Calculation of Coefficient of Concurrent Deviation

To find the coefficient of concurrent deviation, we first need to understand what concurrent deviation is.

Concurrent Deviation:
Concurrent deviation is a measure of the degree of dispersion or scattering of a set of observations in relation to their mean. It is calculated as the sum of the absolute differences between each observation and the mean, divided by the number of observations.

Given:
Pairs of observations: 10
Number of concurrent deviations: 4

Step 1: Find the Mean
To calculate the coefficient of concurrent deviation, we need to find the mean of the given observations. The mean is calculated by summing up all the observations and dividing by the number of observations.

Step 2: Find the Deviations
Next, we need to find the deviation of each observation from the mean. The deviation is calculated by subtracting the mean from each observation.

Step 3: Calculate the Concurrent Deviation
The concurrent deviation is calculated by summing up the absolute differences between each observation and the mean.

Step 4: Calculate the Coefficient of Concurrent Deviation
Finally, the coefficient of concurrent deviation is calculated by dividing the concurrent deviation by the number of observations.

Answer:
To calculate the coefficient of concurrent deviation, we need the actual values of the observations. Once we have the actual values, we can follow the above steps to find the coefficient of concurrent deviation.

As the actual values are not provided in the question, it is not possible to calculate the coefficient of concurrent deviation. However, you can use the given formula and steps to calculate it once you have the actual values.

Note: The coefficient of concurrent deviation is a relative measure and does not have any unit. It is used to compare the dispersion of different sets of observations. The smaller the coefficient of concurrent deviation, the less dispersed the observations are. Conversely, the larger the coefficient of concurrent deviation, the more dispersed the observations are.