This quarter I worked on my survey about how much kids liked math within the Byron School District. I finished getting all of the surveys that I could from the rest of the teachers that I need them from. I was able to get a few more grades done. In the high school mat department I was able to Algebra one done. So I was able to get a few more grades done although I haven't found a more efficient way to get my data where it needs to go with out me having to tally the results and then having to put it there. I have had a hard time
getting some teachers to have the time to do it.

The part of this project that I found to be surprising is the fact that there was three main group responses. One was either I like math, or I am on the dislike to hate side of it, or I don't mind it but I'm on the indifferent side of it. There were some responses like people who wanted to make math more fun by chewing gum or having take home math tests all of the time when tests were given. I honestly thought that just about all of the kids that were surveyed would say that they hate math. Those are the things that I found surprising to me.

Going Mobile in Social Media

Over the past 20 years many people have gone mobile just because it is more easier to get at and to use. For this project I just gather a bunch of information and put it into an infogram. I found a lot of interesting things

For my project I wanted to find out how well of a tool Piazza was for our Statistics class. I gave a survey to the two Stats classes about Piazza. On a scale from one to ten the average of likeness of Piazza is a little over 5. That is not good. The usage was even lower with it being 3.7. Also not good. I also asked about the likes, dislikes, and whether or not we should piazza. Overall, both classes like Piazza and with some changes it could be better to the classes wants.

I found out that the best way to make Piazza better is the give points on discussing essay questions on tests., That way the classmates can learn from each other. Also everyone  is happy with getting free points. The main dislike was the layout of Piazza was hard to find things. But, there are ways to make it better with the help of some videos on the Piazza website. The other main thing was that some people found it hard to get helps with things, because not a lot of people used it. But, with the more usage, this should not be a problem. I found out that Piazza is great for Stats class, but with some minor changes it could be better. And i also found out that more than half of the two classes have are confused by the spelling of Piazza and pizza. I personally learned that people read to fast and can confuse spellings of words between words like Piazza and pizza. Also I was decent in giving the survey because the average likeness of my survey was 6.7.  

Get to the Fork

One of the statistics projects this 2nd quarter was the Minute to Win it team project. The directions were to make a game that could take under a minute to play and collect data from the subjects results. For the team me project, Levi, Sam, and I diced to do our project with forks, nickels, and quarters. We taped forks upside down, and had subjects roll quarters and nickels towards the spaces in between the prongs of the forks to get the best score. If they got it between the prongs of the fork, they got 2 points. If they just hit the fork, it was 1 point. If they completely missed it, they only got 0 points. After all the subjects went, we tallied up the score and got the results.

Subject name	First?	Quarter (points)	Nickel (points)	Difference	Observations	Score
1	Q	1	5	-4	Right handed, hard throw	6
2	N	1	2	-1	Right handed, light throws	3
3	N	2	0	2	Right handed, light throws	2
4	Q	1	2	-1	Right handed, rolls went to the left	3
5	N	5	4	1	Right handed, quick rolls	9
6	N	1	2	-1	Right handed, smooth rolls	3
7	Q	1	4	-3	Left handed, quick rolls	5
8	Q	2	4	-2	Left Handed, light rolls	6
9	Q	6	5	1	Right Handed	11
10	Q	5	3	2	Right Handed, threw softly	8
11	N	6	8	-2	Right handed	14
12	Q	2	5	-3	Left Handed, alternating thows	7
13	N	6	4	2	Right handed	10
14	N	4	2	2	Right Handed,	6
15	N	3	3	0	Right handed	6
16	N	7	6	1	Right Hand	13
17	N	3	0	3	Right Hand, quick throws	3

Politics: A Treasure Chest For Stats

Politics: A Treasure Chest For Stats

Throughout this second quarter of stats at Byron High School I have taken a deep dive into the complexities of political momentum. I used the 270Soft company's election simulator to test how certain aspects of elections affect the outcome. This simulator allowed me to test variables like: endorsements, crusaders, foot soldiers. name notoriety, endorsement cash, media attention, scandals, and so on. All of these aspects were tested one at a time in the simulator and data was collected in an excel spreadsheet. After I tested all of the variables I was able to put the data into a simple percentage equation to get this chart:

As you can see endorsements are the most influential on political momentum, which makes complete sense due to the fact that many voters follow a single politician with great earnest. This chart shows how political momentum influence was broken down throughout this simulation testing process. This information then allowed me to create variable based question to survey people in my school. I surveyed each grade as a strata using questions based on these variables above, allowing the questions to potentially make a change in the political momentum for a candidate. Below are the results of the political leniency information for each grade at Byron High School. 

