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  1. 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.

  2. Jan 24, 2022 · Step 2. Find the first quartile, Q1. To find Q1, multiply 25/100 by the total number of data points (n). This will give you a locator value, L. If L is a whole number, take the average of the Lth value of the data set and the (L +1)^ {th} (L + 1)th value. The average will be the first quartile.

  3. www.mathsisfun.com › data › outliersOutliers - Math is Fun

    So it seems that outliers have the biggest effect on the mean, and not so much on the median or mode. Hint: calculate the median and mode when you have outliers. You can also try the Geometric Mean and Harmonic Mean, they may work better. Mathopolis: Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10.

  4. www.omnicalculator.com › statistics › outlierOutlier Calculator

    Apr 27, 2024 · If we recall the outlier formula from the previous section, we'll see that we need the interquartile range. IQR = Q3 - Q1 = 62 - 42 = 20. Lastly, we need to determine the limits for the outliers. According to the outlier definition in math, in our case, an entry x is an outlier if either. x < Q1 - 1.5 * IQR = 42 - 1.5 * 20 = 42 - 30 = 12. or

  5. Aug 24, 2021 · As a reminder, an outlier must fit the following criteria: outlier < Q1 - 1.5 (IQR) Or . outlier > Q3 + 1.5 (IQR) To see if there is a lowest value outlier, you need to calculate the first part and see if there is a number in the set that satisfies the condition. Outlier < Q1 - 1.5 (IQR) Outlier < 5 - 1.5 (9) Outlier < 5 - 13.5 outlier < - 8.5

  6. Oct 4, 2022 · 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.

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  8. The extreme values in the data are called outliers. Example: For a data set containing 2, 19, 25, 32, 36, 38, 31, 42, 57, 45, and 84. In the above number line, we can observe the numbers 2 and 84 are at the extremes and are thus the outliers.

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