JMP® 8 Demos

Who wants to waste time and money developing products and services that don’t interest or satisfy consumers? You can avoid that risk by using JMP 8 to design and analyze choice experiments. Design surveys that ask respondents to choose among sets of prospective product features. Learn which features your customers want most and how much they are willing to pay for each additional feature. Plus, you can tailor your products and services to the needs of specific market segments.

The JMP development team used a Choice experiment to understand JMP users’ preferences. The accompanying case study shows how.

The Graph Builder in JMP 8 lets you interactively construct graphs. Modify your graph or make another one with just a couple of clicks and drags of your mouse. Get instantaneous feedback as you drag variables onto a graph. Compare visually across grouping levels – all in one plot. Forget about switching between windows to make comparisons and explore your data. Just drag and drop your way to understanding.

In the accompanying case study, the Graph Builder is used to show how the growth rate in the Freddie Mac Home Price Index has changed since 1970. The Graph Builder easily charts each state, allowing quick visual comparison. As an added bonus, you can see how JMP can rapidly reshape data for any analysis.

New platforms in JMP 8 simplify product reliability analysis. Examine the distributional properties of lifetime events with the new Life Distribution platform. Discover failure causes in a system and understand the effects of repair with the new Competing Causes platform. Fit Life by X lets you analyze the effect of an acceleration factor on the lifetime of products and parts under a rich set of assumptions.

Using JMP and SAS together is even easier with JMP 8. You can connect to both SAS 9.1.3 and SAS 9.2. When importing large data sets from SAS, you can take a stratified random sample. SAS statistical graphics now flow seamlessly into JMP, making SAS analyses better than ever. A pair of add-ins shows how JMP and SAS together form a powerful analytic platform.

Now you can export interactive Bubble Plots and Profilers as Adobe Flash animations to insert into presentations, Web pages and other documents. Show your colleagues what you’ve found using JMP and let them interact with the visualizations – and see for themselves.

Three new goal plot features show the capability measures of many process variables in just one picture. Defect rate contours and shaded Cpk regions provide more detail in goal plots. The Cpk goal line inscribed in the goal plot is now adjustable. The points in a goal plots also link directly to their respective columns in the original data table.

JMP 8 offers new and improved capabilities for row states, columns and metadata; file import previewing; multiple currency support; and more.

With the JMP Scripting Language (JSL), you can extend JMP for custom analyses and applications. JMP 8 includes a rich, new interface for calling external functions written in other languages. In this video you’ll see JSL used to access financial functions, such as mortgage calculators, that are not included in JMP.

A new Mixture Profiler in JMP 8shows response contours for models where three or more factors are ingredients in a mixture. This new Profiler is designed for visualizing and optimizing response surfaces resulting from mixture experiments.

Two advanced model selection capabilities for stepwise fitting are new in JMP 8. These capabilities, named All Possible Models and Model Averaging, are ideal for handling screening experiments, as well as for mining data with lots of variables. All Possible Models lets you limit the number of terms and models to examine. With Model Averaging, you can rapidly examine thousands of models to find the terms that matter the most.

JMP 8 has an array of other new features. This video highlights a number of them, including features in these areas: new diagnostics for designed experiments, fitting distributional models in the Distribution platform, missing values and outliers in multivariate analyses, new Bayesian estimation for variance components in Gauge R&R studies, and new graphical themes in surface plots.

The Partition platform now offers automated tree-building. JMP continues to find the best splits until cross-validation indicates that new splits no longer improve the model.