Label | Explanation | Data Type |

Input features | The feature class containing the dependent and independent variables. | Feature Layer |

Dependent variable | The numeric field containing the values that will be modeled. | Field |

Explanatory variable(s) | A list of fields representing independent explanatory variables in the regression model. | Field |

Output feature class | The output feature class that will receive dependent variable estimates and residuals. | Feature Class |

Kernel type | Specifies whether the kernel is constructed as a fixed distance, or if it is allowed to vary in extent as a function of feature density. - Fixed—The spatial context (the Gaussian kernel) used to solve each local regression analysis is a fixed distance.
- Adaptive—The spatial context (the Gaussian kernel) is a function of a specified number of neighbors. Where feature distribution is dense, the spatial context is smaller; where feature distribution is sparse, the spatial context is larger.
| String |

Bandwidth method | Specifies how the extent of the kernel will be determined. When Akaike Information Criterion or Cross Validation is selected, the tool will find the optimal distance or number of neighbors. Typically, you will select either Akaike Information Criterion or Cross Validation when you aren't sure what to use for the Distance or Number of neighbors parameter. Once the tool determines the optimal distance or number of neighbors, however, you'll use the As specified below option. - Akaike Information Criterion—The extent of the kernel is determined using the Akaike Information Criterion.
- Cross Validation—The extent of the kernel is determined using cross validation.
- As specified below—The extent of the kernel is determined by a fixed distance or a fixed number of neighbors. You must specify a value for either the Distance or Number of neighbors parameters.
| String |

Distance (Optional) | The distance to use when the Kernel type parameter is set to Fixed and the Bandwidth method parameter is set to As specified below. | Double |

Number of neighbors (Optional) | The exact number of neighbors to include in the local bandwidth of the Gaussian kernel when the Kernel type parameter is set to Adaptive and the Bandwidth method parameter is set to As specified below. | Long |

Weights (Optional) | The numeric field containing a spatial weighting for individual features. This weight field allows some features to be more important in the model calibration process than others. This is useful when the number of samples taken at different locations varies, values for the dependent and independent variables are averaged, and places with more samples are more reliable (should be weighted higher). If you have an average of 25 different samples for one location but an average of only 2 samples for another location, for example, you can use the number of samples as your weight field so that locations with more samples have a larger influence on model calibration than locations with few samples. | Field |

Coefficient raster workspace (Optional) | The full path to the workspace where the coefficient rasters will be created. When this workspace is provided, rasters are created for the intercept and every explanatory variable. | Workspace |

Output cell size (Optional) | The cell size (a number) or reference to the cell size (a path to a raster dataset) to use when creating the coefficient rasters. The default cell size is the shortest of the width or height of the extent specified in the geoprocessing environment output coordinate system, divided by 250. | Analysis Cell Size |

Prediction locations (Optional) | A feature class containing features representing locations where estimates should be computed. Each feature in this dataset should contain values for all of the explanatory variables specified; the dependent variable for these features will be estimated using the model calibrated for the input feature class data. | Feature Layer |

Prediction explanatory variable(s) (Optional) | A list of fields representing explanatory variables in the Prediction locations feature class. These field names should be provided in the same order (a one-to-one correspondence) as those listed for the input feature class Explanatory variables parameter. If no prediction explanatory variables are given, the output prediction feature class will only contain computed coefficient values for each prediction location. | Field |

Output prediction feature class (Optional) | The output feature class to receive dependent variable estimates for each feature in the Prediction locations feature class. | Feature Class |

### Derived Output

Label | Explanation | Data Type |

Output table | The table with the tool execution summary report diagnostic values. | Table |

Output regression rasters | The workspace where all of the coefficient rasters will be created. | Raster Layer |