Non-profit Project

I’ll admit, the non-profit project sounded like it was going to be a huge hassle. In some retrospect it was. Finding an organization to work with was one of the most challenging parts. At first we called the Ronald McDonald house, but to no avail, they ended up having nothing for us to work on. We then turned to the United Way, because of the fact that my mom volunteered there.

When the lady named Becky at the United Way got back to us with some work to do it was a huge relief. It also felt pretty cool that we had to sign nondisclosure agreements. The main problem though was that none of us knew how to type a report, and her directions were very vague. So at first, we all got together and tackled the first report. It was a slow process, but the report turned out fairly good. However, Becky  wanted graphs and for the next reports to be more summed up. So, we talked to Mr. Pethan, and decided to split into two teams to tackle the remaining reports. Initially, we tried to use Piktochart, then Microsoft word for the reports, but finally realized, with the help of Mr. Pethan, that Google Presentations was the best bet. We finished all the slides, converted them into PDF and then shipped them out to Becky. She liked them, which was very gratifying, and only had us tweak a few minor things.

If I were to do this project again, I wouldn't really change much, except that we would of used Google Presentations right off the bat. Splitting into groups was a good thing, but working on the first report together kind gave everyone a feel for what we wanted out of these reports. This project was definitely the most challenging part of this class, but it was also the most rewarding. There was a much larger sense of purpose while doing this project, because of the fact that it was for an outside organization and that it was going to be put to used for something important. Overall, i’d recommend that you continue to keep the nonprofit project as part of the class, maybe make it optional though for the next class.

Below are some examples of the reports we typed, but censored slightly

Quarter 2 stats

Quarter 2 stats we worked on two projects. The first project we worked on was making Dan's dice football game into a computer game. We used a program called Python to make it. The second project we worked on was our final project which was for the United Way. We had to analyze their surveys for them.

Our first project we started to work on was our dice football game. Dan had made this game earlier and he brought it in. We decided to make it into a stats game by using a program called Python. We didn't know much on how to make a computer game so we had to research a lot on how to do it and had Mr. Pethan help out a lot. The first thing we did was play the board game to get how its played and learn all the rules and strategies. Then we started making the game on python. The coding was pretty confusing, so we needed a lot of help, but we started out by getting the standard rules down. For example, We made a touchdown function so there was a way to score and a first down function. We also made a sack, interception, fumble, field goal, passing, and running functions. We used three dice for the offense and two dice for the defense. After we got all the standard stuff made we made an AI player which allowed us to play against the computer. We had to make our own strategies to see whose was the most effective. This project was a lot of fun and really interesting. It didn't have a lot to do with stats but it introduced us to other things.

Our second project was our final project which was to analyze some surveys for United Way. They wanted us to make their surveys easier to read. So we made a power point of all their data. We would make graphs of all the survey questions that involved multiple choice questions so they could tell exactly how often one answer was selected. For the free response questions we would summarize all the answers and right a short statement of what they all meant. This project helped us a lot in understanding this kind of work and how to work together and split up work in a team.

If I had to do these projects again I would want to research and learn how to code better so we don't have to rely on Mr. Pethan for help. I also would want to just work on this and not have to worry about the modules and final project. We never got to completely finish this project like we wanted too because we had to do other work. The United Way project was pretty interesting too. The only thing I would want to change is have the United Way explain what exactly they wanted because we were confused for awhile on that. I feel that we got a lot out of these projects and had fun doing it. They were really interesting and it was easy to stay on task by letting us have so much freedom on what we could do.

This picture shows our field goal function and a couple other functions from our dice football game.

Failure to Launch

     Growing up, my life revolved around sports! I love to get together with a bunch of friends and just play a pickup game of soccer, or chill on the couch with my family and watch College Football Saturday. Earlier this year I was introduced to the field of sport analytics, and have been interested ever since. I have really considered going into this line of work, just the only problem is that most sports, other than baseball, don't seem to have a very advanced program. I was scrolling through Facebook one night when I came across a Grantland video about a Coach Kelly from Arkansas who always goes for it on 4th down and never punts!

I was immediately intrigued because this seemed like the kind of work I could potentially be interested in doing! I decided I wanted to try to coordinate some sort of project for my stats class around this idea. I came to class the next Monday totally excited to see what I could dig up. I began by reading, "Do Firms Maximize, Evidence From Professional Football" to get a better idea of how stats was being applied to the game of football. The article talked about point possibilities in regards to field position, so I decided to research that more to see if that would give me any project ideas! I found the graph below:

This was a really cool graph, and I found myself concentrating on this when I was watching football the following Saturday. Then it hit me. I wanted to do something like this with my favorite football team, the Nebraska Cornhuskers..........but SOO much for one girl to do by herself! I realized I did not have a very good understanding about how to work with the team stats that I didn't have.

