Bar charts with error bars
This collection of screenshots provides a sampling of capabilities and features from each of the products in the JMP family.
JMP - A collection of interactive learning modules help you learn and teach statistical concepts. One of the available Interactive Learning Tools.
Confidence interval learning module
JMP - Create a contingency table and mosaic plot using Fit Y by X.
Contingency table (cross tabulation)
JMP - Create a variety of control charts for continuous or attribute data, and conduct a capability analysis, using the Control Chart platform.
Control charts and process capability
Build custom menus to quickly access your data or custom applications.
Create add-ins and customize menus (Add-in Builder)
Create custom JMP applications using the Application Builder.
Custom applications (Application Builder)
Customize summary statistics in the Distribution platform to display the statistic you’re looking for.
Customize summary statistics
The Partition platform provides interactive classification and regression trees.
Data mining – classification tree (Partition)
The DOE menu provides a wide variety of experimental designs, including full factorial, screening, response surface, split plot and custom designs.
Design of experiments (DOE)
Analyze industrial experiments, view interactions and optimize with the Fit Model analysis platform.
Design of experiments (DOE) – response surface
Use the Screening platform to analyze two-level screening experiments.
Design of experiments (DOE) – screening analysis
Calculate and display probabilities and percentiles for a variety of distributions. One of the available Interactive Learning Tools.
Distribution and probability calculator
The Excel Import Wizard allows you to get an analysis-ready JMP table from your Excel workbooks in just a few steps.
Excel Import Wizard
Perform factor analysis with Principal Components or Maximum Likelihood and multiple rotation methods.
The Nonlinear platform can fit curved data without the need to pre-impute a formula or starting values. Simply select from one of the models in a rich library, which includes popular bioassay or pharmacokinetic models, and data is fit automatically.
Fit Curve (Nonlinear platform)
Use the reliability Fit Life Distribution platform to fit and compare a variety of distributions.
Hierarchical and KMeans clustering are available from the Multivariate platform.
Create interactive histograms and display customizable summary statistics using the Distribution platform.
Histograms and summary statistics
Use the JMP Excel Add-in to create a JMP data table from an Excel spreadsheet.
Import Excel data into JMP
Send data to MATLAB, execute code and return data to JMP for visualization and analysis. The interface lets JMP seamlessly integrate with MATLAB to extend JMP further for even greater analytic power and flexibility.
Interface to MATLAB
Dynamically explore and visualize Excel models in JMP using the Excel Profiler.
JMP add-in Excel Profiler
The Measurements Systems Analysis (MSA) platform provides an all-in-one method for assessing the variation in your measurement system and gauges.
Measurements systems analysis (MSA)
Analysis of covariance with the Fit Model platform.
Models with continuous and categorical predictors (ANCOVA)
Multivariate analysis of variance with the Fit Model platform.
Multivariate analysis of variance (MANOVA)
Conduct hypothesis tests and construct confidence intervals using Distribution.
One-sample t-test and confidence intervals
Use Fit Y by X for ANOVA and multiple comparison procedures, such as Tukey HSD.
Oneway ANOVA and Tukey HSD
The Prediction Profiler is ideal for model exploration, optimization and Monte Carlo simulations.
Optimization and Monte Carlo simulation
The Partial Least Squares (PLS) platform has rich graphics and detailed reports.
Partial least squares (PLS)
Explore variation explained by each principal component using the Principal Components platform.
Principal components analysis
Graphically explore relationships between variables using Graph Builder.
Regression with grouping variable
Run R code from JMP. Download Multidimensional Scaling, and other R add-ins, from jmp.com/addins.
Running R Code from JMP
Explore correlations between variables using the Multivariate platform.
Scatterplot matrix and correlations
Easily save your work and create custom applications using the JMP Scripting Language (JSL).
Scripting in JMP
Plot continuous data using Fit Y by X, and then fit a line or another regression model.
Simple linear regression
Choose the Stepwise personality in Fit Model for stepwise regression, all possible models, and model averaging.
Plot location-based data on background maps, which now can include a street-level view.
Analyze complex surveys with complete control of question structures, report presentation and statistical tests performed.
The Time Series platform includes ARIMA, Seasonal ARIMA, smoothing models and more.
