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must first be sorted in ascending order: this can be done most easily if working by hand by constructing a draft of the stem-and-leaf display with the leaves unsorted, then sorting the leaves to produce the final stem-and-leaf display. Here is the sorted set of data values that will be used in the
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The stem-and-leaf display is drawn with two columns separated by a vertical line. The stems are listed to the left of the vertical line. It is important that each stem is listed only once and that no numbers are skipped, even if it means that some stems have no leaves. The leaves are listed in
620:. However, stem-and-leaf displays are only useful for moderately sized data sets (around 15â150 data points). With very small data sets a stem-and-leaf displays can be of little use, as a reasonable number of data points are required to establish definitive distribution properties. A
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Next, it must be determined what the stems will represent and what the leaves will represent. Typically, the leaf contains the last digit of the number and the stem contains all of the other digits. In the case of very large numbers, the data values may be rounded to a particular
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Stem-and-leaf displays are useful for displaying the relative density and shape of the data, giving the reader a quick overview of the distribution. They retain (most of) the raw numerical data, often with perfect integrity. They are also useful for highlighting
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For negative numbers, a negative is placed in front of the stem unit, which is still the value X / 10. Non-integers are rounded. This allows the stem and leaf plot to retain its shape, even for more complicated data sets. As in this example below:
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aâabdeghilmnrstwxy bâaeioy câh dâaeio eâadefhlmnrstwx f|aey gâiou hâaeimo iâdfnost jâao kâaioy lâaio mâaeimouy nâaeouy oâbdefhikmnoprsuwxy pâaeio qâi râe sâhiot tâaeio uâghmnprst vâ wâeo xâiu yâaeou zâaeo
92:(typewriter) typestyles that allowed computer technology of the time to easily produce the graphics. Modern computers' superior graphic capabilities have meant these techniques are less often used.
121:
Unlike histograms, stem-and-leaf displays retain the original data to at least two significant digits, and put the data in order, thereby easing the move to order-based inference and
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When there is a repeated number in the data (such as two 72s), the plot must reflect such (so the plot would look like 7 | 2 2 5 6 7 when it has the numbers 72 72 75 76 77).
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may be better suited for such data. With very large data sets, a stem-and-leaf display will become very cluttered, since each data point must be represented numerically. A
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556:{\displaystyle {\begin{array}{r|l}{\text{Stem}}&{\text{Leaf}}\\\hline -2&4\\-1&2\\-0&3\\0&4~6~6\\1&7\\2&5\\3&\\4&\\5&7\end{array}}}
343:{\displaystyle {\begin{array}{r|l}{\text{Stem}}&{\text{Leaf}}\\\hline 4&4~6~7~9\\5&\\6&3~4~6~8~8\\7&2~2~5~6\\8&1~4~8\\9&\\10&6\end{array}}}
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152:(such as the hundreds place) that will be used for the leaves. The remaining digits to the left of the rounded place value are used as the stem.
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Rounding may be needed to create a stem-and-leaf display. Based on the following set of data, the stem plot below would be created:
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In this example, the leaf represents the ones place and the stem will represent the rest of the number (tens place and higher).
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Stem-and-leaf displays can also be used to convey non-numerical information. In this example of valid two-letter words in
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Some railway timetables use stem-and-leaf displays with hours as stems and minutes as leaves
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under 100 shows that the most frequent tens digits are 0 and 1 while the least is 9
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78:. Stemplots became more commonly used in the 1980s after the publication of
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in 1977. The popularity during those years is attributable to their use of
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as stems, it can be easily seen that the three most common initials are
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823:
Exploring Data: An
Introduction to Data Analysis for Social Scientists
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44, 46, 47, 49, 63, 64, 66, 68, 68, 72, 72, 75, 76, 81, 84, 88, 106
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808:
Chance
Encounters: A First Course in Data Analysis and Inference
3010:
1991:
1965:
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397:â23.678758, â12.45, â3.4, 4.43, 5.5, 5.678, 16.87, 24.7, 56.8
63:
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Two-Letter
Scrabble Words Visualized as Stem and Leaf Plot
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may become more appropriate as the data size increases.
