We had a review of many statistical ideas to help with lab 3. The major topic was on regression since it can help model patterns. Ordinary Least Squares (OLS) is simple and one of the most commonly used regression models that can show overall patterns. However OLS has a “global” nature, which means it assumes everything is the same over space. This is a limitation when working with spatial data with heterogeneity. So Geographically Weighed Regression (GWR) has become one of the more popular methods recently due to its ability to create a “local” model that varies over space.

The lab for this week was also focused on GWR and children’s language skills in relation to several variables. Firstly we used the Explanatory Regression Analysis tool to determine which set of variables provides the best relations. We then used OLS tool to produce a general global result, and compared that to results produced using the GWR tool. Finally we produced grouping clusters using the Grouping Analysis tool to see if we can relate the results from OLS and GWR to other variables used to create the groups. I found that only two of the three variables are statistically significant from OLS, and the map of OLS residuals showed no significant spatial patterns. As for the GWR results, the surface map of social score showed good correlation with GWR R² values, so it would seem that social scores and language scores have the strongest relation.

GWR R2 results with Social Score surface.