Time series analysis
Transform variables in a single click in any platform launcher, in the data table, in Graph Builder and through JSL.
The advanced neural network capabilities in JMP Pro include the choice of three activation functions and two layers, a choice of validation methods, as well as gradient boosting.
Advanced neural modeling
Advanced features for partial least squares (PLS) regression in the Fit Model platform. Model effects can include categorical factors, as well as crossed and polynomial terms.
Advanced partial least squares (PLS)
Build cross-validated boosted decision-tree models, which build many simple trees, repeatedly fitting any residual variation from one tree to the next.
The bootstrap forest technique grows dozens of decision trees using random subsets of the available data and then averages the computed influence of each factor in these trees.
Stepwise regression includes the option of stopping rules based on cross-validation.
Cross-validated stepwise regression
JMP Pro includes advanced computational methods for performing exact measures of association and oneway nonparametric exact tests.
Use Generalized Regression in JMP Pro to build better predictive models despite data challenges.
Compare the effect of different architectures on a neural fit with boosting (left) and without (right).
Gradient boosted neural model
Partial least squares (PLS) regression can impute missing data before fitting.
Missing value imputation for partial least squares regression
Build multi-layer, cross-validated neural network models with numerous architectures.
Multi-layer neural network model
Bootstrap any statistic in a JMP report with a single click. This example shows bootstrapping confidence limits around a 10th percentile quantile.
Use the Reliability Block Diagram for complex system design and to locate and address weaknesses in the system.
Reliability block diagram
Uplift models identify the consumer segments most likely to respond favorably to an offer or treatment.
Easily split your data into training, validation and test portions for honest assessment of a model’s predictive ability.
Validation column role for cross-validation
Use variable clustering for quick and easy dimension reduction to make your prediction problems easier to solve.
Severity analysis of adverse events shows differences across treatment groups via dynamically linked graphs.
Adverse Events Analysis
Tree maps like this one can show whether drug-event pairs meet signal criteria.
Adverse Events Tree Map
A simplified Starter menu helps users easily choose specific reports and analyses for dynamic visual exploration.
From one-way plots, select subjects of interest and drill down again to patient profiles.
Drill down to patient profiles
A Hierarchical Cluster report with two-way clustering identifies relationships between individual subjects and the events, findings and interventions included in the safety analyses for the study.
A two-dimensional hierarchical cluster shows relationships between selected events, findings or observations.
Zoom in to select subjects in the Hy’s Law region. Drill down on selected subjects to see patient profiles or subject clusters.
HY's Law Region
The default view for the JMP Clinical bubble plot is a representation of Hy’s Law analysis, showing changes across treatment groups over time for any lab.
Hy's Law analysis using Bubble Plot
The industry-standard Hy’s Law display in JMP Clinical is interactive for selection of subjects. A dashboard containing a scatterplot matrix of transaminases and bilirubin; mosaic plot of days until bilirubin elevation and missing lab tests report tab.
Hy's Law Dashboard
The mapping feature in Graph Builder lets you choose any variable for geographic display.
Mapping with Graph Builder
A new medical monitors dashboard in JMP Clinical 4 displays frequency and count information for adverse events.
Medical monitors dashboard
Use Graph Builder’s Mosaic Plot to compare adverse events, here from the vascular body system, by sex across treatment groups.
Use the zoom tool in JMP Clinical to magnify clusters of interest, and then run a partial correlation analysis on any items in the cluster.
Partial correlation cluster analysis
A patient profile dashboard allowing configuration of clinical information, notes for reviewers and printing to PDF documents.
In this bubble plot, time windows show the change in significance and relative risk for all adverse events and concomitant medications for each day of the trial.
Risk Bubble Plot
From the volcano plot, drill down to a relative risk plot to sort easily by count, relative risk, significance or dictionary-derived terms.
From the severity analysis, select significant adverse events and drill down to the subject level with one-way plots.
From the volcano plot, a Venn diagram helps identify co-occurring adverse events for subjects in the study. Then select subjects for clustering or profiling.
Use paired RNA expression and DNA hybridization data to screen for indications of allele-specific expression with the new Allele Specific Expression Filter process. Examine a summary volcano plot, then drill down to display detailed information for specific SNPs.
