Knowledge (XXG)

Lexis ratio

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as a measure which seeks to evaluate differences between the statistical properties of random mechanisms where the outcome is two-valued — for example "success" or "failure", "win" or "lose". The idea is that the probability of success might vary between different sets of trials in different
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This measure compares the between-set variance of the sample proportions (evaluated for each set) with what the variance should be if there were no difference between in the true proportions of success across the different sets. Thus the measure is used to evaluate how data compares to a
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If the Lexis ratio is significantly below 1, the sampling is referred to as Poissonian (or subnormal); it is equal to 1 the sampling is referred to as Bernoullian (or normal); and if it is above 1 it is referred to as Lexian (or supranormal).
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This ratio ( Q ) is a measure that can be used to distinguish between three types of variation in sampling for attributes: Bernoullian, Lexian and Poissonian. The Lexis ratio is sometimes also referred to as
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is the variance computed from the expected Bernoulli distribution on the basis of the overall average proportion of success. Trials where
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is the variance computed from the expected Bernoulli distribution on the basis of the overall average proportion of success.
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introduced this statistic to test the then commonly held assumption that sampling data could be regarded as homogeneous.
517: 968: 963: 691: 441: 34: 604: 160: 504:() is the variance. The formula for the variance is approximate and holds only for large values of 25:
situations. This ratio is not much used currently having been largely replaced by the use of the
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differ the sampling is said to be Lexian and the dispersion is said to be supranormal.
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Lexis W (1877) Zur Theorie Der Massenerscheinungen in Der Menschlichen Gesellschaft.
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derived from the observed proportions of success in sets in "Lexis trials" and
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derived from the observed proportions of success in sets in "Lexis trials" and
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and these samples have the proportion of the attribute being examined of
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Chuprov showed in 1922 that in the case of statistical homogeneity
381:{\displaystyle Q={\frac {\sum {n_{i}(p_{i}-p)^{2}}}{(k-1)p(1-p)}}} 908:
Lexian sampling occurs in sampling from non homogenous strata.
653:) be constant and let the actual probability of success in the 116:{\displaystyle L^{2}=Q^{2}={\frac {s^{2}}{\sigma _{0}^{2}}}.} 898:
are equal the sampling is said to be Bernoullian; where the
864:{\displaystyle var(successes)=np(1-p)+n(n-1)var(p_{i})} 641:
A closely related concept is the Lexis variation. Let
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be drawn at random. Let the probability of success (
863: 728: 625: 589: 559:{\displaystyle Q={\frac {s^{2}}{\sigma _{0}^{2}}}} 558: 486: 425: 380: 193:falls significantly above or below 1 are known as 181: 145: 115: 8: 729:{\displaystyle p={\frac {1}{k}}\sum {p_{i}}} 739:The variance in the number of successes is 29:in testing for the homogeneity of samples. 852: 747: 719: 714: 701: 693: 617: 612: 606: 586: 580: 574: 548: 543: 533: 527: 519: 466: 443: 403: 333: 317: 304: 299: 293: 285: 173: 168: 162: 137: 131: 102: 97: 87: 81: 72: 59: 53: 928: 487:{\displaystyle var(Q)={\frac {2}{n-1}}} 277:respectively. Then the Lexis ratio is 7: 681:The average probability of success ( 14: 626:{\displaystyle \sigma _{0}^{2}} 182:{\displaystyle \sigma _{0}^{2}} 949:Overdispersion § Binomial 858: 845: 833: 821: 812: 800: 788: 758: 460: 454: 414: 408: 372: 360: 354: 342: 330: 310: 1: 511:An alternative definition is 33:fixed-probability-of-success 985: 500:() is the expectation and 881:) is the variance of the 590:{\displaystyle s^{2}\,} 865: 730: 627: 591: 560: 488: 427: 426:{\displaystyle E(Q)=1} 382: 183: 147: 117: 35:Bernoulli distribution 866: 731: 645:samples each of size 628: 592: 561: 489: 428: 383: 184: 148: 146:{\displaystyle s^{2}} 118: 746: 692: 605: 573: 518: 442: 402: 284: 