The trusted series of workbooks by Philip H. Pollock III and Barry C. Edwards continues with A Microsoft Excel (R)Companion to Political Analysis. In this new guide, students dive headfirst into actual political data working with the ubiquitous Excel software. Students learn by doing with new guided examples, annotated screenshots, step-by-step instructions, and exercises that reflect current scholarly debates in varied subfields of political science, including American politics, comparative politics, law and courts, and international relations. Chapters cover all major topics in political data analysis, from descriptive statistics through logistic regression, all with worked examples and exercises in Excel. No matter their professional goals, students can gain a leg up for their future careers by developing a working knowledge of statistics using Excel. By encouraging students to build on their existing familiarity with the Excel program, instructors can flatten the statistics learning curve and take some of the intimidation out of the learning process. Gain lost time usually spent troubleshooting software to provide students with a smooth transition into political analysis.

The prediction of the valuation of the "quality" of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the "actual" financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify "quality" characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.

You’re beyond the basics—so dive in and really put your spreadsheet skills to work! This supremely organized reference is packed with hundreds of timesaving solutions, troubleshooting tips, and workarounds. It’s all muscle and no fluff. Learn how the experts tackle Excel—and challenge yourself to new levels of mastery. Includes companion eBook and sample files.

Topics include:

•Customizing the Excel workspace

•Best practices for designing and managing worksheets

•Creating formulas and functions

•Performing statistical, what-if, and other data analysis

•Core to advanced charting techniques

•Using graphics and sparklines

•Managing databases and tables

•Automating Excel with macros and custom functions

•Collaborating in Excel online, in the cloud, and more

•Extending Excel