1316:, is a useful determination of flow uniformity for industrial processes. The term is used widely in the design of pollution control equipment, such as electrostatic precipitators (ESPs), selective catalytic reduction (SCR), scrubbers, and similar devices. The Institute of Clean Air Companies (ICAC) references RMS deviation of velocity in the design of fabric filters (ICAC document F-7). The guiding principal is that many of these pollution control devices require "uniform flow" entering and through the control zone. This can be related to uniformity of velocity profile, temperature distribution, gas species (such as ammonia for an SCR, or activated carbon injection for mercury absorption), and other flow-related parameters. The
2400:
1333:. While intra-assay and inter-assay CVs might be assumed to be calculated by simply averaging CV values across CV values for multiple samples within one assay or by averaging multiple inter-assay CV estimates, it has been suggested that these practices are incorrect and that a more complex computational process is required. It has also been noted that CV values are not an ideal index of the certainty of a measurement when the number of replicates varies across samples â in this case standard error in percent is suggested to be superior. If measurements do not have a natural zero point then the CV is not a valid measurement and alternative measures such as the
1812:
2395:{\displaystyle \mathrm {d} F_{c_{\rm {v}}}={\frac {2}{\pi ^{1/2}\Gamma {\left({\frac {n-1}{2}}\right)}}}\exp \left(-{\frac {n}{2\left({\frac {\sigma }{\mu }}\right)^{2}}}\cdot {\frac {{c_{\rm {v}}}^{2}}{1+{c_{\rm {v}}}^{2}}}\right){\frac {{c_{\rm {v}}}^{n-2}}{(1+{c_{\rm {v}}}^{2})^{n/2}}}\sideset {}{^{\prime }}\sum _{i=0}^{n-1}{\frac {(n-1)!\,\Gamma \left({\frac {n-i}{2}}\right)}{(n-1-i)!\,i!\,}}\cdot {\frac {n^{i/2}}{2^{i/2}\cdot \left({\frac {\sigma }{\mu }}\right)^{i}}}\cdot {\frac {1}{(1+{c_{\rm {v}}}^{2})^{i/2}}}\,\mathrm {d} c_{\rm {v}},}
6577:
6563:
6601:
6589:
440:
In most cases, a CV is computed for a single independent variable (e.g., a single factory product) with numerous, repeated measures of a dependent variable (e.g., error in the production process). However, data that are linear or even logarithmically non-linear and include a continuous range for the
1582:
If, for example, the data sets are temperature readings from two different sensors (a
Celsius sensor and a Fahrenheit sensor) and you want to know which sensor is better by picking the one with the least variance, then you will be misled if you use CV. The problem here is that you have divided by a
303:
temperature has a meaningful zero, the complete absence of thermal energy, and thus is a ratio scale. In plain language, it is meaningful to say that 20 Kelvin is twice as hot as 10 Kelvin, but only in this scale with a true absolute zero. While a standard deviation (SD) can be measured in Kelvin,
1541:
Archaeologists often use CV values to compare the degree of standardisation of ancient artefacts. Variation in CVs has been interpreted to indicate different cultural transmission contexts for the adoption of new technologies. Coefficients of variation have also been used to investigate pottery
1199:
The coefficient of variation is useful because the standard deviation of data must always be understood in the context of the mean of the data. In contrast, the actual value of the CV is independent of the unit in which the measurement has been taken, so it is a
1270:. While many natural processes indeed show a correlation between the average value and the amount of variation around it, accurate sensor devices need to be designed in such a way that the coefficient of variation is close to zero, i.e., yielding a constant
732:
3336:"Head-to-head, randomised, crossover study of oral versus subcutaneous methotrexate in patients with rheumatoid arthritis: drug-exposure limitations of oral methotrexate at doses >=15 mg may be overcome with subcutaneous administration"
1288:
In industrial solids processing, CV is particularly important to measure the degree of homogeneity of a powder mixture. Comparing the calculated CV to a specification will allow to define if a sufficient degree of mixing has been reached.
1006:
remains the same.) This estimate is sometimes referred to as the "geometric CV" (GCV) in order to distinguish it from the simple estimate above. However, "geometric coefficient of variation" has also been defined by
Kirkwood as:
833:
1320:
also is used to assess flow uniformity in combustion systems, HVAC systems, ductwork, inlets to fans and filters, air handling units, etc. where performance of the equipment is influenced by the incoming flow distribution.
299:. For example, most temperature scales (e.g., Celsius, Fahrenheit etc.) are interval scales with arbitrary zeros, so the computed coefficient of variation would be different depending on the scale used. On the other hand,
1213:
When the mean value is close to zero, the coefficient of variation will approach infinity and is therefore sensitive to small changes in the mean. This is often the case if the values do not originate from a ratio
1075:
1542:
standardisation relating to changes in social organisation. Archaeologists also use several methods for comparing CV values, for example the modified signed-likelihood ratio (MSLR) test for equality of CVs.
2584:
Liu (2012) reviews methods for the construction of a confidence interval for the coefficient of variation. Notably, Lehmann (1986) derived the sampling distribution for the coefficient of variation using a
614:
3601:"Telomere length measurement validity: the coefficient of variation is invalid and cannot be used to compare quantitative polymerase chain reaction and Southern blot telomere length measurement technique"
1004:
291:
It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningful zero (
2573:
287:
2447:
2960:
2644:
954:
2868:
2821:
1599:
are still 15.81 and 28.46, respectively, because the standard deviation is not affected by a constant offset. The coefficients of variation, however, are now both equal to 5.39%.
295:) and hence allow relative comparison of two measurements (i.e., division of one measurement by the other). The coefficient of variation may not have any meaning for data on an
3475:
435:
375:
2908:
2714:
633:
1184:
1112:
2761:
866:
3822:
Bettinger, Robert L.; Eerkens, Jelmer (April 1999). "Point
Typologies, Cultural Transmission, and the Spread of Bow-and-Arrow Technology in the Prehistoric Great Basin".
1550:
Comparing coefficients of variation between parameters using relative units can result in differences that may not be real. If we compare the same set of temperatures in
553:
2671:
1154:
1750:
898:
228:
108:
166:
2479:
1724:
1649:
1204:. For comparison between data sets with different units or widely different means, one should use the coefficient of variation instead of the standard deviation.
1084:
to the coefficient of variation, for describing multiplicative variation in log-normal data, but this definition of GCV has no theoretical basis as an estimate of
248:
131:
5698:
4243:
1695:
524:
1773:
6627:
6203:
2559:
2539:
2519:
2499:
1805:
1669:
1620:
3724:
Eerkens, Jelmer W.; Bettinger, Robert L. (July 2001). "Techniques for
Assessing Standardization in Artifact Assemblages: Can We Scale Material Variability?".
304:
Celsius, or
Fahrenheit, the value computed is only applicable to that scale. Only the Kelvin scale can be used to compute a valid coefficient of variability.
3164:
Koopmans, L. H.; Owen, D. B.; Rosenblatt, J. I. (1964). "Confidence intervals for the coefficient of variation for the normal and log normal distributions".
6353:
5977:
1579:
are 15.81 and 28.46, respectively. The CV of the first set is 15.81/20 = 79%. For the second set (which are the same temperatures) it is 28.46/68 = 42%.
752:
471:
The data set has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18
4618:
3699:
1266:, often abbreviated SCV. In modeling, a variation of the CV is the CV(RMSD). Essentially the CV(RMSD) replaces the standard deviation term with the
5751:
6190:
1529:
are equal). Its most notable drawback is that it is not bounded from above, so it cannot be normalized to be within a fixed range (e.g. like the
3064:"PsiMLE: A maximum-likelihood estimation approach to estimating psychophysical scaling and variability more reliably, efficiently, and flexibly"
468:
The data set has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1
3313:
4057:
3191:
Diletti, E; Hauschke, D; Steinijans, VW (1992). "Sample size determination for bioequivalence assessment by means of confidence intervals".
4613:
4313:
742:
Many datasets follow an approximately log-normal distribution. In such cases, a more accurate estimate, derived from the properties of the
4122:
4005:
Iglevicz, Boris; Myers, Raymond (1970). "Comparisons of approximations to the percentage points of the sample coefficient of variation".
5217:
4365:
3452:
1013:
6605:
6637:
3020:
441:
independent variable with sparse measurements across each value (e.g., scatter-plot) may be amenable to single CV calculation using a
315:
6000:
5892:
3421:
3119:
561:
4176:
6178:
6052:
4109:
Feltz, Carol J; Miller, G. Edward (1996). "An asymptotic test for the equality of coefficients of variation from k populations".
6236:
5897:
5642:
5013:
4603:
2768:
5227:
6287:
5499:
5306:
5195:
5153:
1259:
1156:
which is of most use in the context of log-normally distributed data. If necessary, this can be derived from an estimate of
4392:
872:
transformation. (In the event that measurements are recorded using any other logarithmic base, b, their standard deviation
6530:
5489:
3040:
484:
442:
5539:
3912:
Krishnamoorthy, K.; Lee, Meesook (February 2014). "Improved tests for the equality of normal coefficients of variation".
6081:
6030:
6015:
6005:
5874:
5746:
5713:
5494:
5324:
31:
6150:
5451:
6632:
6425:
6226:
5205:
4874:
4338:
1533:
which is constrained to be between 0 and 1). It is, however, more mathematically tractable than the Gini coefficient.
6310:
6277:
502:
When only a sample of data from a population is available, the population CV can be estimated using the ratio of the
3218:
Julious, Steven A.; Debarnot, Camille A. M. (2000). "Why Are
Pharmacokinetic Data Summarized by Arithmetic Means?".
6282:
6025:
5784:
5690:
5670:
5578:
5289:
5107:
4590:
4462:
2717:
2586:
959:
56:
5456:
5222:
5080:
6042:
5810:
5531:
5385:
5314:
5234:
5092:
5073:
4781:
4502:
1697:. In the above example, Celsius can only be converted to Fahrenheit through a linear transformation of the form
1346:
1118:
253:
72:
6155:
3149:
3132:
6525:
6292:
5840:
5805:
5769:
5554:
4996:
4905:
4864:
4776:
4306:
4285:
4201:
Krishnamoorthy, K; Lee, Meesook (2013). "Improved tests for the equality of normal coefficients of variation".
3413:
Actex study manual, Course 1, Examination of the
Society of Actuaries, Exam 1 of the Casualty Actuarial Society
1784:
1254:
is equal to its mean, so its coefficient of variation is equal to 1. Distributions with CV < 1 (such as an
1251:
1243:
490:
The data set has a population standard deviation of 8.16 and a coefficient of variation of 8.16 / 100 = 0.0816
80:
5562:
5546:
2918:
2603:
2407:
1602:
Mathematically speaking, the coefficient of variation is not entirely linear. That is, for a random variable
493:
The data set has a population standard deviation of 30.8 and a coefficient of variation of 30.8 / 27.9 = 1.10
3688:
311:
distributed exhibit stationary CV; in contrast, SD varies depending upon the expected value of measurements.
