# Rank Index

Here’s the problem.  I have a list of ‘things’, for example, batch names.  I can also get another list, for example, the start dates of the batches.  How do I sort the batches by date?  The answer: use the Rank Index function.  (more…)

I’m going to take a look at the process of creating a JMP add-in to create a single-file deployment package for a collection of files associated with a JMP script.  (more…)

# Box-Jenkins, and JMP

There are two main classes of model for time series data – autoregressive (AR) and moving average (MA).  The generalisation of the two is referred to as ARIMA – autoregressive integrated moving average.  These models are sometimes referred to as Box-Jenkins models, but more accurately the term “Box-Jenkins” refers to a methodology for model selection. (more…)

# Linear Programming

Did you know that JMP has an LP Solver?  Linear programming (LP) is a technique for optimising a function subject to a set of linear constraints.  (more…)

# Inspired By John Sall

As users of JMP Software we all develop our own opinions of what we like, what we don’t like, and how we think it should evolve.

So it’s very insightful to hear the perspective of the man behind the software – John Sall.  He is hard to ignore, both physically (he is tall!) and intellectually (he is a giant!).  But above all, he knows how we ought to be using the software whereas we just think we know.  (more…)

# Testing For Independent Observations

In this final step of developing the oneway advisor I want to check the assumption of “independence”.  Specifically I want to test whether there is any evidence of serial correlation on the residuals.  (more…)

# Testing For Equal Variance

This is the sixth and penultimate step in constructing the oneway advisor.  The advisor automates four tests associated with the assumptions of a oneway analysis of variance.  In this step a test will be performed to assess whether the data within each level of the grouping variable have equal variance. (more…)

# Testing Residuals for Normality

This is step 5 in the creation of the oneway advisor.  In the previous step code was produced for testing whether the data within each level of the grouping (X) variable were normally distributed.

In this step code will be developed to determine whether the residuals are normally distributed.
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# Checking Distributions Within Group Levels

This is the fourth step in building the oneway advisor.

It’s time to start developing the code that will check the assumptions of the oneway analysis.  The first assumption is that data within each level of the grouping variable are normally distributed.
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# Using Pattern Matching To Inspect Reports

This is the third step in building the oneway advisor.  In the first step the code for the main window was developed.  In the second step the code was revised to handle access to the icon files.

The advisor will validate the assumptions associated with a oneway analysis of variance.  It is assumed that the user has created the oneway analysis prior to running the advisor.  In this step this assumption is validated.
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