Knowledge (XXG)

Weight function

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is a mathematical device used when performing a sum, integral, or average to give some elements more "weight" or influence on the result than other elements in the same set. The result of this application of a weight function is a
2396: 2016: 717: 1149: 2285: 1527: 608: 2201: 2165: 987:. More generally, the expected value of a function of a random variable is the probability-weighted average of the values the function takes on for each possible value of the random variable. 1609: 361: 98: 1741: 939: 208: 2483: 1674: 432: 878: 1296: 2030: 1094: 520: 314: 1905: 46:. Weight functions can be employed in both discrete and continuous settings. They can be used to construct systems of calculus called "weighted calculus" and "meta-calculus". 1404: 822: 1804: 1345: 1933: 1836: 1424: 1368: 167: 973: 787: 1563: 1482: 1764: 1166: 1044: 760: 262: 242: 124: 1459: 2296: 1944: 626: 1006:
function is estimated, this function being a weighted average of the current and various lagged independent variable values. Similarly, a
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of a random variable is the weighted average of the possible values it might take on, with the weights being the respective
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specifies an evolving variable as a weighted average of current and various lagged values of a random variable.
179: 886: 1631: 377: 2540: 2116:{\displaystyle {\frac {\displaystyle \int _{\Omega }f(x)\,w(x)\,dx}{\displaystyle \int _{\Omega }w(x)\,dx}}} 1272: 828: 2402: 1427: 1053: 477: 271: 2448: 2423: 1859: 1616: 1533: 1308: 438: 215: 173:
situation in which all elements have equal weight. One can then apply this weight to various concepts.
43: 39: 1380: 2406: 1007: 999: 795: 101: 2443: 368: 2438: 995: 944: 2512: 2489: 1773: 1314: 991: 739: 104: 1918: 1821: 1409: 1353: 824:, the best estimate of the signal is obtained by averaging all the measurements with weight 137: 1485: 618: 31: 951: 765: 2463: 1539: 1375: 1258:{\displaystyle {\frac {\sum _{i=1}^{n}w_{i}{\boldsymbol {x}}_{i}}{\sum _{i=1}^{n}w_{i}}},} 1003: 1464: 2418: 1749: 1158: 1029: 980: 745: 247: 227: 109: 1432: 2529: 2433: 2428: 2204: 614: 1612: 984: 882:
and the resulting variance is smaller than each of the independent measurements
457: 211: 1530: 735: 530: 449: 127: 35: 2391:{\displaystyle {\langle f,g\rangle }_{w}:=\int _{\Omega }f(x)g(x)\ w(x)\ dx.} 2458: 1023: 221: 131: 2011:{\displaystyle {\frac {1}{\mathrm {vol} (\Omega )}}\int _{\Omega }f(x)\ dx} 998:
is assumed to be affected by both current and lagged (past) values of the
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method weights the difference between fit and data using the same weights
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has finite non-zero weighted volume, then we can replace the unweighted
1936: 1566: 538: 1839: 1371: 1097: 712:{\displaystyle {\frac {\sum _{a\in A}f(a)w(a)}{\sum _{a\in A}w(a)}}.} 1144:{\displaystyle {\boldsymbol {x}}_{1},\dotsc ,{\boldsymbol {x}}_{n}} 1154: 1047: 2485:
