From 2016 Mastering JMP session by Jian Cao @jiancao.View the videos See also JMP 15 Time Series Forecasting videos and resources.   See how to:   Use Time Series Overview and ARIMA Models Use Box-Jenkins Methodology Build ARIMA (AutoRegressive Integrated Moving-Average) models Transform data using differencing, to compute the differences between consecutive observations, to make the time series stationary so that it can be modeled and used for forecasting Save table that includes the forecasted data Use Seasonal ARIMA Build seasonal ARIMA models to handle season components, data that has increasing variability over time or data with a growth pattern that requires data transformation Specify seasonal autoregressive order, seasonal differencing order, seasonal moving average order and the number of periods per season.    Use Transfer Function and X-11 Decomposition Models Understand how transfer functions are used to take into account an intervention, such as a policy change or marketing effort, that occurs during the time series during which the model will be built.  T Model and forecast where the intervention is shown as binary (1 for Yes, 0 for No) Dynamically modify the forecast based on input changes UseX-11 Decomposition to remove trend and seasonal effects using the X-11 method developed by the US Bureau of the Census Understand smoothing methods for quick forecasting available in JMP (simple exponential, double exponential, linear exponential, damped-trend linear exponential, seasonal exponential, and Winters method).  ... View more

An overview of ways to share your results with other JMP users and using JMP Live to share with JMP users and non-users alike.   1) Dynamic reports for those who don't have JMP - JMP Live (a product released October 2019; licensed separately from JMP and JMP Pro) 2) Saving visuals and analyses to data table 3) Combining reports into one-window Dashboards 4) Using Journals to present findings 5) Automatic updating of standard Word/PowerPoint files 6) Archiving multi-table analyses using JMP Projects . ... View more

Rarity is a measure of how unlikely a pattern would be to occur by chance. Jmp Systems Engineer Ross Metusalem @RossM suggests thinking of it in terms of coin flips. A Rarity value of x is similar to the likelihood of flipping a coin and getting x heads in a row.      Try Explore Patterns simple example using instructions and sample data.                                                              ... View more

Materials from the Advanced Mastering webinar using JMP Pro 15.  Use them to explore and practice SEM.  View the video by Kemal Oflus @kemal_oflus .   Also, see more from JMP SEM Developer Laura Castro-Schilo's @LauraCS  2019 JMP Discovery Summit presentation and responses to Community questions.  ... View more

Materials used in live Advanced Mastering JMP webinar given by Jian Cao @jiancao . Main video was recorded in May 2020 using JMP Pro 15.  Other videos use an earlier version of JMP.  All are useful to help you learn ways to use JMP to solve linear mixed modeling challenges.   JMP Pro 15 lets you run, compare and choose the best model for your needs.   ... View more

Materials from live Mastering JMP webinar presented by JMP Systems Engineer Ross Metusalem @RossM. View the video and try Tabulate.   Tabulate Table and Script in Script Window.       ... View more

Journal to help you practice designing a mixture experiment as demonstrated  by Wendy Tseng @wendytseng  in her Mastering JMP presentations.  View the videos for Part 1 and Part 2. ... View more

Mike Anderson @MikeD_Anderson shows how to build dashboards, JMP add-ins and JMP applications.  View the video from his Advanced Mastering JMP session and use his .jmpapp to try it yourself. ... View more

  “You never know what you can do until you try, and very few try unless they have to.”  – C.S. Lewis   Mastering JMP host Gail Massari introduces this popular webinar series designed to show users how to solve common analytics problems. Free, live and interactive, these webinars give you a chance to see JMP in action. To better describe the series, Gail is joined by a couple of regular attendees – Amy Phillips from Proctor & Gamble and Tom Bidwell INEOS Composites. In addition to hearing why they’re such big fans of the series, Amy and Tom tell you how they use insight from these talks to advance their organizations and expand the knowledge of their colleagues. (BTW, live-stream events are provided on demand, for those who cannot attend in person.) ... View more

