In this activity, it is trying to represent on whether or not two variables are correlated or not. In this project we had to choose 2 quantitative variables that we thought might be related to one another and if one affected the other. We chose to do the number of hours somebody has to work a week and the number of hours somebody studies a week. We originally thought that that there was going to be a strong negative correlation between the two variables because depending on if you work more hours weekly, then you possibly would have less time to study. We started out by asking 10 random people the number of hours they worked a week and how many hours they spent studying each week and we got a lot of different responses. After we got the data, we drew a scatter diagram from the data that we had collected so that we could visualize the two variables. After that was done we plugged the data into the calcualtor to get the r value which is the correlation coefficient and it ended up to be -.5006. Next we got the r2 value which is the coefficent of determination and it was .2506 and then found the critical value in the chart given and that was .632. The closer the correlation coefficient is to 1 or -1 the stronger the correlation is either positive or negatively. By comparing the coefficent of determination and the critical value, we concluded that there was a weak negative correlation between the two variables. We concluded this because anything between .3 <|absolute value of r| <.5 is considered to be a weak correlation and from the scatter diagram that we drew we also concluded that it was a negative correlation. This project taught me how to find out if two quantitative variables are related or not. This process can be very useful and is i s interesting to use to see se e if you were right about the guess that you made in the beginning about what you thought was going to happen. Our original intutition was that there would be a strong negative cor relation between the two and it ended up to be weak correlation. It also sho wed me that if there are “outliers” in your data from the group that you are sampling, that it can change the way your conclusion ends up. There were people who didn’t have jobs and had a lot more time to study, then people who work more than 40 hours and have very little time to study, or the people in the middle who balance both out evenly.
