Would you drink hot chocolate if it was this degree outside?
By: Olivia M., and Kenzie F.
To collect data of how many people would or would not drink hot chocolate at a specific weather temperature, we went around and had people write yes or no next to the temperature. Our data consisted of a population of 50 different students. We did the senior class but had to add in a few others to get to 50 students. Collecting our data really wasn’t that hard, just tracking everyone down was a struggle. We had to take into consideration the people who would not answer truthfully and the people who did care. There was one person who did not like hot chocolate at all, so it affected our data a little by having a no on each temperature. They didn’t not drink it because of the weather temperature but because they just don’t like it.
The explanatory variable is the temperature of the weather outside and the response variable is whether or not they would drink the hot chocolate(yes/no). Our scatter plot shows a negative slope. The R is the -0.936 which means it has a strong correlation of the two items going together. This means the colder the temperature the more likely people are to drink hot chocolate. The Y is .567 and the X is 50. This means that 57% of people would drink hot chocolate at 50 degrees according to our data. There is a gap in our scatter plot showing that after a certain degree people were unsure of whether or not they would drink hot chocolate or not. Once the temperature started rising, that is when most people started being sure that they did not want hot chocolate in the hotter weather. The least squares regression line is at -0.0079x+0.964. Every time the degrees increases 1 degree, the amount of people that would drink hot chocolate goes down .0079 percent. For example, if the temperature increases by 80 degrees, the percent of people who would have hot chocolate would decrease by 63%. The marginal change is when the degree goes up the amount of people who drink hot chocolate goes down. The two most influential points on our graph would be (50, 0.7) and (60, 0.36). We chose these points because there is such a large gap between the two. The reason for this is because the degree started to warm up and that is when more people start to not want to drink hot chocolate as much. Our R squared is 0.875 which means it is the ratio explained variation over the total variation. This shows that more people are more likely to drink hot chocolate when the temperature is cold. 88% is the explained and 12% is unexplained. The 88% explained means that we were able to control 88 percent of the factors contributing to our data. The unexplained data at 12% is out of our control, there may be many reasons why this is caused. Some people may not have understood the question, they may have just wrote random answers, too lazy to read/fill out, and/or didn’t care in general. For our extrapolation, our data would predict that If it is 110 degrees outside, 19% of people would drink hot chocolate. For our interpolation, our data would predict that if it is 60 degrees outside about 50 percent of our population would say yes to drinking hot chocolate.
The explanatory variable is the temperature of the weather outside and the response variable is whether or not they would drink the hot chocolate(yes/no). Our scatter plot shows a negative slope. The R is the -0.936 which means it has a strong correlation of the two items going together. This means the colder the temperature the more likely people are to drink hot chocolate. The Y is .567 and the X is 50. This means that 57% of people would drink hot chocolate at 50 degrees according to our data. There is a gap in our scatter plot showing that after a certain degree people were unsure of whether or not they would drink hot chocolate or not. Once the temperature started rising, that is when most people started being sure that they did not want hot chocolate in the hotter weather. The least squares regression line is at -0.0079x+0.964. Every time the degrees increases 1 degree, the amount of people that would drink hot chocolate goes down .0079 percent. For example, if the temperature increases by 80 degrees, the percent of people who would have hot chocolate would decrease by 63%. The marginal change is when the degree goes up the amount of people who drink hot chocolate goes down. The two most influential points on our graph would be (50, 0.7) and (60, 0.36). We chose these points because there is such a large gap between the two. The reason for this is because the degree started to warm up and that is when more people start to not want to drink hot chocolate as much. Our R squared is 0.875 which means it is the ratio explained variation over the total variation. This shows that more people are more likely to drink hot chocolate when the temperature is cold. 88% is the explained and 12% is unexplained. The 88% explained means that we were able to control 88 percent of the factors contributing to our data. The unexplained data at 12% is out of our control, there may be many reasons why this is caused. Some people may not have understood the question, they may have just wrote random answers, too lazy to read/fill out, and/or didn’t care in general. For our extrapolation, our data would predict that If it is 110 degrees outside, 19% of people would drink hot chocolate. For our interpolation, our data would predict that if it is 60 degrees outside about 50 percent of our population would say yes to drinking hot chocolate.
To view our data and graphs please view.... docs.google.com/document/d/1ETbP-98H9fZeKGcbzw73hSG2FI-_hgNcAptg07aEKY4/edit