What does regression analysis primarily estimate in predictive modeling?

Regression analysis primarily estimates the relationships among variables by modeling how one variable, typically called the dependent variable, is affected by one or more independent variables. This statistical method enables analysts to understand the strength, direction, and nature of these relationships, which can be critical for making predictions about future outcomes based on the data.

Through regression, one can determine not just if a relationship exists, but also quantify it, allowing for meaningful interpretation and insights into how changes in predictor variables might influence the response variable. This functionality is essential in various fields, such as economics, biology, engineering, and social sciences, where understanding interactions between variables can lead to better decision-making and improved strategies.

The other options reflect different aspects of analysis and modeling that do not capture the primary focus of regression analysis. For instance, estimating the time until an event occurs typically pertains to survival analysis, while evaluating the success of predictive models relates more to model validation techniques. Moreover, assessing similarity between grouped objects is a characteristic of clustering techniques rather than regression. Therefore, option B accurately represents the core purpose of regression analysis in predictive modeling.