Exercise on ANOVA Table Generation for Simple and Stratified Random Sampling
Tasks :
(Item 1)
Delete all observations with missing values for the variable ACRES92.
(Item 2)
Construct separate datasets according to the 4 REGIONS.
(Item 3)
Obtain 10 different SRS of size 300 from dataset 1 using
set.seed (last 5 digits of your std no)
set.seed (last 5 digits of your std no + 1)
set.seed (last 5 digits of your std no + 2)
set.seed (last 5 digits of your std no + 3)
set.seed (last 5 digits of your std no + 4)
set.seed (last 5 digits of your std no + 5)
set.seed (last 5 digits of your std no + 6)
set.seed (last 5 digits of your std no + 7)
set.seed (last 5 digits of your std no + 8)
set.seed (last 5 digits of your std no + 9)
and compute (and reflect in your paper) the sample variances.
(Item 4)
Construct the population ANOVA table from the stratification obtained in 2.
(Item 5)
Using
set.seed (last 5 digits of your std no + 10) obtain a sample of size 21 from the Northeast stratum.
set.seed (last 5 digits of your std no + 11) obtain a sample of size 103 from the NorthCentral stratum.
set.seed (last 5 digits of your std no + 12) obtain a sample of size 135 from the South stratum.
set.seed (last 5 digits of your std no + 13) obtain a sample of size 41 from the West stratum.
(Item 6A)
Construct the sample ANOVA table
using ybar_SRS for ybar_U (Use 3.a as reference : SRS sample using random seed = 89176).
(Item 6B)
Construct the sample ANOVA table
using ybar_STR for ybar_U.
##############################################
############ STAT 250 Exercise #############
############ 17-Oct-2018 #############
############ John Pauline Pineda #############
##############################################
# Set working directory
setwd("F:/SamplingDesign")
Item 1
##############################################
############ ITEM 1 #############
##############################################
# Delete all observations with missing values
# for the variable ACRES92.
##############################################
# Load the working data
agpop <- read.csv("agpop.dat")
# Initial exploratory analysis
# Check the data dimensions
# 3078 rows and 15 columns
dim(agpop)
## [1] 3078 15
# Generate the data summary
summary(agpop)
## COUNTY STATE ACRES92
## WASHINGTON COUNTY: 30 TX : 254 Min. : -99
## JEFFERSON COUNTY : 25 GA : 159 1st Qu.: 80903
## FRANKLIN COUNTY : 24 KY : 120 Median : 191648
## JACKSON COUNTY : 23 MO : 114 Mean : 306677
## LINCOLN COUNTY : 23 KS : 105 3rd Qu.: 366886
## MADISON COUNTY : 19 IL : 102 Max. :7229585
## (Other) :2934 (Other):2224
## ACRES87 ACRES82 FARMS92 FARMS87
## Min. : -99 Min. : -99 Min. : 0.0 Min. : 0.0
## 1st Qu.: 86236 1st Qu.: 96397 1st Qu.: 295.0 1st Qu.: 318.5
## Median : 199864 Median : 207292 Median : 521.0 Median : 572.0
## Mean : 313016 Mean : 320194 Mean : 625.5 Mean : 678.3
## 3rd Qu.: 372224 3rd Qu.: 377065 3rd Qu.: 838.0 3rd Qu.: 921.0
## Max. :7687460 Max. :7313958 Max. :7021.0 Max. :7590.0
##
## FARMS82 LARGEF92 LARGEF87 LARGEF82
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 345.0 1st Qu.: 8.00 1st Qu.: 8.00 1st Qu.: 8.00
## Median : 616.0 Median : 30.00 Median : 27.00 Median : 25.00
## Mean : 728.1 Mean : 56.18 Mean : 54.86 Mean : 52.62
## 3rd Qu.: 991.0 3rd Qu.: 75.00 3rd Qu.: 70.00 3rd Qu.: 65.00
## Max. :7394.0 Max. :579.00 Max. :596.00 Max. :546.00
##
## SMALLF92 SMALLF87 SMALLF82 REGION
## Min. : 0.00 Min. : 0.00 Min. : 0.00 NC:1054
## 1st Qu.: 13.00 1st Qu.: 17.00 1st Qu.: 16.00 NE: 220
## Median : 29.00 Median : 35.00 Median : 34.00 S :1382
## Mean : 54.09 Mean : 59.54 Mean : 60.97 W : 422
## 3rd Qu.: 59.00 3rd Qu.: 67.00 3rd Qu.: 67.00
## Max. :4298.00 Max. :3654.00 Max. :3522.00
##
# Count the number of rows with missing values for the ACRES92 column
# 19 rows with missing values
nrow(agpop[agpop$ACRES92==-99,])
## [1] 19
# Count the number of rows with missing values for the REGION column
# 0 row with missing values
nrow(agpop[agpop$REGION==-99,])
## [1] 0
# Remove missing values and only keep the needed columns
agpop_complete <- agpop[agpop$ACRES92!=-99,c("ACRES92","REGION")]
# Check the data dimensions
# 3059 rows and 2 columns
dim(agpop_complete)
## [1] 3059 2
# Generate the data summary
summary(agpop_complete)
## ACRES92 REGION
## Min. : 0 NC:1052
## 1st Qu.: 82446 NE: 213
## Median : 193688 S :1376
## Mean : 308582 W : 418
## 3rd Qu.: 368482
## Max. :7229585
# Specify the population size
(N <- nrow(agpop_complete))
## [1] 3059
# Specify the population mean for reference
# Population mean = 308582.4
(agpop_mean <- mean(agpop_complete$ACRES92))
## [1] 308582.4
# Specify the population variance for reference
# Population variance = 1.80891e+11
(agpop_variance <- var(agpop_complete$ACRES92))
## [1] 1.80891e+11
# Specify the population standard deviation for reference
# Population standard deviation = 425312.8
(agpop_sd <- sd(agpop_complete$ACRES92))
## [1] 425312.8
Item 2
##############################################
############ ITEM 2 #############
##############################################
# Create four(4) additional dataframes
# for each of the regions
##############################################
# Specify the number of strata
(H <- nlevels(agpop_complete$REGION))
## [1] 4
# Create data objects for the regions / strata
NCregion <- agpop_complete[agpop_complete$REGION=="NC",]
NEregion <- agpop_complete[agpop_complete$REGION=="NE",]
Sregion <- agpop_complete[agpop_complete$REGION=="S",]
Wregion <- agpop_complete[agpop_complete$REGION=="W",]
# Specify the population size per stratum
# North Central region stratum population size = 1052
(N.NCregion <- nrow(NCregion))
## [1] 1052
# North East region stratum population size = 213
(N.NEregion <- nrow(NEregion))
## [1] 213
# South region stratum population size = 1376
(N.Sregion <- nrow(Sregion))
## [1] 1376
# West region stratum population size = 418
(N.Wregion <- nrow(Wregion))
## [1] 418
Item 3
##############################################
############ ITEM 3 #############
##############################################
# Obtain 10 different SRS of size 300
# from dataset 1
##############################################
# Set the seed numbers
(seedSRS0 <- 89176)
## [1] 89176
(seedSRS1 <- seedSRS0+1)
## [1] 89177
(seedSRS2 <- seedSRS0+2)
## [1] 89178
(seedSRS3 <- seedSRS0+3)
## [1] 89179
(seedSRS4 <- seedSRS0+4)
## [1] 89180
(seedSRS5 <- seedSRS0+5)
## [1] 89181
(seedSRS6 <- seedSRS0+6)
## [1] 89182
(seedSRS7 <- seedSRS0+7)
## [1] 89183
(seedSRS8 <- seedSRS0+8)
## [1] 89184
(seedSRS9 <- seedSRS0+9)
## [1] 89185
# Specify the sample size
(n <- 300)
## [1] 300
# Generate the sample indices for SRS0
set.seed(seedSRS0)
(sampleindices0 <- sample(N,n))
## [1] 602 164 2038 1444 733 1545 2387 234 1927 524 2181 1232 2116 704
## [15] 2867 2191 2641 2343 813 966 616 2089 79 1163 2496 1355 1491 332
## [29] 2355 2878 2722 1586 621 2669 2281 781 3019 71 1652 2731 467 1379
## [43] 162 2674 1542 2876 922 1696 1577 519 545 1767 97 99 881 2534
## [57] 1911 723 3012 333 130 2160 2610 551 247 2499 300 1570 271 2551
## [71] 1020 1899 349 2630 2656 397 1691 2941 2944 1568 2977 2035 122 1429
## [85] 1936 959 1114 2486 639 2834 7 179 537 1450 1666 558 1920 1001
## [99] 2391 1364 268 1057 2672 2718 1113 1251 903 1439 1747 1757 2426 1155
## [113] 823 2798 1019 955 20 336 2440 963 1779 1912 1727 2012 1123 987
## [127] 26 543 1555 2677 57 1842 1917 2190 740 1126 880 1050 2456 2058
## [141] 2872 2412 874 2637 207 1880 106 665 35 2015 2694 1380 1229 2255
## [155] 330 1235 400 2590 2483 576 2250 296 2479 1817 205 2598 1797 2760
## [169] 1306 1070 1520 2654 1075 1605 1308 1623 2799 982 844 2510 1826 789
## [183] 2589 972 780 2042 2787 2506 1081 933 1975 131 1601 2334 2472 1693
## [197] 17 1165 2434 1454 2045 1658 47 1323 2901 2248 315 2103 2724 456
## [211] 3003 2345 1396 1565 735 371 2532 399 1723 884 2962 1383 2608 2491
## [225] 1182 2233 1665 1844 1151 1871 1241 573 1749 1643 2143 2858 1525 1538
## [239] 1793 1583 1497 301 2331 1201 2697 2671 2926 1858 581 756 1869 2306
## [253] 1290 930 72 2619 859 842 2582 1282 775 2392 1141 2273 250 40
## [267] 3031 1857 1271 2080 896 226 2765 2475 1461 1524 598 405 692 892
## [281] 2454 915 701 1481 2554 2292 2948 2840 368 1970 74 1868 2327 999
## [295] 2435 2832 1125 2782 2658 925
# Generate the actual samples for SRS0
(agpop_sampled0 <- agpop_complete[sampleindices0,])
## ACRES92 REGION
## 606 233217 NC
## 164 4768 W
## 2049 245049 NC
## 1450 250475 NC
## 737 184599 NC
## 1551 96540 S
## 2400 41899 S
## 234 156801 W
## 1933 1797466 W
## 528 238609 NC
## 2192 1457339 W
## 1238 47308 NC
## 2127 419760 S
## 708 217191 NC
## 2884 93364 NE
## 2202 1318447 W
## 2656 678590 S
## 2356 974811 NC
## 817 223328 NC
## 970 111913 S
## 620 357684 NC
## 2100 187175 NC
## 79 18818 S
## 1169 126981 S
## 2509 408710 S
## 1361 286337 NC
## 1497 42712 S
## 335 299699 S
## 2368 1361106 NC
## 2895 82967 W
## 2737 307783 S
## 1592 868064 W
## 625 221209 W
## 2684 98449 S
## 2294 44800 S
## 785 202429 NC
## 3038 115487 S
## 71 141260 S
## 1658 180400 S
## 2746 563183 S
## 471 18644 S
## 1385 255498 NC
## 162 229365 W
## 2689 1555905 S
## 1548 118651 S
## 2893 112085 W
## 926 427403 NC
## 1702 51916 S
## 1583 2232575 W
## 523 214452 W
## 549 312173 NC
## 1773 591185 NC
## 97 223889 S
## 99 37606 S
## 885 403375 NC
## 2547 2001152 S
## 1917 1881764 W
## 727 299709 NC
## 3031 54622 S
## 336 296242 S
## 130 156363 S
## 2171 421233 S
## 2624 354917 S
## 555 343870 NC
## 248 641755 W
## 2512 547829 S
## 302 227202 S
## 1576 1424228 W
## 272 546538 W
## 2564 426189 S
## 1024 23062 S
## 1905 3112271 W
## 352 151242 S
## 2645 346653 S
## 2671 536300 S
## 401 109923 S
## 1697 68736 S
## 2958 92761 NC
## 2961 356651 NC
## 1574 598694 W
## 2996 114184 NC
## 2046 275644 NC
## 122 69422 S
## 1435 111549 NC
## 1942 738041 W
## 963 443802 NC
## 1118 36059 S
## 2499 612718 S
## 643 353528 W
## 2851 71803 S
## 7 167832 S
## 179 164130 W
## 541 308497 NC
## 1456 188595 NC
## 1672 56693 S
## 562 317205 NC
## 1926 770155 W
## 1005 41352 S
## 2404 96181 S
## 1370 420778 NC
## 269 32072 W
## 1061 35712 S
## 2687 362642 S
## 2733 328367 S
## 1117 58730 S
## 1257 121153 NC
## 907 141386 NC
## 1445 368849 NC
## 1753 1128346 NC
## 1763 503575 NC
## 2439 191486 S
## 1159 31583 NE
## 827 32318 NC
## 2815 136320 S
## 1023 98545 S
## 959 484093 NC
## 20 47200 S
## 339 44962 S
## 2453 123557 S
## 967 177858 S
## 1785 649612 NC
## 1918 10 W
## 1733 877382 NC
## 2023 138297 NC
## 1127 97643 S
## 991 144904 S
## 26 111315 S
## 547 368114 NC
## 1561 108236 S
## 2692 391842 S
## 57 155914 S
## 1848 297326 NC
## 1923 1769177 W
## 2201 380464 W
## 744 82426 NC
## 1130 6166 S
## 884 222028 NC
## 1054 105068 S
## 2469 31368 S
## 2069 200405 NC
## 2889 89785 NE
## 2425 135469 S
## 878 486997 NC
## 2652 49579 S
## 207 388084 W
## 1886 11644 NE
## 106 367969 S
## 669 69354 NC
## 35 130063 S
## 2026 179280 NC
## 2709 536507 S
## 1386 232189 NC
## 1235 438914 NC
## 2268 90065 NE
## 333 11738 S
## 1241 210638 NC
## 404 168593 S
## 2604 490578 S
## 2496 962576 S
## 580 266083 NC
## 2262 41347 NE
## 298 36230 S
## 2492 123792 S
## 1823 1387740 NC
## 205 342653 W
## 2612 656961 S
## 1803 265048 NC
## 2775 167374 W
## 1312 269147 NC
## 1074 93887 S
## 1526 62833 S
## 2669 525885 S
## 1079 196701 S
## 1611 1197028 W
## 1314 272049 NC
## 1629 70697 S
## 2816 37044 S
## 986 78966 S
## 848 257351 NC
## 2523 208073 S
## 1832 612694 NC
## 793 148662 NC
## 2603 470096 S
## 976 27836 S
## 784 86236 NC
## 2053 219023 NC
## 2804 85954 S
## 2519 513533 S
## 1085 61145 S
## 937 432326 NC
## 1984 135494 NE
## 131 313232 S
## 1607 1629363 W
## 2347 392935 NC
## 2485 11292 S
## 1699 194015 S
## 17 67950 S
## 1171 80241 S
## 2447 91343 S
## 1460 228936 NC
## 2056 210601 NC
## 1664 156027 S
## 47 207226 S
## 1329 250507 NC
## 2918 92074 W
## 2260 20777 NE
## 318 69405 S
## 2114 300829 S
## 2739 260892 S
## 460 68729 S
## 3022 30015 S
## 2358 373787 NC
## 1402 252783 NC
## 1571 3002378 W
## 739 261482 NC
## 375 168861 S
## 2545 471498 S
## 403 19060 S
## 1729 818893 NC
## 888 442362 NC
## 2980 133197 NC
## 1389 252890 NC
## 2622 383573 S
## 2504 396508 S
## 1188 62242 NE
## 2245 125707 NE
## 1671 75496 S
## 1850 250086 NC
## 1155 25470 NE
## 1877 39844 NE
## 1247 193956 NC
## 577 321950 NC
## 1755 1233663 NC
## 1649 162634 S
## 2154 390957 S
## 2875 43332 S
## 1531 125713 S
## 1544 80342 S
## 1799 1425338 NC
## 1589 349938 W
## 1503 79962 S
## 303 70672 S
## 2344 688081 NC
## 1207 77493 NC
## 2712 926093 S
## 2686 617851 S
## 2943 130051 NC
## 1864 360203 NC
## 585 305685 NC
## 760 115517 NC
## 1875 46056 NE
## 2319 82634 S
## 1296 165961 NC
## 934 449151 NC
## 72 56680 S
## 2634 518028 S
## 863 378517 NC
## 846 181020 NC
## 2596 201952 S
## 1288 61832 NC
## 779 105658 NC
## 2405 91858 S
## 1145 116221 S
## 2286 12408 NE
## 251 878447 W
## 40 191810 S
## 3050 28622 S
## 1863 347598 NC
## 1277 181569 NC
## 2091 19088 NC
## 900 499112 NC
## 226 796892 W
## 2780 25810 S
## 2488 165309 S
## 1467 168586 NC
## 1530 98914 S
## 602 542855 NC
## 409 8151 S
## 696 431415 NC
## 896 517623 NC
## 2467 53026 S
## 919 588061 NC
## 705 662629 NC
## 1487 316617 NC
## 2568 412632 S
## 2305 62108 S
## 2965 529966 NC
## 2857 160973 S
## 372 17105 S
## 1979 195626 NE
## 74 151325 S
## 1874 33935 NE
## 2340 496799 NC
## 1003 86074 S
## 2448 182754 S
## 2849 48889 S
## 1129 57789 S
## 2798 17392 S
## 2673 593819 S
## 929 668420 NC
# Generate the sample indices for SRS1
set.seed(seedSRS1)
