When product reliability at use conditions is high, accelerated life tests (ALT) are necessary to reduce test time. By testing parts under more severe conditions, the failure time is much lower...
Découvrez comment automatiser de manière interactive les étapes d'analyse, depuis l'accès aux données jusqu'à l'analyse, en passant par le partage des résultats avec d'autres utilisateurs de JMP, san...
Scoprite come automatizzare in modo interattivo le fasi di analisi – dall'accesso ai dati, passando per l'analisi e la condivisione dei risultati con altri utenti JMP – senza necessità di scrip...
See how to interactively automate analysis steps – from accessing data, through analysis and sharing results with other JMP users – with no JSL scripting required. Learn how to access data and ...
Finden Sie heraus, wie Sie Analyseschritte ganz ohne JSL-Skripte interaktiv automatisieren können – vom Zugriff auf Daten über die Analyse bis hin zum Teilen von Ergebnissen mit anderen JMP-Anwe...
Six Sigma Projects require many files to Define, Measure, Analyze, Improve and Control (DMAIC) your process. Use JMP for Six Sigma process improvement and manage your data, results and all rela...
Why use DOE and the Guided mode of Easy DOE introduced in JMP 17? More rapidly answer “what if?” questions Identify important factors when faced with many Do sensitivity and trade-space analy...
See how to: Process, visualize, prepare to analysis and interpret results for free-form text Launch Text Explorer Identify Terms as the smallest pieces of text, simil...
See how to: Learn how to deploy JMP's interactive quality and SPC capabilities Identify routine (common-cause) and abnormal (special cause) process variation over time Meas...
Presenter: @Stuart_Little See how to deploy JMP’s interactive quality and SPC capabilities to identify routine (common-cause) and abnormal (special-cause) process varia...
See how to: Address key assumptions for MSA (Parts do not change form over time and are not changed by measuring them) Overcome inability to repeat measurements when key ...
See how to: Understand how control charts to identify special causes of variation. Build charts using Control Chart Builder Save Control Charts to PowerPoint S...
See how to: Deploy interactive quality and SPC capabilities to identify routine (common-cause) and abnormal (special-cause) process variation over time Determine if a process is stable ...
See how to: Reduce spatial defect data to a few key clusters Correlate clusters and isolated defects to the manufacturing process in order to give direction for process improvemen...
See how to: Understand the value of QbD Developmental studies lead to enhanced knowledge of a process (design space) Higher levels of quality, faster development and approval of new products...
See how to: Screen multiple processes over time Examine one process at a time using Control Chart Builder Examine and compare multiple processes using Process Screening ...
See how to compare and contrast situational usefulness of two methods for looking at lots of process variables at the same time: Model Driven Multivariate Control Charts (MDMCC) and Process Screening...
See how to: Use Process Stability and Process Screening to help control complicated manufacturing processes Screen processes for many (1000+) p...
Understand measurement system analysis principles, including comparing Gauge R&R to the EMP (Evaluating the Measurement Process) approach described by Don Wheeler in the book EMP II...
See how to deploy JMP’s interactive quality and SPC capabilities to identify routine (common-cause) and abnormal (special-cause) process variation over time. Learn how to use JMP to help you de...
Design Space Profiler, introduced in JMP 17, is one kind of JMP profiler. It shows the rate of in-specification responses using chosen lower and upper spec limits on the factors and helps set s...
See how to: Define MSA - An experiment designed to measure measurement system limitations to determine if measurement system is good enough to use or needs improvement Understand d...
Observational data lends itself to analysis useful for large data sets, data that probably do not exhibit orthogonality and situations where we are interested in prediction rather than interpret...
See how to: Model using Partition, Bootstrap Forests and Boosted Tree Understand pros and cons of decision trees Pros: Uncover non-linear relationships, get results that are ea...
See how to: Understand why transformations stabilize variance, make the error more uniform across the design region, remedy lack of fit and plot predictions in a way that d...