JMP 11 Fitting Linear Models

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V1 of A vs. Then, I used "Fit Special" command to generate a line where the slope is equal to 1. Hence, I create linear regression lines and create regression reports. My question is this. How do I "extract" the numbers from the regression reports and place them in a variable? My purpose is this. I need to tabulate the R square values and y-intercept in single table.

Right now, I am manually typing. This initial part was done with "Save Script to Script Window.

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service , privacy policy and cookie policy , and that your continued use of the website is subject to these policies. I then send the graphs to a JMP report so all graphs will appear in just one window.

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We need harness these abilities in the service of business and statistical visualization so we can routinely do dynamic interactive graphs built into the analytic application. Motion and 3D are particularly of interest. Effective BI has many ingredients; data cleansing, data integration, data retrieval query and reporting and analytics. While these ingredients are important, to be effective analysis results need to motivate others toaction. The best written report has little impact on an organization if it does not motivate action. Motivating action is where data visualization makes a big contribution.

This talk will focus on using data visualization to communicate analytic models and data analysis in ways that motivate analytic consumers to action and effective BI. Two-way tables of non-negative zero and positive numbers are common. The data tables can be large, e. There is a need to make simplify and make sense of these complex data sets particularly when using them to make predictions. Non-negative matrix factorization, NMF, can take advantage of correlations among predictors to create ordered sets of predictors; within the ordered sets, statistical testing can be done sequentially, removing the need for correction for multiple testing within the set.

We turn the one block analysis of micro array data into a two-block problem, where one block uses the observed gene expression levels and the second block uses observed levels -1; we then apply Consensus NMF to find, simultaneously, up- and down-regulated genes. This provides a unified approach to the two-sided testing of micro array data. We also explicate NMF using a whisky taste data set. Computations for this work were done using a complex JMP script.

Simple Linear Regression

As a data mining and visualization tool, JMP tackles some traditional challenges that emerge when mining large, multilevel, longitudinal data sets. Non-linear patterns can be difficult to identify using traditional statistics and presented using line or bar charts.

JMP bubble charts with trails helped our team visualize 6 years of nonlinear operational and financial performance metrics for thousands of retail outlets spanning 20 markets. This allows for complicated interrelationships between variables to be visualized in simple, yet impactful ways without overloading clients with information.

With multilevel data sets, JMP bubble plots allow for exploration of complex data at the highest level of analysis with the option of drilling down to increasingly more micro levels within the same graph.



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This capability is especially beneficial for targeting root causes of patterns and relationships existing at higher levels. It also creates a collective awareness of the discovery path by allowing clients to participate in the exploration journey with experienced analysts.

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More traditional statistical procedures such as stepwise regression and partitioning can then be employed in JMP to dig deeper into the root causes of macro and micro patterns. Just as importantly, JMP provides a powerful platform to convey the results of these more complicated statistical analyses in simple, yet impactful ways that have resonated with our clients.

The team members charged with improving this process first used variability charts and other tools to assess both continuous and attribute measurements. They then undertook a visual study of historical data using histograms, scatterplots, and 3D scatterplots to suggest appropriate specification ranges for four key process responses. Accurate detection of breast cancer is an area of critical importance.

This talk utilizes data from the Diagnostic Wisconsin Breast Cancer Database, which were collected in connection with the development of an automated system to classify biopsied cells as malignant or benign based on digital images. Thirty variables are available as potential inputs to the classification process.

There are unique talents that make JMP a great dynamic visualization front end to SAS, but there are also statistical methods that make JMP and SAS work together much more powerfully than would be possible with either product by itself. One example to be demonstrated is in the field of reliability called degradation analysis.

JMP 11 Fitting Linear Models

I have data that watches units degrade over time, and the goal is to estimate how long before a certain percentage of the units fail by degrading past the lowest acceptable value. One degradation application would be in determining the shelf life of pharmaceuticals, how long they retain acceptable potency, which could be much more powerfully done by a full-scale degradation model, rather than the stability analysis that is common today.

It turns out that doing this involves a fair amount of manipulation with both JMP and SAS, but it is easy to understand once you see it. The use of a JMP script to provide P charts of product failure rates by both date tested and date manufactured, for products sample tested within finished goods stock.

JMP Presentation Abstracts

Both P charts, used in conjunction, provide the support engineer with improved insight on part number quality performance to the quality expectancies. Ramamurthy, Amurthur Experimental Learning: Chemical Informatics Using JMP Stanley Young, National Institute of Statistical Sciences Biologists and informatics scientists often wish to view molecules, manage chemical structure data sets, compute molecular properties and conduct statistical analysis of biological results and properties of molecules.

Heinz Plaumann, BASF Corporation Many technical and business colleagues shy away from maximizing the true value of decision-supporting data due to reluctance to analyze data with the most up-to-date, descriptive and predictive models. Use of JMP Journal in Six Sigma Green and Black Belt Training Amurthur Ramamurthy, Covance Six Sigma methodologies have made considerable inroads as continuous improvement tools of choice initially in manufacturing and more recently in service and transactional environments. Data Visualization; a necessary ingredient in making BI effective and realizing analytic excellence Jon Weisz, SAS Institute Effective BI has many ingredients; data cleansing, data integration, data retrieval query and reporting and analytics.

Visualizing Business Growth Ron Halverson, Halverson Group As a data mining and visualization tool, JMP tackles some traditional challenges that emerge when mining large, multilevel, longitudinal data sets.