Which statistical method is used to model the relationship between one dependent variable and two or more independent variables?

The choice of Multiple Linear Regression is correct because this statistical method is specifically designed to analyze and model the relationship between one dependent variable and multiple independent variables. In this approach, the dependent variable is predicted based on the linear combination of the independent variables, allowing for the assessment of how these variables interact and contribute to the outcome.

Multiple Linear Regression extends the concept of Simple Linear Regression, where only one independent variable is involved. By incorporating two or more independent variables, it provides a more comprehensive understanding of the dynamics at play in the data. This is particularly useful in complex situations where various factors might influence the dependent variable simultaneously.

The method is widely utilized across different fields, including economics, social sciences, and natural sciences, as it can accommodate real-world scenarios where outcomes are often influenced by multiple variables. It also allows for the evaluation of the importance of each independent variable while controlling for the others, leading to robust data-driven decision-making insights.