JMP® for Modeling

The Categorical platform in JMP provides tables, summaries and statistical tests of response data and multiple response data when the measured responses indicate membership of a particular category. Such data is generated in a variety of settings, including measuring test results, classifying defects or side effects, and administering surveys. Partly because of its diverse application, categorical data can be presented in a variety of formats. A particular strength of the Categorical platform is that it can handle this diversity without any need to reshape the data prior to exploration and analysis. One or more columns can be used to define the categories within and between which variation in the response is assessed, and the Categorical report contains the resulting charts of share and frequency, by category. Used in conjunction withthe Data Filter, these charts provide for quick and easy review of large-scale survey data. The report can also display the associated tabulations and cross tabulations, which can be quickly transposed for easier viewing or printing if needed. Depending on the nature of the responses, you can statistically address questions like: Does the pattern of response vary with sample categories, and have they changed over time? For each response category, are the rates the same across sample categories? How closely do the raters agree? What is the relative risk of different treatments?

When your spreadsheet can’t perform the analysis you need or you have so much data that you can’t easily see what it contains,the JMP Add-In for Microsoft Excel allows you to dig deeper. Move your data to JMP with a single click, launching either the Distribution or Graph Builder platform if you choose. Both platforms allow you to interact dynamically with your data in a way that isn’t possible in a spreadsheet, finding new patterns and associations between variables or cases, and leading you to ask new, important questions about what’s happening – and maybe even why. You can also bring the power of the JMP Profiler to your Excel spreadsheets to interactively explore what-if scenarios as Excel calculates the model in the background and JMP provides the visualization. In JMP, you can numerically find input settings that give you the most favorable outcomes and use the Monte Carlo simulator in the Profiler to quickly define and play out these what-if scenarios to see if your theoretically optimum operating policies or settings will survive contact with the real-world.

In many ways, JMP’s innovative Fit Y by X platform is an application in its own right. As the name implies, it allows you to test for and model dependencies between a single input and a single response or outcome. By using pre-assigned modeling types, Fit Y by X is able to unify what is normally considered to be a disparate set of statistical approaches into a coherent, understandable whole. It encompasses regression, logistic regression, ANOVA and contingency analyses, and each area comes complete with its own repertoire of graphs, tables and hypothesis tests. This, in conjunction with JMP’s “unfolding” style of analysis, allows you to be genuinely data driven, making your next analysis decision only in the light of what you actually see in your data. And the results of any statistical tests you choose are presented in ways that can be readily understood. In short, Fit Y by X can support you in applying sound statistical principles to manage risk, no matter what your level of statistical knowledge.

JMP’s Time Series platform allows you to explore, model and forecast univariate time series with the same interactivity and agility found in other JMP platforms. Your modeling approach can be informed by the usual diagnostics, including plots of autocorrelations and partial autocorrelations, variograms, AR coefficients and spectral density plots. Available model types include ARIMA, seasonal ARIMA, smoothing models and transfer function models. Best-fit model coefficients are estimated by maximizing the likelihood, which is computed using a Kalman filter. You can fit a series of ARIMA models in one step, compare and rank all the models you have fit through a central report, and access further details of each fit to assess individual model adequacy as needed. Once you have a model you like, you can make forecasts with uncertainties, as required. When using transfer function models to relate an input series to an output series, you can prewhiten the input and use cross-correlation between the residual series to identify the proper order of the transfer function polynomial. In addition to the capability to fit all the popular time series models, JMP’s interactivity and ease of use will allow you to build the most useful model quickly.