Calculating the Mean Deviation Score: A Guide to Determine Data Dispersion.

mean deviation score

The mean deviation score is a statistical measure used to determine the average distance of a set of data values from their mean

The mean deviation score is a statistical measure used to determine the average distance of a set of data values from their mean. It provides information about the dispersion or spread of the data.

To calculate the mean deviation score, follow these steps:

1. Find the mean of the data set by adding up all the values and dividing by the total number of values.
2. Subtract the mean from each individual data value to determine the deviation of each value from the mean.
3. Take the absolute value of each deviation to ensure that all values are positive. This is because we are interested in the distance from the mean, not the direction.
4. Add up all the absolute deviations.
5. Divide the sum of the absolute deviations by the total number of values to find the mean deviation score.

Here is an example to illustrate the calculation of mean deviation score:

Consider the following data set: 12, 15, 18, 20, 24.

Step 1: Calculate the mean:
Mean = (12 + 15 + 18 + 20 + 24) / 5 = 17.8

Step 2: Calculate the deviation for each value:
Deviation of 12 = 12 – 17.8 = -5.8
Deviation of 15 = 15 – 17.8 = -2.8
Deviation of 18 = 18 – 17.8 = 0.2
Deviation of 20 = 20 – 17.8 = 2.2
Deviation of 24 = 24 – 17.8 = 6.2

Step 3: Take the absolute value of each deviation:
Absolute deviation of -5.8 = 5.8
Absolute deviation of -2.8 = 2.8
Absolute deviation of 0.2 = 0.2
Absolute deviation of 2.2 = 2.2
Absolute deviation of 6.2 = 6.2

Step 4: Add up all the absolute deviations:
Sum of absolute deviations = 5.8 + 2.8 + 0.2 + 2.2 + 6.2 = 17.2

Step 5: Calculate the mean deviation score:
Mean deviation score = 17.2 / 5 = 3.44

Therefore, the mean deviation score for the given data set is 3.44. This means that, on average, each data value deviates from the mean by approximately 3.44 units.

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