Contents
How do you identify the observation influence in regression?
If the predictions are the same with or without the observation in question, then the observation has no influence on the regression model. If the predictions differ greatly when the observation is not included in the analysis, then the observation is influential.
What is an influential point in linear regression?
An influential point is an outlier that greatly affects the slope of the regression line. One way to test the influence of an outlier is to compute the regression equation with and without the outlier.
How do you know if a point is influential in statistics?
Statistics Dictionary One way to test the influence of an outlier is to compute the regression equation with and without the outlier. This type of analysis is illustrated below. The slope is larger when the outlier is present, so this outlier would be considered an influential point.
How are influential observations in a linear regression model?
The following DATA step extracts a subset of n = 84 vehicles from the Sashelp.Cars data, creates a short ID variable for labeling observations, and sorts the data by the response variable, MPG_City. The data are sorted because the DFFITS statistic is graphed against the observation number, which is an arbitrary quantity.
Which is the most influential observation in horsepower?
For the Horsepower estimates, the most influential observations are numbers 1, 4, and 38. For the Weight estimates, the most influential observations are numbers 1, 24, 35, and 38. Notice that several observations (such as 1, 35, and 38) are influential for more than one estimate.
When is an observation considered to be an outlier?
In this lesson, we learn about how data observations can potentially be influential in different ways. If an observation has a response value that is very different from the predicted value based on a model, then that observation is called an outlier.
Which is an example of an influential observation?
The DFFITS graph shows that three observations have a large positive DFFITS value. The observations are the Ford Excursion, the Ford Ranger, and the Madza BB230. For these observations, the predicted value (at the observation) is higher with the observation included in the model than if it were excluded.