Week_14_Assignment_ITCS_6216

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University of North Carolina, Charlotte *

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6216

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Statistics

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Feb 20, 2024

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docx

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How Do You Treat Outlier Values? Show drafts Deciding how to treat outlier values depends on several factors, like the cause of the outlier, the nature of your data, and the analysis you're performing. Here are some common approaches: Investigate the Outliers: Before taking any action, try to understand why the outlier exists. Is it a data entry error, an instrument malfunction, or a genuine but rare event? Investigating the cause can help you decide on the most appropriate approach. Removal:  Simple: This is the most straightforward option, but only if you're confident the outlier is an error or doesn't represent a valid part of your data.  Risks: Removing outliers can reduce the data size and potentially bias your analysis, especially if there are many outliers or they represent a meaningful subgroup. Data Transformation:  Winsorizing: Replace extreme values with predefined thresholds based on the data distribution. This retains some information from the outliers while limiting their impact.  Capping: Similar to winsorizing, but replaces outliers with the highest or lowest non- outlier values.  Logarithmic transformation: Useful for skewed data with outliers in one tail. This transformation compresses the large values, reducing their influence. Robust Methods:  Median absolute deviation (MAD): Less sensitive to outliers than standard deviation for calculating dispersion.  Trimming: Removes a predefined percentage of extreme values from each tail of the distribution.  Rank-based methods: Focus on the relative order of data points instead of absolute values, making them less affected by outliers. Separate Analysis:  Analyze the outliers separately to understand their characteristics and potential contribution to your study. This can be valuable if they represent a distinct subgroup or phenomenon. Additional Tips:

 Document your approach: Clearly explain how you identified and handled outliers in your analysis for transparency and reproducibility.  Assess the impact: Compare results with different approaches to see how sensitive your analysis is to outlier treatment.  Consult with experts: If unsure about the best approach, seek advice from statisticians or data scientists familiar with your specific context. Remember, there's no one-size-fits-all solution. Consider the specific characteristics of your data, analysis goals, and potential biases when choosing how to handle outlier values.

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