11.5
Ausreißer und einflussreiche Punkte
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Q1: What is an outlier in a regression analysis?
An outlier is a data point that does not follow the trend and lies far from the regression line in the vertical direction. For example, a person with few years of schooling but exceptionally high income would be an outlier in an income versus education scatter plot. Outliers appear as extreme values that do not fit the pattern of the graph.
Q2: How do you identify outliers using residuals?
Outliers are identified by calculating residuals, which represent the vertical distance between observed y-values and predicted y-values from the regression equation. Data points located at least two residual standard deviations above or below the regression line are flagged as potential outliers. This quantitative approach helps systematically detect extreme values in your dataset.
Q3: What are influential points and how do they differ from outliers?
Influential points are data points located far from other points in the horizontal direction, unlike outliers which are vertically distant. These points significantly change the regression line's slope when added or removed from the dataset. While outliers have large vertical errors, influential points affect the line's position and angle due to their extreme x-values.
Q4: How can you determine if a point is influential?
To identify an influential point, remove it from the dataset and recalculate the regression line. If the slope changes significantly, the point is influential. Computer software and many calculators automatically identify both outliers and influential points, allowing you to examine their effects on your regression analysis.
Q5: Should outliers always be removed from data analysis?
No. Outliers require careful examination to determine their cause. Some result from data entry errors and should be removed, while others may represent valuable information about the population being studied. The key is investigating what causes a data point to be an outlier before deciding whether to include or exclude it.
Q6: Why do outliers have large residuals?
Outliers have large residuals because the residual represents the vertical distance between the observed data point and the regression line's predicted value. When a point is far from the line vertically, this distance is substantial, resulting in a large error or residual value that signals the point does not fit the overall trend.
Q7: What role do computers play in identifying outliers and influential points?
Computers and calculators automate the identification of outliers and influential points through regression analysis output. This automated detection allows researchers to systematically examine problematic data points without manual calculation. Computer-generated reports help you visualize and evaluate whether these points should be retained or removed from your analysis.