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| Probability Density Function | The formula for the probability density function of the general Weibull distribution is![]() where is the shape parameter, is the location parameter and is thescale parameter. The case where = 0 and = 1 is called the standard Weibull distribution. The case where = 0 is called the 2-parameter Weibull distribution. The equation for the standard Weibull distribution reduces to![]() Since the general form of probability functions can be expressed in terms of the standard distribution, all subsequent formulas in this section are given for the standard form of the function. The following is the plot of the Weibull probability density function. ![]() | ||||||||||||
| Cumulative Distribution Function | The formula for the cumulative distribution function of the Weibull distribution is![]() The following is the plot of the Weibull cumulative distribution function with the same values of as the pdf plots above.![]() | ||||||||||||
| Percent Point Function | The formula for the percent point function of the Weibull distribution is![]() The following is the plot of the Weibull percent point function with the same values of as the pdf plots above.![]() | ||||||||||||
| Hazard Function | The formula for the hazard function of the Weibull distribution is![]() The following is the plot of the Weibull hazard function with the same values of as the pdf plots above.![]() | ||||||||||||
| Cumulative Hazard Function | The formula for the cumulative hazard function of the Weibull distribution is![]() The following is the plot of the Weibull cumulative hazard function with the same values of as the pdf plots above.![]() | ||||||||||||
| Survival Function | The formula for the survival function of the Weibull distribution is![]() The following is the plot of the Weibull survival function with the same values of as the pdf plots above.![]() | ||||||||||||
| Inverse Survival Function | The formula for the inverse survival function of the Weibull distribution is![]() The following is the plot of the Weibull inverse survival function with the same values of as the pdf plots above.![]() | ||||||||||||
| Common Statistics | The formulas below are with the location parameter equal to zero and the scale parameter equal to one.
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| Parameter Estimation | Maximum likelihood estimation for the Weibull distribution is discussed in theReliability chapter (Chapter 8). It is also discussed in Chapter 21 of Johnson, Kotz, and Balakrishnan. | ||||||||||||
| Comments | The Weibull distribution is used extensively in reliability applications to model failure times. | ||||||||||||
| Software | Most general purpose statistical software programs support at least some of the probability functions for the Weibull distribution. | ||||||||||||
Runs Test for Detecting Non-randomness Purpose: Detect Non-Randomness The runs test ( Bradley, 1968 ) can be used to decide if a data set is from a random process. A run is defined as a series of increasing values or a series of decreasing values. The number of increasing, or decreasing, values is the length of the run. In a random data set, the probability that the ( I +1)th value is larger or smaller than the I th value follows a binomial distribution , which forms the basis of the runs test. Typical Analysis and Test Statistics The first step in the runs test is to count the number of runs in the data sequence. There are several ways to define runs in the literature, however, in all cases the formulation must produce a dichotomous sequence of values. For example, a series of 20 coin tosses might produce the f...

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![SQRT[GAMMA((gamma+2)/gamma) - (GAMMA((gamma+1)/gamma))**2]](http://www.itl.nist.gov/div898/handbook/eda/section3/eqns/weisd.gif)

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