Patrick – Computers brought to DIG 210 class on 9/14
I was curious to see the distribution of computers that students were using during class. Personally, I am a Mac user, who has developed a preference for operating in Windows while programming or using Microsoft Excel. Though I have no basis of comparison, I was curious to see if students in a class focused on Data had a stronger preference towards non Mac computers.
My results were found simply by counting the number of computers of each brand that students had during class on 9/14. 14 of the 20 students attending class brought Mac computers with them, while there was one student with each of the following brands: Asus, Dell, Toshiba. The remaining 3 students did not bring computers to class. 70% of the students in class had Mac computers, which is an overwhelming majority. Even in a data focused class, Mac appears to be the most popular choice.
Seth – What can the clothes we wear tell us about the weather outside?
I recorded what each student in class on Thursday (9/14) was wearing. Either shorts, pants or a dress and either a short sleeve shirt or a long sleeve shirt/jacket.
This past week I decided to observe how people decided to dress as the weather begins to turn to lower fall temperatures. On (9/14) during our class it was approximately 70° F and one of the first cooler days of the season. I was curious if people would start to wear warmer clothes and what percentage of people would wear pants and jackets vs. shorts and t-shirts. Furthermore, in this period of time when the weather cools off from the much hotter months I was curious of the probability if someone wore shorts, would they wear a short sleeve shirt or a long sleeve shirt etc.
I found that 65% of the class wore shorts, 25% wore pants, and 10% wore a dress. I also discovered that 50% of the class wore both short sleeves and shorts, while 20% wore long sleeves and pants. These were the two highest combinations, however, some people also mixed what they were wearing (shorts and long sleeve, pants and short sleeve etc.) Also I found that if someone wore shorts, the probability they would wear a short sleeve shirt, was 77%. And if they wore pants there was a 20% chance that they would wear short sleeves. The findings from this experiment coincide with my hypothesis that warmer clothes will appear with the cooler outside temperature. It was also interesting to see that people either wore both pants and long sleeves or shorts and short sleeves, which could definitely coincide with fashion and other factors.
I think it would also be interesting if I made this observation every class period and graphically showed the change in clothes worn to class as well as the outdoor temperature side by side. This would obviously take more than one week but nonetheless could be an interesting representation of average temperature over time and its effect on the clothes we wear.
Charlotte – DIG 210 use of filler words in class discussion, by gender
In my study, I focused on the use of “filler words”, such as ‘like’, ‘um’, ‘uh’, ‘er’, ‘okay’, ‘right’, ‘you know’, and so on, during Tuesday’s class discussion of the week’s reading. I was interested in observing the use of these words as we never notice our own, personal use of them, but we do tend to become aware of their repetition in our language once somebody tells us to look out for them.
In terms of methodology, I split my observations up into male and female groups. I was interested in seeing if one gender used more filler words than the other. From my observations, women used 3.14 filler words per comment in class discussion and men used 3.26 filler words. However, our DIG 210 class is not split equally between genders. Tuesday’s class consisted of 14 men (n = 14) and five women (n = 5). Therefore, it would be unwise to find a conclusion is my data as the subject, or sample size (n), groups were not the same and a fair comparison cannot be drawn.
I think this topic would be very interesting to extrapolate upon in multiple classes across Davidson departments. In particular, I wonder if filler words are more common in specific departments and whether men or women speak more frequently in class discussions at Davidson.
Daniel – DIG 210 Student Hair Color
In our study regarding the DIG 210 students’ hair colors, we found that 64% of students had either brown (n = 6) or blonde hair (n = 6), while students with red hair represented a clear minority with only 10% of the class. As a result, our class differs from other sample groups such as the one that was recently the research subject at Rice University (2016), which showcased an overwhelming majority of 48% (n = 286) as brown-haired while only 21% (n= 127) had blonde hair.
How can it be the case that these two data sets of US student classrooms allow for completely different inferences? On a qualitative level, a variety of reasons ranging from geography to social demographics could be responsible for the differences in probabilities. However, the real statistical illegitimacy that is embedded in these data sets originates from the limited number of observations that prevents the Law of Large Numbers to eliminate selection bias to a sufficient extent. With so few observations, the inferences that can be made about the hair color demographics of Davidson College’s general student body will be inherently flawed.