Predictive analytics for business strategy : reasoning from data to actionable knowledge / Jeffrey T. Prince, Professor of Business Economics & Public Policy, Harold A. Poling Chair in Strategic Management, Kelley School of Business, Indiana University.

By: Prince, Jeff [author.]
Publisher: New York, NY : McGraw-Hill Education, [2019]Copyright date: c2019Description: xviii, 348 pages ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781260084641Subject(s): Business planning -- Study and teaching | Quantitative researchDDC classification: 658.4/012 LOC classification: H62 | .P6844 2019
Contents:
Chapter 1: The Roles of Data and Predictive Analytics in Business Chapter 2: Reasoning with Data Chapter 3: Reasoning from Sample to Population Chapter 4: The Scientific Method: The Gold Standard for Establishing Causality Chapter 5: Linear Regression as a Fundamental Descriptive Tool Chapter 6: Correlation vs. Causality in Regression Analysis Chapter 7: Basic Methods for Establishing Causal Inference Chapter 8: Advances Methods for Establishing Causal Inference Chapter 9: Prediction for a Dichotomous Variable Chapter 10: Identification and Data Assessment
Summary: Designed for courses that provide a conceptual and broad-based introduction to Econometrics and business analytics, Predictive Analytics for Business Strategy, 1st edition provides future managers with a basic understanding of what data can do in forming business strategy without getting into a taxonomy of models and their statistical properties.
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BOOK BOOK COLLEGE LIBRARY
COLLEGE LIBRARY
SUBJECT REFERENCE
658.4012 P9355 2019 (Browse shelf) Available CITU-CL-49916
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Includes index.

About the Author

Jeff Prince

Jeffrey T. Prince is Professor of Business Economics & Public Policy and Harold A. Poling Chair in Strategic Management at Indiana University’s Kelley School of Business. He received his B.A. in economics and B.S. in mathematics and statistics from Miami University in 1998 and earned a Ph.D. in economics from Northwestern University in 2004. Prior to joining Indiana University, he taught graduate and undergraduate courses at Cornell University.

Jeff has won top teaching honors as a faculty member at both Indiana University and Cornell, and as a graduate student at Northwestern. He has a broad research agenda within applied economics, having written and published on topics that include demand in technology and telecommunications markets, internet diffusion, regulation in healthcare, risk aversion in insurance markets, and quality competition among airlines. He is one of a small number of economists to have published in both the top journal in economics (American Economic Review) and the top journal in management (Academy of Management Journal). Professor Prince currently is a co-editor at the Journal of Economics and Management Strategy, and serves on the editorial board for Information Economics and Policy. In his free time, Jeff enjoys activities ranging from poker and bridge to running and racquetball.

Chapter 1: The Roles of Data and Predictive Analytics in Business

Chapter 2: Reasoning with Data

Chapter 3: Reasoning from Sample to Population

Chapter 4: The Scientific Method: The Gold Standard for Establishing Causality

Chapter 5: Linear Regression as a Fundamental Descriptive Tool

Chapter 6: Correlation vs. Causality in Regression Analysis

Chapter 7: Basic Methods for Establishing Causal Inference

Chapter 8: Advances Methods for Establishing Causal Inference

Chapter 9: Prediction for a Dichotomous Variable

Chapter 10: Identification and Data Assessment

Designed for courses that provide a conceptual and broad-based introduction to Econometrics and business analytics, Predictive Analytics for Business Strategy, 1st edition provides future managers with a basic understanding of what data can do in forming business strategy without getting into a taxonomy of models and their statistical properties.

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