6434:
6047:
5987:
5924:
5284:
5146:
5136:
4986:
4900:
3511:"Statistical quality control and routine data processing for radioimmunoassays and immunoradiometric assays"
2778:
903:
743:
6195:
6132:
3411:
6472:
6402:
5887:
5774:
4771:
4668:
4575:
4454:
4353:
3552:"Improving qPCR telomere length assays: Controlling for well position effects increases statistical power"
2872:
2831:
2784:
2732:
2678:
1334:
84:
76:
6593:
5471:
2572:. Statistical inference for the coefficient of variation in normally distributed data is often based on
1783:
Provided that negative and small positive values of the sample mean occur with negligible frequency, the
6497:
6439:
6382:
6208:
6101:
6010:
5736:
5620:
5479:
5361:
5353:
5168:
5064:
5042:
5001:
4966:
4933:
4879:
4854:
4809:
4748:
4708:
4510:
4333:
3492:
2982:
2764:
727:{\displaystyle {\widehat {c_{\rm {v}}}}^{*}={\bigg (}1+{\frac {1}{4n}}{\bigg )}{\widehat {c_{\rm {v}}}}}
6576:
5466:
384:
324:
2878:
2684:
6420:
5995:
5944:
5920:
5882:
5800:
5779:
5731:
5610:
5588:
5557:
5343:
5294:
5212:
5185:
5141:
5097:
4859:
4635:
4515:
3878:
1201:
1122:
624:
619:
But this estimator, when applied to a small or moderately sized sample, tends to be too low: it is a
171:
6567:
6492:
6415:
6096:
5860:
5853:
5815:
5723:
5703:
5675:
5408:
5274:
5269:
5259:
5251:
5069:
5030:
4920:
4910:
4819:
4598:
4554:
4472:
4397:
4299:
3310:
2825:
2597:
2569:
1255:
1247:
1218:
1159:
1087:
319:
6142:
2738:
841:
6581:
6392:
6246:
6091:
5967:
5864:
5848:
5825:
5602:
5336:
5319:
5279:
5190:
5085:
5047:
5018:
4978:
4938:
4884:
4801:
4487:
4482:
4218:
4168:
4090:
4022:
3987:
3929:
3894:
3847:
3839:
3804:
3796:
3749:
3741:
3392:
3243:
1596:
1576:
1239:
503:
462:
88:
40:
529:
187:
6487:
6457:
6449:
6269:
6260:
6185:
6116:
5972:
5957:
5932:
5820:
5761:
5627:
5615:
5241:
5158:
5102:
5025:
4869:
4791:
4570:
4444:
4126:
4053:
4040:
Bennett, B. M. (1976). "On an
Approximate Test for Homogeneity of Coefficients of Variation".
3788:
3630:
3622:
3581:
3532:
3445:"Measuring Degree of Mixing â Homogeneity of powder mix - Mixture quality - PowderProcess.net"
3417:
3365:
3292:
3235:
3200:
3115:
3093:
3085:
3016:
2987:
2972:
2724:
2649:
1278:
1128:
3867:"Standardization of ceramic shape: A case study of Iron Age pottery from northeastern Taiwan"
3444:
1729:
875:
213:
93:
6512:
6467:
6231:
6218:
6111:
6086:
6020:
5952:
5830:
5438:
5331:
5264:
5177:
5124:
4943:
4814:
4608:
4407:
4374:
4210:
4160:
4118:
4082:
4045:
4014:
3977:
3921:
3886:
3831:
3780:
3769:"Ceramic Standardization and Intensity of Production: Quantifying Degrees of Specialization"
3733:
3612:
3571:
3563:
3522:
3355:
3347:
3282:
3274:
3227:
3173:
3144:
3075:
2728:
1530:
620:
141:
2452:
1392:. This follows from the fact that the variance and mean are independent of the ordering of
17:
6429:
6173:
6035:
5962:
5637:
5511:
5484:
5461:
5430:
5057:
5052:
5006:
4736:
4387:
3317:
3037:"What is the difference between ordinal, interval and ratio variables? Why should I care?"
2565:
1700:
1625:
1235:
233:
116:
2716:, is another similar ratio, but is not dimensionless, and hence not scale invariant. See
1674:
508:
4145:
3882:
1755:
6378:
6373:
4836:
4766:
4412:
3576:
3551:
3360:
3335:
3009:
2544:
2524:
2504:
2484:
1790:
1654:
1605:
1293:
1271:
1262:) are considered high-variance. Some formulas in these fields are expressed using the
1231:
296:
191:
134:
4288:
package to test for significant differences between multiple coefficients of variation
4073:
Vangel, Mark G. (1996). "Confidence intervals for a normal coefficient of variation".
3287:
3262:
6621:
6535:
6502:
6365:
6326:
6137:
6106:
5570:
5524:
5129:
4831:
4658:
4422:
4417:
4222:
3933:
3898:
3851:
3808:
3753:
3062:
Odic, Darko; Im, Hee Yeon; Eisinger, Robert; Ly, Ryan; Halberda, Justin (June 2016).
1563:
4688:
4172:
3950:
cvequality: Tests for the equality of coefficients of variation from multiple groups
3351:
3278:
1752:, whereas Kelvins can be converted to Rankines through a transformation of the form
210:
The coefficient of variation (CV) is defined as the ratio of the standard deviation
6477:
6410:
6387:
6302:
5632:
4928:
4826:
4761:
4703:
4625:
4580:
4086:
3247:
3036:
1473:
PigouâDalton transfer principle: when wealth is transferred from a wealthier agent
828:{\displaystyle {\widehat {cv}}_{\rm {raw}}={\sqrt {\mathrm {e} ^{s_{\ln }^{2}}-1}}}
199:
4146:"Estimator and tests for common coefficients of variation in normal distributions"
3527:
3510:
1470:). This follows from the fact that the variance and mean both obey this principle.
1230:
The coefficient of variation is also common in applied probability fields such as
4049:
3890:
3263:"Use of Coefficient of Variation in Assessing Variability of Quantitative Assays"
6520:
6482:
6165:
6066:
5928:
5741:
5708:
5200:
5117:
5112:
4756:
4713:
4693:
4673:
4663:
4432:
4281:
3948:
2977:
2912:
869:
292:
179:
170:, and often expressed as a percentage ("%RSD"). The CV or RSD is widely used in
4237:
3481:. International Society of Electrostatic Precipitation (ISESP) Conference 2018.
3177:
5366:
4846:
4546:
4477:
4427:
4402:
4322:
4214:
4164:
3982:
3965:
3925:
3080:
3063:
1807:
of i.i.d. normal random variables has been shown by
Hendricks and Robey to be
1555:
308:
195:
44:
3792:
3626:
3089:
2677:
moment about the mean, which are also dimensionless and scale invariant. The
5519:
5371:
4991:
4786:
4698:
4683:
4678:
4643:
4123:
10.1002/(SICI)1097-0258(19960330)15:6<647::AID-SIM184>3.0.CO;2-P
3634:
3585:
3369:
3296:
3239:
3097:
1329:
CV measures are often used as quality controls for quantitative laboratory
465:
is 0 and average is 100, giving the coefficient of variation as 0 / 100 = 0
4130:
3536:
3231:
3204:
5035:
4653:
4530:
4525:
4520:
4492:
3617:
3600:
378:
6540:
6241:
4094:
4026:
3991:
3843:
3800:
3745:
3567:
3396:
3193:
International
Journal of Clinical Pharmacology, Therapy, and Toxicology
1551:
1362:) is a list of the values of an economic indicator (e.g. wealth), with
1217:
Unlike the standard deviation, it cannot be used directly to construct
183:
6462:
5443:
5417:
5397:
4648:
4439:
3476:"Improved Methodology for Accurate CFD and Physical Modeling of ESPs"
3311:"FAQ: Issues with Efficacy Analysis of Clinical Trial Data Using SAS"
1559:
1258:) are considered low-variance, while those with CV > 1 (such as a
300:
4018:
3835:
3784:
3737:
3383:
Kirkwood, TBL (1979). "Geometric means and measures of dispersion".
3768:
1330:
1070:{\displaystyle \mathrm {GCV_{K}} ={\mathrm {e} ^{s_{\ln }}\!\!-1}}
175:
4382:
1267:
111:
6351:
5918:
5665:
4964:
4734:
4351:
4295:
3416:(2001 ed.). Winsted, CT: Actex Publications. p. 104.
3133:"Log-normal Distributions across the Sciences: Keys and Clues"
609:{\displaystyle {\widehat {c_{\rm {v}}}}={\frac {s}{\bar {x}}}}
4291:
3474:
Banka, A; Dumont, B; Franklin, J; Klemm, G; Mudry, R (2018).
1520:
assumes its minimum value of zero for complete equality (all
4044:. Experientia Supplementum. Vol. 22. pp. 169â171.
2433:
2118:
4239:
Confidence Interval Estimation for Coefficient of Variation
3966:"The Sampling Distribution of the Coefficient of Variation"
3698:. Policy Support Service, Policy Assistance Division, FAO.
3689:"Policy Impacts on Inequality â Simple Inequality Measures"
3150:
10.1641/0006-3568(2001)051[0341:LNDATS]2.0.CO;2
1537:
As a measure of standardisation of archaeological artefacts
3131:
Limpert, Eckhard; Stahel, Werner A.; Abbt, Markus (2001).
2449:
indicates that the summation is over only even values of
2589:
to give an exact method for the construction of the CI.
453:
In the examples below, we will take the values given as
3866:
627:
data, an unbiased estimator for a sample of size n is:
3015:. Cambridge, UK New York: Cambridge University Press.
2410:
1325:
Laboratory measures of intra-assay and inter-assay CVs
2921:
2881:
2834:
2787:
2741:
2687:
2652:
2606:
2564:
This is useful, for instance, in the construction of
2547:
2527:
2507:
2487:
2455:
1815:
1793:
1787:
of the coefficient of variation for a sample of size
1758:
1732:
1703:
1677:
1657:
1628:
1608:
1162:
1131:
1090:
1016:
962:
906:
878:
868:
is the sample standard deviation of the data after a
844:
755:
636:
564:
532:
511:
387:
327:
256:
236:
216:
144:
119:
96:
6204:
Autoregressive conditional heteroskedasticity (ARCH)
1586:
Comparing the same data set, now in absolute units:
475:
In these examples, we will take the values given as
6511:
6448:
6401:
6364:
6319:
6301:
6268:
6259:
6217:
6164:
6125:
6074:
6065:
5986:
5943:
5873:
5839:
5793:
5760:
5722:
5689:
5601:
5510:
5429:
5384:
5352:
5305:
5250:
5176:
5167:
4977:
4919:
4893:
4845:
4800:
4747:
4634:
4589:
4563:
4545:
4501:
4453:
4373:
4364:
3008:
2954:
2902:
2862:
2815:
2755:
2708:
2665:
2638:
2553:
2533:
2513:
2493:
2473:
2441:
2394:
1799:
1767:
1744:
1718:
1689:
1663:
1643:
1614:
1178:
1148:
1106:
1069:
998:
948:
892:
860:
827:
726:
608:
547:
518:
487:of 0 and a coefficient of variation of 0 / 100 = 0
455:randomly chosen from a larger population of values
429:
369:
281:
242:
222:
160:
125:
102:
4153:Communications in Statistics â Theory and Methods
3687:Bellu, Lorenzo Giovanni; Liberati, Paolo (2006).
2425:
2110:
1347:requirements for a measure of economic inequality
1059:
1058:
698:
670:
174:to express the precision and repeatability of an
3498:. Institute of Clean Air Companies (ICAC). 1996.
5752:Multivariate adaptive regression splines (MARS)
1186:or GCV by inverting the corresponding formula.
2576:for the coefficient of variation. Methods for
4307:
3964:Hendricks, Walter A.; Robey, Kate W. (1936).
999:{\displaystyle {\widehat {cv}}_{\rm {raw}}\,}
377:divided by the average of the quartiles (the
178:. It is also commonly used in fields such as
57:normalized root-mean-square deviation (NRMSD)
8:
3865:Wang, Li-Ying; Marwick, Ben (October 2020).
