What does mean absolute deviation indicates?

Answer 1

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:

• Find the mean (average) value for the set of data. Call it M.
• For each observation, O, calculate the deviation, which is O - M.
• The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.
• Calculate the average of all the absolute deviations.

One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

Answer 2

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:

• Find the mean (average) value for the set of data. Call it M.
• For each observation, O, calculate the deviation, which is O - M.
• The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.
• Calculate the average of all the absolute deviations.

One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:

• Find the mean (average) value for the set of data. Call it M.
• For each observation, O, calculate the deviation, which is O - M.
• The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.
• Calculate the average of all the absolute deviations.

One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:

• Find the mean (average) value for the set of data. Call it M.
• For each observation, O, calculate the deviation, which is O - M.
• The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.
• Calculate the average of all the absolute deviations.

One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

The mean absolute deviation for a set of data is a measure of the spread of data. It is calculated as follows:

• Find the mean (average) value for the set of data. Call it M.
• For each observation, O, calculate the deviation, which is O - M.
• The absolute deviation is the absolute value of the deviation. If O - M is positive (or 0), the absolute value is the same. If not, it is M - O. The absolute value of O - M is written as |O - M|.
• Calculate the average of all the absolute deviations.

One reason for using the absolute value is that the sum of the deviations will always be 0 and so will provide no useful information. The mean absolute deviation will be small for compact data sets and large for more spread out data.

Answer 3

The Mean Absolute Deviation indicates how clustered (close together) the data is, i also indicates the average of the distance of the values and the mean.