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Analysing Economic DataContinuous Random Variables and Probability Density Functions

Analysing Economic Data: Continuous Random Variables and Probability Density Functions [An alternative way of approximating a binomial distribution is considered that leads to a continuous random variable (one that takes on an infinite number of values), known as the normal or Gaussian. Continuous random variables have probability density functions, rather than probability distributions, associated with them, and this leads to probabilities having to be calculated as an area under the function, which requires integral calculus. The standard normal distribution is introduced as a convenient way of calculating normal probabilities and examples of how to do such calculations are provided. Distributions related to the normal — the chi-square, Student’s t and the F — along with the concepts of independence and covariance between random variables are introduced. Methods of simulating random variables and distributions are discussed.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Analysing Economic DataContinuous Random Variables and Probability Density Functions

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/lp/springer-journals/analysing-economic-data-continuous-random-variables-and-probability-EZ4R37RXIE
Publisher
Palgrave Macmillan UK
Copyright
© Palgrave Macmillan, a division of Macmillan Publishers Limited 2014
ISBN
978-1-349-48656-4
Pages
123 –137
DOI
10.1057/9781137401908_9
Publisher site
See Chapter on Publisher Site

Abstract

[An alternative way of approximating a binomial distribution is considered that leads to a continuous random variable (one that takes on an infinite number of values), known as the normal or Gaussian. Continuous random variables have probability density functions, rather than probability distributions, associated with them, and this leads to probabilities having to be calculated as an area under the function, which requires integral calculus. The standard normal distribution is introduced as a convenient way of calculating normal probabilities and examples of how to do such calculations are provided. Distributions related to the normal — the chi-square, Student’s t and the F — along with the concepts of independence and covariance between random variables are introduced. Methods of simulating random variables and distributions are discussed.]

Published: Oct 26, 2015

Keywords: Probability Density Function; Standard Normal Distribution; Normal Random Variable; Discrete Random Variable; Continuous Random Variable

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