Top 50 Metro Areas by Population: Personal Income, Population, Per Capita Personal Income Rank Metro GeoName Total Metro Personal Income 2020 (000s) Per Capita Personal Income 2020 Population 2020 (millions) # of NFL, NBA, MLB, NHL teams 1 New York-Newark-Jersey City, NY-NJ-PA 1574364671 82322 19.124359 9 2 Los Angeles-Long Beach-Anaheim, CA 915132543 69805 13.109903 6 3 Chicago-Naperville-Elgin, IL-IN-WI 636555184 67671 9.406638 5 4 Dallas-Fort Worth-Arlington, TX 473604117 61554 7.694138 4 5 Houston-The Woodlands-Sugar Land, TX 428500645 59893 7.154478 3 6 Washington-Arlington-Alexandria, DC-VA-MD-WV 485550913 76771 6.324629 4 7 Miami-Fort Lauderdale-Pompano Beach, FL 396247347 64190 6.173008
Top 50 Metro Areas by Population: Personal Income, Population, Per Capita Personal Income Rank Metro GeoName Total Metro Personal Income 2020 (000s) Per Capita Personal Income 2020 Population 2020 (millions) # of NFL, NBA, MLB, NHL teams 1 New York-Newark-Jersey City, NY-NJ-PA 1574364671 82322 19.124359 9 2 Los Angeles-Long Beach-Anaheim, CA 915132543 69805 13.109903 6 3 Chicago-Naperville-Elgin, IL-IN-WI 636555184 67671 9.406638 5 4 Dallas-Fort Worth-Arlington, TX 473604117 61554 7.694138 4 5 Houston-The Woodlands-Sugar Land, TX 428500645 59893 7.154478 3 6 Washington-Arlington-Alexandria, DC-VA-MD-WV 485550913 76771 6.324629 4 7 Miami-Fort Lauderdale-Pompano Beach, FL 396247347 64190 6.173008
Chapter9: Sequences, Probability And Counting Theory
Section9.5: Counting Principles
Problem 43SE: A wholesale T-shirt company oilers sizes small, medium, large, and extra-large in organic or non-...
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| Top 50 Metro Areas by Population: Personal Income, Population, Per Capita Personal Income | |||||||
| Rank | Metro GeoName | Total Metro Personal Income 2020 (000s) | Per Capita Personal Income 2020 | Population 2020 (millions) | # of NFL, NBA, MLB, NHL teams | ||
| 1 | New York-Newark-Jersey City, NY-NJ-PA | 1574364671 | 82322 | 19.124359 | 9 | ||
| 2 | Los Angeles-Long Beach-Anaheim, CA | 915132543 | 69805 | 13.109903 | 6 | ||
| 3 | Chicago-Naperville-Elgin, IL-IN-WI | 636555184 | 67671 | 9.406638 | 5 | ||
| 4 | Dallas-Fort Worth-Arlington, TX | 473604117 | 61554 | 7.694138 | 4 | ||
| 5 | Houston-The Woodlands-Sugar Land, TX | 428500645 | 59893 | 7.154478 | 3 | ||
| 6 | Washington-Arlington-Alexandria, DC-VA-MD-WV | 485550913 | 76771 | 6.324629 | 4 | ||
| 7 | Miami-Fort Lauderdale-Pompano Beach, FL | 396247347 | 64190 | 6.173008 | 4 | ||
| 8 | Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 425748748 | 69705 | 6.107906 | 4 | ||
| 9 | Atlanta-Sandy Springs-Alpharetta, GA | 357795984 | 58773 | 6.087762 | 3 | ||
| 10 | Phoenix-Mesa-Chandler, AZ | 262362901 | 51851 | 5.059909 | 4 | ||
| 11 | Boston-Cambridge-Newton, MA-NH | 418178210 | 85724 | 4.878211 | 4 | ||
| 12 | San Francisco-Oakland-Berkeley, CA | 521589857 | 111050 | 4.696902 | 4 | ||
| 13 | Riverside-San Bernardino-Ontario, CA | 212234205 | 45365 | 4.678371 | 0 | ||
| 14 | Detroit-Warren-Dearborn, MI | 251173430 | 58356 | 4.304136 | 4 | ||
| 15 | Seattle-Tacoma-Bellevue, WA | 323176255 | 80420 | 4.018598 | 3 | ||
| 16 | Minneapolis-St. Paul-Bloomington, MN-WI | 245833135 | 67214 | 3.657477 | 4 | ||
| 17 | San Diego-Chula Vista-Carlsbad, CA | 220825596 | 66266 | 3.332427 | 1 | ||
