lab 10

This picture isn't the greatest, but I've included the flowchart in my file to show more detail.

The difference between Shannon and Hartley is Shannon is a much more accurate estimate of information. Both are based on estimating the amount of energy in an information system, but Hartley's method does not take into account as much information as Shannon does when guessing. Shannon's method is, simply put, more certain.

The entropy between the three instructuors was interesting. I guess it makes sense that the less data there is the more likely the measure will be accurate.
Matic had a number for every letter grade, while the other two professors had zeros in at least one area. The more zeros the better, because Mussolini was the most certain.

lab 9

This picture best describes what I think of about inductive modeling.
I like the concept of inductive modeling. It's interesting how it relates so well to how our brains use the idea of modeling and categorization. Our minds basically run the same way computers do and vice versa, and just thinking about that is remarkable. I've read a little more into inductive modeling and the user interface intrigues me the most. Inductive modeling is so efficent when it comes to making data directly available.

I enjoyed doing lab 9. I briefly noted in my previous post that I have little experience with excel, so not only did I learn how to use data analysis, I became way more familiar with Excel itself. I didn't even have it downloaded on my computer before the lab, that's how much I never used it. Having a step by step guide folllowed by a file you do all by yourself was useful, too, because it was a personal challenge to see if I could do everything without looking at instructions.