(sampleindices1 <- sample(N,n))
## [1] 2315 2753 636 1670 1343 2730 162 719 1371 2306 1558 2627 593 152
## [15] 1606 1001 948 2189 1891 1675 1956 1005 3028 60 2847 1344 1645 1395
## [29] 2484 2471 1974 1817 2879 1407 2014 2518 1985 2715 452 2603 2786 389
## [43] 742 2456 1945 557 251 1237 1994 1521 2013 1112 2105 2444 565 713
## [57] 2257 464 1297 2181 457 718 1955 505 2988 1352 664 2701 3023 2217
## [71] 2237 510 2610 2302 1776 2373 1057 1302 2462 1842 1853 1160 1892 2993
## [85] 2741 2451 1794 2329 1369 83 1115 730 553 2164 2149 1052 145 2324
## [99] 1211 2727 633 2298 1981 2071 371 1534 293 965 1659 392 2717 788
## [113] 602 1590 951 2961 1077 1348 3056 2258 48 1639 2433 933 2408 2686
## [127] 584 2748 1186 2726 1393 768 534 727 2137 1180 2668 1426 777 1029
## [141] 2106 2073 811 189 2117 1551 2782 2762 268 2058 2646 202 1397 2454
## [155] 2586 2523 1975 746 1845 1591 303 148 2141 1232 502 1666 759 1273
## [169] 2897 2309 815 257 2429 1142 2191 2514 239 577 2035 240 1392 1261
## [183] 1133 2076 2063 1082 1704 726 270 2067 2440 900 1102 2348 1954 943
## [197] 1710 1844 2725 529 2453 838 857 332 1840 2551 2621 1240 1484 1230
## [211] 1293 2609 80 983 2575 628 2535 117 2279 401 874 620 2213 1295
## [225] 256 2772 1310 2530 1231 1373 1453 1953 277 2892 2087 2254 1350 2251
## [239] 810 1672 1334 9 605 340 1626 1648 1630 2206 34 2387 1495 2745
## [253] 1345 298 1742 1677 494 920 1158 927 781 2386 487 1959 1125 2150
## [267] 2700 1265 497 1360 642 1284 1824 1644 1088 696 1570 1165 2130 2155
## [281] 2729 1908 2731 2669 629 1503 2318 2017 2127 3036 12 2816 27 439
## [295] 376 1880 2945 2795 2366 1512
# Generate the actual samples for SRS1
(agpop_sampled1 <- agpop_complete[sampleindices1,])
## ACRES92 REGION
## 2328 110679 S
## 2768 447463 W
## 640 286711 W
## 1676 230402 S
## 1349 183208 NC
## 2745 344667 S
## 162 229365 W
## 723 249240 NC
## 1377 377059 NC
## 2319 82634 S
## 1564 72515 S
## 2642 588500 S
## 597 318778 NC
## 152 1846497 W
## 1612 1414415 W
## 1005 41352 S
## 952 620144 NC
## 2200 34292 W
## 1897 1838 NE
## 1681 21218 S
## 1964 138620 NE
## 1009 206090 S
## 3047 32093 S
## 60 179319 S
## 2864 52770 S
## 1350 491726 NC
## 1651 98531 S
## 1401 201670 NC
## 2497 103063 S
## 2484 55097 S
## 1983 0 NE
## 1823 1387740 NC
## 2896 304928 W
## 1413 226336 NC
## 2025 215796 NC
## 2531 269146 S
## 1996 831 NE
## 2730 213923 S
## 456 8003 S
## 2617 322324 S
## 2802 61669 S
## 393 13563 S
## 746 270598 NC
## 2469 31368 S
## 1953 57889 NE
## 561 366927 NC
## 252 1341738 W
## 1243 16076 NC
## 2005 56002 NE
## 1527 95736 S
## 2024 122480 NC
## 1116 27469 S
## 2116 157105 S
## 2457 50767 S
## 569 260781 NC
## 717 612112 NC
## 2270 87253 NE
## 468 45845 S
## 1303 374920 NC
## 2192 1457339 W
## 461 121588 S
## 722 344649 NC
## 1963 111974 NE
## 509 53895 S
## 3007 34919 S
## 1358 311849 NC
## 668 464834 NC
## 2716 1396275 S
## 3042 104194 S
## 2229 139918 NE
## 2249 85113 NE
## 514 48755 S
## 2624 354917 S
## 2315 52978 S
## 1782 407678 NC
## 2386 507101 NC
## 1061 35712 S
## 1308 414710 NC
## 2475 32714 S
## 1848 297326 NC
## 1859 1481503 NC
## 1166 43320 S
## 1898 98256 NE
## 3012 106325 S
## 2756 240535 W
## 2464 43202 S
## 1800 138022 NC
## 2342 1243168 NC
## 1375 227156 NC
## 83 313573 S
## 1119 246536 S
## 734 187039 NC
## 557 401625 NC
## 2175 216638 S
## 2160 207118 S
## 1056 191002 S
## 145 358904 S
## 2337 322432 NC
## 1217 186431 NC
## 2742 432887 S
## 637 391050 W
## 2311 70277 S
## 1990 112334 NE
## 2082 204079 NC
## 375 168861 S
## 1540 80761 S
## 295 191140 S
## 969 90033 S
## 1665 112291 S
## 396 198184 S
## 2732 455873 S
## 792 229097 NC
## 606 233217 NC
## 1596 50220 W
## 955 411785 NC
## 2979 94596 NC
## 1081 165391 S
## 1354 536299 NC
## 3075 62307 W
## 2271 219933 NE
## 48 199714 S
## 1645 23929 S
## 2446 94254 S
## 937 432326 NC
## 2421 144267 S
## 2701 764723 S
## 588 312858 NC
## 2763 234576 W
## 1192 50076 NE
## 2741 476493 S
## 1399 187239 NC
## 772 165091 NC
## 538 359755 NC
## 731 98838 NC
## 2148 236766 S
## 1186 91254 S
## 2683 1695484 S
## 1432 270576 NC
## 781 236073 NC
## 1033 3383 S
## 2117 264890 S
## 2084 160734 NC
## 815 267695 NC
## 189 1372778 W
## 2128 566152 S
## 1557 108314 S
## 2798 17392 S
## 2777 256522 W
## 269 32072 W
## 2069 200405 NC
## 2661 428243 S
## 202 1324403 W
## 1403 227783 NC
## 2467 53026 S
## 2600 545664 S
## 2536 680567 S
## 1984 135494 NE
## 750 141703 NC
## 1851 439475 NC
## 1597 1290134 W
## 305 301977 S
## 148 5785707 W
## 2152 230832 S
## 1238 47308 NC
## 506 32865 S
## 1672 56693 S
## 763 333238 NC
## 1279 444407 NC
## 2914 32637 W
## 2322 104862 S
## 819 219402 NC
## 258 1066453 W
## 2442 119419 S
## 1146 58790 S
## 2202 1318447 W
## 2527 622130 S
## 240 1105614 W
## 581 349252 NC
## 2046 275644 NC
## 241 857404 W
## 1398 207611 NC
## 1267 129083 NC
## 1137 81747 S
## 2087 95704 NC
## 2074 187718 NC
## 1086 123655 S
## 1710 104733 S
## 730 282222 NC
## 271 896994 W
## 2078 96060 NC
## 2453 123557 S
## 904 340035 NC
## 1106 176952 S
## 2361 276744 NC
## 1962 158392 NE
## 947 484415 NC
## 1716 67716 S
## 1850 250086 NC
## 2740 545670 S
## 533 236668 NC
## 2466 105519 S
## 842 217288 NC
## 861 162244 NC
## 335 299699 S
## 1846 532901 NC
## 2564 426189 S
## 2636 766037 S
## 1246 18047 NC
## 1490 112896 S
## 1236 193688 NC
## 1299 138594 NC
## 2623 386991 S
## 80 246184 S
## 987 60812 S
## 2589 560355 S
## 632 150021 W
## 2548 451584 S
## 117 142856 S
## 2292 87355 S
## 405 3046 S
## 878 486997 NC
## 624 232879 W
## 2224 76790 NE
## 1301 210897 NC
## 257 1660146 W
## 2788 81768 S
## 1316 131563 NC
## 2543 430377 S
## 1237 254793 NC
## 1379 311161 NC
## 1459 254493 NC
## 1961 188008 NE
## 279 38467 W
## 2909 689639 W
## 2098 139655 NC
## 2267 6197 NE
## 1356 131753 NC
## 2263 81479 NE
## 814 144305 NC
## 1678 37434 S
## 1340 457670 NC
## 9 48022 S
## 609 331211 NC
## 343 716542 S
## 1632 144529 S
## 1654 92192 S
## 1636 93584 S
## 2217 32526 NE
## 34 64755 S
## 2400 41899 S
## 1501 294547 S
## 2760 332686 W
## 1351 600114 NC
## 300 23735 S
## 1748 669049 NC
## 1683 93320 S
## 498 114487 S
## 924 323769 NC
## 1164 114805 NE
## 931 349293 NC
## 785 202429 NC
## 2399 1406379 NC
## 491 38313 S
## 1967 145679 NE
## 1129 57789 S
## 2161 282211 S
## 2715 509017 S
## 1271 14081 NC
## 501 32800 S
## 1366 171412 NC
## 646 197176 W
## 1290 224923 NC
## 1830 446007 NC
## 1650 88386 S
## 1092 136534 S
## 700 201567 NC
## 1576 1424228 W
## 1171 80241 S
## 2141 194253 S
## 2166 239971 S
## 2744 203667 S
## 1914 1289733 W
## 2746 563183 S
## 2684 98449 S
## 633 453647 W
## 1509 99726 S
## 2331 55992 S
## 2028 227382 NC
## 2138 477655 S
## 3055 5693 S
## 12 96427 S
## 2833 100602 S
## 27 196859 S
## 443 77532 S
## 380 113861 S
## 1886 11644 NE
## 2962 120383 NC
## 2812 73097 S
## 2379 2076199 NC
## 1518 89168 S
# Generate the sample indices for SRS2
set.seed(seedSRS2)
(sampleindices2 <- sample(N,n))
## [1] 360 144 2951 378 2484 2362 1613 868 86 1330 2334 675 1143 1239
## [15] 2006 2931 1817 180 2563 1542 2111 944 192 516 699 2432 160 1505
## [29] 653 1396 1728 1651 1272 1098 1392 357 752 3014 2493 2634 817 2312
## [43] 2877 2986 1639 541 951 526 2236 1811 2196 1115 2971 368 1990 2651
## [57] 2515 2345 1852 2285 1661 1899 1298 127 972 2537 1280 2390 1222 297
## [71] 1406 1108 2913 2959 442 1409 988 1453 2158 313 2831 2922 1876 2524
## [85] 917 1681 320 183 2691 437 679 316 573 813 2379 2096 2110 473
## [99] 1455 633 292 461 2137 1710 968 2837 1012 1375 2247 2130 2168 1436
## [113] 1309 128 122 1680 790 267 1714 1106 2808 1142 1399 864 2211 934
## [127] 1121 2564 155 1627 1585 204 1496 2133 939 1495 554 188 1741 3018
## [141] 2827 1834 816 1124 2257 1729 1510 2729 850 386 469 2769 2535 1318
## [155] 1847 710 1958 1849 1032 2789 1366 1532 1920 1865 1480 2612 676 1932
## [169] 2862 1099 941 2668 1398 980 1856 948 1322 2512 2791 2433 421 1670
## [183] 2471 2065 1386 2239 2185 1846 2890 1127 2626 2955 1772 588 2672 6
## [197] 1458 2732 409 953 2392 1610 749 2037 1624 2773 1164 2413 243 402
## [211] 14 3008 1556 1916 3044 1407 983 929 2017 50 1168 1395 1907 2290
## [225] 381 625 608 2378 138 2911 2693 424 592 1454 2080 439 2675 2488
## [239] 2990 2173 1191 2171 1946 76 3059 214 1182 1923 344 1441 2346 1320
## [253] 797 1196 2038 1220 93 1175 2818 1917 1473 2872 2106 2606 1295 1540
## [267] 2556 2024 1702 2847 1934 546 3053 2728 1439 2122 855 2501 1156 413
## [281] 2062 2271 1570 1896 2311 451 580 1757 1588 2966 2 1906 1727 2231
## [295] 1180 1468 645 2721 810 1858
# Generate the actual samples for SRS2
(agpop_sampled2 <- agpop_complete[sampleindices2,])
## ACRES92 REGION
## 364 78739 S
## 144 352322 S
## 2969 8763 NC
## 382 57074 S
## 2497 103063 S
## 2375 846435 NC
## 1619 1178885 W
## 872 271015 NC
## 86 57253 S
## 1336 392615 NC
## 2347 392935 NC
## 679 259923 NC
## 1147 38566 S
## 1245 264 NC
## 2017 171129 NC
## 2948 351633 NC
## 1823 1387740 NC
## 180 487499 W
## 2577 686578 S
## 1548 118651 S
## 2122 633874 S
## 948 510319 NC
## 192 60740 W
## 520 200061 S
## 703 203974 NC
## 2445 54518 S
## 160 334284 W
## 1511 16665 S
## 657 208161 W
## 1402 252783 NC
## 1734 1070528 NC
## 1657 19676 S
## 1278 234823 NC
## 1102 147826 S
## 1398 207611 NC
## 361 45214 S
## 756 314886 NC
## 3033 21871 S
## 2506 357933 S
## 2649 472332 S
## 821 188843 NC
## 2325 262093 S
## 2894 24253 W
## 3005 12175 S
## 1645 23929 S
## 545 314812 NC
## 955 411785 NC
## 530 427215 NC
## 2248 79310 NE
## 1817 304180 NC
## 2207 487534 W
## 1119 246536 S
## 2989 231427 NC
## 372 17105 S
## 2001 65323 NE
## 2666 73948 S
## 2528 166939 S
## 2358 373787 NC
## 1858 314949 NC
## 2298 66165 S
## 1667 204443 S
## 1905 3112271 W
## 1304 130683 NC
## 127 70872 S
## 976 27836 S
## 2550 780925 S
## 1286 31427 NC
## 2403 93098 S
## 1228 137082 NC
## 299 199724 S
## 1412 285496 NC
## 1112 4127 S
## 2930 51208 NC
## 2977 282405 NC
## 446 39712 S
## 1415 321181 NC
## 992 68373 S
## 1459 254493 NC
## 2169 250958 S
## 316 369965 S
## 2848 68584 S
## 2939 327185 NC
## 1882 29606 NE
## 2537 275638 S
## 921 596103 NC
## 1687 36975 S
## 323 265443 S
## 183 168879 W
## 2706 247626 S
## 441 72626 S
## 683 40917 NC
## 319 327611 S
## 577 321950 NC
## 817 223328 NC
## 2392 615479 NC
## 2107 493631 S
## 2121 336285 S
## 477 80396 S
## 1461 221122 NC
## 637 391050 W
## 294 304680 NE
## 465 93061 S
## 2148 236766 S
## 1716 67716 S
## 972 132979 S
## 2854 24924 S
## 1016 200455 S
## 1381 239298 NC
## 2259 81426 NE
## 2141 194253 S
## 2179 687299 S
## 1442 306175 NC
## 1315 112412 NC
## 128 404585 S
## 122 69422 S
## 1686 23007 S
## 794 194312 NC
## 268 459659 W
## 1720 46726 S
## 1110 81401 S
## 2825 52508 S
## 1146 58790 S
## 1405 210829 NC
## 868 339138 NC
## 2222 76466 NE
## 938 641109 NC
## 1125 193137 S
## 2578 102229 S
## 155 729947 W
## 1633 170006 S
## 1591 367482 W
## 204 836989 W
## 1502 126613 S
## 2144 344280 S
## 943 578283 NC
## 1501 294547 S
## 558 287586 NC
## 188 103294 W
## 1747 723816 NC
## 3037 15650 S
## 2844 64856 S
## 1840 724458 NC
## 820 71596 NC
## 1128 247106 S
## 2270 87253 NE
## 1735 855458 NC
## 1516 76673 S
## 2744 203667 S
## 854 197947 NC
## 390 11559 S
## 473 18254 S
## 2785 287442 S
## 2548 451584 S
## 1324 5262 NC
## 1853 657906 NC
## 714 178222 NC
## 1966 109692 NE
## 1855 105085 NC
## 1036 119218 S
## 2806 15714 S
## 1372 272540 NC
## 1538 96474 S
## 1926 770155 W
## 1871 345509 NC
## 1486 134028 NC
## 2626 595420 S
## 680 223764 NC
## 1938 79635 W
## 2879 82849 NE
## 1103 79150 S
## 945 463690 NC
## 2683 1695484 S
## 1404 278841 NC
## 984 137337 S
## 1862 217228 NC
## 952 620144 NC
## 1328 395023 NC
## 2525 329288 S
## 2808 116509 S
## 2446 94254 S
## 425 73869 S
## 1676 230402 S
## 2484 55097 S
## 2076 106573 NC
## 1392 325796 NC
## 2251 388368 NE
## 2196 31249 W
## 1852 301513 NC
## 2907 10302 W
## 1131 23185 S
## 2641 428068 S