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increasing order in a row to the right of each stem.
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Autoregressive conditional heteroskedasticity (ARCH)
74:'s work in the early 1900s, and are useful tools in
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95:This plot has been implemented in Octave and R.
2300:Multivariate adaptive regression splines (MARS)
114:value with a vertical line, and the individual
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27:Format for presentation of quantitative data
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862:
848:
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134:To construct a stem-and-leaf display, the
1513:
29:
821:Elliott, Jane; Catherine Marsh (2008).
651:tournaments outside the US) with their
14:
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2826:KaplanâMeier estimator (product limit)
98:A stem-and-leaf plot is also called a
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2836:Accelerated failure time (AFT) model
810:pp. 49â54 John Wiley and Sons.
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2431:Analysis of variance (ANOVA, anova)
1283:
24:
2526:CochranâMantelâHaenszel statistics
1152:Pearson product-moment correlation
25:
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118:values with symbols on the line.
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110:axis, and identifying the common
3176:Statistical charts and diagrams
2785:Least-squares spectral analysis
778:create a stem-and-leaf display.
129:
62:, to assist in visualizing the
1766:Mean-unbiased minimum-variance
869:
825:(2nd ed.). Polity Press.
806:Wild, C. and Seber, G. (2000)
781:
760:
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738:
709:
13:
1:
3079:Geographic information system
2295:Simultaneous equations models
800:
2262:Coefficient of determination
1873:Uniformly most powerful test
596:{\displaystyle -2\mid 4=-24}
50:is a device for presenting
7:
2831:Proportional hazards models
2775:Spectral density estimation
2757:Vector autoregression (VAR)
2191:Maximum posterior estimator
1423:Randomized controlled trial
10:
3197:
2591:Multivariate distributions
1011:Average absolute deviation
377:{\displaystyle 6\mid 3=63}
3181:Exploratory data analysis
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1330:Sample size determination
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720:Exploratory Data Analysis
123:non-parametric statistics
85:exploratory data analysis
76:exploratory data analysis
3074:Environmental statistics
2596:Elliptical distributions
2389:Generalized linear model
2318:Simple linear regression
2088:HodgesâLehmann estimator
1545:Probability distribution
1454:Stochastic approximation
1016:Coefficient of variation
774:stem functions. They do
702:
606:
34:A stem-and-leaf plot of
2734:Cross-correlation (XCF)
2342:Non-standard predictors
1776:LehmannâScheffĂ© theorem
1449:Adaptive clinical trial
724:(1 ed.). Pearson.
716:Tukey, John W. (1977).