In the end, I know I made the right decision to not spend anymore time trying to make up a project about something I didn't have enough time or information to do. I am disappointed I was unable to do more with my findings and felt like I failed, but luckily there was another girl in my class that was interested in doing something sports related as well. We ended up doing a project on Player/Team Efficiency for the Girls Basketball Team that gave me the chance to work on something that might be similar to what I hope to do in the future. Mr. Pethan always says failure in stats is a GOOD thing! You always learn from failure, and now I know that by going through this process, in the future I won't spend as much time contemplating what I want to do. Now I know that if I spend more than a couple days trying to pull something together, it probably won't be a good project in the end, but there are always other things for me to do!

Taxes, Fencing, and Stats

I spent this quarter working for the Byron Public School District. I put together a group of students and we worked to analyze data about houses' taxable market values. One thing this project taught me was that working with the government can take a very long time. My group is still waiting for data from Olmsted County. When we get the data we will use it to generate a model of projected residential growth in Byron. We will then present to the school board and community. We hope to find that Byron will grow enough that taxes will fall low enough to cover the added cost of a bond for the new school.

Another project I wanted to take on this quarter involved fencing. I wanted to statistically analyze the game to see if any particular factors heavily influenced the number of touches a fencer could get. In order to do this I would either have to collect real life data, which is very hard to do in such a fast paced sport, or build a computer simulator. I decided to build a simulator. I had a very limited programming experience before undertaking this project. I only knew how to program calculators.

I started to teach myself python (a programming language) using http://www.codecademy.com. 
I spent seven hours learning the syntax, and wrote several basic programs. I then laid out logic for my fencing simulator, but realized i didn't know enough about programming to make the simulator work. I will continue to learn more about python, and hopefully I can bring my simulator to life.

This quarter I spent a lot of time learning about things I would have never guessed I would be learning about in a statistics class. Even though my projects are still in the works I have accomplished a ton.

Jacob Ostreng, Quarter 2

This quarter I did a few projects. These projects were difficult to try to find information for and produce results.  The first project I did was with Benton Blank and involved trying to program a survey that looped and would just ask you the same question over and over again. The survey would ask "would you be willing to answer another question?" Then would record how many times you answered yes and end when you say no. This failed when Mr. Pethan gave us the idea to create a stats app that would be used for the tests and homework questions. To make this app Benton and I had to learn how to program in Django, a programming language. The only problem is that Mr. Pethan did not know how to program in this language, so we looked for a class online to try to teach us. When going through the instructions on how to program we learned how to make a blog. It took us a long time to be able to just make blog that can only be edited by one person with admin access. What I learned from this project is that it is extremely difficult to learn something you have very little background knowledge about when you do not have direct contact with someone who is an expert on the subject.

For a final project, Benton, Paul, and I are creating graphs and charts to help show how the growth of Byron will affect the tax rates with the new school levy. To do this we need to obtain property tax information on all of Byron and surrounding townships. The trouble with this is that we do not have direct access to this and have to obtain all this information through the county. As we all know the government does not do much very fast, so we have to postpone the continuation of our project till the first of February. When we get this information we will be able to create the graphs and models we want to. We will then be presenting it at two different informational sessions to let the school and public have a better understanding of how their taxes should change. This will include how the tax rate will go down with an increasing tax base.



This is the Olmsted County building we visited for data
http://upload.wikimedia.org/wikipedia/commons/7/72/OlmstedGovtCenter.JPG

End of Year project

Plan for Data Usage

We gathered a large quantity of data from this restaurant. We took all of this data and found out trends in different projects. Among the things we found were, the most sold item and the item that generated the most revenue. This information could be useful to this fast food restaurant in the way that they could heavily advertise the products that bring the most revenue, so that the company as a whole can continue to expand. With them being aware of these statistics they could be able to more effectively raise prices in terms that they would be able to increase overall 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. We may be able to predict what they will sell more of and make more money off of in the future. All of information and findings are only predictions now and what not because we never presented it to the owner.

Hypothesis of What We May Be Able to Find

We could use this information to figure out how a new sandwich might do in their restaurant. We have found that their are a few sandwiches and meals that sell a lot because of their price, and their are a few things that sell purely because they come with something else so are automatically added. Then there are other things that are free to add so those are not really that important.

Information We Discovered

Most fast food restaurants could make a lot of money from selling special sauces, but that probably would not be a good plan, since ranch was a top “selling” item. That is probably because one of the chicken menu items comes with sauce so the main reason for that is that that was added on to that order.  We also found that pop, generates an enormous amount of revenue. It is one of the top selling items and does not cost the business hardly anything to produce. It is advertising for the pop companies so they really don’t charge too much for a large quantity.  We found most profitable items to be Drink C, Meal B, Sandwich G and Meal C. We concluded that since these items produce the most amount of revenue, their prices may be increased in the future. They may want to try to lower prices of a few items with high solo revenue but not many sold, so that way more could get sold.

