Bonjour à tous,      Serez vous capable de construire le meilleur hélicoptère et gagner un drone? U n challenge est organisé autour de la construction d'un helicoptere en papier.    Vous trouverez les instructions ici: https://www.jmp.com/en_gb/offers/jmp-paper-helicopter-experiment-2019.html?   Un drone est mis en jeu si vous soumettez votre approche et résultats!!!   Bonne chance à tous!   Florence     ... View more

Bonjour,    Les inscriptions au Discovery Summit sont maintenant ouvertes! https://discoverysummit.jmp/en/2019/europe/overview.html Je vous encourage à vous y inscrire dès maintenant!   De plus, certains d'entre vous pourraient être interessés par les formations proposées en amont du Discovery et les places y seront limitées. https://discoverysummit.jmp/en/2019/europe/training.html   Au plaisir de vous voir à Copenhague! Florence ... View more

Bonjour à tous,    Pour faire suite à notre rencontre du 14 novembre, veuillez trouver en pièce attachée mon projet JMP concernant l'approche Quality By Design.  Pour ceux qui découvrent le QbD, vous en trouverez quelques mots ici: https://www.industriepharma.fr/quality-by-design-la-nouvelle-philosophie,36592   Je reste bien évidemment à votre disposition pour toute question sur le sujet. Bien cordialement,  Florence Kussener ... View more

Bonjour à tous,   Pour ceux qui n'ont pas pu assister au Discovery Summit et qui souhaitent avoir une idée des nouveautés de JMP14 en attendant le 22 mars, je vous propose de parcourir ce blog de Dan Valente qui vous détaillera les moments forts de la présentation de John Sall avec un lien sur sa présentation. https://community.jmp.com/t5/JMP-Blog/JMP-14-is-here-what-is-difficult-becomes-easy/ba-p/52979 https://community.jmp.com/t5/Discovery-Summit-Europe-2018/Plenary-Session-John-Sall-Projections-and-Encapsulations/ta-p/53175   Bonne journée à tous et réservez deja vos dates pour le prochain Discovery Summit à Copenhague les 13 et 14 Mars 2019!   Florence ... View more

Bonjour à tous,   Même si l'agenda n'est pas encore complètement en ligne, sachez que vous pouvez vous inscrire au Discovery Summit 2018: https://discoverysummit.jmp/en/2018/europe/overview.html   Notez au passage les formations en amont du Discovery: https://discoverysummit.jmp/en/2018/europe/training.html   Je vous souhaite à tous de bonnes et joyeuses fêtes de fin d'année. Bien cordialement, Florence ... View more

Bonjour à tous,   Il est temps de s'inscrire au JMP Discovery Summit 2017. L'ensemble des papiers et posters sont maintenant en ligne sur le site web: http://www.jmp.com/en_gb/events/discovery-summit/prague-2017/abstracts.html#posters   De plus, je voulais signaler la présence de deux formations en amont de cet évènement. Une sur la fiabilité, et une sur les plans d'expériences. http://www.jmp.com/en_gb/events/discovery-summit/prague-2017/training.html   Celles ci sont complétées par les tutoriaux proposés par les développeurs du mardi: http://www.jmp.com/en_gb/events/discovery-summit/prague-2017/tutorials.html   Bref, de nombreuses occasions d'améliorer vos connaissances de JMP et ses applications, qui seront fortement enrichies par les échanges avec vos pairs européens ainsi que les développeurs. Au plaisir de vous retrouver à Prague, Florence ... View more

Bonjour à tous,   Avec quelques jours d'avance sur le Père Noël, je vous propose ci-joint les cartes géographiques des nouvelles régions de France. Celles ci seront proposées dans la version 14 de JMP. Mais en attendant, vous pouvez utiliser celles-ci.   Plus d'informations sur leur utilisation ici: http://www.jmp.com/support/help/13/Custom_Map_Files.shtml   Bonne journée à vous tous, Florence     ... View more

Bonjour,   Suite à cette belle journée d'échanges lors de la rencontre des utilisateurs francophones de JMP, veuillez trouver le journal de ma présentation concernant l'exploration et l'analyse de données textuelles.   En complément vous trouverez des informations au niveau des présentations anglophones suivantes: http://www.jmp.com/en_us/events/ondemand/technically-speaking/tackling-unstructured-data-with-text-exploration.html http://www.jmp.com/en_us/events/ondemand/mastering-jmp/text-explorer.html   Je reste bien évidemment à vote disposition pour toute question sur le sujet. Au plaisir de vous voir à Prague! Florence ... View more

Bonjour, Je suggère à ceux qui sont concernés par le SPC et l'analyse de processus, et plus particulièrement ceux qui ont plusieurs paramètres à suivre, de jeter un oeil sur la nouvelle plateforme "criblage de processus" que vous trouverez dans le menu Analyse -> Criblage -> Criblage des processus. Cette plateforme va vous permettre d'extraire au sein de vos processus ceux qui peuvent avoir une dérive, les processus instables, ou ceux non capable de répondre aux spécifications requises. Bien cordialement, Florence ... View more

Bonjour à tous, Cette rentrée 2016 va s'accompagner de la sortie de JMP13. Cette nouvelle version de JMP sera disponible le 21 septembre, lors de la présentation de John Sall durant le Discovery Summit US. Je vous encourage vivement à suivre cette présentation qui sera diffusée en ligne. Vous trouverez les informations relatives au niveau du lien: Announcing JMP 13, live from Discovery Summit | JMP Bien Cordialement, Florence ... View more

