Step up your Excel skills with our 6-page laminated guide focusing on tips and tricks for using data efficiently while ensuring data quality. Curtis Frye, author of multiple books on Excel, creator of many Lynda.com videos and an experienced corporate trainer used his experience and knowledge to cover the most relevant functions for users at different levels. This is the second in the Excel 2016 series. Suggested uses: Workplace -- Kept conveniently at your desk for easy reference; Company Training -- reduce help-desk calls and keep productivity flowing for a team or for your entire company; Students/Teachers/Parents -- help for classroom or homework; College Professors/Students -- offers a range of guides for different levels.

Expert systems--problem-solving computer programs that contain the encoded knowledge of experts in a specific application area such as financial planning--represent a crucial turning point in how the typical organization utilizes its computer environment. This volume, written for practitioners in finance and accounting as well as MIS managers who wish to broaden their expertise, offers a comprehensive look at the use of expert systems in the everyday operations of finance and accounting. The author presents selected areas that are viable candidates for expert systems, demonstrating the ways in which organizations can successfully augment their present management information systems with expert systems. Actual programs using a typical PC expert system shell (EXSYS) further illustrate the relative ease with which expert systems for finance and accounting can be developed, implemented, and maintained.

Divided into four parts, the book begins by offering a framework for developing expert systems in finance and accounting. In the second part, Thierauf reviews the current state of development for programming languages in expert systems, the computer hardware necessary to run expert systems, and expert system shells useful in developing business expert systems. Part three presents an in-depth examination of the procedures used in developing expert systems, while the final part focuses on typical applications of expert systems in finance and accounting. Following a chapter on general business and nonbusiness applications, Thierauf addresses finance applications of expert systems and then uses the EXSYS program to develop demonstration and field prototypes and operational expert systems for standard finance applications. The final two chapters take the same approach to accounting applications. An appendix lists vendors of artificial intelligence/expert systems hardware and software.

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference - t-test and U-test; correlation - Spearman's rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal-Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. - Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel - Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. - Amazon 5-star review It has been very easy to follow and will be perfect for anyone. - Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. - Goodreads, 4-star review

Contents:
Part 1: First Steps with Excel Making Sense of Pupil Performance Figures How Can We Describe Performance? How Can We Investigate the Relationship between Sets of Marks? Confidence in Sample Estimates Creating an Expectancy Table Part 2A: Scales and their Interpretation Part 2B: Handling Test Data Appendices

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.