Another advantage of the general log-linear and proportional hazards models presented in this chapter is that they allow 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, X1 and X2.
For the units from Lot 1, X1 = 1 and X2 = 0.
For the units from Lot 2, X1 = 0 and X2 = 1.
For the units from Lot 3, X1 = 0 and X2 = 0.
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 Eqn. (1) we get:
where:
X1 and X2 are the indicator variables, as defined above.
where
T
is the temperature.
The data can now be entered in ALTA PRO and, with the assumption of an underlying life distribution and using MLE, the parameters of this model can be obtained.
See Also:
General Log-Linear Relationship
Proportional Hazards Model
Go to Weibull.com
Go to ReliaSoft.com
©1998-2007. ReliaSoft Corporation. ALL RIGHTS RESERVED.