if you're good at one, are you bad at the other?
The Purpose of our Study
The purpose of our study is to see if there is an inverse relationship between how well you do in algebra as opposed to how well you do in geometry. This means, if you are essentially successful at one, you will have a harder time with the other, or vise-versa. We decided to research this study when we found, personally, that one class was easier compared to the other and found that numerous other individuals had similar results.
Abstract
The purpose of our study was to find out if there is an association of how well you do in algebra 1 vs. geometry. We chose this as our topic because both of us had a hard time with geometry but algebra was much easier for us and we wanted to know if it was common to be good at one and bad at the other. We first cearched for other studies or information that could relate to our study and to see if someone else had already conducted the same study. We looked on many sites and we decided that no one had done the same study or a study similar to our topic. We had found one site that talked a little bit about what we were researching but they did not do any research or provide any data to support their opinions.
Since we did not find any actual data to go with our question, we wanted to find out if there was a indeed any kind of association between how well you do in algebra vs. geometry. Our population of interest were Juniors and Seniors at North Olmsted High School, we then started our random sampling by selecting 75 surveys without any errors, in which we obtained by generating a random 100 juniors and seniors and sending them surveys during school. We found that most of the time, people had the same grade for one as they did for the other. Any variation however was always close, for example if they had an A in one they had a B in the other or vise versa. We did a paired t-test to see if there was any association between the grade in those two classes. We computed a t-value of .13 and a p-val or .894. Our data was not relevant because when testing our assumptions we discovered that the data was not normally distributed. Therefore we can not come to a conclusion.