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 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
A contingency analysis determines whether there is a relationship between two categorical variables. A caterer, for example, might be interested in knowing whether entrée selections at an event were related to gender, given the following data: The contingency platform in JMP requires the X and Y variables to be contained
Understanding your customers has always been important, but it's even more so when marketing products and services related to winter snow sports. Winter sports can have a short season and a limited number of consumers. How do you find them, identify their needs, create new promotions and assess whether the
Over the past few weeks, we’ve looked at the rise of Excel’s usage and popularity, and I've noted that Excel has made forays into analytics. Today, we’ll look at one type of analysis: scenario-based planning and optimization. Using Excel add-ins, you can create what-if analysis and scenario-based planning using Monte
In a recent blog post, I introduced an add-in, the Bubble Shape Editor, that makes it easier to use custom shapes in place of the default circle in Bubble Plot. The add-in takes advantage of JMP's ability to draw shapes based on SVG path specifications. When custom shapes were introduced
Halloween is here. It’s a time when the weather gets colder and the leaves fall from the trees. A time when kids dress up in fun costumes and go trick-or-treating. A time when we start thinking about how to spend the holidays with our family and friends. And if you’re like me,
If you never have to deal with missing data, please count your blessings — nothing to see here today! However, for those of us who are not so lucky, I’ve got some good news: First, throughout JMP 11, we have added capabilities for dealing with missing data. The Reliability Growth
Bubble Plots are very popular for their ability to display many dimensions at once (X, Y, Size, Color, ID, Time). As the name suggests, a traditional Bubble Plot uses a circular shape to represent each row or aggregation of rows based on the ID(s). The radius of the circle is
Note: This blog post is contributed by Alex Spancake, who was the JMP Academic Team's summer intern. Mia Stephens worked with Alex on this project. During my summer internship with the JMP Academic Team, I developed a number of textbook add-ins, which package data sets and other teaching resources into