Mean Absolute Deviation

Mean Absolute Deviation (M.A.D.) is a measure of the dispersion or variability of a set of data points. It is calculated by taking the average of the absolute differences between each data point and the mean of the data set.

1. Find the mean of the data set. 2. Subtract the mean from each data point and take the absolute value of each result. 3. Find the average of these absolute values.

A higher Mean Absolute Deviation indicates greater variability or spread in the data set.

A lower Mean Absolute Deviation indicates that the data points are closer to the mean, showing less variability.

If the M.A.D. is 0, it means all data points are identical and there is no variability.

The Mean Absolute Deviation is 1.2.

The Mean is the average of a data set, while the Mean Absolute Deviation measures how much the data points deviate from that average.

You can compare the M.A.D. values of the two data sets; the one with the higher M.A.D. has more variability.

M.A.D. = (1/n) * Σ|xi - mean|, where n is the number of data points and xi represents each data point.

A dot plot visually represents data points, allowing you to see the spread and variability, which can help in comparing M.A.D. between different data sets.