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Also known as the Median Absolute Deviation method, it is similar to Z-score method with some changes in parameters. Since mean and standard deviations are heavily influenced by outliers, instead of them we will be using median and absolute deviation from median.

Suppose x follows a standard normal distribution. The MAD will converge to the median of the half normal distribution, which is the 75% percentile of a normal distribution, and N(0.75) is approximately equal to 0.6745.

First we will import

`Numpy`

as`np`

and`scipy.stats`

as`stats`

`import numpy as <<your code goes here>> import scipy.stats as <<your code goes here>>`

Next, we will use the same datapoints we used previously

`x = [5, 5, 5, -99, 5, 5, 5, 5, 5, 5, 88, 5, 5, 5]`

Now, we will define a function

`calculate_rzscore`

that will detect the outliers using the robust z-score method`def <<your code goes here>>(data): out=[] med = np.median(data) ma = stats.median_absolute_deviation(data) for i in data: z = (0.6745*(i-med))/ (np.median(ma)) if np.abs(z) > 3: out.append(i) print("Outliers:",out)`

Finally, we will call the function using our datapoints

`calculate_rzscore(<<your code goes here>>)`

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