Show You can save predicted values, residuals, and related measures to the dataset as new variables. Each selection adds one or more new variables to the active data file. Predicted valuesValues that the regression model predicts for each case.UnstandardizedThe value the model predicts for the dependent variable.StandardizedA transformation of each predicted value into its standardized form. That is, the mean predicted value is subtracted from the predicted value, and the difference is divided by the standard deviation of the predicted values. Standardized predicted values have a mean of 0 and a standard deviation of 1.AdjustedThe predicted value for a case when that case is excluded from the calculation of the regression coefficients.Standard errors of predicted meansStandard errors of the predicted values. An estimate of the standard deviation of the average value of the dependent variable for cases that have the same values of the independent variables.DistancesMeasures to identify cases with unusual combinations of values for the independent variables and cases that may have a large impact on the regression model.Mahalanobis distanceA measure of how much a case's values on the independent variables differ from the average of all cases. A large Mahalanobis distance identifies a case as having extreme values on one or more of the independent variables.Cook's distancesA measure of how much the residuals of all cases would change if a particular case were excluded from the calculation of the regression coefficients. A large Cook's D indicates that excluding a case from computation of the regression statistics changes the coefficients substantially.Leverage valuesMeasures the influence of a point on the fit of the regression. The centered leverage ranges from 0 (no influence on the fit) to (N-1)/N.ResidualsThe actual value of the dependent variable minus the value predicted by the regression equation.UnstandardizedThe difference between an observed value and the value predicted by the model.StandardizedThe residual divided by an estimate of its standard deviation. Standardized residuals, which are also known as Pearson residuals, have a mean of 0 and a standard deviation of 1.StudentizedThe residual divided by an estimate of its standard deviation that varies from case to case, depending on the distance of each case's values on the independent variables from the means of the independent variables.DeletedThe residual for a case when that case is excluded from the calculation of the regression coefficients. It is the difference between the value of the dependent variable and the adjusted predicted value.Studentized deletedThe deleted residual for a case divided by its standard error. The difference between a Studentized deleted residual and its associated Studentized residual indicates how much difference eliminating a case makes on its own prediction.Influence statisticsThe change in the regression coefficients (DfBeta[s]) and predicted values (DfFit) that results from the exclusion of a particular case. Standardized DfBetas and DfFit values are also available along with the covariance ratio.DfBetasThe difference in beta value is the change in the regression coefficient that results from the exclusion of a particular case. A value is computed for each term in the model, including the constant.Standardized DfBetasStandardized difference in beta value. The change in the regression coefficient that results from the exclusion of a particular case. You may want to examine cases with absolute values greater than 2 divided by the square root of N, where N is the number of cases. A value is computed for each term in the model, including the constant.DfFitsThe difference in fit value is the change in the predicted value that results from the exclusion of a particular case.Standardized DfFitsStandardized difference in fit value. The change in the predicted value that results from the exclusion of a particular case. You may want to examine standardized values which in absolute value exceed 2 times the square root of p/N, where p is the number of parameters in the model and N is the number of cases.Covariance ratiosThe ratio of the determinant of the covariance matrix with a particular case excluded from the calculation of the regression coefficients to the determinant of the covariance matrix with all cases included. If the ratio is close to 1, the case does not significantly alter the covariance matrix.Prediction intervalsThe upper and lower bounds for both mean and individual prediction intervals.MeanLower and upper bounds (two variables) for the prediction interval of the mean predicted response.IndividualLower and upper bounds (two variables) for the prediction interval of the dependent variable for a single case.Confidence IntervalEnter a value between 1 and 99.99 to specify the confidence level for the two Prediction Intervals. Mean or Individual must be selected before entering this value. Typical confidence interval values are 90, 95, and 99.What is the difference between predicted and residual in regression analysis?The predicted values are calculated from the estimated regression equation; the residuals are calculated as actual minus predicted. Some procedures can calculate standard errors of residuals, predicted mean values, and individual predicted values.
What is predicted and residual?Residual = actual y value − predicted y value , r i = y i − y i ^ . Having a negative residual means that the predicted value is too high, similarly if you have a positive residual it means that the predicted value was too low. The aim of a regression line is to minimise the sum of residuals.
What does residual mean in regression analysis?The difference between an observed value of the response variable and the value of the response variable predicted from the regression line.
What does residual vs predicted plot tell us?This "residuals versus weight" plot can be used to determine whether we should add the predictor weight to the model that already contains the predictor age. In general, if there is some non-random pattern to the plot, it indicates that it would be worthwhile adding the predictor to the model.
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