Which procedure attempts to predict a single dependent variable using correlations with several other variables?

The procedure that focuses on predicting a single dependent variable using correlations with several other variables is known as multiple regression. This analytical method involves examining the relationship between one dependent variable and two or more independent variables. By doing so, multiple regression allows researchers to understand how different factors may influence the dependent variable simultaneously.

In multiple regression, each independent variable contributes information to the prediction of the dependent variable, enhancing the model's overall accuracy and allowing for a more nuanced understanding of the data. This differs from simple regression, which only considers one independent variable, and logistic regression, which is specifically designed for situations where the dependent variable is categorical rather than continuous. Chi-square analysis, on the other hand, is used to assess relationships between categorical variables and does not serve the purpose of predicting a dependent variable based on correlations with multiple independent variables.

Therefore, multiple regression is the appropriate choice for predicting a single dependent variable based on the interplay of several other variables.