647:(the word list used in
3130:Mathematics portal
2951:Engineering statistics
2859:NelsonâAalen estimator
2436:Analysis of covariance
2323:Ordinary least squares
2247:Pearson product-moment
1651:Statistical functional
1562:Empirical distribution
1395:Controlled experiments
1124:Frequency distribution
902:Descriptive statistics
645:Collins Scrabble Words
597:
557:
378:
344:
39:
3046:Population statistics
2988:System identification
2722:Autocorrelation (ACF)
2650:Exponential smoothing
2564:Discriminant analysis
2559:Canonical correlation
2423:Partition of variance
2285:Regression validation
2129:(JonckheereâTerpstra)
2028:Likelihood-ratio test
1717:Frequentist inference
1629:Locationâscale family
1550:Sampling distribution
1515:Statistical inference
1482:Cross-sectional study
1469:Observational studies
1428:Randomized experiment
1257:Stem-and-leaf display
1059:Central limit theorem
598:
558:
379:
345:
106:values onto a common
58:format, similar to a
44:stem-and-leaf display
33:
2969:Probabilistic design
2554:Principal components
2397:Exponential families
2349:Nonlinear regression
2328:General linear model
2290:Mixed effects models
2280:Errors and residuals
2257:Confounding variable
2159:Bayesian probability
2137:Van der Waerden test
2127:Ordered alternative
1892:Multiple comparisons
1771:RaoâBlackwellization
1734:Estimating equations
1690:Statistical distance
1408:Factorial experiment
941:Arithmetic-Geometric
569:
409:
356:
169:
70:. They evolved from
3041:Official statistics
2964:Methods engineering
2645:Seasonal adjustment
2413:Poisson regressions
2333:Bayesian regression
2272:Regression analysis
2252:Partial correlation
2224:Regression analysis
1823:Prediction interval
1818:Likelihood interval
1808:Confidence interval
1800:Interval estimation
1761:Unbiased estimators
1579:Model specification
1459:Up-and-down designs
1147:Partial correlation
1103:Index of dispersion
1021:Interquartile range
139:following example:
3061:Spatial statistics
2941:Medical statistics
2841:First hitting time
2795:Whittle likelihood
2446:Degrees of freedom
2441:Multivariate ANOVA
2374:Heteroscedasticity
2186:Bayesian estimator
2151:Bayesian inference
2000:KolmogorovâSmirnov
1885:Randomization test
1855:Testing hypotheses
1828:Tolerance interval
1739:Maximum likelihood
1634:Exponential family
1567:Density estimation
1527:Statistical theory
1487:Natural experiment
1433:Scientific control
1350:Survey methodology
1036:Standard deviation
745:Function in Octave
681:{{{annotations}}}
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48:stem-and-leaf plot
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3036:National accounts
3006:Actuarial science
2998:Social statistics
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2818:Survival function
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2665:Granger causality
2506:Contingency table
2481:Survival analysis
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2310:Linear regression
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2176:Credible interval
