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 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.

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

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.

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

The 2014 Electricity Profiles publication provides an overall picture of the electricity sector of over 200 countries and areas on an internationally comparable basis, for the years 2009-2014. It displays detailed information on production, trade and consumption of electricity, on net installed capacity and thermal power plant inputs and efficiency relevant to each of these countries and areas. This is the third issue of Electricity Profiles as a stand-alone publication, replacing the previous series of Energy Balances and Electricity Profiles.