Exploratory Data Analysis with JMP®
Exploratory data analysis (EDA) helps find structure in data – whether in small samples or large volumes of data collected from many domains. The interactive graphics and robust data analysis capabilities in JMP make it an ideal alternative to Excel for EDA and other types of statistical data analysis.
The in-memory architecture of JMP means you don’t have to wait for a server to return data analysis results, even with significant volumes of data.
Heuristic, open-ended and dynamic, EDA often involves significant data quality and aggregation steps as users analyze data and try different visualizations to tell its story most accurately. EDA is a data analysis tool that can also guide you in building a useful model.
- Data Selection and Management
- Linked Interactive Graphs
- Linked Interactive Analysis
Handling incongruent cases appropriately is an important step in EDA. Individual rows in a data table may be selected, colored, marked, labeled and excluded or hidden directly from any visualisation in which they are displayed, and such changes immediately propagate to all open displays. You can use Missing Data Pattern to quickly segregate cases that are incomplete, while Summary allows you to aggregate detail-level data into a linked table to allow visualizations at a higher level of granularity. The Data Filter can make all displays conditional on your selection of variables and their levels and ranges, as you make them. This allows you to rapidly review, characterize and appropriately handle all cases that satisfy the currently imposed condition. Cases can also be colored by variable using standard or custom themes.
Perception is personal, and the open-ended nature of EDA means that you will develop your own style of analysis. JMP provides a wide repertoire of visualizations so that there are few limitations. Various tools allow you to pan and probe these displays, and zoom in as required. The Graph Builder is a powerful and innovative platform that allows you to interactively build trellis displays with multiple x and y grouping variables and containing graphical segments such as bar charts, histograms, line charts and contour plots. And if the dimensionality of the data is high, you can use the Parallel Plot with coloring and transparency to reveal structure when there are many cases. But often insight comes from using multiple visualizations simultaneously, and JMP’s linking and Data Filter make this approach even more useful.
With JMP you can be genuinely data-driven. In many cases you can peruse an initial exploratory analysis directly from the visualization itself, making choices that are informed by what you actually see rather than by what you expect. Typically, tabular output is appended directly to the same report window, and the display will be augmented by a visual representation of the analysis results (such as a regression line with confidence intervals). And, with the correct options set, you can make the analysis results instantly respond to the selections you make in the Data Filter.
- Selected JMP capabilities in the area of Exploratory Data Analysis
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