Outliers in Regression Analysis Conceptual Overview: Explore the effects of outl

Question

Outliers in Regression Analysis Conceptual Overview: Explore the effects of outliers in regression analysis. An observation's standardized residual provides an index of how unusual that observation is relative to the regression model of the data. In essence the standardized residual is a measure of each point's distance from the best-fitting regression line (displayed in green in this graph) Move the mouse over each data point to reveal its standardized residual. Examine the standardized residuals of points close to and far from the line to confirm that the standardized residuals are indeed larger for points far from the line. If all observations fit the model well, then omitting one observation should not change the analysis very much Moving the mouse over the data point not only reveals its standardized residual but also shows the regression line with that data point omitted. How does ommitting certain data points effect the regression analysis?

Explanation / Answer

1) The largest standarddised residual in the data will be of the point which is close to (x=3, y=80)

This point s clearly the outlier in the data

2) Removing the largest standardised residual will increase the r square value from 0.4977 to close to 0.8

Check the regression line by moving the mouse to the point (x=3, y=80)

The answer should be close to 0.80

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