The First Systems of Weighted Differential and Integral Calculus
2280:{\displaystyle \langle f,g\rangle :=\int _{\Omega }f(x)g(x)\ dx} 534: 1100:
is now interpreted in the physical sense) and locations
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are two functions, one can generalize the unweighted
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which is also the weighted average of the positions
1522:{\displaystyle w\colon \Omega \to \mathbb {R} ^{+}} 2390: 2279: 2195: 2159: 2115: 2010: 1927: 1899: 1830: 1798: 1758: 1735: 1668: 1603: 1557: 1521: 1476: 1453: 1418: 1398: 1362: 1339: 1307:In the continuous setting, a weight is a positive 1290: 1257: 1143: 1088: 1038: 967: 933: 872: 816: 781: 754: 711: 603:{\displaystyle {\frac {1}{|A|}}\sum _{a\in A}f(a)} 602: 514: 437:One common application of weighted sums arises in 426: 355: 308: 256: 236: 202: 161: 118: 92: 2196:{\displaystyle g\colon \Omega \to {\mathbb {R} }} 2160:{\displaystyle f\colon \Omega \to {\mathbb {R} }} 1604:{\displaystyle f\colon \Omega \to \mathbb {R} } 18:Construct related to weighted sums and averages 2482:Jane Grossman, Michael Grossman, Robert Katz. 533:non-empty set, one can replace the unweighted 42:, and are closely related to the concept of a 356:{\displaystyle w\colon A\to \mathbb {R} ^{+}} 93:{\displaystyle w\colon A\to \mathbb {R} ^{+}} 8: 2314: 2302: 2228: 2216: 1736:{\displaystyle \int _{\Omega }f(x)w(x)\,dx} 59:In the discrete setting, a weight function 2331: 2318: 2301: 2298: 2238: 2214: 2188: 2187: 2186: 2172: 2152: 2151: 2150: 2136: 2103: 2085: 2072: 2059: 2041: 2034: 2032: 1981: 1954: 1948: 1946: 1920: 1867: 1861: 1823: 1806:in order for this integral to be finite. 1789: 1775: 1751: 1726: 1696: 1690: 1639: 1633: 1597: 1596: 1582: 1541: 1513: 1509: 1508: 1493: 1466: 1434: 1411: 1390: 1386: 1385: 1382: 1355: 1330: 1316: 1282: 1277: 1274: 1243: 1233: 1222: 1210: 1205: 1198: 1188: 1177: 1170: 1168: 1153:then the lever will be in balance if the 1135: 1130: 1114: 1109: 1106: 1080: 1061: 1055: 1031: 959: 953: 934:{\textstyle \sigma ^{2}=1/\sum _{i}w_{i}} 925: 915: 906: 894: 888: 863: 858: 853: 848: 836: 830: 808: 803: 797: 773: 767: 747: 679: 637: 630: 628: 576: 564: 556: 550: 548: 485: 479: 385: 379: 347: 343: 342: 327: 279: 273: 249: 229: 203:{\displaystyle f\colon A\to \mathbb {R} } 196: 195: 181: 139: 111: 84: 80: 79: 64: 2508:Meta-Calculus: Differential and Integral 1536:. In this context, the weight function 2475: 1669:{\displaystyle \int _{\Omega }f(x)\ dx} 1278: 1206: 1131: 1110: 427:{\displaystyle \sum _{a\in A}f(a)w(a).} 34:. Weight functions occur frequently in 873:{\textstyle w_{i}=1/{\sigma _{i}^{2}}} 1291:{\displaystyle {\boldsymbol {x}}_{i}} 7: 762:measured multiple independent times 734:Weighted means are commonly used in 100:is a positive function defined on a 1089:{\displaystyle w_{1},\ldots ,w_{n}} 515:{\displaystyle \sum _{a\in B}w(a).