Histogram Statistics Report

With the Histogram Statistics Report you can analyze the statistical information of the data in your histograms. To access the statistics report, right-mouse click on a histogram to open the histogram context menu and select Statistics Report.... The form that opens shows the statistics for the histogram you selected to create the report for. Use the 'Chart' drop-down list to select any histogram in your solution.

The Histogram Statistics Report is divided into two sections. The top section, the 'Data Statistics' table, shows the statistics of the data series of the histogram. The bottom section, the 'Distribution Model Statistics' table, shows the statistics of assigned distribution models, in line with their above data series. When a filter is applied to a histogram, all the statistics are computed from the filtered data. The report is automatically updated when you make changes to the source histogram.

Weighting

Select Unweighted to view the data statistics without weighting applied, or Weighted, to apply weighting to the data statistics. This setting is independent from weighting used in the distribution model 'Autofit to data' calibration. Weights are only applied for data from a 3D grid, where the cell volumes are used as weights, and well-log data, where the sample spacings are used as weights. All other source types will be unweighted regardless of the setting.

Exporting a statistics report

You can export the displayed statistical information as a .csv file. To do so:

1. Click on the button to open the browser.
2. The 'File name' will be populated with the 'Chart' name selected in the drop-down list. The name is editable.
3. Browse to the location where you want to store the exported file and click Save.
You can also select cells of the statistics report table and copy them to the clipboard.

Statistics definitions

Differences are noted where different value definitions apply to data statics or distribution model statistics.

The following statistics information is shown in the report:

• Source type  The selected source type of the data: 2D Grid, 3D Grid, 3D Mesh, Marker set, Tri-mesh, Polyline set, Point set, or Wellbore.
• Source  The name of the data source object.
• Wellbores  Included only if the source type is 'Wellbore'. The selected wellbores are listed.
• Property  The name of the property.
• Realization  The realization number or time step.
• Weighting  This row indicates whether the statistics for the series are weighted. A chart may contain multiple series from different source types, not all of which can be weighted.
• Filter  The name of the applied property-based filter.
• Distribution model  The name of the distribution model.
• Distribution type  The distribution model type: Gaussian, Lognormal, Triangular, or Uniform.
• Count  The number of observations in the series.
• Effective sample size  The Kish effective sample size for weighted data, the sum of weights squared, divided by the sum of squared weights: For unweighted or equally weighted data the effective sample size is the same as the count.
• Minimum  
  • The smallest series value in data statistics.
  • The minimum setting for uniform or triangular distribution models.
• Maximum  
  • The largest series value in data statistics.
  • The maximum setting for uniform or triangular distribution models.
• Minimum truncation   The minimum truncation setting for Gaussian, lognormal, or triangular distribution models.
• Maximum truncation  The maximum truncation setting for Gaussian, lognormal, or triangular distribution models.
• P10  The 10th percentile of the data or distribution model.
• P50 (Median)  The 50th percentile of the data or distribution model. Also known as the median.
• P90  The 90th percentile of the data or distribution model.
• Mean  The average of the data or distribution model.
• Mode  The most likely value of the distribution model.
• Standard deviation  The standard deviation of the data or distribution model, a measure of distribution spread in relation to the mean.
• Variance  The square of the standard deviation.
• Skewness  The skewness of the data or distribution model, a measure of distribution asymmetry. Symmetric distributions have a skewness of zero. Distributions with asymmetric tails to the right are positively skewed. Distributions with asymmetric tails to the left are negatively skewed.
• Kurtosis  The kurtosis of the data or distribution model, a measure of the heaviness of the distribution tails. An untruncated Gaussian distribution has a kurtosis of 3. Distributions with kurtosis less than 3 have lighter tails than a Gaussian distribution. Distributions with kurtosis greater than 3 have heavier tails. Note: The kurtosis should not be confused with the “excess kurtosis,” another common measure of tail heaviness. The excess kurtosis is equal to the kurtosis minus 3.
• Coefficient of determination, R2  A measure of the goodness of fit of a distribution model to its data. Larger values indicate better fits. The coefficient of determination of a distribution model is computed between the X-axis data of the series and the X-axis predictions of the model at the corresponding cumulative probabilities. The largest possible value for a perfect fit is 1. A model that is just a constant equal to the average of the data, sometimes known as a “baseline model,” has a coefficient of determination of zero. A negative coefficient of determination indicates that the model is worse than a baseline model. Note: The coefficient of determination, sometimes known as the R squared, is often confused with the square of the correlation coefficient, but they are not the same thing except for certain special cases.

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