Data Visualization with JMP®
Data visualization is an essential part of JMP – for both statistical discovery and communication of results. Its combination of interactive graphics and robust analysis speeds innovation and sets this desktop software from SAS apart from other statistical packages.
One of the original and integral design goals of JMP was to provide a graphic for every statistic and a statistic for every graphic. Even in an area as numerically focused as model design, graphics can provide important insights, whether you’re creating a designed experiment, spotting clusters of outliers in collected data, or simulating and optimizing multiple responses.
Static graphs have dominated reports and presentations for years. But they offer only limited insight, because they are unable to show enough information in a way that is easily consumed. In contrast, JMP provides dynamic graphs, offering a more efficient way to interact with your data, to see trends and patterns that lead to better understanding of cause and effect, and to solve problems.
To communicate findings with decision makers or colleagues, you need to summarize results in a way that will promote greater understanding. JMP facilitates communication with standout tools for easily creating visualizations that synthesize results into graphics that elicit understanding more readily than tables of numbers. These features include linked histograms, mapping tools, Graph Builder and bubble plots. In fact, decision makers don’t even need to be JMP users to experience JMP visualizations. Dynamic graphs can be embedded as Flash (.swf) files in reports, easily exported in a variety of file formats or displayed in our free Graph Builder App for the iPad®.
- Effective Communication
- Geographic Maps
- Density Graphs
- Excel and JMP Together
Whether you are building models, exploring data, spotting significant trends and patterns, or communicating findings, you have access to effective visualization tools in nearly every analysis platform in JMP. And these graphics are always tied to the numbers behind them.
A table of parameter estimates from a logistic regression may not be the easiest way to understand how significant factors predict a response – and it certainly is not the most effective way to share findings with others.
Dynamically linked histograms, however, are an example of how an effective visualization can communicate significant effects. They are also approachable for others not familiar with your data or model – simply point and click on one histogram, and then see the selection of data points in the rest of the histograms.
You can add maps to all relevant JMP graphs. Use high-quality maps built in to JMP, or use your favorite Web Map Service to get custom map images on the fly. And you can go beyond geographic mapping in JMP: Add your own shapes, such as for a manufacturing plant, office building or retail store.
You may need to see your data in more detail than simple bar or line graphs provide. It may be helpful to understand where the data concentrates and where it is sparser. Use the innovative point-and-click interface of Graph Builder to display the density of your data without having to display all the individual data points. Density or contour graphs show how your data is spread across multiple variables.
You can use your Microsoft Excel spreadsheets to conduct what-if analyses and analyze potential outcomes. Using the JMP Add-In for Excel, simply specify the inputs and outputs on your spreadsheet, and launch the JMP Profiler. JMP uses the Excel spreadsheet to recalculate results as you adjust the inputs using a slide bar. You can also perform Monte Carlo simulation to predict potential outcomes based on your targets.
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