* Hello, my name is Roger Hoerl. I'm a professor of statistics at Union College in Schenectady, New York. Prior to coming to Union, I spent 30 years in the private sector, the last 17 with GE Global Research in upstate New York. You're about to embark on a journey, taking a course on statistical thinking for industrial problem solving. This is a topic near and dear to my heart. So I'm excited for you. I hope you're excited. Some of you may be a little concerned about what you're actually going to do, perhaps you're concerned about having to memorize a bunch of formulas, focus on number crunching, having to write software code. That's really not the focus of this course. This course is really about the application of the scientific method to solving problems and driving improvement. It's really about how to think about problems, how to think about approaching problems, how to obtain data, how to make sense of data, and how to utilize that data to drive improvement. It's based on some fundamental principles, statistical thinking that is, and one of the principles it's based on is the principle that all processes can produce information that enables improvement. Every process, not just industrial processes, but all processes can produce information that we can use to drive improvement. If we look at the history of the scientific method, what we see is that learning occurs when two things come together. One is an informative event, something noteworthy happens, and there's also a perceptive observer, that is someone who's trained and understands how to look at things and actually learn from the events that occur. So that's how learning occurs. We get the informative event and the perceptive observer at the same time. This course is really going to focus on helping each of you be a perceptive observer. It's going to help each one of you understand how to look at data, how to learn from data, and how to respond to data in a logical way so that your conclusions are actionable and can actually solve problems, can actually drive improvement. So this course is a very important course. It's one step in an ultimate journey that each of you might take to optimize your ability to solve problems and drive improvement. Another key principle I want to talk about is the fact that basic tools, the kind of tools you're going to go over in this course, tend to be a lot more effective than people think. That is, oftentimes people think, well, I have to have the latest and greatest, the most advanced, the most sophisticated tools if I'm going to solve problems and drive improvement. That's actually not true. Let's take a look at this graph. In this graph, we're looking at what I call the sweet spot for statistical thinking. This is the area where statistical thinking methods and tools apply across the board. The vast majority of problems that we face every day are really low complexity and medium complexity problems. Using the thought processes that you're going to get from this course and the basic tools, you're going to be in a position to solve the majority of the problems that you face on a day in, day out basis. With more advanced training, you'll be in a position to solve even more complex problems. But even with just the material that you're going to get in this course, you're going to be able to apply it to solve real problems, make real improvements in your workplace. So I want to encourage you. I'm excited about the course. I hope you're excited about it. I can guarantee you the tools and the thought processes that you're going to get out of this course will be directly applicable to your workplace, to the real problems that you face day in and day out. Best of luck.