New in JMP 15. If you have data in  tables i n PDF files, you can get them from FILE>OPEN. JMP recognizes the PDF file and opens the PDF Import Wizard, goes through the file, previews it and tries to locate tables to automatically import.      You can also locate and import a specific table by indicating you want to ignore other tables and then clicking, in the PDF Preview Window, on the table you want . To get column headers, go to the hotspot and indicate the number of rows to use as headers, right click on the place where you want the row divider and ADD ROW DIVIDER.   Similarly, if JMP did not figure out where the columns need to be, you can also create a column divider..   Systems Engineer Jerry Fish @JerryFish  shows how.   ... View more

Materials from Meijian Guan's Advanced Mastering JMP Session.  They include a subset of the PBMC data set used in his demo.   View the video.   The full, original PBMC Dataset is available to the public. ... View more

Video from the live Mastering JMP webinar  Phil Kay presented on this topic and the attached journal are available for viewing and to practice what you learned.  See Phil's related blog. ... View more

Video from Jian Cao's Advanced Mastering session on Times Series Forecasting using JMP 15.  See also Time Series Forecasting and Analysis videos.   Materials used for this presentation, including a copy of Peng Liu's paper on State Space Models and how JMP implements them, are attached.   A more detailed set of Discovery Summit  Munich 2020 materials on this topic is also available.  ... View more

The Art of Statistics Sir David Spiegelhalter Winton Professor of the Public Understanding of Risk, University of Cambridge     David Spiegelhalter's most recent book is The Art of Statistics:   How to Learn from Data. ... View more

JMP® 15: Instant Graphical Gratification John Sall Co-Founder and Executive Vice President, SAS   JMP 15 and JMP Pro 15 give you more options for exploring and analyzing your data interactively. From enhanced data preparation and modeling to instant graphical gratification, you’ll discover new ways to access and understand your data. You’ll also find a new way to publish, share and communicate those findings.     As part of this presentation, John Sall shows how to use JMP to explore, analyze and understand patterns related to the current Coronavirus worldwide outbreak. Download the COVID-19 data from John Hopkins University at GitHub to use with this journal.  ... View more

In JMP, a p-value between .01 and .05 is colored red; less than .01 is orange; greater than .05 is black.  Color-coding can help when you are analyzing a number of different effects at once and want to look in reports for visual cues of  statistical significance.   P-values help us make inferences about populations from the sample we analyze. They relate to the null hypothesis - which is that there is no effect in the population from which a sample was taken.    In simple terms,a low p-value suggests that the null hypothesis is wrong, and that there really is an effect out there in the population.    For example, a sample of 50 individuals might show a correlation between two variables. The null hypothesis states that there is no correlation out there in the whole population, but a low p-value for the correlation suggests that the null hypothesis is wrong (i.e., there really is a correlation in the population). Thinking of p as standing for probability, one might think of a low p-value as indicating that the result we see in our sample would be low probability if there weren’t actually an effect out in the population.   Statisticians typically consider a p-value  ≤ 0.05 statistically significant. In some cases, people look for p-values <.01.   Ross Metusalem @RossM  explains:     ... View more

Modern crop improvement programs harness the power of plant genetic variability to drive selection of desirable traits and genomic selection methods can be applied to breeding experiments. See how to use JMP Genomics to predict, cross-evaluate and simulate genetic diversity and genomic selection in plant breeding programs.   The attached materials and video from Luciano Silva's live webinar on Analyzing Genetic Diversity using JMP Genomics cover predictive modeling, biomarker signature cross-validation and optimization, learning curve analysis and genomic selection model scoring.   The video and JMP Genomics Journal will be available here by Feb. 17. ... View more

Fils used in Hyde Miller's @HydeMiller Mastering JMP webinar, Collaborating Using JMP Live.  View the video. ... View more