} 309:{\displaystyle \sum _{a\in A}f(a);} 2332: 2239: 2180: 2144: 2086: 2042: 1982: 1968: 1961: 1958: 1955: 1922: 1900:{\displaystyle \int _{E}w(x)\ dx,} 1825: 1746:Note that one may need to require 1697: 1640: 1590: 1501: 1413: 1357: 738:to compensate for the presence of 14: 456:, one can replace the unweighted 1399:{\displaystyle \mathbb {R} ^{n}} 817:{\displaystyle \sigma _{i}^{2}} 2373: 2367: 2358: 2352: 2346: 2340: 2265: 2259: 2253: 2247: 2183: 2147: 2100: 2094: 2069: 2063: 2056: 2050: 1996: 1990: 1971: 1965: 1882: 1876: 1786: 1780: 1723: 1717: 1711: 1705: 1654: 1648: 1593: 1565:is sometimes referred to as a 1552: 1546: 1504: 1448: 1436: 1327: 1321: 700: 694: 670: 664: 658: 652: 597: 591: 565: 557: 506: 500: 418: 412: 406: 400: 338: 300: 294: 192: 150: 144: 75: 1: 2290:to a weighted bilinear form 1026:: if one has a collection of 1770:with respect to the weight 2572: 2546:Combinatorial optimization 2454:Riemann–Stieltjes integral 1850:can be generalized to the 1679:can be generalized to the 2405:for examples of weighted 1799:{\displaystyle w(x)\,dx} 1340:{\displaystyle w(x)\,dx} 1928:{\displaystyle \Omega } 1831:{\displaystyle \Omega } 1419:{\displaystyle \Omega } 1370:, which is typically a 1363:{\displaystyle \Omega } 1157:of the lever is at the 162:{\displaystyle w(a):=1} 134:. The weight function 2403:orthogonal polynomials 2392: 2281: 2197: 2161: 2117: 2012: 1929: 1901: 1832: 1800: 1760: 1737: 1670: 1605: 1559: 1523: 1478: 1455: 1420: 1400: 1364: 1341: 1292: 1259: 1238: 1193: 1145: 1090: 1040: 969: 935: 874: 818: 783: 756: 726:weights are relevant. 722:In this case only the 713: 604: 516: 428: 357: 310: 258: 238: 204: 163: 120: 94: 2536:Mathematical analysis 2449:Measure (mathematics) 2424:Numerical integration 2393: 2282: 2198: 2162: 2118: 2013: 1930: 1902: 1833: 1801: 1768:absolutely integrable 1761: 1738: 1671: 1606: 1560: 1524: 1479: 1456: 1421: 1401: 1365: 1342: 1293: 1260: 1218: 1173: 1146: 1091: 1041: 970: 968:{\displaystyle w_{i}} 936: 875: 819: 784: 782:{\displaystyle f_{i}} 757: 714: 605: 517: 439:numerical integration 429: 358: 311: 259: 239: 205: 164: 126:, which is typically 121: 95: 2407:orthogonal functions 2297: 2213: 2171: 2135: 2031: 1945: 1919: 1860: 1822: 1774: 1750: 1689: 1632: 1581: 1558:{\displaystyle w(x)} 1540: 1492: 1465: 1433: 1410: 1381: 1354: 1315: 1273: 1167: 1105: 1054: 1030: 1008:moving average model 1000:independent variable 952: 887: 829: 796: 766: 746: 627: 547: 478: 470:weighted cardinality 378: 326: 272: 248: 228: 180: 138: 110: 63: 2551:Functional analysis 2444:Kernel (statistics) 868: 813: 369:conical combination 169:corresponds to the 2556:Types of functions 2439:Linear combination 2388: 2277: 2193: 2157: 2113: 2110: 2079: 2008: 1925: 1897: 1828: 1796: 1756: 1733: 1666: 1601: 1573:General definition 1555: 1529:is a non-negative 1519: 1477:{\displaystyle dx} 1474: 1451: 1416: 1396: 1360: 1337: 1303:Continuous weights 1288: 1255: 1141: 1086: 1036: 996:dependent variable 965: 945:maximum likelihood 931: 920: 870: 854: 814: 799: 779: 752: 742:. For a