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Ordinary Least Squares (OLS)


Performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables.

Learn more about how Ordinary Least Squares regression works


OLS Regression
Ordinary Least Squares Regression: predicted values in relation to observed values


  • The primary output for this tool is a report file which is written as messages at the bottom of the Geoprocessing pane during tool execution. You may access the messages by hovering over the progress bar, clicking on the pop-out button, or expanding the messages section in the Geoprocessing pane. You may also access the messages for a previous run of Exploratory Regression via the Geoprocessing History.

  • The OLS tool also produces an output feature class and optional tables with coefficient information and diagnostics. All of these are accessible from the messages at the bottom of the Geoprocessing pane . The output feature class is automatically added to the table of contents, with a hot/cold rendering scheme applied to model residuals. A full explanation of each output is provided in Interpreting OLS results.

  • Results from OLS regression are only trustworthy if your data and regression model satisfy all of the assumptions inherently required by this method. Consult the table Common Regression Problems, Consequences, and Solutions in Regression Analysis Basics to ensure your model is properly specified.

  • Dependent and Explanatory variables should be numeric fields containing a variety of values. OLS cannot solve when variables have all the same value (all the values for a field are 9.0, for example). Linear regression methods, like OLS, are not appropriate for predicting binary outcomes (for example, all of the values for the dependent variable are either 1 or 0).

  • The Unique ID field links model predictions to each feature. Consequently, the Unique ID values must be unique for every feature, and typically should be a permanent field that remains with the feature class. If you don't have a Unique ID field, you can easily create one by adding a new integer field to your feature class table and calculating the field values to be equal to the FID/OID field. You cannot use the FID/OID field directly for the Unique ID parameter.

  • Whenever there is statistically significant spatial autocorrelation of the regression residuals the OLS model will be considered misspecified and, consequently, results from OLS regression are unreliable. Be sure to run the Spatial Autocorrelation tool on your regression residuals to assess this potential problem. Statistically significant spatial autocorrelation of regression residuals almost always indicates one or more key explanatory variables are missing from the model.

  • You should visually inspect the over- and underpredictions evident in your regression residuals to see if they provide clues about potential missing variables from your regression model. It sometimes helps to run Hot Spot Analysis on the residuals to help you visualize spatial clustering of the over- and underpredictions.

  • When misspecification is the result of trying to model nonstationary variables using a global model (OLS is a global model), then Geographically Weighted Regression may be used to improve predictions and to better understand the nonstationarity (regional variation) inherent in your explanatory variables.

  • When the result of a computation is infinity or undefined, the output for nonshapefiles will be Null; for shapefiles the output will be -DBL_MAX (-1.7976931348623158e+308, for example).

  • Model summary diagnostics are written to the OLS summary report and the optional diagnostic output table. Both include diagnostics for the corrected Akaike Information Criterion (AICc), Coefficient of Determination, Joint F statistic, Wald statistic, Koenker's Breusch-Pagan statistic, and the Jarque-Bera statistic. The diagnostic table also includes uncorrected AIC and Sigma-squared values.

  • The optional coefficient and/or diagnostic output tables, if they already exist, will be overwritten when the Geoprocessing Option to Allow geoprocessing tools to overwrite existing datasets is checked on.

  • On machines configured with the ArcGIS language packages for Arabic and other right-to-left languages, you might notice missing text or formatting problems in the PDF Output Report File. These problems are addressed in this article.

  • Map layers can be used to define the Input Feature Class. When using a layer with a selection, only the selected features are included in the analysis.

  • Caution:

    When using shapefiles, keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from nonshapefile inputs may store or interpret null values as zero. In some cases, nulls are stored as very large negative values in shapefiles. This can lead to unexpected results. See Geoprocessing considerations for shapefile output for more information.


OrdinaryLeastSquares_stats (Input_Feature_Class, Unique_ID_Field, Output_Feature_Class, Dependent_Variable, Explanatory_Variables, {Coefficient_Output_Table}, {Diagnostic_Output_Table}, {Output_Report_File})
ParameterExplanationData Type

The feature class containing the dependent and independent variables for analysis.

Feature Layer

An integer field containing a different value for every feature in the Input Feature Class.


The output feature class to receive dependent variable estimates and residuals.

Feature Class

The numeric field containing values for what you are trying to model.


A list of fields representing explanatory variables in your regression model.


The full path to an optional table that will receive model coefficients, standardized coefficients, standard errors, and probabilities for each explanatory variable.


The full path to an optional table that will receive model summary diagnostics.


The path to the optional PDF file you want the tool to create. This report file includes model diagnostics, graphs, and notes to help you interpret the OLS results.


Code sample

OrdinaryLeastSquares example 1 (Python window)

The following Python window script demonstrates how to use the OrdinaryLeastSquares tool.

import arcpy
arcpy.env.workspace = r"c:\data"
arcpy.OrdinaryLeastSquares_stats("USCounties.shp", "MYID","olsResults.shp", 
OrdinaryLeastSquares example 2 (stand-alone Python script)

The following stand-alone Python script demonstrates how to use the OrdinaryLeastSquares tool.

# Analyze the growth of regional per capita incomes in US
# Counties from 1969 -- 2002 using Ordinary Least Squares Regression

# Import system modules
import arcpy

# Set property to overwrite existing outputs
arcpy.env.overwriteOutput = True

# Local variables...
workspace = r"C:\Data"

    # Set the current workspace (to avoid having to specify the full path to the feature classes each time)
    arcpy.env.workspace = workspace

    # Growth as a function of {log of starting income, dummy for South
    # counties, interaction term for South counties, population density}
    # Process: Ordinary Least Squares... 
    ols = arcpy.OrdinaryLeastSquares_stats("USCounties.shp", "MYID", 
                        "olsResults.shp", "GROWTH",

    # Create Spatial Weights Matrix (Can be based off input or output FC)
    # Process: Generate Spatial Weights Matrix... 
    swm = arcpy.GenerateSpatialWeightsMatrix_stats("USCounties.shp", "MYID",
                        "#", "#", "#", 6) 

    # Calculate Moran's Index of Spatial Autocorrelation for 
    # OLS Residuals using a SWM File.  
    # Process: Spatial Autocorrelation (Morans I)...      
    moransI = arcpy.SpatialAutocorrelation_stats("olsResults.shp", "Residual",
                        "NO_REPORT", "GET_SPATIAL_WEIGHTS_FROM_FILE", 
                        "EUCLIDEAN_DISTANCE", "NONE", "#", 

    # If an error occurred when running the tool, print out the error message.

Licensing information

  • ArcGIS Desktop Basic: Yes
  • ArcGIS Desktop Standard: Yes
  • ArcGIS Desktop Advanced: Yes

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