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Nov 30, 2021 · Sort your data from low to high. Identify the first quartile (Q1), the median, and the third quartile (Q3). Calculate your IQR = Q3 – Q1. Calculate your upper fence = Q3 + (1.5 * IQR) Calculate your lower fence = Q1 – (1.5 * IQR) Use your fences to highlight any outliers, all values that fall outside your fences.
Oct 23, 2019 · When you decide to remove outliers, document the excluded data points and explain your reasoning. You must be able to attribute a specific cause for removing outliers. Another approach is to perform the analysis with and without these observations and discuss the differences.
Nov 15, 2021 · 1. Remove it. We can simply remove it from the data and make a note of this when reporting the results. 2. Perform a transformation on the data. Instead of removing the outlier, we could try performing a transformation on the data such as taking the square root or the log of all of the data values.
If it is obvious that the outlier is due to incorrectly entered or measured data, you should drop the outlier: For example, I once analyzed a data set in which a woman’s weight was recorded as 19 lbs. I knew that was physically impossible. Her true weight was probably 91, 119, or 190 lbs, but since I didn’t know which one, I dropped the ...
Aug 24, 2021 · In simple terms, an outlier is an extremely high or extremely low data point relative to the nearest data point and the rest of the neighboring co-existing values in a data graph or dataset you're working with. Outliers are extreme values that stand out greatly from the overall pattern of values in a dataset or graph.
Oct 4, 2022 · Example: Distortion of results due to outliers You calculate the average running time for all participants using your data. The average is much lower when you include the outlier compared to when you exclude it. Your standard deviation also increases when you include the outlier, so your statistical power is lower as well.
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Apr 2, 2023 · In the third exam/final exam example, you can determine if there is an outlier or not. If there is an outlier, as an exercise, delete it and fit the remaining data to a new line. For this example, the new line ought to fit the remaining data better. This means the SSE should be smaller and the correlation coefficient ought to be closer to 1 or -1.
