Data - Regression with Life Data
main topic      

Enter three types of columns in the worksheet:

·    the response variable (failure times) - see Failure times.

·    censoring indicators for the response variables, if needed

·    predictor variables, which may be factors (categorical variables) or covariates (continuous variables). For factors, Minitab estimates the coefficients for k - 1 design variables (where k is the number of levels), to compare the effect of different levels on the response variable. For covariates, Minitab estimates the coefficient associated with the covariate to describe its effect on the response variable.

Unless you specify a predictor as a factor, the predictor is assumed to be a covariate. In the model, terms may be created from these predictor variables and treated as factors, covariates, interactions, or nested terms. The model can include up to 9 factors and 50 covariates. Factors may be crossed or nested. Covariates may be crossed with each other or with factors, or nested within factors. See How to specify the model terms.

You can enter up to ten samples per analysis.

Depending on the type of censoring you have, you will set up your worksheet in column or table form. You can also structure the worksheet as raw data, or as frequency data. For details, see Worksheet Structure for Regression with Life Data.

Factor columns can be numeric or text. Minitab by default designates the lowest numeric or text value as the reference level. To change the reference level, see Factor variables and reference levels.

Minitab automatically excludes all observations with missing values from all calculations.

How you run the analysis depend on whether your data are uncensored/right censored or uncensored/arbitrarily censored: