# Weibull distribution

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The Weibull distribution is often used to represent failure time in reliability models. It is similar in shape to the gamma distribution, but tends to be less skewed and tail-heavy. It is a continuous distribution over the positive real numbers.

Weibull(10, 4) →

## Functions

Both parameters most be positive, i.e., $shape, scale > 0$. The «scale» parameter is optional and defaults to 1.

### Weibull(shape, scale, over)

The distribution function. Use this to define a chance variable or other uncertain quantity as having a Weibull distribution.

You can use the optional «over» parameter to generate independent and identically distributed distributions over one or more indicated indexes.

### DensWeibull(x, shape, scale )

The density on $x\ge 0$ is given by:

$p(x) = {{shape}\over{scale}} \left({x\over{scale}}\right)^{shape-1} \exp\left(-(x/{scale})^{shape}\right)$

### CumWeibull(x, shape, scale )

The Weibull distribution has a cumulative density on $x\ge 0$ given by:

$F(x) = 1 - \exp\left({-\left({x\over{scale}}\right)^{shape}}\right)$

and F(x) = 0 for x < 0.

### CumWeibullInv(p, shape, scale )

The inverse cumulative distribution, or quantile function. Returns the «p»th fractile/quantile/percentile.

$F^{-1}(p) = scale * \left( \ln\left( 1\over{1-p} \right)\right)^{1/shape}$

## Statistics

The theoretical statistics (i.e., without sampling error) for the Weibull distribution are as follows. I use $\alpha = 1/shape$ and $\beta = scale$.

• Mean = $\beta \Gamma\left( 1 + \alpha\right)$
• Mode = $\left\{ \begin{array}{ll} \beta \left( 1 - \alpha \right)^\alpha & \alpha>1 \\ 0 & \alpha \leq 1 \end{array}\right.$
• Median = $\beta \left( \ln 2\right)^\alpha$
• Variance = $\beta^2 \Gamma(1+2\alpha) - \left(\beta \Gamma(1+\alpha)\right)^2$

## Parameter Estimation

Suppose you have sampled historic data in Data, indexed by I, and you want to find the parameters for the best-fit Weibull distribution. The parameters can be estimated using a linear regression as follows:

Index bm := ['b', 'm'];
Var Fx := (Rank(Data, I) - 0.5)/Size(I);
Var Z := Ln(-Ln(1 - Fx));
Var fit := Regression(Z, Array(bm, [1, Ln(Data)]), I, bm);
Var shape := fit[bm = 'm'];
Var b := fit[bm = 'b'];
Var scale := Exp(-b/shape);
[shape, scale]