Difference between revisions of "AP Statistics Curriculum 2007 Pareto"
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'''Definition''': Pareto distribution is a skewed, heavy-tailed distribution that is sometimes used to model that distribution of incomes. The basis of the distribution is that a high proportion of a population has low income while only a few people have very high incomes. | '''Definition''': Pareto distribution is a skewed, heavy-tailed distribution that is sometimes used to model that distribution of incomes. The basis of the distribution is that a high proportion of a population has low income while only a few people have very high incomes. | ||
| − | <br />'''Probability density function''': For X | + | <br />'''Probability density function''': For <math>X\sim Pareto(x_m,\alpha)\!</math>, the Pareto probability density function is given by |
:<math>\frac{\alpha x_m^\alpha}{x^{\alpha+1}}</math> | :<math>\frac{\alpha x_m^\alpha}{x^{\alpha+1}}</math> | ||
where | where | ||
| − | *<math>x_m</math> is the minimum possible value of X | + | *<font size="3"><math>x_m</math></font> is the minimum possible value of X |
| − | *<math>\alpha</math> is a positive parameter which determines the concentration of data towards the mode | + | *<font size="3"><math>\alpha</math></font> is a positive parameter which determines the concentration of data towards the mode |
| − | *x is a random variable (<math>x>x_m</math>) | + | *x is a random variable (<font size="3"><math>x>x_m</math></font>) |
<br />'''Cumulative density function''': The Pareto cumulative distribution function is given by | <br />'''Cumulative density function''': The Pareto cumulative distribution function is given by | ||
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where | where | ||
| − | *<math>x_m</math> is the minimum possible value of X | + | *<font size="3"><math>x_m</math></font> is the minimum possible value of X |
| − | *<math>\alpha</math> is a positive parameter which determines the concentration of data towards the mode | + | *<font size="3"><math>\alpha</math></font> is a positive parameter which determines the concentration of data towards the mode |
| − | *x is a random variable (<math>x>x_m</math>) | + | *x is a random variable (<font size="3"><math>x>x_m</math></font>) |
<br />'''Moment generating function''': The Pareto moment-generating function is | <br />'''Moment generating function''': The Pareto moment-generating function is | ||
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where | where | ||
| − | *<math>\Gamma(-\alpha,-x_m t)=\int_{-x_m t}^\infty t^{-\alpha-1}e^{-t}dt</math> | + | *<math>\textstyle\Gamma(-\alpha,-x_m t)=\int_{-x_m t}^\infty t^{-\alpha-1}e^{-t}dt</math> |
<br />'''Expectation''': The expected value of Pareto distributed random variable x is | <br />'''Expectation''': The expected value of Pareto distributed random variable x is | ||
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:<math>Var(X)=\frac{x_m^2 \alpha}{(\alpha-1)^2(\alpha-2)}</math> for <math>\alpha>2\!</math> | :<math>Var(X)=\frac{x_m^2 \alpha}{(\alpha-1)^2(\alpha-2)}</math> for <math>\alpha>2\!</math> | ||
| − | |||
===Applications=== | ===Applications=== | ||
Revision as of 15:54, 11 July 2011
Pareto Distribution
Definition: Pareto distribution is a skewed, heavy-tailed distribution that is sometimes used to model that distribution of incomes. The basis of the distribution is that a high proportion of a population has low income while only a few people have very high incomes.
Probability density function: For \(X\sim Pareto(x_m,\alpha)\!\), the Pareto probability density function is given by
\[\frac{\alpha x_m^\alpha}{x^{\alpha+1}}\]
where
- \(x_m\) is the minimum possible value of X
- \(\alpha\) is a positive parameter which determines the concentration of data towards the mode
- x is a random variable (\(x>x_m\))
Cumulative density function: The Pareto cumulative distribution function is given by
\[1-(\frac{x_m}{x})^\alpha\]
where
- \(x_m\) is the minimum possible value of X
- \(\alpha\) is a positive parameter which determines the concentration of data towards the mode
- x is a random variable (\(x>x_m\))
Moment generating function: The Pareto moment-generating function is
\[M(t)=\alpha(-x_m t)^\alpha\Gamma(-\alpha,-x_m t)\!\]
where
- \(\textstyle\Gamma(-\alpha,-x_m t)=\int_{-x_m t}^\infty t^{-\alpha-1}e^{-t}dt\)
Expectation: The expected value of Pareto distributed random variable x is
\[E(X)=\frac{\alpha x_m}{\alpha-1}\] for \(\alpha>1\!\)
Variance: The Pareto variance is
\[Var(X)=\frac{x_m^2 \alpha}{(\alpha-1)^2(\alpha-2)}\] for \(\alpha>2\!\)
Applications
The Pareto distribution is sometimes expressed more simply as the “80-20 rule”, which describes a range of situations. In customer support, it means that 80% of problems come from 20% of customers. In economics, it means 80% of the wealth is controlled by 20% of the population. Examples of events that may be modeled by Pareto distribution include:
- The sizes of human settlements (few cities, many villages)
- The file size distribution of Internet traffic which uses the TCP protocol (few larger files, many smaller files)
- Hard disk drive error rates
- The values of oil reserves in oil fields (few large fields, many small fields)
- The length distribution in jobs assigned supercomputers (few large ones, many small ones)
- The standardized price returns on individual stocks
- The sizes of sand particles
- The sizes of meteorites
- The number of species per genus
- The areas burned in forest fires
- The severity of large casualty losses for certain businesses, such as general liability, commercial auto, and workers compensation
Example
Suppose that the income of a certain population has a Pareto distribution with \(\alpha=3\) and \(x_m=1000\). Compute the proportion of the population with incomes between 2000 and 4000.
We can compute this as follows:
\[P(2000\le X\le 4000)=\sum_{x=2000}^{4000}\frac{3\times 1000^3}{x^{3+1}}=0.109375\]
The figure below shows this result using SOCR distributions
