Trend tests for Parametric Growth Curve

When fitting a parametric growth curve model, you want to select a model that results in a good fit to your data. The hypotheses for the trend tests are as follows:
  • H0: No trend exists (homogeneous Poisson process)
  • H1: A trend exists (nonhomogeneous Poisson process)

By default, Minitab provides five trend tests: MIL-Hdbk-189 (pooled), MIL-Hdbk-189 (TTT-based), Laplace (pooled), Laplace (TTT-based), and Anderson-Darling. For more information, go to Trend tests (also called goodness-of-fit tests).

Example Output

Trend Tests MIL-Hdbk-189 Laplace’s TTT-based Pooled TTT-based Pooled Anderson-Darling Test Statistic 378.17 378.28 0.86 -0.40 0.94 P-Value 0.107 0.448 0.388 0.688 0.389 DF 424 400


For the air conditioning data, the p-values for the goodness-of-fit tests are 0.107, 0.448, 0.388, 0.688, and 0.389. Because all p-values are greater than α = 0.05, the engineer can conclude that there is not enough evidence that a trend exists in the data. This result is consistent with a shape of 1 for the power-law process.

Although the power-law process provides an adequate fit, using a two-parameter model is not necessary when there is no trend in the data. Therefore, the engineer may want to consider using the simpler homogeneous Poisson process to model these data.

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