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.

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?