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1744:Method of moments
1613:Parametric family
1574:Statistical model
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1418:Random assignment
1340:Statistical power
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1119:Contingency table
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956:Generalized/power
636:Non-numerical use
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52:quantitative data
16:(Redirected from
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3016:Crime statistics
2910:
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2780:Fourier analysis
2767:Frequency domain
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2660:Structural break
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2569:Cluster analysis
2516:Log-linear model
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2379:Homoscedasticity
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2113:(KruskalâWallis)
2098:
2083:
2038:Cross validation
2023:
2005:AndersonâDarling
1952:
1939:
1938:
1910:Likelihood-ratio
1902:Parametric tests
1880:Permutation test
1863:1- & 2-tails
1754:Minimum distance
1726:Point estimation
1722:
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1673:Optimal decision
1624:
1523:
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1492:Quasi-experiment
1442:Adaptive designs
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1157:Rank correlation
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616:and finding the
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2927:Clinical trials
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2033:Model selection
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1979:Goodness of fit
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1783:Median unbiased
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389:Stem unit: 10.0
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2922:Bioinformatics
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2727:partial (PACF)
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2674:Specific tests
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2574:Classification
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2556:
2551:
2546:
2540:
2538:
2532:
2531:
2529:
2528:
2523:
2521:McNemar's test
2518:
2513:
2508:
2503:
2497:
2495:
2485:
2484:
2460:
2459:
2456:
2455:
2452:
2451:
2449:
2448:
2443:
2438:
2433:
2427:
2425:
2419:
2418:
2416:
2415:
2399:
2393:
2391:
2385:
2384:
2382:
2381:
2376:
2371:
2366:
2361:
2359:Semiparametric
2356:
2351:
2345:
2343:
2339:
2338:
2336:
2335:
2330:
2325:
2320:
2314:
2312:
2306:
2305:
2303:
2302:
2297:
2292:
2287:
2282:
2276:
2274:
2268:
2267:
2265:
2264:
2259:
2254:
2249:
2243:
2241:
2231:
2230:
2227:
2226:
2221:
2215:
2207:
2206:
2203:
2202:
2199:
2198:
2196:
2195:
2194:
2193:
2183:
2178:
2173:
2172:
2171:
2166:
2155:
2153:
2147:
2146:
2143:
2142:
2140:
2139:
2134:
2133:
2132:
2124:
2116:
2100:
2097:(MannâWhitney)
2092:
2091:
2090:
2077:
2076:
2075:
2064:
2062:
2056:
2055:
2053:
2052:
2051:
2050:
2045:
2040:
2030:
2025:
2022:(ShapiroâWilk)
2017:
2012:
2007:
2002:
1997:
1989:
1983:
1981:
1975:
1974:
1972:
1971:
1963:
1954:
1942:
1936:
1934:Specific tests
1930:
1929:
1926:
1925:
1923:
1922:
1917:
1912:
1906:
1904:
1898:
1897:
1895:
1894:
1889:
1888:
1887:
1877:
1876:
1875:
1865:
1859:
1857:
1851:
1850:
1848:
1847:
1846:
1845:
1840:
1830:
1825:
1820:
1815:
1810:
1804:
1802:
1796:
1795:
1793:
1792:
1787:
1786:
1785:
1780:
1779:
1778:
1773:
1758:
1757:
1756:
1751:
1746:
1741:
1730:
1728:
1719:
1713:
1712:
1710:
1709:
1704:
1699:
1698:
1697:
1687:
1682:
1681:
1680:
1670:
1669:
1668:
1663:
1658:
1648:
1643:
1638:
1637:
1636:
1631:
1626:
1610:
1609:
1608:
1603:
1598:
1588:
1587:
1586:
1581:
1571:
1570:
1569:
1559:
1558:
1557:
1547:
1542:
1537:
1531:
1529:
1519:
1518:
1506:
1505:
1502:
1501:
1498:
1497:
1495:
1494:
1489:
1484:
1479:
1473:
1471:
1465:
1464:
1462:
1461:
1456:
1451:
1445:
1443:
1439:
1438:
1436:
1435:
1430:
1425:
1420:
1415:
1410:
1405:
1399:
1397:
1391:
1390:
1388:
1387:
1385:Standard error
1382:
1377:
1372:
1371:
1370:
1365:
1354:
1352:
1346:
1345:
1343:
1342:
1337:
1332:
1327:
1322:
1317:
1315:Optimal design
1312:
1307:
1301:
1299:
1289:
1288:
1276:
1275:
1272:
1271:
1268:
1267:
1265:
1264:
1259:
1254:
1249:
1244:
1239:
1234:
1229:
1224:
1219:
1214:
1209:
1204:
1199:
1194:
1188:
1186:
1180:
1179:
1177:
1176:
1171:
1170:
1169:
1164:
1154:
1149:
1143:
1141:
1135:
1134:
1132:
1131:
1126:
1121:
1115:
1113:
1112:Summary tables
1109:
1108:
1106:
1105:
1099:
1097:
1091:
1090:
1087:
1086:
1084:
1083:
1082:
1081:
1076:
1071:
1061:
1055:
1053:
1047:
1046:
1044:
1043:
1038:
1033:
1028:
1023:
1018:
1013:
1007:
1005:
999:
998:
996:
995:
990:
985:
984:
983:
978:
973:
968:
963:
958:
953:
948:
946:Contraharmonic
943:
938:
927:
925:
916:
906:
905:
893:
892:
890:
889:
884:
878:
875:
874:
867:
866:
859:
852:
844:
838:
837:
831:
818:
802:
799:
796:
795:
780:
759:
748:
737:
730:
707:
706:
704:
701:
693:
692:
684:
679:
672:
671:
670:
669:
639:
637:
634:
608:
605:
604:
603:
592:
589:
586:
583:
580:
577:
574:
563:
548:
545:
543:
540:
539:
535:
532:
531:
527:
524:
523:
520:
517:
515:
512:
511:
508:
505:
503:
500:
499:
496:
490:
484:
481:
479:
476:
475:
472:
469:
467:
464:
461:
460:
457:
454:
452:
449:
446:
445:
442:
439:
437:
434:
431:
430:
422:
415:
414:
399:
398:
391:
390:
387:
386:Leaf unit: 1.0
384:
373:
370:
367:
364:
361:
350:
335:
332:
330:
327:
326:
322:
319:
318:
315:
309:
303:
300:
298:
295:
294:
291:
285:
279:
273:
270:
268:
265:
264:
261:
255:
249:
243:
237:
234:
232:
229:
228:
224:
221:
220:
217:
211:
205:
199:
196:
194:
191:
190:
182:
175:
174:
145:
144:
131:
128:
26:
9:
6:
4:
3:
2:
3193:
3182:
3179:
3177:
3174:
3173:
3171:
3156:
3155:
3146:
3144:
3143:
3134:
3132:
3131:
3126:
3120:
3118:
3117:
3108:
3107:
3104:
3090:
3087:
3085:
3084:Geostatistics
3082:
3080:
3077:
3075:
3072:
3070:
3067:
3066:
3064:
3062:
3058:
3052:
3051:Psychometrics
3049:
3047:
3044:
3042:
3039:
3037:
3034:
3032:
3029:
3027:
3024:
3022:
3019:
3017:
3014:
