Evaluation of the accuracy of a model via linear regression based on confidence region and intervals

Linear regression can be used to evaluate the accuracy of predictive models through visual assessment, offering an alternative to purely analytical statistical tests. This study proposes a graphical method based on confidence intervals and a joint confidence region (JCR) to assess model accuracy in contexts where predictions inform operational decisions, forecasting, and simulation. Three models were evaluated using confidence intervals for the intercept and slope, as well as the JCR for the parameter vector. The JCR is preferred over separate confidence intervals or Bonferroni adjustments for simultaneously testing whether the intercept is zero and the slope is one, as it provides a more efficient and intuitive assessment. Only one of the three evaluated models was accurate. The JCR yields results equivalent to the joint F-test but is easier to interpret: if the point (0, 1) lies within the region, the model is considered accurate. A graph displaying the JCR alongside separate and Bonferroni confidence intervals enables modelers to visually identify whether inaccuracies stem from the intercept or slope. This makes the JCR a practical and accessible tool for evaluating predictive accuracy, particularly in applications where reliable model performance is essential for decision support and operational planning.