Final answer:
The Cleary Model uses regression analyses to determine relationships between dependent and independent variables, which is crucial for geographers and social scientists in making predictions and policy decisions.
Step-by-step explanation:
The Cleary Model uses regression analyses to determine whether there is a relationship between cause and effect variables. Regression analysis, particularly geographic information systems (GIS) enabled regression, is a sophisticated statistical technique used by geographers and other social scientists. It aids in comprehending the strength and direction of causality between a dependent variable (such as obesity) and one or more independent variables (like the prevalence of fast food joints, ethnicity, income, access to parks). This method can be applied to various situations, such as the potential effects of opening new businesses on local crime rates, to predict neighborhood crime trends more accurately. The analytical capabilities of regression models make them ecologically realistic and practical tools for policy analysis, urban planning, and social science research.
In essence, these models help us understand not only if certain factors are associated with an outcome, but also how strong those associations are. This can be critical for making predictions and decisions based on the model's findings. For instance, if there is a strong negative linear relationship between two variables, they would be prime candidates for a linear regression analysis to investigate the nature of their interaction.