Preventa de eBooks de Estadística y probabilidad

A novel framework for how entrepreneurs can use Bayesian reasoning to make decisions.This edited volume introduces and explores the concept of Bayesian entrepreneurship, a novel framework for understanding entrepreneurial decision-making under uncertainty. It brings together contributions from leading scholars to examine how entrepreneurs form beliefs about opportunities, learn through experimentation, and make strategic decisions.The Bayesian approach emphasizes the critical interaction between an entrepreneurs prior beliefs and their capacity for learning and experimentation to update those beliefs. This dynamic process allows entrepreneurs to formulate and test theories about value creation and capture, attract resources and capabilities, and implement effective entrepreneurial strategies. The volume is organized around four key themes: the foundations and distinguishing features of Bayesian entrepreneurship how entrepreneurs form and update prior beliefs in entrepreneurial decision-making the role of experimentation and learning in entrepreneurial strategy applications to entrepreneurship education and practice Bayesian Entrepreneurship offers both theoretical contributions advancing our understanding of entrepreneurial decision-making and practical insights for entrepreneurs and educators.

A comprehensive survey of the dominant methods for separating perceptual from decisional effects and for studying perceptual interactions.Human performance in any perceptual or cognitive task can change for a variety of reasons. Signal detection theory (SDT) and its multidimensional generalization, general recognition theory (GRT), are by far the dominant methods for determining whether a change in performance is due to a change in perception or a change in how perceptual or cognitive information is used to select a response. In addition, GRT is the dominant method for studying perceptual interactions.In this book, author F. Gregory Ashby covers how SDT and GRT are used in thousands of published articles that span an enormous range of fields, including vision and all other areas of perception, memory, decision-making, eyewitness testimony, response time modeling, face perception, visual search, categorization, perceived similarity, preference, stereotyping, implicit learning, fMRI data analysis, and food science. The book includes examples that illustrate how the various methods are applied, as well as Matlab code to perform several key computations.

Bringing Integrity to the Stories Numbers Tell When we tell stories with numbers, we depend on the field of statisticsbut how do we know that those stories are true? Almost everything in life is connected to data, and that data can help us make sense of truth in a world that feels increasingly challenging to navigate as Christians.But in a world where we are told to "do our own research," it can be nearly impossible to know whether were just gravitating toward stories that confirm our own untested biases, or whether were analyzing the data in a way that is illuminating truth.Exploring a range of contemporary topics, professor of statistics Jason Wilson shows both non-experts and experts alike that statistics has something to say when it relates to the polarizing issues that we face today. Exploring topics such as gambling, Covid, and media bias, Wilson shows that the ways we collect, organize, analyze, and interpret data have something to say in conversation with the Christian faith.In Statistics and Faith, youll find examinations of contemporary issues using statistical concepts, tools, and theories; case studies from history that show how statistics can be used in ways that are helpful or harmful; application chapters based on over fifteen years of research; and reflection questions to help you explore how to do statistics with integrity. This book is ideal for professors seeking to help students explore the history and application of statistics from a Christian worldview as well as for Christians who are curious about how we understand truth through numbers. It serves as an ideal supplementary text for statistics courses and other disciplines that incorporate statistical methods, and it offers an insightful perspective on how statistics in conversation with Christian faith can speak to todays polarizing issues, empowering readers to discern truth through data in a noisy world.

Thinking in Math teaches readers how to understand mathematics by experimenting with it. Using short, focused Python programs, readers explore how mathematical ideas behave, change, and interact developing intuition through observation rather than memorization.Mathematics often feels abstract because it is taught as static symbols on a page. But real understanding comes from seeing how ideas behave when you change them.Thinking in Math brings an experimental mindset to mathematics, using Python as a tool for exploration. Across eighteen carefully chosen topics, readers write and modify small programs to observe patterns, test assumptions, and see mathematical concepts unfold in real time.This is not a programming book, and it is not a traditional math textbook. Instead, it treats math the way scientists treat physical systems: by running experiments, asking what if?, and learning from what happens. With only basic arithmetic and algebra as prerequisites, readers develop mathematical intuition by doing, not by rote.