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Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment R is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations. In the second edition, the R code has been updated throughout to take advantage of new R packages and to illustrate better coding style. New sections have been added covering bootstrap methods, multinomial and multivariate normal distributions, the delta method, numerical methods for Bayesian inference, and nonlinear least squares. Also, the use of matrix algebra has been expanded, but remains optional, providing instructors with more options regarding the amount of linear algebra required.

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Genre: Mathematical statistics
Author: Randall Pruim
Publisher: American Mathematical Soc.
Release: 2018-04-04
File: 820 Pages
ISBN-13: 9781470428488

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In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors’ use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

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Genre: Mathematics
Author: Dennis Wackerly
Publisher: Nelson Education
Release: 2007-10-10
File: 944 Pages
ISBN-13: 9781111798789

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Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems. There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said π? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc. Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/~eugened. This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.

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Genre: Mathematics
Author: Eugene Demidenko
Publisher: John Wiley & Sons
Release: 2019-11-12
File: 880 Pages
ISBN-13: 9781118387986

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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods

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Genre: Mathematics
Author: Kandethody M. Ramachandran
Publisher: Elsevier
Release: 2014-09-14
File: 826 Pages
ISBN-13: 9780124171329

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This updated Second Edition of Research Methods, Statistics, and Applications consistently integrates methods and statistics to prepare students for both graduate work and critical analysis of research as professionals and informed citizens. Maintaining the conversational writing style, multiple examples, and hands-on applications of key concepts that made the first edition so accessible, Kathrynn A. Adams and Eva K. Lawrence enhance the new edition with additional coverage of online data collection, inferential statistics, and regression and ANOVA, as well as a wide range of diverse examples. In every chapter, the authors develop and apply research topics and examples from the current research literature across all aspects of the research process. New to this Edition New diverse examples from current research literature in criminal justice, politics, education, and counseling expose students to different research designs in the social sciences and demonstrate commonalities. New chapter-ending The Big Picture sections with appropriate charts and tables encourage students to consider decisions about specific statistical analyses. Two separate chapters (Inferential Statistics and Comparing Your Sample to a Known or Expected Score) now allow instructors to focus on the theoretical concepts associated with inferential statistics before introducing each specific inferential statistic to enhance student understanding. Expanded coverage of inferential statistics includes more discussion of APA guidelines for appropriate statistics and more focus on effect sizes and confidence intervals. New consistent headings make it easy for students to quickly locate information and for instructors to identify sections they may wish to focus on, skip, or present in a different order.

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Genre: Social Science
Author: Kathrynn A. Adams
Publisher: SAGE Publications
Release: 2018-02-02
File: 672 Pages
ISBN-13: 9781506350479

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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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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Genre: Mathematics
Author: Gareth James
Publisher: Springer Science & Business Media
Release: 2013-06-24
File: 426 Pages
ISBN-13: 9781461471387

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Statistics and Probability for Engineering Applications provides a complete discussion of all the major topics typically covered in a college engineering statistics course. This textbook minimizes the derivations and mathematical theory, focusing instead on the information and techniques most needed and used in engineering applications. It is filled with practical techniques directly applicable on the job. Written by an experienced industry engineer and statistics professor, this book makes learning statistical methods easier for today's student. This book can be read sequentially like a normal textbook, but it is designed to be used as a handbook, pointing the reader to the topics and sections pertinent to a particular type of statistical problem. Each new concept is clearly and briefly described, whenever possible by relating it to previous topics. Then the student is given carefully chosen examples to deepen understanding of the basic ideas and how they are applied in engineering. The examples and case studies are taken from real-world engineering problems and use real data. A number of practice problems are provided for each section, with answers in the back for selected problems. This book will appeal to engineers in the entire engineering spectrum (electronics/electrical, mechanical, chemical, and civil engineering); engineering students and students taking computer science/computer engineering graduate courses; scientists needing to use applied statistical methods; and engineering technicians and technologists. * Filled with practical techniques directly applicable on the job * Contains hundreds of solved problems and case studies, using real data sets * Avoids unnecessary theory

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Genre: Mathematics
Author: William DeCoursey
Publisher: Elsevier
Release: 2003-05-14
File: 396 Pages
ISBN-13: 9780080489759

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