Holy cow I am way behind the learning curve for these new websites.

For my project I would like to perform an experiment that describes the effect the amount of time in a popcorn popper (explanitory variable) has on the popping of popcorn kernels. I will pour kernels into the popper multiple times and time how long it takes for the first pop once it is started.

Ok here goes comes the data.

In this experiment I have two quantative variables. They are the the number of popcorn kernels vs the amount of time it takes for the first kernel to pop. I tried to eliminate all lurking variables by keeping the conditions of each trial the same. The popcorn air popper was plugged in for one minute to warm up so as to not bias the first trial with a longer time. However, I noticed that when I left the cover off, the popping time rose dramatically (the first pop of 5 kernels). This can be taken as an outlier and I will provide another graph to illustrate the impact it has on the rest of the data. Also, during counting of the kernels there were a number of damaged and deformed kernels. Rather than discarding them, I used them in the experiment and took them as one of the experiment's environments (a lurking variable that was randomly distributed throughout the trials). During the popping phases of the trials, several kernels flew out of the popper. Again, this was taken as part of the experiment and the renegade kernels were not thrown back into the popper. All kernels used were Orville Reddenbocker and the kernels left over from previous trials were not reused in later trials. Also the time between trials was kept to a minimum so as to not allow a cooldown of the popper.

I've used Microsoft Excel to help plot the data. As you can see, the data slopes downward from left to right, indicating a negative association.
This contradicts my original hypothesis in which I thought that there would be a strong positive association. The square of the correlation is 0.403, which translates to a correlation of -0.635 (not a very strong correlation. This can be explained by the outlier at the beginning of the data. If the outlier was removed, then the data would look like this:

As you can see, the square of the correlation dramatically increases (0.6372) as well as the correlation of the data, (-0.7982). This results in a much stronger relationship within the data. This "enlarged" picture of the data also suggests that the relationship may not be exactly linear. The time it takes for the first kernel to pop as more and more kernels are added tends to level out over time. More trials would be needed to either prove or disprove this.

In either case, the results of this experiment present that as you add more and more kernels, the popping time for the first kernel decreases and then level off. The average value of popping time for the first kernel (including the outlier) was 0:42 with a standard deviation of 0:13. The median in the data equaled 0:39. Feel free to ask me any questions. Thanks.

For my project I would like to perform an experiment that describes the effect the amount of time in a popcorn popper (explanitory variable) has on the popping of popcorn kernels. I will pour kernels into the popper multiple times and time how long it takes for the first pop once it is started.

Ok here goes comes the data.

In this experiment I have two quantative variables. They are the the number of popcorn kernels vs the amount of time it takes for the first kernel to pop. I tried to eliminate all lurking variables by keeping the conditions of each trial the same. The popcorn air popper was plugged in for one minute to warm up so as to not bias the first trial with a longer time. However, I noticed that when I left the cover off, the popping time rose dramatically (the first pop of 5 kernels). This can be taken as an outlier and I will provide another graph to illustrate the impact it has on the rest of the data. Also, during counting of the kernels there were a number of damaged and deformed kernels. Rather than discarding them, I used them in the experiment and took them as one of the experiment's environments (a lurking variable that was randomly distributed throughout the trials). During the popping phases of the trials, several kernels flew out of the popper. Again, this was taken as part of the experiment and the renegade kernels were not thrown back into the popper. All kernels used were Orville Reddenbocker and the kernels left over from previous trials were not reused in later trials. Also the time between trials was kept to a minimum so as to not allow a cooldown of the popper.

I've used Microsoft Excel to help plot the data. As you can see, the data slopes downward from left to right, indicating a negative association.

This contradicts my original hypothesis in which I thought that there would be a strong positive association. The square of the correlation is 0.403, which translates to a correlation of -0.635 (not a very strong correlation. This can be explained by the outlier at the beginning of the data. If the outlier was removed, then the data would look like this:

As you can see, the square of the correlation dramatically increases (0.6372) as well as the correlation of the data, (-0.7982). This results in a much stronger relationship within the data. This "enlarged" picture of the data also suggests that the relationship may not be exactly linear. The time it takes for the first kernel to pop as more and more kernels are added tends to level out over time. More trials would be needed to either prove or disprove this.

In either case, the results of this experiment present that as you add more and more kernels, the popping time for the first kernel decreases and then level off. The average value of popping time for the first kernel (including the outlier) was 0:42 with a standard deviation of 0:13. The median in the data equaled 0:39. Feel free to ask me any questions. Thanks.