Social Science Data Sources & Statistical Methods

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.

"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."

This book contains advice on basic numeracy, including common errors, the recognized techniques for solving financial problems, analyzing information, forecasting and effective decision making. Over 100 charts, graphs, tables and feature boxes highlight key points. Emphasis is on how to present and communicate numerical information. Included is a dictionary of terms covering from Amortization to Zero-sum game.

This electronic reference work offers explanations of numerous statistical techniques and tests.

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.

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.

"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."

This book shows how to perform statistical analysis computationally, rather than mathematically with programs written in Python. By working through a case study, it describes the process of exploratory data analysis--from collecting data and generating statistics to identifying patterns and testing hypotheses. It explores distributions, rules of probability, visualization, and other tools. New chapters on regression, time series analysis, survival analysis, and analytic methods are included.

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics.

Suitable for self study, Uses real examples and real data sets that will be familiar to the audience. Introduction to the bootstrap is included - this is a modern method missing in many other books.