quantity 709: 690: 648: 600: 587: 512: 496: 424: 396: 353: 306: 290: 254: 234: 200: 159: 116: 90: 55:General definition 2401:See the entry on 2378: 2363: 2270: 2111: 2001: 1975: 1887: 1759:{\displaystyle f} 1681:weighted integral 1659: 1250: 1039:{\displaystyle n} 911: 755:{\displaystyle f} 704: 675: 633: 572: 570: 481: 381: 275: 257:{\displaystyle A} 237:{\displaystyle f} 119:{\displaystyle A} 2563: 2520: 2503: 2497: 2480: 2397: 2395: 2394: 2389: 2376: 2361: 2336: 2335: 2323: 2322: 2317: 2286: 2284: 2283: 2278: 2268: 2243: 2242: 2202: 2200: 2199: 2194: 2192: 2191: 2166: 2164: 2163: 2158: 2156: 2155: 2122: 2120: 2119: 2114: 2112: 2090: 2089: 2046: 2045: 2035: 2023:weighted average 2017: 2015: 2014: 2009: 1999: 1986: 1985: 1976: 1974: 1964: 1949: 1934: 1932: 1931: 1926: 1911:Weighted average 1906: 1904: 1903: 1898: 1885: 1872: 1871: 1837: 1835: 1834: 1829: 1805: 1803: 1802: 1797: 1765: 1763: 1762: 1757: 1742: 1740: 1739: 1734: 1701: 1700: 1675: 1673: 1672: 1667: 1657: 1644: 1643: 1610: 1608: 1607: 1602: 1600: 1564: 1562: 1561: 1556: 1528: 1526: 1525: 1520: 1518: 1517: 1512: 1486:Lebesgue measure 1483: 1481: 1480: 1475: 1460: 1458: 1457: 1454:{\displaystyle } 1452: 1425: 1423: 1422: 1417: 1405: 1403: 1402: 1397: 1395: 1394: 1389: 1369: 1367: 1366: 1361: 1346: 1344: 1343: 1338: 1299: 1297: 1295: 1294: 1289: 1287: 1286: 1281: 1264: 1262: 1261: 1256: 1251: 1249: 1248: 1247: 1237: 1232: 1216: 1215: 1214: 1209: 1203: 1202: 1192: 1187: 1171: 1152: 1150: 1148: 1147: 1142: 1140: 1139: 1134: 1119: 1118: 1113: 1095: 1093: 1092: 1087: 1085: 1084: 1066: 1065: 1045: 1043: 1042: 1037: 1018:The terminology 976: 974: 972: 971: 966: 964: 963: 942: 940: 938: 937: 932: 930: 929: 919: 910: 899: 898: 881: 879: 877: 876: 871: 869: 867: 862: 852: 841: 840: 823: 821: 820: 815: 812: 807: 788: 786: 785: 780: 778: 777: 761: 759: 758: 753: 718: 716: 715: 710: 705: 703: 689: 673: 647: 631: 619:weighted average 609: 607: 606: 601: 586: 571: 569: 568: 560: 551: 521: 519: 518: 513: 495: 433: 431: 430: 425: 395: 362: 360: 359: 354: 352: 351: 346: 315: 313: 312: 307: 289: 263: 261: 260: 255: 243: 241: 240: 235: 209: 207: 206: 201: 199: 176:If the function 168: 166: 165: 160: 125: 123: 122: 117: 99: 97: 96: 91: 89: 88: 83: 50:Discrete weights 32:weighted average 2571: 2570: 2566: 2565: 2564: 2562: 2561: 2560: 2526: 2525: 2524: 2523: 2504: 2500: 2481: 2477: 2472: 2464:Window function 2415: 2327: 2300: 2295: 2294: 2234: 2211: 2210: 2169: 2168: 2133: 2132: 2129: 2081: 2037: 2029: 2028: 1977: 1953: 1943: 1942: 1917: 1916: 1913: 1863: 1858: 1857: 1852:weighted volume 1820: 1819: 1818:is a subset of 1812: 1810:Weighted volume 1772: 1771: 1748: 1747: 1692: 1687: 1686: 1635: 1630: 1629: 1579: 1578: 1575: 1538: 1537: 1507: 1490: 1489: 1463: 1462: 1431: 1430: 1408: 1407: 1406:, for instance 1384: 1379: 1378: 1376:Euclidean space 1352: 1351: 1313: 1312: 1305: 1276: 1271: 1270: 1269: 1239: 1217: 1204: 1194: 1172: 1165: 1164: 1129: 1108: 1103: 1102: 1101: 1076: 1057: 1052: 1051: 1050:, with weights 1028: 1027: 