Before executing the tools and examining the results, let’s review some terminology:
• Dependent variable (Y): what you are trying to model or predict (residential burglary
incidents, for example).
• Explanatory variables (X): variables you believe influence or help explain the dependent
variable (like: income, the number of vandalism incidents, or households).
• Coefficients (β): values, computed by the regression tool, reflecting the relationship and
strength of each explanatory variable to the dependent variable.
• Residuals (ε): the portion of the dependent variable that isn’t explained by the model; the
model under and over predictions.
Before executing the tools and examining the results, let’s review some terminology: • Dependent variable (Y): what you are trying to model or predict (residential burglary incidents, for example). • Explanatory variables (X): variables you believe influence or help explain the dependent variable (like: income, the number of vandalism incidents, or households). • Coefficients (β): values, computed by the regression tool, reflecting the relationship and strength of each explanatory variable to the dependent variable. • Residuals (ε): the portion of the dependent variable that isn’t explained by the model; the model under and over predictions.
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