Problem

One problem we have is that this particular restaurant, is owned in many places so they cannot just change their one store without getting permission from the owner of all of them.  That is a problem. They really can’t do anything with our information because all of this restaurant would have to have a giant meeting and talk to the owner and I don’t think he would listen to a few teenagers on what to do with the company he has made so much money with. We also wouldn't really know how to contact this person, or if they would even respond or anything.

Figure 1: Items Sold With Total Revenue.

Figure 2:  Slope CI

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.

Failure

During this second quarter, Isaac and I worked on a number of different projects.  We started off with our Pet Survey, but that ended up as a bust for us.  We considered that as a failure for our project as it all fell apart.  After writing our reflections on our pet survey, we started to discuss that failure.  This got us thinking about failure, which ended up being the focal point of our final project from the quarter.  

Starting this project we got help from Ms. Hegna, who helped us figure out what it was we were trying to discover and what we wanted to get our surveyees to think about.  The main questions we asked in this survey was if people have learned from past failures, whether or not they believe failure is essential to being successful in the future and if they believe failure is good.  We got a very wide variety of answers but the majority of people agreed with what we believed saying that they strongly agree for those statements.  We had a list of statements from which we had to take a small handful of statements that we thought would best evaluate their opinions.  We also wanted to make sure the survey was easy to understand.  The answers we got were very skewed, but in a good way.  Most answers we got were agreeing that failure is good, and important in being successful.  I am very pleased with our results thus far, seeing other peoples thoughts about failure.  This project has been a big help to me and will help me with real life problems and situations in my future.

If there is some things that I could change about our final project, it would be the amount of time we had available to us as we wanted to have this survey be on a much bigger scale and be sent to a number of people, instead of just teachers and students.  I think that we worked very efficiently on this project.  This project was more to me than elective points to help my grade in this class.  This project helped my views of how failing, will help you if you view it in the right mindset.  Almost every successful person has failed in their past, and that is what helps them to succeed.  

Donation Statistics

The main project I worked on this quarter involved analyzing the data of donations given to the Hope Lodge from the past year. The Hope Lodge is affiliated with the American Cancer Society and is funded through donations, so the ability to analyze these donations was a good opportunity to apply statistics to a situation outside of school.


When beginning this project, Kristin and I decided that we wanted to focus on either analyzing donations given to the Hope Lodge or interpreting the results of guest surveys. We met with the manager in Rochester to discuss our ideas and found that there was an abundance of donations data in easily accessible spreadsheets, so we decided that the best approach would be to work with this data.

The first step was to reformat the spreadsheets. Various details such as price, quantity, category, and location of each individual donation were included in the spreadsheets. However, besides the month each donation was given, there was minimal organization in the data. In order to ensure that the analysis process went as efficient as possible, we decided to reorganize the spreadsheets before beginning any other work. While this was time consuming at first, it ended up saving a lot of time in the long run.

After organizing the data into consistent groups, I analyzed the different categories according to the quantity of donations given from November 2012- October 2013 in total, then broken down into each individual month. Additionally, I used the same process to find the total monetary value of each donation category. Later, I decided it would be interesting to see a comparison between the total values and the values divided by each month.

 

To display the results, I created a few visual aids using various pie charts and bar graphs. These were later put into a presentation that included the cumulative analysis of all the donations.

This project was a good reminder for me of the numerous applications statistics has in the world. The ability to gather and interpret data into results that can benefit the Hope Lodge was a positive aspect of its completion. I also found that working on this project was a good experience that allowed me to further my understanding of concepts related to statistics.  Overall, this project has improved my ability to time manage as well as taught me how helpful statistics is in a variety of different ways.

Bullying Results

The largest project I worked on this quarter was going through bully survey results all the way from 2005.  I worked with Manda Pahl and she accomplished the feat of finding out the confidence interval for spring vs. fall amounts of bullying.  You can find more of that information in her blog!  We met up with the counselors a couple of times who handed us paper copies of the data and then virtually sent Mr. Pethan data as well.  Our first step was to figure out what we wanted to do with all the years of data they had conveniently collected.  I decided to work with separate grades and genders. 



As you can see, gender results varied on the differing seasons/years.  I was surprised to find out that there was not a consistent trend within genders! I expected the complete opposite, finding that one gender was bullied more frequently.  I found this out by putting it into statkey under descriptive statistics for two categorical variables.  The variables were gender and whether or not they had been bullied in that particular season.  I then found proportions and made the graph pictured above!


Next, I considered individual grades and how they were effected by bullying.  My confidence intervals are as follows:

- We are 95% confident that between 26.6% and 36.8% of 5th graders are bullied every season.

- We are 95% confident that between 27.3 and 34.9% of 6th graders are bullied every season.

- We are 95% confident that between 22.2% and 29.5% of 7th graders are bullied every season.

- We are 95% positive that between 21.9% and 30.9% of 8th graders are bullied every season.  However, we excluded the first 3 seasons from the 8th grade reports since they were not included in the surveys.

Here is an example of the 5th grade confidence interval:

I put the following grades in the same way as the 5th graders and got the results listed above.

