Figures of Chance I: Chance in Literature and the Arts (16th–21st Centuries) proposes a transhistorical analysis that will serve as a reference work on the evolution of literary and artistic representations of chance and contingency. Alongside its multidisciplinary companion volume (Figures of Chance II), it considers how the projective and predictive capacity of societies is shaped by representations and cultural models of a reality that is understood, to varying degrees, to be contingent, unpredictable, or chaotic. Giving special emphasis to the French context while also developing broad cross-cultural comparisons, this volume examines the dialogue between evolving conceptions and changing representations of chance, from Renaissance figures of Fortune to the data-driven world of the present. Written by recognized specialists of each of the periods studied, it identifies and historicizes the main fictional and factual modes of portraying, narrating, and comprehending chance in the West.
Figures of Chance II: Chance in Theory and Practice proposes a multidisciplinary analysis of cultural phenomena related to notions of chance and contingency. Alongside its transhistorical companion volume (Figures of Chance I), it considers how the projective and predictive capacity of societies is shaped by representations and cultural models of a reality that is understood, by varying degrees, to be contingent, unpredictable, or chaotic. This volume reevaluates the role played by figurative representations of chance in contemporary discourses about chance and contingency. Written by seven interdisciplinary teams, and encompassing philosophy, literature, history of science, sociology, mathematics, cognitive science, information science, and art history, this text puts scientific conceptions of chance into dialogue with their contemporary literary and artistic representations. It thus brings out the central role played by art in the human perception of chance, and in our methods for projecting the future, in order to better understand contemporary human attitudes in the face of risk.
A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel® functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel® add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance.
The Gambling Addiction Client Workbook is an evidence-based program that uses treatments including motivational enhancement, cognitive-behavioral therapy, skills training, medication, and 12-step facilitation. This workbook walks clients through self-reflective activities and exercises meant to help them recognize the underlying motivations and causes of their gambling addiction and to learn the tools necessary for recovery. The Third Edition of this workbook includes coverage of all 12 steps of recovery. Chapters focused on honesty and relapse prevention as well as a personal recovery plan contribute to client success. About the Author Robert R. Perkinson is the clinical director of Keystone Treatment Center in Canton, South Dakota. He is a licensed psychologist; licensed marriage & family therapist; internationally certified alcohol and drug counselor; and a nationally certified gambling counselor and supervisor. In addition to the best-selling The Gambling Addiction Client Workbook, Third Edition, Dr. Perkinson is the author of The Alcoholism and Drug Abuse Client Workbook, Third Edition and Chemical Dependency Counseling: A Practical Guide, Fifth Edition.
Provides a Solid Foundation for Statistical Modeling and Inference and Demonstrates Its Breadth of Applicability Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists addresses core issues in post-calculus probability and statistics in a way that is useful for statistics and mathematics majors as well
