Lab 10: Shannon and Hartley aka The Masters of Uncertainty

From the lab I determined the difference between Shannon's and Hartley's measures of uncertainty was Shannon integrated probability when solving for the results while Hartley does not at all. Both the measures however are logarithmic because they use logs when solving for them.

Linear Regression

From Lab 9 on Linear Regression, I learned how to use regression statistics that include the variables "m, b, and r" to calculate the best fit line with Microsoft Excel. I discovered it was more simple to procure what "m, b, and r" equal with Excel than with a calculator because Excel does the calculations automatically with a few non-complex adjustments and clicks of the mouse. I also learned how to create a scatter chart in Lab 9 while also adding a trend line to the chart to produce the best fit line. Next I learned how to add in the data analysis pack to Excel and from that I could compare my chart with the data analysis pack to compare my answer. Inductive modeling was emphasized throughout the lab because I felt like I learned how to take little tiny details to turn them into a generalized hypothesis. For example, the "m, b, and r" variables were easy to understand what they were used for after doing the formulas myself. Inductive modeling is useful in the world because it takes the tiny bits of pieces to build them into something bigger. It is very similiar to building a computer. With a computer you need the parts first or the little pieces such as the motherboard, memory chips, etc... before you can actually make the computer. It is necessary to understand the little things first or the foundation before learning about the broader subject in the inductive modeling world.