We also concluded that on average, according the most recent surveys, that 94.9% of students know how to report bullying but 74.3% of students bullied do not report it.

It was an interesting topic to take time to look into and I'm glad I got the opportunity!

A Better Statkey

        Going into this quarter, I had some different ideas for products to work on, but it wasn't until shortly into the the class that Mr. Pethan gave me the idea that has consumed me for many many weeks.  We use Statkey on a daily basis in all of our core modules and even for our projects, but I see a major problem with it; the experience is too separated.  You need to navigate from page to page copying and pasting your data back and forth only to have 3 or 4 tabs open just to get all of your information.  This is coupled by the lack of data recognition; if you don't know what type of data you have, you are left guessing what to do with it.  Also, Statkey is a powerful resource that can be very easily manipulated.  Since it does all of the work for you, when it comes to exams, you might lose what this course is all about, knowing how to analyze statistical data.  

        This is where I decided to create my own version of Statkey, which I have called "Data Window" as of now.  By going to the main page of the site, you are presented with an open window to put your data into.

From here you can enter in your data just like Statkey, except you don't have to specify which type of data you are entering.  The algorithms decide which type of information you have entered, and then navigates you to the specific page based on the variables entered.  On this page you are presented with all of the information that was separated on Statkey.  For example, on the 1 quantitative variable page, you see the histogram, boxplot, confidence interval, and hypothesis test graphs with an ever-present dotplot to always know what your data is.  Here is a snippet of the page, which is still a work in progress:

You can see the tabs in which you can easily switch between the graphs that you care about, and the data is kept so you don't have to refresh the page or load new pages.  This along with tools that allow administrators to choose just how much information is displayed on each page allows for a true learning experience.  For example, while everyone is still learning about the unit and exploring how the site interprets the data, they will have access to all of the features, and when taking a test, certain settings could be disabled, such as direct page navigation or not displaying all of the graphs completely.

        Although I have made a lot of progress on this project and I think that it has some very cool features, I am by no means close to being finished with it.  I haven't even really focused on other types of input, such as 2 categorical or 2 quantitative, but since I have more knowledge of the framework and its implementation, this will not take long.  But for now I have focused on making the experience of just 1 quantitative variable as nice as it can be.  Once this is complete I will move onto the other sites and the tools to go along with it.  Kudos to Mr. Pethan for giving me such a great idea and hopefully I will make it a usable tool for future classes.

Basketball on Paper

          When given the opportunity to analyze the girl's basketball game stats, Margaret and I jumped at the chance. Especially since I am on the basketball team, this was a unique opportunity for me to have. Our goal was to find the overall efficiency of the team using areas of statistics that we thought were valuable to the team. Although we encountered many obstacles and potential errors throughout the process, we were able to come up with a fairly accurate analysis of the team as a whole as well as each individual player.

          This idea originated from the book "Basketball on Paper". We were able to read bits and pieces of this book to gain an understanding of the overview of basketball stats and what is important when it comes to making a team. We consulted with Mr. Bernards, as he is doing the same efficiency rating with his eighth grade team. Mr. Pocius helped us get started by giving us his own player efficiency rating formula. He formulated this based on what he thought was needed in each player. The formula is below:

[Points + Rebounds + (2 x Assists) + (2 x Steals) + (2 x Blocks)] / [(2 x FG Missed) + FT missed + (2 x Turnovers) + (2 x Fouls)]

           At first, Margaret and I started to watch each game and take our own statistics so we knew for sure that they were right. After about three games though, we realized that it was a tedious job and we weren't even sure if our stats were as accurate as we thought they were. We resorted to using the stat sheets that are taken on the bench during the game. Although there could have been a couple mistakes, we know that there is a small margin of error, and it was a lot less busywork for us. We took the first eleven games and did a team efficiency rating and a player efficiency rating and put it all on a google spreadsheet. Part of it is shown below.

          We then did confidence intervals for each of the players as well as the team. This worked better than averages because it eliminates outliers. There were many girls who would have either a really good game or a really bad game, and their averages would be skewed by it. Using a confidence interval helped get rid of this problem. Along with the formula from Mr. Pocius is a ranking scale (poor to excellent). We were able to analyze the team and how we changed from game to game. One thing we found was that there tends to be a pattern in the games. We will have a really high efficiency game (a peak) and then the next two or three would be a significant drop. One question that arises from that is why that pattern is so prevalent? This is something that our project did not answer, but would be something interesting to find in the future.  

 

          I would love to go deeper into this project if there was more time, but for now, I am content with what we have here. I learned a lot about how stats really do matter and how much more you can do with them than just looking at them individually. It really goes to show that everyone contributes to the team, and if the player efficiencies are overall pretty low, the team efficiency will also be low. We had a lot of fun with this project and although it was a challenge at time, it was definitely beneficial to do!