| 18 | Tampa-St. Petersburg-Clearwater, FL | 169629192 | 52291 | 3.243963 | 3 | ||
| 19 | Denver-Aurora-Lakewood, CO | 208852979 | 69822 | 2.991231 | 4 | ||
| 20 | St. Louis, MO-IL | 170697159 | 60844 | 2.805473 | 3 | ||
| 21 | Baltimore-Columbia-Towson, MD | 186759714 | 66695 | 2.800189 | 2 | ||
| 22 | Charlotte-Concord-Gastonia, NC-SC | 152149128 | 56682 | 2.684276 | 2 | ||
| 23 | Orlando-Kissimmee-Sanford, FL | 127277824 | 48223 | 2.639374 | 1 | ||
| 24 | San Antonio-New Braunfels, TX | 129592611 | 50022 | 2.590732 | 1 | ||
| 25 | Portland-Vancouver-Hillsboro, OR-WA | 157150249 | 62603 | 2.510259 | 1 | ||
| 26 | Sacramento-Roseville-Folsom, CA | 146881794 | 61852 | 2.374749 | 1 | ||
| 27 | Las Vegas-Henderson-Paradise, NV | 118678768 | 51244 | 2.315963 | 1 | ||
| 28 | Pittsburgh, PA | 147040883 | 63675 | 2.309246 | 3 | ||
| 29 | Austin-Round Rock-Georgetown, TX | 148993898 | 64913 | 2.295303 | 0 | ||
| 30 | Cincinnati, OH-KY-IN | 133097872 | 59607 | 2.232907 | 2 | ||
| 31 | Kansas City, MO-KS | 126169318 | 58057 | 2.173212 | 2 | ||
| 32 | Columbus, OH | 120320535 | 56252 | 2.138946 | 1 | ||
| 33 | Indianapolis-Carmel-Anderson, IN | 126361669 | 60431 | 2.091019 | 1 | ||
| 34 | Cleveland-Elyria, OH | 120269831 | 58846 | 2.043807 | 3 | ||
| 35 | San Jose-Sunnyvale-Santa Clara, CA | 239730405 | 121619 | 1.97116 | 0 | ||
| 36 | Nashville-Davidson--Murfreesboro--Franklin, TN | 121744536 | 62076 | 1.961232 | 2 | ||
| 37 | Virginia Beach-Norfolk-Newport News, VA-NC | 94883251 | 53310 | 1.779824 | 0 | ||
| 38 | Providence-Warwick, RI-MA | 98890061 | 60897 | 1.62389 | 0 | ||
| 39 | Jacksonville, FL | 87532441 | 55125 | 1.587892 | 1 | ||
| 40 | Milwaukee-Waukesha, WI | 95447587 | 60499 | 1.577676 | 2 | ||
| 41 | Oklahoma City, OK | 75099831 | 52688 | 1.425375 | 1 | ||
| 42 | Raleigh-Cary, NC | 86478668 | 60884 | 1.420376 | 1 | ||
| 43 | Memphis, TN-MS-AR | 68992049 | 51155 | 1.348678 | 1 | ||
| 44 | Richmond, VA | 79704113 | 61148 | 1.303469 | 0 | ||
| 45 | New Orleans-Metairie, LA | 73652443 | 57891 | 1.272258 | 2 | ||
| 46 | Louisville/Jefferson County, KY-IN | 70652096 | 55676 | 1.268993 | 0 | ||
| 47 | Salt Lake City, UT | 71932167 | 58008 | 1.240029 | 1 | ||
| 48 | Hartford-East Hartford-Middletown, CT | 80910874 | 67343 | 1.201483 | 0 | ||
| 49 | Buffalo-Cheektowaga, NY | 62784969 | 55777 | 1.125637 | 2 | ||
| 50 | Birmingham-Hoover, AL | 60136271 | 55074 | 1.091921 | 0 |
1) Estimate the standard deviation of the error term in the simple linear regression model.
1) Calculate SE (b1), the estimate of the standard deviation, of the least squares slope b1
1a) what is the value of the test statistic for testing H0: B1 = 0?
2) Calculate a 90% confidence interval for the slope of the least squares regression line of # of teams on Population size.
2a) For the metro areas that have at least 1 professional team, how much does their population have to increase to support another team?
Transcribed Image Text:Professional sports teams in the National Football League (NFL), Major League Baseball (MLB), National Basketball Association (NBA), and National Hockey League (NHL) are in cities across the United States. Population size of the surrounding metropolitan area is an important consideration when deciding to locate a team of one of the above leagues in a city,
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