Focused more on concepts than on statistical methods, it shows readers how to properly use statistics to analyze data and demonstrates how computer software is an integral part of this analysis. "Using Statistics" scenarios discuss how statistics is used in a real business setting. Includes contemporary business applications, many with real data sets, and an integrated case that runs throughout chapters. "PHSTAT," a custom designed Excel add-in, is packaged with each book. Introduction and Data Collection. Presenting Data in Tables and Charts. Summarizing and Describing Numerical Data. Basic Probability and Probability Distributions. Sampling Distributions and Confidence Interval Estimation. Fundamentals of Hypothesis Testing: One-Sample Tests. Two-Sample and C-Sample Tests with Numerical Data. Hypothesis Testing with Categorical Data. Statistical Applications in Quality and Productivity Management. The Simple Linear Regression Model and Correlations. Introduction to Multiple Regression. Time Series Analysis. An accessible introduction or refresher on statistics for those in accounting, marketing, management, economics, and finance.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    Several of the data display strategies from previous editions are now presented in re-envisioned and reorganized formats to enhance reader accessibility and comprehension. The Third Edition's presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Miles and Huberman′s original research studies are profiled and accompanied with new examples from Salda�a′s recent qualitative work. The book′s most celebrated chapter, Drawing and Verifying Conclusions, is retained and revised, and the chapter on report writing has been greatly expanded, and is now called Writing About Qualitative Research. Comprehensive and authoritative, Qualitative Data Analysis has been elegantly revised for a new generation of qualitative researchers.

    Robert Kaplan and Dennis Saccuzzo provide students with a current analysis of the most widely used psychological tests in schools, professional training programs, business, industry, the military, and clinical settings. The authors offer a clear picture of how psychological tests are constructed, how they are used, and how an understanding of them can make a difference in their careers and everyday lives. Comprehensive and accurate, yet interesting and personally relevant, this book gets and keeps students' attention through the use of informal discussions and real-life examples.

    Blending both the practical and theoretical, the concrete and abstract, this book is both enjoyable to read and thoroughly researched. By clearly explaining different theories and the significance of cultural patterns and having readers practice what they learn via examples in the book, Intercultural Competence better prepares readers to interact in intercultural relationships. The book also provides a discussion of important ethical and social issues relating to intercultural communication. The authors cover U.S. cultures as well as global cultural issues.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Offers students with the tools necessary to do ethical research in psychology and to understand the research they learn about in psychology courses and in the media.

    This text aims to motivate the need for tools with concrete applications, providing simple assumptions that match the application.

    This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you:Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theoryData management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

    Updated web links and expanded appendices provide cutting edge information on action research along with new case studies and examples.

    In a field as rapidly expanding and diverse as I/O psychology, it's not easy finding a book with just the right balance. Fortunately, Paul Spector's Fourth Edition of Industrial and Organizational Psychology: Research and Practice is up to the job. A balance between cutting-edge topics and core material This Fourth Edition covers both the core material as well as newly emerging topics and cutting-edge findings. Inside, you'll find 125 new references, as well as the latest thinking on team job analysis, drug testing, group problem solving, group diversity, cross-cultural issues in leadership, and more. A balance between research and practice The text's balanced presentation of research and practice equips you with an understanding of the whole field. This balanced approach helps you see the relationship between the science and application of I/O psychology. You'll find detailed summaries of current research, including international studies. In addition, cases introduce you to the wide array of settings and applied work that involve I/O psychologists. A balance between employee performance and well-being I/O psychology isn't just about how the organization can use psychology to improve the efficiency and effectiveness of its workforce; it's also about improving the health of the workforce. This Fourth Edition provides more balance between these two over-arching areas of the field. Connect to online resources from the author's classroom. The author's website features class notes from his I/O psychology course, practice exams, internet exercises, I/O career information, and links to I/O instructor and student resources. Go to www.wiley.com/college/spector for help presenting and studying the material in your textbook, and use these resources to go beyond the text.

     Douglas Montgomery arms readers with the most effective approach for learning how to design, conduct, and analyze experiments that optimize performance in products and processes. He shows how to use statistically designed experiments to obtain information for characterization and optimization of systems, improve manufacturing processes, and design and develop new processes and products. You will also learn how to evaluate material alternatives in product design, improve the field performance, reliability, and manufacturing aspects of products, and conduct experiments effectively and efficiently. Discover how to improve the quality and efficiency of working systems with this highly-acclaimed book. This 6th Edition: Places a strong focus on the use of the computer, providing output from two software products: Minitab and DesignExpert. Presents timely, new examples as well as expanded coverage on adding runs to a fractional factorial to de-alias effects. Includes detailed discussions on how computers are currently used in the analysis and design of experiments. Offers new material on a number of important topics, including follow-up experimentation and split-plot design. Focuses even more sharply on factorial and fractional factorial design.

    I've examined every one and Cipani's is clearly the best. The assessment part is great, and I particularly like the protocol format for interventions. Cipani's text nicely fills a big gap between research and application. Were I teaching a seminar to clinicians, I think the text would be perfect. -- Brian A. Iwata, PhD, University of FloridaProfessionals who work in mental health and educational settings are frequently faced with clients (children, adolescents, adults) who engage in serious problem behaviors. Such behaviors often impact the client's welfare and ability to live, work, and be educated in mainstream environments. Children and adolescents who manifest these behaviors are particularly vulnerable to these disruptions, which can have a far-reaching impact on their development and future prospects.This practical book, written both for clinician/educators and high-level students, creates a function-based behavioral diagnostic classification system, the first of its kind, as well as treatment protocols that fit such a diagnostic system. Heavily practitioner-oriented, the book will address the full range of behaviors - ranging from aggression, self-injury, stereotypic behavior (repetitive body movements), tantrums, and non-compliance - with real life and hypothetical cases to help clinicians think through the full range of treatment options. Unique in moving beyond functional assessment to assessment diagnosis and treatment, this book will be highly useful for mental health clinicians, students of Advanced Behavior Analysis, and special education practitioners among others.Professor Cipani has also prepared extensive ancillary material for use in teaching this book and will make it available to anyone who has adopted it for course use. Instructors who have adopted the title may inquire of Professor Cipani at ennioc26@hotmail.com