Author: Rand R. Wilcox
Publisher: Oxford University Press
Release Date: 2009-07-15
This introductory statistics textbook for non-statisticians covers basic principles, concepts, and methods routinely used in applied research. What sets this text apart is the incorporation of the many advances and insights from the last half century when explaining basic principles. These advances provide a foundation for vastly improving our ability to detect and describe differences among groups and associations among variables and provide a deeper and more accurate sense of when basic methods perform well and when they fail. Assuming no prior training, Wilcox introduces students to basic principles and concepts in a simple manner that makes these advances and insights, as well as standard ideas and methods, easy to understand and appreciate.
Facts101 is your complete guide to Basic Statistics, Understanding Conventional Methods and Modern Insights. In this book, you will learn topics such as Probability and Related Concepts, Sampling Distributions, Estimation, and Hypothesis Testing 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.
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: 9780195315103 .
Author: Rand R. Wilcox
Publisher: Springer Science & Business Media
Release Date: 2010-03-18
Genre: Social Science
Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques, even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming the reader has any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research. The second edition of this book includes a number of advances and insights that have occurred since the first edition appeared. Included are new results relevant to medians, regression, measures of association, strategies for comparing dependent groups, methods for dealing with heteroscedasticity, and measures of effect size.
Author: Rand R. Wilcox
Publisher: Gulf Professional Publishing
Release Date: 2003-01
Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible. * Assumes no previous training in statistics * Explains how and why modern statistical methods provide more accurate results than conventional methods * Covers the latest developments on multiple comparisons * Includes recent advances in risk-based methods * Features many illustrations and examples using data from real studies * Describes and illustrates easy-to-use s-plus functions for applying cutting-edge techniques * Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books
In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.
Using R for Statistics will get you the answers to most of the problems you are likely to encounter when using a variety of statistics. This book is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics.
Author: Jason W. Osborne
Release Date: 2012-01-10
Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.
Author: Ken Black
Publisher: John Wiley & Sons
Release Date: 2011-10-25
Genre: Business & Economics
Black's latest outstanding pedagogy of Business Statistics includes the use of extra problems called "Demonstration Problems" to provide additional insight and explanation to working problems, and presents concepts, topics, formulas, and application in a manner that is palatable to a vast audience and minimizes the use of "scary" formulas. Every chapter opens up with a vignette called a "Decision Dilemma" about real companies, data, and business issues. Solutions to these dilemmas are presented as a feature called "Decision Dilemma Solved." In this edition all cases and "Decision Dilemmas" are updated and revised and 1/3 have been replaced for currency. There is also a significant number of additional problems and an extremely competitive collection of databases (containing real data) on: international stock markets, consumer food, international labor, financial, energy, agribusiness, 12-year gasoline, manufacturing, and hospital.
Author: Tilman M. Davies
Publisher: No Starch Press
Release Date: 2016-12-16
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: *The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops *Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R *How to access R’s thousands of functions, libraries, and data sets *How to draw valid and useful conclusions from your data *How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Easy to understand and fun to read, Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. Learn simple data types, and basic math and text operations Use data-wrangling techniques with Python’s built-in data structures Explore Python code structure, including the use of functions Write large programs in Python, with modules and packages Dive into objects, classes, and other object-oriented features Examine storage from flat files to relational databases and NoSQL Use Python to build web clients, servers, APIs, and services Manage system tasks such as programs, processes, and threads Understand the basics of concurrency and network programming
Author: Pawel Cichosz
Publisher: John Wiley & Sons
Release Date: 2014-11-17
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Author: Rand R. Wilcox
Publisher: John Wiley & Sons
Release Date: 2016-06-07
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox’ Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.
Author: Charles Henry Brase
Publisher: Cengage Learning
Release Date: 2016-12-05
UNDERSTANDABLE STATISTICS: CONCEPTS AND METHODS, Twelfth Edition, is a thorough yet accessible program designed to help you overcome any apprehensions you may have about statistics and to master the subject. The authors provide clear guidance and informal advice while showing you the links between statistics and the world. To reinforce this approach—and make the material interesting as well as easier to understand—the book integrates real-life data from a variety of sources, including journals, periodicals, newspapers, and the Internet. You'll also have opportunities to develop your critical-thinking and statistical literacy skills through special features and exercises throughout the text. The use of graphing calculators, Excel, Minitab, Minitab ExpressTM, and SPSS is covered, although not required. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.