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Standard deviation and absolute deviation independent practice 2026

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  4. Proceed to each problem listed in the document. For each data set, calculate the mean absolute deviation or standard deviation as instructed. You can write your answers directly in the spaces provided next to each question.
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Mean absolute deviation around the mean MAD has been proposed to be used in place of standard deviation since it corresponds better to real life. Because the MAD is a simpler measure of variability than the standard deviation, it can be useful in school teaching.
Step 1 We find the mean of the dataset i.e. (2+4+8+10)/4 = 6. Step 3 And add them i.e. 4+2+2+4 = 12. Step 4 Finally, we divide this sum by the total number of values in the dataset (4) that will give us the mean deviation. The answer is 12/4 = 3.
The mean absolute deviation of the given set of data {12, 4, 6, 12, 10, 8, 4} is 3.43 (rounded to two decimal places). The mean absolute deviation measures the average distance of each data point from the mean of the set, indicating the overall variability of the data.
The mean absolute deviation (MAD) of the numbers 6, 2, 8, 4, 8, 6, 8, 8 is 1.75. This is calculated by first finding the mean of the data and then determining the average of the absolute differences from the mean. In this case, the mean is 6.25, and the absolute deviations sum to 14, leading to a MAD of 1.75.
Take each number in the data set, subtract the mean, and take the absolute value. Then take the sum of the absolute values. Now compute the mean absolute deviation by dividing the sum above by the total number of values in the data set. Finally, round to the nearest tenth.

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The mean absolute deviation (MAD) of the numbers 6, 2, 8, 4, 8, 6, 8, 8 is 1.75. This is calculated by first finding the mean of the data and then determining the average of the absolute differences from the mean.
It is calculated by finding the distance of each data value from the mean and then calculating the mean of those distances. The MAD is a measure of spread, like the range, because it gives us an idea of how much the data are spread out. If the data are tightly clustered around the mean, the MAD is low.

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