Distribution Fitting Software & Articles

Gamma Distribution

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The Gamma distribution is defined in terms of a shape factor, a scale factor, and a location factor. The two-parameter version of this distribution is more commonly used in practice. The three-parameter Gamma distribution is also referred to as the Pearson Type III distribution.

Parameters

- shape parameter ()
- scale parameter ()
- location parameter ( yields the two-parameter Gamma distribution)

Domain

Probability Density Function (PDF)

Gamma distribution PDF

Gamma Distribution Fitting

EasyFit allows to automatically or manually fit the Gamma distribution and 55 additional distributions to your data, compare the results, and select the best fitting model using the goodness of fit tests and interactive graphs. Watch the short video about EasyFit and get your free trial.

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Gamma Distribution Graphs and Properties

EasyFit displays all graphs and properties of the Gamma distribution, presenting the results in an easy to read & understand manner. EasyFit calculates statistical moments (mean, variance etc.), quantiles, tail probabilities depending on the distribution parameters you specify.

Random Numbers from the Gamma Distribution

You can easily generate random numbers from the Gamma distribution in a variety of ways:

  • directly from EasyFit
  • in Excel sheets using the worksheet functions provided by EasyFitXL
  • in your VBA applications using the EasyFitXL library

Excel Worksheet and VBA Functions

EasyFitXL enables you to use the following functions in your Excel sheets and VBA applications:

Function Name
Description
GammaPdf Probability Density Function
GammaCdf Cumulative Distribution Function
GammaHaz Hazard Function
GammaInv Inverse CDF (Quantile Function)
GammaRand Random Numbers
GammaMean Mean
GammaVar Variance
GammaStdev Standard Deviation

Learn more: EasyFit Help on the Gamma distribution

Applications

The Gamma distribution has a variety of applications. In particular, it can be used to model:

  • the queuing systems;
  • the flow of items through manufacturing and distribution processes;
  • the load on web servers;
  • the probability of ruin & value (risk management).