Sam B

My favorite project I did this quarter was the pancake experiment I did with Michael, Nicole, and Justin. In this experiment we had each subject try two different kinds of pancakes. One kind was regular, and one had cake mix mixed with it. I liked this experiment because the results were interesting and I love pancakes.

One of the main problems we had with this experiment was how the pancakes looked.  The cake mix ones did not turn out nearly as well as the regular ones.  I think that the appearance of the pancakes may have had something to do with how many people liked them.  It would have been a good idea if I had brought my non stick pan with me to class because we couldn’t find very much pam.

To test which was better we made a bunch of pancakes and cut them up and gave each test subject a sample of each and some syrup to dip them in.  It was hard to survey the whole class because some of the first subjects told their friends that the pancakes weren’t very good so they didn’t want to take the survey and said they were “busy”.

Overall people liked the regular pancakes over the cake mix ones, but it was very close.  From the results we got you couldn’t really say that there was a definite winner.  This experiment definitely could have been done better, but this one was easily the most memorable out of the ones I did this quarter.

Gapminder

My first project that I started out doing was the GapMinder project.  That was a quick short project that showed relationship between two different things.  My two things that I choose to talk about was murder (per 100,000 people) and income per person.  I put the video on Youtube after I recorded it and talked about it.  After that I focused a lot on the units and tried my best to get good scores on the tests.

Mr. Pethan emailed me and told me that I should consider doing a project on Byron graduates from the past 5 years.  This immediately sparked my interest and I began searching Facebook and asking graduates from Byron if they would be willing to participate in my survey.   After people agreed to take my survey, I came up with a few quick questions that I could ask them.  I sent it out to them and I realized that I sort of failed because most of the answers were either yes or no.  After writing a short reflection about what I did and how it went wrong, I decided that I wanted to make a bigger and better survey that I could send out to more people and eventually possibly get it sent out to all alumni too see how they do after graduation.

Quarter 2 post

The one and only big project I worked on this quarter was the Call of Duty Statistics. This presentation consisted of getting information on every single gun in the game Call of Duty Ghosts. I also worked on this glorious project with Ian Whitney and Michael Hanson.

We took very tedious steps to analyze the data. We found a website that had all of the stats of all of the guns including stats on the damage, fire rate, accuracy, and headshot multiplier. We put all of those stats into excel, and found the z-scores for all of the stats. We all worked together well, and did our parts equally.

After that, we started making the presentation. We went into extreme detail on the presentation, and I think that it looks awesome. I'm so proud of my boys, and the way we all worked together to make this awesome presentation. We put a ton of effort into making it. Also, we had all the info for each and every gun on Call of Duty Ghosts. We even ranked them all by their class (AR, submachine, snipers). We also ranked them as a unit overall, and found out statistically, the top 3 guns were all snipers and rifles.

We then managed to do our own test on the top 10 guns, and found out that assault rifles actually performed better than snipers and rifles. I thought that we made the setup of the games pretty well, and pretty accurate. We tried as best as we could to leave out as many variables as possible.

This project really helped me out in not only just playing Call of Duty, but in many other different ways as well. It also helped me put a more organized presentation. Also it helped me on finding the z-scores, and ranking things statistically. I feel very confident in this project, and I'm very happy with the way it turned out.

Citations for pics:
www.gamerheadlines.com
www.ap.ign.com

Smog Score in Hybrids; Levi

I did a project that involved the statistical analysis of the smog score for hybrid vehicles. The smog score is based on how much emission it has from itself. I did go into other data about other things for hybrid vehicles. Overall I spent about 11 hours and 21 minutes on this project. I decided to blog about this project because it was my favorite and I loved this project. I love learning about vehicle data and things about vehicles, but hybrid vehicles are the most interest to me. So I went into detail about different hybrid vehicle smog scoring. The smog score is based on how much CO2 is executed from the exhaust pipe per so many miles.

In my graph you can see that hybrids have got more better on the scale scores. You can see that from 2000 to 2003 there was one that was pretty good compared to the rest. Hybrids get better but there are still a few that are at 5 for their score and there are none at 10. There are a few vehicle companies that made hybrids that are at 6 and below in 2008 and newer years.  These would be Chevy, Buick, BMW and Infinity that made a few hybrids that were at 6 and below. There was one company that stayed strong and that was Toyota with good scores. They have stayed good all the years from 2001 to 2012.

