The value of a sports franchise is directly related to theamount of revenue that a franchise can generate.  Thedata below represents the value in 2017 ($ in millions) and theannual revenue ($ in millions) for the 30 Major League Baseballteams.
Team | Revenue ($ in millions) | Value ($ in millions) |
Baltimore Orioles | 253 | 1175 |
Boston Red Sox | 434 | 2700 |
Chicago White Sox | 269 | 1350 |
Cleveland Indians | 271 | 920 |
Detroit Tigers | 275 | 1200 |
Houston Astros | 299 | 1450 |
Kansas City Royals | 246 | 950 |
Los Angeles Angels | 350 | 1750 |
Minnesota Twins | 249 | 1025 |
New York Yankees | 526 | 3700 |
Oakland Athletics | 216 | 880 |
Seattle Mariners | 289 | 1400 |
Tampa Bay Rays | 205 | 825 |
Texas Rangers | 298 | 1550 |
Toronto Blue Jays | 278 | 1300 |
Arizona Diamondbacks | 253 | 1150 |
Atlanta Braves | 275 | 1500 |
Chicago Cubs | 434 | 2675 |
Cincinnati Reds | 229 | 915 |
Colorado Rockies | 248 | 1000 |
Los Angeles Dodgers | 462 | 2750 |
Miami Marlins | 206 | 940 |
Milwaukee Brewers | 239 | 925 |
New York Mets | 332 | 2000 |
Philadelphia Phillies | 325 | 1650 |
Pittsburgh Pirates | 265 | 1250 |
St. Louis Cardinals | 310 | 1800 |
San Diego Padres | 259 | 1125 |
San Francisco Giants | 428 | 2650 |
Washington Nationals | 304 | 1600 |
1. Using Excel or JMP, construct a scatterplot of value versusthe revenue for the 30 MLB teams in 2016.  Provide a copyof the resulting scatterplot. (3 points)
2. Based upon your scatterplot, does it appear that the linearmodel is a reasonable approximation of the data? Comment on thedirection and form of the relationship. (2 points)
3.  Using Minitab provide (or attach) the simplelinear regression analysis for predicting a team’s value based uponits revenue. (2 points)
4. State the slope for the simple linear regression analysis andinterpret this value in this context. (2 points)
5. State the y-intercept for the simple linear regressionanalysis and interpret, if applicable. (1 point)
6.  State the standard error of the regressionanalysis and interpret that value.  (2 points)
7. State the coefficient of determination and interpret thevalue in this context. (2 points)
8. State the sum of square errors. (1 point)
SSE=504849.5953
9. State the standard error of the slope. (1point)
SEb(1)=sqrt(504849.5953/30-2)/sqrt(186742.7)=134.277/432.137=0.3107
Source:www.forbes.com
10. Calculate and interpret the 95% confidence interval forslope.   (2 points)
8.6507+-2.048(0.3107)=8.6507+-0.6363=(8.0144,
   We are 95% confident that the slope of theinterval is between 8.0144 and 9.287.
11.  From the coefficient of determination, standarderror of regression, and the confidence interval for slope doesthat model appear to fit well?  Explain.  (2points)
