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Detection of High Leverage Points in Regression Model in Apple Data |
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PP: 135-140 |
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doi:10.18576/jsapl/090303
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Author(s) |
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Rizwan Yousuf,
Manish Sharma,
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Abstract |
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| An outlier in regression analysis is an observation with a large residual compared to the other observations in the data set. Outliers and influential points must be identified as part of the regression analysis. To identify and exclude unusual values from data, outlier detection methods have been applied. In this paper, Outliers are identified in regression models for the Apple data set. Ordinary residuals are not ideal for diagnostic purposes; rather, a modified version is recommended. Next, we have used the new approach of Modified OLS after handling HLP for detecting outliers. Real data was used to test the new approach’s performance. The modified had shown better results as compared to OLS.
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