Parameter estimates table for Parametric Growth Curve

The parameter estimates define the best-fitting parameter estimates for the chosen model. All other parametric growth curve graphs and statistics are based on this model.

The value of the shape (β) depends on whether your system is improving, deteriorating, or remaining stable.
  • If 0 < β < 1, the failure/repair rate is decreasing. Thus, your system is improving over time.
  • If β = 1, the failure/repair rate is constant. Thus, your system is remaining stable over time.
  • If β > 1, the failure/repair rate is increasing. Thus, your system is deteriorating over time.

You cannot determine from the estimated parameters whether the selected model fits the data well. Use the plots and trend tests to determine whether the model adequately fits the data.

Example output

System: System Model: Power-Law Process Estimation Method: Maximum Likelihood
Parameter Estimates Standard 95% Normal CI Parameter Estimate Error Lower Upper Shape 1.10803 0.067 0.984256 1.24738 Scale 128.763 22.489 91.4369 181.325


For the air conditioning data, Minitab used the maximum likelihood method of estimation for the power-law process model. The estimated shape parameter is 1.10803 and the estimated scale parameter is 128.763.

The engineer can be 95% confident that the interval (0.984256, 1.24738) contains the true shape for the population. Because the shape estimate is not significantly different from 1, the engineer can conclude that the systems are failing at a constant rate over time.

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