JMP®

Challenges

Many organizations rely on “A-B testing” for experimental design, but testing one situation against another with many factors in flux is a very slow way to learn about your business.

In contrast, design of experiments (DOE) in JMP offers a proven and practical approach for exploring and exploiting the multifactor opportunities that exist in almost all real-world situations. Using multifactor experiments, you learn more quickly, at minimal cost, by teasing out not just the effect of an individual factor, but also the combined impact of two or more factors. JMP offers leading-edge capabilities for design of experiments, so you can design the best experiments to answer specific questions. JMP also offers a rich set of analyses tailored to your design in a form you can easily use.

Instead of fitting your problem to a text­book design, you fit the design to your problem with the budget you have. The unique Custom Designer constructs an optimal design for your problem, taking into account specific conditions such as time, budget and other experimental constraints.

Once you have completed your sophis­ticated analyses, employing basic tools for visual analysis is often the best way to communicate results and motivate action. The interactivity of JMP gives you the tools to share the meaning in graphs and not just the graphs themselves.

The market demands continual improvement, which is why you strive to accelerate time to market, protect your brand by minimizing customer complaints, and deliver products and services that consistently meet or exceed customer expectations. JMP has the necessary tools to be at the heart of your quality program, provid­ing a wide range of relevant graphical and statistical capabilities.

Benefits

Minimize data drudgery

How much time do you spend prepar­ing your data for analysis? For many data analysts this is a constant chore. JMP has long respected this fact and worked to make data preparation easier, faster and more reliable.

No matter what your data cleaning tasks, JMP automates the process. What is hard or even impossible in other software is easy in JMP. And even if you can’t clean your data directly, JMP includes methods to minimize the impact of data problems on your analysis, often eliminating the need (and effort required) to make your data pristine.

Once your data resides in a JMP table, you can take advantage of rich reshaping and restructuring tools, merge all of your data into a single file, and easily perform intelligent and interactive table joins.

Before you analyze your data, you should check to make sure it is clean, that the values are consistent and encoded well. JMP offers many ways to do this. One of the best is with the Distribution platform. If you spot outliers, simply grab them and they are selected in the table, thanks to dynamic linking in JMP.

Having a visual interface to your data is a powerful advantage of JMP. You will soon wonder how much you were miss­ing before you were able to immediately grab what you see in your data.

When many different individuals enter categories, naming can become inconsistent. To accurately tabulate the categories and use them for prediction, they must match up consistently. JMP has a Recode utility to make consoli­dation of categories easy rather than time-consuming. You can select a set of categories and choose which of them to make representative of the group. You can also tell JMP to automatically consolidate categories that are very similar to each other. This can be an incredible time saver, especially when there are hundreds or thousands of unique entries.

Other tools for data cleanup in JMP include:

• Screening for outliers.
• Screening for entry errors, error codes or missing values/missing value codes you might not have accounted for in your data.
• Creating formula columns or derived variables; ratio columns or response transformations.
• Data property cleanup.
• Binning continuous data into discrete categories.
• Splitting strings of delimited text into multiple columns.
• Making indicator variables.
• Standardizing attributes across many similar-type columns.

Make compelling data visualizations

Spreadsheets don’t easily reveal patterns and trends in data sets, yet seeing patterns helps you make discoveries. JMP provides rich and dynamic visualization tools, making statistical discovery easier and more effective, leading to innovation.

JMP frees you from the narrow path so you can explore your data dynami­cally and allow it to tell you what is interesting. Move through your data quickly and with agility until you find the visualization that best communicates the story in your data.

Graph Builder is the best way to begin exploring and graphing your data. Interactively build simple or complex graphical displays just by dragging and dropping. Simply drag the vari­ables into position, choose the graph element from an icon palette, and customize the display to get your final, publication-quality result. Graph Builder always gives you options that make sense for your situation.

You can also add background maps to all relevant JMP graphs using high-quality, built-in geographic images, or plot data on street-level maps that include cities, roads and bodies of water. With Bubble Plot, you can create animated data movies, showing changes in many dimensions over time.

Dynamic linking perhaps best illustrates the magic of JMP. You can select ele­ments from any graph and instantly see the selection propagate to multiple views, yielding insights that other software simply can’t reveal.

Perform basic data analysis

Employing basic tools for visual analysis is often the best way to communicate results and motivate action in an organization. And frequently, your first step in a statistical data inquiry consists of investigating variables one at a time, a process known as univariate analysis. In JMP, once you’ve identified the columns that interest you, the Distribution platform automatically provides graphs and statistics based on the variable’s defined modeling type.

Quickly get histograms, summary statistics, box plots and quantiles for continuous data, capability analysis, distribution fitting and frequencies for nominal or ordinal values.

Key capabilities in JMP for basic statistical analysis include:

• Histograms, box plots.
• Descriptive statistics.
• One- and two-sample t-tests, ANOVA, regression, nonparametric tests.
• Distribution fitting.
• Fitting splines and curves to data.
• Statistical calculators and simulators; power and sample size calculation.

Group and filter data with ease

In any business, the quicker you can learn and adapt to ever-changing cus­tomer needs, the quicker you can get ahead of your competition. To acceler­ate this learning cycle, you need to be able to notice patterns in your data, focus on the most important, and act quickly. You can’t waste time generating a stack of reports to wade through, or worse, writing custom code and waiting for output before acting.

JMP brings a radically different approach to the daily task of slicing and dicing data. Its grouping and filtering paradigm allows for instant, in-memory recasting of report output. Imagine how quickly you can focus on interesting areas when you can update reports on the fly just by clicking through levels of a categorical variable. With one click, you can even switch the analysis focus to a new metric entirely.

Grouping and filtering tools in JMP include:

• Local and global data filters for focusing on specific parts of your data table, with or without condi­tional statements. The ability to save favorite filter settings brings efficiency to routine filtering tasks.
• Easy-to-define row markers, colors and labels that enrich graphical reports and data tables.
• Column Switcher for swapping vari­ables within a graphical or statistical report. Stepping through variables manually or by animation allows you to spot patterns and anomalies when you have hundreds of vari­ables.
• Easy creation of By variables in many analysis platforms generates multiple copies of the same analysis; multiple subset analysis in a single click.
• Transforms for generating derived variables on the fly. Stay in the flow while you are analyzing data and create many statistical or mathematical transformed columns of your data with a single click.
• Graph filtering. Use a graph to filter another graph.