This breakthrough study examines how business decisions explain successful and unsuccessful performance. Real world and academic research is evaluated, including interviews and cases studies, to create a model of how decisions and performance are connected for businesses of all sizes. Recommendations are made to optimize decision making and projections about the future of decision making and performance are provided.

Introduction to Business covers the scope and sequence of most introductory business courses. The book provides detailed explanations in the context of core themes such as customer satisfaction, ethics, entrepreneurship, global business, and managing change. Introduction to Business includes hundreds of current business examples from a range of industries and geographic locations, which feature a variety of individuals. The outcome is a balanced approach to the theory and application of business concepts, with attention to the knowledge and skills necessary for student success in this course and beyond. This is an adaptation of Introduction to Business by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.

This volume presents novel concepts to help physicians and health care providers better understand the thought processes and approaches used in clinical decision-making and how we develop those skills as we transition from being a medical student to post-graduate trainee to independent practitioner. Approaches presented range from simple rules of thumb, pattern recognition, and heuristics, to more formulaic methods such as standard operating procedures, checklists, evidence-based medicine, mathematical modeling, and statistics. Ways to recognize and manage errors and how our decision-making can be improved, are also discussed. An Introduction to Medical Decision-Making presents several innovative techniques to allow the reader to use the principles presented and integrate the ethical, humanistic and social aspects of decision-making with the pragmatic and knowledge-based aspects of clinical medicine. It also highlights how our thinking processes, emotions, and biases affect decision-making. This invaluable resource will allow students and physicians to evaluate and critically discuss their decisions objectively to become more efficient and effective, and maximize the quality of care they provide.

Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics. The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making. Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision. With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics. - Teaches managers how they can use business analytics to formulate and solve business problems to enhance managerial decision-making - Explains the processes needed to develop, report and analyze business data - Describes how to use and apply business analytics software - Offers expanded coverage on the value and application of prescriptive analytics - Includes a wealth of illustrative exercises that are newly organized by difficulty level - Winner of the 2017 Textbook and Academic Authors Association's (TAA) Most Promising New Textbook Award in the prior edition

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

How to improve decision-making skills in realistic situations and do it in a reasonably nonmathematical fashion. Develops practical techniques for deciding upon the best strategies in a variety of situations. Provides methods for reducing complex problems to easily-drawn decision diagrams (trees), supported by real-world examples. Includes detailed cases that employ the methods described in the text. Each chapter contains illustrative examples and exercises.

Hackman (social and organizational psychology, Harvard U.) identifies the factors of being a team leader that will enable a team to work together efficiently to achieve organizational goals. He suggests that five conditions are necessary: having a real team, a compelling direction, an enabling team structure, a supportive organizational context, and expert team coaching. He integrates insights from interviews with team leaders with concepts from the social sciences. Annotation copyrighted by Book News, Inc., Portland, OR

Written primarily for students taking courses in managerial economics in Britain and Europe, The Business Economics and Managerial Decision Making analyses the growth and development of privately owned firms and also the decisions made by firms operating in both private and public sector enterprises. Coverage is clear and concise, and avoids specialist techniques such as linear programming, which in a European context tend to belong in courses dealing with operations research. The book also avoids straying into areas of industrial economics, instead retaining a sharp focus on relevant issues such as the theory of the firm and the varying objectives that may be adopted in practice. Key sections are supported by case studies of real firms and actual decisions made.

This up-to-date business analytics textbook (published in July 2020) will get you harnessing the power of the R programming language to: manipulate and model data, discover and communicate insight, to visually communicate that insight, and successfully advocate for change within an organization. Book Description A frequent teaching-award winning professor with an analytics-industry background shares his hands-on guide to learning business analytics. It is the first textbook addressing a complete and modern business analytics workflow that includes data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders. Book Highlights Content that is accessible to anyone, even most analytics beginners. If you have taken a stats course, you are good to go. Assumes no knowledge of the R programming language. Provides introduction to R, RStudio, and the Tidyverse. Provides a solid foundation and an implementable workflow for anyone wading into the Bayesian inference waters. Provides a complete workflow within the R-ecosystem; there is no need to learn several programming languages or work through clunky interfaces between software tools. First book introducing two powerful R-packages - `causact` for visual modelling of business problems and `greta` which is an R interface to `TensorFlow` used for Bayesian inference. Uses the intuitive coding practices of the `tidyverse` including using `dplyr` for data manipulation and `ggplot2` for data visualization. Datasets that are freely and easily accessible. Code for generating all results and almost every visualization used in the textbook. Do not learn statistical computation or fancy math in a vacuum, learn it through this guide within the context of solving business problems.