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.

Microsoft Excel

From utilizing the simulation for task #3, I have ardously discovered the program proved De Morgan's Law as true because the equation (a and b)= not a or b are the same. For example, I took output 1 "A Not And B" and it was the same for output 2 which is "Not A OR Not B" Even though I interchanged the inputs, both outputs came out with the same result.

Logic Circuit 1

From the truth table whenever I added 0's for both inputs, my output came up as 1. Adding a 1 to input A and a 0 for input B or vice versa came up with an output of 0. Plugging a 1 into both inputs gives me an output of 1.

Converting Numbers & the Number System

To convert the # 110010101 to a decimal, I started to utilize the powers of 2 increasing the exponent by 1 each time. For example, I started on the rightmost side with the numeral 1 and made it equal to 2 to the zero, then 0 to the first and so on. Eventually, using these powers I got up to 2 to the 9th. Next I added up all the ones which was 1+4+16+128+256 which equals 405.

The number 529 can be converted into binary by dividing the digit by 2 and thus taking the quotient of that and dividing by 2 until the final remainder is 0. In the dividing process, after getting the quotients you need to take the remainder which can be equal to 0 or 1 and starting from the rightmost side fill in the binary number like so:

529 divided by 2= 264 1
264 " "= 137 0
137 " "= 68 1
68" "= 34 0
34" "= 17 0
17" "= 8 1
8" "= 4 0
4" "= 2 0
2" "= 1 1
1" "= 1/2 0

Thus the answer is 0100100101.


The positional number system is when the value of each digit by its position. In other words "Order Matters". For example, the # 312 is different from the value 213. The non-positional number system is when symbols are utilized to represent numbers like the Roman numeral system is non-positional. For example V would represent the numeral 5.

Global Swarming

Ch.6 in the text-book was one of the most intriguing chapters I've read this year because I found it fascinating that websites like Amazon.com have found ways to get people to buy more products through them. The consumer aspect is interesting becaue it is very profitable to the company utilizing the consumer information to advertise products to them. Amazon uses consumer information by taking the items that consumers are looking at and making some sort of network that shows what other people are buying along with that object. The network is a never-ending trail because consumers can click on any object through the website but yet there will be a never-ending cycle of recommended products. Companies that master how to do this can predict what consumers like and want.