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.

Filename: an-introduction-to-statistical-methods-and-data-analysis.pdf
ISBN: 0534251226
Release Date: 2001
Number of pages: 1152
Author: Lyman Ott
Publisher: Duxbury Press

Download and read online An Introduction to Statistical Methods and Data Analysis in PDF and EPUB Statistics is a thought process. In this comprehensive introduction to statistical methods and data analysis, the process is presented utilizing a four-step approach: 1) gathering data, 2) summarizing data, 3) analyzing data, and 4) communicating the results of data analyses.

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.

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 .

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.

Filename: an-introduction-to-statistical-methods-and-data-analysis-north-scituate-duxbury-press.pdf
ISBN: OCLC:490573971
Release Date: 1977
Number of pages: 730
Author: Lyman Ott
Publisher:

Download and read online An Introduction to Statistical Methods and Data Analysis North Scituate Duxbury Press in PDF and EPUB

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.

Filename: an-introduction-to-statistical-inference-and-its-applications-with-r.pdf
ISBN: 1584889489
Release Date: 2009-06-23
Number of pages: 496
Author: Michael W. Trosset
Publisher: CRC Press

Download and read online An Introduction to Statistical Inference and Its Applications with R in PDF and EPUB Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.

Filename: introduction-to-statistical-methods-in-modern-genetics.pdf
ISBN: 9781482287394
Release Date: 2000-02-23
Number of pages: 247
Author: M.C. Yang
Publisher: CRC Press

Download and read online Introduction to Statistical Methods in Modern Genetics in PDF and EPUB Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. Introduction to Statistical Methods in Modern Genetics distinguishes between the necessary and unnecessary complexity in a presentation designed for graduate-level statistics students. The author keeps derivations simple, but does so without losing the mathematical details. He also provides the required background in modern genetics for those looking forward to entering this arena. Along with some of the statistical tools important in genetics applications, students will learn: How a gene is found How scientists have separated the genetic and environmental aspects of a person's intelligence How genetics are used in agriculture to improve crops and domestic animals What a DNA fingerprint is and why there are controversies about it Although the author assumes students have a foundation in basic statistics, an appendix provides the necessary background beyond the elementary, including multinomial distributions, inference on frequency tables, and discriminant analysis. With clear explanations, a multitude of figures, and exercise sets in each chapter, this text forms an outstanding entrée into the rapidly expanding world of genetic data analysis.

Filename: introduction-to-statistics-and-data-analysis.pdf
ISBN: 9783319461625
Release Date: 2017-02-07
Number of pages:
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.

Filename: machine-learning-and-data-science.pdf
ISBN: 9781634620987
Release Date: 2015-11-01
Number of pages: 282
Author: Daniel D. Gutierrez
Publisher: Technics Publications

Download and read online Machine Learning and Data Science in PDF and EPUB A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field – recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

Filename: introduction-to-statistical-methods-for-clinical-trials.pdf
ISBN: 9781584880271
Release Date: 2007-11-19
Number of pages: 464
Author: Thomas D. Cook
Publisher: CRC Press

Download and read online Introduction to Statistical Methods for Clinical Trials in PDF and EPUB Clinical trials have become essential research tools for evaluating the benefits and risks of new interventions for the treatment and prevention of diseases, from cardiovascular disease to cancer to AIDS. Based on the authors’ collective experiences in this field, Introduction to Statistical Methods for Clinical Trials presents various statistical topics relevant to the design, monitoring, and analysis of a clinical trial. After reviewing the history, ethics, protocol, and regulatory issues of clinical trials, the book provides guidelines for formulating primary and secondary questions and translating clinical questions into statistical ones. It examines designs used in clinical trials, presents methods for determining sample size, and introduces constrained randomization procedures. The authors also discuss how various types of data must be collected to answer key questions in a trial. In addition, they explore common analysis methods, describe statistical methods that determine what an emerging trend represents, and present issues that arise in the analysis of data. The book concludes with suggestions for reporting trial results that are consistent with universal guidelines recommended by medical journals. Developed from a course taught at the University of Wisconsin for the past 25 years, this textbook provides a solid understanding of the statistical approaches used in the design, conduct, and analysis of clinical trials.