Tag: Analytical Application Development

Scagnostics JMP Add-In: A new way to explore your data

Scagnostics, scatterplot diagnostics, was discovered by John and Paul Tukey and later popularized by Leland Wilkinson in Graph-Theoretic Scagnostics (2005). These analyses were redefined in High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions (2006). The beauty of scagnostics is the ability to visually explore a data

JMP add-in measures distance between 2 points

JMP has many tools and features that allow you to interactively explore and analyze data. But what if you just want to measure the distance between two points? You could compute the distance with the standard distance formula, but what if the coordinates are latitude and longitude pairs? The distance

Lookup tables in JMP

A few days ago, I showed a customer how she could use lookup tables in JMP, and I thought it would be a good idea to share this with everyone. Those of you who have used lookup tables elsewhere already know how handy they can be. For those who have

Simple nonlinear least squares curve fitting in JMP

In his Walking Randomly blog, Mike Croucher shows how to fit a simple nonlinear curve using five different statistical programming libraries: R, MATLAB, Maple, Julia and Python/numpy. The idea is to provide concrete examples for a commonly asked modeling question that is simple to state but not so simple to

JMP add-in summarizes sample data (or your data)

Sometimes when you hear about a new feature in JMP, you want to try it out. You may not have the right kind of data on hand, or you may just want to see a canned example. The JMP sample data is a rich resource full of these good examples.

Creating a Gantt chart using JMP

A useful way for project managers to visualize a project's work breakdown structure is to create a Gantt chart. The Gantt chart is essentially an illustration of the work schedule that a project plans to follow, showing when each element of the project will start and complete. This is a commonly

PCA and illustrative variables add-in for JMP

Principal Component Analysis (PCA) is a traditional method in data analysis and, more specifically, in multivariate analysis. PCA was developed by Karl Pearson in 1901. The goal of PCA is to reduce the dimensionality in a set of correlated variables into a smaller set of uncorrelated variables that explain the majority

Image analysis of an elephant's foot in JMP

This is a picture of the bottom of an elephant's foot. As you might guess from looking at this picture, this is not a very happy elephant. Elephants kept in captivity often spend their time walking on pavement or other hard surfaces. This is not the substrate they are used

JMP content & information sharing in organizations

Companies of all sizes use JMP every day to make their products and processes better. As the number of JMP users at an organization grows, sharing information about JMP and discoveries made with JMP becomes more important. Let's tackle these two issues separately. Sharing information about JMP At a recent

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