Institution: Helmut-Schmidt-University Hamburg, hosted by Univ.-Prof. Dr. Sven Knoth, WiSo (email@example.com)
Presenter: Volker Kraft, JMP Academic Ambassador
Time: 25th November 2016, 10am – 5pm
Location: Helmut-Schmidt-Universität, Holstenhofweg 85, 22043, Hamburg, WiSo Hörsaal 3 (or PC-Pool)
Registration: Please click here an fill out the form to register for the workshop.
Number of attendees: 25 max. (hands-on only)
10-12: Introduction to JMP, Design of Experiments and Predictive Modeling (live demo & discussion)
12-13: Lunch break – pizza session by JMP
13-17: Kriging Workshop – attendees should have JMP 13 pre-installed (see www.jmp.com/trial for 30 days trial license)
Morning: Anybody interested to see JMP 13, or to get ready for the afternoon hands-on workshop.
Afternoon: Anybody with applications that require to work with functions, often of many variables, that are costly to evaluate.
Knowledge of linear regression, statistical modeling, and stochastic processes is helpful but is not required for the workshop. Similarly, basic knowledge of R and/or JMP will be helpful for the hands-on lab component, but is not mandatory.
JMP is an easy-to-use, standalone statistics and graphics software from SAS Institute. It includes comprehensive capabilities for every academic field, and its interactive point-and-click interface and linked analyses and graphics make it ideal for research and for use in statistics courses, from the introductory to the advanced levels. JMP runs on Windows and Macintosh operating systems and also functions as an easy, point-and-click interface to SAS®, R, MATLAB and Excel.
The JMP INTRO SESSION introduces the interactive user interface of JMP. Sample applications will focus on Experimental Design and Data Modeling. Get to know about JMP academic resources and where to find help.
KRIGING (or Gaussian process regression) has proven to be of great interest when trying to approximate a costly to evaluate function in a closed form. The principle aim of the workshop is to show how to build useful surrogate models using this approach, and to make clear the assumptions that such models rely on. Furthermore, once it exists, we will show how a surrogate model can be used for optimization.
Lab sessions will use both R and JMP. The main aim of the lab is to quickly find optimal settings of a catapult numerical simulator that can fire the longest shot.
Workshop content and installation instructions for R (packages) and JMP will be shared mid of November.