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Inferential Statistical Methods for Energy Risk Managers

  • Nigel Da Costa Lewis
Part of the Finance and Capital Markets Series book series (FCMS)

Abstract

Unlike descriptive statistics, inferential statistics are procedures for determining whether it is possible to make generalizations based on the data collected from a sample. Such generalizations are about an unobserved population. A population consists of all values (past and future) of the random variable of interest. In most circumstances the exact value of a population parameter such as the mean or variance will be unknown, and we will have to make some conjecture about its true value. In Chapter 3, we used sample estimators such as the mean, median, skew, and kurtosis, to provide estimates of the respective population parameters. When a sample is drawn from a population, the evidence contained within it may bolster our conjecture about population values or it may indicate that the conjecture is untenable. Hypothesis testing is a formal mechanism by which we can make and test inferential statements about the characteristics of a population. It uses the information contained in a sample to assess the validity of a conjecture about a specific population parameter.

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Further Reading

  1. Crow, E. L., Davis, F. A. and Maxfield, M. W. (1960) Statistics Manual, Dover Publications Inc., New York.Google Scholar
  2. Lewis, Nigel Da Costa (2003) Market Risk Modeling: Applied Statistical Methods for Practitioners, Risk Books, London.Google Scholar
  3. Lewis, Nigel Da Costa (2004) Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management, John Wiley & Sons, Inc., New York.Google Scholar
  4. Rowntree, D. (1981) Statistics Without Tears, Penguin Books, Middlesex, England.Google Scholar

Copyright information

© Nigel Da Costa Lewis 2005

Authors and Affiliations

  • Nigel Da Costa Lewis

There are no affiliations available

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