 

Failure Survey

During the 2nd Quarter, the most memorable project that I did was with Chris Roberts. We did a Failure Survey as our final project. Mr. Pethan wanted to know what went on in the minds of students. We asked him specifically what part of their mind that he wanted to know about it. His response; "what do students think about Failing?" We set up a survey for students to take, along with some parents. Jen Hegna was a huge help for us. She helped us come up with the comments and configure a scale from Strongly Disagree to Strongly Agree. We got this survey out pretty late but it didn't stop students from doing it, and having us get some results. With about 27 people tested, here are some of the results.

As you can see, with the questions asked, and the answers given, there are a lot of people that have positive reactions to failure. From looking at this data, it makes you less afraid to fail. If everyone thinks failure is a positive experience, then why is it so frowned upon? I really enjoyed seeing the results from this experiment because in a way, it proved that nobody is in a position to judge someone. Data like this can change that mindsets of individuals. This project is definitely a positive way to end stats class because I was finally able to put together a successful project with the help of Chris Roberts.

Dice Football Computer Game

For quarter 2 stats we needed a project to do so Joe, Dan and I, started brainstorming of a project that we should do. Then Dan came up with the idea of a dice football game that his family played when he was younger. We thought it would be a good idea because it had to do a lot with this class.

We had to use python to make the game and Joe, Dan and I had no idea what it was and how to use it. Mr. Pethan showed us some videos on how to do it. We had to make a bunch of codes to make our game work. The game worked pretty much like the normal game of football like you could get TD's and Interceptions. We had to make codes for all of that to work in the game.

We had the player either select pass or run. If they chose run they would roll the dice and whatever they rolled they would get unless the defensive roll was doubles. If they chose to pass it they would have to get even number if they get doubles they would automatically get a touchdown. If they defense got doubles they would get a sack if it adds up to what the offensive rolled it could be an interception.

If we had to do this project again, we would probably make a function for kicking off and make the game a little better. Overall it was a very fun and difficult project to do.

Statistics Goes Beyond the Classroom

Last quarter, I did a project to help improve the stats class I was learning in, and this quarter I got to do a project using statistics to help the community around me. I worked with Hope Lodge, an organization in Rochester that offers free residence facilities and programs for cancer patients. I volunteer at Hope Lodge, so my connections helped give me a jump start on the project, but it was interesting to get another perspective of ways I can help.

Madison and I worked together to do an analysis of their yearly donations. Since Hope Lodge relies so heavily on outside donations, this project offered a lot of insight into what keeps the Hope Lodge running. After talking with the manager and presenting our ideas to her, we knew that the donations project was going to be the most beneficial for the Hope Lodge. The project included a lot of tedious work because we took piles of raw data and turned them into something meaningful. What were once spreadsheets of words and numbers turned into easily understandable graphs that made results clear. Below is an example of a graph I made. It divides all of the donations into varying price categories.



Going even further with the price categories, I divided them into the purposes Hope Lodge uses the donations for. The difficult part about categorizing the donations by purposes was renaming each donation by using standardized labels. Here is an example of the graph of purposes of donations within the 0-$49.99 price range.



This project took a lot of time and effort, and while the results have importance, the manager's to-do list is way too long for her to be able to complete this analysis each year. This made me think about what I could do to help Hope Lodge obtain these results each year without having our help. As a result, I created a new donations input system on Google Forms. This system standardizes all entries while offering enough flexibility for the varying donations received each year. The best part about it is the summary of responses that creates graphs just like the ones I made above with the click of a button. The input process is very easy, anyone from volunteers to staff could do it, and the results are easily accessible at any time.

Overall, this project was a big undertaking, but being able to help an organization that does so much good in the community made it all worth it. I was satisfied with the analysis we did, but I am even more excited to see what Hope Lodge can do with the new input system in the future! Who would have thought a school project could really make a difference?

Fast Food Restaurant Findings

Plan for Data Usage

We gathered a large quantity of data from this restaurant. We took all of this data and found out different trends in certain products. Among the things we found were, the most sold item and the item that generated the most revenue. This information could be useful to this fast food restaurant in the way that they could heavily advertise the products that bring the most revenue, so that the company as a whole can continue to expand. With them being aware of these statistics they could be able to more effectively raise prices in terms that they would be able to increase overall 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 of in the future.

Hypothesis of What We May Be Able to Find

We could use this information to figure out how a new sandwich might do in their industry because of what we have found. We have found that their are a few sandwiches and meals that sell a lot because of their price, and their are a few things that sell purely because they come with something else so are automatically added. Then there are other things that are free to add so those are not really that important.

Information We Discovered

Most fast food restaurants could make a lot of money from selling special sauces, but that probably would not be a good plan, since ranch was a top “selling” item. We also found that pop, generates an enormous amount of revenue. It is one of the top selling items and does not cost the business hardly anything to produce. We found most profitable items to be Drink C, Meal B, Sandwich G and Meal C. We concluded that since these items produce the most amount of revenue, their 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.

Figure 2: Confidence Interval Items to Revenue.

