The prediction interval is a range that is likely to contain a single future response for a selected combination of variable settings.
Use the prediction intervals (PI) to assess the precision of the predictions. The prediction intervals help you assess the practical significance of your results. If a prediction interval extends outside of acceptable boundaries, the predictions might not be sufficiently precise for your requirements.
With a 95% PI, you can be 95% confident that a single response will be contained in the interval given the settings of the predictors that you specified. The prediction interval is always wider than the confidence interval because of the added uncertainty involved in predicting a single response versus the mean response.
For example, a materials engineer at a furniture manufacturer develops a simple regression model to predict the stiffness of particleboard from the density of the board. The engineer verifies that the model meets the assumptions of the analysis. Then, the analyst uses the model to predict the stiffness.
The regression equation predicts that the stiffness for a new observation with a density of 25 is -21.53 + 3.541*25, or 66.995. Although such an observation is unlikely to have a stiffness of exactly 66.995, the prediction interval indicates that the engineer can be 95% confident that the actual value will be between approximately 48 and 86.