Project 2: Data Collection

College Algebra 161

November 15, 2012

Intelligence vs. Brain size

The Data Collection project was designed to teach students how to collect, and organize, describe and document data using Excel lists and graphs. I chose this particular subject to research to further my understanding of the evolution of human species. “Can intelligence and brain size be directly related, and as intelligence increases, what happens to the size of our brains?” I conducted my research through the internet by searching for previous, creditable research by someone trained the in the field of Anthropology. The website that I found to have to most useful information needed to conduct an extensive research with adequate background history in the subject was Creation Studies.org. The website contained an article written by the institute’s chief technical advisor, Steven Rowitt, Th.M., Ph.D. After reviewing the information contained in the article, I was able to formulate a hypothesis. My hypothesis is that as humans evolve, and intelligence increases, so does the size of the brain.

The tools used in this project were the website from which I obtained the information and Microsoft excel which I used to document and chart the data. Using that data I was able to formulate a graph, and a mathematical model that could test and support my hypothesis. The graph shows you the trend of growth in brain size, per ____(one thousand years. However you decide to chart it)---------- The mathematical model formulated from the graphed data, will allow future testing to see if the trend still continues, or if the size of a human brain reaches a maximum or minimum. The goal was to chart previous data collected by experts to support my hypothesis as well as predict and test the size of human brains in the future if the trend continued and develop a linear equation to represent the findings. I began by collecting 12 points of data of the average size of human brains at a specific time (years) in history. I recorded the average size of the brain in the year that correlated it. After collecting the data, I plotted the data in Excel and used a best line fit to give me a linear equation/linear regression model to represent my data. See table below:

We entered the data is as follows: The independent variable was the number of rubber bands which represented the x axis. The dependent variable was how far the egg fell, which represented the y axis. We chose a domain of 0 to 25 because the number of rubber bands we used ranged from 0 bands to 15 bands. By choosing a domain or an x-axis of this amount, it gives you a graph that allows you to see the line past 15 rubber bands. We went with a range for of 0 to 90 inches because according to our data, the maximum number of inches that the egg dropped was 67 inches so in order to get a better picture of the data we extended the y-axis to 90 inches. The linear regression model that fitted our data was D(r) = 3.948r + 5.758, with the y-intercept being (0, 5.758) and m= 3.948 inches. Interpretation for the data in the context of the study based on our linear regression model, is at zero rubber bands, the egg would fall 5.758 inches, and with each added rubber band the egg would fall an additional 3.948 inches. To test this linear regression equation we were given a length of 67 inches. To mathematically solve for 67 inches to predict the number of rubber bands needed, we solved for (r) as follows: D(r) = 3.948r + 5.758

67(r) = 3.948r + 5.758

r = 15.5

What we concluded from our mathematical prediction was that it would take 15.5 rubber bands to have a successful fall of 67 inches. Because it was not realistic to use 15.5 rubber bands, we went with 15 instead. This was a realistic prediction because the length that the egg fell was 66 inches, without imposing any damage to the egg and leaving us 1 inch from the original test value of 67 inches....

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