Question
Denise Lau is an avid football fan and religiously follows every game of the National Football League. During the 2017 season, she meticulously keeps a
Denise Lau is an avid football fan and religiously follows every game of the National Football League. During the 2017 season, she meticulously keeps a record of how each quarterback has played throughout the season. Denise is making a presentation at the local NFL fan club about these quarterbacks. The accompanying table shows a portion of the data that Denise has recorded with the following variables: the players name (Player), teams name (Team), completed passes (Comp), attempted pass (Att), completion percentage (Pct), total yards thrown (Yds), average yards per attempt (Avg), yards thrown per game (Yds/G), number of touchdowns (TD), and number of interceptions (Int).
Player | Team | Comp | Att | Pct | Yds | Avg | Yds/G | TD | Int |
Aaron Rodgers | GB | 154 | 238 | 64.7 | 1675 | 7 | 239.3 | 16 | 6 |
Alex Smith | KC | 341 | 505 | 67.5 | 4042 | 8 | 269.5 | 26 | 5 |
Andy Dalton | CIN | 297 | 496 | 59.9 | 3320 | 6.7 | 207.5 | 25 | 12 |
Ben Roethlisberger | PIT | 360 | 561 | 64.2 | 4251 | 7.6 | 283.4 | 28 | 14 |
Blaine Gabbert | ARI | 95 | 171 | 55.6 | 1086 | 6.4 | 217.2 | 6 | 6 |
Blake Bortles | JAX | 315 | 523 | 60.2 | 3687 | 7 | 230.4 | 21 | 13 |
Brett Hundley | GB | 192 | 316 | 60.8 | 1836 | 5.8 | 166.9 | 9 | 12 |
Brock Osweiler | DEN | 96 | 172 | 55.8 | 1088 | 6.3 | 181.3 | 5 | 5 |
Bryce Petty | NYJ | 55 | 112 | 49.1 | 544 | 4.9 | 136 | 1 | 3 |
C.J. Beathard | SF | 123 | 224 | 54.9 | 1430 | 6.4 | 204.3 | 4 | 6 |
Cam Newton | CAR | 291 | 492 | 59.1 | 3302 | 6.7 | 206.4 | 22 | 16 |
Carson Palmer | ARI | 164 | 267 | 61.4 | 1978 | 7.4 | 282.6 | 9 | 7 |
Carson Wentz | PHI | 265 | 440 | 60.2 | 3296 | 7.5 | 253.5 | 33 | 7 |
Case Keenum | MIN | 325 | 481 | 67.6 | 3547 | 7.4 | 236.5 | 22 | 7 |
Dak Prescott | DAL | 308 | 490 | 62.9 | 3324 | 6.8 | 207.8 | 22 | 13 |
Derek Carr | OAK | 323 | 515 | 62.7 | 3496 | 6.8 | 233.1 | 22 | 13 |
Deshaun Watson | HOU | 126 | 204 | 61.8 | 1699 | 8.3 | 242.7 | 19 | 8 |
DeShone Kizer | CLE | 255 | 476 | 53.6 | 2894 | 6.1 | 192.9 | 11 | 22 |
Drew Brees | NO | 386 | 536 | 72 | 4334 | 8.1 | 270.9 | 23 | 8 |
Drew Stanton | ARI | 79 | 159 | 49.7 | 894 | 5.6 | 178.8 | 6 | 5 |
Eli Manning | NYG | 352 | 571 | 61.6 | 3468 | 6.1 | 231.2 | 19 | 13 |
Jacoby Brissett | IND | 276 | 469 | 58.8 | 3098 | 6.6 | 193.6 | 13 | 7 |
Jameis Winston | TB | 282 | 442 | 63.8 | 3504 | 7.9 | 269.5 | 19 | 11 |
Jared Goff | LA | 296 | 477 | 62.1 | 3804 | 8 | 253.6 | 28 | 7 |
Jay Cutler | MIA | 266 | 429 | 62 | 2666 | 6.2 | 190.4 | 19 | 14 |
Jimmy Garoppolo | SF | 120 | 178 | 67.4 | 1560 | 8.8 | 260 | 7 | 5 |
Joe Flacco | BAL | 352 | 549 | 64.1 | 3141 | 5.7 | 196.3 | 18 | 13 |
Josh McCown | NYJ | 267 | 397 | 67.3 | 2926 | 7.4 | 225.1 | 18 | 9 |
Kirk Cousins | WAS | 347 | 540 | 64.3 | 4093 | 7.6 | 255.8 | 27 | 13 |
Marcus Mariota | TEN | 281 | 453 | 62 | 3232 | 7.1 | 215.5 | 13 | 15 |
Matt Moore | MIA | 78 | 127 | 61.4 | 861 | 6.8 | 215.2 | 4 | 5 |
Matt Ryan | ATL | 342 | 529 | 64.7 | 4095 | 7.7 | 255.9 | 20 | 12 |
Matthew Stafford | DET | 371 | 565 | 65.7 | 4446 | 7.9 | 277.9 | 29 | 10 |
Mike Glennon | CHI | 93 | 140 | 66.4 | 833 | 6 | 208.2 | 4 | 5 |
Mitchell Trubisky | CHI | 196 | 330 | 59.4 | 2193 | 6.6 | 182.8 | 7 | 7 |
Nick Foles | PHI | 57 | 101 | 56.4 | 537 | 5.3 | 76.7 | 5 | 2 |
Philip Rivers | LAC | 360 | 575 | 62.6 | 4515 | 7.9 | 282.2 | 28 | 10 |
Russell Wilson | SEA | 339 | 553 | 61.3 | 3983 | 7.2 | 248.9 | 34 | 11 |
Ryan Fitzpatrick | TB | 96 | 163 | 58.9 | 1103 | 6.8 | 183.8 | 7 | 3 |
Tom Brady | NE | 385 | 581 | 66.3 | 4577 | 7.9 | 286.1 | 32 | 8 |
Tom Savage | HOU | 125 | 223 | 56.1 | 1412 | 6.3 | 176.5 | 5 | 6 |
Trevor Siemian | DEN | 206 | 349 | 59 | 2285 | 6.5 | 207.7 | 12 | 14 |
Tyrod Taylor | BUF | 263 | 420 | 62.6 | 2799 | 6.7 | 186.6 | 14 | 4 |
a. Conduct principal component analysis on all the numerical variables in the data set. Should you use the covariance matrix or the correlation matrix for the analysis in this case?
multiple choice
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Use the covariance matrix because the variables have different scales
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Use the covariance matrix because the variables have the same scale
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Use the correlation matrix because the variables have different scales
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Use the correlation matrix because the variables have the same scale
b. Allow the maximum number of principal components to be calculated by the software. How many principal components are computed? What percent of the total variability is accounted for by the first three principal components? How many principal components must be retained in order to account for at least 80% of the total variance in the data? (Show your final answer as a percentage point with 2 decimal places.)
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c. Which original variable is given the highest weight to compute the first principal component? Which original variable is given the highest weight to compute the second principal component?
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*****all data from table is included,,, nothing else to add ******
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