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.

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.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace.

Applying statistical results to real life situations can be difficult or futile if you can't be certain what the results actually mean. This reference guide provides readers with the frequently elusive link between statistical results and practical applications. Students will learn the basic concepts and principles of statistics and probability, without getting bogged down in complicated theories and abstractions. Many statistics texts rely too heavily on mathematical formulas. Kault restores the emphasis to understanding statistical results and using common sense in decision making. Everyday examples bring the concepts to life. In the entry on hypothesis testing, Kault examines how a statistical result incorrectly overruled the common sense of many doctors. A chapter on random variables shows the chance that students will carry cell phones, and a chapter on categorical measurements uses statistics to determine the efficiency of a new treatment for a serious disease. Each chapter ends with questions that will help students further understand important concepts. Useful on its own and perfect as a means of expanding classroom discussions, this book is ideal for high school students or anyone needing to review the basics of statistics.

Straightforward Statistics is written in plain language and connects material in a clear, logical manner to help students across the social and behavioral sciences develop a "big picture" understanding of foundational statistics. Each new chapter is purposefully connected with the previous chapter for a gradual accrual of knowledge from simple to more complex concepts—this effective, cumulative approach to statistics through logical transitions eases students into statistics and prepares them for success in more advanced quantitative coursework and their own research.