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2015 | OriginalPaper | Buchkapitel

4. Some Probability Distributions

verfasst von : Michael O. Finkelstein, Bruce Levin

Erschienen in: Statistics for Lawyers

Verlag: Springer New York

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Abstract

We continue the discussion of random variables that was begun in Section 1.​1 at p. 1.

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Fußnoten
1
More precisely, if we represent the discrete probability P[X = x] geometrically as the area of a narrow rectangle centered at x and extending horizontally half-way to the two neighboring values of x, then the probability density function is the approximate height of the rectangle in the limit, as the neighboring values of x come closer together and P[X = x] approaches 0.
 
2
Trials in such a sequence are called “Bernoulli trials” in honor of Jakob Bernoulli (1654–1705), whose Ars Conjectandi was one of the earliest treatises on mathematical probability.
 
3
The correction is not applied if the observed value already differs from the expected value by less than 1/2.
 
4
Deciding in advance as a test specification to reject the null hypothesis when the P-value falls below 0.05 ensures that the maximum rate of Type I error is no more than 0.05.
 
5
A still smaller P-value, in which the two-tailed test remains statistically significant, is obtainable using the generalized likelihood ratio method. See Section 5.​6 at p. 201.
 
6
Exponential distributions are common waiting-time distributions; see Section 4.8.
 
7
A uniform distribution assigns equal probability density to all points in a given interval. The uniform distribution on the interval [0, 1] has relative frequency function \( f(x)=1 \) for \( x\in \left[0,1\right] \) and \( f(x)=0 \) elsewhere. A uniformly distributed random variable X has mean 1/2 and variance 1/12. The probability that X ≤ x is simply x for any x between 0 and 1. Its skewness is zero by symmetry.
 
Metadaten
Titel
Some Probability Distributions
verfasst von
Michael O. Finkelstein
Bruce Levin
Copyright-Jahr
2015
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4419-5985-0_4