We were initially given the results of the survey. These results clumped all the answers in a percentage. Because of this layout, we were unable to dig into the information and accurately conclude any trends. I was however, able to compare those bullied vs. the season. Using the information we had, I made a table comparing the percentage bullied in the Spring and the percentage bullied in the Fall. After plugging these numbers into StatKey, I found a significant difference in these seasons.
Year

Percent Bullied

Spring 2005

36.5%

Fall 2005

24.41%

Spring 2006

36.31%

Fall 2006

27.76%

Spring 2007

31%

Fall 2007

29.58%

Spring 2008

32.26%

Fall 2008

22.31%

Spring 2009

30.78%

Fall 2009

19%

Spring 2010

25.4%

Fall 2010

n/a

Spring 2011

41.19%

Fall 2011

23.94%

Spring 2012

34.3%

Fall 2012

22.31%

Spring 2013

35.66%

Fall 2013

22.9%

This confidence interval shows that we are 95% percent confident that students are bullied on average from 6% to 13% more in the Spring versus the Fall. This means, on average, the percentage bullied increase about 9% with a margin of error of about 3%. This is a significantly large jump in bullying. We decided to conclude that this trend may be due to "Spring Fever" and students may be more comfortable around this point in the year. We also noticed that most of bullying occurs outdoors. Since it is warmer during Spring, this would make sense that more bullying, particularly outside, would happen.
After meeting with the counselor again, we were able to obtain the raw data. This was very useful since we could compare almost any of the results. Due to limited time, we only compared a few things. I wanted to see if there was any correlation between gender and whether or not they were bullied. I took the proportion of females who were bullied over the total females and the males who were bullied over total males for each season. Using the data, I created two graphs to visualize the results.
In the top graph you can see the spikes in each year. This is not caused by gender but by the seasons which I explained earlier. If you look at the top graph you can see that the lines are, for the most part, close together. There are no significant drops or spikes in gender. In some years, their proportions were almost equal. In the bottom graph, you can see the comparison as bars. For the first few years, a higher percentage of females were bullied. From 200708, the proportion of males is higher. I didn't see any real patterns with this data, so we decided to conclude that those bullied isn't affected by gender. For further support of this conclusion, hypothesis tests and confidence intervals could be done, but we were unable to find time to do it.
After meeting with the counselor again, we were able to obtain the raw data. This was very useful since we could compare almost any of the results. Due to limited time, we only compared a few things. I wanted to see if there was any correlation between gender and whether or not they were bullied. I took the proportion of females who were bullied over the total females and the males who were bullied over total males for each season. Using the data, I created two graphs to visualize the results.
In the top graph you can see the spikes in each year. This is not caused by gender but by the seasons which I explained earlier. If you look at the top graph you can see that the lines are, for the most part, close together. There are no significant drops or spikes in gender. In some years, their proportions were almost equal. In the bottom graph, you can see the comparison as bars. For the first few years, a higher percentage of females were bullied. From 200708, the proportion of males is higher. I didn't see any real patterns with this data, so we decided to conclude that those bullied isn't affected by gender. For further support of this conclusion, hypothesis tests and confidence intervals could be done, but we were unable to find time to do it.
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