Final answer:
The question pertains to multiple regression, a statistical technique used to understand the relationship between one dependent variable and multiple independent variables. It is particularly useful in analyzing the impact of different factors on a single outcome, such as attitudes towards a city of residence.
Step-by-step explanation:
The research design described in the question is an example of a multiple regression analysis. Multiple regression is used to examine the relationship between one dependent variable and two or more independent variables. In this case, the attitudes toward a respondent's city of residence, which are measured on an 11-point scale, constitute the dependent variable, while the duration of residence in the city and the importance attached to weather are the two independent variables.
The concept of regression analysis is integral in fields such as geography and psychology for illustrating the impact of independent variables on a dependent variable. For instance, geographers might use regression analysis to determine how various factors, like income or access to parks, influence obesity rates by utilizing Geographic Information System (GIS) software for enhanced analysis. Similarly, in psychological studies, researchers analyze how specific situations influence attitudes and beliefs, which can then be evaluated through attitude scales like the Likert-type scale often employed in personality assessments.
In the context of explaining attitudes toward a city of residence, the multiple regression approach allows the researcher to quantify how much each independent variable—duration of residence and weather importance—contributes to the residents' attitudes. Therefore, this statistical technique is most suitable to understand the sequential influence of these factors, providing valuable insights that can guide urban planning and policy decisions.