Which analysis expresses the variance between forecast and actual results as an absolute percentage?

The analysis that expresses the variance between forecast and actual results as an absolute percentage is residual analysis. This type of analysis is commonly used in budgetary and financial evaluations to quantify how much the actual results deviate from expected values. By focusing on the residuals, or the differences between observed (actual) values and predicted values, this approach can provide valuable insights into the accuracy of forecasting models.

Using percentage values allows stakeholders to easily interpret the significance of variances and adjust their expectations or models accordingly. This is particularly useful in capital and operating budgeting, where precise forecasting is crucial for effective financial planning and resource allocation. The clarity and directness of using a percentage makes it an effective tool for communicating results to various stakeholders who may not have financial expertise.

Other types of analysis, while useful in their respective contexts, do not specifically focus on expressing the variance in this manner. For instance, residue analysis tends to involve examining leftover amounts after certain calculations, location quotient analysis looks at economic data relative to a broader geographical context, and p-value analysis pertains to statistical significance in hypothesis testing rather than the financial variance between actual and forecasted data.