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.
Sometimes it is useful to access the installation folder of an add-in. This post describes how to do that.
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.
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.
In my previous post I introduced the sample data table Pet Survey. I created a column formula to classify each respondent to determine whether they owned a cat, a dog, or both. In this simple example, there were signs of the problems that arise when processing unstructured text data. My classification of “dog” missed out responses referring to huskies; my classification of “cat” incorrectly included references to cattle. I looked at the Text Explorer platform and focused on the output contained in the lists of terms and phrases. In this post I want to focus on workflow: using the functionality within Text Explorer platform to gain meaningful insights into my data, and to answer specific questions.
In this post I will walk through some of the common tasks that are undertaken when we process unstructured text-based data. This will also give me the opportunity to introduce the terminology associated with text processing.
Traditionally statistical methods have focused on the use of numerical data, perhaps partitioned by classification data. A classic example of this would be oneway analysis of variance, or linear multiple regression containing classification variables that had been internally coded as integer values.
JSL is often described as a scripting language. Personally I think that doesn’t do it justice. I prefer to think of it as a programming language. The difference? For me an obvious difference is that instead of using hard-coded values I want to use variables. In particular I want to use variables to handle column references.