Template:One-parameter weibull distribution - Revision history

Richard House: Redirected page to The Weibull Distribution

2012-08-13T01:31:03Z

Redirected page to The Weibull Distribution

← Older revision		Revision as of 01:31, 13 August 2012
Line 1:		Line 1:
	~~====~~ The ~~1-Parameter~~ Weibull Distribution ~~====~~		#REDIRECT [[The Weibull Distribution]]

	~~The 1-parameter Weibull ''pdf'' is obtained by again setting~~
	~~<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:~~

	~~<math> f(t)={ \frac{C}{\eta }}\left( {\frac{t}{\eta }}\right) ^{C-1}e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math>~~

	~~where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.~~

	Note that in the formulation of the 1-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience with identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Chris Kahn: /* The 1-Parameter Weibull Distribution */

2012-04-24T22:46:17Z

The 1-Parameter Weibull Distribution

← Older revision		Revision as of 22:46, 24 April 2012
Line 8:		Line 8:
	where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.		where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

	Note that in the formulation of the 1-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the 1-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience with identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Chris Kahn: /* The One-Parameter Weibull Distribution */

2012-04-24T22:41:27Z

The One-Parameter Weibull Distribution

← Older revision		Revision as of 22:41, 24 April 2012
Line 1:		Line 1:
	==== The ~~One~~-Parameter Weibull Distribution ====		==== The 1-Parameter Weibull Distribution ====

	The ~~one~~-parameter Weibull ''pdf'' is obtained by again setting		The 1-parameter Weibull ''pdf'' is obtained by again setting
	<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:		<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:

Line 8:		Line 8:
	where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.		where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

	Note that in the formulation of the ~~one~~-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the 1-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Harry Guo: /* The One-Parameter Weibull Distribution */

2012-02-09T21:35:41Z

The One-Parameter Weibull Distribution

← Older revision		Revision as of 21:35, 9 February 2012
Line 4:		Line 4:
	<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:		<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:

	<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>		<math> f(t)={ \frac{C}{\eta }}\left( {\frac{t}{\eta }}\right) ^{C-1}e^{-\left( {\frac{t}{ \eta }}\right) ^{C}} \,\!</math>

	where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.		where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

	Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Nicolette Young at 20:32, 7 February 2012

2012-02-07T20:32:33Z

← Older revision		Revision as of 20:32, 7 February 2012
Line 9:		Line 9:

	Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

	~~[[Category:Needs an example]]~~

Nicolette Young at 20:30, 7 February 2012

2012-02-07T20:30:12Z

← Older revision		Revision as of 20:30, 7 February 2012
Line 9:		Line 9:

	Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

			[[Category:Needs an example]]

Nicolette Young: /* The One-Parameter Weibull Distribution */

2012-02-07T19:54:34Z

The One-Parameter Weibull Distribution

← Older revision		Revision as of 19:54, 7 February 2012
Line 4:		Line 4:
	<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:		<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:

	:<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>		<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>

	where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.		where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

	Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Nicolette Young: /* The One-Parameter Weibull Distribution */

2012-02-07T19:53:16Z

The One-Parameter Weibull Distribution

← Older revision		Revision as of 19:53, 7 February 2012
Line 4:		Line 4:
	<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:		<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:

	::<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>		:<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>

	where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.		where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

	Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.		Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Nicolette Young: /* The One-Parameter Weibull Distribution */

2012-02-07T17:16:24Z

The One-Parameter Weibull Distribution

← Older revision		Revision as of 17:16, 7 February 2012
Line 1:		Line 1:
	=== The One-Parameter Weibull Distribution ===		==== The One-Parameter Weibull Distribution ====

	The one-parameter Weibull ''pdf'' is obtained by again setting		The one-parameter Weibull ''pdf'' is obtained by again setting

Nicolette Young: Created page with '=== The One-Parameter Weibull Distribution === The one-parameter Weibull ''pdf'' is obtained by again setting $\gamma=0 \,\!$ and assuming $\beta=C=Constant \,\…'$

2012-01-24T16:06:22Z

Created page with '=== The One-Parameter Weibull Distribution === The one-parameter Weibull ''pdf'' is obtained by again setting <math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\…'

New page

=== The One-Parameter Weibull Distribution ===

The one-parameter Weibull ''pdf'' is obtained by again setting
<math>\gamma=0 \,\!</math> and assuming <math>\beta=C=Constant \,\!</math> assumed value or:

::<math> f(T)={ \frac{C}{\eta }}\left( {\frac{T}{\eta }}\right) ^{C-1}e^{-\left( {\frac{T}{ \eta }}\right) ^{C}} \,\!</math>

where the only unknown parameter is the scale parameter, <math>\eta\,\!</math>.

Note that in the formulation of the one-parameter Weibull, we assume that the shape parameter <math>\beta \,\!</math> is known ''a priori'' from past experience on identical or similar products. The advantage of doing this is that data sets with few or no failures can be analyzed.

Template:One-parameter weibull distribution - Revision history

Richard House: Redirected page to The Weibull Distribution

Chris Kahn: /* The 1-Parameter Weibull Distribution */

Chris Kahn: /* The One-Parameter Weibull Distribution */

Harry Guo: /* The One-Parameter Weibull Distribution */

Nicolette Young at 20:32, 7 February 2012

Nicolette Young at 20:30, 7 February 2012

Nicolette Young: /* The One-Parameter Weibull Distribution */

Nicolette Young: /* The One-Parameter Weibull Distribution */

Nicolette Young: /* The One-Parameter Weibull Distribution */

Nicolette Young: Created page with '=== The One-Parameter Weibull Distribution === The one-parameter Weibull ''pdf'' is obtained by again setting \gamma=0 \,\! and assuming \beta=C=Constant \,\…'

Nicolette Young: Created page with '=== The One-Parameter Weibull Distribution === The one-parameter Weibull ''pdf'' is obtained by again setting $\gamma=0 \,\!$ and assuming $\beta=C=Constant \,\…'$