Quiz on regression
Check how much you remember from previous sections by answering the questions below.
What is the primary purpose of using spatial regression models in geographic data analysis?
✗To eliminate random variables from a model.
✗To predict outcomes without considering geographic variations.
✓To incorporate the spatial dimension and analyze the influence of location on the dependent variable.
✗To avoid using regression altogether in geographic studies.
In the OLS linear regression model, what does \(\alpha\) represent?
✗The coefficient for the independent variable.
✗The predicted value.
✓The intercept, or constant, in the model.
✗The residual error.
Which of the following describes geographically weighted regression (GWR)?
✗A regression model that assumes global homogeneity in variable relationships.
✗A method that produces a single global estimate for variable relationships.
✗A regression model used exclusively for time-series data.
✓A method that examines how relationships between dependent and independent variables vary across geographic locations.
In a spatial fixed effects model, what is the purpose of including variables such as districts?
✗To improve the performance of the OLS model by creating spatial clusters.
✗To apply temporal weighting to the regression model.
✗To fix the \(\beta\) coefficients across all locations.
✓To allow the intercept to vary geographically.
What is the primary advantage of using Geographically Weighted Regression (GWR) over a standard OLS regression?
✗GWR is faster than OLS.
✗GWR creates a global model for the entire dataset.
✓GWR can model local variations and geographic heterogeneity in relationships.
✗GWR requires fewer variables to perform well.
When residuals from a regression model exhibit spatial autocorrelation, this indicates:
✗The residuals are randomly distributed across space.
✗The model perfectly explains the variance in the dependent variable.
✗There is no spatial heterogeneity in the model.
✓The residuals have a spatial pattern that may suggest spatial heterogeneity.