1020:weight function 1016: 1004:distributed lag 955: 950: 949: 948: 921: 890: 885: 884: 883: 832: 827: 826: 825: 794: 793: 769: 764: 763: 744: 743: 732: 674: 632: 625: 624: 555: 545: 544: 476: 475: 376: 375: 341: 324: 323: 321:weight function 270: 269: 246: 245: 226: 225: 178: 177: 136: 135: 108: 107: 78: 61: 60: 57: 52: 23:weight function 19: 12: 11: 5: 2569: 2567: 2559: 2558: 2553: 2548: 2543: 2541:Measure theory 2538: 2528: 2527: 2522: 2521: 2505:Jane Grossman. 2498: 2474: 2473: 2471: 2468: 2467: 2466: 2461: 2456: 2451: 2446: 2441: 2436: 2431: 2426: 2421: 2419:Center of mass 2414: 2411: 2399: 2398: 2387: 2384: 2381: 2375: 2372: 2369: 2366: 2360: 2357: 2354: 2351: 2348: 2345: 2342: 2339: 2334: 2330: 2326: 2321: 2316: 2313: 2310: 2307: 2304: 2288: 2287: 2276: 2273: 2267: 2264: 2261: 2258: 2255: 2252: 2249: 2246: 2241: 2237: 2233: 2230: 2227: 2224: 2221: 2218: 2190: 2185: 2182: 2179: 2176: 2154: 2149: 2146: 2143: 2140: 2128: 2125: 2124: 2123: 2109: 2106: 2102: 2099: 2096: 2093: 2088: 2084: 2078: 2075: 2071: 2068: 2065: 2062: 2058: 2055: 2052: 2049: 2044: 2040: 2019: 2018: 2007: 2004: 1998: 1995: 1992: 1989: 1984: 1980: 1973: 1970: 1967: 1963: 1960: 1957: 1952: 1924: 1912: 1909: 1908: 1907: 1896: 1893: 1890: 1884: 1881: 1878: 1875: 1870: 1866: 1827: 1811: 1808: 1795: 1792: 1788: 1785: 1782: 1779: 1755: 1744: 1743: 1732: 1729: 1725: 1722: 1719: 1716: 1713: 1710: 1707: 1704: 1699: 1695: 1677: 1676: 1665: 1662: 1656: 1653: 1650: 1647: 1642: 1638: 1599: 1595: 1592: 1589: 1586: 1574: 1571: 1554: 1551: 1548: 1545: 1516: 1511: 1506: 1503: 1500: 1497: 1473: 1470: 1450: 1447: 1444: 1441: 1438: 1415: 1393: 1388: 1359: 1336: 1333: 1329: 1326: 1323: 1320: 1304: 1301: 1285: 1280: 1266: 1265: 1254: 1246: 1242: 1236: 1231: 1228: 1225: 1221: 1213: 1208: 1201: 1197: 1191: 1186: 1183: 1180: 1176: 1159:center of mass 1138: 1133: 1128: 1125: 1122: 1117: 1112: 1083: 1079: 1075: 1072: 1069: 1064: 1060: 1035: 1015: 1012: 981:expected value 962: 958: 928: 924: 918: 914: 909: 905: 902: 897: 893: 866: 861: 857: 851: 847: 844: 839: 835: 811: 806: 802: 776: 772: 751: 731: 728: 720: 719: 708: 702: 699: 696: 693: 688: 685: 682: 678: 672: 669: 666: 663: 660: 657: 654: 651: 646: 643: 640: 636: 611: 610: 599: 596: 593: 590: 585: 582: 579: 575: 567: 563: 559: 554: 523: 522: 511: 508: 505: 502: 499: 494: 491: 488: 484: 435: 434: 423: 420: 417: 414: 411: 408: 405: 402: 399: 394: 391: 388: 384: 371:is defined as 350: 345: 340: 337: 334: 331: 317: 316: 305: 302: 299: 296: 293: 288: 285: 282: 278: 265:is defined as 253: 233: 198: 194: 191: 188: 185: 158: 155: 152: 149: 146: 143: 115: 87: 82: 77: 74: 71: 68: 56: 53: 51: 48: 17: 13: 10: 9: 6: 4: 3: 2: 2568: 2557: 2554: 2552: 2549: 2547: 2544: 2542: 2539: 2537: 2534: 2533: 2531: 2518: 2517:0-9771170-2-2 2514: 2510: 2509: 2502: 2499: 2495: 2494:0-9771170-1-4 2491: 2487: 2486: 2479: 2476: 2469: 2465: 2462: 2460: 2457: 2455: 2452: 2450: 2447: 2445: 2442: 2440: 2437: 2435: 2434:Weighted mean 2432: 2430: 2429:Orthogonality 2427: 2425: 2422: 2420: 2417: 2416: 2412: 