Dice Football Simulation Game on Python

To start off the second quarter, we wanted to think of project that we would enjoy doing everyday when we went to stats class. After a lot of brainstorming, we came up with an idea. We decided to use a game that my family made a long time ago at my house, called dice football, and try to make it into a simulation game on the computer. We thought this would be a good project for stats class because not only does the game have to do with probability and math, but we can also simulate games hundreds of times to find data, which is what this class is really about.

None of us, Joe, Buster or I, knew anything about the Python Program or how to code. So with the help of Mr. Pethan, and even a little bit from Evan Richardson,  we were able to come up with a series of codes to make our game on the computer. We had to come up with codes to simulate three dice being rolled on offense and two dice being rolled on defense. Depending on what each team rolled, and the play type called by the offense, different outcomes can occur. Just like in regular football, in our game you could score touchdowns or field goals, fumble the ball, or throw an interception. We had to make functions for all of these which took a lot of time and hard work. While working on this project, we also wanted to know which strategy was best for the game (when was the best time to pass, run, or kick a field goal). We decided to create individual "teams" with their own unique functions. For example, one could make a function so they could only run on 3rd down if you had 3 or less yards to go. By making these funcitions, and repeating the results on python, we were able to see patterns of which functions worked better than others. I learned a ton from this project, especially in coding. While I'm still not that great of a coder, I learned the basics needed if I was ever to do something like this again. This project also is great in that it helps you learn how to overcome obstacles. For example, we could take three or four days just to get a function  for runs, pass, kickoffs, or scoring. While we had to be patient, it was worth it in the end to see our game actually work.

If I was to do this project again, the only thing I would do is work on it more and figure out more codes to make the game more specific and fun. For example, we never really made a kickoff or onside function because we never had the time. I would have liked to completely finish the project, but between the modules and our other final project, we never really had the time. Overall, it was a very fun project to work on and it taught me a lot about statistics and the basics of coding. Below are examples of the type of coding we did in Python.

Bowling: Crankers vs Tweeners

In the first quarter of Statistics I completed a few projects. One of my favorite projects was determining what type of bowler is better. The three types are a cranker, tweener, and stroker.  I determined that every single top bowler is either a cranker or tweener. After collecting all the data it was clear that there is no difference between the two. You may wondering how I determined that crankers had to have high revolutions on the ball, above average ball speed, and exploding pins. Tweeners had an average ball speed and gradual hook.

Each type of bowler has its pros and cons though. Crankers have the ability to throw powerful strikes, even on not-perfect shots, due to the high number of revolutions and speed on the ball. However, with that comes the risk of splits, which crankers are more prone to throw than some of the other bowling styles. Now tweeners are typically versatile bowlers due to their ability to take parts from each style of bowling to form their own. They can consistently place their shots where they want and turn up the power when necessary. However, it can be difficult to make such changes during a match, and tweeners have to weigh the consequences prior to making any such adjustment. 

Overall I think I did very well on this project. It was interesting to see how my results panned out. I had always wondered which type of bowler was better. A cranker or tweener are equally as good.. I think that depending on the condition of the lane, one can be better than the other.

An Analysis of Simple Rockets

For one of my statistics projects I chose to analyze a game called SimpleRockets. I wanted to find out the most efficient way to achieve orbit. So I ran numerous tests to figure it out. I measured the height of the rockets apoapsis and the time it took the rocket to reach apoapsis. If the rocket escaped orbit I also measured its escape velocity and the time it took to escape orbit.

I was surprised to find the largest engine was not the most efficient engine. In fact the second smallest engine used with the second largest fuel tank was the best combination to achieve orbit. I attribute this to air resistance in the atmosphere. The larger engines consumed more fuel while they were still in the atmosphere. But the smaller engines had plenty of fuel left after the escaped the atmosphere.

In addition to running tests I was able to look at a bit of the games code. I found all of the different parts values such as mass, fuel consumption, thrust, wet weight, dry weight, and turning ability. I then looked though the planets and found their mass, orbit, and atmospheric density. Seeing these values helped me to come to my conclusion that the atmosphere is responsible for making large ships inefficient.

Greatest Sport State of the US (currently)

Sports fans around the US each have their own claims on what team is best and which state has the best sports.  Is there a way actually figure out who is the best of the best? I decided to come up with a way to calculate who really is the sports state of america and I displayed this throughout my project.  First, I looked through all 50 states, plus Washington D.C., and took the teams that had at least one MLB, one NFL, and one NBA team and threw out the rest.  I figured this would show the states that were overall the sports state, rather than dominating in one or two types of sports.  This gave me a total of 15 states (Washington D.C. included). I looked at each state and wrote down every MLB, NFL, and NBA team they had. Then I went onto ESPN and found the winning percentages of the past three completed seasons for each individual team.  After that, I calculated the average winning percentage of those three seasons.  If a state had more than one team of the same sport, I then made an average winning percentage for that sport. If they only had one team, I would just use that single team for the sport average.  Finally, I found an average of each sport to show the state's overall winning percentage.

