Data Collection

The past week I went to 15 classrooms and collected data on teachers. I gave them a small 2-question survey.

I’m still trying to prove that the classroom door is a distraction, so now I’m turning to teachers. I plan on trying to answer these two questions,

Does the classroom door distract students more? or teachers?

And, Do the students opinions on how the classroom door effects themselves, differ from the opinions of how teachers see the door effecting the students?  (this is a confusing sentence I know)

Next step is to review my data for teachers, compare, and begin the calculations!



Confidence Interval corrections—Grace and Emily

When constructing a confidence interval for the true proportion of students at Edison that have a GPA of 3.0 or higher, we realized we made a mistake on our last confidence interval for the proportions of students at Edison who use Twitter at least once a week. We used the wrong sample proportion. So, we fixed our first interval and constructed our GPA interval. Here is our work:image

How many people use Twitter?—Grace and Emily

After our chi-square test for independence  suggested there was no association between using Twitter and having a GPA of 3.0 or higher, we had to construct a confidence interval for the amount of people who use Twitter at least once a week at Edison. Here are the results:image.jpeg Next we will construct a confidence interval for how many people at Edison have a GPA of 3.0+.

Significance Test for Music vs. No Music

We have carried out a significance test with our null being that the true mean score on a 15 question multiple choice test assessing students who took place in the experiment would be the same whether students listened to classical music or no music while studying.

Using a two-sample T test we calculated a p-value of .048. With a standard alpha level of .05 this data means that we can reject our null hypothesis because there is convincing evidence that supports the alternative – the true mean scores on the tests are greater when students listen to classical music versus no music at all.

From here we are going to apply what we have been learning about Chi-Square Tests and apply one of the methods from Chapter 11 to compare the results that listening to different genres of music while studying has on test performance.

-Paige, Jason & Brett

Tilapia significance test

When talking to Mrs. Harrell about our project, Megan and I realized some issues that we could possibly have with our conditions. We decided that we would be looking at the time it took to catch the fish. Our random condition is checked, and our 10% condition is okay, but not our large counts condition. We realized that we did not sample the first from each group 30 times. This means that the graphs of our data have to be approximately normal when we graph them in order to further our calculations. Though, we are not sure this will happen since our times did change because of getting better at sampling the fish and so forth. If we find that it isn’t approximately normal, then we will have to go in and sample more of the fish to get more times.

Twitter vs. GPA findings (Emily and Grace)

After performing a chi-square test for independence, trying to find an association between Twitter usage and GPA, it turns out there is NO association. With this, we were a little disappointed but we decided to do a 180 degree turn with what we were planning. Instead of telling students to stop using Twitter, we are going to inform students that using Twitter does not have an affect on GPA so keep tweeting! Here is a picture of our work:IMG_0001Next, we will find a confidence interval for the proportion of students at Edison who use Twitter at least once a week.

Finished data collection on Tilapia (Small VS. Large Tank)

Before data collection, to be accurate with the idea of making hypothesises (null and alternative hypothesis) before the data was collected, I made sure to set up these before the data was collected.
The hypothesises are as follows:
Null= U1-U2=0
Alternative= U1-U2 <0 (I think I would use a less than sign here because I believe the mean weight of the fish in the large tank will be greater than the fish in the small tank)
where U1 is the mean weight of the fish in the small tank and U2 is the mean weight of the fish in the large tank
For the past 5 weeks, I've been going to the innovation lab each Tuesday during 5th period. My data collection is finally done! I have to admit that as each week progressed, the fish were a lot larger than before and so much more difficult to handle, splashing water literally everywhere! Next, I will need to create a line graph to help to visually see the progression of the mean weight of the fish over time.

Confidence in Freethrows Pt 1

To complete my data for the probability of freethrows, I finally was able to watch game film for the second half of the season and record the amount of freethrows made and attempted there to add into my data. The second half of the season, my team shot an additional 104 freethrows but only made 57 of them, equaling 55%.

When I include this with the rest of my data, the total of freethrows is 145/237 (61%) which is a drop by 5%.

I will continue on with this project by finding the confidence interval for which it falls in.

Sports vs. Different Aspects of Fitness GT Strength in Numbers Post #1

In the wake of our previous project, we have come to the conclusion that most of our data, with the exception of our body mass index (BMI) figures, are not entirely reliable .  Accordingly, we plan to continue forth with the analysis of our BMI calculations and their application to each of the sports we have randomly selected (boys: football, soccer, swim, tennis, volleyball; girls: basketball, cross country, soccer, golf, water polo).  Previously, we limited the data we collected to solely varsity athletes.  However, we would like to extend our data to include all levels of high school sports.  Therefore, before doing anything else, we now have to randomly sample more athletes from each of the sports we have already selected while making sure we do not exceed 10% of the total number of athletes in each sport.