The goal of PCA is to reduce the dimensionality in a set of correlated variables into a smaller set of uncorrelated variables that explain the majority of the variation in the original variables. Illustrative variables have no impact on the construction of the new components. They are used to help interpret the components and relate them to other variables. Fig1 - example of illustratives variables on the two components axis ... View more

Completely interactive, this application allows users to explore the capability analysis of a column which has specification limits in its metadata. The goal is to see the impact of changing the standard deviation, or specification limit on capability analysis and, more specifically, on CpK and Cp. To use it, please run the add-in and choose a column which has some specifications limits coded. You can use the file Semiconductor Capability.jmp from the Sample Directory in JMP Help, and use whatever column that you want (VPM7 is a good illustration of the capability animation tool). ... View more

In quality improvement, it’s common to talk about the "voice of the process" (intrinsic variation in the outputs of an in-control process) and the "voice of the customer" (specification limits that express the range of output values customers will not be unhappy with). A capability analysis compares these two "voices," and summarizes the result via the well-known capability indices C p and C pk . These indices are both defined so that larger values correspond to better overall performance. C p compares the process variation to the width of the spec window, and C pk is a measure of how well this variation is centered on the target value (mid-way between the spec limits). When calculating process capability from a normal fit to your data, the Capability Animation feature (available in JMP via Analysis > Quality > Capability or Analysis > Distribution > Capability Analysis) allows you to assess the relationship between C pk  and a proposed shift in the mean or a change in the specification limits. For example, in Figure 1 we see a normal fit to output from a process that has a standard deviation of 3.51, and for which there is a lower spec limit (LSL) of 70 and an upper spec limit (USL) of 100. Figure 1 also shows the effect of shifting the original mean (72.8, in gray) to a new, higher value (78.12, in blue) that is closer to the target. So if we were (somehow . . . ) able improve the centering of the process in this way, we could expect the C pk value to increase from 0.27 to 0.77. Note that in this case, the process variation is assumed to be unchanged, so the value of C p is unchanged also.     My colleague William Zhou and I developed an add-in that extends this built-in Capability Animation to include the option of exploring how changes in the process variation may affect both C pk and C p . This add-in is now available in the JMP File Exchange.  You can install the add-in by double clicking on it, and then use it by selecting Add-Ins > Capability Animation. Figure 2 illustrates that reducing the standard deviation of the process from 3.51 to 1.96 (without shifting the mean) increases the C pk from 0.27 to 0.48 and Cp from 1.42 to 2.54.  This new add-in allows you to perform what-if analysis to determine how any changes to the process mean, process standard deviation or specification limits are likely to impact the values of C pk and C p . This can help you to focus your process improvement efforts in the best way, either by re-engineering the process itself, or by renegotiating requirements with customers to get new spec limits.     ... View more

The JMP team held a seminar on Sept. 17 in France that focused on design of experiments. Bradley Jones and Peter Goos led this half-day event, and they covered various aspects of design of experiments using four different case studies. They introduced the definitive screening design, which is new in JMP 11. This type of design is very helpful for minimizing the number of experiments and maximizing the quality of information, such as curvature. You can find a technical description and advantages of this design in Jones' blog post introducing definitive screening designs. Jones and Goos also discussed blocking, split plots and constraints. The example of the use of split-plot design for a wind turbine was a nice illustration of the applicability of this type of design, which was initially used in agriculture. Attendees were fascinated by Jones' demonstration, as you can see in the photo below. Jacques Goupy gave us the honor of attending the seminar and presenting the new edition of his book Introduction aux plans d’experiences, which has a new chapter about split-plot designs. ... View more

Principal Component Analysis (PCA) is a traditional method in data analysis and, more specifically, in multivariate analysis. PCA was developed by Karl Pearson in 1901. The goal of PCA is to reduce the dimensionality in a set of correlated variables into a smaller set of uncorrelated variables that explain the majority of the variation in the original variables. It is a helpful technique when exploring the correlation structure in a set of predictor variables and in predictive modeling or Principal Component Regression (PCR) where the principal components are used as predictors in regression models. Here is a simple example:  Let's consider thisdata set about cars from Probabilités, Analyse de données et Statistique. The data set has 18 rows with eight numerical columns. We could investigate  relationships between pairs of variables. For example, maximum speed is correlated with power. But with a large number of variables, it is more convenient to summarize the pairwise relationships with a correlation matrix, and we observe a large number of positive correlations. We also see that maximum speed is most strongly correlated with cylinder and power. Because of the correlation structure in this set of variables, we can expect to reduce the number of dimensions using PCA. We see that we have two distinct groups regarding the second component. One group is related to size characteristics of the car (length, width, weight), and the second group is related to motor characteristics (power, maximal speed, cylinder). Illustrative Variables Illustrative variables have no impact on the construction of the new components. They are used to help interpret the components and relate them to other variables (in this case, price and weight to power ratio). You can download the JMP add-in for PCA with illustrative variables from the JMP File Exchange. (Download requires a free SAS profile.) This add-in will allow you to add illustrative variables to a PCA . Using our previous example, PCA with illustrative variables allows us to visualizethe relationship between the price and ratio of weight to power (orange variable in the previous data set) and the principal components without using these additional variables to build the PCA. We can see that price is clearly correlated with all variables and grouped with the number of cylinders, power and speed-max. So an expensive car is a big car or a powerful car. The second illustrative variable, the weight to power ratio, is negatively correlated with the power of the car regarding the two components and is negatively correlated with weight regarding the first component. It is clearly due to the fact that the weight can have negative impact on the power of a car. References G. SAPORTA, « Probabilités, Analyse de données et Statistique », TECHNIP, 2006 (in French). ... View more