This would allow columns to be restricted to certain expected formats. ... View more

Right now you can right-click an open window listed in the home window and select Close All But This. It would be great if this were also in the top Window menu (especially in scripting windows). ... View more

I am having trouble using Ignore Platform Preferences() with Fit Model. In this example, I turn on AICc in the preferences, and then try to use Ignore Platform Preferences to run Fit Model without the AICc output, but it still appears. Is there something wrong with my syntax?   As a bonus question, is there a more global option to ignore all preferences and platform preferences when running a script? Almost in the same way that names default to here(1) creates a unique namespace for that script, an option to ignore all user preferences when running platforms in a script?   Thanks.   set platform preferences(Fit Least Squares(AICc(1))); get platform preferences(Fit Least Squares); open("$sample_data/big class.jmp"); Fit model( Ignore Platform Preferences( 1 ), Y( :height ), Effects( :weight ), Personality( "Standard Least Squares" ), Emphasis( "Effect Leverage" ), Run() ); ... View more

Level: Beginner Andrew Karl, Statistician, Adsurgo Heath Rushing, Principal Consultant and Co-Founder, Adsurgo     Recently, a medical device customer wanted a script to evaluate different, standard acceptance sampling plans using operating characteristic (OC) curves. They also wanted to find the limit from an average outgoing quality (AOQ) curve for rectifying inspections. Although they were experienced JMP users, they did not know about the availability of single- and double-sample OC curves found only in the JMP Starter menu. Furthermore, they had not utilized Graph Builder for evaluating common deviations (nothing goes as planned) from these sampling plans or for developing AOQ curves. Although scripting can be quite effective and efficient, writing scripts from scratch can be overwhelming for new JMP users. However, the existing capabilities of JMP can be exploited and easily adapted for routine and/or recurring analysis of common quality questions. This talk with demonstrate the adaptation of the existing capabilities of JMP to explore and evaluate deviations from standard quality approaches such as acceptance sampling, process control and capability, and design of experiments. ... View more

Andrew Karl, PhD, Statistician, Adsurgo Heath Rushing, Principal Consultant and Co-Founder, Adsurgo   This presentation was voted Best Contributed Paper.   Moving to a new city? Both of us did. Use JMP to analyze your commute times? We did. Which parts of a city provide a reasonable commute time to your job? Your spouse’s job? Your child’s school? Or to all three, on average? Which parts of a city are most isolated from your multi-location business branches (e.g., banks, stores)? Which neighborhoods suffer the largest commute variability throughout the day? JMP can show us. A space-filling design is used to sample points (starting locations) within the latitude and longitude boundaries of the area of consideration (e.g., city). A JSL script then communicates each of the starting points, along with one or more destinations and a departure time, to the Google Maps API to retrieve (and then scrape) rush-hour and normal drive times and distances. Times from our specific commuting locations are plotted using Graph Builder with background street maps. The heat map replaces the points with contours of commute times for both one destination (e.g., the airport) and a sum of driving times for multiple locations (e.g., home-to-work and home-to-school). The column switcher allows us to visualize a smooth progression of commute times across a region throughout the day and week. ... View more