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  2. Outlier - Wikipedia

    en.wikipedia.org/wiki/Outlier

    The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.

  3. Winsorizing - Wikipedia

    en.wikipedia.org/wiki/Winsorizing

    The distribution of many statistics can be heavily influenced by outliers, values that are 'way outside' the bulk of the data. A typical strategy to account for, without eliminating altogether, these outlier values is to 'reset' outliers to a specified percentile (or an upper and lower percentile) of the data. For example, a 90% winsorization ...

  4. Noisy data - Wikipedia

    en.wikipedia.org/wiki/Noisy_data

    If actual outliers are not removed from the data set, they corrupt the results to a small or large degree depending on circumstances. If valid data is identified as an outlier and is mistakenly removed, that also corrupts results. Fraud: Individuals may deliberately skew data to influence the results toward a desired conclusion.

  5. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. [1]

  6. 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 ...

  7. 8 Big Outliers That Should Revert to the Mean - AOL

    www.aol.com/news/2013-09-11-8-big-outliers-that...

    Crowds don't tolerate excess, so something happens to pull outliers back toward a happy medium. Averages act like gravity. Reversion to the mean is probably the second most powerful law in finance ...

  8. Opinion - Surprises, outliers, oddities: What to anticipate ...

    www.aol.com/opinion-surprises-outliers-oddities...

    A memorable outlier was the Washington Post/ABC News late-campaign poll in Wisconsin in 2020, which reported Joe Biden was 17 points ahead in the state. Biden carried Wisconsin by less than 1 point.

  9. 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 ]