Allele Specific Expression Filter process
Overlay continuous variables such as p-values, intensities, counts or fold changes on simple and complex genomes to identify interesting regions, then drill down to view detailed statistical results and tracks.
JMP Genomics 5 offers enhanced flexibility for movement of dendrograms and labels in cluster heat plots.
Cluster heat plots
Significant enhancements to copy number partitioning let users display segment means, shade segments relative to a referencevalue, and filter results to display segmentsthat meet a pre-specified cutoff.
Copy number partitioning
New segmentation summary plots can be filtered interactively to identify shared regions of copy number loss or gain.
Copy number segmentation
Overlay continuous variables such as p-values, intensities, counts or fold changes on simple and complex genomes, with the option to display single chromosomes as circular. Identify interesting regions, then drill down to view detailed results and tracks.
Display singular chromosomes as singular
Examine individual variations in copy number (left and upper right) or guide your search for shared regions with summary plots (lower right).
Examine copy number variations
Create, compress, and incorporate genetic distance matrices into association tests that simultaneously correct for relatedness and population structure. Distance matrices may also be created to display relationships between continuous measures, or custom distance matrices clustered and visualized.
Genetic distance matrices
Analyze mRNA-seq data to find interesting patterns of differential expression with interactive volcano plot.
Interactive volcano plot
The customizable JMP Genomics Starter window, new in version 5, makes it easy for new and existing users to access tools appropriate to their analysis areas.
JMP Genomics Starter window
Scale count data across samples using TMM normalization, compare TMM factors between samples, and view kernel density plots of normalized data.
Kernel Density MA Plot by Sample after TMM Normalization
View p-values from statistical tests individually by chromosome, or create custom, multichromosome views.
Identify genomic regions that contain marker genotypes shared identical by state between related or unrelated individuals.
Screen for SNP pair interactions using flexible filters, and drill down to view plots that display the details of trait values and SNP genotypes.
Screen for SNP pair interactions
Display summaries of your statistical analyses in genome context to identify interesting regions with pre-built settings for commonly used genomes, or by creating custom genome views. Then drill down to overlay gene, histogram, SNP and heat plot tracks on statistical results. Here, p-values across a genomic region are overlaid with co-localized genes and a histogram track that summarizes raw exon-level data for two samples of particular interest.
Summarize statistical analyses in genome context
Interactive triangular plots let you calculate and visualize linkage disequilibrium measures, identify LD blocks and zoom into interesting regions.
See batch effects in your data and remove them prior to statistical analysis.
Visualize batch effects
Visualize shared patterns with multiway Venn diagrams. Overlay statistical findingsand annotation categories to drill down on the most important gene sets.
Visualize shared patterns with multiway Venn diagrams
Examine a summary volcano plot to identify pathways that are over- or under-represented in your significant gene list, using a variety of enrichment tests.
Volcano plot to visualize Analysis of Variance
A box plot and bar chart shown in edit mode. Multiple graphs can be displayed at the same time.
Box plots and side-by-side bar charts
Customize the look and feel of your graphs. Change graph properties, colors, line widths, marker sizes and more.Display contour plots and violin plots using the contour icon.
Variables can be dragged and dropped to any of the drop zones to change parameters of the graph.
Drag and drop variables
Fit a regression line, and add confidence intervals and prediction intervals.
Fitted line with confidence and prediction intervals
Drag one variable to the X or Y zone and use the histogram icon to create a histogram.
Histogram with one variable
Add a Group X or Group Y variable to create separate graphs. Add a variety of available summary statistics.
Histograms with summary statistics
Import JMP data tables from iTunes or Dropbox, or any website, or open JMP data from an e-mail attachment.
Import JMP data
Fit a number of functions to data including linear, quadratic and cubic fits.
Linear, quadratic, and cubic fits
Graph builder includes online samples and resources to help you get started quickly.
Online samples and help screen
Build multivariate scatterplots to visualize correlations between variables.
Scatterplots and contours
Build stacked bar charts, or a variety of other bar chart styles, with or without summary statistics.
Stacked bar chart
Zoom into the values of individual points using the magnifier by tapping and holding the screen.
Tap and hold to zoom
Use the Wrap zone to display graphs when there are several categories of a grouping variable.