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| = How do I produce random variables which follow a negatively skewed distribution? = Most distributions such as the exponential and log-Normal distributions are positively skewed with the mode of the distribution occurring for lower values. |
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| = How do I produce random variables which follow a negative skew distribution? = |
The Weibull distribution is negatively skewed and may be generated [http://www.taygeta.com/random/weibull.xml using random variables which are uniform on the interval (0,1).] |
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| Most distributions such as the exponential and log-Normal distributions are positive skewed with the model of the distribution for lower values. | The below produces an open ended negatively skewed weibull distribution with parameters, 2 and 20. It has a median of |
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| [http://www.uib.no/people/ngbnk/kurs/notes/node31.html The Gamma distribution] which has two parameters, $$\alpha$$ and $$\beta$$ may produce negative skew where the model occurs for higher values (values > 0) when $$/alpha$$ is a lot greater than $$\beta$$. It also has no maximum value. | $$2^text{-0.05}ln(2)^text{0.05}$$ = 0.95. which is of form $$A^text{-1/B}ln(2)^text{1/B}$$ where A and B are the two parameters of the Weibull distribution. (See [http://www.weibull.com/AccelTestWeb/weibull_distribution.htm here for formulae).] {{{ compute alpha=2. compute beta=20. compute rvw=((-1/alpha)*(ln(1-rv.uniform(0,1))))**(1/beta). compute med=((alpha)**(-1/beta))*(ln(2)**(1/beta)). exe. }}} |
How do I produce random variables which follow a negatively skewed distribution?
Most distributions such as the exponential and log-Normal distributions are positively skewed with the mode of the distribution occurring for lower values.
The Weibull distribution is negatively skewed and may be generated [http://www.taygeta.com/random/weibull.xml using random variables which are uniform on the interval (0,1).]
The below produces an open ended negatively skewed weibull distribution with parameters, 2 and 20. It has a median of
$$2text{-0.05}ln(2)text{0.05}$$ = 0.95.
which is of form
$$Atext{-1/B}ln(2)text{1/B}$$
where A and B are the two parameters of the Weibull distribution. (See [http://www.weibull.com/AccelTestWeb/weibull_distribution.htm here for formulae).]
compute alpha=2. compute beta=20. compute rvw=((-1/alpha)*(ln(1-rv.uniform(0,1))))**(1/beta). compute med=((alpha)**(-1/beta))*(ln(2)**(1/beta)). exe.