"The Encyclopedia features two major types of entries: definitions, consisting of a paragraph or two, provide a quick explanation of a methodological term; and topical treatments or essays discussing the nature, history, application/example and implication of using a certain method. Also included are suggested readings and references for future study."
Essays explain qualitative research methods and provide info on further reading. Includes numerous methodologies & techniques, data collection & analysis, research design & planning, ethics, rigor, dissemination & writing, theoretical frameworks, etc.
"Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used in marketing, economics, transportation, health, tourism, and other areas to develop and modify products, services, policies, and programs, specifically ones that can be described in terms of attributes."
In addition to general advice on basic numeracy, the guide points out common errors and explains the recognized techniques for solving financial problems, analyzing information of any kind, forecasting and effective decision making. Over 100 charts, graphs, tables and feature boxes highlight key points, and great emphasis is put on the all-important aspect of how you present and communicate numerical information effectively and honestly. At the back of the book is an extensive A-Z dictionary of terms covering everything from amortization to zero-sum game.
Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.
This handbook includes survey essays on statistics processes useful in areas of financial economics and financial econometrics, such as GARCH-modeling, stochastic volatility modeling, continuous time processes, cointegration and unit roots, as well as special topics related to risk, time series, and simulation based methods.
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. Develop an understanding of probability and statistics by writing and testing code.