## 2973 263514 NC
## 1778 1182658 NC
## 592 223638 NC
## 2687 362642 S
## 6 107259 S
## 1464 152529 NC
## 2747 484907 S
## 413 54233 S
## 957 687593 NC
## 2405 91858 S
## 1616 99746 W
## 753 128867 NC
## 2048 113329 NC
## 1630 104426 S
## 2789 96833 S
## 1170 157505 S
## 2426 272121 S
## 244 13296 W
## 406 97215 S
## 14 109555 S
## 3027 117168 S
## 1562 114083 S
## 1922 1166009 W
## 3063 1234542 W
## 1413 226336 NC
## 987 60812 S
## 933 380403 NC
## 2028 227382 NC
## 50 224370 S
## 1174 222768 S
## 1401 201670 NC
## 1913 1532887 W
## 2303 156853 S
## 385 21697 S
## 629 1371605 W
## 612 236265 NC
## 2391 903980 NC
## 138 115019 S
## 2928 1639965 W
## 2708 632622 S
## 428 24239 S
## 596 260780 NC
## 1460 228936 NC
## 2091 19088 NC
## 443 77532 S
## 2690 667177 S
## 2501 765139 S
## 3009 59184 S
## 2184 71839 W
## 1197 118152 NE
## 2182 148848 W
## 1954 161643 NE
## 76 293745 S
## 3078 1484583 W
## 214 1016851 W
## 1188 62242 NE
## 1929 517952 W
## 347 611336 S
## 1447 507875 NC
## 2359 601034 NC
## 1326 205031 NC
## 801 187079 NC
## 1202 94755 NE
## 2049 245049 NC
## 1226 233921 NC
## 93 262021 S
## 1181 165349 S
## 2835 167858 S
## 1923 1769177 W
## 1479 160576 NC
## 2889 89785 NE
## 2117 264890 S
## 2620 330173 S
## 1301 210897 NC
## 1546 86096 S
## 2570 545666 S
## 2035 169017 NC
## 1708 53690 S
## 2864 52770 S
## 1940 1949420 W
## 550 275319 NC
## 3072 1208776 W
## 2743 461127 S
## 1445 368849 NC
## 2133 268038 S
## 859 198680 NC
## 2514 563993 S
## 1161 9882 NE
## 417 22212 S
## 2073 164607 NC
## 2284 5636 NE
## 1576 1424228 W
## 1902 87638 NE
## 2324 69897 S
## 455 33785 S
## 584 347599 NC
## 1763 503575 NC
## 1594 883479 W
## 2984 308460 NC
## 2 47146 W
## 1912 1209335 W
## 1733 877382 NC
## 2243 234391 NE
## 1186 91254 S
## 1474 120036 NC
## 649 311296 W
## 2736 501692 S
## 814 144305 NC
## 1864 360203 NC
# Generate the sample indices for SRS3
set.seed(seedSRS3)
(sampleindices3 <- sample(N,n))
## [1] 2799 3017 639 2426 2504 3045 1450 1796 211 1587 2000 2444 172 381
## [15] 1349 1521 750 1983 467 1786 2105 2003 110 2774 2601 186 194 1977
## [29] 1869 2723 1295 628 2907 1541 1014 1694 2883 471 768 270 2614 2338
## [43] 1043 1042 964 2127 2656 1366 1317 1854 503 633 746 62 23 803
## [57] 362 951 2381 2048 3031 1630 2990 1388 1303 1722 1310 1525 280 649
## [71] 3021 523 2475 1309 2751 1678 2832 2543 933 1536 370 3055 1766 1024
## [85] 1671 2005 2496 1940 1755 2585 198 2437 2319 2156 2508 978 2749 574
## [99] 1320 812 1652 348 1616 1765 934 2564 2809 2342 2014 425 1238 1069
## [113] 2741 2474 2291 531 2344 608 1053 1566 1098 2238 2416 1524 97 401
## [127] 897 624 2044 356 590 2992 1895 2073 1144 970 2239 756 2193 212
## [141] 948 1235 355 759 2126 865 2371 1194 2880 2934 542 1193 1044 2791
## [155] 3016 368 2086 1478 680 823 754 2784 1262 1588 1419 2457 2522 679
## [169] 1152 918 1639 2383 144 1227 758 1127 1908 1584 1586 2207 2365 2793
## [183] 269 2822 1457 1601 939 2434 1330 15 1622 730 2387 1714 1833 2220
## [197] 1105 166 1960 3044 2684 968 185 493 2835 990 1497 217 1490 267
## [211] 3035 2469 2200 2712 1033 2353 845 2727 1170 1374 2113 410 1501 851
## [225] 2307 2024 616 1118 2655 2363 1293 678 2385 278 2789 1916 1465 2611
## [239] 498 1200 699 1582 3050 2616 1411 2517 833 505 2644 1890 2838 2152
## [253] 1297 2427 694 2899 2772 65 1826 1213 128 2885 569 516 37 1119
## [267] 2278 8 461 2351 857 2988 609 813 3037 235 3036 906 2118 2873
## [281] 2651 1721 1260 28 1481 1229 2395 2379 245 781 148 1835 874 847
## [295] 48 2865 1270 1163 1868 1937
# Generate the actual samples for SRS3
(agpop_sampled3 <- agpop_complete[sampleindices3,])
## ACRES92 REGION
## 2816 37044 S
## 3036 178160 S
## 643 353528 W
## 2439 191486 S
## 2517 408824 S
## 3064 908320 W
## 1456 188595 NC
## 1802 658572 NC
## 211 517114 W
## 1593 631377 W
## 2011 174627 NE
## 2457 50767 S
## 172 1774664 W
## 385 21697 S
## 1355 153188 NC
## 1527 95736 S
## 754 402212 NC
## 1992 3803 NE
## 471 18644 S
## 1792 428769 NC
## 2116 157105 S
## 2014 102024 NE
## 110 281895 S
## 2790 84677 S
## 2615 70165 S
## 186 978831 W
## 194 119514 W
## 1986 145329 NE
## 1875 46056 NE
## 2738 863384 S
## 1301 210897 NC
## 632 150021 W
## 2924 12611 W
## 1547 186297 S
## 1018 159966 S
## 1700 23140 S
## 2900 670149 W
## 475 45450 S
## 772 165091 NC
## 271 896994 W
## 2628 774804 S
## 2351 561312 NC
## 1047 144254 S
## 1046 5256 S
## 968 156590 S
## 2138 477655 S
## 2671 536300 S
## 1372 272540 NC
## 1323 405029 NC
## 1860 298115 NC
## 507 88829 S
## 637 391050 W
## 750 141703 NC
## 62 112620 S
## 23 82466 S
## 807 182836 NC
## 366 32976 S
## 955 411785 NC
## 2394 1006831 NC
## 2059 227327 NC
## 3050 28622 S
## 1636 93584 S
## 3009 59184 S
## 1394 403597 NC
## 1309 443496 NC
## 1728 777675 NC
## 1316 131563 NC
## 1531 125713 S
## 282 2086292 W
## 653 193908 W
## 3040 55827 S
## 527 321728 NC
## 2488 165309 S
## 1315 112412 NC
## 2766 107663 W
## 1684 131767 S
## 2849 48889 S
## 2556 569212 S
## 937 432326 NC
## 1542 93180 S
## 374 27561 S
## 3074 1720737 W
## 1772 1269572 NC
## 1028 120959 S
## 1677 68577 S
## 2016 194022 NC
## 2509 408710 S
## 1947 140380 W
## 1761 639709 NC
## 2599 463450 S
## 198 1287057 W
## 2450 36978 S
## 2332 173188 S
## 2167 660214 S
## 2521 548351 S
## 982 140810 S
## 2764 58522 W
## 578 322401 NC
## 1326 205031 NC
## 816 158788 NC
## 1658 180400 S
## 351 79270 S
## 1622 1688070 W
## 1771 439846 NC
## 938 641109 NC
## 2578 102229 S
## 2826 37777 S
## 2355 641911 NC
## 2025 215796 NC
## 429 36074 S
## 1244 190706 NC
## 1073 117768 S
## 2756 240535 W
## 2487 32892 S
## 2304 108848 S
## 535 341923 NC
## 2357 560057 NC
## 612 236265 NC
## 1057 112409 S
## 1572 2338866 W
## 1102 147826 S
## 2250 36963 NE
## 2429 236912 S
## 1530 98914 S
## 97 223889 S
## 405 3046 S
## 901 319686 NC
## 628 111510 W
## 2055 68344 NC
## 360 96730 S
## 594 219832 NC
## 3011 52748 S
## 1901 325 NE
## 2084 160734 NC
## 1148 126839 S
## 974 80864 S
## 2251 388368 NE
## 760 115517 NC
## 2204 1119004 W
## 212 759649 W
## 952 620144 NC
## 1241 210638 NC
## 359 8679 S
## 763 333238 NC
## 2137 323534 S
## 869 765688 NC
## 2384 1066060 NC
## 1200 106971 NE
## 2897 35678 W
## 2951 293134 NC
## 546 456954 NC
## 1199 18793 NE
## 1048 119533 S
## 2808 116509 S
## 3035 21164 S
## 372 17105 S
## 2097 129416 NC
## 1484 93053 NC
## 684 223561 NC
## 827 32318 NC
## 758 119370 NC
## 2800 45451 S
## 1268 75345 NC
## 1594 883479 W
## 1425 268447 NC
## 2470 116696 S
## 2535 523049 S
## 683 40917 NC
## 1156 74484 NE
## 922 286989 NC
## 1645 23929 S
## 2396 259517 NC
## 144 352322 S
## 1233 231557 NC
## 762 297003 NC
## 1131 23185 S
## 1914 1289733 W
## 1590 1644001 W
## 1592 868064 W
## 2218 119566 NE
## 2378 485748 NC
## 2810 166477 S
## 270 1004360 W
## 2839 18367 S
## 1463 89683 NC
## 1607 1629363 W
## 943 578283 NC
## 2447 91343 S
## 1336 392615 NC
## 15 121504 S
## 1628 72621 S
## 734 187039 NC
## 2400 41899 S
## 1720 46726 S
## 1839 290608 NC
## 2232 55023 NE
## 1109 110173 S
## 166 452347 W
## 1968 54986 NE
## 3063 1234542 W
## 2699 358211 S
## 972 132979 S
## 185 725118 W
## 497 174020 S
## 2852 78977 S
## 994 125133 S
## 1503 79962 S
## 217 137530 W
## 1496 149027 S
## 268 459659 W
## 3054 59846 S
## 2482 177522 S
## 2211 694304 W
## 2727 1806639 S
## 1037 278675 S
## 2366 502469 NC
## 849 160930 NC
## 2742 432887 S
## 1176 97312 S
## 1380 430451 NC
## 2124 583098 S
## 414 123702 S
## 1507 43498 S
## 855 201739 NC
## 2320 19486 S
## 2035 169017 NC
## 620 357684 NC
## 1122 100 S
## 2670 30268 S
## 2376 325998 NC
## 1299 138594 NC
## 682 263425 NC
## 2398 271200 NC
## 280 104010 W
## 2806 15714 S
## 1922 1166009 W
## 1471 165225 NC
## 2625 553226 S
## 502 40783 S
## 1206 246403 NC
## 703 203974 NC
## 1588 636514 W
## 3069 1344561 W
## 2630 698832 S
## 1417 232592 NC
## 2530 251249 S
## 837 236436 NC
## 509 53895 S
## 2659 724706 S
## 1896 10365 NE
## 2855 141766 S
## 2163 481244 S
## 1303 374920 NC
## 2440 130167 S
## 698 303715 NC
## 2916 58750 W
## 2788 81768 S
## 65 167923 S
## 1832 612694 NC
## 1219 40871 NC
## 128 404585 S
## 2902 1086045 W
## 573 346569 NC
## 520 200061 S
## 37 128357 S
## 1123 74678 S
## 2291 160659 S
## 8 177189 S
## 465 93061 S
## 2364 1657305 NC
## 861 162244 NC
## 3007 34919 S
## 613 241422 NC
## 817 223328 NC
## 3056 1868333 W
## 235 260728 W
## 3055 5693 S
## 910 399835 NC
## 2129 599536 S
## 2890 996742 W
## 2666 73948 S
## 1727 858267 NC
## 1266 48236 NC
## 28 134555 S
## 1487 316617 NC
## 1235 438914 NC
## 2408 165547 S
## 2392 615479 NC
## 246 177333 W
## 785 202429 NC
## 148 5785707 W
## 1841 236950 NC
## 878 486997 NC
## 851 80958 NC
## 48 199714 S
## 2882 24848 NE
## 1276 318125 NC
## 1169 126981 S
## 1874 33935 NE
## 1943 494304 W
# Generate the sample indices for SRS4
set.seed(seedSRS4)
(sampleindices4 <- sample(N,n))
## [1] 753 965 3037 675 95 2929 1932 432 274 2110 1644 1459 858 2072
## [15] 1434 969 1787 1329 491 2293 1014 2030 1400 852 1845 2695 851 766
## [29] 2983 2222 1262 1662 1045 2443 1703 1645 2618 840 1059 2561 1788 1347
## [43] 2245 2900 3031 2447 2230 1257 980 513 3040 2387 1511 2327 2619 2003
## [57] 1467 1156 998 588 1883 344 2800 2773 799 2882 2461 1027 2415 1369
## [71] 2404 2760 1702 1277 2302 244 2351 954 2099 684 413 544 671 1362
## [85] 1171 1667 975 1061 1802 1407 1647 2736 2975 2852 1719 359 1901 1364
## [99] 2474 953 2949 845 1261 1134 415 1922 50 1212 323 234 843 298
## [113] 1224 1174 2041 2176 1919 2394 61 150 2503 2195 857 1917 289 1405
## [127] 2846 667 1648 2505 1463 307 436 2129 2470 1957 959 547 2741 1384
## [141] 1197 910 451 186 1747 1367 2699 686 906 1949 2751 270 135 1515
## [155] 426 2108 1313 2652 2284 2868 2861 2913 1934 181 990 2819 2858 2001
## [169] 1231 311 1124 2674 1388 2743 1609 2654 103 1426 506 381 141 31
## [183] 3 2615 2553 1536 1480 1038 871 1104 1527 550 812 1715 2546 1830
## [197] 20 1397 1296 2501 199 1718 2076 2497 113 2596 1054 1067 788 2454
## [211] 467 1479 2104 692 340 2661 414 1938 2917 410 1892 995 2973 838
## [225] 2395 2259 545 2873 889 1250 492 634 2445 2097 897 566 793 1183
## [239] 1554 1051 1339 949 1749 1592 2050 1557 3013 130 162 440 1595 916
## [253] 1094 1481 1275 3044 2520 2247 2305 720 2349 1222 823 1433 981 44
## [267] 1676 1956 213 1007 1085 1472 2605 1178 2604 792 1126 1254 34 773
## [281] 2549 1738 1196 187 994 2491 2571 2267 2509 2870 587 21 2728 80
## [295] 2786 810 2495 2565 2823 2753
# Generate the actual samples for SRS4
(agpop_sampled4 <- agpop_complete[sampleindices4,])
## ACRES92 REGION
## 757 336450 NC
## 969 90033 S
## 3056 1868333 W
## 679 259923 NC
## 95 210692 S
## 2946 189905 NC
## 1938 79635 W
## 436 45624 S
## 275 462086 W
## 2121 336285 S
## 1650 88386 S
## 1465 204171 NC
## 862 282862 NC
## 2083 286698 NC
## 1440 265245 NC
## 973 5419 S
## 1793 521389 NC
## 1335 422916 NC
## 495 71097 S
## 2306 68858 S
## 1018 159966 S
## 2041 109820 NC
## 1406 219440 NC
## 856 96219 NC
## 1851 439475 NC
## 2710 835337 S
## 855 201739 NC
## 770 197724 NC
## 3002 73430 S
## 2234 101816 NE
## 1268 75345 NC
## 1668 127663 S
## 1049 42642 S
## 2456 141357 S
## 1709 89063 S
## 1651 98531 S
## 2633 424701 S
## 844 134960 NC
## 1063 4469 S
## 2575 370140 S
## 1794 3887635 NC
## 1353 270332 NC
## 2257 39561 NE
## 2917 20529 W
## 3050 28622 S
## 2460 56253 S
## 2242 4702 NE
## 1263 22056 NC
## 984 137337 S
## 517 115516 S
## 3059 2720903 W
## 2400 41899 S
## 1517 24845 S
## 2340 496799 NC
## 2634 518028 S
## 2014 102024 NE
## 1473 219042 NC
## 1161 9882 NE
## 1002 10919 S
## 592 223638 NC
## 1889 61748 NE
## 347 611336 S
## 2817 51374 S
## 2789 96833 S
## 803 148609 NC
## 2899 748088 W
## 2474 200097 S
## 1031 20803 S
## 2428 104457 S
## 1375 227156 NC
## 2417 96874 S
## 2775 167374 W
## 1708 53690 S
## 1283 206781 NC
## 2315 52978 S
## 245 299142 W
## 2364 1657305 NC
## 958 702549 NC
## 2110 726481 S
## 688 259498 NC
## 417 22212 S
## 548 415104 NC
## 675 238906 NC
## 1368 100774 NC
## 1177 44623 S
## 1673 93728 S
## 979 42602 S
## 1065 33155 S
## 1808 340471 NC
## 1413 226336 NC
## 1653 7046 S
## 2751 267924 W
## 2994 86091 NC
## 2869 56289 S
## 1725 38394 S
## 363 77659 S
## 1907 2085387 W
## 1370 420778 NC
## 2487 32892 S
## 957 687593 NC
## 2966 145980 NC
## 849 160930 NC
## 1267 129083 NC
## 1138 40181 S
## 419 36260 S
## 1928 2579730 W
## 50 224370 S
## 1218 41037 NC
## 326 244185 S
## 234 156801 W
## 847 79235 NC
## 300 23735 S
## 1230 5965 NC
## 1180 54459 S
## 2052 177194 NC
## 2187 74375 W
## 1925 1646707 W
## 2407 96550 S
## 61 96435 S
## 150 5989961 W
## 2516 2405018 S
## 2206 167880 W
## 861 162244 NC
## 1923 1769177 W
## 291 55263 NE
## 1411 245827 NC
## 2863 20107 S
## 671 135163 NC
## 1654 92192 S
## 2518 