Quarter II: Project Summaries

This quarter we had a hard time coming up with ideas for projects, so we picked something and ran with it.  What started as an attempt to "troll" survey participants, to see how many questions they would be willing to answer,  quickly decayed when Google Forms refused to let us format the survey in such a way that would allow us to effectively do this.  When this occurred Mr. Pethan encouraged us to try and program our own survey, which again diverged into programming a class web application to act as a medium for reviewing the class material.  This all come to a grinding halt as we realized how difficult it is to learn to program when you keep switching languages (Rails to Django) and when there is no one there to mentor you, aside from a few non-interactive online tutorials.  Although we learned a lot through this project,the our attempt at reaching our goal was a blithering failure.

The second project we did this quarter was our community outreach project.  For this we set out to analyze the expected growth of Byron in relation to the expansion of the tax base as applicable to the prospective levy involved in funding the proposal for a new K-2 building for the Byron Public School District.  This may seem like a mouthful, but in actuality it could be a relatively simple project if it were not for one thing.  Our primary issue is that all of the data that we require for this project is held by Olmsted County, and as with any request submitted to the government, everything takes time as you work your way through the bureaucratic system.  If it were not for the multi-week waiting periods for information or the multiple phone transfers between departments looking for someone to help, this project would have gone a lot more smoothly.

Black Friday

Thinking of a project to do was a difficult task for me.  I couldn't quite figure out what to do.  Then, I realized that Black Friday was in a week and it would be perfect to conduct a survey on the preferences and happenings on Black Friday.  There are many different times, items, places, and money totals involved with this huge shopping holiday and I was very curious to find it all out. I looked online, trying to find information and data, but it just wasn't giving me what I wanted.  I decided to conduct a survey and have about 20-50 students take it.  I had one person at each table in lunch A take the survey.  The survey was 13 questions.

Here is what the survey looked like:

After we collected the survey results, they were put into a spreadsheet where we could compare all the results. We took key parts of the data and found averages. This gave us an idea of exactly what is most common with certain things on Black Friday. Then, we displayed it in a powerpoint using visuals to provide a better understanding of the stats we collected. 

Here are the powerpoint slides:

 

This project provided me with a better idea of what is common on Black Friday and opened my eyes to the variety of times, items and people involved with it.  I think if I had more time to work on this, I would look into providing a more detailed survey and using a much bigger sample size.  This would give a better variety of ages, rather than just high school students.  Overall, doing this project was very interesting and something I actually wanted to learn about. 

Quarter 2 of Stats

This quarter I spent most of my time on two projects that I had a lot of passion for. The first one I started to work on was putting together basketball stats for the Varsity Byron Boys Basketball team. The reason I chose this project is because I was already on the basketball team as manager so I thought why not take stats and get graded for taking stats and helping the coaches see what we are doing good on and what we need improvements on. Here is an example of just one of the games we played where I kept stats on.

This game is Byron Varsity vs Dover Eyota Varsity, we won this game by quite a bit and you can see that just by looking at the score and how much we kept them from shooting. 

The other project I did this quarter was a project on the stats of all the guns in Call of Duty Ghosts. I did this project with Nate Levy & Michael Hanson. We looked up all the stats about everything little detail about each gun then we put it into a Google spreadsheet and figured out the z-scores for each category. And then we did an overall z-score of all the guns looking at what we thought were the most important qualities to have when using a gun. After we found out the top 10 best guns using our z-scores, we tested them out. Our results were not the same and figured rate of fire mattered more than we thought it did. This is an example of the graph we made.

Fast Food Findings

Plan for Data Usage

We gathered a large quantity of data from this restaurant. We took all of this data and found different trends in the number of certain products sold during the day. Among the data of all the items, we were trying to figure out the most popular item and the item that generated the most revenue for this restaurant. This information could be useful to this fast food restaurant in many ways. The data we found could possibly help the restaurant by telling them what they should advertise more of in order to make more money. They could also figure out the most popular item which could allow them to slightly raise the price in order to make more money on the item and increase their profit. With us having the ability to analyze each menu item that they sell and knowing how many they sell, we create more precise conclusions. For example, we may be able to predict what they will sell more of and make more money off in the future.

Hypothesis of What We May Be Able to Find

We could use this information to figure out how well a new sandwich might do in the fast food industry because of what we have found. We determined there are a few sandwiches and meals that sell because of their price. We also know certain items or meals sell more because of what is added to the meal automatically like a drink. This will affect the number of items sold along with the profit.

