An Introduction to Statistical Methods and Data Analysis

Filename: an-introduction-to-statistical-methods-and-data-analysis.pdf
ISBN: 9781305465527
Release Date: 2015-05-28
Number of pages: 1296
Author: R. Lyman Ott
Publisher: Cengage Learning

Download and read online An Introduction to Statistical Methods and Data Analysis in PDF and EPUB Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh 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 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.


An Introduction to Statistical Methods and Data Analysis

Filename: an-introduction-to-statistical-methods-and-data-analysis.pdf
ISBN: 053437123X
Release Date: 2001-06-01
Number of pages: 201
Author: Michael Longnecker
Publisher: Duxbury Press

Download and read online An Introduction to Statistical Methods and Data Analysis in PDF and EPUB Provides worked-out solutions to odd-numbered exercises.


An Introduction to Statistical Methods and Data Analysis

Filename: an-introduction-to-statistical-methods-and-data-analysis.pdf
ISBN: 9781467279130
Release Date: 2016-10-16
Number of pages: 100
Author: CTI Reviews
Publisher: Cram101 Textbook Reviews

Download and read online An Introduction to Statistical Methods and Data Analysis in PDF and EPUB Facts101 is your complete guide to An Introduction to Statistical Methods and Data Analysis. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.


An Introduction to Statistical Methods and Data Analysis

Filename: an-introduction-to-statistical-methods-and-data-analysis.pdf
ISBN: 1428814019
Release Date: 2006-10
Number of pages: 144
Author: 5th Edition Ott and Longnecker
Publisher: Academic Internet Pub Incorporated

Download and read online An Introduction to Statistical Methods and Data Analysis in PDF and EPUB Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780534251222 .






Statistical Methods for Data Analysis in Particle Physics

Filename: statistical-methods-for-data-analysis-in-particle-physics.pdf
ISBN: 9783319201764
Release Date: 2015-07-24
Number of pages: 172
Author: Luca Lista
Publisher: Springer

Download and read online Statistical Methods for Data Analysis in Particle Physics in PDF and EPUB This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP). First, an introduction to probability theory and basic statistics is given, mainly as reminder from advanced undergraduate studies, yet also in view to clearly distinguish the Frequentist versus Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on upper limits as many applications in HEP concern hypothesis testing, where often the main goal is to provide better and better limits so as to be able to distinguish eventually between competing hypotheses or to rule out some of them altogether. Many worked examples will help newcomers to the field and graduate students to understand the pitfalls in applying theoretical concepts to actual data.


Introduction to Statistics and Data Analysis

Filename: introduction-to-statistics-and-data-analysis.pdf
ISBN: 9783319461625
Release Date: 2017-01-26
Number of pages: 456
Author: Christian Heumann
Publisher: Springer

Download and read online Introduction to Statistics and Data Analysis in PDF and EPUB 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. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. 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 all methods to their own applications.


An Introduction to Statistical Learning

Filename: an-introduction-to-statistical-learning.pdf
ISBN: 9781461471387
Release Date: 2013-06-24
Number of pages: 426
Author: Gareth James
Publisher: Springer Science & Business Media

Download and read online An Introduction to Statistical Learning in PDF and EPUB 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.


Statistical Methods for Survival Data Analysis

Filename: statistical-methods-for-survival-data-analysis.pdf
ISBN: 9781118593059
Release Date: 2013-09-23
Number of pages: 512
Author: Elisa T. Lee
Publisher: John Wiley & Sons

Download and read online Statistical Methods for Survival Data Analysis in PDF and EPUB Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences. Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes: Marginal and random effect models for analyzing correlated censored or uncensored data Multiple types of two-sample and K-sample comparison analysis Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of the presented material Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.


An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

Filename: an-introduction-to-data-analysis-and-uncertainty-quantification-for-inverse-problems.pdf
ISBN: 9781611974911
Release Date: 2017-07-06
Number of pages: 269
Author: Luis Tenorio
Publisher: SIAM

Download and read online An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems in PDF and EPUB Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics. This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications. An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems÷includes many examples that explain techniques which are useful to address general problems arising in uncertainty quantification, Bayesian and non-Bayesian statistical methods and discussions of their complementary roles, and analysis of a real data set to illustrate the methodology covered throughout the book.


Statistical Methods for Categorical Data Analysis

Filename: statistical-methods-for-categorical-data-analysis.pdf
ISBN: 1781906599
Release Date: 2008-11-13
Number of pages: 296
Author: Daniel Powers
Publisher: Emerald Group Publishing

Download and read online Statistical Methods for Categorical Data Analysis in PDF and EPUB This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/


Statistical Methods for Spatial Data Analysis

Filename: statistical-methods-for-spatial-data-analysis.pdf
ISBN: 9780203491980
Release Date: 2004-12-20
Number of pages: 512
Author: Oliver Schabenberger
Publisher: CRC Press

Download and read online Statistical Methods for Spatial Data Analysis in PDF and EPUB Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.