What type of analysis involves assessing how well a model explains observed outcomes?

The choice of regression analysis is accurate because this method specifically focuses on understanding the relationship between a dependent variable and one or more independent variables. In regression analysis, we aim to assess how well the chosen model predicts or explains the observed outcomes of the dependent variable based on the variations in the independent variables.

The performance of a regression model is often evaluated using metrics such as R-squared, which measures the proportion of variance in the dependent variable that can be explained by the independent variables included in the model. This quantifies the effectiveness of the model in capturing the relationships inherent in the data.

In contrast, variance analysis typically deals with understanding the differences between planned and actual results in a budget context, while residual analysis involves examining the discrepancies between observed and predicted values to assess the fit of the model. Descriptive analysis focuses more on summarizing and describing the characteristics of data rather than explaining relationships between variables, which is not the primary goal when evaluating how well a model explains outcomes.

This is why regression analysis is the most appropriate choice for the type of analysis that addresses the explanation of observed outcomes in relation to the fitted model.