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| Probability Density Function | The general formula for the probability density function of the gamma distribution is where The case where 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 gamma probability density function. | ||||||||||||||
| Cumulative Distribution Function | The formula for the cumulative distribution function of the gamma distribution is where The following is the plot of the gamma cumulative distribution function with the same values of | ||||||||||||||
| Percent Point Function | The formula for the percent point function of the gamma distribution does not exist in a simple closed form. It is computed numerically.The following is the plot of the gamma percent point function with the same values of | ||||||||||||||
| Hazard Function | The formula for the hazard function of the gamma distribution is The following is the plot of the gamma hazard function with the same values of | ||||||||||||||
| Cumulative Hazard Function | The formula for the cumulative hazard function of the gamma distribution is where The following is the plot of the gamma cumulative hazard function with the same values of | ||||||||||||||
| Survival Function | The formula for the survival function of the gamma distribution is where The following is the plot of the gamma survival function with the same values of | ||||||||||||||
| Inverse Survival Function | The gamma inverse survival function does not exist in simple closed form. It is computed numberically.The following is the plot of the gamma inverse survival function with the same values of | ||||||||||||||
| 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 | The method of moments estimators of the gamma distribution are where The equations for the maximum likelihood estimation of the shape and scale parameters are given in Chapter 18 of Evans, Hastings, and Peacock and Chapter 17 of Johnson, Kotz, and Balakrishnan. These equations need to be solved numerically; this is typically accomplished by using statistical software packages. | ||||||||||||||
| Software | Some general purpose statistical software programs support at least some of the probability functions for the gamma 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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