2100:
But you could also pick many samples and use, say, the median of your sample as the estimator. Or you could just guess the value 0 no matter what (this would be a bad estimator but it would still be an estimator!). The variance of the estimator is going to be the amount by which the estimator varies about ITS mean, not the true mean. The MSE is the amount that the estimator varies about its TRUE mean, which in this example is the number m. For an unbiased estimator, the MSE and the variance are the same. But often, it is not possible to find an unbiased estimator, or in cases a biased estimator might be preferred. I hope this answers the questions given here.
74:
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tell me something in
English, I can understand it. But if you write something in a mathematical equation using symbols that are by conventions known only to those who have studied mathematics formally, I will not understand you. Hence, if you tell me "the mean squared error equals the average (mean) of the squares of the variance" I know what you mean. I don't know what
2691:
exact choice of loss function has little bearing on the result. However, in other situations, it is used because it approximates some loss function arising in utility theory. In other situations, it might be inappropriate. Still more, there are circumstances where there are compelling theoretical reasons to use it--such as its direct relationship to the
2690:
I think that a statement "The error is phrased as a mean of squares ... because ..." is problematic because it does not specify what is meant by "because". I think that there are different things going on here, which is that MSE is used in some circumstances solely out of convenience and because the
2807:
This is a very poorly written article. I ran into MSE, and wanted to learn more. I have a strong math background. Yes, I couldn't make heads or tails of the article. For the most part, the article simply spouts complex equations and obscure verbiage with little or no context. It fails to answer such
2544:
I agree with the previous comment that this article is pretty useless to anyone but a math major. In my opinion, most people look up MSE to get a general idea what it is and how to calculate it - and not for the ultra-precise mathematical dedinition. If I don't know what the MSE is, I am very likely
2099:
1. The mean, m, is a fixed number, but it is unknown. Now, suppose you take a sample from this random variable. If you try to estimate m, your estimator is taking the sample and using it to guess m. A simple case would be to take one sample and have your guess for a be whatever value is picked.
2884:
I have to agree. I'm looking at whether to use Root Mean Square Error to perform image difference calculation in a program. That article is unintelligible to me, but since RMSE is just sqrt(MSE), I thought this article might help me. It did not at all. What is the purpose of MSE? What are the
2366:
I agree whole heartedly with this sweeping critique. Like many mathematics articles on wikipedia, it's written by experts for experts instead of by experts for laymen, but since laymean don't really understand where to start asking questions, the problem is never fixed. I speak
English and if you
2670:
Defining terms, no. But is defining the variables used in the examples section possible? I would do it myself if only I had the knowledge. Inclusion of the variable names would, in one fell swoop, change this article's value to me from nearly useless to something frequently referenced. It is very
2652:
Defining technical terms within in the article is not the appropriate when it leads to duplication elsewhere on wikipedia. Rather, these terms should be referenced on other pages. This is the whole point of wikipedia! Knowledge is based around the idea of a web of knowledge, not a more-or-less
1949:
Someone has suggested that the page for Root mean square deviation (RMSD) be merged with mean squared error. I do not think that it makes sense to do this for several reasons: 1. MSE is a measure of error, whereas RMSD method for comparing two biological structures. 2. RMSD is used almost
2118:
Well, the MSE is a random variable itself that needs to be estimated. It's not just a number. If it has been estimated, it gives a measures of the variation of an estimator with repect to a known parameter. But it is not the variance as it also accounts for the bias of the estimator.
414:
might be wrong. At least the formulas presented are completely different from those shown in Mood, Graybill and Boes (1974) Introduction to the Theory of
Statistics (see pages 229 and 294). The formulas presented in MGB, which is a classic, are more complex and include terms in
2885:
pros/cons vs the Mean
Absolute Error? When would it be useful to use MSE over other error formulas? Why are we squaring things? I understand that MSE is a statistics concept, but you shouldn't have to be a statistician to read the first 3 paragraphs of this article. ----
307:
Even though nobody's responded in three years, I'm going to add my support that something should be changed. The title says "mean squared error", but the first line says "mean square error". Maybe there should be consistency within the same article, at least?
2602:, but there are some disagreements about how to do this. In particular, I have been editing the page in order to make it more concise, and removing the explanations/expositions of topics that are duplicated elsewhere. The way I look at things is this:
2703:. On these grounds, I would like to say that I don't think we should avoid normative statements about the loss functions, rather, I think we really ought to include them, and to discuss in more detail exactly why MSE is used in different situations.
2779:
SSD refers to Sum of
Squared Differences and that redirects to this page. A DAB requires an article show the acronym for which it is linking. If someone can add SSD in reference to Sum of Squared Differences, then they can add the link back to
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2545:
also not to know what all those other greek symbols on the page mean... (and worse - I can't even google them). What about a simple paragraph for the layman first, along the lines of MSE = average((y-x)^2) / average((y^2 - x^2))...
2729:, of which we then take either the expectation or an average (depending on usage). Therefore Mean Squared Error is correct, Mean Square Error is incorrect. (Similarly, chi-square distribution is incorrect. Chi-squared is correct).
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basic questions as: What's the purpose of MSE? What are a few practical examples of MSE? This is probably less a criticism of wikipedia, which is just a medium. It's just an observation of poor writing and communication skills.
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redirects to this page, and appropriately so. However, because of this, and because that term is fairly commonly used, and also because this is a question of naming and definitions, I think that the remark about the term
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I conclude that a deviation is the additive opposite of an error. I agree that both words indicate differences, but they have not exactly the same meaning, and it is inappropriate to use theme as synonims.
1855:, despite these problems. I will wait for comments during the next days and will proceed to make some changes in the article to reflect these findings if there is no disagreement or more insights. --
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2016:
typically stands for "deviation", not for "distance". The distance and the difference between two scalar values are not exactly the same thing: the distance is the absolute value of the difference.
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exclusively in the context of protein folding, whereas MSE is used to describe statistics 3. Merging the articles would result in losing the meaning of the RMSD article.
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480:, which is the same as the variance in this case, seems a rather tedious task. I think someone with some expertise in this area should have a look on this issue. --
721:. A possible explanation is that this expression simplifies to the one presented in the article, but to be in the safe side, an expert review would be advisable.--
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I'm surprised that there is no link to max likelihood. Namely, if data is
Gaussian distributed, the ML is the same as minimizing MSE. This justifies the MSE.
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2087:
MSE has a lot in common with variance but they are not the same! As an example, suppose you are trying to estimate the mean of a random variable that has a
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This article confuses two distinct usages of MSE. I've clarified the distinction in the first section, but other sections still have the same confusion.
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2647:
Using technical terms does not necessarily make the article less accessible, nor does replacing them with expanded explanations make it less more so.
2030:. There are errors "of estimate" as well as errors "of measurement", and they are all with respect to the (often unknown) real value of the variable.
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1338:. The general result, without distributional assumptions, is, I believe, the one presented in Mood, Graybill and Boes (1974, p. 229 and 294) that is
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146:
There is another article published on
Knowledge and titled "Root mean square error". Notice the use of the adjective "square", rather than "square
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For the sake of consistency, I suggest to use "square" everywhere, including the title of this article, and indicate in the text that "square
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2829:), but someone always comes along to rewrite it in the most incomprehensible way possible. When pressed, they'll claim to support the
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Specific suggestion: If someone agrees with me on the following statement, then it would be helpful if added into the article--
1980:
RMSD is used in disciplines other than bioinformatics/biostatistics—try googling RMSD and "electrical engineering", for example.
2065:
Agreed that the article could be made more friendly to those of use who haven't studied statistical theory. BTW, MSE and RMSE
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It is also true that a google search yields about twice as many hits for "mean square error" as "mean squared error".
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The article does not give explicit formulae of the MSE for the estimators in the example. Could someone fill this in?
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builds off a number of other topics. It would be hard to understand MSE without understanding concepts like
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There have been attempts in the past to at least make sure the lede is in "plain
English" (for example, see
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2342:? I agree that it has a lower variance, but this is offset when calculating the MSE by the bias term.
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1896:: the residual sum of squares divided by the number of degrees of freedom... Note that, although the
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means, except that I'm able to guess from the context. Please state all these equations in
English.
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2054:"MSE is also sometimes called the variance; RMSE is also sometimes called the standard deviation."
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2116:"MSE is also sometimes called the variance; RMSE is also sometimes called the standard deviation."
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on
Knowledge. If you would like to participate, please visit the project page, where you can join
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2335:{\displaystyle S^{2}={\frac {1}{n-1}}\sum _{i=1}^{n}\left(X_{i}-{\overline {X}}\,\right)^{2}}
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2224:{\displaystyle S^{2}={\frac {1}{n}}\sum _{i=1}^{n}\left(X_{i}-{\overline {X}}\,\right)^{2}}
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On the other hand, "error" is the difference between an estimated value of a variable and
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In regression analysis, the term mean squared error is sometimes used to refer to the
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1844:{\displaystyle \operatorname {MSE} (S_{n}^{2})<\operatorname {MSE} (S_{n-1}^{2})}
1117:, presented in the article, is not general but is correct under the assumption that
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I'm no statistician, but if people say I'm right, I'm happy to write something.
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2461:{\displaystyle \operatorname {MSE} ({\hat {\theta }})=\operatorname {E} {\big }.}
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Note that root mean squared deviation is different than root mean squared error.
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1764:{\displaystyle \operatorname {MSE} (S_{n}^{2})={\frac {2n-1}{n^{2}}}\sigma ^{4}}
1598:{\displaystyle \operatorname {MSE} (S_{n}^{2})={\frac {2n+1}{n^{2}}}\sigma ^{4}}
962:{\displaystyle \operatorname {MSE} (S_{n}^{2})={\frac {2n-1}{n^{2}}}\sigma ^{4}}
601:, the fourth central moment. The MSE expression in Mood, Graybill and Boyes for
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2048:
Sweeping critique: This article is pretty useless to anyone but a math major.
86:
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the variance and standard deviation. To equate them would be inaccurate. --
1679:{\displaystyle \operatorname {MSE} (S_{n-1}^{2})={\frac {2}{n-1}}\sigma ^{4}}
1273:. This result follows, for instance, from the assumption of normality on the
1110:{\displaystyle \operatorname {MSE} (S_{n-1}^{2})={\frac {2}{n-1}}\sigma ^{4}}
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RMSE = estimator of average error, RMSD = estimator of average distance.
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After some research I believe I've found out what is going on: the result
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I'm pretty sure that's correct, but I won't add it without confirmation.
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is clearly wrong and will be corrected. The correct result, derived from
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Some indication of how MSE differs from the variance would be useful.
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Providing a practical example with real numbers would be desirable.
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Deviation is the difference between the real value of a variable and
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implies they're looking at a multivariate estimator, most likely the
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I first want to say that I am fully committed to making this page
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should remain at the very top of the page, with the definition.
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1449:{\displaystyle \operatorname {MSE} (S_{n-1}^{2})={\frac {1}{n}}}
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is a more natural way to measure the error of an estimate of a
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linear exposition of knowledge like is found in most textbooks.
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I find that Numerical Recipes has a good description of this.
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They're measuring the same thing: differences or variation.
505:, which is a different animal than the univariate ones. --
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requires understanding more subtle concepts like that of a
1904:, it is consistent, given the consistency of the predictor.
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We have to choose; It cannot be both biased and unbiased.
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its estimated or expected or predicted or "desired" value
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No, it is indeed the univariate estimator in this case:
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2618:. Also, to understand the difference between MSE and
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2860:https://en.wikipedia.org/Mean_squared_error#Mean
1887:Also contradictory: In the section "regression"
2868:https://stats.stackexchange.com/a/375227/99274
2864:https://stats.stackexchange.com/q/375101/99274
2725:Also, with regard to square vs squared: It's
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2231:has a lower MSE than the unbiased estimator
2002:My opinion: -- PdL -- January 11 2007 (UTC)
1178:{\displaystyle (n-1)S_{n-1}^{2}/\sigma ^{2}}
1902:an unbiased estimator of the error variance
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344:I suspect that the MSEs presented for
2831:WP:Make technical articles accessible
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2128:In Examples, is it really true that
289:A disambiguation is also necessary.
79:This article is within the scope of
2950:High-importance Statistics articles
1894:unbiased estimate of error variance
38:It is of interest to the following
2753:Errors and residuals in statistics
2519:{\displaystyle \operatorname {E} }
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735:The formula presented as MSE for
767:is clearly wrong if the MSE for
442:. The derivation of the MSE for
99:Knowledge:WikiProject Statistics
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2955:WikiProject Statistics articles
1926:I've clarified it in the text.
119:This article has been rated as
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1300:s used in the computation of
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318:10:02, 15 November 2010 (UTC)
302:17:22, 23 December 2007 (UTC)
93:and see a list of open tasks.
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1983:Merging the articles should
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2945:C-Class Statistics articles
2803:Very Poorly Written Article
2742:22:24, 13 August 2008 (UTC)
1936:18:21, 19 August 2016 (UTC)
1331:{\displaystyle S_{n-1}^{2}}
798:{\displaystyle S_{n-1}^{2}}
632:{\displaystyle S_{n-1}^{2}}
473:{\displaystyle S_{n-1}^{2}}
375:{\displaystyle S_{n-1}^{2}}
276:Root mean square deviation
2971:
2930:22:46, 10 March 2021 (UTC)
2900:Link to maximum likelihood
2895:22:45, 6 August 2019 (UTC)
2862:is hard to understand see
2851:15:56, 9 August 2011 (UTC)
2818:15:39, 9 August 2011 (UTC)
1851:is indeed correct for the
969:, accepting the result on
805:is correct (not sure). As
322:I agree, so I'll move it.
142:INCONSISTENT ARTICLE TITLE
2023:(for instance, the mean).
1879:03:03, 10 July 2008 (UTC)
1865:13:15, 25 June 2008 (UTC)
1205:{\displaystyle \chi ^{2}}
1029:12:45, 24 June 2008 (UTC)
760:{\displaystyle S_{n}^{2}}
731:01:48, 23 June 2008 (UTC)
515:22:41, 22 June 2008 (UTC)
490:21:06, 22 June 2008 (UTC)
407:{\displaystyle S_{n}^{2}}
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884:it is easy to show that
546:in this context is just
539:{\displaystyle \mu _{4}}
435:{\displaystyle \mu _{4}}
2870:. Can this be improved?
2866:, which I discuss here
2749:residual sum of squares
1771:. The conclusion that
242:Root mean square error
2526:is the symbol for the
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82:WikiProject Statistics
28:This article is rated
2771:Removed link from SSD
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340:MSE examples wrong?
215:" is not correct")
105:Statistics articles
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2577:squared error loss
2572:Squared error loss
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34:content assessment
2916:comment added by
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1961:My two cents: --
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1664:
1583:
1431:
1389:
1095:
947:
846:
696:
654:
594:{\displaystyle E}
287:
286:
208:Root mean square
139:
138:
135:
134:
131:
130:
2962:
2932:
2789:
2788:§ Music Sorter §
2718:multiple usages.
2563:
2525:
2523:
2522:
2517:
2490:
2488:
2487:
2482:
2467:
2465:
2464:
2459:
2454:
2453:
2447:
2446:
2431:
2430:
2422:
2416:
2415:
2397:
2396:
2388:
2346:
2341:
2339:
2338:
2333:
2331:
2330:
2325:
2321:
2318:
2310:
2305:
2304:
2288:
2283:
2268:
2266:
2252:
2247:
2246:
2230:
2228:
2227:
2222:
2220:
2219:
2214:
2210:
2207:
2199:
2194:
2193:
2177:
2172:
2157:
2149:
2144:
2143:
1987:the RMSD tie-in.
1850:
1848:
1847:
1842:
1836:
1831:
1800:
1795:
1770:
1768:
1767:
1762:
1760:
1759:
1750:
1748:
1747:
1738:
1724:
1715:
1710:
1685:
1683:
1682:
1677:
1675:
1674:
1665:
1663:
1649:
1640:
1635:
1604:
1602:
1601:
1596:
1594:
1593:
1584:
1582:
1581:
1572:
1558:
1549:
1544:
1519:
1517:
1516:
1511:
1506:
1505:
1472:
1471:
1455:
1453:
1452:
1447:
1442:
1441:
1432:
1430:
1419:
1408:
1403:
1402:
1390:
1382:
1373:
1368:
1337:
1335:
1334:
1329:
1326:
1321:
1299:
1297:
1296:
1291:
1289:
1288:
1272:
1270:
1269:
1264:
1237:
1235:
1234:
1229:
1211:
1209:
1208:
1203:
1201:
1200:
1184:
1182:
1181:
1176:
1174:
1173:
1164:
1158:
1153:
1116:
1114:
1113:
1108:
1106:
1105:
1096:
1094:
1080:
1071:
1066:
1018:
1016:
1015:
1010:
1004:
999:
968:
966:
965:
960:
958:
957:
948:
946:
945:
936:
922:
913:
908:
878:
876:
875:
870:
867:
862:
847:
842:
831:
825:
820:
804:
802:
801:
796:
793:
788:
766:
764:
763:
758:
755:
750:
720:
718:
717:
712:
707:
706:
697:
695:
684:
673:
668:
667:
655:
647:
638:
636:
635:
630:
627:
622:
600:
598:
597:
592:
587:
586:
577:
576:
545:
543:
542:
537:
535:
534:
479:
477:
476:
471:
468:
463:
441:
439:
438:
433:
431:
430:
413:
411:
410:
405:
402:
397:
381:
379:
378:
373:
370:
365:
279:Root mean square
245:Root mean square
160:
125:importance scale
107:
106:
103:
100:
97:
76:
69:
68:
63:
55:
48:
31:
25:
24:
16:
2970:
2969:
2965:
2964:
2963:
2961:
2960:
2959:
2935:
2934:
2911:
2902:
2805:
2787:
2773:
2720:
2688:
2624:random variable
2606:A concept like
2596:
2569:
2553:146.203.126.246
2546:
2508:
2507:
2473:
2472:
2438:
2372:
2371:
2296:
2295:
2291:
2290:
2256:
2238:
2233:
2232:
2185:
2184:
2180:
2179:
2135:
2130:
2129:
2046:
1944:
1889:
1773:
1772:
1751:
1739:
1725:
1688:
1687:
1666:
1653:
1607:
1606:
1585:
1573:
1559:
1522:
1521:
1497:
1463:
1458:
1457:
1433:
1420:
1409:
1394:
1340:
1339:
1302:
1301:
1280:
1275:
1274:
1240:
1239:
1214:
1213:
1192:
1187:
1186:
1165:
1119:
1118:
1097:
1084:
1038:
1037:
971:
970:
949:
937:
923:
886:
885:
832:
807:
806:
769:
768:
737:
736:
698:
685:
674:
659:
641:
640:
603:
602:
578:
568:
548:
547:
526:
521:
520:
500:
444:
443:
422:
417:
416:
384:
383:
346:
345:
342:
179:Sum of squares
166:Preferred name
144:
121:High-importance
104:
101:
98:
95:
94:
62:High‑importance
61:
32:on Knowledge's
29:
12:
11:
5:
2968:
2966:
2958:
2957:
2952:
2947:
2937:
2936:
2901:
2898:
2872:CarlWesolowski
2856:
2855:
2854:
2853:
2843:DanielPenfield
2804:
2801:
2772:
2769:
2768:
2767:
2719:
2716:
2693:expected value
2687:
2684:
2655:
2654:
2649:
2648:
2644:
2643:
2612:expected value
2595:
2592:
2568:
2565:
2528:Expected value
2515:
2503:
2493:134.114.203.37
2480:
2469:
2468:
2457:
2452:
2445:
2441:
2437:
2434:
2428:
2425:
2419:
2414:
2409:
2406:
2403:
2400:
2394:
2391:
2385:
2382:
2379:
2353:131.203.101.15
2329:
2324:
2316:
2313:
2308:
2303:
2299:
2294:
2287:
2282:
2279:
2276:
2272:
2265:
2262:
2259:
2255:
2250:
2245:
2241:
2218:
2213:
2205:
2202:
2197:
2192:
2188:
2183:
2176:
2171:
2168:
2165:
2161:
2155:
2152:
2147:
2142:
2138:
2113:
2112:
2081:
2080:
2079:
2078:
2077:
2076:
2071:DanielPenfield
2045:
2042:
2041:
2040:
2039:
2038:
2037:
2036:
2035:
2034:
2031:
2028:its real value
2024:
2017:
2004:
2003:
1995:
1994:
1993:
1992:
1991:
1990:
1989:
1988:
1981:
1978:
1969:
1968:
1963:DanielPenfield
1943:
1940:
1939:
1938:
1913:Livingthingdan
1888:
1885:
1884:
1883:
1882:
1881:
1867:
1840:
1835:
1830:
1827:
1824:
1820:
1816:
1813:
1810:
1807:
1804:
1799:
1794:
1790:
1786:
1783:
1780:
1758:
1754:
1746:
1742:
1737:
1734:
1731:
1728:
1722:
1719:
1714:
1709:
1705:
1701:
1698:
1695:
1673:
1669:
1662:
1659:
1656:
1652:
1647:
1644:
1639:
1634:
1631:
1628:
1624:
1620:
1617:
1614:
1592:
1588:
1580:
1576:
1571:
1568:
1565:
1562:
1556:
1553:
1548:
1543:
1539:
1535:
1532:
1529:
1520:. The result
1509:
1504:
1500:
1496:
1493:
1490:
1487:
1484:
1481:
1478:
1475:
1470:
1466:
1445:
1440:
1436:
1429:
1426:
1423:
1418:
1415:
1412:
1406:
1401:
1397:
1393:
1388:
1385:
1380:
1377:
1372:
1367:
1364:
1361:
1357:
1353:
1350:
1347:
1325:
1320:
1317:
1314:
1310:
1287:
1283:
1262:
1259:
1256:
1253:
1250:
1247:
1227:
1224:
1221:
1199:
1195:
1172:
1168:
1163:
1157:
1152:
1149:
1146:
1142:
1138:
1135:
1132:
1129:
1126:
1104:
1100:
1093:
1090:
1087:
1083:
1078:
1075:
1070:
1065:
1062:
1059:
1055:
1051:
1048:
1045:
1019:as correct. --
1008:
1003:
998:
995:
992:
988:
984:
981:
978:
956:
952:
944:
940:
935:
932:
929:
926:
920:
917:
912:
907:
903:
899:
896:
893:
882:
881:
880:
879:
866:
861:
858:
855:
851:
845:
841:
838:
835:
829:
824:
819:
815:
792:
787:
784:
781:
777:
754:
749:
745:
733:
710:
705:
701:
694:
691:
688:
683:
680:
677:
671:
666:
662:
658:
653:
650:
626:
621:
618:
615:
611:
590:
585:
581:
575:
571:
567:
564:
561:
558:
555:
533:
529:
517:
507:DanielPenfield
498:
467:
462:
459:
456:
452:
429:
425:
401:
396:
392:
369:
364:
361:
358:
354:
341:
338:
337:
336:
335:
334:
310:67.174.115.100
285:
284:
277:
274:
268:
267:
260:
257:
251:
250:
243:
240:
234:
233:
226:
223:
217:
216:
209:
206:
200:
199:
192:
189:
183:
182:
180:
177:
171:
170:
167:
164:
143:
140:
137:
136:
133:
132:
129:
128:
117:
111:
110:
108:
91:the discussion
77:
65:
64:
56:
44:
43:
37:
26:
13:
10:
9:
6:
4:
3:
2:
2967:
2956:
2953:
2951:
2948:
2946:
2943:
2942:
2940:
2933:
2931:
2927:
2923:
2919:
2915:
2908:
2905:
2899:
2897:
2896:
2892:
2888:
2882:
2881:
2877:
2873:
2869:
2865:
2861:
2852:
2848:
2844:
2840:
2836:
2832:
2828:
2824:
2823:
2822:
2821:
2820:
2819:
2815:
2811:
2802:
2800:
2799:
2795:
2791:
2790:
2783:
2778:
2770:
2766:
2762:
2758:
2754:
2750:
2746:
2745:
2744:
2743:
2739:
2735:
2730:
2728:
2727:squared error
2723:
2717:
2715:
2714:
2710:
2706:
2702:
2698:
2694:
2685:
2683:
2682:
2678:
2674:
2668:
2667:
2663:
2659:
2651:
2650:
2646:
2645:
2641:
2637:
2633:
2629:
2625:
2621:
2617:
2613:
2609:
2605:
2604:
2603:
2601:
2593:
2591:
2590:
2586:
2582:
2578:
2573:
2566:
2564:
2562:
2558:
2554:
2550:
2542:
2541:
2537:
2533:
2529:
2504:
2502:
2498:
2494:
2455:
2443:
2435:
2432:
2423:
2407:
2401:
2389:
2380:
2377:
2370:
2369:
2368:
2364:
2362:
2358:
2354:
2350:
2343:
2327:
2322:
2311:
2306:
2301:
2297:
2292:
2285:
2280:
2277:
2274:
2270:
2263:
2260:
2257:
2253:
2248:
2243:
2239:
2216:
2211:
2200:
2195:
2190:
2186:
2181:
2174:
2169:
2166:
2163:
2159:
2153:
2150:
2145:
2140:
2136:
2126:
2125:
2122:
2117:
2111:
2107:
2103:
2098:
2094:
2090:
2086:
2085:
2084:
2075:
2072:
2068:
2064:
2063:
2062:
2061:
2060:
2059:
2058:
2055:
2052:
2049:
2043:
2032:
2029:
2025:
2022:
2018:
2015:
2011:
2008:
2007:
2006:
2005:
2001:
2000:
1999:
1998:
1997:
1996:
1986:
1982:
1979:
1977:
1973:
1972:
1971:
1970:
1967:
1964:
1960:
1959:
1958:
1957:
1956:
1955:
1954:
1951:
1947:
1941:
1937:
1933:
1929:
1925:
1924:
1923:
1922:
1918:
1914:
1909:
1905:
1903:
1901:
1895:
1886:
1880:
1876:
1872:
1868:
1866:
1862:
1858:
1854:
1833:
1828:
1825:
1822:
1818:
1811:
1808:
1805:
1797:
1792:
1788:
1781:
1778:
1756:
1752:
1744:
1740:
1735:
1732:
1729:
1726:
1720:
1712:
1707:
1703:
1696:
1693:
1671:
1667:
1660:
1657:
1654:
1650:
1645:
1637:
1632:
1629:
1626:
1622:
1615:
1612:
1590:
1586:
1578:
1574:
1569:
1566:
1563:
1560:
1554:
1546:
1541:
1537:
1530:
1527:
1502:
1494:
1491:
1488:
1479:
1473:
1468:
1464:
1438:
1434:
1427:
1424:
1421:
1416:
1413:
1410:
1404:
1399:
1395:
1386:
1383:
1378:
1370:
1365:
1362:
1359:
1355:
1348:
1345:
1323:
1318:
1315:
1312:
1308:
1285:
1281:
1257:
1254:
1251:
1245:
1225:
1222:
1219:
1197:
1193:
1170:
1166:
1161:
1155:
1150:
1147:
1144:
1140:
1133:
1130:
1127:
1102:
1098:
1091:
1088:
1085:
1081:
1076:
1068:
1063:
1060:
1057:
1053:
1046:
1043:
1035:
1034:
1033:
1032:
1031:
1030:
1026:
1022:
1001:
996:
993:
990:
986:
979:
976:
954:
950:
942:
938:
933:
930:
927:
924:
918:
910:
905:
901:
894:
891:
864:
859:
856:
853:
849:
843:
839:
836:
833:
827:
822:
817:
813:
790:
785:
782:
779:
775:
752:
747:
743:
734:
732:
728:
724:
703:
699:
692:
689:
686:
681:
678:
675:
669:
664:
660:
651:
648:
624:
619:
616:
613:
609:
583:
573:
569:
565:
562:
553:
531:
527:
518:
516:
512:
508:
504:
496:
495:
494:
493:
492:
491:
487:
483:
465:
460:
457:
454:
450:
427:
423:
399:
394:
390:
367:
362:
359:
356:
352:
339:
333:
329:
325:
321:
320:
319:
315:
311:
306:
305:
304:
303:
299:
295:
290:
282:
278:
275:
273:
270:
269:
265:
261:
258:
256:
253:
252:
248:
244:
241:
239:
236:
235:
231:
227:
224:
222:
219:
218:
214:
210:
207:
205:
202:
201:
197:
194:("Mean square
193:
190:
188:
185:
184:
181:
178:
176:
173:
172:
168:
165:
162:
161:
158:
156:
151:
149:
141:
126:
122:
116:
113:
112:
109:
92:
88:
84:
83:
78:
75:
71:
70:
66:
60:
57:
54:
50:
45:
41:
35:
27:
23:
18:
17:
2912:— Preceding
2909:
2906:
2903:
2883:
2857:
2833:, but their
2827:this version
2810:97.126.59.10
2806:
2786:
2774:
2731:
2726:
2724:
2721:
2689:
2669:
2656:
2597:
2576:
2570:
2547:— Preceding
2543:
2505:
2470:
2365:
2344:
2127:
2115:
2114:
2082:
2066:
2056:
2053:
2050:
2047:
2027:
2020:
2013:
2009:
1984:
1975:
1952:
1948:
1945:
1910:
1907:
1899:
1897:
1893:
1891:
883:
343:
291:
288:
280:
271:
263:
254:
246:
237:
229:
220:
212:
203:
195:
191:Mean square
186:
174:
154:
152:
147:
145:
120:
80:
40:WikiProjects
2673:Cranhandler
2532:MahdiEynian
2347:—Preceding
262:Mean square
228:Mean square
169:Other name
2939:Categories
2887:Cowlinator
2695:, whereas
2600:accessible
1871:Bluemaster
1857:Bluemaster
1021:Bluemaster
723:Bluemaster
482:Bluemaster
283:deviation
266:deviation
96:Statistics
87:statistics
59:Statistics
2777:WP:DABNOT
2632:estimator
2926:contribs
2918:Cosine12
2914:unsigned
2640:estimate
2636:estimand
2620:variance
2616:variance
2549:unsigned
2349:unsigned
2097:variance
2067:estimate
1985:preserve
2835:actions
2044:Content
1898:MSE is
1456:, with
163:Symbol
123:on the
30:C-class
2841:. --
2751:. See
2734:Zaqrfv
2705:Cazort
2701:median
2658:Cazort
2638:, and
2628:sample
2581:Cazort
2102:Cazort
2095:m and
2012:in RMS
1928:Loraof
294:Cazort
249:error
232:error
36:scale.
2839:words
2757:3mta3
2630:, an
2121:Squim
2091:with
1686:, is
2922:talk
2891:talk
2876:talk
2847:talk
2814:talk
2794:talk
2775:Per
2761:talk
2738:talk
2709:talk
2677:talk
2662:talk
2626:, a
2614:and
2585:talk
2557:talk
2536:talk
2497:talk
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324:Qwfp
314:talk
298:talk
272:RMSD
238:RMSE
115:High
2378:MSE
1942:MSE
1900:not
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1779:MSE
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2941::
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1363:−
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1638:2
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1469:4
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1439:4
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1411:n
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1392:[
1387:n
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1376:)
1371:2
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1356:S
1352:(
1324:2
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911:2
906:n
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898:(
865:2
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854:n
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828:=
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791:2
786:1
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776:S
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42::
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