Essentials of Excel VBA, Python, and R - Lee, Cheng-Few

Résumé :
This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry.

Biographie:
Cheng-Few Lee is a Distinguished Professor of Finance at Rutgers Business School, Rutgers University and was chairperson of the Department of Finance from 1988-1995. He has also served on the faculty of the University of Illinois (IBE Professor of Finance) and the University of Georgia. He has maintained academic and consulting ties in Taiwan, Hong Kong, China and the United States for the past three decades. He has been a consultant to many prominent groups including, the American Insurance Group, the World Bank, the United Nations, The Marmon Group Inc., Wintek Corporation, and Polaris Financial Group.

Sommaire:
Chapter 1. Introduction.- Chapter 2. Data Collection, Presentation, and Yahoo Finance.- Chapter 3. Histograms and the Rate of Returns of JPM and JNJ.- Chapter 4. Numerical Summary Measures on Stock Rates of Return and Market Rates of Return.- Chapter 5. Probability Concepts and their Analysis.- Chapter 6. Discrete Random Variables and Probability Distributions.- Chapter 7. The Normal and Lognormal Distributions.- Chapter 8. Sampling Distributions and Central Limit Theorem.- Chapter 9. Other Continuous Distributions.- Chapter 10. Estimation.- Chapter 11. Hypothesis Testing.- Chapter 12. Analysis of Variance and Chi-Square Tests.- Chapter 13. Simple Linear Regression and the Correlation Coefficient.- Chapter 14. Simple Linear Regression and Correlation: Analyses and Applications.- Chapter 15. Multiple Linear Regression.- Chapter 16. Residual and Regression Assumption Analysis.- Chapter 17. Nonparametric Statistics.- Chapter 18. Time Series: Analysis, Model, and Forecasting.- Chapter 19.Index Numbers and Stock Market Indexes.- Chapter 20. Sampling Surveys: Methods and Applications.- Chapter 21. Statistical Decision Theory.- Chapter 22. Sources of Risks and their Determination.- Chapter 23. Risk-Aversion, Capital Asset Allocation, and Markowitz Portfolio Selection Model.- Chapter 24. Capital Asset Pricing Model and Beta Forecasting.- Chapter 25. Single-Index Models for Portfolio Selection.- Chapter 26. Sharpe Performance Measure and Treynor Performance Measure Approach to Portfolio Analysis.