3) Repeat parts (b)-(d) of Problem 2 with y Number of veterans ("veterans") and x = population ("totpop”). How does the standard error using SRS compare with that using ratio estimation? Here is the Problem 2 2) The data file counties.csv contains information on land area, population, number of physicians, unemployment, and a number of other quantities for an SRS of 100 of the 3141 counties in the United States (U.S. Census Bureau, 1994). The total land area for the United States is 3,536,278 square miles; 1993 population was estimated to be 255,077,536. a) Draw a histogram of the farm populations (the variable “farmpop") for the 100 counties. Comment on the shape of the distribution. b) Estimate the total farm population in the United States, along with its standard error, using Nỹ. c) Plot the farm populations vs. land areas (variables “farmpop” and “landarea”) for each county, and find the sample correlation coefficient between them. Which method do you think is more appropriate for these data: ratio estimation or regression estimation or none of them? d) Using ratio estimation, use the auxiliary variable land area to estimate the total farm population in the United States, along with the standard error. e) The “true” value for total farm population is 3,871,583. Which method of estimation came closer: SRS or ratio estimation? Here is the baseball.csv data. A B C D E F G H M N ○ P Q R S 1 RN State 2 27 AL 3 48 AL County Escambia Marshall landarea totpop physician enroll percpub civlabor unemp farmpop numfarm farmacre fedgrant fedciv milit veterans percviet 948 36023 24 567 73524 44 6931 11928 95.4 15247 1339 531 414 90646 122.3 85 370 3723 27.1 98.6 38803 3189 1592 1582 136599 235.7 316 748 8510 29.1 4 85 AK Prince of 7325 6408 7 1317 98.6 2787 383 71 2 214 32.2 126 63 809 44.6 5 126 AR Cross 6 158 AR Newton 7 186 CA Butte 8 254 CO Custer 9 286 CO Ouray 10 305 CT Hartford 11 340 FL Hardee 616 19261 823 7649 1640 188377 739 2140 542 2497 736 847009 637 20084 11 4066 99.1 8336 704 762 492 339830 81.4 87 107 1505 23.9 3 1579 99.2 3280 270 600 562 98106 31.7 71 44 807 25.5 327 27899 94.5 77500 7303 2818 2030 494530 688 570 577 23958 27.6 1 364 97.5 789 42 145 130 150334 7 11 10 347 37.8 3 429 99.3 1919 109 112 88 -99 5.7 5 11 337 35.9 2851 128982 90 470164 32673 623 656 60277 4051.1 8504 2975 93683 24.9 12 350 FL Lake 13 371 FL St. Lucie 14 422 GA Crisp 573 274 953 161228 161106 20377 11 3802 167 19777 176 99.1 9368 987 1202 1130 303892 60.6 55 44 2071 21.4 91.5 58285 5182 1582 1285 232657 664.2 499 403 26923 21.6 22769 90.6 65078 8966 257 522 297433 543.7 536 348 23205 21.1 20 4112 95.1 8980 573 341 192 112431 67.3 64 170 1893 32.3 15 432 GA Echols 404 2291 0 483 100 875 39 162 80 13745 4.8 5 19 242 30.2 16 527 GA Walton 329 40750 29 7210 95.2 17404 955 756 469 65220 96.2 93 307 3551 28.4 17 559 ID Camas 1075 755 0 147 100 365 24 66 117 174842 5.9 21 0 82 32.9 18 586 ID 19 606 IL 20 617 IL Cook Ford 21 630 IL Jasper 22 639 IL Lake 23 698 IN Boone Shoshone 2634 13644 946 5139341 486 13914 494 10519 448 541047 423 38381 9 2683 97.8 5041 981 29 46 5148 59.5 203 75 2070 29.3 15153 853115 81.5 2715405 196796 196 389 46907 20151.2 61976 16480 457880 24.2 11 3 1093 24 702 IN Clark 375 89658 81 109 2555 1994 88975 7062 15779 97.6 91.7 7265 5828 481 434 1477 729 297013 54.4 69 40 1741 32.2 2795 88.9 319950 14863 586 95.1 21157 716 2258 894 262198 448 82349 822 227524 31.3 1641.8 83.6 52 31 1073 22.7 9399 21454 53060 32.7 111 213 4283 29.5 25 703 IN Clay 358 25078 11 4506 92.4 47787 95.9 10971 3025 1310 691 118810 314.6 2980 488 11222 31.8 748 1811 646 162594 82.9 80 137 2874 22.2 26 743 IN Martin 336 10510 4 1923 95.2 5353 374 695 361 67373 244.9 5240 109 1359 31.2 27 780 IN counties Washingt (+) 515 24398 9 4443 99.1 11411 882 2119 1034 195118 63.4 69 128 2745 29.7 4030 www ممم 102 20 ་