3682:
3680:
3678:
3676:
3674:
1651:is equal to the coefficient of variation of
3650:Economic Inequality and Income Distribution
3648:Champernowne, D. G.; Cowell, F. A. (1999).
3493:"F7 - Fabric Filter Gas Flow Model Studies"
1388:is independent of the ordering of the list
1375:, then the following requirements are met:
282:{\displaystyle CV={\frac {\sigma }{\mu }}.}
6361:
6348:
6265:
6071:
5940:
5915:
5686:
5662:
5390:
5173:
4974:
4961:
4744:
4731:
4370:
4361:
4348:
4314:
4300:
4292:
3871:Journal of Archaeological Science: Reports
3114:(3rd Ed). New York: Freeman, 1995. p. 58.
1345:The coefficient of variation fulfills the
4242:(Thesis). Georgia State University. p.3.
3981:
3616:
3575:
3526:
3359:
3286:
3148:
3079:
2946:
2937:
2931:
2926:
2920:
2892:
2886:
2880:
2854:
2845:
2839:
2833:
2807:
2798:
2792:
2786:
2745:
2740:
2698:
2692:
2686:
2657:
2651:
2629:
2624:
2619:
2612:
2607:
2605:
2546:
2526:
2506:
2486:
2454:
2442:{\textstyle \sideset {}{^{\prime }}\sum }
2432:
2427:
2415:
2413:
2412:
2411:
2409:
2382:
2381:
2372:
2371:
2358:
2354:
2344:
2336:
2335:
2330:
2314:
2302:
2288:
2270:
2266:
2251:
2247:
2241:
2234:
2227:
2179:
2171:
2150:
2135:
2124:
2117:
2112:
2100:
2098:
2097:
2096:
2082:
2078:
2068:
2060:
2059:
2054:
2031:
2023:
2022:
2017:
2014:
2000:
1992:
1991:
1986:
1972:
1964:
1963:
1958:
1955:
1943:
1929:
1915:
1875:
1870:
1857:
1853:
1843:
1831:
1830:
1825:
1816:
1814:
1792:
1757:
1731:
1702:
1676:
1656:
1627:
1607:
1175:
1168:
1167:
1161:
1145:
1136:
1130:
1103:
1096:
1095:
1089:
1050:
1045:
1040:
1038:
1028:
1017:
1015:
995:
982:
981:
965:
964:
961:
945:
924:
911:
905:
889:
883:
877:
857:
850:
845:
843:
809:
804:
799:
794:
791:
775:
774:
758:
757:
754:
711:
710:
704:
703:
697:
696:
681:
669:
668:
659:
646:
645:
639:
638:
635:
594:
589:
573:
572:
566:
565:
563:
534:
533:
531:
515:
510:
418:
409:
396:
388:
386:
358:
349:
336:
328:
326:
266:
255:
235:
215:
186:when doing quality assurance studies and
153:
145:
143:
118:
95:
3947:Marwick, Ben; Krishnamoorthy, K (2019).
2955:{\displaystyle \sigma _{W}^{2}/\mu _{W}}
2639:{\displaystyle {\mu _{k}}/{\sigma ^{k}}}
1583:relative value rather than an absolute.
461:The data set has constant values. Its
3599:Eisenberg, Dan T. A. (30 August 2016).
3220:Journal of Biopharmaceutical Statistics
2999:
1566:are their associated absolute values):
6278:KaplanâMeier estimator (product limit)
3011:The Cambridge Dictionary of Statistics
2763:(or its square) is referred to as the
949:{\displaystyle s_{\ln }=s_{b}\ln(b)\,}
3970:The Annals of Mathematical Statistics
3605:International Journal of Epidemiology
3455:from the original on 14 November 2017
3261:Reed, JF; Lynn, F; Meade, BD (2002).
3043:from the original on 15 December 2008
2541:is even, sum only over odd values of
1117:For many practical purposes (such as
7:
6628:Statistical deviation and dispersion
6588:
6288:Accelerated failure time (AFT) model
4182:from the original on 6 December 2013
2863:{\displaystyle \mu _{k}/\sigma ^{k}}
2816:{\displaystyle \sigma ^{2}/\mu ^{2}}
1499:) without altering their rank, then
87:. It is defined as the ratio of the
6600:
5883:Analysis of variance (ANOVA, anova)
3663:Campano, F.; Salvatore, D. (2006).
2428:
2113:
1341:As a measure of economic inequality
5978:CochranâMantelâHaenszel statistics
4604:Pearson product-moment correlation
4042:Contribution to Applied Statistics
3705:from the original on 5 August 2016
2383:
2373:
2337:
2172:
2061:
2024:
1993:
1965:
1867:
1832:
1817:
1622:, the coefficient of variation of
1169:
1097:
1041:
1029:
1025:
1021:
1018:
989:
986:
983:
795:
782:
779:
776:
712:
647:
574:
316:quartile coefficient of dispersion
25:
4246:from the original on 1 March 2014
3556:American Journal of Human Biology
1268:Root Mean Square Deviation (RMSD)
1246:is often more important than the
430:{\displaystyle {(Q_{1}+Q_{3})/2}}
370:{\displaystyle {(Q_{3}-Q_{1})/2}}
314:A more robust possibility is the
190:, by economists and investors in
6599:
6587:
6575:
6562:
6561:
3334:Schiff, MH; et al. (2014).
2903:{\displaystyle \sigma ^{2}/\mu }
2709:{\displaystyle \sigma ^{2}/\mu }
2574:McKay's chi-square approximation
2501:is odd, sum over even values of
1264:squared coefficient of variation
1190:Comparison to standard deviation
6237:Least-squares spectral analysis
4266:Testing Statistical Hypothesis.
3352:10.1136/annrheumdis-2014-205228
3279:10.1128/CDLI.9.6.1235-1239.2002
2769:signal-to-noise ratio (imaging)
1250:. The standard deviation of an
477:the entire population of values
5218:Mean-unbiased minimum-variance
4087:10.1080/00031305.1996.10473537
2774:Other related ratios include:
2351:
2320:
2221:
2203:
2165:
2153:
2075:
2044:
1428:Population independence â If {
1260:hyper-exponential distribution
942:
936:
599:
539:
415:
389:
355:
329:
154:
146:
1:
6531:Geographic information system
5747:Simultaneous equations models
3652:. Cambridge University Press.
3410:Broverman, Samuel A. (2001).
3309:Sawant, S.; Mohan, N. (2011)
1337:coefficient are recommended.
1179:{\displaystyle c_{\rm {v}}\,}
1107:{\displaystyle c_{\rm {v}}\,}
1080:This term was intended to be
900:is converted to base e using
485:population standard deviation
443:maximum-likelihood estimation
5714:Coefficient of determination
5325:Uniformly most powerful test
4050:10.1007/978-3-0348-5513-6_16
3891:10.1016/j.jasrep.2020.102554
2756:{\displaystyle \mu /\sigma }
2414:
2099:
1558:(both relative units, where
861:{\displaystyle {s_{\ln }}\,}
32:Coefficient of determination
6283:Proportional hazards models
6227:Spectral density estimation
6209:Vector autoregression (VAR)
5643:Maximum posterior estimator
4875:Randomized controlled trial
3528:10.1093/clinchem/20.10.1255
3509:Rodbard, D (October 1974).
65:relative standard deviation
18:Relative standard deviation
6654:
6043:Multivariate distributions
4463:Average absolute deviation
4144:Forkman, Johannes (2009).
3953:. R package version 0.2.0.
3667:. Oxford University Press.
2718:Normalization (statistics)
2587:non-central t-distribution
1597:sample standard deviations
1577:sample standard deviations
1371:being the wealth of agent
1274:over their working range.
548:{\displaystyle {\bar {x}}}
29:
6638:Income inequality metrics
6557:
6360:
6347:
6031:Structural equation model
5939:
5914:
5685:
5661:
5393:
5367:Score/Lagrange multiplier
4973:
4960:
4782:Sample size determination
4743:
4730:
4360:
4347:
4329:
4215:10.1007/s00180-013-0445-2
4165:10.1080/03610920802187448
4075:The American Statistician
3926:10.1007/s00180-013-0445-2
3696:EASYPol, Analytical tools
3110:Sokal RR & Rohlf FJ.
3081:10.3758/s13428-015-0600-5
3068:Behavior Research Methods
3039:. GraphPad Software Inc.
1508:decreases and vice versa.
1440:appended to itself, then
1119:sample size determination
504:sample standard deviation
6526:Environmental statistics
6048:Elliptical distributions
5841:Generalized linear model
5770:Simple linear regression
5540:HodgesâLehmann estimator
4997:Probability distribution
4906:Stochastic approximation
4468:Coefficient of variation
4268:2nd ed. New York: Wiley.
4203:Computational Statistics
3914:Computational Statistics
3767:Roux, Valentine (2003).
3178:10.1093/biomet/51.1-2.25
2875:(or relative variance),
2666:{\displaystyle \mu _{k}}
1785:probability distribution
1252:exponential distribution
1244:exponential distribution
1242:. In these fields, the
1149:{\displaystyle s_{ln}\,}
81:probability distribution
49:coefficient of variation
30:Not to be confused with
6186:Cross-correlation (XCF)
5794:Non-standard predictors
5228:LehmannâScheffĂ© theorem
4901:Adaptive clinical trial
4264:Lehmann, E. L. (1986).
3983:10.1214/aoms/1177732503
3550:Eisenberg, Dan (2015).
3007:Everitt, Brian (1998).
1745:{\displaystyle b\neq 0}
893:{\displaystyle s_{b}\,}
744:log-normal distribution
223:{\displaystyle \sigma }
103:{\displaystyle \sigma }
6582:Mathematics portal
6403:Engineering statistics
6311:NelsonâAalen estimator
5888:Analysis of covariance
5775:Ordinary least squares
5699:Pearson product-moment
5103:Statistical functional
5014:Empirical distribution
4847:Controlled experiments
4576:Frequency distribution
4354:Descriptive statistics
4111:Statistics in Medicine
3316:24 August 2011 at the
3267:Clin Diagn Lab Immunol
2956:
2904:
2873:Variance-to-mean ratio
2864:
2817:
2757:
2710:
2679:variance-to-mean ratio
2667:
2640:
2555:
2535:
2515:
2495:
2475:
2443:
2396:
2146:
1801:
1769:
1746:
1720:
1691:
1665:
1645:
1616:
1335:intraclass correlation
1300:, also referred to as
1180:
1150:
1108:
1071:
1000:
956:, and the formula for
950:
894:
862:
829:
728:
610:
549:
520:
431:
371:
307:Measurements that are
283:
244:
224:
162:
161:{\displaystyle |\mu |}
127:
104:
85:frequency distribution
6498:Population statistics
6440:System identification
6174:Autocorrelation (ACF)
6102:Exponential smoothing
6016:Discriminant analysis
6011:Canonical correlation
5875:Partition of variance
5737:Regression validation
5581:(JonckheereâTerpstra)
5480:Likelihood-ratio test
5169:Frequentist inference
5081:Locationâscale family
5002:Sampling distribution
4967:Statistical inference
4934:Cross-sectional study
4921:Observational studies
4880:Randomized experiment
4709:Stem-and-leaf display
4511:Central limit theorem
3449:www.powderprocess.net
3232:10.1081/BIP-100101013
2983:Sampling (statistics)
2957:
2905:
2865:
2818:
2765:signal-to-noise ratio
2758:
2711:
2668:
2641:
2556:
2536:
2516:
2496:
2476:
2474:{\displaystyle n-1-i}
2444:
2397:
2095:
1802:
1770:
1747:
1721:
1692:
1666:
1646:
1617:
1281:, the CV is known as
1181:
1151:
1109:
1072:
1001:
951:
895:
863:
830:
729:
611:
550:
521:
432:
372:
284:
245:
225:
163:
128:
105:
27:Statistical parameter
6421:Probabilistic design
6006:Principal components
5849:Exponential families
5801:Nonlinear regression
5780:General linear model
5742:Mixed effects models
5732:Errors and residuals
5709:Confounding variable
5611:Bayesian probability
5589:Van der Waerden test
5579:Ordered alternative
5344:Multiple comparisons
5223:RaoâBlackwellization
5186:Estimating equations
5142:Statistical distance
4860:Factorial experiment
4393:Arithmetic-Geometric
4236:Liu, Shuang (2012).
