Detect outliers on logistic regressions

  • Updated

When fitting a regression line against a set of data, outlier data points can negatively impact the calculation of the regression and lead to misleading results.

Using Outlier Detection on an Insights Dashboard Block

Note that outlier detection is subject to error.

  1. Select the “Scatter plot” chart type, and specify the appropriate x- and y-axes

  2. Under the “Analysis” tab, choose “4-parameter logistic” as the regression line type

    1. Note: Outlier detection is currently only available on logistic regressions, not linear regressions

  3. Select either “Detect” or “Detect and exclude” under the “Outliers” dropdown

    1. Detect: Detects outlier data point(s) and marks them with a red X.

      1. Regressions, aggregations, and error values are computed using the full dataset, including any outliers.

    2. Detect and exclude: Detects outlier data point(s) and marks them with a semi-transparent, red X.

      1. These outlier data point(s) are excluded from the regression, aggregation, and error bar calculations, which are automatically re-calculated upon excluding outliers.

  4. Customize the sensitivity of outlier detection by adjusting the slider below the “Outliers” dropdown

    1. Filter out fewer outliers by dragging the slider to the left (i.e., less aggressive detection) or filter out more outliers by dragging the slider to the right (i.e., more aggressive detection)

The algorithm implemented for outlier detection on logistic regressions is based on the ROUT method, and its details can be found in this paper.



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