Information We Discovered

We discovered that many fast food restaurants could make a lot of money by selling their sauces. They probably will not sell their sauce because people do not want to buy sauce to dip their food in. Ranch was a top "selling" item but they do not make any money on it because it is free for the consumer to purchase. We also found out pop is one of the most profitable items. This is because the restaurant does not cost much to be produced. We found the most profitable items are Drink C, Meal B, Sandwich G, and Meal C. Because these items are the top selling, we believe these prices may be increased in the future.

Figure 1: Items Sold with Total Revenue

We found a confidence interval for the total revenue of all products. We found that we are 95% confident that this fast food restaurant makes between $.84 and $1.46 on every item they sell. This shows that the number of items sold has some effect on the total revenue made by the fast food restaurant.

Figure 2: Confidence Interval

Bullying Statistics

At the Byron Middle School, surveys are taken twice every year - once in the Fall and again in the Spring. Data has continued to accumulate over 8 years but the question begs: What do we do with it? Katie Gilbertson and I decided to volunteer to tackle the data the counselors have given us. With this information, we hoped to find the obvious as well as the not-so-obvious trends in bullying.

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 2007-08, 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. 

K/d Cause and Effect

I'll start with the basics of the project, Brady and I decided to devise a experiment to study the cause and effect relationship of the Kills to Death ratio of a game called Black Ops 2. The cause in this experiment were different attachments on the same gun, using the same map, and even using bots instead of real players. But to be completely honest, this experiment was completely made up, now yes we came up with the original idea to do this experiment and yes we did all of the work for it, but it was a kind of a pointless experiment. Because I'm pretty sure neither of us, well at least me, didn't care about the outcome of it. I guess we chose this experiment because we both had the game, we both thought it would be interesting and we both thought it would be sort of easy.

In actuality though the experiment took a pretty descent amount of time and effort. After we came up with the idea of the experiment we had to brainstorm on exactly what we were going to be doing. We had to decide how many guns to use, what guns to use, the attachments, the perks, the difficulty of the bots, how many test subjects we were going to have, we even had to decide on which map to use. And even after all of this we still had to go back to Mr. Pethan to confirm that all of these things were okay to do. On top of that we had to go find test subjects and test them, then take all of this data and plug it into Statkey to figure out what it all meant.

My favorite part about this experiment, other than working with Brady, was the conclusion that we came to after looking at the results. It turns out that having no attachment turned out to be the best choice of the attachments that we experimented with. This completely caught us off guard and was really surprising to find out.

The Most Dangerous State

We chose 8 different factors that made the state dangerous. We choose murders, pneumonia, heart disease, lung cancer, traffic accidents, earthquakes, tornadoes, and hurricanes. Each state we did we did 100,000 people.  We made graphs for each individual factor and then we made a z-score and then made a ranking system using z score then made a mega graph. Louisiana was the most dangerous state followed by Mississippi and Texas. The most safest state was Utah followed by Minnesota and Vermont. The southern states were the most dangerous because they lived next to a hurricane could happen and tornadoes happen.

I learned more about how to use z score better. I learned a lot about America and where most of natural disasters happen and also where some caused deaths happen like smoking and people get lung cancer. I found out where I would want to live and where I wouldn't want to live. I found out to make an easy way to make graph then put them on cool power point website.

If I did this over again I would probably add more causes of death. This was a very fun project. It was a cool way to find out where the safest places to live and not to live.

Paws and claws visitor sheets

During this quarter Ian and I went to Paws and Claws and asked if they had any data for us to enter. They gave us a huge container full visitor sheets, these sheets contained information about the person visiting and there interests in adopting. The first step was to set up a document so that data entry would become quick and efficient. After entering about 100 sheets I looked at the data and found out that most of their visitors are planing on adopting an animals in there care. I also found out that people who visit Paws and Claws are more likely to be between the ages of 25 and 44. This is important because Paws and Claws are able market toward this age group rather than wasting money on an age bracket that rarely comes in. I also found out that over half of there visitors have children. This is also important information because then Paws and Claws is able to train there animals to be more child friendly. With this new information Paws and Claws will be able to increase there adoption rates and find more animals loving homes. Other things that I looked at where other pets, if they had their shots, what length of hair for their cat do they want, if they want their cat declawed or not, and also if the cat will be inside or out. All of this data found nothing conclusive enough to prove anything.

During this project I learned how to use Google drive to create and survey and with the help of Mr. Pethan I was able to view the summary in a quick and easy way. After I finished entering in 100 sheets of data I changed the set up to include all the questions so that Paws & Claws will be able to use this form in the future. The very first set up I made did not include the names and contact information of the people because that would first take too long and second would be a volition in privacy. I added that afterward to the form so that this form could be used in the future.