3012:
3009:
3007:
3004:
3003:
3001:
2999:
2995:
2989:
2986:
2984:
2981:
2979:
2975:
2972:
2970:
2967:
2965:
2962:
2960:
2957:
2956:
2954:
2952:
2948:
2942:
2939:
2937:
2934:
2932:
2928:
2925:
2923:
2920:
2919:
2917:
2915:
2914:Biostatistics
2911:
2907:
2903:
2898:
2894:
2876:
2875:Log-rank test
2873:
2872:
2870:
2866:
2860:
2857:
2856:
2854:
2852:
2848:
2842:
2839:
2837:
2834:
2832:
2829:
2827:
2824:
2823:
2821:
2819:
2815:
2812:
2810:
2806:
2796:
2793:
2791:
2788:
2786:
2783:
2781:
2778:
2776:
2773:
2772:
2770:
2768:
2764:
2758:
2755:
2753:
2750:
2748:
2746:(BoxâJenkins)
2742:
2740:
2737:
2735:
2732:
2728:
2725:
2724:
2723:
2720:
2719:
2717:
2715:
2711:
2705:
2702:
2700:
2699:DurbinâWatson
2697:
2695:
2689:
2687:
2684:
2682:
2681:DickeyâFuller
2679:
2678:
2676:
2672:
2666:
2663:
2661:
2658:
2656:
2655:Cointegration
2653:
2651:
2648:
2646:
2643:
2641:
2638:
2636:
2633:
2631:
2630:Decomposition
2628:
2627:
2625:
2621:
2618:
2616:
2612:
2602:
2599:
2598:
2597:
2594:
2593:
2592:
2589:
2585:
2582:
2581:
2580:
2577:
2575:
2572:
2570:
2567:
2565:
2562:
2560:
2557:
2555:
2552:
2550:
2547:
2545:
2542:
2541:
2539:
2537:
2533:
2527:
2524:
2522:
2519:
2517:
2514:
2512:
2509:
2507:
2504:
2502:
2501:Cohen's kappa
2499:
2498:
2496:
2494:
2490:
2486:
2482:
2478:
2474:
2470:
2465:
2461:
2447:
2444:
2442:
2439:
2437:
2434:
2432:
2429:
2428:
2426:
2424:
2420:
2414:
2410:
2406:
2400:
2398:
2395:
2394:
2392:
2390:
2386:
2380:
2377:
2375:
2372:
2370:
2367:
2365:
2362:
2360:
2357:
2355:
2354:Nonparametric
2352:
2350:
2347:
2346:
2344:
2340:
2334:
2331:
2329:
2326:
2324:
2321:
2319:
2316:
2315:
2313:
2311:
2307:
2301:
2298:
2296:
2293:
2291:
2288:
2286:
2283:
2281:
2278:
2277:
2275:
2273:
2269:
2263:
2260:
2258:
2255:
2253:
2250:
2248:
2245:
2244:
2242:
2240:
2236:
2232:
2225:
2222:
2220:
2217:
2216:
2212:
2208:
2192:
2189:
2188:
2187:
2184:
2182:
2179:
2177:
2174:
2170:
2167:
2165:
2162:
2161:
2160:
2157:
2156:
2154:
2152:
2148:
2138:
2135:
2131:
2125:
2123:
2117:
2115:
2109:
2108:
2107:
2104:
2103:Nonparametric
2101:
2099:
2093:
2089:
2086:
2085:
2084:
2078:
2074:
2073:Sample median
2071:
2070:
2069:
2066:
2065:
2063:
2061:
2057:
2049:
2046:
2044:
2041:
2039:
2036:
2035:
2034:
2031:
2029:
2026:
2024:
2018:
2016:
2013:
2011:
2008:
2006:
2003:
2001:
1998:
1996:
1994:
1990:
1988:
1985:
1984:
1982:
1980:
1976:
1970:
1968:
1964:
1962:
1960:
1955:
1953:
1948:
1944:
1943:
1940:
1937:
1935:
1931:
1921:
1918:
1916:
1913:
1911:
1908:
1907:
1905:
1903:
1899:
1893:
1890:
1886:
1883:
1882:
1881:
1878:
1874:
1871:
1870:
1869:
1866:
1864:
1861:
1860:
1858:
1856:
1852:
1844:
1841:
1839:
1836:
1835:
1834:
1831:
1829:
1826:
1824:
1821:
1819:
1816:
1814:
1811:
1809:
1806:
1805:
1803:
1801:
1797:
1791:
1788:
1784:
1781:
1777:
1774:
1772:
1769:
1768:
1767:
1764:
1763:
1762:
1759:
1755:
1752:
1750:
1747:
1745:
1742:
1740:
1737:
1736:
1735:
1732:
1731:
1729:
1727:
1723:
1720:
1718:
1714:
1708:
1705:
1703:
1700:
1696:
1693:
1692:
1691:
1688:
1686:
1683:
1679:
1678:loss function
1676:
1675:
1674:
1671:
1667:
1664:
1662:
1659:
