Code folding allows you to collapse a block of code – you can use it to focus on high-level structure without getting lost in the detail.

To enable code folding enable to option under the Script Editor section of Preferences, found under the File menu.

I use it with my user-defined functions to give me an overview of contents within an include file. Combined with appropriately placed comments this helps to summarise the contents of a library of functions. Here is an example:

Process capability is a well-established technique for evaluating the degree to which a process is capable of delivering a product within specification. But what if the specifications are unknown or at best tentative?

The calculations of process capability analysis can be reversed so that for a given set of target capability values the associated specification limits can be generated. The calculation is straight-forward for a normal distribution but needs a bit more thought when it comes to asymmetric distributions.

The lognormal distribution is a commonly used distribution for modelling asymmetric data. It’s just the log of a Normal distribution right? Well no, it’s actually the other way around. You take the log of a lognormal distribution to arrive at a normal distribution. Is it just me, but I always have a bit of a mental block about this, it always feels a bit back to front.

In this post I will explore the relationship between a lognormal distribution and a normal distribution.

In my last post I introduced the idea of using the JSL script editor as a simple command line calculator; and prior to that I discussed how process capability indices (Cp,Cpk) are a convenient shorthand notation but suffer from lack of transparency. Today I will bring these two themes together by showing how I can use the JSL script editor to calculate defective parts per million (dppm) for a given set of capability indices Cp and Cpk.

You don’t need to be a programmer to make productive use of the JSL script editor. The editor can be used by non-programmers as a simple command-line calculator that provides access to JMP’s library of mathematical and statistical functions.

In this post I describe an approach that I use to teach statistics. The goal is to use JMP to help develop an intuitive understanding of some common concepts: hypothesis testing, p-values and reference distributions.