This comprehensive textbook equips students of economics and business, as well as industry professionals, with essential principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Through real-world business examples, it illustrates the practical use of univariate, bivariate, and multivariate statistical methods.

The content spans a broad range of topics, from data collection and scaling to the presentation and fundamental univariate analysis of quantitative data, while also demonstrating advanced analytical techniques for exploring multivariate relationships. The book systematically covers all topics typically included in university-level courses on statistics and advanced applied data analysis. Beyond theoretical discussion, it offers hands-on guidance for using statistical software tools such as Excel, SPSS, Stata, and R.

In this completely revised and updated second edition, new sections on logistic regression are included, along with enhanced examples and solutions using R for all covered statistical methods. This edition provides a robust resource for mastering applied statistics in both academic and professional settings.

Is the second revised and extended edition Offers a broad overview of the most important statistical and multivariate methods Uses Excel, SPSS, Stata, and R showing readers how to perform even complex data analyses independently

Autorentext

Thomas Cleff is Professor of Quantitative Methods for Business and Economics and has been Dean of the Business School at Pforzheim University for more than 10 years. He is a member of many international academic advisory boards worldwide and an expert in international accreditation and academic partnerships. His research focuses on quantitative analysis in international marketing, innovation research and brand research.

Inhalt

Chapter 1. Statistics and Empirical Research.- Chapter 2. From Disarray to Dataset.- Chapter 3. Univariate Data Analysis.- Chapter 4. Bivariate Association.- Chapter 5. Classical Measurement Theory.- Chapter 6. Calculating Probability.- Chapter 7. Random Variables and Probability Distributions.- Chapter 8. Parameter Estimation.- Chapter 9. Hypothesis Testing.- Chapter 10. Regression Analysis.- Chapter 11. Logistic Regression.- Chapter 12. Time Series and Indices.- Chapter 13. Cluster Analysis.- Chapter 14. Factor Analysis.