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What do the Studentized deleted residuals help identify?
Studentized deleted residuals The observation is omitted to determine how the model behaves without this potential outlier. If an observation has a large Studentized deleted residual (if its absolute value is greater than 2), it may be an outlier in your data. Use the deleted residual to help you detect outliers.
What are standardized residuals in regression?
The standardized residual is a measure of the strength of the difference between observed and expected values. It’s a measure of how significant your cells are to the chi-square value.
How are deleted residuals calculated?
The basic idea is to delete the observations one at a time, each time refitting the regression model on the remaining n–1 observations. Then, we compare the observed response values to their fitted values based on the models with the ith observation deleted. This produces (unstandardized) deleted residuals.
When to show the studentized residual in regression?
Let’s show all of the variables in our regression where the studentized residual exceeds +2 or -2, i.e., where the absolute value of the residual exceeds 2.
When to use standardized residuals to identify outliers?
When trying to identify outliers, one problem that can arise is when there is a potential outlier that influences the regression model to such an extent that the estimated regression function is “pulled” towards the potential outlier, so that it isn’t flagged as an outlier using the standardized residual criterion.
What does solid line in studentized residuals mean?
The solid line represents the estimated regression line for all four data points, while the dashed line represents the estimated regression line for the data set containing just the three data points — with the red data point omitted. Observe that, as expected, the red data point “pulls” the estimated regression line towards it.
What happens when a regression line is miscalculated?
If the regression line was computed correctly, the point of averages of the residual plot will be on the x axis, and the residuals will not have a trend: the correlation coefficient for the residuals and X will be zero. If the residuals have a trend, the slope of the regression line was miscalculated.