Probability plots
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The Accelerated Life Testing command draws several probability plots to help you assess the fit of the chosen distribution. You can draw probability plots for the standardized and Cox-Snell residuals. You can use these plots to assess whether a particular distribution fits your data. In general, the closer the points fall to the fitted line, the better the fit.

You can also choose to draw probability plots for each level of the accelerating variable based on individual fits or on the fitted model. You can use these plots to assess whether the distribution, transformation, and assumption of equal shape (Weibull or exponential) or scale (other distributions) are appropriate. The probability plot based on the fitted model includes fitted lines that are based on the chosen distribution and transformation. If the points do not fit the lines adequately, then consider a different transformation or distribution.

The probability plot based on the individual fits includes fitted lines that are calculated by individually fitting the distribution to each level of the accelerating variable. If the distributions have equal shape (Weibull or exponential) or scale (other distributions) parameters, then the fitted lines should be approximately parallel. The points should fit the line adequately if the chosen distribution is appropriate.

Minitab provides one goodness-of-fit measure: the Anderson-Darling statistic. A smaller Anderson-Darling statistic indicates that the distribution provides a better fit. You can use the Anderson-darling statistic to compare the fit of competing models.

For a discussion of probability plots, see Probability plots.