566400 S
## 1469 54082 NC
## 309 31693 S
## 440 60811 S
## 2140 558313 S
## 2483 183178 S
## 1965 192116 NE
## 963 443802 NC
## 551 261494 NC
## 2756 240535 W
## 1390 377000 NC
## 1203 61797 NE
## 914 482434 NC
## 455 33785 S
## 186 978831 W
## 1753 1128346 NC
## 1373 407953 NC
## 2714 167569 S
## 690 354480 NC
## 910 399835 NC
## 1957 203704 NE
## 2766 107663 W
## 271 896994 W
## 135 45609 S
## 1521 96919 S
## 430 53944 S
## 2119 358446 S
## 1319 145545 NC
## 2667 479889 S
## 2297 32392 S
## 2885 149503 NE
## 2878 96704 NE
## 2930 51208 NC
## 1940 1949420 W
## 181 183569 W
## 994 125133 S
## 2836 21507 S
## 2875 43332 S
## 2012 5709 NE
## 1237 254793 NC
## 314 57853 S
## 1128 247106 S
## 2689 1555905 S
## 1394 403597 NC
## 2758 63116 W
## 1615 962450 W
## 2669 525885 S
## 103 105721 S
## 1432 270576 NC
## 510 47000 S
## 385 21697 S
## 141 136309 S
## 31 104364 S
## 3 141338 W
## 2629 436040 S
## 2567 378003 S
## 1542 93180 S
## 1486 134028 NC
## 1042 247266 S
## 875 380969 NC
## 1108 44490 S
## 1533 89816 S
## 554 202249 NC
## 816 158788 NC
## 1721 179554 S
## 2559 599637 S
## 1836 330369 NC
## 20 47200 S
## 1403 227783 NC
## 1302 1249 NC
## 2514 563993 S
## 199 517860 W
## 1724 103773 S
## 2087 95704 NC
## 2510 371257 S
## 113 143104 S
## 2610 345138 S
## 1058 159794 S
## 1071 154082 S
## 792 229097 NC
## 2467 53026 S
## 471 18644 S
## 1485 289729 NC
## 2115 1034980 S
## 696 431415 NC
## 343 716542 S
## 2676 517272 S
## 418 73659 S
## 1944 48968 W
## 2934 323482 NC
## 414 123702 S
## 1898 98256 NE
## 999 69310 S
## 2991 365511 NC
## 842 217288 NC
## 2408 165547 S
## 2272 30613 NE
## 549 312173 NC
## 2890 996742 W
## 893 671506 NC
## 1256 73437 NC
## 496 142824 S
## 638 587693 W
## 2458 161902 S
## 2108 513789 S
## 901 319686 NC
## 570 280797 NC
## 797 207766 NC
## 1189 334040 NE
## 1560 100433 S
## 1055 117868 S
## 1345 252658 NC
## 953 537457 NC
## 1755 1233663 NC
## 1598 1271160 W
## 2061 142624 NC
## 1563 342237 S
## 3032 148842 S
## 130 156363 S
## 162 229365 W
## 444 71379 S
## 1601 248215 W
## 920 572989 NC
## 1098 258035 S
## 1487 316617 NC
## 1281 236799 NC
## 3063 1234542 W
## 2533 670459 S
## 2259 81426 NE
## 2318 136151 S
## 724 709106 NC
## 2362 861129 NC
## 1228 137082 NC
## 827 32318 NC
## 1439 242018 NC
## 985 43447 S
## 44 201892 S
## 1682 22089 S
## 1964 138620 NE
## 213 318156 W
## 1011 100468 S
## 1089 63446 S
## 1478 329999 NC
## 2619 517671 S
## 1184 109108 S
## 2618 736407 S
## 796 196537 NC
## 1130 6166 S
## 1260 88322 NC
## 34 64755 S
## 777 220057 NC
## 2562 518788 S
## 1744 1019300 NC
## 1202 94755 NE
## 187 686876 W
## 998 60294 S
## 2504 396508 S
## 2585 409501 S
## 2280 153897 NE
## 2522 263925 S
## 2887 58891 NE
## 591 392835 NC
## 21 175209 S
## 2743 461127 S
## 80 246184 S
## 2802 61669 S
## 814 144305 NC
## 2508 416631 S
## 2579 573827 S
## 2840 52469 S
## 2768 447463 W
# Generate the sample indices for SRS5
set.seed(seedSRS5)
(sampleindices5 <- sample(N,n))
## [1] 998 1667 455 472 749 1571 150 1915 2 935 1976 2075 2166 352
## [15] 2791 2902 702 303 2069 93 1250 2677 2431 2262 1008 2763 66 331
## [29] 202 321 2877 1991 6 325 1295 2061 374 1316 1465 3039 1195 2109
## [43] 868 2491 634 211 40 1903 1938 2832 3035 1505 106 983 1240 1621
## [57] 2888 1019 1096 10 1685 1749 9 1779 261 2953 90 619 1259 1762
## [71] 1601 2205 251 2955 1324 2405 1071 270 960 1201 2101 1029 945 2998
## [85] 2340 328 379 1720 1879 1602 1893 1973 2539 2745 2522 2493 327 2747
## [99] 1874 1661 638 117 16 234 1575 2046 506 2247 676 2718 3016 1370
## [113] 2803 1643 2731 354 855 2755 1395 1488 1442 1289 1600 1538 2768 570
## [127] 1155 487 92 751 2917 2286 2009 707 2578 923 2199 2612 1345 693
## [141] 2468 42 704 2778 1553 2011 1228 1030 2415 2169 2705 1273 2813 767
## [155] 2996 3019 2535 1688 1812 629 255 77 986 2386 1537 2781 2906 2503
## [169] 1337 2190 2957 1788 2092 1026 1535 228 808 2275 665 2414 2189 569
## [183] 2647 2337 2560 872 1895 2932 2106 2384 1480 203 2469 866 1074 2501
## [197] 1928 2155 990 2726 528 759 1522 1711 1924 1603 2042 578 2576 1317
## [211] 1310 830 1441 1843 2367 1751 2936 2774 27 1890 969 966 2589 1266
## [225] 1885 583 477 440 2934 1666 1900 840 2925 289 1475 2993 140 1278
## [239] 2375 1630 1692 80 1967 857 2135 1560 1366 764 1196 2939 644 365
## [253] 679 1580 1033 1916 1546 215 1599 2302 1408 2396 343 166 2031 453
## [267] 2700 123 428 3022 2005 2138 1151 2465 1224 551 233 2359 690 2859
## [281] 1539 2972 656 2570 2399 914 2683 2102 1313 2443 2697 631 2256 2143
## [295] 1897 750 351 2559 860 856
# Generate the actual samples for SRS5
(agpop_sampled5 <- agpop_complete[sampleindices5,])
## ACRES92 REGION
## 1002 10919 S
## 1673 93728 S
## 459 45448 S
## 476 46014 S
## 753 128867 NC
## 1577 2277936 W
## 150 5989961 W
## 1921 905235 W
## 2 47146 W
## 939 700869 NC
## 1985 242637 NE
## 2086 202324 NC
## 2177 577693 S
## 355 161936 S
## 2808 116509 S
## 2919 4043 W
## 706 186425 NC
## 305 301977 S
## 2080 87954 NC
## 93 262021 S
## 1256 73437 NC
## 2692 391842 S
## 2444 98669 S
## 2275 63159 NE
## 1012 69711 S
## 2778 91568 S
## 66 104199 S
## 334 132208 S
## 202 1324403 W
## 324 86706 S
## 2894 24253 W
## 2002 115071 NE
## 6 107259 S
## 328 95833 S
## 1301 210897 NC
## 2072 113892 NC
## 378 166511 S
## 1322 68778 NC
## 1471 165225 NC
## 3058 2704163 W
## 1201 71890 NE
## 2120 447212 S
## 872 271015 NC
## 2504 396508 S
## 638 587693 W
## 211 517114 W
## 40 191810 S
## 1909 1343237 W
## 1944 48968 W
## 2849 48889 S
## 3054 59846 S
## 1511 16665 S
## 106 367969 S
## 987 60812 S
## 1246 18047 NC
## 1627 52974 S
## 2905 9603 W
## 1023 98545 S
## 1100 63674 S
## 10 137426 S
## 1691 155213 S
## 1755 1233663 NC
## 9 48022 S
## 1785 649612 NC
## 262 834018 W
## 2971 208888 NC
## 90 326808 S
## 623 353683 NC
## 1265 115338 NC
## 1768 745815 NC
## 1607 1629363 W
## 2216 172366 NE
## 252 1341738 W
## 2973 263514 NC
## 1330 186573 NC
## 2418 47319 S
## 1075 128719 S
## 271 896994 W
## 964 312717 NC
## 1207 77493 NC
## 2112 372901 S
## 1033 3383 S
## 949 328094 NC
## 3017 88571 S
## 2353 1859161 NC
## 331 100764 S
## 383 82549 S
## 1726 594587 NC
## 1885 7799 NE
## 1608 675569 W
## 1899 43989 NE
## 1982 1890 NE
## 2552 553047 S
## 2760 332686 W
## 2535 523049 S
## 2506 357933 S
## 330 106721 S
## 2762 484156 W
## 1880 24716 NE
## 1667 204443 S
## 642 140701 W
## 117 142856 S
## 16 99466 S
## 234 156801 W
## 1581 135126 W
## 2057 17138 NC
## 510 47000 S
## 2259 81426 NE
## 680 223764 NC
## 2733 328367 S
## 3035 21164 S
## 1376 304032 NC
## 2820 24201 S
## 1649 162634 S
## 2746 563183 S
## 358 138208 S
## 859 198680 NC
## 2770 373582 W
## 1401 201670 NC
## 1494 127351 S
## 1448 252074 NC
## 1295 113422 NC
## 1606 893872 W
## 1544 80342 S
## 2783 78691 S
## 574 431185 NC
## 1159 31583 NE
## 491 38313 S
## 92 20589 S
## 755 169622 NC
## 2934 323482 NC
## 2299 94193 S
## 2020 205105 NC
## 711 94681 NC
## 2592 576468 S
## 927 441417 NC
## 2210 473316 W
## 2626 595420 S
## 1351 600114 NC
## 697 171938 NC
## 2481 92773 S
## 42 35748 S
## 708 217191 NC
## 2794 51604 S
## 1559 230524 S
## 2022 202188 NC
## 1234 29161 NC
## 1034 159710 S
## 2428 104457 S
## 2180 818736 W
## 2720 1020756 S
## 1279 444407 NC
## 2830 195476 S
## 771 285730 NC
## 3015 7710 S
## 3038 115487 S
## 2548 451584 S
## 1694 44000 S
## 1818 316551 NC
## 633 453647 W
## 256 2286947 W
## 77 250819 S
## 990 299321 S
## 2399 1406379 NC
## 1543 31587 S
## 2797 112944 S
## 2923 59890 W
## 2516 2405018 S
## 1343 280089 NC
## 2201 380464 W
## 2975 113548 NC
## 1794 3887635 NC
## 2103 207333 S
## 1030 48509 S
## 1541 218154 S
## 228 84172 W
## 812 251603 NC
## 2288 89935 S
## 669 69354 NC
## 2427 256272 S
## 2200 34292 W
## 573 346569 NC
## 2662 587316 S
## 2350 270665 NC
## 2574 128533 S
## 876 407464 NC
## 1901 325 NE
## 2949 26456 NC
## 2117 264890 S
## 2397 448834 NC
## 1486 134028 NC
## 203 57418 W
## 2482 177522 S
## 870 351941 NC
## 1078 71324 S
## 2514 563993 S
## 1934 2364443 W
## 2166 239971 S
## 994 125133 S
## 2741 476493 S
## 532 330080 NC
## 763 333238 NC
## 1528 140209 S
## 1717 119855 S
## 1930 1233794 W
## 1609 683088 W
## 2053 219023 NC
## 582 191291 NC
## 2590 443027 S
## 1323 405029 NC
## 1316 131563 NC
## 834 220959 NC
## 1447 507875 NC
## 1849 409715 NC
## 2380 701352 NC
## 1757 552707 NC
## 2953 361918 NC
## 2790 84677 S
## 27 196859 S
## 1896 10365 NE
## 973 5419 S
## 970 111913 S
## 2603 470096 S
## 1272 36272 NC
## 1891 106324 NE
## 587 268520 NC
## 481 95876 S
## 444 71379 S
## 2951 293134 NC
## 1672 56693 S
## 1906 2080760 W
## 844 134960 NC
## 2942 414240 NC
## 291 55263 NE
## 1481 402202 NC
## 3012 106325 S
## 140 159013 S
## 1284 188958 NC
## 2388 322784 NC
## 1636 93584 S
## 1698 115854 S
## 80 246184 S
## 1976 4 NE
## 861 162244 NC
## 2146 253652 S
## 1566 89807 S
## 1372 272540 NC
## 768 203428 NC
## 1202 94755 NE
## 2956 232591 NC
## 648 744295 W
## 369 85075 S
## 683 40917 NC
## 1586 2000266 W
## 1037 278675 S
## 1922 1166009 W
## 1552 95121 S
## 215 116083 W
## 1605 1968857 W
## 2315 52978 S
## 1414 399193 NC
## 2409 36633 S
## 346 4123 S
## 166 452347 W
## 2042 65266 NC
## 457 120839 S
## 2715 509017 S
## 123 102560 S
## 432 31529 S
## 3041 32633 S
## 2016 194022 NC
## 2149 494277 S
## 1155 25470 NE
## 2478 144953 S
## 1230 5965 NC
## 555 343870 NC
## 233 423785 W
## 2372 297819 NC
## 694 300127 NC
## 2876 209677 NE
## 1545 124202 S
## 2990 348602 NC
## 660 752032 W
## 2584 576013 S
## 2412 142729 S
## 918 537914 NC
## 2698 82721 S
## 2113 218803 S
## 1319 145545 NC
## 2456 141357 S
## 2712 926093 S
## 635 159358 W
## 2269 89045 NE
## 2154 390957 S
## 1903 415263 W
## 754 402212 NC
## 354 253330 S
## 2573 422464 S
## 864 245099 NC
## 860 285169 NC
# Generate the sample indices for SRS6
set.seed(seedSRS6)
(sampleindices6 <- sample(N,n))
## [1] 2060 2679 2198 2416 758 2629 2346 1245 2295 2993 347 135 2179 2521
## [15] 645 109 480 1288 235 1887 2680 1881 466 2870 2191 952 925 2567
## [29] 1528 2073 1344 2959 2238 754 1951 1660 301 1241 605 1622 81 461
## [43] 1511 2898 902 612 280 1441 2133 2092 3058 2701 1138 293 920 1956
## [57] 839 2786 1986 2366 1026 2049 2675 150 1250 1690 3011 1404 2978 2298
## [71] 130 2705 3010 697 513 278 1353 1474 2612 14 129 1318 239 2013
## [85] 410 654 2102 2822 308 2798 2640 834 1539 1248 1402 1113 820 753
## [99] 2670 1506 960 395 3052 3000 1559 1171 2688 604 2658 360 2447 516
## [113] 394 1975 2857 45 956 759 884 455 1294 2971 2917 118 1260 1445
## [127] 1662 789 1532 1854 787 562 2364 3043 927 56 896 936 1906 1236
## [141] 2177 733 1127 975 803 923 421 1836 1940 1216 148 1583 1542 2472
## [155] 327 2454 964 3021 1645 1168 1861 1358 2676 1813 485 712 1867 593
## [169] 1276 863 2635 2753 433 2770 1840 1919 962 127 1194 566 658 682
## [183] 1926 564 197 833 1280 953 2501 2044 2478 1120 7 1269 1635 2606
## [197] 1281 2229 725 390 1178 879 2274 1111 802 1185 2154 1009 2887 898
## [211] 1567 1273 2342 553 2248 2748 3025 1761 870 904 2967 2797 310 622
## [225] 1074 1366 41 2765 2734 832 1489 126 1706 482 2357 788 2172 1204
## [239] 514 2808 298 481 1114 2421 1589 517 1319 1190 1781 184 2212 20
## [253] 709 2931 1225 1468 2935 816 2297 387 2369 2036 2482 2926 1794 508
## [267] 111 1574 422 386 2257 836 2326 2646 2084 1852 271 1442 2158 2504
## [281] 935 2837 1187 555 1530 2096 1860 2825 1730 336 1109 840 2809 2325
## [295] 727 1173 1242 946 2979 2010
# Generate the actual samples for SRS6
(agpop_sampled6 <- agpop_complete[sampleindices6,])
