Introduction to Statistics Through Resampling Methods and Microsoft Office Excel
Introduction to Statistics Through Resampling Methods and Microsoft Office Excel

Author: Phillip I. Good

Publisher: John Wiley & Sons

Published: 2005-07-22

Total Pages: 245

ISBN-13: 0471741760

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Learn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers quickly master and learn to apply statistical methods, such as bootstrap, decision trees, t-test, and permutations to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: * Tests and estimation procedures for one, two, and multiple samples * Model building * Multivariate analysis * Complex experimental design Throughout the text, Microsoft Office Excel(r) is used to illustrate new concepts and assist readers in completing exercises. An Excel Primer is included as an Appendix for readers who need to learn or brush up on their Excel skills. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided: * More than 100 exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills * Companion FTP site provides access to all data sets discussed in the text * An Instructor's Manual is available upon request from the publisher * Dozens of thought-provoking questions in the final chapter assist readers in applying statistics to solve real-life problems * Helpful appendices include an index to Excel and Excel add-in functions This text serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited to both students and practitioners.

Data Mining for Business Analytics
Data Mining for Business Analytics

Author: Galit Shmueli

Publisher: John Wiley & Sons

Published: 2019-10-14

Total Pages: 608

ISBN-13: 111954985X

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Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Intermediate Microeconomics with Microsoft Excel
Intermediate Microeconomics with Microsoft Excel

Author: Humberto Barreto

Publisher: Cambridge University Press

Published: 2009-06-15

Total Pages: 593

ISBN-13: 0521899028

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This unique text uses Microsoft Excel® workbooks to instruct students. In addition to explaining fundamental concepts in microeconomic theory, readers acquire a great deal of sophisticated Excel skills and gain the practical mathematics needed to succeed in advanced courses. In addition to the innovative pedagogical approach, the book features explicitly repeated use of a single central methodology, the economic approach. Students learn how economists think and how to think like an economist. With concrete, numerical examples and novel, engaging applications, interest for readers remains high as live graphs and data respond to manipulation by the user. Finally, clear writing and active learning are features sure to appeal to modern practitioners and their students. The website accompanying the text is found at www.depauw.edu/learn/microexcel.

Introductory Econometrics
Introductory Econometrics

Author: Humberto Barreto

Publisher: Cambridge University Press

Published: 2006

Total Pages: 810

ISBN-13: 9780521843195

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This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.

Microsoft Excel 2010
Microsoft Excel 2010

Author: Wayne L. Winston

Publisher:

Published: 2011

Total Pages: 700

ISBN-13: 9780735643369

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An award-winning business professor and corporate consultant shares the best of his real-world experience in this practical, scenario-focused guide--fully updated for Excel 2010.

Monte Carlo and Quasi-Monte Carlo Sampling
Monte Carlo and Quasi-Monte Carlo Sampling

Author: Christiane Lemieux

Publisher: Springer Science & Business Media

Published: 2009-04-03

Total Pages: 373

ISBN-13: 038778165X

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Quasi–Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the last two decades. Their successful implementation on practical problems, especially in finance, has motivated the development of several new research areas within this field to which practitioners and researchers from various disciplines currently contribute. This book presents essential tools for using quasi–Monte Carlo sampling in practice. The first part of the book focuses on issues related to Monte Carlo methods—uniform and non-uniform random number generation, variance reduction techniques—but the material is presented to prepare the readers for the next step, which is to replace the random sampling inherent to Monte Carlo by quasi–random sampling. The second part of the book deals with this next step. Several aspects of quasi-Monte Carlo methods are covered, including constructions, randomizations, the use of ANOVA decompositions, and the concept of effective dimension. The third part of the book is devoted to applications in finance and more advanced statistical tools like Markov chain Monte Carlo and sequential Monte Carlo, with a discussion of their quasi–Monte Carlo counterpart. The prerequisites for reading this book are a basic knowledge of statistics and enough mathematical maturity to follow through the various techniques used throughout the book. This text is aimed at graduate students in statistics, management science, operations research, engineering, and applied mathematics. It should also be useful to practitioners who want to learn more about Monte Carlo and quasi–Monte Carlo methods and researchers interested in an up-to-date guide to these methods.

Meta-Analysis with R
Meta-Analysis with R

Author: Guido Schwarzer

Publisher: Springer

Published: 2015-10-08

Total Pages: 256

ISBN-13: 3319214160

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This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.

Learning Landscape Ecology
Learning Landscape Ecology

Author: Sarah E. Gergel

Publisher: Springer Science & Business Media

Published: 2006-04-18

Total Pages: 323

ISBN-13: 0387216138

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Filled with numerous exercises this practical guide provides a real hands-on approach to learning the essential concepts and techniques of landscape ecology. The knowledge gained enables students to usefully address landscape- level ecological and management issues. A variety of approaches are presented, including: group discussion, thought problems, written exercises, and modelling. Each exercise is categorised as to whether it is for individual, small group, or whole class study.

R in Action
R in Action

Author: Robert Kabacoff

Publisher: Manning Publications

Published: 2015-03-03

Total Pages: 475

ISBN-13: 9781617291388

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R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is a language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. Purchase of the print book comes with an offer of a free PDF eBook from Manning. Also available is all code from the book.