This book presents statistical concepts and techniques in simple, everyday language to help readers gain a better understanding of how they work and how to interpret them correctly. Each self-contained chapter features a description of the statistic including how it is used and the information it provides, how to calculate the formula, the strengths and weaknesses of each technique, the conditions needed for its use, and an example that uses and interprets the statistic. A glossary of terms and symbols is also included along with an Interactive CD with PowerPoint presentations and problems and solutions for each chapter. This brief paperback is an ideal supplement for statistics, research methods, or any course that uses statistics, or as a handy reference tool to refresh one's memory about key concepts. The actual research examples are from a variety of fields, including psychology and education.

Statistics in Plain English is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. Each chapter begins with a brief overview of a statistic (or set of statistics) that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. Chapters also include an example of the statistic (or statistics) used in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems. Thoroughly updated throughout, this edition features several key additions and changes. First, a new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added, providing an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression). Next, the chapter on non-parametric statistics has been enhanced with in-depth descriptions of Mann-Whitney U, Kruskal-Wallis, and Wilcoxon Signed-Rank analyses, in addition to the detailed discussion of the Chi-square statistic found in the previous edition. These nonparametric statistics are widely used when dealing with nonnormally distributed data. This edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression, as well as more coverage of the normal distribution in statistics. Finally, the book features a multitude of real-world examples throughout to aid student understanding and provides them with a solid understanding of how several statistics techniques commonly used by researchers in the social sciences work. Statistics in Plain English is suitable for a wide range of readers, including students taking their first statistics course, professionals who want to refresh their statistical memory, and undergraduate or graduate students who need a concise companion to a more complicated text used in their class. The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.

This inexpensive paperback provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. New features in the third edition include: a new chapter on Factor and Reliability Analysis especially helpful to those who do and/or read survey research, new "Writing it Up" sections demonstrate how to write about and interpret statistics seen in books and journals, a website at http: //www.psypress.com/statistics-in-plain-english with PowerPoint presentations, interactive problems (including an overview of the problem's solution for Instructors) with an IBM SPSS dataset for practice, videos of the author demonstrating how to calculate and interpret most of the statistics in the book, links to useful websites, and an author blog, new section on understanding the distribution of data (ch. 1) to help readers understand how to use and interpret graphs, many more examples, tables, and charts to help students visualize key concepts. Statistics in Plain English, Third Edition is an ideal supplement for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.

Harvey Brightman's accessible, easy-to-understand new book focuses on helping readers learn essential statistical concepts and data analysis. In an intuitive and non-mathematical writing style, Brightman uses actual business applications and covers practical insights in business problem solving using Microsoft Excel as the primary computational tool. His clear, to-the-point presentation gives students a 'map' for learning what data analysis techniques to use and when to use them. Brightman presents descriptive and inferential methods in sequential chapters, and introduces probability only as needed and then only on a very limited basis.

Sherri Jackson's straightforward, conversational introduction to statistics presents just what its title promises -- a plain and simple overview of statistics that is clear, concise, and sparing in its use of jargon. Ideal for behavioral sciences majors, STATISTICS PLAIN AND SIMPLE, Fourth Edition, is designed to build students' confidence in understanding, calculating, and interpreting statistics. It instills a strong awareness of the interaction between statistical methods and research methods. It also helps students develop a solid working knowledge of basic statistical cautions in research design, a strong understanding of the concept of significance, and the critical thinking skills necessary to apply these ideas. A modular format presents the material in brief segments that make concepts manageable. Jackson shows why each statistical technique is necessary before explaining it, and skillfully uses narrative to connect one module to the next. 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 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.

This book is meant to be a supplement to a more detailed statistics textbook, such as that recommended for a statistics course in the social sciences. Also, as a reference book to refresh your memory about statistical concepts.