2919:
2879:
2832:
2785:
2739:
2720:for further ratios.
2685:
2650:
2604:
2600:are similar ratios,
2598:Standardized moments
2570:confidence intervals
2545:
2525:
2505:
2485:
2453:
2420:
2408:
2105:
1813:
1791:
1756:
1730:
1719:{\displaystyle ax+b}
1701:
1675:
1655:
1644:{\displaystyle aX+b}
1626:
1606:
1219:confidence intervals
1202:dimensionless number
1160:
1129:
1123:confidence intervals
1088:
1014:
960:
904:
876:
842:
753:
634:
625:normally distributed
562:
530:
509:
483:The data set has a
385:
325:
254:
243:{\displaystyle \mu }
234:
214:
172:analytical chemistry
142:
126:{\displaystyle \mu }
117:
94:
6493:Official statistics
6416:Methods engineering
6097:Seasonal adjustment
5865:Poisson regressions
5785:Bayesian regression
5724:Regression analysis
5704:Partial correlation
5676:Regression analysis
5275:Prediction interval
5270:Likelihood interval
5260:Confidence interval
5252:Interval estimation
5213:Unbiased estimators
5031:Model specification
4911:Up-and-down designs
4599:Partial correlation
4555:Index of dispersion
4473:Interquartile range
3883:2020JArSR..33j2554W
3665:Income distribution
2936:
2826:Standardized moment
2422:
2416:
2107:
2101:
1690:{\displaystyle b=0}
1256:Erlang distribution
1248:normal distribution
1121:and calculation of
814:
526:to the sample mean
519:{\displaystyle s\,}
320:interquartile range
188:ANOVA gauge R&R
6633:Statistical ratios
6513:Spatial statistics
6393:Medical statistics
6293:First hitting time
6247:Whittle likelihood
5898:Degrees of freedom
5893:Multivariate ANOVA
5826:Heteroscedasticity
5638:Bayesian estimator
5603:Bayesian inference
5452:KolmogorovâSmirnov
5337:Randomization test
5307:Testing hypotheses
5280:Tolerance interval
5191:Maximum likelihood
5086:Exponential family
5019:Density estimation
4979:Statistical theory
4939:Natural experiment
4885:Scientific control
4802:Survey methodology
4488:Standard deviation
3824:American Antiquity
3773:American Antiquity
3726:American Antiquity
3618:10.1093/ije/dyw191
3568:10.1002/ajhb.22690
3515:Clinical Chemistry
3199:(Suppl 1): S51â8.
2952:
2922:
2900:
2860:
2813:
2753:
2706:
2663:
2636:
2551:
2531:
2511:
2491:
2471:
2439:
2392:
1797:
1768:{\displaystyle ax}
1765:
1742:
1716:
1687:
1661:
1641:
1612:
1546:Examples of misuse
1477:to a poorer agent
1399:Scale invariance:
1240:reliability theory
1176:
1146:
1104:
1067:
996:
946:
890:
858:
825:
800:
724:
606:
545:
516:
463:standard deviation
427:
367:
279:
240:
220:
158:
123:
100:
89:standard deviation
41:probability theory
6615:
6614:
6553:
6552:
6549:
6548:
6488:National accounts
6458:Actuarial science
6450:Social statistics
6343:
6342:
6339:
6338:
6335:
6334:
6270:Survival function
6255:
6254:
6117:Granger causality
5958:Contingency table
5933:Survival analysis
5910:
5909:
5906:
5905:
5762:Linear regression
5657:
5656:
5653:
5652:
5628:Credible interval
5597:
5596:
5380:
5379:
5196:Method of moments
5065:Parametric family
5026:Statistical model
4956:
4955:
4952:
4951:
4870:Random assignment
4792:Statistical power
4726:
4725:
4722:
4721:
4571:Contingency table
4541:
4540:
4408:Generalized/power
4059:978-3-0348-5515-0
2988:Variance function
2973:Information ratio
2725:signal processing
2554:{\displaystyle i}
2534:{\displaystyle n}
2514:{\displaystyle i}
2494:{\displaystyle n}
2404:where the symbol
2369:
2309:
2296:
2236:
2195:
2093:
2007:
1950:
1937:
1899:
1891:
1800:{\displaystyle n}
1664:{\displaystyle X}
1615:{\displaystyle X}
1425:is a real number.
1279:actuarial science
978:
823:
771:
746:, is defined as:
721:
694:
656:
604:
602:
583:
542:
274:
55:), also known as
16:(Redirected from
6645:
6603:
6602:
6591:
6590:
6580:
6579:
6565:
6564:
6468:Crime statistics
6362:
6349:
6266:
6232:Fourier analysis
6219:Frequency domain
6199:
6146:
6112:Structural break
6072:
6021:Cluster analysis
5968:Log-linear model
5941:
5916:
5857:
5831:Homoscedasticity
5687:
5663:
5582:
5574:
5566:
5565:(KruskalâWallis)
5550:
5535:
5490:Cross validation
5475:
5457:AndersonâDarling
5404:
5391:
5362:Likelihood-ratio
5354:Parametric tests
5332:Permutation test
5315:1- & 2-tails
5206:Minimum distance
5178:Point estimation
5174:
5125:Optimal decision
5076:
4975:
4962:
4944:Quasi-experiment
4894:Adaptive designs
4745:
4732:
4609:Rank correlation
4371:
4362:
4349:
4316:
4309:
4302:
4293:
4269:
4262:
4256:
4255:
4253:
4251:
4233:
4227:
4226:
4209:(1â2): 215â232.
4198:
4192:
4191:
4189:
4187:
4181:
4150:
4141:
4135:
4134:
4106:
4100:
4098:
4070:
4064:
4063:
4037:
4031:
4030:
4002:
3996:
3995:
3985:
3961:
3955:
3954:
3944:
3938:
3937:
3920:(1â2): 215â232.
3909:
3903:
3902:
3862:
3856:
3855:
3819:
3813:
3812:
3764:
3758:
3757:
3721:
3715:
3714:
3712:
3710:
3704:
3693:
3684:
3669:
3668:
3660:
3654:
3653:
3645:
3639:
3638:
3620:
3611:(4): 1295â1298.
3596:
3590:
3589:
3579:
3547:
3541:
3540:
3530:
3506:
3500:
3499:
3497:
3489:
3483:
3482:
3480:
3471:
3465:
3464:
3462:
3460:
3441:
3435:
3434:
3432:
3430:
3407:
3401:
3400:
3380:
3374:
3373:
3363:
3331:
3325:
3307:
3301:
3300:
3290:
3273:(6): 1235â1239.
3258:
3252:
3251:
3215:
3209:
3208:
3188:
3182:
3181:
3161:
3155:
3154:
3152:
3128:
3122:
3108:
3102:
3101:
3083:
3059:
3053:
3052:
3050:
3048:
3033:
3027:
3026:
3014:
3004:
2961:
2959:
2958:
2953:
2951:
2950:
2941:
2935:
2930:
2909:
2907:
2906:
2901:
2896:
2891:
2890:
2869:
2867:
2866:
2861:
2859:
2858:
2849:
2844:
2843:
2822:
2820:
2819:
2814:
2812:
2811:
2802:
2797:
2796:
2762:
2760:
2759:
2754:
2749:
2729:image processing
2715:
2713:
2712:
2707:
2702:
2697:
2696:
2672:
2670:
2669:
2664:
2662:
2661:
2645:
2643:
2642:
2637:
2635:
2634:
2633:
2623:
2618:
2617:
2616:
2566:hypothesis tests
2560:
2558:
2557:
2552:
2540:
2538:
2537:
2532:
2520:
2518:
2517:
2512:
2500:
2498:
2497:
2492:
2480:
2478:
2477:
2472:
2448:
2446:
2445:
2440:
2438:
2437:
2436:
2431:
2424:
2423:
2421:
2401:
2399:
2398:
2393:
2388:
2387:
2386:
2376:
2370:
2368:
2367:
2366:
2362:
2349:
2348:
2343:
2342:
2341:
2340:
2315:
2310:
2308:
2307:
2306:
2301:
2297:
2289:
2279:
2278:
2274:
2260:
2259:
2255:
2242:
2237:
2235:
2201:
2200:
2196:
2191:
2180:
2151:
2145:
2134:
2123:
2122:
2121:
2116:
2109:
2108:
2106:
2094:
2092:
2091:
2090:
2086:
2073:
2072:
2067:
2066:
2065:
2064:
2042:
2041:
2030:
2029:
2028:
2027:
2015:
2013:
2009:
2008:
2006:
2005:
2004:
1999:
1998:
1997:
1996:
1977:
1976:
1971:
1970:
1969:
1968:
1956:
1951:
1949:
1948:
1947:
1942:
1938:
1930:
1916:
1900:
1898:
1897:
1896:
1892:
1887:
1876:
1866:
1865:
1861:
1844:
1839:
1838:
1837:
1836:
1835:
1820:
1806:
1804:
1803:
1798:
1774:
1772:
1771:
1766:
1751:
1749:
1748:
1743:
1725:
1723:
1722:
1717:
1696:
1694:
1693:
1688:
1670:
1668:
1667:
1662:
1650:
1648:
1647:
1642:
1621:
1619:
1618:
1613:
1531:Gini coefficient
1490: >
1185:
1183:
1182:
1177:
1174:
1173:
1172:
1155:
1153:
1152:
1147:
1144:
1143:
1113:
1111:
1110:
1105:
1102:
1101:
1100:
1076:
1074:
1073:
1068:
1066:
1057:
1056:
1055:
1054:
1044:
1034:
1033:
1032:
1005:
1003:
1002:
997:
994:
993:
992:
980:
979:
974:
966:
955:
953:
952:
947:
929:
928:
916:
915:
899:
897:
896:
891:
888:
887:
867:
865:
864:
859:
856:
855:
854:
834:
832:
831:
826:
824:
816:
815:
813:
808:
798:
792:
787:
786:
785:
773:
772:
767:
759:
733:
731:
730:
725:
723:
722:
717:
716:
715:
705:
702:
701:
695:
693:
682:
674:
673:
664:
663:
658:
657:
652:
651:
650:
640:
621:biased estimator
615:
613:
612:
607:
605:
603:
595:
590:
585:
584:
579:
578:
577:
567:
554:
552:
551:
546:
544:
543:
535:
525:
523:
522:
517:
436:
434:
433:
428:
426:
422:
414:
413:
401:
400:
376:
374:
373:
368:
366:
362:
354:
353:
341:
340:
288:
286:
285:
280:
275:
267:
249:
247:
246:
241:
229:
227:
226:
221:
169:
167:
165:
164:
159:
157:
149:
132:
130:
129:
124:
109:
107:
106:
101:
21:
6653:
6652:
6648:
6647:
6646:
6644:
6643:
6642:
6618:
6617:
6616:
6611:
6574:
6545:
6507:
6444:
6430:quality control
6397:
6379:Clinical trials
6356:
6331:
6315:
6303:Hazard function
6297:
6251:
6213:
6197:
6160:
6156:BreuschâGodfrey
6144:
6121:
6061:
6036:Factor analysis
5982:
5963:Graphical model
5935:
5902:
5869:
5855:
5835:
5789:
5756:
5718:
5681:
5680:
5649:
5593:
5580:
5572:
5564:
5548:
5533:
5512:Rank statistics
5506:
5485:Model selection
5473:
5431:Goodness of fit
5425:
5402:
5376:
5348:
5301:
5246:
5235:Median unbiased
5163:
5074:
5007:Order statistic
4969:
4948:
4915:
4889:
4841:
4796:
4739:
4737:Data collection
4718:
4630:
4585:
4559:
4537:
4497:
4449:
4366:Continuous data
4356:
4343:
4325:
4320:
4278:
4273:
4272:
4263:
4259:
4249:
4247:
4235:
4234:
4230:
4200:
4199:
4195:
4185:
4183:
4179:
4148:
4143:
4142:
4138:
4108:
4107:
4103:
4072:
4071:
4067:
4060:
4039:
4038:
4034:
4019:10.2307/1267363
4004:
4003:
3999:
3963:
3962:
3958:
3946:
3945:
3941:
3911:
3910:
3906:
3864:
3863:
3859:
3836:10.2307/2694276
3821:
3820:
3816:
3785:10.2307/3557072
3766:
3765:
3761:
3738:10.2307/2694247
3723:
3722:
3718:
3708:
3706:
3702:
3691:
3686:
3685:
3672:
3662:
3661:
3657:
3647:
3646:
3642:
3598:
3597:
3593:
3549:
3548:
3544:
3521:(10): 1255â70.
3508:
3507:
3503:
3495:
3491:
3490:
3486:
3478:
3473:
3472:
3468:
3458:
3456:
3443:
3442:
3438:
3428:
3426:
3424:
3409:
3408:
3404:
3382:
3381:
3377:
3333:
3332:
3328:
3318:Wayback Machine
3308:
3304:
3260:
3259:
3255:
3217:
3216:
3212:
3190:
3189:
3185:
3163:
3162:
3158:
3130:
3129:
3125:
3109:
3105:
3061:
3060:
3056:
3046:
3044:
3035:
3034:
3030:
3023:
3006:
3005:
3001:
2996:
2969:
2942:
2917:
2916:
2882:
2877:
2876:
2850:
2835:
2830:
2829:
2803:
2788:
2783:
2782:
2771:in particular.
2767:in general and
2737:
2736:
2727:, particularly
2688:
2683:
2682:
2653:
2648:
2647:
2625:
2608:
2602:
2601:
2595:
2582:
2543:
2542:
2523:
2522:
2503:
2502:
2483:
2482:
2451:
2450:
2426:
2406:
2405:
2377:
2350:
2331:
2329:
2319:
2284:
2283:
2262:
2261:
2243:
2202:
2181:
2175:
2152:
2111:
2074:
2055:
2053:
2043:
2018:
2016:
1987:
1985:
1978:
1959:
1957:
1925:
1924:
1920:
1911:
1907:
1877:
1871:
1849:
1848:
1826:
1821:
1811:
1810:
1789:
1788:
1781:
1754:
1753:
1728:
1727:
1699:
1698:
1673:
1672:
1653:
1652:
1624:
1623:
1604:
1603:
1548:
1539:
1528:
1519:
1507:
1498:
1489:
1465:
1448:
1416:
1405:
1387:
1370:
1361:
1343:
1327:
1310:%RMS Uniformity
1236:queueing theory
1228:
1210:
1197:
1192:
1163:
1158:
1157:
1132:
1127:
1126:
1091:
1086:
1085:
1046:
1039:
1024:
1012:
1011:
967:
963:
958:
957:
920:
907:
902:
901:
879:
874:
873:
846:
840:
839:
793:
760:
756:
751:
750:
740:
738:Log-normal data
706:
686:
641:
637:
632:
631:
568:
560:
559:
528:
527:
507:
506:
500:
451:
405:
392:
383:
382:
345:
332:
323:
322:
252:
251:
232:
231:
212:
211:
208:
192:economic models
140:
139:
138:
115:
114:
92:
91:
35:
28:
23:
22:
15:
12:
11:
5:
6651:
6649:
6641:
6640:
6635:
6630:
6620:
6619:
6613:
6612:
6610:
6609:
6597:
6585:
6571:
6558:
6555:
6554:
6551:
6550:
6547:
6546:
6544:
6543:
6538:
6533:
6528:
6523:
6517:
6515:
6509:
6508:
6506:
6505:
6500:
6495:
6490:
6485:
6480:
6475:
6470:
6465:
6460:
6454:
6452:
6446:
6445:
6443:
6442:
6437:
6432:
6423:
6418:
6413:
6407:
6405:
6399:
6398:
6396:
6395:
6390:
6385:
6376:
6374:Bioinformatics
6370:
6368:
6358:
6357:
6352:
6345:
6344:
6341:
6340:
6337:
6336:
6333:
6332:
6330:
6329:
6323:
6321:
6317:
6316:
6314:
6313:
6307:
6305:
6299:
6298:
6296:
6295:
6290:
6285:
6280:
6274:
6272:
6263:
6257:
6256:
6253:
6252:
6250:
6249:
6244:
6239:
6234:
6229:
6223:
6221:
6215:
6214:
6212:
6211:
6206:
6201:
6193:
6188:
6183:
6182:
6181:
6179:partial (PACF)
6170:
6168:
6162:
6161:
6159:
6158:
6153:
6148:
6140:
6135:
6129:
6127:
6126:Specific tests
6123:
6122:
6120:
6119:
6114:
6109:
6104:
6099:
6094:
6089:
6084:
6078:
6076:
6069:
6063:
6062:
6060:
6059:
6058:
6057:
6056:
6055:
6040:
6039:
6038:
6028:
6026:Classification
6023:
6018:
6013:
6008:
6003:
5998:
5992:
5990:
5984:
5983:
5981:
5980:
5975:
5973:McNemar's test
5970:
5965:
5960:
5955:
5949:
5947:
5937:
5936:
5919:
5912:
5911:
5908:
5907:
5904:
5903:
5901:
5900:
5895:
5890:
5885:
5879:
5877:
5871:
5870:
5868:
5867:
5851:
5845:
5843:
5837:
5836:
5834:
5833:
5828:
5823:
5818:
5813:
5811:Semiparametric
5808:
5803:
5797:
5795:
5791:
5790:
5788:
5787:
5782:
5777:
5772:
5766:
5764:
5758:
5757:
5755:
5754:
5749:
5744:
5739:
5734:
5728:
5726:
5720:
5719:
5717:
5716:
5711:
5706:
5701:
5695:
5693:
5683:
5682:
5679:
5678:
5673:
5667:
5666:
5659:
5658:
5655:
5654:
5651:
5650:
5648:
5647:
5646:
5645:
5635:
5630:
5625:
5624:
5623:
5618:
5607:
5605:
5599:
5598:
5595:
5594:
5592:
5591:
5586:
5585:
5584:
5576:
5568:
5552:
5549:(MannâWhitney)
5544:
5543:
5542:
5529:
5528:
5527:
5516:
5514:
5508:
5507:
5505:
5504:
5503:
5502:
5497:
5492:
5482:
5477:
5474:(ShapiroâWilk)
5469:
5464:
5459:
5454:
5449:
5441:
5435:
5433:
5427:
5426:
5424:
5423:
5415:
5406:
5394:
5388:
5386:Specific tests
5382:
5381:
5378:
5377:
5375:
5374:
5369:
5364:
5358:
5356:
5350:
5349:
5347:
5346:
5341:
5340:
5339:
5329:
5328:
5327:
5317:
5311:
5309:
5303:
5302:
5300:
5299:
5298:
5297:
5292:
5282:
5277:
5272:
5267:
5262:
5256:
5254:
5248:
5247:
5245:
5244:
5239:
5238:
5237:
5232:
5231:
5230:
5225:
5210:
5209:
5208:
5203:
5198:
5193:
5182:
5180:
5171:
5165:
5164:
5162:
5161:
5156:
5151:
5150:
5149:
5139:
5134:
5133:
5132:
5122:
5121:
5120:
5115:
5110:
5100:
5095:
5090:
5089:
5088:
5083:
5078:
5062:
5061:
5060:
5055:
5050:
5040:
5039:
5038:
5033:
5023:
5022:
5021:
5011:
5010:
5009:
4999:
4994:
4989:
4983:
4981:
4971:
4970:
4965:
4958:
4957:
4954:
4953:
4950:
4949:
4947:
4946:
4941:
4936:
4931:
4925:
4923:
4917:
4916:
4914:
4913:
4908:
4903:
4897:
4895:
4891:
4890:
4888:
4887:
4882:
4877:
4872:
4867:
4862:
4857:
4851:
4849:
4843:
4842:
4840:
4839:
4837:Standard error
4834:
4829:
4824:
4823:
4822:
4817:
4806:
4804:
4798:
4797:
4795:
4794:
4789:
4784:
4779:
4774:
4769:
4767:Optimal design
4764:
4759:
4753:
4751:
4741:
4740:
4735:
4728:
4727:
4724:
4723:
4720:
4719:
4717:
4716:
4711:
4706:
4701:
4696:
4691:
4686:
4681:
4676:
4671:
4666:
4661:
4656:
4651:
4646:
4640:
4638:
4632:
4631:
4629:
4628:
4623:
4622:
4621:
4616:
4606:
4601:
4595:
4593:
4587:
4586:
4584:
4583:
4578:
4573:
4567:
4565:
4564:Summary tables
4561:
4560:
4558:
4557:
4551:
4549:
4543:
4542:
4539:
4538:
4536:
4535:
4534:
4533:
4528:
4523:
4513:
4507:
4505:
4499:
4498:
4496:
4495:
4490:
4485:
4480:
4475:
4470:
4465:
4459:
4457:
4451:
4450:
4448:
4447:
4442:
4437:
4436:
4435:
4430:
4425:
4420:
4415:
4410:
4405:
4400:
4398:Contraharmonic
4395:
4390:
4379:
4377:
4368:
4358:
4357:
4352:
4345:
4344:
4342:
4341:
4336:
4330:
4327:
4326:
4321:
4319:
4318:
4311:
4304:
4296:
4290:
4289:
4277:
4276:External links
4274:
4271:
4270:
4257:
4228:
4193:
4136:
4101:
4065:
4058:
4032:
4013:(1): 166â169.
3997:
3956:
3939:
3904:
3857:
3830:(2): 231â242.
3814:
3779:(4): 768â782.
3759:
3732:(3): 493â504.
3716:
3670:
3655:
3640:
3591:
3542:
3501:
3484:
3466:
3436:
3422:
3402:
3375:
3326:
3302:
3253:
3210:
3183:
3172:(1â2): 25â32.
3156:
3143:(5): 341â352.
3123:
3103:
3074:(2): 445â462.
3054:
3028:
3022:978-0521593465
3021:
2998:
2997:
2995:
2992:
2991:
2990:
2985:
2980:
2975:
2968:
2965:
2964:
2963:
2962:(windowed VMR)
2949:
2945:
2940:
2934:
2929:
2925:
2910:
2899:
2895:
2889:
2885:
2870:
2857:
2853:
2848:
2842:
2838:
2823:
2810:
2806:
2801:
2795:
2791:
2752:
2748:
2744:
2705:
2701:
2695:
2691:
2660:
2656:
2632:
2628:
2622:
2615:
2611:
2594:
2593:Similar ratios
2591:
2581:
2578:
2550:
2530:
2510:
2490:
2470:
2467:
2464:
2461:
2458:
2435:
2430:
2419:
2391:
2385:
2380:
2375:
2365:
2361:
2357:
2353:
2347:
2339:
2334:
2328:
2325:
2322:
2318:
2313:
2305:
2300:
2295:
2292:
2287:
2282:
2277:
2273:
2269:
2265:
2258:
2254:
2250:
2246:
2240:
2233:
2230:
2226:
2223:
2220:
2217:
2214:
2211:
2208:
2205:
2199:
2194:
2190:
2187:
2184:
2178:
2174:
2170:
2167:
2164:
2161:
2158:
2155:
2149:
2144:
2141:
2138:
2133:
2130:
2127:
2120:
2115:
2104:
2089:
2085:
2081:
2077:
2071:
2063:
2058:
2052:
2049:
2046:
2040:
2037:
2034:
2026:
2021:
2012:
2003:
1995:
1990:
1984:
1981:
1975:
1967:
1962:
1954:
1946:
1941:
1936:
1933:
1928:
1923:
1919:
1914:
1910:
1906:
1903:
1895:
1890:
1886:
1883:
1880:
1874:
1869:
1864:
1860:
1856:
1852:
1847:
1842:
1834:
1829:
1824:
1819:
1796:
1780:
1777:
1764:
1761:
1741:
1738:
1735:
1715:
1712:
1709:
1706:
1686:
1683:
1680:
1660:
1640:
1637:
1634:
1631:
1611:
1547:
1544:
1538:
1535:
1524:
1515:
1510:
1509:
1503:
1494:
1485:
1471:
1461:
1444:
1436:} is the list
1426:
1414:
1403:
1397:
1383:
1366:
1357:
1353:(with entries
1342:
1339:
1326:
1323:
1294:fluid dynamics
1272:absolute error
1232:renewal theory
1227:
1224:
1223:
1222:
1215:
1209:
1206:
1196:
1193:
1191:
1188:
1171:
1166:
1142:
1139:
1135:
1099:
1094:
1078:
1077:
1065:
1062:
1053:
1049:
1043:
1037:
1031:
1027:
1023:
1020:
991:
988:
985:
977:
973:
970:
944:
941:
938:
935:
932:
927:
923:
919:
914:
910:
886:
882:
853:
849:
836:
835:
822:
819:
812:
807:
803:
797:
790:
784:
781:
778:
770:
766:
763:
739:
736:
735:
734:
720:
714:
709:
700:
692:
689:
685:
680:
677:
672:
667:
662:
655:
649:
644:
617:
616:
601:
598:
593:
588:
582:
576:
571:
541:
538:
514:
499:
496:
495:
494:
491:
488:
473:
472:
469:
466:
450:
447:
425:
421:
417:
412:
408:
404:
399:
395:
391:
365:
361:
357:
352:
348:
344:
339:
335:
331:
297:interval scale
278:
273:
270:
265:
262:
259:
239:
219:
207:
204:
156:
152:
148:
135:absolute value
122:
99:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
6650:
6639:
6636:
6634:
6631:
6629:
6626:
6625:
6623:
6608:
6607:
6598:
6596:
6595:
6586:
6584:
6583:
6578:
6572:
6570:
6569:
6560:
6559:
6556:
6542:
6539:
6537:
6536:Geostatistics
6534:
6532:
6529:
6527:
6524:
6522:
6519:
6518:
6516:
6514:
6510:
6504:
6503:Psychometrics
6501:
6499:
6496:
6494:
6491:
6489:
6486:
6484:
6481:
6479:
6476:
6474:
6471:
6469:
6466:
6464:
6461:
6459:
6456:
6455:
6453:
6451:
6447:
6441:
6438:
6436:
6433:
6431:
6427:
6424:
6422:
6419:
6417:
6414:
6412:
6409:
6408:
6406:
6404:
6400:
6394:
6391:
6389:
6386:
6384:
6380:
6377:
6375:
6372:
6371:
6369:
6367:
6366:Biostatistics
6363:
6359:
6355:
6350:
6346:
6328:
6327:Log-rank test
6325:
6324:
6322:
6318:
6312:
6309:
6308:
6306:
6304:
6300:
6294:
6291:
6289:
6286:
6284:
6281:
6279:
6276:
6275:
6273:
6271:
6267:
6264:
6262:
6258:
6248:
6245:
6243:
6240:
6238:
6235:
6233:
6230:
6228:
6225:
6224:
6222:
6220:
6216:
6210:
6207:
6205:
6202:
6200:
6198:(BoxâJenkins)
6194:
6192:
6189:
6187:
6184:
6180:
6177:
6176:
6175:
6172:
6171:
6169:
6167:
6163:
6157:
6154:
6152:
6151:DurbinâWatson
6149:
6147:
6141:
6139:
6136:
6134:
6133:DickeyâFuller
6131:
6130:
6128:
6124:
6118:
6115:
6113:
6110:
6108:
6107:Cointegration
6105:
6103:
6100:
6098:
6095:
6093:
6090:
6088:
6085:
6083:
6082:Decomposition
6080:
6079:
6077:
6073:
6070:
6068:
6064:
6054:
6051:
6050:
6049:
6046:
6045:
6044:
6041:
6037:
6034:
6033:
6032:
6029:
6027:
6024:
6022:
6019:
6017:
6014:
6012:
6009:
6007:
6004:
6002:
5999:
5997:
5994:
5993:
5991:
5989:
5985:
5979:
5976:
5974:
5971:
5969:
5966:
5964:
5961:
5959:
5956:
5954:
5953:Cohen's kappa
5951:
5950:
5948:
5946:
5942:
5938:
5934:
5930:
5926:
5922:
5917:
5913:
5899:
5896:
5894:
5891:
5889:
5886:
5884:
5881:
5880:
5878:
5876:
5872:
5866:
5862:
5858:
5852:
5850:
5847:
5846:
5844:
5842:
5838:
5832:
5829:
5827:
5824:
5822:
5819:
5817:
5814:
5812:
5809:
5807:
5806:Nonparametric
5804:
5802:
5799:
5798:
5796:
5792:
5786:
5783:
5781:
5778:
5776:
5773:
5771:
5768:
5767:
5765:
5763:
5759:
5753:
5750:
5748:
5745:
5743:
5740:
5738:
5735:
5733:
5730:
5729:
5727:
5725:
5721:
5715:
5712:
5710:
5707:
5705:
5702:
5700:
5697:
5696:
5694:
5692:
5688:
5684:
5677:
5674:
5672:
5669:
5668:
5664:
5660:
5644:
5641:
5640:
5639:
5636:
5634:
5631:
5629:
5626:
5622:
5619:
5617:
5614:
5613:
5612:
5609:
5608:
5606:
5604:
5600:
5590:
5587:
5583:
5577:
5575:
5569:
5567:
5561:
5560:
5559:
5556:
5555:Nonparametric
5553:
5551:
5545:
5541:
5538:
5537:
5536:
5530:
5526:
5525:Sample median
5523:
5522:
5521:
5518:
5517:
5515:
5513:
5509:
5501:
5498:
5496:
5493:
5491:
5488:
5487:
5486:
5483:
5481:
5478:
5476:
5470:
5468:
5465:
5463:
5460:
5458:
5455:
5453:
5450:
5448:
5446:
5442:
5440:
5437:
5436:
5434:
5432:
5428:
5422:
5420:
5416:
5414:
5412:
5407:
5405:
5400:
5396:
5395:
5392:
5389:
5387:
5383:
5373:
5370:
5368:
5365:
5363:
5360:
5359:
5357:
5355:
5351:
5345:
5342:
5338:
5335:
5334:
5333:
5330:
5326:
5323:
5322:
5321:
5318:
5316:
5313:
5312:
5310:
5308:
5304:
5296:
5293:
5291:
5288:
5287:
5286:
5283:
5281:
5278:
5276:
5273:
5271:
5268:
5266:
5263:
5261:
5258:
5257:
5255:
5253:
5249:
5243:
5240:
5236:
5233:
5229:
5226:
5224:
5221:
5220:
5219:
5216:
5215:
5214:
5211:
5207:
5204:
5202:
5199:
5197:
5194:
5192:
5189:
5188:
5187:
5184:
5183:
5181:
5179:
5175:
5172:
5170:
5166:
5160:
5157:
5155:
5152:
5148:
5145:
5144:
5143:
5140:
5138:
5135:
5131:
5130:loss function
5128:
5127:
5126:
5123:
5119:
5116:
5114:
5111:
5109:
5106:
5105:
5104:
5101:
5099:
5096:
5094:
5091:
5087:
5084:
5082:
5079:
5077:
5071:
5068:
5067:
5066:
5063:
5059:
5056:
5054:
5051:
5049:
5046:
5045:
5044:
5041:
5037:
5034:
5032:
5029:
5028:
5027:
5024:
5020:
5017:
5016:
5015:
5012:
5008:
5005:
5004:
5003:
5000:
4998:
4995:
4993:
4990:
4988:
4985:
4984:
4982:
4980:
4976:
4972:
4968:
4963:
4959:
4945:
4942:
4940:
4937:
4935:
4932:
4930:
4927:
4926:
4924:
4922:
4918:
4912:
4909:
4907:
4904:
4902:
4899:
4898:
4896:
4892:
4886:
4883:
4881:
4878:
4876:
4873:
4871:
4868:
4866:
4863:
4861:
4858:
4856:
4853:
4852:
4850:
4848:
4844:
4838:
4835:
4833:
4832:Questionnaire
4830:
4828:
4825:
4821:
4818:
4816:
4813:
4812:
4811:
4808:
4807:
4805:
4803:
4799:
4793:
4790:
4788:
4785:
4783:
4780:
4778:
4775:
4773:
4770:
4768:
4765:
4763:
4760:
4758:
4755:
4754:
4752:
4750:
4746:
4742:
4738:
4733:
4729:
4715:
4712:
4710:
4707:
4705:
4702:
4700:
4697:
4695:
4692:
4690:
4687:
4685:
4682:
4680:
4677:
4675:
4672:
4670:
4667:
4665:
4662:
4660:
4659:Control chart
4657:
4655:
4652:
4650:
4647:
4645:
4642:
4641:
4639:
4637:
4633:
4627:
4624:
4620:
4617:
4615:
4612:
4611:
4610:
4607:
4605:
4602:
4600:
4597:
4596:
4594:
4592:
4588:
4582:
4579:
4577:
4574:
4572:
4569:
4568:
4566:
4562:
4556:
4553:
4552:
4550:
4548:
4544:
4532:
4529:
4527:
4524:
4522:
4519:
4518:
4517:
4514:
4512:
4509:
4508:
4506:
4504:
4500:
4494:
4491:
4489:
4486:
4484:
4481:
4479:
4476:
4474:
4471:
4469:
4466:
4464:
4461:
4460:
4458:
4456:
4452:
4446:
4443:
4441:
4438:
4434:
4431:
4429:
4426:
4424:
4421:
4419:
4416:
4414:
4411:
4409:
4406:
4404:
4401:
4399:
4396:
4394:
4391:
4389:
4386:
4385:
4384:
4381:
4380:
4378:
4376:
4372:
4369:
4367:
4363:
4359:
4355:
4350:
4346:
4340:
4337:
4335:
4332:
4331:
4328:
4324:
4317:
4312:
4310:
4305:
4303:
4298:
4297:
4294:
4287:
4283:
4280:
4279:
4275:
4267:
4261:
4258:
4245:
4241:
4240:
4232:
4229:
4224:
4220:
4216:
4212:
4208:
4204:
4197:
4194:
4178:
4174:
4170:
4166:
4162:
4158:
4154:
4147:
4140:
4137:
4132:
4128:
4124:
4120:
4116:
4112:
4105:
4102:
4096:
4092:
4088:
4084:
4080:
4076:
4069:
4066:
4061:
4055:
4051:
4047:
4043:
4036:
4033:
4028:
4024:
4020:
4016:
4012:
4008:
4007:Technometrics
4001:
3998:
3993:
3989:
3984:
3979:
3976:(3): 129â32.
3975:
3971:
3967:
3960:
3957:
3952:
3951:
3943:
3940:
3935:
3931:
3927:
3923:
3919:
3915:
3908:
3905:
3900:
3896:
3892:
3888:
3884:
3880:
3876:
3872:
3868:
3861:
3858:
3853:
3849:
3845:
3841:
3837:
3833:
3829:
3825:
3818:
3815:
3810:
3806:
3802:
3798:
3794:
3790:
3786:
3782:
3778:
3774:
3770:
3763:
3760:
3755:
3751:
3747:
3743:
3739:
3735:
3731:
3727:
3720:
3717:
3701:
3697:
3690:
3683:
3681:
3679:
3677:
3675:
3671:
3666:
3659:
3656:
3651:
3644:
3641:
3636:
3632:
3628:
3624:
3619:
3614:
3610:
3606:
3602:
3595:
3592:
3587:
3583:
3578:
3573:
3569:
3565:
3561:
3557:
3553:
3546:
3543:
3538:
3534:
3529:
3524:
3520:
3516:
3512:
3505:
3502:
3494:
3488:
3485:
3477:
3470:
3467:
3454:
3450:
3446:
3440:
3437:
3425:
3423:9781566983969
3419:
3415:
3414:
3406:
3403:
3398:
3394:
3390:
3386:
3379:
3376:
3371:
3367:
3362:
3357:
3353:
3349:
3345:
3341:
3340:Ann Rheum Dis
3337:
3330:
3327:
3323:
3322:PharmaSUG2011
3319:
3315:
3312:
3306:
3303:
3298:
3294:
3289:
3284:
3280:
3276:
3272:
3268:
3264:
3257:
3254:
3249:
3245:
3241:
3237:
3233:
3229:
3225:
3221:
3214:
3211:
3206:
3202:
3198:
3194:
3187:
3184:
3179:
3175:
3171:
3167:
3160:
3157:
3151:
3146:
3142:
3138:
3134:
3127:
3124:
3121:
3120:0-7167-2411-1
3117:
3113:
3107:
3104:
3099:
3095:
3091:
3087:
3082:
3077:
3073:
3069:
3065:
3058:
3055:
3042:
3038:
3032:
3029:
3024:
3018:
3013:
3012:
3003:
3000:
2993:
2989:
2986:
2984:
2981:
2979:
2976:
2974:
2971:
2970:
2966:
2947:
2943:
2938:
2932:
2927:
2923:
2914:
2911:
2897:
2893:
2887:
2883:
2874:
2871:
2855:
2851:
2846:
2840:
2836:
2827:
2824:
2808:
2804:
2799:
2793:
2789:
2780:
2777:
2776:
2775:
2772:
2770:
2766:
2750:
2746:
2742:
2734:
2730:
2726:
2721:
2719:
2703:
2699:
2693:
2689:
2680:
2676:
2658:
2654:
2630:
2626:
2620:
2613:
2609:
2599:
2592:
2590:
2588:
2579:
2577:
2575:
2571:
2567:
2562:
2548:
2528:
2508:
2488:
2468:
2465:
2462:
2459:
2456:
2417:
2402:
2389:
2378:
2363:
2359:
2355:
2345:
2332:
2326:
2323:
2316:
2311:
2303:
2298:
2293:
2290:
2285:
2280:
2275:
2271:
2267:
2263:
2256:
2252:
2248:
2244:
2238:
2231:
2228:
2224:
2218:
2215:
2212:
2209:
2206:
2197:
2192:
2188:
2185:
2182:
2176:
2168:
2162:
2159:
2156:
2147:
2142:
2139:
2136:
2131:
2128:
2125:
2102:
2087:
2083:
2079:
2069:
2056:
2050:
2047:
2038:
2035:
2032:
2019:
2010:
2001:
1988:
1982:
1979:
1973:
1960:
1952:
1944:
1939:
1934:
1931:
1926:
1921:
1917:
1912:
1908:
1904:
1901:
1893:
1888:
1884:
1881:
1878:
1872:
1862:
1858:
1854:
1850:
1845:
1840:
1827:
1822:
1808:
1794:
1786:
1778:
1776:
1762:
1759:
1739:
1736:
1733:
1713:
1710:
1707:
1704:
1684:
1681:
1678:
1658:
1638:
1635:
1632:
1629:
1609:
1600:
1598:
1593:
1590:
1587:
1584:
1580:
1578:
1573:
1572:Fahrenheit:
1570:
1567:
1565:
1564:Rankine scale
1561:
1557:
1553:
1545:
1543:
1536:
1534:
1532:
1527:
1523:
1518:
1514:
1506:
1502:
1497:
1493:
1488:
1484:
1480:
1476:
1472:
1469:
1464:
1460:
1456:
1452:
1447:
1443:
1439:
1435:
1431:
1427:
1424:
1420:
1413:
1409:
1402:
1398:
1395:
1391:
1386:
1382:
1378:
1377:
1376:
1374:
1369:
1365:
1360:
1356:
1352:
1348:
1340:
1338:
1336:
1332:
1324:
1322:
1319:
1315:
1311:
1307:
1303:
1299:
1295:
1290:
1286:
1284:
1283:unitized risk
1280:
1275:
1273:
1269:
1265:
1261:
1257:
1253:
1249:
1245:
1241:
1237:
1233:
1225:
1221:for the mean.
1220:
1216:
1212:
1211:
1208:Disadvantages
1207:
1205:
1203:
1194:
1189:
1187:
1164:
1140:
1137:
1133:
1124:
1120:
1115:
1092:
1083:
1063:
1060:
1051:
1047:
1035:
1010:
1009:
1008:
975:
971:
968:
939:
933:
930:
925:
921:
917:
912:
908:
884:
880:
871:
851:
847:
820:
817:
810:
805:
801:
788:
768:
764:
761:
749:
748:
747:
745:
737:
718:
707:
690:
687:
683:
678:
675:
665:
660:
653:
642:
630:
629:
628:
626:
622:
596:
591:
586:
580:
569:
558:
557:
556:
536:
512:
505:
497:
492:
489:
486:
482:
481:
480:
478:
470:
467:
464:
460:
459:
458:
456:
448:
446:
444:
438:
423:
419:
410:
406:
402:
397:
393:
380:
363:
359:
350:
346:
342:
337:
333:
321:
317:
312:
310:
305:
302:
298:
294:
289:
276:
271:
268:
263:
260:
257:
237:
217:
205:
203:
201:
197:
193:
189:
185:
181:
177:
173:
150:
136:
120:
113:
97:
90:
86:
82:
78:
74:
70:
66:
62:
58:
54:
50:
46:
42:
37:
33:
19:
6604:
6592:
6573:
6566:
6478:Econometrics
6428: /
6411:Chemometrics
6388:Epidemiology
6381: /
6354:Applications
6196:ARIMA model
6143:Q-statistic
6092:Stationarity
5988:Multivariate
5931: /
5927: /
5925:Multivariate
5923: /
5863: /
5859: /
5633:Bayes factor
5532:Signed rank
5444:
5418:
5410:
5398:
5093:Completeness
4929:Cohort study
4827:Opinion poll
4762:Missing data
4749:Study design
4704:Scatter plot
4626:Scatter plot
4619:Spearman's Ï
4581:Grouped data
4467:
4265:
4260:
4248:. Retrieved
4238:
4231:
4206:
4202:
4196:
4186:23 September
4184:. Retrieved
4159:(2): 21â26.
4156:
4152:
4139:
4114:
4110:
4104:
4081:(1): 21â26.
4078:
4074:
4068:
4041:
4035:
4010:
4006:
4000:
3973:
3969:
3959:
3949:
3942:
3917:
3913:
3907:
3874:
3870:
3860:
3827:
3823:
3817:
3776:
3772:
3762:
3729:
3725:
3719:
3707:. Retrieved
3695:
3664:
3658:
3649:
3643:
3608:
3604:
3594:
3562:(4): 570â5.
3559:
3555:
3545:
3518:
3514:
3504:
3487:
3469:
3457:. Retrieved
3448:
3439:
3427:. Retrieved
3412:
3405:
3391:(4): 908â9.
3388:
3384:
3378:
3343:
3339:
3329:
3324:, Paper PO08
3321:
3305:
3270:
3266:
3256:
3226:(1): 55â71.
3223:
3219:
3213:
3196:
3192:
3186:
3169:
3165:
3159:
3140:
3136:
3126:
3111:
3106:
3071:
3067:
3057:
3045:. Retrieved
3031:
3010:
3002:
2773:
2722:
2674:
2596:
2583:
2563:
2403:
1809:
1782:
1779:Distribution
1601:
1594:
1591:
1588:
1585:
1581:
1574:
1571:
1568:
1549:
1540:
1525:
1521:
1516:
1512:
1511:
1504:
1500:
1495:
1491:
1486:
1482:
1478:
1474:
1467:
1462:
1458:
1454:
1450:
1445:
1441:
1437:
1433:
1429:
1422:
1418:
1411:
1407:
1400:
1393:
1389:
1384:
1380:
1379:Anonymity â
1372:
1367:
1363:
1358:
1354:
1350:
1344:
1328:
1317:
1314:Velocity RMS
1313:
1309:
1305:
1301:
1297:
1291:
1287:
1282:
1276:
1263:
1229:
1226:Applications
1198:
1116:
1081:
1079:
837:
741:
618:
501:
476:
474:
454:
452:
439:
313:
309:log-normally
306:
290:
230:to the mean
209:
200:neuroscience
73:standardized
68:
64:
60:
52:
48:
38:
36:
6606:WikiProject
6521:Cartography
6483:Jurimetrics
6435:Reliability
6166:Time domain
6145:(LjungâBox)
6067:Time-series
5945:Categorical
5929:Time-series
5921:Categorical
5856:(Bernoulli)
5691:Correlation
5671:Correlation
5467:JarqueâBera
5439:Chi-squared
5201:M-estimator
5154:Asymptotics
5098:Sufficiency
4865:Interaction
4777:Replication
4757:Effect size
4714:Violin plot
4694:Radar chart
4674:Forest plot
4664:Correlogram
4614:Kendall's Ï
4250:25 February
3047:22 February
2978:Omega ratio
2913:Fano factor
2580:Alternative
2481:, i.e., if
1318:Percent RMS
1302:Percent RMS
870:natural log
318:, half the
293:ratio scale
180:engineering
75:measure of
61:percent RMS
6622:Categories
6473:Demography
6191:ARMA model
5996:Regression
5573:(Friedman)
5534:(Wilcoxon)
5472:Normality
5462:Lilliefors
5409:Student's
5285:Resampling
5159:Robustness
5147:divergence
5137:Efficiency
5075:(monotone)
5070:Likelihood
4987:Population
4820:Stratified
4772:Population
4591:Dependence
4547:Count data
4478:Percentile
4455:Dispersion
4388:Arithmetic
4323:Statistics
4282:cvequality
4117:(6): 647.
3877:: 102554.
3385:Biometrics
3346:(8): 1â3.
3166:Biometrika
3137:BioScience
2994:References
2779:Efficiency
2733:reciprocal
1671:only when
1592:Rankine:
1569:Celsius:
1556:Fahrenheit
1195:Advantages
498:Estimation
445:approach.
206:Definition
196:psychology
77:dispersion
45:statistics
5854:Logistic
5621:posterior
5547:Rank sum
5295:Jackknife
5290:Bootstrap
5108:Bootstrap
5043:Parameter
4992:Statistic
4787:Statistic
4699:Run chart
4684:Pie chart
4679:Histogram
4669:Fan chart
4644:Bar chart
4526:L-moments
4413:Geometric
4223:120898013
3934:120898013
3899:224904703
3852:163198451
3809:147444325
3793:0002-7316
3754:163507589
3627:0300-5771
3090:1554-3528
2944:μ
2924:σ
2898:μ
2884:σ
2852:σ
2837:μ
2805:μ
2790:σ
2751:σ
2743:μ
2704:μ
2690:σ
2655:μ
2627:σ
2610:μ
2466:−
2460:−
2434:′
2429:∑
2418:∑
2312:⋅
2294:μ
2291:σ
2281:⋅
2239:⋅
2216:−
2210:−
2186:−
2173:Γ
2160:−
2148:
2140:−
2119:′
2114:∑
2103:∑
2036:−
1953:⋅
1935:μ
1932:σ
1913:−
1905:
1882:−
1868:Γ
1851:π
1737:≠
1589:Kelvin:
1082:analogous
1061:−
976:^
934:
818:−
769:^
719:^
661:∗
654:^
600:¯
581:^
540:¯
343:−
272:μ
269:σ
238:μ
218:σ
194:, and in
151:μ
121:μ
98:σ
6568:Category
6261:Survival
6138:Johansen
5861:Binomial
5816:Isotonic
5403:(normal)
5048:location
4855:Blocking
4810:Sampling
4689:QâQ plot
4654:Box plot
4636:Graphics
4531:Skewness
4521:Kurtosis
4493:Variance
4423:Heronian
4418:Harmonic
4244:Archived
4177:Archived
4173:29168286
3700:Archived
3635:27581804
3586:25757675
3453:Archived
3370:24728329
3314:Archived
3297:12414755
3240:10709801
3112:Biometry
3098:25987306
3041:Archived
2967:See also
1421:) where
1125:) it is
1114:itself.
449:Examples
379:midhinge
133:(or its
71:), is a
6594:Commons
6541:Kriging
6426:Process
6383:studies
6242:Wavelet
6075:General
5242:Plug-in
5036:L space
4815:Cluster
4516:Moments
4334:Outline
4131:8731006
4095:2685039
4027:1267363
3992:2957564
3879:Bibcode
3844:2694276
3801:3557072
3746:2694247
3709:13 June
3577:4478151
3537:4370388
3397:2530139
3361:4112421
3248:2805094
3205:1601532
2673:is the
2521:and if
1552:Celsius
1417:(α
623:. For
184:physics
110:to the
6463:Census
6053:Normal
6001:Manova
5821:Robust
5571:2-way
5563:1-way
5401:-test
5072:
4649:Biplot
4440:Median
4433:Lehmer
4375:Center
4221:
4171:
4129:
4093:
4056:
4025:
3990:
3932:
3897:
3850:
3842:
3807:
3799:
3791:
3752:
3744:
3633:
3625:
3584:
3574:
3535:
3429:7 June
3420:
3395:
3368:
3358:
3295:
3288:130103
3285:
3246:
3238:
3203:
3118:
3096:
3088:
3019:
2735:ratio
2731:, the
2646:where
1560:kelvin
1481:(i.e.
1423:α
1331:assays
1296:, the
1238:, and
1214:scale.
838:where
301:Kelvin
63:, and
47:, the
6087:Trend
5616:prior
5558:anova
5447:-test
5421:-test
5413:-test
5320:Power
5265:Pivot
5058:shape
5053:scale
4503:Shape
4483:Range
4428:Heinz
4403:Cubic
4339:Index
4219:S2CID
4180:(PDF)
4169:S2CID
4149:(PDF)
4091:JSTOR
4023:JSTOR
3988:JSTOR
3930:S2CID
3895:S2CID
3848:S2CID
3840:JSTOR
3805:S2CID
3797:JSTOR
3750:S2CID
3742:JSTOR
3703:(PDF)
3692:(PDF)
3496:(PDF)
3479:(PDF)
3459:2 May
3393:JSTOR
3244:S2CID
1726:with
1457:}) =
1349:. If
1312:, or
176:assay
79:of a
6320:Test
5520:Sign
5372:Wald
4445:Mode
4383:Mean
4252:2014
4188:2013
4127:PMID
4054:ISBN
3789:ISSN
3711:2016
3631:PMID
3623:ISSN
3582:PMID
3533:PMID
3461:2018
3431:2014
3418:ISBN
3366:PMID
3293:PMID
3236:PMID
3201:PMID
3116:ISBN
3094:PMID
3086:ISSN
3049:2008
3017:ISBN
1595:The
1575:The
1562:and
1554:and
1410:) =
1306:%RMS
112:mean
43:and
5500:BIC
5495:AIC
4211:doi
4161:doi
4119:doi
4083:doi
4046:doi
4015:doi
3978:doi
3922:doi
3887:doi
3832:doi
3781:doi
3734:doi
3613:doi
3572:PMC
3564:doi
3523:doi
3356:PMC
3348:doi
3283:PMC
3275:doi
3228:doi
3174:doi
3145:doi
3076:doi
2723:In
2568:or
1902:exp
1292:In
1277:In
381:),
182:or
83:or
69:RSD
39:In
6624::
4284::
4217:.
4207:29
4205:.
4175:.
4167:.
4157:38
4155:.
4151:.
4125:.
4115:15
4113:.
4089:.
4079:50
4077:.
4052:.
4021:.
4011:12
4009:.
3986:.
3972:.
3968:.
3928:.
3918:29
3916:.
3893:.
3885:.
3875:33
3873:.
3869:.
3846:.
3838:.
3828:64
3826:.
3803:.
3795:.
3787:.
3777:68
3775:.
3771:.
3748:.
3740:.
3730:66
3728:.
3694:.
3673:^
3629:.
3621:.
3609:45
3607:.
3603:.
3580:.
3570:.
3560:27
3558:.
3554:.
3531:.
3519:20
3517:.
3513:.
3451:.
3447:.
3389:35
3387:.
3364:.
3354:.
3344:73
3342:.
3338:.
3320:,
3291:.
3281:.
3269:.
3265:.
3242:.
3234:.
3224:10
3222:.
3197:30
3195:.
3170:51
3168:.
3141:51
3139:.
3135:.
3092:.
3084:.
3072:48
3070:.
3066:.
2915:,
2828:,
2781:,
2681:,
2561:.
1775:.
1449:({
1308:,
1304:,
1298:CV
1285:.
1234:,
1052:ln
931:ln
913:ln
852:ln
806:ln
555::
479:.
457:.
437:.
250:,
202:.
137:,
59:,
53:CV
5445:G
5419:F
5411:t
5399:Z
5118:V
5113:U
4315:e
4308:t
4301:v
4286:R
4254:.
4225:.
4213::
4190:.
4163::
4133:.
4121::
4099:.
4097:.
4085::
4062:.
4048::
4029:.
4017::
3994:.
3980::
3974:7
3936:.
3924::
3901:.
3889::
3881::
3854:.
3834::
3811:.
3783::
3756:.
3736::
3713:.
3637:.
3615::
3588:.
3566::
3539:.
3525::
3463:.
3433:.
3399:.
3372:.
3350::
3299:.
3277::
3271:9
3250:.
3230::
3207:.
3180:.
3176::
3153:.
3147::
3100:.
3078::
3051:.
3025:.
2948:W
2939:/
2933:2
2928:W
2894:/
2888:2
2856:k
2847:/
2841:k
2809:2
2800:/
2794:2
2747:/
2700:/
2694:2
2675:k
2659:k
2631:k
2621:/
2614:k
2549:i
2529:n
2509:i
2489:n
2469:i
2463:1
2457:n
2390:,
2384:v
2379:c
2374:d
2364:2
2360:/
2356:i
2352:)
2346:2
2338:v
2333:c
2327:+
2324:1
2321:(
2317:1
2304:i
2299:)
2286:(
2276:2
2272:/
2268:i
2264:2
2257:2
2253:/
2249:i
2245:n
2232:!
2229:i
2225:!
2222:)
2219:i
2213:1
2207:n
2204:(
2198:)
2193:2
2189:i
2183:n
2177:(
2169:!
2166:)
2163:1
2157:n
2154:(
2143:1
2137:n
2132:0
2129:=
2126:i
2088:2
2084:/
2080:n
2076:)
2070:2
2062:v
2057:c
2051:+
2048:1
2045:(
2039:2
2033:n
2025:v
2020:c
2011:)
2002:2
1994:v
1989:c
1983:+
1980:1
1974:2
1966:v
1961:c
1945:2
1940:)
1927:(
1922:2
1918:n
1909:(
1894:)
1889:2
1885:1
1879:n
1873:(
1863:2
1859:/
1855:1
1846:2
1841:=
1833:v
1828:c
1823:F
1818:d
1795:n
1763:x
1760:a
1740:0
1734:b
1714:b
1711:+
1708:x
1705:a
1685:0
1682:=
1679:b
1659:X
1639:b
1636:+
1633:X
1630:a
1610:X
1526:i
1522:x
1517:v
1513:c
1505:v
1501:c
1496:j
1492:x
1487:i
1483:x
1479:j
1475:i
1468:x
1466:(
1463:v
1459:c
1455:x
1453:,
1451:x
1446:v
1442:c
1438:x
1434:x
1432:,
1430:x
1419:x
1415:v
1412:c
1408:x
1406:(
1404:v
1401:c
1396:.
1394:x
1390:x
1385:v
1381:c
1373:i
1368:i
1364:x
1359:i
1355:x
1351:x
1170:v
1165:c
1141:n
1138:l
1134:s
1098:v
1093:c
1064:1
1048:s
1042:e
1036:=
1030:K
1026:V
1022:C
1019:G
990:w
987:a
984:r
972:v
969:c
943:)
940:b
937:(
926:b
922:s
918:=
909:s
885:b
881:s
848:s
821:1
811:2
802:s
796:e
789:=
783:w
780:a
777:r
765:v
762:c
713:v
708:c
699:)
691:n
688:4
684:1
679:+
676:1
671:(
666:=
648:v
643:c
597:x
592:s
587:=
575:v
570:c
537:x
513:s
424:2
420:/
416:)
411:3
407:Q
403:+
398:1
394:Q
390:(
364:2
360:/
356:)
351:1
347:Q
338:3
334:Q
330:(
277:.
264:=
261:V
258:C
198:/
168:)
155:|
147:|
67:(
51:(
34:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.