Learning Excel for Economics & Business: Excel Topics

This advanced, 2-volume 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. This first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits.

Now in its second edition, this title 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. Readers can look to Volume II for dedicated content on financial derivatives, risk management, and machine learning.

This publication shows how to visualize, plan, and put into motion an idea for creating a start-up company. The contents follow a six-step process: presenting a case study of a business start-up; creating goals and objectives; determining expenses; estimating potential sales revenue; predicting marketing costs; and finalizing the financial analysis. Written around an IT startup case study, the book presents a host of Excel worksheets describing the case study along with accompanying blank forms.

This work guides you through basic statistics to test if your data can be used to perform regression predictions and time series forecasts. The exercises covered use real-life data from Kaggle such as demand for seasonal air tickets and credit card fraud detection. You’ll learn how to apply the grouping K-means algorithm that helps you find segments of your data that are impossible to see with other analyses like business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you’ll be able to detect the outliers that could indicate possible fraud or a bad function in the network packets.

Turn your financial data into insightful decisions with this straightforward guide to financial modeling with Excel. Whether you work at a mom-and-pop retail store or a multinational corporation, you can learn how to build budgets, project your profits into the future, model capital depreciation, value your assets, and more. You'll learn by doing as this book walks you through practical, hands-on exercises to help you build powerful models using just a regular version of Excel.

Feel like you've barely scratched the surface of what Excel’s hundreds of built-in functions can do? Use this book, and you'll soon be organizing, analyzing, and interpreting data like a pro. This easy-to-use guide to Excel formulas and functions shows how to: work with financial functions like PMT, PPMT, NPER, RATE, and PV; calculate mean, median, mode, standard deviation, and many more statistical functions; troubleshoot formulas for common errors, and validate your data to avoid mistakes. It covers how to work with dates, times, logic operators, conditions, and basic and advanced mathematical functions. Intended both for novice and more advanced users.

A hands-on guide to using Excel in the business context that contains step-by-step instructions of how to solve common business problems using financial models. It includes downloadable Excel templates, a list of shortcuts and tons of tips. Learn to create financial models to help make business decisions while applying modelling best practice methodology, tools and techniques. This book focuses on the topics most relevant to finance professionals, and covers these features in detail, with real world examples.