This publication presents data visualization of economic statistics relevant for cross-border production arrangements analysis, focusing on Bangladesh, Bhutan, India, Kazakhstan, the Kyrgyz Republic, Maldives, Nepal, Pakistan, and Sri Lanka. It was computed from ADB's multi-regional input-output database which serves the increasing demand for structured, relevant, timely, and accurate data, especially with the onset of various economic research projects on global value chains. Supply and use tables and input-output tables in the publication address the emerging need for more systematic and comprehensive approaches in data management, economic analysis, and policy research for national economies around the world.

Risk, Uncertainty, and Profit is a groundbreaking work of economic theory, distinguishing between risk, which is by nature measurable and quantifiable, and uncertainty, which can be neither be measured nor quantified. We begin with an analysis of the functions of profit, risk and uncertainty in the economy. Frank H. Knight introduces his work with a discussion on profit and how there are conflicts about its nature between various economic theorists. As the title implies, the author's chief concern is the interplay between making a profit, incurring risk, and determining if there is uncertainty. Risks are different from uncertainty in that they can be measured and protected against. For example a location chosen for a factory or farm may have a measured risk of flooding in a given year. Businesses, insurers and investors alike can be made aware of this, and behave according to the quantified risk.

This report showcases the role that technology can play in improving the quality, timeliness, and frequency of agricultural statistics in Asia and the Pacific. It is a special supplement to the Key Indicators for Asia and the Pacific 2018. This report summarizes how other innovations, such as drones, computer-assisted personal interviewing, and artificial intelligence hold promise in transforming the field of agricultural statistics.

MS Excel is one of the most powerful tools available to a business manager. In this book, the author provides an advanced level of skill sets and brings actionable insights to the user. Hence, the material in this version has been organized as follows: Financial functions; Conditional math and statistical functions; Data analysis; Decision making; Data cleaning and use of macros; Auditors. The objective is to give readers a flavor of how the vast array of functions can be used to make life easier and more efficient. Amazing results can be achieved by mastering Excel at a basic level. Readers who execute the given functions on a workbook simultaneously and experience the journey will find the learning curve the steepest.

It's estimated that 80 percent of an organization's data contains location attributes, but many don't understand how to unlock the potential of this data for their organizations to make better decisions. You have just been handed the keys by finding this book. Readers will unlock these methods by learning about location analytics as well as taking a deep dive into the Planned Grocery (R) platform created in part by the author. The Planned Grocery (R) location analytics platform has been mentioned in the Wall Street Journal (twice), Forbes, Bloomberg, and Business Insider. A sampling of clients of Planned Grocery (R) include: Philips Edison and Company, Just Fresh, Slate Retail REIT, Wegmans, and Whole Foods. The practical information in this book is designed to prepare you to recognize and take advantage of situations where you and your organization can become more successful using location analytics. This will be accomplished by taking you through an explanation of the fundamentals of location analytics, by looking at various case studies, by learning how to identify and analyze spatial data sets, and by learning about the companies that are doing interesting work in this space

Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts. This book is intended to present key concepts related to quantitative analysis in business. It is targeted to business students, undergraduate and graduate, taking an introductory core course. Topics covered include knowledge management, visualization, sampling and hypothesis testing, regression (simple, multiple, and logistic), as well as optimization modeling. It concludes with a brief overview of data mining. Concepts are demonstrated with worked examples.

This book deals with Business Analytics (BA) - an emerging area in modern business decision making. Business analytics is a data driven decision making approach that uses statistical and quantitative analysis along with data mining, management science, and fact-based data to measure past business performance to guide an organization in business planning and effective decision making. Business Analytics tools are also used to predict future business outcomes with the help of forecasting and predictive modeling. In this age of technology, massive amount of data are collected by companies. Successful companies use their data as an asset and use them for competitive advantage. Business Analytics is helping businesses in making informed business decisions and automating and optimizing business processes. Successful business analytics depends on the quality of data. Skilled analysts, who understand the technologies and their business, use business analytics tools as an organizational commitment to data-driven decision making.

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.