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''This article also appears in the [[Multivariable_Relationships:_General_Log-Linear_and_Proportional_Hazard|Accelerated Life Testing Data Analysis Reference]] book.'' </noinclude>

When a test involves multiple accelerating stresses or requires the inclusion of an engineering variable, a general [[Multivariable_Relationships:_General_Log-Linear_and_Proportional_Hazard|multivariable relationship]] is needed. Such a relationship is the general log-linear relationship, which describes a life characteristic as a function of a vector of <math>n\,\!</math> stresses, or <math>\underline{X}=({{X}_{1}},{{X}_{2}}...{{X}_{n}}).\,\!</math> ALTA includes this relationship and allows up to eight stresses. Mathematically the relationship is given by: 

::<math>L(\underline{X})={{e}^{{{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}}}}\,\!</math>

where:

*<math>{{\alpha }_{0}}\,\!</math> and <math>{{\alpha }_{j}}\,\!</math> are model parameters.

*<math>X\,\!</math> is a vector of <math>n\,\!</math> stresses.

This relationship can be further modified through the use of transformations and can be reduced to the relationships discussed previously, if so desired. As an example, consider a single stress application of this relationship and an inverse transformation on <math>X,\,\!</math> such that <math>V=1/X\,\!</math> or: 

::<math>\begin{align}
  & L(V)= & {{e}^{{{\alpha }_{0}}+\tfrac{{{\alpha }_{1}}}{V}}} =\ & {{e}^{{{\alpha }_{0}}}}{{e}^{\tfrac{{{\alpha }_{1}}}{V}}}  
\end{align}\,\!</math>

It can be easily seen that the generalized log-linear relationship with a single stress and an inverse transformation has been reduced to the [[Arrhenius Relationship|Arrhenius relationship]], where: 

::<math>\begin{align}
  & C= & {{e}^{{{\alpha }_{0}}}} \\ 
 & B= & {{\alpha }_{1}}  
\end{align}\,\!</math>

or: 

::<math>L(V)=C{{e}^{\tfrac{B}{V}}}\,\!</math>

Similarly, when one chooses to apply a logarithmic transformation on <math>X\,\!</math> such that <math>V=\ln (X)\,\!</math>, the relationship would reduce to the [[Inverse Power Law (IPL) Relationship|Inverse Power Law relationship]]. Furthermore, if more than one stress is present, one could choose to apply a different transformation to each stress to create combination relationships similar to the [[Temperature-Humidity Relationship|Temperature-Humidity]] and the [[Temperature-NonThermal Relationship|Temperature-Non Thermal]]. ALTA has three built-in transformation options, namely:

{|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
|-
|None||	X=V||	Exponential LSR
|-
|Reciprocal||	 <math>V=1/X\,\!</math>|| 	Arrhenius LSR
|-
|Logarithmic||	 <math>V=\ln (X)\,\!</math>|| 	Power LSR
|}

The power of the relationship and this formulation becomes evident once one realizes that 6,651 unique life-stress relationships are possible (when allowing a maximum of eight stresses). When combined with the life distributions available in ALTA, almost 20,000 models can be created.

==Using the GLL Model==
Like the previous relationships, the general log-linear relationship can be combined with any of the available life distributions by expressing a life characteristic from that distribution with the GLL relationship. A brief overview of the GLL-distribution models available in ALTA is presented next.

===GLL Exponential===
The GLL-exponential model can be derived by setting <math>m=L(\underline{X})\,\!</math> in the exponential ''pdf'', yielding the following GLL-exponential ''pdf'': 

<math>f(t,\underline{X})={{e}^{-\left( {{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}} \right)}}{{e}^{-\left( {{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}} \right)\cdot t}}\,\!</math>

The total number of unknowns to solve for in this model is <math>n+1\,\!</math> (i.e., <math>{{a}_{0}},{{a}_{1}},...{{a}_{n}}).\,\!</math>

===GLL Weibull===
The GLL-Weibull model can be derived by setting <math>\eta =L(\underline{X})\,\!</math> in Weibull ''pdf'', yielding the following GLL-Weibull ''pdf'': 

<math>f(t,\underline{X})=\beta \cdot {{t}^{\beta -1}}{{e}^{-\beta \left( {{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}} \right)}}{{e}^{-{{t}^{\beta }}{{e}^{-\beta \left( {{\alpha }_{0}}+\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}} \right)}}}}\,\!</math>

The total number of unknowns to solve for in this model is <math>n+2\,\!</math> (i.e., <math>\beta ,{{a}_{0}},{{a}_{1}},...{{a}_{n}}).\,\!</math>

===GLL Lognormal===
The GLL-lognormal model can be derived by setting <math>\breve{T}=L(\underline{X})\,\!</math> 
in the lognormal ''pdf'', yielding the following GLL-lognormal ''pdf'': 

<math>f(t,\underline{X})=\frac{1}{t\text{ }{{\sigma }_{{{T}'}}}\sqrt{2\pi }}{{e}^{-\tfrac{1}{2}{{\left( \tfrac{{T}'-{{\alpha }_{0}}-\underset{j=1}{\overset{n}{\mathop{\sum }}}\,{{\alpha }_{j}}{{X}_{j}}}{{{\sigma }_{{{T}'}}}} \right)}^{2}}}}\,\!</math>

The total number of unknowns to solve for in this model is <math>n+2\,\!</math> (i.e., <math>{{\sigma }_{{{T}'}}},{{a}_{0}},{{a}_{1}},...{{a}_{n}}).\,\!</math>