Over the years, I’ve modified my JMP elevator speech – you know, the summary of your product that you can say in the brief span of an elevator ride. Here is my latest: JMP is computer software that people use to understand information and patterns about that information. It analyzes information patterns, might predict future patterns, and immediately displays graphs that help you understand, in a glance, what it found. So, as I was exploring options for a new graphic for our Mastering JMP webinars, I chose this rendering of a JMP graph, because it shows, in a way that almost anyone could appreciate, the pattern for the result of a prediction. It shows: A regression analysis (the diagonal blue line) Each category of information (triangle, circle x, diamond) A graphical representation of the uncertainty about the regression analysis results (the shaded, arched lighter blue bands around the diagonal blue line; they help us see the range where the diagonal blue line might actually be)   So, without deciphering any tables of statistics, from this simple graphic, anyone could see where the observations are for each of the categories (triangle, circle x, diamond). And, by looking the diagonal line's increase toward the upper right, they could see where the next observation would fall. Over the past 35 years he has been developing JMP software, John Sall, SAS Executive Vice President, has consistently emphasized the importance of graphs and plots for showing and interpreting statistics and statistical results. Recently, John specifically made the case for the importance of using effective summaries and graphs to portray the behavior of processes. For his paper, Scaling-up process characterization, he was awarded the 2019 Søren Bisgaard Award. The award is given annually for the article published in Quality Engineering that is considered to be of greatest potential for advancing the practice of quality improvement. In the summary for his paper, John wrote: “The goal of statistical analysis is to produce reports to effectively and efficiently portray the behavior of processes in context. When there are many processes to portray, effective summaries and graphs that give a fair picture of process health are needed. Engineers want to start with the overall picture and then dig into the details as justified by the summaries. Engineers want good default analyses so that they do not have to spend time making decisions about the methods and details to use in the analysis. A good report communicates an understandable portrait of what the data say.” We thank John Sall, the American Society for Quality, Quality Engineering and Taylor and Francis for allowing us to share this paper. We hope you will read it and see how John describes and gives examples of using JMP graphs and reports to characterize process conformance, changes and stability. Scaling-up process characterization is an Accepted Manuscript of an article published by Taylor & Francis in Quality Engineering, Volume 30, 2018, Issue 1: Special Issue on the Fifth Stu Hunter Research Conference on December 7, 2017. Available online:  https://doi.org/10.1080/08982112.2017.1361539 ... View more

Yes, you have the option to share the data with those who have permission to access the JMP Live report. In his Mastering JMP session on collaborating using JMP Live, @HydeMiller explains.   The information button shows 1) which groups can access the report 2) when the report was last updated and 3) a link to the data table if the author has made it accessible.  The ability to share the data allows you to use the JMP Live report as a sort of repository of the most recent data, alleviating the need to send the table to others after it is updated.       ... View more

Please join us for an afternoon session with presentations about practical applications of JMP.  It will be held after lunch following the New in JMP 15 and New in JMP Pro 15 morning presentation by JMP staff who will demonstrate and answer questions about new capabilities.   Location:  SAS Arlington Training Center 1530 Wilson Blvd Suite 1000 Arlington, VA 22209   1:30 - 4:00 pm   The afternoon MAJUG meeting agenda and registration form are being finalized.  Presentations will include: Extracting Valuable Practical Information From Experimental Models Created for Quality by Design Presented by Rob Lievense, Senior Systems Engineer, JMP   Extracting Valuable Practical Information From Experimental Models Created for Quality by Design JMP DOE models can be used dynamically to provide stakeholders with reliable estimates of the quality performance for new products. The design and execution of structured, multivariate experiments allow scientists and engineers to efficiently define a robust design space for pharmaceutical and medical device manufacturing. Models created through the DOE platform in JMP are used to determine settings of the critical process parameters (CPPs) that ensure a robust process to make products that meet the requirements for Critical Quality Attributes (CQAs). Regulatory submissions that include such QbD elements demonstrate that risks have been mitigated; however, a great deal of practical information can be extracted with simulations of the model. This presentation utilizes the experimental model with historical information and subject matter expertise to project the likely operational performance of a product. The DOE model Prediction Profiler is used with the simulator to dynamically predict a population of future results with patterns of real-world variation included in the inputs. This dynamic modeling is an excellent tool for setting manufacturing card limits determining a manufacturing control space defined with estimates of the percentage defects. The analyses allow for the inclusion of the measurement uncertainty as an added noise factor for the response.   ... View more