Maximum likelihood estimates of the parameters in the distribution are calculated by maximizing the likelihood function with respect to the parameters. For a given data set, the maximum likelihood estimates are the most likely values for the distribution parameters.
The Newton-Raphson algorithm is used to calculate maximum likelihood estimates of the distribution parameters. The Newton-Raphson algorithm is an iterative numerical method for calculating the maximum of a function. 1
Minitab calculates the parameter estimates using the maximum likelihood method for all the distributions except the lognormal distribution. For the lognormal distribution, Minitab calculates unbiased parameter estimates.
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| CDF |
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| Mean |
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| Stdev |
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| Term | Description |
|---|---|
| μ | Scale parameter |
| σ | Shape parameter |
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| CDF |
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| Mean | αβ |
| Stdev | αβ2 |
| Term | Description |
|---|---|
| α | Shape parameter |
| β | Scale parameter |
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| CDF |
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| Mean | θ |
| Stdev | θ |
| Term | Description |
|---|---|
| θ | Scale parameter |
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| CDF |
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| Mean |
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| Stdev |
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| Term | Description |
|---|---|
| μ | Location parameter |
| σ | Scale parameter |
| γ | Euler's constant (approximately equals 0.5772) |
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|
| CDF |
![]() |
| Mean |
![]() |
| Stdev |
![]() |
| Term | Description |
|---|---|
| α | Scale parameter |
| β | Shape parameter |
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|
| CDF |
![]() |
| Mean |
![]() |
| Stdev |
![]() |
| Term | Description |
|---|---|
| μ | Location parameter |
| σ | Scale parameter |
| γ | Euler's constant (approximately equals 0.5772) |
![]() |
|
| CDF |
![]() |
| Mean | μ |
| Stdev |
![]() |
| Term | Description |
|---|---|
| μ | Location parameter |
| σ | Scale parameter |
![]() |
|
| CDF |
![]() |
| Mean |
![]() |
| Stdev |
![]() |
| Term | Description |
|---|---|
| μ | Location parameter |
| σ | Scale parameter |