===GLL Likelihood Function===
The maximum likelihood estimation method can be used to determine the parameters for the GLL relationship and the selected life distribution. For each distribution, the likelihood function can be derived, and the parameters of model (the distribution parameters and the GLL parameters) can be obtained by maximizing the log-likelihood function. For example, the log-likelihood function for the Weibull distribution is given by: 

::<math>\begin{align}
  & \ln (L)= & \Lambda =\underset{i=1}{\overset{{{F}_{e}}}{\mathop \sum }}\,{{N}_{i}}\ln \left[ \beta \cdot T_{i}^{\beta -1}{{e}^{-T_{i}^{\beta }\cdot {{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{\sum}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}}}}{{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{\sum}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}} \right] -\underset{i=1}{\overset{S}{\mathop \sum }}\,N_{i}^{\prime }{{\left( T_{i}^{\prime } \right)}^{\beta }}{{e}^{-\beta \left( {{\alpha }_{0}}+\mathop{\sum}_{j=1}^{n}{{a}_{j}}{{x}_{i,j}} \right)}}+\overset{FI}{\mathop{\underset{i=1}{\mathop{\underset{}{\overset{}{\mathop \sum }}\,}}\,}}\,N_{i}^{\prime \prime }\ln [R_{Li}^{\prime \prime }-R_{Ri}^{\prime \prime }]  
\end{align}\,\!</math>

where:

::<math>\begin{align}
  & R_{Li}^{\prime \prime }= & {{e}^{-{{\left( T_{Li}^{\prime \prime }{{e}^{{{\alpha }_{0}}+\underset{j=1}{\mathop{\overset{n}{\mathop{\mathop{}_{}^{}}}\,}}\,{{\alpha }_{j}}{{x}_{j}}}} \right)}^{\beta }}}} \\ 
 & R_{Ri}^{\prime \prime }= & {{e}^{-{{\left( T_{Ri}^{\prime \prime }{{e}^{{{\alpha }_{0}}+\underset{j=1}{\mathop{\overset{n}{\mathop{\mathop{}_{}^{}}}\,}}\,{{\alpha }_{j}}{{x}_{j}}}} \right)}^{\beta }}}}  
\end{align}\,\!</math>

and:

*<math>{{F}_{e}}\,\!</math> is the number of groups of exact times-to-failure data points.

*<math>{{N}_{i}}\,\!</math> is the number of times-to-failure in the <math>{{i}^{th}}\,\!</math> time-to-failure data group.

*<math>\lambda \,\!</math> is the failure rate parameter (unknown).

*<math>{{T}_{i}}\,\!</math> is the exact failure time of the <math>{{i}^{th}}\,\!</math> group.

*<math>S\,\!</math> is the number of groups of suspension data points.

*<math>N_{i}^{\prime }\,\!</math> is the number of suspensions in the <math>{{i}^{th}}\,\!</math> group of suspension data points.

*<math>T_{i}^{\prime }\,\!</math> is the running time of the <math>{{i}^{th}}\,\!</math> suspension data group.

*<math>FI\,\!</math> is the number of interval data groups.

*<math>N_{i}^{\prime \prime }\,\!</math> is the number of intervals in the <math>{{i}^{th}}\,\!</math> group of data intervals.

*<math>T_{Li}^{\prime \prime }\,\!</math> is the beginning of the <math>{{i}^{th}}\,\!</math> interval.

*<math>T_{Ri}^{\prime \prime }\,\!</math> is the ending of the <math>{{i}^{th}}\,\!</math> interval.

==GLL Example==
{{:General_Log-Linear_Relationship_Example}}

<noinclude>=Indicator Variables=
Another advantage with the General Log-Linear models is that it allows for simultaneous analysis of continuous and categorical variables. Categorical variables are variables that take on discrete values such as the lot designation for products from different manufacturing lots. In this example, lot is a categorical variable, and it can be expressed in terms of indicator variables. Indicator variables only take a value of 1 or 0. For example, consider a sample of test units. A number of these units were obtained from Lot 1, others from Lot 2, and the rest from Lot 3. These three lots can be represented with the use of indicator variables, as follows:

*Define two indicator variables, <math>{{X}_{1}}\,\!</math> and <math>{{X}_{2}}.\,\!</math> 

*For the units from Lot 1, <math>{{X}_{1}}=1,\,\!</math> and <math>{{X}_{2}}=0.\,\!</math> 

*For the units from Lot 2, <math>{{X}_{1}}=0,\,\!</math> and <math>{{X}_{2}}=1.\,\!</math> 

*For the units from Lot 3, <math>{{X}_{1}}=0,\,\!</math> and <math>{{X}_{2}}=0.\,\!</math> 

Assume that an accelerated test was performed with these units, and temperature was the accelerated stress. In this case, the GLL relationship can be used to analyze the data. From the GLL relationship we get:

::<math>L(\underline{X})={{e}^{{{\alpha }_{0}}+{{\alpha }_{1}}{{X}_{1}}+{{\alpha }_{2}}{{X}_{2}}+{{\alpha }_{3}}{{X}_{3}}}}\,\!</math>

where:

*<math>{{X}_{1}}\,\!</math> and <math>{{X}_{2}}\,\!</math> are the indicator variables, as defined above.

*<math>{{X}_{3}}=\tfrac{1}{T},\,\!</math> where <math>T\,\!</math> is the temperature.

The data can now be entered in ALTA and, with the assumption of an underlying life distribution and using MLE, the parameters of this model can be obtained.</noinclude>