Select the analysis options for Accelerated Life Testing

Stat > Reliability/Survival > Test Plans > Accelerated Life Testing > Options
  • Estimate model parameters: Select to estimate the model parameters from the data.
    Use starting estimates
    If you have starting estimates, enter one column to apply to all the response variables, or enter a separate column for each response variable. Each column must contain one value for each coefficient in the regression table, in the order that the coefficients appear in the regression table.
    The maximum likelihood solution may not converge if the starting estimates are not in the neighborhood of the true solution. If the algorithm does not converge to a solution, specify what you think are good starting values. For more information, go to Entering values for estimated model parameters.
    Maximum number of iterations
    Enter a positive integer to specify the maximum number of iterations for the maximum likelihood method. The default value is 20. Minitab uses the modified Newton-Raphson algorithm, a recursive method, to calculate the maximum likelihood estimates. If Minitab reaches the maximum number of iterations before convergence, the command terminates.
    Set shape (Weibull) or scale (other distributions) at
    To estimate other model coefficients while keeping the shape or scale parameter fixed, enter one value to use as the shape or scale parameter for all the response variables, or enter a number of values that is equal to the number of response variables.
  • Use historical estimates: Select to enter your own estimates for the model parameters. Enter one column to apply to all the response variables, or enter a separate column for each response variable. Each column must contain one value for each coefficient in the regression table, in the order that the coefficients appear in the regression table.

    You can use historical estimates to cross-validate the model with an independent sample. For more information, go to Entering values for estimated model parameters.