Regression with Life Data
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Stat > Reliability/Survival > Regression with Life Data

Use Regression with Life Data to see whether one or more predictors affect the failure time of a product. The goal is to come up with a model that predicts failure time. This model uses explanatory variables to explain changes in the response variable, for example why some products fail quickly and some survive for a long time. The model can include factors, covariates, interactions, and nested terms.

Regression with Life Data differs from Minitab's regression commands in that it accepts censored data and uses different distributions.

To do regression with life data, you must enter the following information:

·    the response variable (failure times).

·    model terms, which consist of any number of predictor variables and when appropriate, various interactions between predictors and nested terms. See How to specify the model terms. Some of these variables may be factors.

Dialog box items

Responses are uncens/right censored data: Choose if your data is uncensored or right censored.

Responses are uncens/ arbitrarily censored data: Choose if your data is uncensored or arbitrarily censored.

Variables/Start variables: Enter up to 10 columns (10 different samples) containing the start times.

End variables: If you have uncensored or arbitrarily censored data, enter up to 10 columns (10 different samples) of end times.

Freq. columns (optional): Enter a column for each variable containing the frequency data.

Model: Enter the model terms - see How to specify the model terms. If any of those predictors are factors, enter them again in Factors.

Factors (optional): Enter any variables in the model that are factors.

Assumed distribution: Choose one of eight common lifetime distributions: Weibull (default), smallest extreme value, exponential, normal, lognormal, logistic, and loglogistic.