Introduction To Statistics Resampling Methods And Microsoft Office Excelr
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| Author | : Phillip I. Good |
| Publisher | : John Wiley & Sons |
| Total Pages | : 245 |
| Release | : 2005-07-22 |
| Genre | : Mathematics |
| ISBN | : 0471741760 |
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.
| Author | : Phillip I. Good |
| Publisher | : |
| Total Pages | : 231 |
| Release | : 2005 |
| Genre | : |
| ISBN | : |
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; and 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 learn
| Author | : S. N. Lahiri |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 400 |
| Release | : 2003-08-07 |
| Genre | : Mathematics |
| ISBN | : 9780387009285 |
"The book can be used as a graduate-level text for a special topics course on resampling methods for dependent data and also as a research monograph for statisticians and econometricians who want to learn more about the topic and want to apply the methods in their own research."--BOOK JACKET.
| Author | : Phillip I. Good |
| Publisher | : John Wiley & Sons |
| Total Pages | : 0 |
| Release | : 2011-05-03 |
| Genre | : Mathematics |
| ISBN | : 0470927143 |
This book grew out of an online interactive offered through statcourse.com, and it soon became apparent to the author that the course was too limited in terms of time and length in light of the broad backgrounds of the enrolled students. The statisticians who took the course needed to be brought up to speed both on the biological context as well as on the specialized statistical methods needed to handle large arrays. Biologists and physicians, even though fully knowledgeable concerning the procedures used to generate microaarrays, EEGs, or MRIs, needed a full introduction to the resampling methods—the bootstrap, decision trees, and permutation tests, before the specialized methods applicable to large arrays could be introduced. As the intended audience for this book consists both of statisticians and of medical and biological research workers as well as all those research workers who make use of satellite imagery including agronomists and meteorologists, the book provides a step-by-step approach to not only the specialized methods needed to analyze the data from microarrays and images, but also to the resampling methods, step-down multi-comparison procedures, multivariate analysis, as well as data collection and pre-processing. While many alternate techniques for analysis have been introduced in the past decade, the author has selected only those techniques for which software is available along with a list of the available links from which the software may be purchased or downloaded without charge. Topical coverage includes: very large arrays; permutation tests; applying permutation tests; gathering and preparing data for analysis; multiple tests; bootstrap; applying the bootstrap; classification methods; decision trees; and applying decision trees.
| Author | : Phillip I. Good |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 296 |
| Release | : 1999 |
| Genre | : Mathematics |
| ISBN | : |
This new book is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and researcher professionals in science, engineering, and technology. Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. Students, professionals, and researchers will find it a particularly useful guide to modern resampling methods and their applications.
| Author | : Galit Shmueli |
| Publisher | : John Wiley & Sons |
| Total Pages | : 608 |
| Release | : 2019-10-14 |
| Genre | : Mathematics |
| ISBN | : 111954985X |
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
| Author | : Humberto Barreto |
| Publisher | : Cambridge University Press |
| Total Pages | : 593 |
| Release | : 2009-06-15 |
| Genre | : Business & Economics |
| ISBN | : 0521899028 |
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.
| Author | : Humberto Barreto |
| Publisher | : Cambridge University Press |
| Total Pages | : 810 |
| Release | : 2006 |
| Genre | : Business & Economics |
| ISBN | : 9780521843195 |
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.
| Author | : Wayne L. Winston |
| Publisher | : |
| Total Pages | : 700 |
| Release | : 2011 |
| Genre | : Computers |
| ISBN | : 9780735643369 |
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.
| Author | : Guido Schwarzer |
| Publisher | : Springer |
| Total Pages | : 256 |
| Release | : 2015-10-08 |
| Genre | : Medical |
| ISBN | : 3319214160 |
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.