1657:
1654:
1653:
1652:
1649:
1647:
1644:
1642:
1639:
1635:
1632:
1630:
1627:
1625:
1619:
1616:
1615:
1614:
1611:
1607:
1604:
1602:
1599:
1597:
1594:
1593:
1592:
1589:
1585:
1582:
1580:
1577:
1576:
1575:
1572:
1568:
1565:
1564:
1563:
1560:
1556:
1553:
1552:
1551:
1548:
1546:
1543:
1541:
1538:
1536:
1533:
1532:
1530:
1528:
1524:
1520:
1516:
1511:
1507:
1493:
1490:
1488:
1485:
1483:
1480:
1478:
1475:
1474:
1472:
1470:
1466:
1460:
1457:
1455:
1452:
1450:
1447:
1446:
1444:
1440:
1434:
1431:
1429:
1426:
1424:
1421:
1419:
1416:
1414:
1411:
1409:
1406:
1404:
1401:
1400:
1398:
1396:
1392:
1386:
1383:
1381:
1380:Questionnaire
1378:
1376:
1373:
1369:
1366:
1364:
1361:
1360:
1359:
1356:
1355:
1353:
1351:
1347:
1341:
1338:
1336:
1333:
1331:
1328:
1326:
1323:
1321:
1318:
1316:
1313:
1311:
1308:
1306:
1303:
1302:
1300:
1298:
1294:
1290:
1286:
1281:
1277:
1263:
1260:
1258:
1255:
1253:
1250:
1248:
1245:
1243:
1240:
1238:
1235:
1233:
1230:
1228:
1225:
1223:
1220:
1218:
1215:
1213:
1210:
1208:
1207:Control chart
1205:
1203:
1200:
1198:
1195:
1193:
1190:
1189:
1187:
1185:
1181:
1175:
1172:
1168:
1165:
1163:
1160:
1159:
1158:
1155:
1153:
1150:
1148:
1145:
1144:
1142:
1140:
1136:
1130:
1127:
1125:
1122:
1120:
1117:
1116:
1114:
1110:
1104:
1101:
1100:
1098:
1096:
1092:
1080:
1077:
1075:
1072:
1070:
1067:
1066:
1065:
1062:
1060:
1057:
1056:
1054:
1052:
1048:
1042:
1039:
1037:
1034:
1032:
1029:
1027:
1024:
1022:
1019:
1017:
1014:
1012:
1009:
1008:
1006:
1004:
1000:
994:
991:
989:
986:
982:
979:
977:
974:
972:
969:
967:
964:
962:
959:
957:
954:
952:
949:
947:
944:
942:
939:
937:
934:
933:
932:
929:
928:
926:
924:
920:
917:
915:
911:
907:
903:
898:
894:
888:
885:
883:
880:
879:
876:
872:
865:
860:
858:
853:
851:
846:
845:
842:
834:
832:0-7456-2282-8
828:
824:
819:
817:
816:0-471-32936-3
813:
809:
805:
804:
792:
791:
784:
777:
773:
769:
763:
757:
756:Function in R
752:
746:
741:
733:
731:0-201-07616-0
727:
722:
721:
712:
708:
695:
688:
682:
676:
668:
654:
650:
646:
633:
631:
627:
623:
619:
615:
590:
587:
584:
581:
578:
575:
572:
564:
546:
541:
533:
525:
518:
513:
506:
501:
494:
488:
482:
477:
470:
465:
462:
455:
450:
447:
440:
435:
432:
405:
404:
403:
396:
395:
394:
388:
385:
371:
368:
365:
362:
359:
351:
333:
328:
320:
313:
307:
301:
296:
289:
283:
277:
271:
266:
259:
253:
247:
241:
235:
230:
222:
215:
209:
203:
197:
192:
165:
164:
163:
160:
156:
153:
151:
142:
141:
140:
137:
127:
124:
119:
117:
113:
109:
105:
101:
96:
93:
91:
87:
86:
81:
77:
73:
72:Arthur Bowley
69:
65:
61:
57:
53:
49:
45:
37:
36:prime numbers
32:
19:
3152:
3140:
3121:
3114:
3026:Econometrics
2976: /
2959:Chemometrics
2936:Epidemiology
2929: /
2902:Applications
2744:ARIMA model
2691:Q-statistic
2640:Stationarity
2536:Multivariate
2479: /
2475: /
2473:Multivariate
2471: /
2411: /
2407: /
2181:Bayes factor
2080:Signed rank
1992:
1966:
1958:
1946:
1641:Completeness
1477:Cohort study
1375:Opinion poll
1310:Missing data
1297:Study design
1256:
1252:Scatter plot
1174:Scatter plot
1167:Spearman's Ï
1129:Grouped data
822:
807:
793:, 2020-10-01
789:
