Final answer:
Regression is the statistical method used to quantify the response in one variable when another variable changes. It differs from correlation, which measures how two variables move together, as regression can also infer causality in a well-designed study.
Step-by-step explanation:
The general concept used to quantify the response in one variable when another variable changes is known as regression. Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. This approach can determine the strength and direction of the relationships, aiming to predict the dependent variable's behavior based on the values of the independent variables.
For instance, in studying the impact of fast food restaurants on obesity rates in a neighborhood, regression would consider not just the presence of fast food joints but also other factors like ethnicity, income, and access to parks. The outcome of such analysis could help to better understand and address the obesity issue by considering all contributing factors.
It is important to note that regression differs from correlation, which measures the association between two variables, indicating how they move together without implying a cause-and-effect relationship. While correlation is represented by a coefficient ranging from -1 to +1 known as the correlation coefficient (), regression focuses on creating predictive models that can include multiple variables affecting the outcome. Unlike correlation, regression can provide a basis for inferring causality if the study is well-designed.