What does logWorth measure that is not included in the anova table of a Fit Model analysis?

[#1 · Accepted solution]

Hi @hartpjb,

The logWorth values shown in the effect summary table are -log transformations of each model effect's p-value. This representation can be useful when interpreting the degree of difference in importance between your model effects (i.e. potentially easier to compare than p-values). The Analysis of Variance table is used in evaluating the overall model. More information on these tables can be found in JMP's help documentation: Effect Summary and Analysis of Variance.

[#2]

Hi Jeff, Thanks for the explanation. logWorth appears unique to JMP. I find no reference to it in books on statistical methods - e.g. Sokal and Rohlf 'Biometry' or Zar 'Biostatistical analysis' or Snedecor 'Statistical methods'. Is logWorth peculiar to certain disciplines? - I am a biologist and perhaps have a limited view of the statistical field.

[#3]

Hi @hartpjb,

Data transformations are used in many disciplines; some transformation are functional (i.e. log transform of concentration when evaluating bioassay data) while others are more for aesthetic/visualization purposes. The latter is the case for LogWorth. As mentioned above, this transformation can make it easier to visualize and interpret truncated p-values. Two model effects may have a p-value show as 0.0000, leading you to think they are equally important; when transformed, though, you may find that one model effect is more important than another.

I can't speak to whether LogWorth is specific to JMP - if you're curious and want a more clear answer then you may consider tuning into our virtual JMP Discovery Conference next week. There is a 'Meet the Experts' session where you can chat with our Development Team, the great minds behind JMP