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Starting this Fall, I will be teaching Applied Statistics to undergraduate students majoring in Computer Science and in Software Engineering. I am designing the course around real-life examples (published papers, conference talks, blogged case studies, etc.) of the use of probability and statistics in our field. I need examples.

I prefer examples that show an appropriate use of a model or a statistic, but I will be glad to also teach from a few papers that use a model or statistic in a clearly invalid way. If you send me a paper, or a link to one, I would appreciate it if you would tell me what you think is good about it (for a stats student to study) and why you think that.

Background of the Students

The typical student in the course has studied discrete math, including some combinatorics, and has at least two semesters of calculus. Only some students will have multivariable calculus.

By this point in their studies, most of the students have taken courses in psychology, logic, programming (up to at least Algorithms & Data Structures), two laboratory courses in an experimental science (chemistry, physics, & biology), and some humanities courses, including a course on research sources/systems (how to navigate the research literature).

My Approach to the Course

In one semester, applied stats courses often cover the concept of probability, discrete distributions, continuous distributions, descriptive statistics, basic inferential statistics and an introduction to stochastic models. The treatment of these topics is often primarily theoretical, with close attention to the underlying theorems and to derivation of the attributes of the distributions and their key statistics. Here are three examples of frequently-used texts for these courses. I chose these because you can see their table of contents on the amazon site.

I’d rather teach a more applied course. Rather than working through the topics in a clear linear order, I’d like to start with a list of 25 examples (case studies) and teach enough theory and enough computation to understand each case.

For example, consider a technical support department receiving customer calls. How many staff do they need? How long will customers have to wait before their call is answered? To answer questions like these, students would learn about the Erlang distributions. I’d want them to learn some of the underlying mathematics of the distribution, the underlying model (see this description of the model for example) and how this practical model connects to the mathematical model, and gain some experience with the distribution via simulation.

Examples Wanted

The biggest complaint that students (at Florida Tech and at several other schools, long ago) have voiced to me about statistics is that they don’t understand how the math applies to anything that they care about now or will ever care about. They don’t see the application to their field or to their personal work. Because of that, I have a strong preference for examples from Computer Science / Software Engineering / Information Security / Computer Information Systems.

Several of my students will also relate well to biostatistics examples. Some will work well with quantitative finance.

In the ideal course (a few years from now), a section of the class that focuses on a specific statistic, a specific model, or a specific type problem will have links to several papers that cover essentially the same concepts but in different disciplines. Once I have a computing-related primary example of a topic, I can flesh it out with related examples from other fields.

Please send suggestions either as comments on this blog or by email to me. A typical suggestion would include

  • a link to a paper, a presentation, a blog post or some other source that I can reach and read.
  • comments from you identifying what part of the material I should teach
  • comments from you explaining why this is a noteworthy example
  • any other notes on how it might be taught, why it is important, etc.

Thanks for your help!

 

 

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