During nearly one hundred years of application, statistical thinking has proved itself valuable in many fields of human endeavour. The era of Big Data promises to make such application ubiquitous, and yet ‘statistics’ remains a topic that many, even most, people struggle with. At the same time, the practice of statistics has been intimately connected with what is computationally feasible, so that the choice of enabling software becomes a vital consideration in the spread and successful adoption of these methods. And although often seen as very different worlds, the fundamental challenge of building confidence and facility in the application of statistical approaches is largely common between industry and academia.

In this seminar you will see how the interactive and visual nature of JMP supports the process of training, teaching and learning statistics in the workplace, in professional development, and in the classroom.

You will see how the methods available in JMP, and the way they are surfaced, simultaneously reduce the barrier to their effective use, and extend their applicability. This means you can develop and deploy the analytical capability you need more quickly and easily than before, supporting data-driven innovation and problem solving throughout your operations.


Teaching and Learning Industrial Statistics - Professor David Meintrup

Building useful statistical models is many-faceted, involving a blend of statistical knowledge, domain-specific knowledge, software knowledge and technique, inventiveness and perseverance. The link between data exploration and modeling is vital, and is supported well by the interactivity and agility of JMP. These same properties also support the interrogation and dissemination of the results of modeling, which is often equally important.
This presentation shows how JMP is well adapted to the needs of engineers and scientists who need to build empirical models, allowing them to leverage their own knowledge effectively, but without imposing unnecessary statistical burdens.

Teaching and Learning Industrial Experimentation - Professor Peter Goos

In the application of statistics, the use of statistically designed experiments is pre-eminent. Using computer-generated designs, the custom designer in JMP allows you to fit your design to the problem at hand, giving maximum assurance that you will collect just the data you need to quickly progress to the next cycle of learning. This presentation shows how using such designs has distinct advantages, both in pedagogy (because it unifies a miscellany of classical designs into a coherent framework that is easier to understand), and in extending practical

JMP Resources for Teaching and Learning - Volker Kraft:

JMP is used by many top companies and universities globally. This success is partly due to free teaching and learning resources which make the learning process with JMP effective, efficient and enjoyable. This session will recommend some prominent resources and tools which help instructors to develop compelling training materials that allow new and advanced JMP users to leverage the data they collect most effectively.

Register now for this free webinar.

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