2410: 2408: 2404: 2385: 2382: 2379: 2370: 2364: 2355: 2349: 2343: 2337: 2328: 2324: 2319: 2311: 2308: 2305: 2293: 2292: 2291: 2274: 2271: 2262: 2256: 2250: 2244: 2235: 2231: 2225: 2222: 2219: 2209: 2208: 2207: 2206: 2205:bilinear form 2177: 2174: 2141: 2138: 2127:Bilinear form 2126: 2107: 2104: 2097: 2091: 2082: 2076: 2073: 2066: 2060: 2053: 2047: 2038: 2027: 2026: 2025: 2024: 2005: 2002: 1993: 1987: 1978: 1950: 1941: 1940: 1939: 1938: 1910: 1894: 1891: 1888: 1879: 1873: 1868: 1864: 1856: 1855: 1854: 1853: 1849: 1845: 1841: 1817: 1809: 1807: 1793: 1790: 1783: 1777: 1769: 1753: 1730: 1727: 1720: 1714: 1708: 1702: 1693: 1685: 1684: 1683: 1682: 1663: 1660: 1651: 1645: 1636: 1628: 1627: 1626: 1625: 1622: 1618: 1614: 1587: 1584: 1572: 1570: 1568: 1549: 1543: 1535: 1532: 1514: 1498: 1495: 1487: 1471: 1468: 1445: 1442: 1439: 1429: 1391: 1377: 1373: 1350: 1334: 1331: 1324: 1318: 1310: 1302: 1300: 1283: 1252: 1244: 1240: 1234: 1229: 1226: 1223: 1219: 1211: 1199: 1195: 1189: 1184: 1181: 1178: 1174: 1163: 1162: 1161: 1160: 1156: 1136: 1126: 1123: 1120: 1115: 1099: 1081: 1077: 1073: 1070: 1067: 1062: 1058: 1049: 1046:objects on a 1033: 1025: 1021: 1013: 1011: 1009: 1005: 1001: 997: 994:in which the 993: 988: 986: 985:probabilities 982: 977: 960: 956: 946: 926: 922: 916: 912: 907: 903: 900: 895: 891: 864: 859: 855: 849: 845: 842: 837: 833: 809: 804: 800: 792: 774: 770: 749: 741: 737: 729: 727: 725: 706: 697: 691: 686: 683: 680: 676: 667: 661: 655: 649: 644: 641: 638: 634: 623: 622: 621: 620: 616: 615:weighted mean 594: 588: 583: 580: 577: 573: 561: 552: 543: 542: 541: 540: 536: 532: 528: 509: 503: 497: 492: 489: 486: 482: 474: 473: 472: 471: 467: 463: 459: 455: 451: 447: 442: 440: 421: 415: 409: 403: 397: 392: 389: 386: 382: 374: 373: 372: 370: 366: 348: 335: 332: 329: 322: 303: 297: 291: 286: 283: 280: 276: 268: 267: 266: 264: 251: 231: 223: 217: 213: 189: 186: 183: 174: 172: 156: 153: 147: 141: 133: 129: 113: 106: 103: 85: 72: 69: 66: 54: 49: 47: 45: 41: 37: 33: 29: 24: 16: 2507: 2501: 2484: 2478: 2400: 2289: 2130: 2022: 2020: 1914: 1851: 1847: 1843: 1815: 1813: 1745: 1680: 1678: 1620: 1576: 1426:could be an 1306: 1267: 1022:arises from 1019: 1017: 989: 978: 733: 723: 721: 612: 526: 524: 469: 465: 461: 453: 445: 443: 436: 365:weighted sum 364: 320: 319:but given a 318: 219: 175: 170: 58: 28:weighted sum 27: 22: 20: 15: 1838:, then the 1619:, then the 992:regressions 458:cardinality 220:unweighted 218:, then the 2530:Categories 2470:References 1621:unweighted 1531:measurable 736:statistics 730:Statistics 452:subset of 171:unweighted 36:statistics 2459:Weighting 2333:Ω 2329:∫ 2315:⟩ 2303:⟨ 2240:Ω 2236:∫ 2229:⟩ 2217:⟨ 2184:→ 2181:Ω 2178:: 2148:→ 2145:Ω 2142:: 2087:Ω 2083:∫ 2043:Ω 2039:∫ 1983:Ω 1979:∫ 1969:Ω 1923:Ω 1865:∫ 1826:Ω 1698:Ω 1694:∫ 1641:Ω 1637:∫ 1594:→ 1591:Ω 1588:: 1505:→ 1502:Ω 1499:: 1414:Ω 1358:Ω 1220:∑ 1175:∑ 1124:… 1071:… 1024:mechanics 1014:Mechanics 913:∑ 892:σ 856:σ 801:σ 684:∈ 677:∑ 642:∈ 635:∑ 581:∈ 574:∑ 490:∈ 483:∑ 390:∈ 383:∑ 339:→ 333:: 284:∈ 277:∑ 193:→ 187:: 132:countable 76:→ 70:: 2413:See also 1624:integral 1617:function 1615:-valued 1534:function 1461:. Here 1428:interval 1347:on some 1311:such as 791:variance 724:relative 216:function 214:-valued 102:discrete 40:analysis 2519:, 1981. 