 I displayed all of this in a spreadsheet in Microsoft Excel.

After many calculations I found the top states/teams for each sport 

And last but not least, I determined that Massachusetts is the overall best sports state of the US based upon the last three seasons.  (Meanwhile, we unfortunately have Minnesota in last place) 

Perfect Student

This quarter, Hope and I worked together on a perfect student project.  The idea of this project was to find a way to rank students on their qualities, not just their grades.  We came up with a list of 19 characteristics we found important.  We asked students to rank the importance of each characteristic on a scale of 1-5.  After getting results from the survey we averaged the results to figure out what characteristics were most important.

Results from Survey

We compared our results to what would be the perfect student.  The perfect student would have a score of 5 (light purple).  The average we got shows our ranking out of 5 (dark purple).

Average of the Results we got

Along with finding the most important characteristics, we asked students to rank themselves on a scale of 1-5.  From these rankings, we found the average of their answers to find their ranking.  The top 5 characteristics (trustworthy/honest, respectfulness, responsibility, dedication/hard working, and standing up for what you believe in) were weighted stronger then the other characteristics.  From this we found the maximum, minimum, median, and mean.  

Results: 

Maximum:    4.32

Minimum:    1.68

Mean:          3.7

Median:       3.84

Even though the class rated themselves, I don't think everyone was completely honest about all of the characteristics.  To continue this project next quarter, we will be ranking people on these characteristics based on what we see -- hopefully the results will be more exact.  We may also ask teachers or other students to rank some students to see how accurate the students ranked themselves.  From this project I figured out what qualities were important to students.  One thing that shocked me was eagerness to learn was on the bottom of the importance ranking.  I thought it would be more important.  I learned that no student thinks they are perfect so if we were to use this ranking over our GPA, the top person would not have a perfect score like the top for GPA could have a 4.0.

Freshman Survey

For my project I decided to take a survey of all the freshmen in school and see what is going through their heads. The questions I chose were because I had been wondering about what the answers were. I wanted to know what the kids in the school thought of the iPads and the lunches, the last questions were just for fun and I wanted to see how the freshmen handled some questions that were not what was to be expected. It took me about a week to come up with the questions, make the survey, send the survey out and get the responses. My first plan was to go to all of the seminar rooms and have the freshmen take it on paper, then I went high-tech and decided to have them take it on their iPad's. I sent the survey out in a email to all of the freshmen in school on Friday and waited over the weekend for the results.

Of the one hundred and fifty freshmen only 78 responded. Once the results were in I compiled them and took a random, cluster, convenience and nonrandom samples, then I compared the results to each of the samples and then to the results of the whole survey. The data I got back showed that some of the bad ways of sampling had the most accurate data, however that could have been a fluke: it is possible to get good data with a bad sample, but it is not likely to happen. The point of this survey was to compare the results of all of the different types of samples, and to answer some questions that I had been wondering about for a while. I have not confirmed which is better Xbox or PS 3, so I will need to create further surveys to confirm my results, and why I got the results I did for this survey.

I can now confirm that the freshmen class likes the iPad's overall, and that the freshmen are indifferent about school lunches. Some things I learned from working on this survey are that, freshmen are lazy even when it comes to taking a two minute survey, that even the worst samples can give the most accurate data (but not very often), and I also learned that it takes a lot of work to set up and send out a survey and then compile the results I got back.

Math Survey of the school district

This quarter I had to do a project that was for electives and I had a hard time thinking about what to study. Then Mr. Pethan suggested that I write down ideas about thing that I really disliked and hated. So I wrote down a few thing and then realized that the one thing that I really hated was math. I decided to survey a class from every grade to get a more proper representation of what other kids liked about math or what they didn't like about math.

So first off I had to do a cluster sample and picked one classroom from every grade.  The first thing that I did was make the survey, then I picked all of grades 1st through 8th graders and then I picked 4 math classes in the high school that I was going to survey. After I got the classes figured out I sent emails to the teachers that I wanted to survey there classes and got there permission to do it. Next I set up a date that work for them all to take it and then I went and got it done. Here is Mrs. Fuchstieners 4th grade class results:

Throughout the course of my project I learned that paper copy surveys are really time consuming and fairly boring to go through and look through them all. I have a hunch that most of the elementary and part of the middle school kids that will take my survey will like math while a few of them won't like math. I learned that even when kids are young they are pretty smart people for their age because of some of the answers that these kids give. One of the response that I got was a great one or at least I thought that it was. It said that math needs "To get rid of some words that mean the same things as other". I guess the one main thing that I learned was that even kids in elementary school thinks that math needs to be more fun than what it already was. Over the years math has not only got less fun but more troublesome to the majority of the people. I was talking with my parents and they told me that math has changed so much over the past few years they never even heard of it or would know how to do it anymore. Today even in school I hear even some of the smarter kids say that math isn't what it used to be. So those are the things that I have learned and observed. My point of this project is to see what makes people either like math or hate math. 

