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

Interpretation

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.

By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy