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...'''influential observation''' is an observation for a [[Estimation theory|statistical calculation]] whose deletion from the dataset would noticeably change the [ ...nce.<ref>{{cite web |first=Larry |last=Winner |title=Influence Statistics, Outliers, and Collinearity Diagnostics |date=March 25, 2002 |url=http://stat.ufl.edu ...

{{Short description|Statistical indicators of the deviation of a sample}} ...le, such as sample [[standard deviation]], which are greatly influenced by outliers. ...

In [[statistical graphics]] and [[scientific visualization]], the '''contour boxplot'''<ref ...range for interpretation because the 50% central region is not affected by outliers or extreme values, and gives a less biased visualization of the curves' spr ...

{{short description|Greatest and least values in a statistical data sample}} ...extremely high or low. However, the sample maximum and minimum need not be outliers, if they are not unusually far from other observations. ...

...doi = 10.1145/335191.335388| title = LOF: Identifying Density-based Local Outliers| year = 2000| last1 = Breunig | first1 = M. M.| last2 = Kriegel | first2 = ...| last4 = Sander | first4 = J. R. | chapter = OPTICS-OF: Identifying Local Outliers | doi = 10.1007/978-3-540-48247-5_28 | title = Principles of Data Mining an ...

{{Short description|Concept in statistical analysis}} ...ate analysis''' is one of the simplest forms of [[Statistics|quantitative (statistical) analysis]].<ref name=babbie>[[Earl R. Babbie]], ''The Practice of Social R ...

...ws' Plots |url=https://www.jstor.org/stable/1403442 |journal=International Statistical Review / Revue Internationale de Statistique |volume=59 |issue=2 |pages=175 [[Category:Statistical charts and diagrams]] ...

{{Short description|Statistical term}} ...|first3=Trevor |last4=Tibshirani |first4=Robert |title=An introduction to statistical learning: with applications in R |date=2021 |publisher=Springer |location=N ...

{{Short description|Statistical property quantifying how much a collection of data is spread out}} ...blisher=U.S. Department of Commerce}}</ref> Common examples of measures of statistical dispersion are the [[variance]], [[standard deviation]], and [[interquartil ...

...ges = 721–730 |author1=Cornish-Bowden, A. |author2=Eisenthal, R. | title = Statistical considerations in estimation of enzyme kinetic parameters by the direct lin ==Resistance to outliers and incorrect weighting== ...

....|title=Least Median of Squares Regression|journal=Journal of the American Statistical Association|date=1984|volume=79|issue=388|pages=871–880|doi=10.1080/0162145 [[robust regression]], which can resist [[outliers]] in the data. ...

...an also affect whether the [[Maximum likelihood estimation|MLE]] procedure statistical programs use to implement beta regressions converge. Furthermore the MLE pr * no outliers ("[[Cook's distance]] to determine outliers"<ref name="auto2"/>) ...

...'), or '''least trimmed sum of squares''', is a [[robust statistics|robust statistical method]] that fits a function to a set of data whilst not being unduly affe ...itle=Least Median of Squares Regression |journal=[[Journal of the American Statistical Association]] |volume=79 |issue= 388|pages=871–880 |jstor=2288718 |doi=10.1 ...

{{Short description|Statistical accuracy measure}} ...tive error on line item level. Furthermore, it is better protected against outliers and the bias effect mentioned in the previous paragraph than the two other ...

...ion theory|estimate]] of a [[variance]] (or a [[standard deviation]]) is [[statistical significance|significantly]] larger than a group of variances (or standard ...Levene's test|Levene's]] test and the [[Brown–Forsythe test]] to check a [[statistical data]] set for [[homogeneity of variance]]s. An even simpler way to check ...

{{Short description|Statistical optimality criterion}} ...ute values''' ('''LAV'''), is a statistical [[optimality criterion]] and a statistical [[optimization (mathematics)|optimization]] technique based on [[maxima and ...

...is [[median]]-based, rather than [[mean]]-based, thus is less sensitive to outliers, and can be a robust alternative to other similarity metrics, such as [[Pea ...ht midcorrelation has been implemented in the [[R (programming language)|R statistical programming language]] as the function <code>bicor</code> as part of the WG ...

...gnition (CVPR'05) |chapter=Robust L₁ Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming |volume=1|pages=739–746| ...=2|pages=318–334|doi=10.1137/1015032|citeseerx=10.1.1.454.9868}}</ref> and statistical optimality when the data set is generated by a true [[Multivariate normal d ...

...nguage=en}}</ref><ref>{{cite book |last1=Kanji |first1=Gopal K. |title=100 Statistical Tests |date=18 July 2006 |publisher=SAGE |isbn=978-1-4462-2250-8 |url=https ...name=addis/> They also have the disadvantage of being less certain in the statistical estimate.<ref name=addis/> ...

...functions]] representing the price paid for inaccuracy of predictions in [[statistical classification|classification problem]]s (problems of identifying which cat as a proxy for expected risk.<ref name="mitlec" /> (See [[statistical learning theory]] for a more detailed description.) ...