Jow-Ran Chang

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.This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

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.This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures. One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS—also helpful in some analytical methods not possible or practical to do in Excel.