## ACRES92 REGION
## 2071 107157 NC
## 2694 572607 S
## 2209 1466580 W
## 2429 236912 S
## 762 297003 NC
## 2644 507135 S
## 2359 601034 NC
## 1251 9391 NC
## 2308 194822 S
## 3012 106325 S
## 350 300622 S
## 135 45609 S
## 2190 766373 W
## 2534 370572 S
## 649 311296 W
## 109 107841 S
## 484 37923 S
## 1294 347420 NC
## 235 260728 W
## 1893 25011 NE
## 2695 268058 S
## 1887 68627 NE
## 470 55310 S
## 2887 58891 NE
## 2202 1318447 W
## 956 450829 NC
## 929 668420 NC
## 2581 477515 S
## 1534 182009 S
## 2084 160734 NC
## 1350 491726 NC
## 2977 282405 NC
## 2250 36963 NE
## 758 119370 NC
## 1959 259540 NE
## 1666 113654 S
## 303 70672 S
## 1247 193956 NC
## 609 331211 NC
## 1628 72621 S
## 81 269122 S
## 465 93061 S
## 1517 24845 S
## 2915 55360 W
## 906 484823 NC
## 616 309508 NC
## 282 2086292 W
## 1447 507875 NC
## 2144 344280 S
## 2103 207333 S
## 3077 397883 W
## 2716 1396275 S
## 1142 61883 S
## 295 191140 S
## 924 323769 NC
## 1964 138620 NE
## 843 175124 NC
## 2802 61669 S
## 1997 396721 NE
## 2379 2076199 NC
## 1030 48509 S
## 2060 202927 NC
## 2690 667177 S
## 150 5989961 W
## 1256 73437 NC
## 1696 65136 S
## 3030 258 S
## 1410 196959 NC
## 2997 241778 NC
## 2311 70277 S
## 130 156363 S
## 2720 1020756 S
## 3029 74760 S
## 701 433246 NC
## 517 115516 S
## 280 104010 W
## 1359 643762 NC
## 1480 459671 NC
## 2626 595420 S
## 14 109555 S
## 129 122871 S
## 1324 5262 NC
## 240 1105614 W
## 2024 122480 NC
## 414 123702 S
## 658 477839 W
## 2113 218803 S
## 2839 18367 S
## 310 40039 S
## 2815 136320 S
## 2655 562612 S
## 838 164025 NC
## 1545 124202 S
## 1254 48029 NC
## 1408 288810 NC
## 1117 58730 S
## 824 139523 NC
## 757 336450 NC
## 2685 497106 S
## 1512 230838 S
## 964 312717 NC
## 399 41972 S
## 3071 1364948 W
## 3019 74268 S
## 1565 75551 S
## 1177 44623 S
## 2703 564382 S
## 608 495769 NC
## 2673 593819 S
## 364 78739 S
## 2460 56253 S
## 520 200061 S
## 398 72636 S
## 1984 135494 NE
## 2874 83047 S
## 45 173468 S
## 960 423064 NC
## 763 333238 NC
## 888 442362 NC
## 459 45448 S
## 1300 566981 NC
## 2989 231427 NC
## 2934 323482 NC
## 118 173861 S
## 1266 48236 NC
## 1451 291846 NC
## 1668 127663 S
## 793 148662 NC
## 1538 96474 S
## 1860 298115 NC
## 791 29837 NC
## 566 332358 NC
## 2377 661474 NC
## 3062 2415873 W
## 931 349293 NC
## 56 231243 S
## 900 499112 NC
## 940 443290 NC
## 1912 1209335 W
## 1242 154482 NC
## 2188 139483 W
## 737 184599 NC
## 1131 23185 S
## 979 42602 S
## 807 182836 NC
## 927 441417 NC
## 425 73869 S
## 1842 226042 NC
## 1947 140380 W
## 1222 256236 NC
## 148 5785707 W
## 1589 349938 W
## 1548 118651 S
## 2485 11292 S
## 330 106721 S
## 2467 53026 S
## 968 156590 S
## 3040 55827 S
## 1651 98531 S
## 1174 222768 S
## 1867 228167 NC
## 1364 310184 NC
## 2691 510079 S
## 1819 321080 NC
## 489 49043 S
## 716 73142 NC
## 1873 25439 NE
## 597 318778 NC
## 1282 324111 NC
## 867 337300 NC
## 2650 515960 S
## 2768 447463 W
## 437 73417 S
## 2786 47010 S
## 1846 532901 NC
## 1925 1646707 W
## 966 22553 NC
## 127 70872 S
## 1200 106971 NE
## 570 280797 NC
## 662 435069 W
## 686 377512 NC
## 1932 324476 W
## 568 225835 NC
## 197 600073 W
## 837 236436 NC
## 1286 31427 NC
## 957 687593 NC
## 2514 563993 S
## 2055 68344 NC
## 2491 204146 S
## 1124 46110 S
## 7 167832 S
## 1275 3786 NC
## 1641 64031 S
## 2620 330173 S
## 1287 168073 NC
## 2241 106390 NE
## 729 203749 NC
## 394 10192 S
## 1184 109108 S
## 883 201798 NC
## 2287 20458 NE
## 1115 132678 S
## 806 301962 NC
## 1191 38853 NE
## 2165 299263 S
## 1013 226206 S
## 2904 19526 W
## 902 366764 NC
## 1573 449970 W
## 1279 444407 NC
## 2355 641911 NC
## 557 401625 NC
## 2260 20777 NE
## 2763 234576 W
## 3044 82154 S
## 1767 521343 NC
## 874 565274 NC
## 908 517376 NC
## 2985 335517 NC
## 2814 51442 S
## 312 52259 S
## 626 325338 W
## 1078 71324 S
## 1372 272540 NC
## 41 204487 S
## 2780 25810 S
## 2749 192288 W
## 836 204165 NC
## 1495 151743 S
## 126 357416 S
## 1712 5897 S
## 486 108967 S
## 2370 584231 NC
## 792 229097 NC
## 2183 24740 W
## 1210 14104 NC
## 518 93078 S
## 2825 52508 S
## 300 23735 S
## 485 138803 S
## 1118 36059 S
## 2434 159927 S
## 1595 951780 W
## 521 926607 W
## 1325 103665 NC
## 1196 63473 NE
## 1787 649634 NC
## 184 206138 W
## 2223 310672 NE
## 20 47200 S
## 713 358920 NC
## 2948 351633 NC
## 1231 66789 NC
## 1474 120036 NC
## 2952 163145 NC
## 820 71596 NC
## 2310 66809 S
## 391 42678 S
## 2382 425288 NC
## 2047 248400 NC
## 2495 352488 S
## 2943 130051 NC
## 1800 138022 NC
## 512 54445 S
## 111 298547 S
## 1580 1334041 W
## 426 137637 S
## 390 11559 S
## 2270 87253 NE
## 840 172348 NC
## 2339 1026353 NC
## 2661 428243 S
## 2095 241787 NC
## 1858 314949 NC
## 272 546538 W
## 1448 252074 NC
## 2169 250958 S
## 2517 408824 S
## 939 700869 NC
## 2854 24924 S
## 1193 95402 NE
## 559 343367 NC
## 1536 80272 S
## 2107 493631 S
## 1866 339358 NC
## 2842 63991 S
## 1736 627774 NC
## 339 44962 S
## 1113 291526 S
## 844 134960 NC
## 2826 37777 S
## 2338 444440 NC
## 731 98838 NC
## 1179 82470 S
## 1248 64973 NC
## 950 227349 NC
## 2998 167191 NC
## 2021 126195 NC
# Generate the sample indices for SRS7
set.seed(seedSRS7)
(sampleindices7 <- sample(N,n))
## [1] 874 2480 2280 2085 3007 250 2379 513 3053 1839 2138 1968 2060 50
## [15] 1296 1283 261 627 2089 1593 2065 1440 1904 1348 309 1550 114 2739
## [29] 1668 1093 2623 2603 587 779 1832 1155 2268 552 1003 1041 789 2362
## [43] 1851 1941 1741 655 1145 236 3054 121 829 1005 1431 3001 1877 1451
## [57] 1170 1660 1397 149 737 1816 1700 2666 2436 2111 1583 316 2020 2744
## [71] 84 2670 2932 2376 637 2691 802 2179 1536 1304 617 611 1210 2510
## [85] 1386 2858 2046 1872 1307 2361 2724 668 2056 970 530 1827 505 1426
## [99] 717 2637 1878 79 2659 2357 2274 1647 1223 1044 938 1378 1760 1169
## [113] 2719 482 2585 1376 1618 1066 1142 1244 39 2901 108 2217 1205 2180
## [127] 1384 2214 673 2731 59 1315 2460 1966 1559 302 1350 1475 1801 131
## [141] 2711 2751 2253 619 2240 1752 2981 1562 1085 2676 1701 1634 1869 1774
## [155] 2516 1218 3009 2347 432 285 1032 2868 2701 2437 1117 2944 2587 2108
## [169] 2880 2663 1859 1335 1068 808 2908 2318 764 2336 1217 487 1799 1545
## [183] 1354 1215 539 2943 1720 2021 217 932 2504 47 672 1894 1977 2928
## [197] 1038 1421 136 2247 1608 1311 1762 187 1531 2665 601 1871 2142 2416
## [211] 1411 629 887 2061 1740 2862 284 169 2146 1351 2389 592 869 2533
## [225] 2783 31 2446 272 145 796 2354 760 2715 667 467 2789 1931 2165
## [239] 2378 1214 905 559 1679 752 264 883 2160 2410 65 512 2485 1108
## [253] 410 2311 1577 911 765 82 2917 2534 2418 1098 2199 2495 1084 1528
## [267] 2529 1645 2735 846 2900 231 2388 1632 839 1705 1897 2136 720 2649
## [281] 184 1778 2387 1078 139 2695 621 1123 411 25 476 735 2635 2583
## [295] 278 2301 1227 1830 2712 1549
# Generate the actual samples for SRS7
(agpop_sampled7 <- agpop_complete[sampleindices7,])
## ACRES92 REGION
## 878 486997 NC
## 2493 204391 S
## 2293 74733 S
## 2096 41666 NC
## 3026 64332 S
## 251 878447 W
## 2392 615479 NC
## 517 115516 S
## 3072 1208776 W
## 1845 223949 NC
## 2149 494277 S
## 1977 169313 NE
## 2071 107157 NC
## 50 224370 S
## 1302 1249 NC
## 1289 377693 NC
## 262 834018 W
## 631 80333 W
## 2100 187175 NC
## 1599 912154 W
## 2076 106573 NC
## 1446 256023 NC
## 1910 526407 W
## 1354 536299 NC
## 311 57179 S
## 1556 141245 S
## 114 186829 S
## 2754 50357 W
## 1674 148135 S
## 1097 66380 S
## 2638 318658 S
## 2617 322324 S
## 591 392835 NC
## 783 222435 NC
## 1838 322120 NC
## 1159 31583 NE
## 2281 62740 NE
## 556 224811 NC
## 1007 127161 S
## 1045 76141 S
## 793 148662 NC
## 2375 846435 NC
## 1857 417698 NC
## 1948 624606 W
## 1747 723816 NC
## 659 271143 W
## 1149 86856 S
## 237 167106 W
## 3073 592754 W
## 121 79803 S
## 833 142482 NC
## 1009 206090 S
## 1437 219894 NC
## 3020 19956 S
## 1883 2636 NE
## 1457 345673 NC
## 1176 97312 S
## 1666 113654 S
## 1403 227783 NC
## 149 1891644 W
## 741 251277 NC
## 1822 403584 NC
## 1706 55309 S
## 2681 141215 S
## 2449 196733 S
## 2122 633874 S
## 1589 349938 W
## 319 327611 S
## 2031 223216 NC
## 2759 434183 W
## 84 108046 S
## 2685 497106 S
## 2949 26456 NC
## 2389 1417516 NC
## 641 103246 W
## 2706 247626 S
## 806 301962 NC
## 2190 766373 W
## 1542 93180 S
## 1310 366534 NC
## 621 442247 NC
## 615 302487 NC
## 1216 244927 NC
## 2523 208073 S
## 1392 325796 NC
## 2875 43332 S
## 2057 17138 NC
## 1878 46610 NE
## 1313 79183 NC
## 2374 322802 NC
## 2739 260892 S
## 672 144435 NC
## 2067 87036 NC
## 974 80864 S
## 534 333115 NC
## 1833 414763 NC
## 509 53895 S
## 1432 270576 NC
## 721 369952 NC
## 2652 49579 S
## 1884 97186 NE
## 79 18818 S
## 2674 847608 S
## 2370 584231 NC
## 2287 20458 NE
## 1653 7046 S
## 1229 61535 NC
## 1048 119533 S
## 942 228178 NC
## 1384 181292 NC
## 1766 495509 NC
## 1175 110699 S
## 2734 1712044 S
## 486 108967 S
## 2599 463450 S
## 1382 197530 NC
## 1624 490988 W
## 1070 91365 S
## 1146 58790 S
## 1250 118764 NC
## 39 166949 S
## 2918 92074 W
## 108 108913 S
## 2229 139918 NE
## 1211 165371 NC
## 2191 1154399 W
## 1390 377000 NC
## 2225 129323 NE
## 677 571807 NC
## 2746 563183 S
## 59 106206 S
## 1321 482991 NC
## 2473 233312 S
## 1975 300559 NE
## 1565 75551 S
## 304 86026 S
## 1356 131753 NC
## 1481 402202 NC
## 1807 528731 NC
## 131 313232 S
## 2726 917186 S
## 2766 107663 W
## 2265 104292 NE
## 623 353683 NC
## 2252 86402 NE
## 1758 384213 NC
## 3000 221357 NC
## 1568 78230 S
## 1089 63446 S
## 2691 510079 S
## 1707 266067 S
## 1640 43056 S
## 1875 46056 NE
## 1780 489384 NC
## 2529 518316 S
## 1224 72777 NC
## 3028 56555 S
## 2360 1204465 NC
## 436 45624 S
## 287 19830 NE
## 1036 119218 S
## 2885 149503 NE
## 2716 1396275 S
## 2450 36978 S
## 1121 182605 S
## 2961 356651 NC
## 2601 356170 S
## 2119 358446 S
## 2897 35678 W
## 2678 415694 S
## 1865 193556 NC
## 1341 305831 NC
## 1072 229838 S
## 812 251603 NC
## 2925 710546 W
## 2331 55992 S
## 768 203428 NC
## 2349 462238 NC
## 1223 1402 NC
## 491 38313 S
## 1805 298854 NC
## 1551 96540 S
## 1360 231610 NC
## 1221 64084 NC
## 543 274905 NC
## 2960 182339 NC
## 1726 594587 NC
## 2032 4060 NC
## 217 137530 W
## 936 451362 NC
## 2517 408824 S
## 47 207226 S
## 676 209437 NC
## 1900 75531 NE
## 1986 145329 NE
## 2945 366593 NC
## 1042 247266 S
## 1427 356164 NC
## 136 114762 S
## 2259 81426 NE
## 1614 381104 W
## 1317 107810 NC
## 1768 745815 NC
## 187 686876 W
## 1537 137267 S
## 2680 2891640 S
## 605 364172 NC
## 1877 39844 NE
## 2153 347480 S
## 2429 236912 S
## 1417 232592 NC
## 633 453647 W
## 891 316317 NC
## 2072 113892 NC
## 1746 688468 NC
## 2879 82849 NE
## 286 86581 NE
## 169 163036 W
## 2157 156748 S
## 1357 117701 NC
## 2402 62989 S
## 596 260780 NC
## 873 592207 NC
## 2546 394805 S
## 2799 68326 S
## 31 104364 S
## 2459 245681 S
## 273 219612 W
## 145 358904 S
## 800 161745 NC
## 2367 545064 NC
## 764 234973 NC
## 2730 213923 S
## 671 135163 NC
## 471 18644 S
## 2806 15714 S
## 1937 82100 W
## 2176 216268 S
## 2391 903980 NC
## 1220 92809 NC
## 909 544071 NC
## 563 328885 NC
## 1685 27901 S
## 756 314886 NC
## 265 633279 W
## 887 547483 NC
## 2171 421233 S
## 2423 258265 S
## 65 167923 S
## 516 38691 S
## 2498 19131 S
## 1112 4127 S
## 414 123702 S
## 2324 69897 S
## 1583 2232575 W
## 915 273841 NC
## 769 295844 NC
## 82 98919 S
## 2934 323482 NC
## 2547 2001152 S
## 2431 57216 S
## 1102 147826 S
## 2210 473316 W
## 2508 416631 S
## 1088 117599 S
## 1534 182009 S
## 2542 571684 S
## 1651 98531 S
## 2750 1449976 W
## 850 80069 NC
## 2917 20529 W
## 231 304592 W
## 2401 213603 S
## 1638 63067 S
## 843 175124 NC
## 1711 121404 S
## 1903 415263 W
## 2147 314987 S
## 724 709106 NC
## 2664 193885 S
## 184 206138 W
## 1784 437826 NC
## 2400 41899 S
## 1082 135850 S
## 139 131353 S
## 2710 835337 S
## 625 221209 W
## 1127 97643 S
## 415 40608 S
## 25 166490 S
## 480 12733 S
## 739 261482 NC
## 2650 515960 S
## 2597 686578 S
## 280 104010 W
## 2314 72500 S
## 1233 231557 NC
## 1836 330369 NC
## 2727 1806639 S
## 1555 273117 S
# Generate the sample indices for SRS8
set.seed(seedSRS8)
(sampleindices8 <- sample(N,n))
## [1] 1447 1300 1772 2697 2272 711 2832 441 1523 1285 2080 1774 1751 1645
## [15] 385 2085 170 957 1489 726 397 2523 627 2633 2471 111 1575 1948
## [29] 388 1717 859 805 2210 2829 2343 1999 2592 2084 548 387 2564 509
## [43] 2623 376 287 1094 2381 686 1899 2729 491 816 1365 1190 192 2111
## [57] 2256 1216 2515 1373 1741 1969 1857 2022 1665 2208 2215 836 1024 1090
## [71] 1292 1142 2101 1491 154 489 282 458 1711 261 1071 1016 2091 2605
## [85] 1367 653 1038 2263 920 1658 2452 1260 1427 1444 156 968 1055 2879
## [99] 753 1357 1423 661 898 2191 1460 2269 216 2234 1062 2510 2087 2669
## [113] 1971 2137 2200 222 1046 345 2174 46 1982 1424 223 2015 1532 2325
## [127] 1746 1529 2124 2149 1110 48 925 1238 327 1217 299 200 1975 1226
## [141] 2079 2854 2330 1004 1674 85 533 2143 105 295 2690 2894 204 2300
## [155] 2202 1614 391 1069 714 1622 1947 131 179 2944 481 267 161 1888
## [169] 2024 382 3057 2252 487 2947 2052 369 2283 1443 1623 1207 1773 2399
## [183] 1001 398 1932 2110 1045 874 1695 2747 3050 2653 2132 374 3055 2642
## [197] 2025 2230 132 829 77 2370 652 189 1458 913 2021 2848 1805 2918
## [211] 2721 1640 109 1788 2001 2260 3017 2270 2744 277 2956 1661 538 1733
## [225] 2809 2514 2135 2153 2954 2599 1853 2170 2728 473 1675 2209 2520 938
## [239] 1461 2422 2649 2484 2425 2684 1303 1163 592 2504 1167 1441 2485 2168
## [253] 735 2429 2571 2940 738 575 459 1687 465 1959 1471 52 2144 1293
## [267] 2497 888 151 3042 2013 2490 2584 1053 2613 904 1525 2098 2955 2333
## [281] 728 1155 781 868 2102 2395 1124 742 921 1235 2069 2041 2791 501
## [295] 2784 2687 1179 1476 1333 1822
# Generate the actual samples for SRS8
(agpop_sampled8 <- agpop_complete[sampleindices8,])
