Hello, data analysis and business intelligent team,
As well known, with the assistance of Exploratory Spatial Data Analysis (ESDA) tools in ArcGIS, we can get deep insight about our data, so that those analysis results can help us to choose the best interpolation algorithm and also the right analysis methods.
Similarly, the Exploratory Regression (ER) analysis tool is also widely used for data analysis practitioners to easily find a properly specified OLS model (Ordinary Least Squares). Sometimes, it certainly works good, especially, when no extreme outliers in the datasets.
However, many factors and technical considerations drive us to consider the combination of both ESDA and ER analysis methods together in practice, because we believe that will significantly improve the reliability of the analysis model and prediction accuracy, in particular, for detection of outliers (spatially over time) ...
So, as data analysts, please share any relevant thoughts about this? For example, advantages and benefits, some cautions ... when combing both together in your applications.
Regards,
