Applied Statistics and Multivariate Data Analysis for Business and Economics

Thomas Cleff is a Professor of Quantitative Methods for Business and Economics at Pforzheim University, Germany, and a Research Associate at the Centre for European Economic Research (ZEW), Mannheim, Germany. Since joining Pforzheim University in 2000, Cleff has spearheaded the development of an international dual-degree program, was appointed Vice Dean in 2012 and Dean of the Business School in 2014. He is an expert on international accreditation and partnership programmes between institutions of higher education. In 2013, he became a board member at the Network of International Business and Economic Schools (NIBES), and a member of the international advisory board at the South American Education Quality Accreditation Agency (EQUAA); in 2016 he joined the AACSB Initial Accreditation Committee. He has served as a Visiting Professor at several universities, including ESCEM Tours-Poitiers, France; Simon Fraser University, Vancouver, Canada; TEC de Monterrey, Mexico; and Gadjah Mada University, Indonesia.Cleff holds degrees in Economics and Management from Pantheon-Sorbonne University, France and the University of Wuppertal, Germany. He was a Senior Researcher at the Centre for European Economic Research (ZEW) in Mannheim from 1997 to 2000, where he led a variety of international research projects in the fields of innovation and strategic management. His main research interests are in international marketing, white-collar crime, brand research, innovation, and industry studies.

“The book will be a very good textbook in applied statistics and multivariate data analysis for students in economics and business, and also for practitioners in firms that use those methods in their work.” (Anatoliy Swishchuk, zbMATH 1429.62002, 2020)

• Statistics and Empirical Research.- From Disarray to Dataset.- Univariate Data Analysis.- Bivariate Association.- Classical Measurement Theory.-Calculating Probability.- Random Variables and Probability Distributions.- Parameter Estimation.- Hypothesis Testing.- Regression Analysis.- Time Series and Indices.- Cluster Analysis.- Factor Analysis.