## ACRES92 REGION
## 1453 359434 NC
## 1306 241148 NC
## 1778 1182658 NC
## 2712 926093 S
## 2285 9631 NE
## 715 385560 NC
## 2849 48889 S
## 445 31394 S
## 1529 262371 S
## 1291 183760 NC
## 2091 19088 NC
## 1780 489384 NC
## 1757 552707 NC
## 1651 98531 S
## 389 33641 S
## 2096 41666 NC
## 170 12594 W
## 961 471658 NC
## 1495 151743 S
## 730 282222 NC
## 401 109923 S
## 2536 680567 S
## 631 80333 W
## 2648 675927 S
## 2484 55097 S
## 111 298547 S
## 1581 135126 W
## 1956 97869 NE
## 392 4519 S
## 1723 142312 S
## 863 378517 NC
## 809 124694 NC
## 2221 221981 NE
## 2846 297064 S
## 2356 974811 NC
## 2010 205954 NE
## 2606 386546 S
## 2095 241787 NC
## 552 336131 NC
## 391 42678 S
## 2578 102229 S
## 513 53291 S
## 2638 318658 S
## 380 113861 S
## 289 65987 NE
## 1098 258035 S
## 2394 1006831 NC
## 690 354480 NC
## 1905 3112271 W
## 2744 203667 S
## 495 71097 S
## 820 71596 NC
## 1371 290627 NC
## 1196 63473 NE
## 192 60740 W
## 2122 633874 S
## 2269 89045 NE
## 1222 256236 NC
## 2528 166939 S
## 1379 311161 NC
## 1747 723816 NC
## 1978 205105 NE
## 1863 347598 NC
## 2033 335575 NC
## 1671 75496 S
## 2219 57960 NE
## 2226 76997 NE
## 840 172348 NC
## 1028 120959 S
## 1094 110637 S
## 1298 326804 NC
## 1146 58790 S
## 2112 372901 S
## 1497 42712 S
## 154 246038 W
## 493 119873 S
## 284 9975 NE
## 462 205573 S
## 1717 119855 S
## 262 834018 W
## 1075 128719 S
## 1020 144828 S
## 2102 216318 NC
## 2619 517671 S
## 1373 407953 NC
## 657 208161 W
## 1042 247266 S
## 2276 52760 NE
## 924 323769 NC
## 1664 156027 S
## 2465 257000 S
## 1266 48236 NC
## 1433 199292 NC
## 1450 250475 NC
## 156 1981938 W
## 972 132979 S
## 1059 84434 S
## 2896 304928 W
## 757 336450 NC
## 1363 395071 NC
## 1429 249046 NC
## 665 489993 W
## 902 366764 NC
## 2202 1318447 W
## 1466 257217 NC
## 2282 252052 NE
## 216 1354262 W
## 2246 129503 NE
## 1066 218145 S
## 2523 208073 S
## 2098 139655 NC
## 2684 98449 S
## 1980 110150 NE
## 2148 236766 S
## 2211 694304 W
## 222 207448 W
## 1050 133173 S
## 348 105621 S
## 2185 174872 W
## 46 67962 S
## 1991 218306 NE
## 1430 253281 NC
## 223 322823 W
## 2026 179280 NC
## 1538 96474 S
## 2338 444440 NC
## 1752 1165695 NC
## 1535 175231 S
## 2135 469883 S
## 2160 207118 S
## 1114 87574 S
## 48 199714 S
## 929 668420 NC
## 1244 190706 NC
## 330 106721 S
## 1223 1402 NC
## 301 43314 S
## 200 7 W
## 1984 135494 NE
## 1232 277400 NC
## 2090 136612 NC
## 2871 131366 S
## 2343 417697 NC
## 1008 210275 S
## 1680 58384 S
## 85 34115 S
## 537 345567 NC
## 2154 390957 S
## 105 183895 S
## 297 9135 S
## 2705 507449 S
## 2911 1465788 W
## 204 836989 W
## 2313 195697 S
## 2213 139820 W
## 1620 1063086 W
## 395 178861 S
## 1073 117768 S
## 718 169292 NC
## 1628 72621 S
## 1955 0 NE
## 131 313232 S
## 179 164130 W
## 2961 356651 NC
## 485 138803 S
## 268 459659 W
## 161 2108834 W
## 1894 58758 NE
## 2035 169017 NC
## 386 8518 S
## 3076 879694 W
## 2264 109438 NE
## 491 38313 S
## 2964 248862 NC
## 2063 261320 NC
## 373 62983 S
## 2296 90995 S
## 1449 316809 NC
## 1629 70697 S
## 1213 19844 NC
## 1779 335465 NC
## 2412 142729 S
## 1005 41352 S
## 402 25802 S
## 1938 79635 W
## 2121 336285 S
## 1049 42642 S
## 878 486997 NC
## 1701 144858 S
## 2762 484156 W
## 3069 1344561 W
## 2668 220355 S
## 2143 397909 S
## 378 166511 S
## 3074 1720737 W
## 2657 658204 S
## 2036 88899 NC
## 2242 4702 NE
## 132 111895 S
## 833 142482 NC
## 77 250819 S
## 2383 284888 NC
## 656 224369 W
## 189 1372778 W
## 1464 152529 NC
## 917 485656 NC
## 2032 4060 NC
## 2865 82736 S
## 1811 430972 NC
## 2935 84091 NC
## 2736 501692 S
## 1646 53902 S
## 109 107841 S
## 1794 3887635 NC
## 2012 5709 NE
## 2273 177215 NE
## 3036 178160 S
## 2283 1468 NE
## 2759 434183 W
## 279 38467 W
## 2974 78772 NC
## 1667 204443 S
## 542 336254 NC
## 1739 369140 NC
## 2826 37777 S
## 2527 622130 S
## 2146 253652 S
## 2164 353045 S
## 2972 31777 NC
## 2613 519043 S
## 1859 1481503 NC
## 2181 118818 W
## 2743 461127 S
## 477 80396 S
## 1681 21218 S
## 2220 199056 NE
## 2533 670459 S
## 942 228178 NC
## 1467 168586 NC
## 2435 110215 S
## 2664 193885 S
## 2497 103063 S
## 2438 146868 S
## 2699 358211 S
## 1309 443496 NC
## 1169 126981 S
## 596 260780 NC
## 2517 408824 S
## 1173 123762 S
## 1447 507875 NC
## 2498 19131 S
## 2179 687299 S
## 739 261482 NC
## 2442 119419 S
## 2585 409501 S
## 2957 195287 NC
## 742 443475 NC
## 579 615034 NC
## 463 44599 S
## 1693 67491 S
## 469 51836 S
## 1967 145679 NE
## 1477 137747 NC
## 52 89228 S
## 2155 282659 S
## 1299 138594 NC
## 2510 371257 S
## 892 164081 NC
## 151 1151284 W
## 3061 1542262 W
## 2024 122480 NC
## 2503 432939 S
## 2598 494177 S
## 1057 112409 S
## 2627 531206 S
## 908 517376 NC
## 1531 125713 S
## 2109 412673 S
## 2973 263514 NC
## 2346 236608 NC
## 732 164158 NC
## 1159 31583 NE
## 785 202429 NC
## 872 271015 NC
## 2113 218803 S
## 2408 165547 S
## 1128 247106 S
## 746 270598 NC
## 925 409839 NC
## 1241 210638 NC
## 2080 87954 NC
## 2052 177194 NC
## 2808 116509 S
## 505 21973 S
## 2800 45451 S
## 2702 518371 S
## 1185 123932 S
## 1482 126474 NC
## 1339 416570 NC
## 1828 310042 NC
# Generate the sample indices for SRS9
set.seed(seedSRS9)
(sampleindices9 <- sample(N,n))
## [1] 383 2338 1022 1243 1445 33 812 2921 841 49 2850 679 926 671
## [15] 2917 1358 455 2603 2722 897 883 2 2967 1769 2521 1925 1506 2634
## [29] 1834 2702 2586 658 357 2494 1071 1234 386 1814 2743 68 988 943
## [43] 2008 7 2060 1859 1921 2272 654 1853 2147 231 2454 1970 1354 621
## [57] 275 445 1504 241 1286 2064 247 2473 1062 1846 1292 2178 1177 2236
## [71] 646 1640 297 449 2347 650 2555 2233 403 84 2510 1947 1952 1569
## [85] 3002 590 2891 1905 220 678 1523 348 823 1784 2177 1593 1096 1152
## [99] 3009 1120 1295 1548 1464 990 1496 88 174 855 1610 2669 2372 2248
## [113] 1851 2605 1399 1894 1188 2693 2214 1681 853 1038 71 2268 1979 1611
## [127] 123 378 432 913 2647 1517 444 2112 2254 1410 601 2190 2964 1899
## [141] 2322 3032 3050 2251 1892 1370 2345 40 187 138 966 2537 330 899
## [155] 662 1935 2197 831 2990 1369 2103 2922 1417 2747 869 20 1467 2033
## [169] 1492 2453 309 3028 914 2962 2642 546 1981 2317 118 638 600 1494
## [183] 337 97 553 2195 2331 3006 396 197 2466 363 1279 2364 113 1032
## [197] 1698 2051 2386 1082 1883 1080 1470 401 2577 522 894 2471 2264 722
## [211] 2888 2310 212 982 703 1739 1957 1556 448 292 1543 2781 2000 2057
## [225] 705 871 2580 965 1669 2479 1217 2255 2262 1014 1753 1386 2416 1602
## [239] 1984 711 249 2985 78 1406 1289 185 582 459 957 1595 567 1256
## [253] 327 340 2595 1081 1253 1786 1155 2221 1740 1314 2382 2161 857 2632
## [267] 198 1368 935 1790 2137 562 2048 2956 1764 300 664 816 2622 2328
## [281] 1668 2871 2899 2144 2184 258 166 952 2040 577 2501 2587 2840 456
## [295] 1047 2428 128 880 2460 2340
# Generate the actual samples for SRS9
(agpop_sampled9 <- agpop_complete[sampleindices9,])
## ACRES92 REGION
## 387 5901 S
## 2351 561312 NC
## 1026 3224 S
## 1249 336273 NC
## 1451 291846 NC
## 33 85821 S
## 816 158788 NC
## 2938 426884 NC
## 845 121710 NC
## 49 138437 S
## 2867 38967 S
## 683 40917 NC
## 930 465527 NC
## 675 238906 NC
## 2934 323482 NC
## 1364 310184 NC
## 459 45448 S
## 2617 322324 S
## 2737 307783 S
## 901 319686 NC
## 887 547483 NC
## 2 47146 W
## 2985 335517 NC
## 1775 737273 NC
## 2534 370572 S
## 1931 1868074 W
## 1512 230838 S
## 2649 472332 S
## 1840 724458 NC
## 2717 459120 S
## 2600 545664 S
## 662 435069 W
## 361 45214 S
## 2507 442173 S
## 1075 128719 S
## 1240 199733 NC
## 390 11559 S
## 1820 305724 NC
## 2758 63116 W
## 68 96194 S
## 992 68373 S
## 947 484415 NC
## 2019 80507 NC
## 7 167832 S
## 2071 107157 NC
## 1865 193556 NC
## 1927 1896131 W
## 2285 9631 NE
## 658 477839 W
## 1859 1481503 NC
## 2158 280533 S
## 231 304592 W
## 2467 53026 S
## 1979 195626 NE
## 1360 231610 NC
## 625 221209 W
## 277 200674 W
## 449 104768 S
## 1510 30050 S
## 242 331639 W
## 1292 262207 NC
## 2075 104197 NC
## 248 641755 W
## 2486 49452 S
## 1066 218145 S
## 1852 301513 NC
## 1298 326804 NC
## 2189 402023 W
## 1183 55657 S
## 2248 79310 NE
## 650 207552 W
## 1646 53902 S
## 299 199724 S
## 453 73023 S
## 2360 1204465 NC
## 654 211039 W
## 2569 496742 S
## 2245 125707 NE
## 407 156805 S
## 84 108046 S
## 2523 208073 S
## 1955 0 NE
## 1960 58963 NE
## 1575 1619482 W
## 3021 81096 S
## 594 219832 NC
## 2908 355360 W
## 1911 1138681 W
## 220 234781 W
## 682 263425 NC
## 1529 262371 S
## 351 79270 S
## 827 32318 NC
## 1790 335849 NC
## 2188 139483 W
## 1599 912154 W
## 1100 63674 S
## 1156 74484 NE
## 3028 56555 S
## 1124 46110 S
## 1301 210897 NC
## 1554 361003 S
## 1470 414394 NC
## 994 125133 S
## 1502 126613 S
## 88 350402 S
## 174 597766 W
## 859 198680 NC
## 1616 99746 W
## 2684 98449 S
## 2385 1726299 NC
## 2260 20777 NE
## 1857 417698 NC
## 2619 517671 S
## 1405 210829 NC
## 1900 75531 NE
## 1194 27622 NE
## 2708 632622 S
## 2225 129323 NE
## 1687 36975 S
## 857 189136 NC
## 1042 247266 S
## 71 141260 S
## 2281 62740 NE
## 1988 102733 NE
## 1617 889294 W
## 123 102560 S
## 382 57074 S
## 436 45624 S
## 917 485656 NC
## 2662 587316 S
## 1523 98816 S
## 448 168051 S
## 2123 242097 S
## 2267 6197 NE
## 1416 174314 NC
## 605 364172 NC
## 2201 380464 W
## 2982 343115 NC
## 1905 3112271 W
## 2335 724776 NC
## 3051 9335 S
## 3069 1344561 W
## 2263 81479 NE
## 1898 98256 NE
## 1376 304032 NC
## 2358 373787 NC
## 40 191810 S
## 187 686876 W
## 138 115019 S
## 970 111913 S
## 2550 780925 S
## 333 11738 S
## 903 479903 NC
## 666 78813 W
## 1941 235826 W
## 2208 39559 W
## 835 242777 NC
## 3009 59184 S
## 1375 227156 NC
## 2114 300829 S
## 2939 327185 NC
## 1423 119595 NC
## 2762 484156 W
## 873 592207 NC
## 20 47200 S
## 1473 219042 NC
## 2044 127867 NC
## 1498 88522 S
## 2466 105519 S
## 311 57179 S
## 3047 32093 S
## 918 537914 NC
## 2980 133197 NC
## 2657 658204 S
## 550 275319 NC
## 1990 112334 NE
## 2330 138573 S
## 118 173861 S
## 642 140701 W
## 604 293266 NC
## 1500 126352 S
## 340 56704 S
## 97 223889 S
## 557 401625 NC
## 2206 167880 W
## 2344 688081 NC
## 3025 40837 S
## 400 37973 S
## 197 600073 W
## 2479 150309 S
## 367 49397 S
## 1285 22488 NC
## 2377 661474 NC
## 113 143104 S
## 1036 119218 S
## 1704 130879 S
## 2062 74037 NC
## 2399 1406379 NC
## 1086 123655 S
## 1889 61748 NE
## 1084 44548 S
## 1476 438142 NC
## 405 3046 S
## 2591 461249 S
## 526 239800 NC
## 898 603755 NC
## 2484 55097 S
## 2277 67388 NE
## 726 402310 NC
## 2905 9603 W
## 2323 93970 S
## 212 759649 W
## 986 78966 S
## 707 258014 NC
## 1745 396154 NC
## 1965 192116 NE
## 1562 114083 S
## 452 11969 S
## 294 304680 NE
## 1549 110124 S
## 2797 112944 S
## 2011 174627 NE
## 2068 269163 NC
## 709 180675 NC
## 875 380969 NC
## 2594 32436 S
## 969 90033 S
## 1675 13310 S
## 2492 123792 S
## 1223 1402 NC
## 2268 90065 NE
## 2275 63159 NE
## 1018 159966 S
## 1759 600845 NC
## 1392 325796 NC
## 2429 236912 S
## 1608 675569 W
## 1994 92683 NE
## 715 385560 NC
## 250 103470 W
## 3004 73407 S
## 78 30196 S
## 1412 285496 NC
## 1295 113422 NC
## 185 725118 W
## 586 314887 NC
## 463 44599 S
## 961 471658 NC
## 1601 248215 W
## 571 272831 NC
## 1262 224030 NC
## 330 106721 S
## 343 716542 S
## 2609 2234262 S
## 1085 61145 S
## 1259 89173 NC
## 1792 428769 NC
## 1159 31583 NE
## 2233 39412 NE
## 1746 688468 NC
## 1320 360500 NC
## 2395 367239 NC
## 2172 1051384 S
## 861 162244 NC
## 2647 491015 S
## 198 1287057 W
## 1374 267066 NC
## 939 700869 NC
## 1796 357067 NC
## 2148 236766 S
## 566 332358 NC
## 2059 227327 NC
## 2974 78772 NC
## 1770 841736 NC
## 302 227202 S
## 668 464834 NC
## 820 71596 NC
## 2637 527837 S
## 2341 279202 NC
## 1674 148135 S
## 2888 43987 NE
## 2916 58750 W
## 2155 282659 S
## 2195 530960 W
## 259 420233 W
## 166 452347 W
## 956 450829 NC
## 2051 48050 NC
## 581 349252 NC
## 2514 563993 S
## 2601 356170 S
## 2857 160973 S
## 460 68729 S
## 1051 136869 S
## 2441 44415 S
## 128 404585 S
## 884 222028 NC
## 2473 233312 S
## 2353 1859161 NC
# Specify the overall sample size
(nSRS <- nrow(agpop_sampled0) +
nrow(agpop_sampled1) +
nrow(agpop_sampled2) +
nrow(agpop_sampled3) +
nrow(agpop_sampled4) +
nrow(agpop_sampled5) +
nrow(agpop_sampled6) +
nrow(agpop_sampled7) +
nrow(agpop_sampled8) +
nrow(agpop_sampled9) )
## [1] 3000
# Compute the variances for the 10 SRS samples
# SRS0 variance = 182430448622
(agpop_SRS0variance <- var(agpop_sampled0$ACRES92))
## [1] 182430448622
# SRS1 variance = 224952698097
(agpop_SRS1variance <- var(agpop_sampled1$ACRES92))
## [1] 224952698097
# SRS2 variance = 139476147012
(agpop_SRS2variance <- var(agpop_sampled2$ACRES92))
## [1] 139476147012
# SRS3 variance = 253766693746
(agpop_SRS3variance <- var(agpop_sampled3$ACRES92))
## [1] 253766693746
# SRS4 variance = 314396869049
(agpop_SRS4variance <- var(agpop_sampled4$ACRES92))
