Introduction to Statistics and Data Analysis PDF Download

Author: Roxy Peck
Publisher:
ISBN: 9789814878135
Category : Mathematical statistics
Languages : en
Pages : 0

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Author: Lisa Daniels
Publisher: SAGE Publications
ISBN: 1506371825
Category : Social Science
Languages : en
Pages : 525

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Book Description
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: Roxy Peck
Publisher:
ISBN: 9781305649835
Category : Mathematics
Languages : en
Pages : 844

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Book Description
INTRODUCTION TO STATISTICS AND DATA ANALYSIS introduces you to the study of statistics and data analysis by using real data and attention-grabbing examples. The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps you grasp concepts and cement your comprehension. You'll also find coverage of most major technologies as a problem-solving tool, plus hands-on activities in each chapter that allow you to practice statistics firsthand.

Author: R. Lyman Ott
Publisher: Cengage Learning
ISBN: 9780495109150
Category : Probabilities
Languages : en
Pages : 0

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Book Description
Provides worked-out solutions to odd-numbered exercises.

Author: Howard M. Reid
Publisher: SAGE Publications
ISBN: 1483301575
Category : Social Science
Languages : en
Pages : 633

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Book Description
Using a truly accessible and reader-friendly approach, Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, by Howard M. Reid, redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks.

Author: Barbara Illowsky
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 2106

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Book Description
Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.

Author: Peter Dalgaard
Publisher: Springer Science & Business Media
ISBN: 0387790543
Category : Mathematics
Languages : en
Pages : 370

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Book Description
This 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: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category : Computers
Languages : en
Pages : 322

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Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author: Thomas Dietz
Publisher: John Wiley & Sons
ISBN: 1405169028
Category : Mathematics
Languages : en
Pages : 613

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Book Description
Introduction to Social Statistics is a basic statistics text with a focus on the use of models for thinking through statistical problems, an accessible and consistent structure with ongoing examples across chapters, and an emphasis on the tools most commonly used in contemporary research. Lively introductory textbook that uses three strategies to help students master statistics: use of models throughout; repetition with variation to underpin pedagogy; and emphasis on the tools most commonly used in contemporary research Demonstrates how more than one statistical method can be used to approach a research question Enhanced learning features include a ‘walk-through’ of statistical concepts, applications, features, advanced topics boxes, and a ‘What Have We Learned’ section at the end of each chapter Supported by a website containing instructor materials including chapter-by-chapter PowerPoint slides, answers to exercises, and an instructor guide Visit www.wiley.com/go/dietz for additional student and instructor resources.

Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617

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Book Description
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.