Contents
What is internally studentized residuals?
The good thing about internally studentized residuals is that they quantify how large the residuals are in standard deviation units, and therefore can be easily used to identify outliers: Recall that Minitab flags any observation with an internally studentized residual that is larger than 2 (in absolute value).
What is a studentized residual plot?
A studentized residual is calculated by dividing the residual by an estimate of its standard deviation. Studentized residuals falling outside the red limits are potential outliers. This plot does not show any obvious violations of the model assumptions. We also do not see any obvious outliers or unusual observations.
What is a studentized residual used for?
In statistics, a studentized residual is the quotient resulting from the division of a residual by an estimate of its standard deviation. It is a form of a Student’s t-statistic, with the estimate of error varying between points. This is an important technique in the detection of outliers.
What is the studentized residual for the Red Data Point?
As you can see, the studentized residual (” TRES1 “) for the red data point is t4 = -19.7990. Now we just have to decide if this is large enough to deem the data point influential. To do that we rely on the fact that, in general, studentized residuals follow a t distribution with ( n – k –2) degrees of freedom.
How to calculate studentized residuals in Stat 462?
An example. Consider the following plot of n = 4 data points (3 blue and 1 red): 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.
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 are the values of studentized deleted residuals?
Three of the studentized deleted residuals — -1.7431, 0.1217, and, 1.6361 — are all reasonable values for this distribution. But, the studentized deleted residual for the fourth (red) data point — -19.799 — sticks out like a very sore thumb.