Use Regression with Life Data to determine whether one or more predictors affect the failure time of a product.
This analysis determines a model that estimates the expected failure time of an item based on the values of the predictors. By using explanatory variables to explain changes in the response variable, the model helps you to determine why some items fail quickly and others survive for a long time. Using the model predictions, you can estimate the reliability of a product or system.
A model for regression with life data can include factors (categorical variables, such as manufacturer, design, or location), covariates (continuous variables, such as temperature, voltage, or pressure), as well as interactions between these terms.
Unlike other regression analyses, regression with life data accepts censored data and uses different distributions to model the data. You can also use this analysis to estimate other percentiles besides the 50th percentile.
To perform regression with life data, choose.
If your response data are binary (only two possible outcomes), instead of continuous measurements of failure time (or other units), use Probit Analysis.