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.

The idea of object-orientation is not new to JSL, but user-created objects require a complex code structure that wraps data and functions into namespaces (for example, see the navigation wizard).

In version 14, there is explicit support for classes which dramatically simplifies the process of creating reusable objects. I thought I would introduce them by means of a real- example: a notification window that shows progress when stepping through a sequence of time-consuming steps.

A wizard is a familiar user-interface mechanism for scrolling through a sequence of steps of more generally scrolling through a series of content. In this post I illustrate how this functionality can be implemented through the use of an object-oriented framework. (more…)

It turns out that the prediction profiler has a hidden secret. And not just some easter egg feature that is just a bit of fun. This secret is core to how you use the profiler – and might just totally change how you use it in future.

A neuron is a single node within a neural network. By analogy with neurons within the brain we can think of a neuron “firing” in response to an input trigger, and we can think of machine learning as the process of training the neuron to recognise that input trigger.