161: 130: 52: 622: 553: 178: 107: 969:Statistical ratios 964:Summary statistics 861: 726: 623: 608: 597:is the (weighted) 587: 556: 539: 484: 423: 378: 179: 164: 153:is the (weighted) 143: 113: 93: 709: 554: 482: 376: 108: 976: 936: 933: 870: 868: 867: 862: 857: 856: 735: 733: 732: 727: 725: 724: 723: 710: 702: 632: 630: 629: 624: 621: 616: 596: 594: 593: 588: 585: 584: 565: 563: 562: 557: 555: 552: 547: 538: 537: 528: 493: 491: 490: 485: 483: 481: 467: 432: 430: 429: 424: 387: 385: 384: 379: 377: 375: 340: 339: 338: 337: 322: 321: 309: 308: 294: 221:samples of size 188: 186: 185: 180: 177: 172: 152: 150: 149: 144: 142: 141: 122: 120: 119: 114: 109: 106: 101: 92: 91: 82: 77: 76: 64: 63: 27:chi-squared test 984: 983: 979: 978: 977: 975: 974: 973: 954: 953: 945: 940: 939: 934: 930: 925: 914: 904: 897: 887: 880: 848: 744: 743: 715: 690: 689: 677: 670: 663: 639: 637:Lexis variation 603: 602: 599:sample variance 576: 571: 570: 529: 516: 515: 471: 440: 439: 400: 399: 341: 329: 313: 300: 295: 282: 281: 276: 269: 262: 255: 248: 241: 234: 227: 215: 159: 158: 155:sample variance 133: 128: 127: 83: 68: 55: 50: 49: 12: 11: 5: 982: 980: 972: 971: 966: 956: 955: 952: 951: 944: 941: 938: 937: 927: 926: 924: 921: 913: 910: 902: 895: 885: 878: 872: 871: 860: 855: 851: 847: 844: 841: 838: 835: 832: 829: 826: 823: 820: 817: 814: 811: 808: 805: 802: 799: 796: 793: 790: 787: 784: 781: 778: 775: 772: 769: 766: 763: 760: 757: 754: 751: 737: 736: 722: 718: 713: 708: 705: 700: 697: 675: 668: 661: 638: 635: 620: 615: 611: 583: 579: 567: 566: 551: 546: 542: 536: 532: 526: 523: 480: 477: 474: 470: 465: 462: 459: 456: 453: 450: 447: 434: 433: 422: 419: 416: 413: 410: 407: 389: 388: 374: 371: 368: 365: 362: 359: 356: 353: 350: 347: 344: 336: 332: 328: 325: 320: 316: 312: 307: 303: 298: 292: 289: 274: 267: 260: 253: 246: 239: 232: 225: 214: 211: 201:respectively. 176: 171: 167: 140: 136: 124: 123: 112: 105: 100: 96: 90: 86: 80: 75: 71: 67: 62: 58: 13: 10: 9: 6: 4: 3: 2: 981: 970: 967: 965: 962: 961: 959: 950: 947: 946: 942: 932: 929: 922: 920: 918: 917:Wilhelm Lexis 911: 909: 906: 901: 894: 889: 884: 877: 853: 849: 842: 839: 836: 830: 827: 824: 818: 815: 809: 806: 803: 797: 794: 791: 785: 782: 779: 776: 773: 770: 767: 764: 761: 755: 752: 749: 742: 741: 740: 720: 716: 711: 706: 703: 698: 695: 688: 687: 686: 684: 679: 674: 667: 660: 656: 652: 648: 644: 636: 634: 618: 613: 609: 600: 581: 577: 549: 544: 540: 534: 530: 524: 521: 514: 513: 512: 509: 507: 503: 499: 494: 478: 475: 472: 468: 463: 457: 451: 448: 445: 437: 420: 417: 411: 405: 398: 397: 396: 393: 369: 366: 363: 357: 351: 348: 345: 334: 326: 323: 318: 314: 305: 301: 296: 290: 287: 280: 279: 278: 273: 266: 259: 252: 245: 238: 231: 224: 220: 217:Let there be 212: 210: 208: 202: 200: 196: 192: 174: 169: 165: 156: 138: 134: 110: 103: 98: 94: 88: 84: 78: 73: 69: 65: 60: 56: 48: 47: 46: 44: 40: 36: 30: 28: 23: 19: 931: 915: 907: 899: 892: 890: 882: 875: 873: 738: 682: 680: 672: 665: 658: 654: 650: 646: 642: 640: 568: 510: 505: 501: 497: 495: 438: 435: 394: 390: 271: 264: 257: 250: 243: 236: 229: 222: 218: 216: 206: 203: 198: 194: 190: 125: 42: 38: 31: 17: 15: 891:If all the 874:where var( 195:supernormal 20:is used in 18:Lexis ratio 958:Categories 923:References 657:sample be 213:Definition 199:subnormal, 22:statistics 828:− 807:− 712:∑ 610:σ 541:σ 476:− 367:− 349:− 324:− 297:∑ 166:σ 95:σ 943:See also 671:, ... , 242:, ... , 45:, where 912:History 270:, ..., 496:where 126:Where 685:) is 569:here 436:and 197:and 16:The 502:var 41:or 960:: 888:. 678:. 664:, 508:. 263:, 256:, 235:, 228:, 209:. 903:i 900:p 896:i 893:p 886:i 883:p 879:i 876:p 859:) 854:i 850:p 846:( 843:r 840:a 837:v 834:) 831:1 825:n 822:( 819:n 816:+ 813:) 810:p 804:1 801:( 798:p 795:n 792:= 789:) 786:s 783:e 780:s 777:s 774:e 771:c 768:c 765:u 762:s 759:( 756:r 753:a 750:v 721:i 717:p 707:k 704:1 699:= 696:p 683:p 676:k 673:p 669:2 666:p 662:1 659:p 655:k 651:p 647:n 643:k 619:2 614:0 582:2 578:s 550:2 545:0 535:2 531:s 525:= 522:Q 506:n 498:E 479:1 473:n 469:2 464:= 461:) 458:Q 455:( 452:r 449:a 446:v 421:1 418:= 415:) 412:Q 409:( 406:E 373:) 370:p 364:1 361:( 358:p 355:) 352:1 346:k 343:( 335:2 331:) 327:p 319:i 315:p 311:( 306:i 302:n 291:= 288:Q 275:k 272:p 268:3 265:p 261:2 258:p 254:1 251:p 247:k 244:n 240:3 237:n 233:3 230:n 226:1 223:n 219:k 207:L 191:L 175:2 170:0 139:2 135:s 111:. 104:2 99:0 89:2 85:s 79:= 74:2 70:Q 66:= 61:2 57:L 43:Q 39:L

Index

statistics
chi-squared test
Bernoulli distribution
sample variance
sample variance
Wilhelm Lexis
Overdispersion § Binomial
Categories
Summary statistics
Statistical ratios

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