This week's assignment consisted of using tools in ArcGIS to perform a regression analysis. Previously, similar assessments to these had been carried out in Excel, but ArcGIS takes it a couple steps further. While in Excel you can determine all of the necessary elements for a regression analysis such as correlation, adjusted R-squared, P-value and everything else for all of your variables, ArcGIS helps to determine the performance of the analysis and which variables should be included or excluded.
The performance of the model is determined using the Ordinary Least Squares tool, which generates a regression analysis using dependent and independent variables from a feature class. The results of this tool can be viewed to help to determine which variables work better, and which ones could be biased or redundant and should possibly be excluded. One way this tool works very well is how it analyzes the residuals and determines spatial autocorrelation and whether explanatory variables are missing. If there is an issue, it advises to use the Spatial Autocorrelation tool on the residuals, which tells whether the residuals are randomly distributed or clustered. This is an especially useful tool for improving models because it's a simple, straightforward method to determine the distribution of each variable and can help pinpoint issues and potential problems with the regression analysis.
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