Which of the following is a common method to assess model appropriateness in regression?

Assessing model appropriateness in regression often involves examining the residuals, which are the differences between the observed values and the values predicted by the model. Residual plots are a vital tool in this process. By plotting the residuals against the predicted values or against an independent variable, you can identify patterns that indicate whether the assumptions of regression analysis have been met, such as linearity and homoscedasticity (constant variance of residuals).

If the residuals display a random scatter around zero, it suggests that the model is appropriate and that the linearity assumption holds. Conversely, if you see patterns, such as a curve or clustering, it indicates potential issues, like non-linearity or varying variances, which may necessitate model adjustments or alternative modeling techniques.

The other methods mentioned, such as histograms, box plots, and pie charts, provide useful information about the distribution of data or categorical variables but do not directly assess the appropriateness of a regression model in the same way that residual plots do. Therefore, the correct option clearly highlights a key aspect of regression diagnostics.