An Introduction to Elementary Applied Statistics
Author: Peter Edward Dress
Publisher:
Published: 1961
Total Pages: 356
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Peter Edward Dress
Publisher:
Published: 1961
Total Pages: 356
ISBN-13:
DOWNLOAD EBOOKAuthor: Marvin Lentner
Publisher:
Published: 1972
Total Pages: 428
ISBN-13: 9780800500221
DOWNLOAD EBOOKAuthor: Gareth James
Publisher: Springer Nature
Published: 2023-08-01
Total Pages: 617
ISBN-13: 3031387473
DOWNLOAD EBOOKAn 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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: David D. Hanagal
Publisher: Alpha Science International, Limited
Published: 2009
Total Pages: 0
ISBN-13: 9781842655313
DOWNLOAD EBOOKIntroduction to Applied Statistics: A Non-Calculus Based Approach expresses our intent to introduce readers to statistics as it is used in practice. Introduction to Applied statistics is concerned with gaining understanding from data; it focuses on problem solving rather than on methods that may be useful in specific settings. A text cannot fully imitate practice, because it must teach specific methods in a logical order and must use data that are not the reader s own. This book makes non-statisticians, under-graduates, graduates, scientists and researchers understand statistics easily. The proofs of theorem and lemmas are not that necessary at the elementary level but it is necessary to know when, where and why to use these tools instead of proving a theorem or lemma. This book is very brief and compact in size and covers only the most necessary topics useful in day to day life.
Author: William Henry Foddy
Publisher:
Published: 1988-01-01
Total Pages: 238
ISBN-13: 9780063121010
DOWNLOAD EBOOKAuthor: Sanjeev Kulkarni
Publisher: John Wiley & Sons
Published: 2011-06-09
Total Pages: 267
ISBN-13: 1118023463
DOWNLOAD EBOOKA thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
Author: Christopher Hay-Jahans
Publisher: CRC Press
Published: 2019-01-02
Total Pages: 419
ISBN-13: 0429827261
DOWNLOAD EBOOKThe R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.
Author: Peter Dalgaard
Publisher: Springer Science & Business Media
Published: 2008-06-27
Total Pages: 370
ISBN-13: 0387790543
DOWNLOAD EBOOKThis book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
Author: F.M. Dekking
Publisher: Springer Science & Business Media
Published: 2006-03-30
Total Pages: 485
ISBN-13: 1846281687
DOWNLOAD EBOOKSuitable 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
Author: Second Wind Productions LLC
Publisher:
Published: 2018-01-30
Total Pages: 277
ISBN-13: 9781524955144
DOWNLOAD EBOOK