## [1] 314396869049
# SRS5 variance = 333860409002
(agpop_SRS5variance <- var(agpop_sampled5$ACRES92))
## [1] 333860409002
# SRS6 variance = 317005336115
(agpop_SRS6variance <- var(agpop_sampled6$ACRES92))
## [1] 317005336115
# SRS7 variance = 130788548393
(agpop_SRS7variance <- var(agpop_sampled7$ACRES92))
## [1] 130788548393
# SRS8 variance = 174062836146
(agpop_SRS8variance <- var(agpop_sampled8$ACRES92))
## [1] 174062836146
# SRS9 variance = 140718991424
(agpop_SRS9variance <- var(agpop_sampled9$ACRES92))
## [1] 1.40719e+11
# Summarize the variances for the 10 SRS samples
SRSname <- c("SRS0","SRS1","SRS2","SRS3","SRS4","SRS5","SRS6","SRS7","SRS8","SRS9")
SRSvariances <- c(format(round(agpop_SRS0variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS1variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS2variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS3variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS4variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS5variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS6variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS7variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS8variance,2),nsmall=2,scientific=FALSE),
format(round(agpop_SRS9variance,2),nsmall=2,scientific=FALSE))
(SRSVarianceSummary <- as.data.frame(cbind(SRSname,SRSvariances)))
## SRSname SRSvariances
## 1 SRS0 182430448622.45
## 2 SRS1 224952698096.95
## 3 SRS2 139476147012.26
## 4 SRS3 253766693746.11
## 5 SRS4 314396869049.06
## 6 SRS5 333860409001.66
## 7 SRS6 317005336115.26
## 8 SRS7 130788548392.68
## 9 SRS8 174062836145.60
## 10 SRS9 140718991424.04
Item 4
##############################################
############ ITEM 4 #############
##############################################
# Construct the population ANOVA table
# from the stratification obtained in 3
##############################################
# Specify the population size
(N <- nrow(agpop_complete))
## [1] 3059
# Specify the population mean for reference
# Population mean = 308582.4
(agpop_mean <- mean(agpop_complete$ACRES92))
## [1] 308582.4
# Specify the population variance for reference
# Population variance = 1.80891e+11
(agpop_variance <- var(agpop_complete$ACRES92))
## [1] 1.80891e+11
# Gather the stratified population information
# Specify the population size per stratum
# North Central region stratum population size = 1052
(N.NCregion <- nrow(NCregion))
## [1] 1052
# North East region stratum population size = 213
(N.NEregion <- nrow(NEregion))
## [1] 213
# South region stratum population size = 1376
(N.Sregion <- nrow(Sregion))
## [1] 1376
# West region stratum population size = 418
(N.Wregion <- nrow(Wregion))
## [1] 418
# Specify the population mean per stratum for reference
# North Central region stratum population mean = 326570.8
(NCregion_mean <- mean(NCregion$ACRES92))
## [1] 326570.8
# North East region stratum population mean = 93600.31
(NEregion_mean <- mean(NEregion$ACRES92))
## [1] 93600.31
# South region stratum population mean = 200009.2
(Sregion_mean <- mean(Sregion$ACRES92))
## [1] 200009.2
# West region stratum population mean = 730266.9
(Wregion_mean <- mean(Wregion$ACRES92))
## [1] 730266.9
# Specify the population variance per stratum for reference
# North Central region stratum population variance = 7.35429e+10
(NCregion_variance <- var(NCregion$ACRES92))
## [1] 73542921422
# North East region stratum population variance = 6.22619e+09
(NEregion_variance <- var(NEregion$ACRES92))
## [1] 6226188633
# South region stratum population variance = 5.96004e+10
(Sregion_variance <- var(Sregion$ACRES92))
## [1] 59600425689
# West region stratum population variance = 6.99922e+11
(Wregion_variance <- var(Wregion$ACRES92))
## [1] 699922245636
# Compute for STR Population SSB
# SSB for the stratified population = 1.00733e+14
(STRPop_SSB <- (N.NCregion * (NCregion_mean-agpop_mean)^2) +
(N.NEregion * (NEregion_mean-agpop_mean)^2) +
(N.Sregion * (Sregion_mean-agpop_mean)^2) +
(N.Wregion * (Wregion_mean-agpop_mean)^2))
## [1] 1.00733e+14
# Compute for STR Population SSW
# SSW for the stratified population = 4.524317e+14
(STRPop_SSW <- ((N.NCregion-1) * NCregion_variance) +
((N.NEregion-1) * NEregion_variance) +
((N.Sregion-1) * Sregion_variance) +
((N.Wregion-1) * Wregion_variance))
## [1] 4.524317e+14
# Compute for STR Population SST
# SST for the stratified population = 5.531647e+14
(STRPop_SST <- (N-1) * agpop_variance)
## [1] 5.531647e+14
# Double check SST using the computed values for SSB and SSW
# SST for the stratified population = 5.531647e+14
(STRPop_SST_Check <- STRPop_SSB + STRPop_SSW)
## [1] 5.531647e+14
# Generate the ANOVA for the Stratified Population
(STRSourceOfVariation <- c("SSB","SSW","SST"))
## [1] "SSB" "SSW" "SST"
(STRDF <- c(H-1,N-H,N-1))
## [1] 3 3055 3058
(STRSumOfSquares <- c(format(round(STRPop_SSB,2),nsmall=2,scientific=FALSE),
format(round(STRPop_SSW,2),nsmall=2,scientific=FALSE),
format(round(STRPop_SST,2),nsmall=2,scientific=FALSE)))
## [1] "100733006012057.16" "452431724156330.06" "553164730168387.19"
(STRAnovaSummary <- as.data.frame(cbind(STRSourceOfVariation,STRDF,STRSumOfSquares)))
## STRSourceOfVariation STRDF STRSumOfSquares
## 1 SSB 3 100733006012057.16
## 2 SSW 3055 452431724156330.06
## 3 SST 3058 553164730168387.19
Item 5
##############################################
############ ITEM 5 #############
##############################################
# set.seed(last 5 digits of your std no + 10)
# obtain a sample of size 21 from the Northeast stratum.
# set.seed(last 5 digits of your std no + 11)
# obtain a sample of size 103 from the NorthCentral stratum.
# set.seed(last 5 digits of your std no + 12)
# obtain a sample of size 135 from the South stratum.
# set.seed(last 5 digits of your std no + 13)
# obtain a sample of size 41 from the West stratum.
##############################################
(seedSTR1 <- seedSRS0+10)
## [1] 89186
(seedSTR2 <- seedSRS0+11)
## [1] 89187
(seedSTR3 <- seedSRS0+12)
## [1] 89188
(seedSTR4 <- seedSRS0+13)
## [1] 89189
# Specify the sample size for each stratum
(n.NEregion <- 21)
## [1] 21
(n.NCregion <- 103)
## [1] 103
(n.Sregion <- 135)
## [1] 135
(n.Wregion <- 41)
## [1] 41
# Specify the overall sample size
(nSTR <- n.NCregion + n.NEregion + n.Sregion + n.Wregion)
## [1] 300
# Generate the sample indices for each stratum
set.seed(seedSTR1)
(sampleindices10.NEregion <- sample(N.NEregion,n.NEregion))
## [1] 125 190 170 23 89 154 50 100 137 194 79 70 110 66 95 3 83
## [18] 81 90 116 52
set.seed(seedSTR2)
(sampleindices11.NCregion <- sample(N.NCregion,n.NCregion))
## [1] 850 309 77 582 887 326 1 623 508 859 522 927 427 770
## [15] 704 81 521 505 899 784 11 246 746 655 85 22 71 502
## [29] 183 118 563 650 862 12 500 734 406 929 553 449 107 281
## [43] 845 999 284 781 203 182 89 793 686 889 1041 906 525 815
## [57] 628 986 257 357 592 204 886 144 660 743 99 83 922 325
## [71] 666 399 907 991 891 739 748 962 703 285 269 175 926 26
## [85] 1047 384 368 109 683 3 430 848 243 323 259 752 167 293
## [99] 20 863 707 332 882
set.seed(seedSTR3)
(sampleindices12.Sregion <- sample(N.Sregion,n.Sregion))
## [1] 679 13 355 73 822 181 1072 241 1040 540 587 606 116 240
## [15] 1357 473 88 1211 155 456 767 813 402 58 1281 43 968 427
## [29] 268 1077 1145 1070 814 463 1233 1030 1058 886 1261 669 615 1027
## [43] 309 981 160 1222 77 1323 327 1279 975 545 200 1156 464 881
## [57] 17 531 734 1374 746 877 4 137 947 782 199 1137 666 303
## [71] 630 730 695 39 163 1361 723 1300 1069 1174 1224 1041 969 44
## [85] 988 223 232 976 1112 1109 9 1324 1132 457 1039 561 1330 1097
## [99] 983 1282 853 618 842 604 790 367 1054 1235 302 702 1159 1215
## [113] 275 1104 70 101 157 224 1352 22 774 502 548 75 786 845
## [127] 659 96 297 1276 234 780 1020 1135 258
set.seed(seedSTR4)
(sampleindices13.Wregion <- sample(N.Wregion,n.Wregion))
## [1] 201 351 243 288 418 203 183 398 248 20 36 402 318 180 49 301 364
## [18] 312 415 267 6 131 123 338 67 395 380 397 140 382 174 26 214 285
## [35] 90 170 136 256 103 146 230
# Generate the actual samples
(NEregion_sampled10 <- NEregion[sampleindices10.NEregion,])
## ACRES92 REGION
## 2010 205954 NE
## 2278 203026 NE
## 2257 39561 NE
## 1164 114805 NE
## 1971 171722 NE
## 2241 106390 NE
## 1882 29606 NE
## 1983 0 NE
## 2223 310672 NE
## 2282 252052 NE
## 1961 188008 NE
## 1902 87638 NE
## 1994 92683 NE
## 1898 98256 NE
## 1978 205105 NE
## 286 86581 NE
## 1965 192116 NE
## 1963 111974 NE
## 1972 45820 NE
## 2001 65323 NE
## 1884 97186 NE
(NCregion_sampled11 <- NCregion[sampleindices11.NCregion,])
## ACRES92 REGION
## 2036 88899 NC
## 877 353371 NC
## 601 229818 NC
## 1387 339372 NC
## 2073 164607 NC
## 894 512728 NC
## 525 328970 NC
## 1428 332910 NC
## 1313 79183 NC
## 2045 28983 NC
## 1327 255453 NC
## 2344 688081 NC
## 1232 277400 NC
## 1813 1069778 NC
## 1747 723816 NC
## 605 364172 NC
## 1326 205031 NC
## 1310 366534 NC
## 2085 253383 NC
## 1827 186806 NC
## 535 341923 NC
## 814 144305 NC
## 1789 270005 NC
## 1460 228936 NC
## 609 331211 NC
## 546 456954 NC
## 595 362109 NC
## 1307 260125 NC
## 751 446750 NC
## 686 377512 NC
## 1368 100774 NC
## 1455 323465 NC
## 2048 113329 NC
## 536 315448 NC
## 1305 221193 NC
## 1777 750913 NC
## 1211 165371 NC
## 2346 236608 NC
## 1358 311849 NC
## 1254 48029 NC
## 675 238906 NC
## 849 160930 NC
## 2031 223216 NC
## 2945 366593 NC
## 852 119318 NC
## 1824 375188 NC
## 771 285730 NC
## 750 141703 NC
## 613 241422 NC
## 1836 330369 NC
## 1729 818893 NC
## 2075 104197 NC
## 2988 207128 NC
## 2092 120519 NC
## 1330 186573 NC
## 1858 314949 NC
## 1433 199292 NC
## 2932 97521 NC
## 825 206885 NC
## 925 409839 NC
## 1397 130358 NC
## 772 165091 NC
## 2072 113892 NC
## 712 203590 NC
## 1465 204171 NC
## 1786 296164 NC
## 623 353683 NC
## 607 353570 NC
## 2339 1026353 NC
## 893 671506 NC
## 1471 165225 NC
## 1204 42572 NC
## 2093 148479 NC
## 2937 386857 NC
## 2077 219037 NC
## 1782 407678 NC
## 1791 296016 NC
## 2379 2076199 NC
## 1746 688468 NC
## 853 144722 NC
## 837 236436 NC
## 743 67998 NC
## 2343 417697 NC
## 550 275319 NC
## 2995 147207 NC
## 952 620144 NC
## 936 451362 NC
## 677 571807 NC
## 1726 594587 NC
## 527 321728 NC
## 1235 438914 NC
## 2034 196759 NC
## 811 305634 NC
## 891 316317 NC
## 827 32318 NC
## 1795 772453 NC
## 735 371936 NC
## 861 162244 NC
## 544 236409 NC
## 2049 245049 NC
## 1750 1048701 NC
## 900 499112 NC
## 2068 269163 NC
(Sregion_sampled12 <- Sregion[sampleindices12.Sregion,])
## ACRES92 REGION
## 1649 162634 S
## 18 61426 S
## 509 53895 S
## 78 30196 S
## 2169 250958 S
## 334 132208 S
## 2594 32436 S
## 395 178861 S
## 2561 493227 S
## 1140 245986 S
## 1501 294547 S
## 1520 80902 S
## 121 79803 S
## 394 10192 S
## 3036 178160 S
## 1073 117768 S
## 93 262021 S
## 2734 1712044 S
## 307 83681 S
## 1056 191002 S
## 2114 300829 S
## 2160 207118 S
## 1002 10919 S
## 63 78176 S
## 2835 167858 S
## 48 199714 S
## 2489 117608 S
## 1027 46321 S
## 422 25376 S
## 2599 463450 S
## 2668 220355 S
## 2592 576468 S
## 2161 282211 S
## 1063 4469 S
## 2786 47010 S
## 2551 123756 S
## 2580 688330 S
## 2407 96550 S
## 2815 136320 S
## 1639 31184 S
## 1529 262371 S
## 2548 451584 S
## 463 44599 S
## 2502 337351 S
## 312 52259 S
## 2745 344667 S
## 82 98919 S
## 3002 73430 S
## 481 95876 S
## 2833 100602 S
## 2496 962576 S
## 1145 116221 S
## 353 59642 S
## 2679 526276 S
## 1064 6158 S
## 2402 62989 S
## 22 138135 S
## 1131 23185 S
## 1704 130879 S
## 3053 35836 S
## 1716 67716 S
## 2332 173188 S
## 9 48022 S
## 142 31190 S
## 2468 37550 S
## 2129 599536 S
## 352 151242 S
## 2660 551148 S
## 1636 93584 S
## 457 120839 S
## 1544 80342 S
## 1700 23140 S
## 1665 112291 S
## 44 201892 S
## 316 369965 S
## 3040 55827 S
## 1693 67491 S
## 2854 24924 S
## 2591 461249 S
## 2697 54580 S
## 2747 484907 S
## 2562 518788 S
## 2490 125092 S
## 49 138437 S
## 2509 408710 S
## 377 213943 S
## 386 8518 S
## 2497 103063 S
## 2635 432379 S
## 2632 576893 S
## 14 109555 S
## 3003 2531 S
## 2655 562612 S
## 1057 112409 S
## 2560 801159 S
## 1175 110699 S
## 3009 59184 S
## 2619 517671 S
## 2504 396508 S
## 2836 21507 S
## 2308 194822 S
## 1532 198955 S
## 2297 32392 S
## 1518 89168 S
## 2137 323534 S
## 967 177858 S
## 2576 749504 S
## 2788 81768 S
## 456 8003 S
## 1672 56693 S
## 2682 402011 S
## 2738 863384 S
## 429 36074 S
## 2626 595420 S
## 75 92708 S
## 106 367969 S
## 309 31693 S
## 378 166511 S
## 3031 54622 S
## 27 196859 S
## 2121 336285 S
## 1102 147826 S
## 1148 126839 S
## 80 246184 S
## 2133 268038 S
## 2300 109652 S
## 1629 70697 S
## 101 168848 S
## 451 32657 S
## 2830 195476 S
## 388 52651 S
## 2127 419760 S
## 2541 543750 S
## 2658 487573 S
## 412 43775 S
(Wregion_sampled13 <- Wregion[sampleindices13.Wregion,])