2496:, 1980. 2021:by the 1937:average 1567:density 1309:measure 1155:fulcrum 1096:(where 613:by the 539:average 468:by the 44:measure 2515:  2492:  2377:  2362:  2269:  2000:  1886:  1840:volume 1766:to be 1658:  1372:subset 1349:domain 1098:weight 531:finite 450:finite 363:, the 128:finite 1846:) of 1611:is a 1374:of a 1048:lever 789:with 529:is a 464:| of 448:is a 210:is a 2513:ISBN 2490:ISBN 2167:and 1842:vol( 1613:real 1488:and 1002:, a 979:The 943:The 740:bias 535:mean 212:real 38:and 2131:If 1915:If 1814:If 1577:If 1484:is 990:In 617:or 537:or 525:If 444:If 367:or 244:on 224:of 222:sum 130:or 105:set 30:or 2532:: 2511:, 2488:, 2409:. 2325::= 2232::= 1569:. 441:. 154::= 21:A 2386:. 2383:x 2380:d 2374:) 2371:x 2368:( 2365:w 2359:) 2356:x 2353:( 2350:g 2347:) 2344:x 2341:( 2338:f 2320:w 2312:g 2309:, 2306:f 2275:x 2272:d 2266:) 2263:x 2260:( 2257:g 2254:) 2251:x 2248:( 2245:f 2226:g 2223:, 2220:f 2189:R 2175:g 2153:R 2139:f 2108:x 2105:d 2101:) 2098:x 2095:( 2092:w 2077:x 2074:d 2070:) 2067:x 2064:( 2061:w 2057:) 2054:x 2051:( 2048:f 2006:x 2003:d 1997:) 1994:x 1991:( 1988:f 1972:) 1966:( 1962:l 1959:o 1956:v 1951:1 1895:, 1892:x 1889:d 1883:) 1880:x 1877:( 1874:w 1869:E 1848:E 1844:E 1816:E 1794:x 1791:d 1787:) 1784:x 1781:( 1778:w 1754:f 1731:x 1728:d 1724:) 1721:x 1718:( 1715:w 1712:) 1709:x 1706:( 1703:f 1664:x 1661:d 1655:) 1652:x 1649:( 1646:f 1598:R 1585:f 1553:) 1550:x 1547:( 1544:w 1515:+ 1510:R 1496:w 1472:x 1469:d 1449:] 1446:b 1443:, 1440:a 1437:[ 1392:n 1387:R 1335:x 1332:d 1328:) 1325:x 1322:( 1319:w 1298:. 1284:i 1279:x 1253:, 1245:i 1241:w 1235:n 1230:1 1227:= 1224:i 1212:i 1207:x 1200:i 1196:w 1190:n 1185:1 1182:= 1179:i 1151:, 1137:n 1132:x 1127:, 1121:, 1116:1 1111:x 1082:n 1078:w 1074:, 1068:, 1063:1 1059:w 1034:n 975:. 961:i 957:w 941:. 927:i 923:w 917:i 908:/ 904:1 901:= 896:2 880:, 865:2 860:i 850:/ 846:1 843:= 838:i 834:w 810:2 805:i 775:i 771:f 750:f 707:. 701:) 698:a 695:( 692:w 687:A 681:a 671:) 668:a 665:( 662:w 659:) 656:a 653:( 650:f 645:A 639:a 598:) 595:a 592:( 589:f 584:A 578:a 566:| 562:A 558:| 553:1 527:A 510:. 507:) 504:a 501:( 498:w 493:B 487:a 466:B 462:B 460:| 454:A 446:B 422:. 419:) 416:a 413:( 410:w 407:) 404:a 401:( 398:f 393:A 387:a 349:+ 344:R 336:A 330:w 304:; 301:) 298:a 295:( 292:f 287:A 281:a 252:A 232:f 197:R 190:A 184:f 157:1 151:) 148:a 145:( 142:w 114:A 86:+ 81:R 73:A 67:w

Index

weighted average
statistics
analysis
measure
discrete
set
finite
countable
real
function
sum
conical combination
numerical integration
finite
cardinality
finite
mean
average
weighted mean
weighted average
statistics
bias
variance
maximum likelihood
expected value
probabilities
regressions
dependent variable
independent variable
distributed lag

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