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Author : Lyman Ott
Publisher : Brooks/Cole
Page : 752 pages
File Size : 53,7 Mb
Release : 1977
Category : Mathematics
ISBN : UOM:39015046750926
Author : R. Lyman Ott,Micheal T. Longnecker
Publisher : Cengage Learning
Page : 1303 pages
File Size : 47,7 Mb
Release : 2008-12-30
Category : Mathematics
ISBN : 9780495017585
Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author : Lyman Ott,Michael Longnecker
Publisher : Cengage Learning
Page : 1296 pages
File Size : 52,9 Mb
Release : 2010
Category : Mathematical statistics
ISBN : 0495109142
Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, 6th Edition, International Edition provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.
Author : Luca Lista
Publisher : Springer
Page : 257 pages
File Size : 51,5 Mb
Release : 2017-10-13
Category : Science
ISBN : 9783319628400
This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation).
Author : Christian Heumann,Michael Schomaker,Shalabh
Publisher : Springer Nature
Page : 584 pages
File Size : 52,5 Mb
Release : 2023-01-30
Category : Mathematics
ISBN : 9783031118333
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor
Publisher : Springer Nature
Page : 617 pages
File Size : 45,8 Mb
Release : 2023-08-01
Category : Mathematics
ISBN : 9783031387470
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, 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 : Melinda C. Mills,Nicola Barban,Felix C. Tropf
Publisher : MIT Press
Page : 433 pages
File Size : 51,8 Mb
Release : 2020-02-18
Category : Science
ISBN : 9780262357449
A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.
Author : Rudolf J. Freund,William J. Wilson
Publisher : Elsevier
Page : 694 pages
File Size : 55,5 Mb
Release : 2003-01-07
Category : Mathematics
ISBN : 9780080498225
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
Author : Lisa Daniels,Nicholas Minot
Publisher : SAGE Publications
Page : 513 pages
File Size : 48,5 Mb
Release : 2019-01-11
Category : Social Science
ISBN : 9781506371825
An Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.
Author : Carlos Andre Reis Pinheiro,Mike Patetta
Publisher : SAS Institute
Page : 169 pages
File Size : 45,8 Mb
Release : 2021-08-06
Category : Computers
ISBN : 9781953329622
Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
Author : Cheryl Ann Willard
Publisher : Routledge
Page : 350 pages
File Size : 46,7 Mb
Release : 2020-02-27
Category : Psychology
ISBN : 9780429523151
Statistical Methods: An Introduction to Basic Statistical Concepts and Analysis, Second Edition is a textbook designed for students with no prior training in statistics. It provides a solid background of the core statistical concepts taught in most introductory statistics textbooks. Mathematical proofs are deemphasized in favor of careful explanations of statistical constructs. The text begins with coverage of descriptive statistics such as measures of central tendency and variability, then moves on to inferential statistics. Transitional chapters on z-scores, probability, and sampling distributions pave the way to understanding the logic of hypothesis testing and the inferential tests that follow. Hypothesis testing is taught through a four-step process. These same four steps are used throughout the text for the other statistical tests presented including t tests, one- and two-way ANOVAs, chi-square, and correlation. A chapter on nonparametric tests is also provided as an alternative when the requirements cannot be met for parametric tests. Because the same logical framework and sequential steps are used throughout the text, a consistency is provided that allows students to gradually master the concepts. Their learning is enhanced further with the inclusion of "thought questions" and practice problems integrated throughout the chapters. New to the second edition: Chapters on factorial analysis of variance and non-parametric techniques for all data Additional and updated chapter exercises for students to test and demonstrate their learning Full instructor resources: test bank questions, Powerpoint slides, and an Instructor Manual
Author : John K. Taylor,Cheryl Cihon
Publisher : CRC Press
Page : 296 pages
File Size : 44,9 Mb
Release : 2004-01-14
Category : Mathematics
ISBN : 9781135439545
Since the first edition of this book appeared, computers have come to the aid of modern experimenters and data analysts, bringing with them data analysis techniques that were once beyond the calculational reach of even professional statisticians. Today, scientists in every field have access to the techniques and technology they need to analyze stat
Author : Brian J. Fogarty
Publisher : SAGE
Page : 462 pages
File Size : 44,9 Mb
Release : 2018-11-24
Category : Social Science
ISBN : 9781526459725
"One of the few books that provide an accessible introduction to quantitative data analysis with R. A particular strength of the text is the focus on ′real world′ examples which help students to understand why they are learning these methods." - Dr Roxanne Connelly, University of York Relevant, engaging, and packed with student-focused learning features, this book provides the step-by-step introduction to quantitative research and data every student needs. Gradually introducing applied statistics and R, it uses examples from across the social sciences to show you how to apply abstract statistical and methodological principles to your own work. At a student-friendly pace, it enables you to: - Understand and use quantitative data to answer questions - Approach surrounding ethical issues - Collect quantitative data - Manage, write about, and share the data effectively Supported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives you not only the tools you need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what you have learned.
Author : R. Lyman Ott,Micheal T. Longnecker
Publisher : Unknown
Page : 256 pages
File Size : 40,8 Mb
Release : 2015-07-20
Category : Foreign Language Study
ISBN : 1305269489
Contains fully worked-out solutions to all of the odd-numbered exercises in the text, giving students a way to check their answers and ensure that they took the correct steps to arrive at an answer.
Author : Lyman Ott,William Mendenhall,Richard F. Larson
Publisher : North Scituate, Mass. : Duxbury Press
Page : 550 pages
File Size : 42,8 Mb
Release : 1978
Category : Mathematics
ISBN : MINN:31951D003897530