Average Standard Error Arcgis
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Average Standard Error Of The Mean
Sign In user My Profile Sign Out Go ArcGIS Pro HomeGet StartedHelpTool ReferenceArcPySDKCommunity Performing cross-validation and validation average percent error Available with Geostatistical Analyst license. Cross-validationValidationPrediction error statistics Before you produce the final surface, you should have some idea of how well the model predicts the values median standard error at unknown locations. Cross-validation and validation help you make an informed decision as to which model provides the best predictions. The calculated statistics serve as diagnostics that indicate whether the model and its associated parameter values are reasonable.Cross-validation and validation use the following idea—remove one or more data locations and predict their associated data using
Percentage Standard Error
the data at the rest of the locations. In this way, you can compare the predicted value to the observed value and obtain useful information about the quality of your interpolation model. Cross-validationCross-validation uses all the data to estimate the trend and autocorrelation models. It removes each data location one at a time and predicts the associated data value. For example, the diagram below shows 10 data points. Cross-validation omits a point (red point) and calculates the value at this location using the remaining 9 points (blue points). The predicted and actual values at the location of the omitted point are compared. This procedure is repeated for a second point, and so on. For all points, cross-validation compares the measured and predicted values. In a sense, cross-validation cheats a little by using all the data to estimate the trend and autocorrelation models. After completing cross-validation, some data locations may be set aside as unusual if they contain large errors, requiring the trend and autocorrelatio
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Cross Validation
Marketplace Get apps and data for your organization Documentation Pricing Support Esri Sign In user My Profile Sign http://pro.arcgis.com/en/pro-app/help/analysis/geostatistical-analyst/performing-cross-validation-and-validation.htm Out Go ArcMap HomeGet StartedMapAnalyzeManage DataToolsExtensions Using ordinary kriging to create a prediction standard error map Available with Geostatistical Analyst license. Ordinary kriging provides a standard error map that shows the uncertainty related to the predicted http://desktop.arcgis.com/en/arcmap/latest/extensions/geostatistical-analyst/using-ordinary-kriging-to-create-a-prediction-standard-error-map.htm values. Right-click the geostatistical layer in the ArcMap table of contents that was created using ordinary kriging and click Change output to Prediction Standard Error. Related TopicsUnderstanding ordinary krigingUsing ordinary kriging to create a prediction mapCreating a prediction map using ordinary kriging with a data transformationUsing ordinary kriging with detrending to create a prediction map Feedback on this topic? ArcGIS for Desktop Home Documentation Pricing Support ArcGIS Platform ArcGIS Online ArcGIS for Desktop ArcGIS for Server ArcGIS for Developers ArcGIS Solutions ArcGIS Marketplace About Esri About Us Careers Insiders Blog User Conference Developer Summit Esri © Copyright 2016 Environmental Systems Research Institute, Inc. | Privacy | Legal
for Developers Tools to build location-aware apps ArcGIS Solutions Free template maps and apps for your industry ArcGIS Marketplace Get apps and data for your organization http://desktop.arcgis.com/en/arcmap/latest/extensions/geostatistical-analyst/performing-cross-validation-and-validation.htm Documentation Pricing Support Esri Sign In user My Profile Sign Out Go ArcMap HomeGet StartedMapAnalyzeManage DataToolsExtensions Performing cross-validation and validation Available with Geostatistical Analyst license. Cross-validationValidationPlotsPrediction error statistics Before you produce the final surface, you should have some idea of how well the model predicts the values at unknown locations. Cross-validation and validation help you make an informed decision as to standard error which model provides the best predictions. The calculated statistics serve as diagnostics that indicate whether the model and/or its associated parameter values are reasonable.Cross-validation and validation use the following idea—remove one or more data locations and predict their associated data using the data at the rest of the locations. In this way, you can compare the predicted value to the observed value average standard error and obtain useful information about the quality of your kriging model (for example, the semivariogram parameters and the searching neighborhood).Cross-validationCross-validation uses all the data to estimate the trend and autocorrelation models. It removes each data location one at a time and predicts the associated data value. For example, the diagram below shows 10 data points. Cross-validation omits a point (red point) and calculates the value at this location using the remaining 9 points (blue points). The predicted and actual values at the location of the omitted point are compared. This procedure is repeated for a second point, and so on. For all points, cross-validation compares the measured and predicted values. In a sense, cross-validation "cheats" a little by using all the data to estimate the trend and autocorrelation models. After completing cross-validation, some data locations may be set aside as unusual if they contain large errors, requiring the trend and autocorrelation models to be refit.Cross-validation is performed automatically, and results are shown in the last step of the Geostatistical Wizard. Cross-validation can also be performed manually using the Cross Validation geoprocessing tool.ValidationValidation first