What technique is used when predicting outcomes using several variables?

Multiple Linear Regression is the correct technique for predicting outcomes when considering several variables. This statistical method is an extension of simple linear regression, which involves only one independent variable. In contrast, multiple linear regression allows analysts to evaluate the relationship between one dependent variable and two or more independent variables simultaneously.

This capability is particularly useful in scenarios where the outcome is influenced by multiple factors, enabling a more nuanced and accurate prediction. The method provides a framework to assess how changes in the independent variables impact the dependent variable, given that relationships in real-world data are rarely isolated to one factor. Moreover, multiple linear regression not only helps in making predictions but also in quantifying the strength and form of the relationships between variables.

While logistic regression is suitable for binary outcomes, and linear regression is limited to one independent variable, both do not accommodate the complexity of multiple influencing variables as effectively as multiple linear regression does. A box plot, on the other hand, is a data visualization tool that is used to summarize and illustrate the distribution of a dataset, but it does not predict outcomes based on variables. Therefore, multiple linear regression stands out as the appropriate choice for this question.