## ACRES92 REGION
## 1583 2232575 W
## 2772 1294703 W
## 1625 1454669 W
## 1948 624606 W
## 3078 1484583 W
## 1585 699409 W
## 663 4428 W
## 3058 2704163 W
## 1907 2085387 W
## 162 229365 W
## 178 775829 W
## 3062 2415873 W
## 2206 167880 W
## 660 752032 W
## 191 72471 W
## 2189 402023 W
## 2897 35678 W
## 2200 34292 W
## 3075 62307 W
## 1926 770155 W
## 148 5785707 W
## 274 576397 W
## 266 119287 W
## 2759 434183 W
## 209 647446 W
## 2928 1639965 W
## 2913 1291118 W
## 3057 441321 W
## 521 926607 W
## 2915 55360 W
## 654 211039 W
## 168 450236 W
## 1596 50220 W
## 1944 48968 W
## 232 330826 W
## 650 207552 W
## 280 104010 W
## 1915 843401 W
## 246 177333 W
## 626 325338 W
## 1612 1414415 W
Item 6.A
##############################################
############ ITEM 6.A ##############
##############################################
# Construct the sample ANOVA table
# using ybar_SRS for ybar_mu
# NOTE : use 3.a as reference
# ( SRS sample using random seed = 89176 )
##############################################
# Specify the sample mean per SRS sample for reference
# SRS0 sample mean = 331645.1
(agpop_sampled0_samplemean <- mean((agpop_sampled0$ACRES92)))
## [1] 331645.1
# SRS1 sample mean = 315117.4
(agpop_sampled1_samplemean <- mean((agpop_sampled1$ACRES92)))
## [1] 315117.4
# SRS2 sample mean = 312988.4
(agpop_sampled2_samplemean <- mean((agpop_sampled2$ACRES92)))
## [1] 312988.4
# SRS3 sample mean = 349607.7
(agpop_sampled3_samplemean <- mean((agpop_sampled3$ACRES92)))
## [1] 349607.7
# SRS4 sample mean = 334447.2
(agpop_sampled4_samplemean <- mean((agpop_sampled4$ACRES92)))
## [1] 334447.2
# SRS5 sample mean = 353240.2
(agpop_sampled5_samplemean <- mean((agpop_sampled5$ACRES92)))
## [1] 353240.2
# SRS6 sample mean = 324067.9
(agpop_sampled6_samplemean <- mean((agpop_sampled6$ACRES92)))
## [1] 324067.9
# SRS7 sample mean = 308757.7
(agpop_sampled7_samplemean <- mean((agpop_sampled7$ACRES92)))
## [1] 308757.7
# SRS8 sample mean = 311701.3
(agpop_sampled8_samplemean <- mean((agpop_sampled8$ACRES92)))
## [1] 311701.3
# SRS9 sample mean = 309142.5
(agpop_sampled9_samplemean <- mean((agpop_sampled9$ACRES92)))
## [1] 309142.5
# Specify the sample variance per SRS for reference
# SRS0 sample variance = 182430448622
(agpop_sampled0_samplevariance <- var((agpop_sampled0$ACRES92)))
## [1] 182430448622
# SRS1 sample variance = 224952698097
(agpop_sampled1_samplevariance <- var((agpop_sampled1$ACRES92)))
## [1] 224952698097
# SRS2 sample variance = 139476147012
(agpop_sampled2_samplevariance <- var((agpop_sampled2$ACRES92)))
## [1] 139476147012
# SRS3 sample variance = 253766693746
(agpop_sampled3_samplevariance <- var((agpop_sampled3$ACRES92)))
## [1] 253766693746
# SRS4 sample variance = 314396869049
(agpop_sampled4_samplevariance <- var((agpop_sampled4$ACRES92)))
## [1] 314396869049
# SRS5 sample variance = 333860409002
(agpop_sampled5_samplevariance <- var((agpop_sampled5$ACRES92)))
## [1] 333860409002
# SRS6 sample variance = 317005336115
(agpop_sampled6_samplevariance <- var((agpop_sampled6$ACRES92)))
## [1] 317005336115
# SRS7 sample variance = 130788548393
(agpop_sampled7_samplevariance <- var((agpop_sampled7$ACRES92)))
## [1] 130788548393
# SRS8 sample variance = 174062836146
(agpop_sampled8_samplevariance <- var((agpop_sampled8$ACRES92)))
## [1] 174062836146
# SRS9 sample variance = 1.40719e+11
(agpop_sampled9_samplevariance <- var((agpop_sampled9$ACRES92)))
## [1] 1.40719e+11
# Create Sum of Squares function
SSFunction <- function(rowdata,target){
rowdata_count <- 1
rowdata_sqddiff <- 0
rowdata_length <- nrow(rowdata)
while (rowdata_count < rowdata_length + 1 ) {
rowdata_unit <- rowdata[rowdata_count,]
rowdata_sqddiff <- rowdata_sqddiff + (rowdata_unit-target)^2
rowdata_count = rowdata_count + 1
}
return(rowdata_sqddiff)
}
# Compute for SRS Sample SSB
(SRSSample_SSB <- (n * (agpop_sampled0_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled1_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled2_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled3_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled4_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled5_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled6_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled7_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled8_samplemean-agpop_sampled0_samplemean)^2) +
(n * (agpop_sampled9_samplemean-agpop_sampled0_samplemean)^2) )
## [1] 871039831387
# Compute for STR Sample SSW
(SRSSample_SSW_0 <- SSFunction(as.data.frame(agpop_sampled0$ACRES92),agpop_sampled0_samplemean))
## [1] 5.45467e+13
(SRSSample_SSW_1 <- SSFunction(as.data.frame(agpop_sampled1$ACRES92),agpop_sampled1_samplemean))
## [1] 6.726086e+13
(SRSSample_SSW_2 <- SSFunction(as.data.frame(agpop_sampled2$ACRES92),agpop_sampled2_samplemean))
## [1] 4.170337e+13
(SRSSample_SSW_3 <- SSFunction(as.data.frame(agpop_sampled3$ACRES92),agpop_sampled3_samplemean))
## [1] 7.587624e+13
(SRSSample_SSW_4 <- SSFunction(as.data.frame(agpop_sampled4$ACRES92),agpop_sampled4_samplemean))
## [1] 9.400466e+13
(SRSSample_SSW_5 <- SSFunction(as.data.frame(agpop_sampled5$ACRES92),agpop_sampled5_samplemean))
## [1] 9.982426e+13
(SRSSample_SSW_6 <- SSFunction(as.data.frame(agpop_sampled6$ACRES92),agpop_sampled6_samplemean))
## [1] 9.47846e+13
(SRSSample_SSW_7 <- SSFunction(as.data.frame(agpop_sampled7$ACRES92),agpop_sampled7_samplemean))
## [1] 3.910578e+13
(SRSSample_SSW_8 <- SSFunction(as.data.frame(agpop_sampled8$ACRES92),agpop_sampled8_samplemean))
## [1] 5.204479e+13
(SRSSample_SSW_9 <- SSFunction(as.data.frame(agpop_sampled9$ACRES92),agpop_sampled9_samplemean))
## [1] 4.207498e+13
(SRSSample_SSW <- SRSSample_SSW_0 +
SRSSample_SSW_1 +
SRSSample_SSW_2 +
SRSSample_SSW_3 +
SRSSample_SSW_4 +
SRSSample_SSW_5 +
SRSSample_SSW_6 +
SRSSample_SSW_7 +
SRSSample_SSW_8 +
SRSSample_SSW_9 )
## [1] 6.612262e+14
# Compute for STR Sample SST
(SRSSample_SST_0 <- SSFunction(as.data.frame(agpop_sampled0$ACRES92),agpop_sampled0_samplemean))
## [1] 5.45467e+13
(SRSSample_SST_1 <- SSFunction(as.data.frame(agpop_sampled1$ACRES92),agpop_sampled0_samplemean))
## [1] 6.734281e+13
(SRSSample_SST_2 <- SSFunction(as.data.frame(agpop_sampled2$ACRES92),agpop_sampled0_samplemean))
## [1] 4.180779e+13
(SRSSample_SST_3 <- SSFunction(as.data.frame(agpop_sampled3$ACRES92),agpop_sampled0_samplemean))
## [1] 7.597304e+13
(SRSSample_SST_4 <- SSFunction(as.data.frame(agpop_sampled4$ACRES92),agpop_sampled0_samplemean))
## [1] 9.400702e+13
(SRSSample_SST_5 <- SSFunction(as.data.frame(agpop_sampled5$ACRES92),agpop_sampled0_samplemean))
## [1] 9.996417e+13
(SRSSample_SST_6 <- SSFunction(as.data.frame(agpop_sampled6$ACRES92),agpop_sampled0_samplemean))
## [1] 9.480182e+13
(SRSSample_SST_7 <- SSFunction(as.data.frame(agpop_sampled7$ACRES92),agpop_sampled0_samplemean))
## [1] 3.926293e+13
(SRSSample_SST_8 <- SSFunction(as.data.frame(agpop_sampled8$ACRES92),agpop_sampled0_samplemean))
## [1] 5.216412e+13
(SRSSample_SST_9 <- SSFunction(as.data.frame(agpop_sampled9$ACRES92),agpop_sampled0_samplemean))
## [1] 4.222689e+13
(SRSSample_SST <- SRSSample_SST_0 +
SRSSample_SST_1 +
SRSSample_SST_2 +
SRSSample_SST_3 +
SRSSample_SST_4 +
SRSSample_SST_5 +
SRSSample_SST_6 +
SRSSample_SST_7 +
SRSSample_SST_8 +
SRSSample_SST_9 )
## [1] 6.620973e+14
# SSB for the SRS sample = 871039831387
SRSSample_SSB
## [1] 871039831387
# SSW for the SRS sample = 6.612262e+14
SRSSample_SSW
## [1] 6.612262e+14
# SST for the SRS sample = 6.620973e+14
SRSSample_SST
## [1] 6.620973e+14
# Double check Sample SST using the computed values for SSB and SSW
# SST for the SRS sample = 7.425776e+13
(SRSSample_SST_Check <- SRSSample_SSB + SRSSample_SSW)
## [1] 6.620973e+14
# Generate the ANOVA for the SRS Samples
(SRSSampleSourceOfVariation <- c("SSB","SSW","SST"))
## [1] "SSB" "SSW" "SST"
(SRSSampleDF <- c(H-1,nSRS-H,nSRS-1))
## [1] 3 2996 2999
(SRSSampleSumOfSquares <- c(format(round(SRSSample_SSB,2),nsmall=2,scientific=FALSE),
format(round(SRSSample_SSW,2),nsmall=2,scientific=FALSE),
format(round(SRSSample_SST,2),nsmall=2,scientific=FALSE)))
## [1] "871039831386.56" "661226234304214.75" "662097274135601.50"
(SRSSampleAnovaSummary <- as.data.frame(cbind(SRSSampleSourceOfVariation,SRSSampleDF,SRSSampleSumOfSquares)))
## SRSSampleSourceOfVariation SRSSampleDF SRSSampleSumOfSquares
## 1 SSB 3 871039831386.56
## 2 SSW 2996 661226234304214.75
## 3 SST 2999 662097274135601.50
Item 6.B
##############################################
############ ITEM 6.B ##############
##############################################
# Construct the sample ANOVA table
# using ybar_STR for ybar_mu
##############################################
# Specify the sample mean per stratum for reference
# North East region stratum sample mean = 128784.7
(NEregion_samplemean <- mean(NEregion_sampled10$ACRES92))
## [1] 128784.7
# North Central region stratum sample mean = 334934
(NCregion_samplemean <- mean(NCregion_sampled11$ACRES92))
## [1] 334934
# South region stratum sample mean = 217776
(Sregion_samplemean <- mean(Sregion_sampled12$ACRES92))
## [1] 217776
# West region stratum sample mean = 838613.6
(Wregion_samplemean <- mean(Wregion_sampled13$ACRES92))
## [1] 838613.6
# Compute the stratified sample mean ( equation 3.2 )
# Stratified sample mean = 336705.5
(agpop_sampled_stratified_mean <- (N.NCregion/N)*NCregion_samplemean +
(N.NEregion/N)*NEregion_samplemean +
(N.Sregion/N)*Sregion_samplemean +
(N.Wregion/N)*Wregion_samplemean )
## [1] 336705.5
# Create Sum of Squares function
SSFunction <- function(rowdata,target){
rowdata_count <- 1
rowdata_sqddiff <- 0
rowdata_length <- nrow(rowdata)
while (rowdata_count < rowdata_length + 1 ) {
rowdata_unit <- rowdata[rowdata_count,]
rowdata_sqddiff <- rowdata_sqddiff + (rowdata_unit-target)^2
rowdata_count = rowdata_count + 1
}
return(rowdata_sqddiff)
}
# Compute for STR Sample SSB
(STRSample_SSB <- (n.NCregion * (NCregion_samplemean-agpop_sampled_stratified_mean)^2) +
(n.NEregion * (NEregion_samplemean-agpop_sampled_stratified_mean)^2) +
(n.Sregion * (Sregion_samplemean-agpop_sampled_stratified_mean)^2) +
(n.Wregion * (Wregion_samplemean-agpop_sampled_stratified_mean)^2) )
## [1] 1.314603e+13
# Compute for STR Sample SSW
(STRSample_SSW_NE <- SSFunction(as.data.frame(NEregion_sampled10$ACRES92),NEregion_samplemean))
## [1] 127912221885
(STRSample_SSW_NC <- SSFunction(as.data.frame(NCregion_sampled11$ACRES92),NCregion_samplemean))
## [1] 7.643786e+12
(STRSample_SSW_S <- SSFunction(as.data.frame(Sregion_sampled12$ACRES92),Sregion_samplemean))
## [1] 7.695654e+12
(STRSample_SSW_W <- SSFunction(as.data.frame(Wregion_sampled13$ACRES92),Wregion_samplemean))
## [1] 4.564438e+13
(STRSample_SSW <- STRSample_SSW_NE + STRSample_SSW_NC + STRSample_SSW_S + STRSample_SSW_W)
## [1] 6.111174e+13
# Compute for STR Sample SST
(STRSample_SST_NE <- SSFunction(as.data.frame(NEregion_sampled10$ACRES92),agpop_sampled_stratified_mean))
## [1] 1.035764e+12
(STRSample_SST_NC <- SSFunction(as.data.frame(NCregion_sampled11$ACRES92),agpop_sampled_stratified_mean))
## [1] 7.644109e+12
(STRSample_SST_S <- SSFunction(as.data.frame(Sregion_sampled12$ACRES92),agpop_sampled_stratified_mean))
## [1] 9.605122e+12
(STRSample_SST_W <- SSFunction(as.data.frame(Wregion_sampled13$ACRES92),agpop_sampled_stratified_mean))
## [1] 5.597277e+13
(STRSample_SST <- STRSample_SST_NC + STRSample_SST_NE + STRSample_SST_S + STRSample_SST_W)
## [1] 7.425776e+13
# SSB for the stratified sample = 1.314603e+13
STRSample_SSB
## [1] 1.314603e+13
# SSW for the stratified sample = 6.111174e+13
STRSample_SSW
## [1] 6.111174e+13
# SST for the stratified sample = 7.425776e+13
STRSample_SST
## [1] 7.425776e+13
# Double check Sample SST using the computed values for SSB and SSW
# SST for the stratified sample = 7.425776e+13
(STRSample_SST_Check <- STRSample_SSB + STRSample_SSW)
## [1] 7.425776e+13
# Generate the ANOVA for the Stratified Samples
(STRSampleSourceOfVariation <- c("SSB","SSW","SST"))
## [1] "SSB" "SSW" "SST"
(STRSampleDF <- c(H-1,n-H,n-1))
## [1] 3 296 299
(STRSampleSumOfSquares <- c(format(round(STRSample_SSB,2),nsmall=2,scientific=FALSE),
format(round(STRSample_SSW,2),nsmall=2,scientific=FALSE),
format(round(STRSample_SST,2),nsmall=2,scientific=FALSE)))
## [1] "13146026098979.40" "61111736466354.48" "74257762565333.86"
(STRSampleAnovaSummary <- as.data.frame(cbind(STRSampleSourceOfVariation,STRSampleDF,STRSampleSumOfSquares)))
## STRSampleSourceOfVariation STRSampleDF STRSampleSumOfSquares
## 1 SSB 3 13146026098979.40
## 2 SSW 296 61111736466354.48
## 3 SST 299 74257762565333.86