### DM 02 Data Modeling Project

 The summer olympic games will be held in London, England from July 27 - August 12. The 22nd Winter Olympic games will be held in Sochi City, Russia In February of 2014.  The Olympic Committee has asked your company, The Linear Regression Alliance (LRA), to create a report regarding the world records of various olympic events in the sports of swimming and speed skating.  The Olympic Committee hopes to be able to predict the record time in the future olympic games, which will help the committee decide what should be the olympic qualifying times.  Project Rubric

Step 1: Create a new page on your website and name it "DM 02 Project."  This is where you will post your report.  Make sure there is a link to it on your navigation pane.

Step 2: Obtain the World Record Progression Data for your event from the following link:  World Record Progression Data.

Step 3:  Write an introduction to your report.  This introduction should include the event you are investigating and a table that includes the last 10 world record changes, the record holder (country in parentheses), and the record time in seconds (you may have to convert the times from minutes:seconds to seconds).  If the world record was broken twice in one year, only use the best record for that year.

Step 4: Create a scatter plot for your data.  You can use Google Spreadsheet or Microsoft Excel to do this.  Make sure to label your axes.  Does the scatter plot support a good fit or a bad fit?  Why?

Step 5: Find the equation for the line of best fit for your data.  Make sure you let x = 0 represent the first year you have data for.  Graph it on your scatter plot in Google Spreadsheet or Microsoft Excel.  Include the correlation coefficient in your report.  Does the correlation coefficient support a good fit or a bad fit?  Why?

Step 6: Interpret the meaning of the slope and y-intercept of your prediction equation in the context of the problem.

Step 7: Find the residuals of your data set.  Include them in a new table along with your data values for year and world record times.

Step 8: Create a residual plot for your data.  You can use Google Spreadsheet or Microsoft Excel to do this.  Make sure to label your axes.  Does the residual plot support a good fit or a bad fit?  Why?

Step 9:  What do you predict the record time of your summer event to be for the 2016 Olympics in Rio De Jinero, Brazil or your winter event to be for the 2018 olympics in Pyeongchang, South Korea?

Step 10:  Decide if this linear model is a good fit for your data set. Make sure that you explain (or re-explain) why each test is good or not good in your overall analysis.