783:
775:
772:Matplotlib's
762:
751:
740:
719:
711:
680:
642:
610:
400:
392:
161:
157:
154:
146:
136:observations
133:
130:Construction
120:
115:
111:
107:
103:
99:
97:
94:
83:
68:distribution
47:
43:
41:
3154:WikiProject
3069:Cartography
3031:Jurimetrics
2983:Reliability
2714:Time domain
2693:(LjungâBox)
2615:Time-series
2493:Categorical
2477:Time-series
2469:Categorical
2404:(Bernoulli)
2239:Correlation
2219:Correlation
2015:JarqueâBera
1987:Chi-squared
1749:M-estimator
1702:Asymptotics
1646:Sufficiency
1413:Interaction
1325:Replication
1305:Effect size
1262:Violin plot
1242:Radar chart
1222:Forest plot
1212:Correlogram
1162:Kendall's Ï
150:place value
82:'s book on
3170:Categories
3021:Demography
2739:ARMA model
2544:Regression
2121:(Friedman)
2082:(Wilcoxon)
2020:Normality
2010:Lilliefors
1957:Student's
1833:Resampling
1707:Robustness
1695:divergence
1685:Efficiency
1623:(monotone)
1618:Likelihood
1535:Population
1368:Stratified
1320:Population
1139:Dependence
1095:Count data
1026:Percentile
1003:Dispersion
936:Arithmetic
871:Statistics
801:References
766:Examples:
90:monospaced
80:John Tukey
2402:Logistic
2169:posterior
2095:Rank sum
1843:Jackknife
1838:Bootstrap
1656:Bootstrap
1591:Parameter
1540:Statistic
1335:Statistic
1247:Run chart
1232:Pie chart
1227:Histogram
1217:Fan chart
1192:Bar chart
1074:L-moments
961:Geometric
630:histogram
588:−
579:∣
573:−
463:−
448:−
433:−
363:∣
60:histogram
56:graphical
3116:Category
2809:Survival
2686:Johansen
2409:Binomial
2364:Isotonic
1951:(normal)
1596:location
1403:Blocking
1358:Sampling
1237:QâQ plot
1202:Box plot
1184:Graphics
1079:Skewness
1069:Kurtosis
1041:Variance
971:Heronian
966:Harmonic
768:MATLAB's
653:initials
649:Scrabble
626:box plot
622:dot plot
614:outliers
100:stemplot
18:Stemplot
3142:Commons
3089:Kriging
2974:Process
2931:studies
2790:Wavelet
2623:General
1790:Plug-in
1584:L space
1363:Cluster
1064:Moments
882:Outline
3011:Census
2601:Normal
2549:Manova
2369:Robust
2119:2-way
2111:1-way
1949:-test
1620:
1197:Biplot
988:Median
981:Lehmer
923:Center
829:
814:
728:
492:
486:
311:
305:
287:
281:
275:
257:
251:
245:
239:
213:
207:
201:
2635:Trend
2164:prior
2106:anova
1995:-test
1969:-test
1961:-test
1868:Power
1813:Pivot
1606:shape
1601:scale
1051:Shape
1031:Range
976:Heinz
951:Cubic
887:Index
703:Notes
607:Usage
565:Key:
352:Key:
66:of a
64:shape
54:in a
2868:Test
2068:Sign
1920:Wald
993:Mode
931:Mean
827:ISBN
812:ISBN
770:and
726:ISBN
663:and
618:mode
425:Leaf
418:Stem
185:Leaf
178:Stem
2048:BIC
2043:AIC
776:not
628:or
46:or
3172::
667:.
659:,
591:24
372:63
329:10
125:.
116:y
42:A
1993:G
1967:F
1959:t
1947:Z
1666:V
1661:U
863:e
856:t
849:v
835:.
734:.
665:e
661:a
657:o
585:=
582:4
576:2
547:7
542:5
534:4
526:3
519:5
514:2
507:7
502:1
495:6
489:6
483:4
478:0
471:3
466:0
456:2
451:1
441:4
436:2
369:=
366:3
360:6
334:6
321:9
314:8
308:4
302:1
297:8
290:6
284:5
278:2
272:2
267:7
260:8
254:8
248:6
242:4
236:3
231:6
223:5
216:9
210:7
204:6
198:4
193:4
112:x
108:x
104:y
20:)
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