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To find the mean absolute deviation (MAD) of the dataset, we first need to find the mean of the dataset.
Mean = (125 + 198 + 209 + 213 + 101 + 178) / 6
Mean = 1024 / 6
Mean = 170.67 (rounded to the nearest hundredth)
Next, we find the absolute deviation of each data point from the mean:
|125 - 170.67| = 45.67
|198 - 170.67| = 27.33
|209 - 170.67| = 38.33
|213 - 170.67| = 42.33
|101 - 170.67| = 69.67
|178 - 170.67| = 7.33
Now, find the mean absolute deviation by adding up all the absolute deviations and dividing by the number of data points:
MAD = (45.67 + 27.33 + 38.33 + 42.33 + 69.67 + 7.33) / 6
MAD = 230.66 / 6
MAD ≈ 38.4 (rounded to the nearest tenth)
Therefore, the mean absolute deviation of the dataset is approximately 38.4.