Fits and predictions for Orthogonal Regression

Interpretation

Use the fitted value for the predictor to investigate any unusual residuals. If the fitted value of the predictor variable is much larger or much smaller than the observed value, investigate the cause.

Interpretation

Use the fitted value for the response variable to investigate any unusual residuals. If the fitted value of the response variable is much larger or smaller than the observed value, investigate the cause.

Interpretation

Examine the residuals to determine how well the model fits the data. In general, the residuals should be randomly distributed with no obvious patterns and no unusual values. If a residual is unusual, you can determine whether the residual is unusual because of the fitted x-value, the fitted y-value, or both values.

Interpretation

Use the standardized residuals to help you detect outliers. Standardized residuals that are greater than 2 and less than −2 are usually considered large enough to investigate.

Standardized residuals are useful because raw residuals might not be good indicators of outliers. The variance of each raw residual can differ by the x-values associated with it. This unequal scale causes it to be difficult to assess the sizes of the raw residuals. Standardizing the residuals solves this problem by converting the different variances to a common scale.

Interpretation

Use the predicted value to estimate the value of a new response value.

Interpretation

Use the standard deviation of the predicted value to measure the precision of the estimate of the prediction. The smaller the standard deviation, the more precise the prediction is. Standard deviations are always positive.