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Steps in Computing The Median Absolute Deviation​

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Final answer:

The Median Absolute Deviation (MAD) is computed by finding the dataset's median, computing the absolute difference from the median for each data point, and then finding the median of those absolute differences. Use of calculators or computer software is recommended for accuracy in computation.

Step-by-step explanation:

The Median Absolute Deviation (MAD) is a robust measure of variability that is less sensitive to outliers in a dataset compared to the standard deviation. To compute the MAD, you must follow several steps:

  1. Find the median of the dataset.
  2. Subtract the median from each data point and take the absolute value of these differences. These values are the absolute deviations from the median.
  3. Find the median of these absolute deviations. This value is the MAD.

Given that calculations for measures of variability such as the standard deviation or MAD can involve many data points and detailed arithmetic, it is often practical and accurate to use a calculator or computer software. These tools help to ensure that intermediate steps are not rounded prematurely, which could affect the accuracy of the final result. For high school students learning statistics, understanding these calculations is important, but using technology can make the computation much easier.

For example, if you are tasked with analyzing data and need to compute the standard deviation, mean, or MAD, using a software program like Excel or a calculator can save time and reduce the potential for errors. Remember to select the appropriate summary statistics function (like sx or ox on a TI calculator), and ensure you understand what these statistical measures indicate about your dataset, especially how they reflect the spread and central tendency.

User Nbar
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How to calculate median absolute deviation?
Sort the dataset and find the median.
Subtract the median from each data point.
Find the absolute value of each number.
Sort the numbers.
Find the median of the new dataset.
User Rafael Higa
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3.8k points