Experimentation for Improvement

Por: Coursera . en: ,

  • Introduction
    • We perform experiments all the time, so let's learn some terminology that we will use throughout the course. We show plenty of examples, and see how to analyze an experiment. We end by pointing out: "how not to run an experiment".
  • Analysis of experiments by hand
    • The focus is on manual calculations. Why? Because you have to understand the most basic building blocks of efficient experiments. We look at systems with 2 and 3 variables (factors). Don't worry; the computer will do the work in the next module.
  • Using computer software to analyze experiments
    • Now we use free software to do the work for us. You can even run the software through a website (without installing anything special). We look at systems with 2, 3 and 4 factors. Most importantly we focus on the software interpretation.
  • Getting more information, with fewer experiments
    • This is where the course gets tough and rough, but real. The quiz at the end if a tough one, so take it several times to be sure you have mastered the material - that's all that matters - understanding. We want to do as few experiments as possible, while still learning the most we can. Feel free to skip to module 5, which is the crucial learning from the whole course. You can come back here later. In module 4 we show how to do *practical* experiments that practitioners use everyday. We learn about important safeguards to ensure that we are not mislead by Mother Nature.
  • Response surface methods (RSM) to optimize any system
    • This is the goal we've been working towards: how to optimize any system. We start gently. We optimize a system with 1 factor and we also show why optimizing one factor at a time is misleading. We spend several videos to show how to optimize a system with 2 variables.
  • Wrap-up and future directions
    • We close up the course and point out the next steps you might follow to extend what you have learned here.