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  2. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    Where gap is the absolute difference between the outlier in question and the closest number to it. If Q > Q table, where Q table is a reference value corresponding to the sample size and confidence level, then reject the questionable point. Note that only one point may be rejected from a data set using a Q test.

  3. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    The methods must manage real-time data, diverse device types, and scale effectively. Garbe et al. [19] have introduced a multi-stage anomaly detection framework that improves upon traditional methods by incorporating spatial clustering, density-based clustering, and locality-sensitive hashing. This tailored approach is designed to better handle ...

  4. Outlier - Wikipedia

    en.wikipedia.org/wiki/Outlier

    The modified Thompson Tau test is a method used to determine if an outlier exists in a data set. [23] The strength of this method lies in the fact that it takes into account a data set's standard deviation, average and provides a statistically determined rejection zone; thus providing an objective method to determine if a data point is an outlier.

  5. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  6. Influential observation - Wikipedia

    en.wikipedia.org/wiki/Influential_observation

    An outlier may be defined as a data point that differs markedly from other observations. [6] [7] A high-leverage point are observations made at extreme values of independent variables. [8] Both types of atypical observations will force the regression line to be close to the point. [2]

  7. Robust Regression and Outlier Detection - Wikipedia

    en.wikipedia.org/wiki/Robust_Regression_and...

    The book has seven chapters. [1] [4] The first is introductory; it describes simple linear regression (in which there is only one independent variable), discusses the possibility of outliers that corrupt either the dependent or the independent variable, provides examples in which outliers produce misleading results, defines the breakdown point, and briefly introduces several methods for robust ...

  8. Data collection - Wikipedia

    en.wikipedia.org/wiki/Data_collection

    Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business ...

  9. Peirce's criterion - Wikipedia

    en.wikipedia.org/wiki/Peirce's_criterion

    In data sets containing real-numbered measurements, the suspected outliers are the measured values that appear to lie outside the cluster of most of the other data values. The outliers would greatly change the estimate of location if the arithmetic average were to be used as a summary statistic of location.