Cryptography

In this quarter of statistics, we were assigned to come up with our own projects, whether those projects were improving the course, finding new and interesting data, or some thing else was completely up to you.  The big project that I started was trying to find a way to easily teach the basic concepts of cryptography and how you can use statistics, in addition to some basic math, to crack some elementary ciphers.  I will be the first to admit that when I started out on this project I knew next to nothing about creating or breaking ciphers, so I had to do a lot of research on types of ciphers, how they work, some basic number theory, and the processes you must under go in order to analyze and break the cipher.  As soon I had a good amount of reach done, I started creating a power point to summarize what I'd learned, in hopes of using it teach others.  This power point is, at this point, still a work in progress, and I hope to eventually expand upon it, creating a video series to teach the concepts necessary in cryptography and cryptanalysis.




Through this project I have learned a lot about the applications of statistics and other forms of math.  I think that it is a good thing to be teaching students how math can be used or has been used in the past, other wise they will think is math is just a class you have to take that will never be used beyond an elementary school level, cryptography is good example of this.  Since this project is still a work in progress, I hope to learn and more and add to/expand upon it in order to better teach others, as well as to better myself.

ACL Injuries

Throughout this quarter of Stats class, we have learned many things.  The thing that I did and enjoyed was researching ACL injuries.  This year I have seen many professional athletes go down with torn ACL's. This started to make me wonder and I became interested in how likely it is for an ACL injury to occur.  To find how likely an ACL injury is to occur you can use this graph from BMJ Publsihing group.

To calculate the occurrence of ACL injuries with your team and in real life situations, you need to plug numbers given into an equation of players multiplied by exposures multiplied again by the injury rate above then divided by 1000. The number of ACL injuries that you would expect to see from a hockey team of 40 players who are on the ice 85 times during the season would be about once every 5 seasons or .204. Surprisingly, the Byron Bears did not suffer any ACL injuries to their football team this season. They had a roster of 45 players and the probability of an ACL injury occurring is .445 ACL injuries per season.

Throughout this process I learned some very interesting things, whether it be about the ACL or how often of an occurrence ACL injuries are. I also talked to my friends dad who is a general surgeon and he was talking to me about the ACL and other ligaments of the knee. That was very good for me as I was able to understand what I was researching more in depth. This project also took some time to complete. I was also more interested because of the injury that Adrian Peterson suffered a couple years ago. I wanted to know the chances that a football team will have players with an ACL injury. For a pro football team that does not make the playoffs, the number of ACL tears per season would be 1.602, obviously some teams might have more than others just depending on other factors that go into it. Being able to put these numbers and predictions into real life situations is what I enjoyed most about this project. I am excited to see at the end of the season how many injuries teams have as by those calculations, there should be around 51 ACL tears. Through 7 weeks there were 38 ACL tears, which would surpass the expected number of 51. This could be due to multiple things including the high speed in the pros and the physicality of the game. I had a lot of fun working on this project and learned many cool things!

Teacher's Expectations

I worked on a group project on what high school seniors are expected to accomplish vs. what high school seniors are capable of accomplishing.  I did the teacher's perspective for surveying.  These were the average results of 16 randomly selected teachers:

	How many hrs. seniors should spend on ALL homework in one week?	How many hours of work should a senior do a week?	How many hours of sleep should a senior get each week?	How many hours on extracurriculars (sports, volunteer, music) a week?	How many hrs. should a senior have a week of free time?
avg. hours/week	15.8	16.4	56.2	13.8	13.7

As you can tell from the table above, seniors are expected to be quite productive in a week with only 168 hours.  Here is the work I did to show that there are not enough hours in a week based on teacher perspective.  (I rounded to the nearest hour since students will vary anyways). 

Hours in a week 168
Homework hrs. -16
Work hrs. -16
Sleep hrs. -56
Extracurricular hrs. -14
"Free time" hrs -14
School hrs. -35

This leaves us with 17 hours in a week and we have to include prep time (amount of time to get ready) in a day which is about 7 hours in a week.  Leaving us with 10 hours!  Then you need to eat which is about 1.5 hours a day leaving us with NEGATIVE 0.5 hours in a week!  That does not include our transportation from Point A to Point B during a week-long time period.

I tried to work mostly on what teachers expected from us while Manda got the students perspective.  First we took all the different departments of teachers and did an SRS to get teachers to survey from each department.   We were going to survey 20 teachers, but we had 4 teachers as undercoverage because they weren't in the building while we surveyed.  After we asked all the teachers the survey questions, I made a table with the data.  I found the average hours per week for each question (the first table above).  Then I included school, eating, and prep time to get the results.

I learned that to take a decent survey you need to be very specific on the questions you ask, how you ask them, and who you ask.  It all needs to be worked out so you try to not have bias.  It's difficult to survey when you know what you want the results to be!

The point of this survey was to prove that as a senior in high school, a lot is expected from us!  Of course, every senior is different and results may vary from senior to senior.