Mean Deviation
Even though Range and Quartile Deviation give an idea about the spread of individual items of a series, they do not try to calculate their dispersion from its average. If the variations of items were calculated from the average, such a measure of dispersion would through light on the formation of the series and the spread of items round the central value. Mean deviation (M.D) is such a
measure of dispersion.
Mean deviation of a series is the arithmetic average of the deviations of various items from a measure of central tendency. In aggregating the deviations, algebraic signs of the deviations are not taken into account. It is because, if the algebraic signs were taken into account, the sum of deviations from the mean should be zero and that from median is nearly zero. Theoretically the deviations can be taken from any of the three averages, namely, arithmetic mean, median or mode; but, mode is usually not considered as it is less stable. Between mean and median, the latter is supposed to be better because, the sum of the deviations from the median is less than the sum of the deviations from the méan.
While doing problems, if the type of the average is mentioned, we take that average: otherwise we consider mean or median as the case may be.
This measure of dispersion has found favour with economists and business men due to its simplicity in calculation. For forecasting of business cycles, this measure has been found more useful than others. it is also good for small sample studies where elaborate statistical analysis is not needed.
Where D represents deviations from mean or median, ignoring signs, and N the total number of items.
MD is an absolute measure of dispersion. The relative measure of MD is coefficient of MD, defined as:
Value will be least, if we are calculating it from median
Value will be higher, if calculated from the mean
Since it ignores signs of deviations, it is not suitable for open-end distribution
Mean Deviation from Arithmetic Mean
Individual Series
STEPS:-
Find Mean using the equation \( {{{\frac{ΣX}{N}} }} \)
Take deviations of individual values from mean, |d| (modulus) = (x - X̄), ignoring signs
MDX̄ = \( {{{\frac{Σ|D|}{N}} }} \) (N = number of items)
Relative measure of MD is coefficient of MD. Coefficient of MD = \( {{{\frac{MD}{Mean}} }} \)
Let us find the value of mean deviation and its coefficient from the following data.
Table 6.11
Roll No.
Marks
1
12
2
18
3
23
4
18
5
25
6
15
7
9
8
14
9
6
10
23
11
19
12
10
$$N\,=\,12$$
$$ X̄\,=\, {{{\frac{ΣX}{N}} }} $$
$$ =\, {{{\frac{192}{12}} }} $$
$$ =\,16 $$
Now we need to find modulus d. For that we creates a table as shown below.
In order to calculate MD and its coefficient for continuous series, we use the same method described earlier. Here we the devition from midvalues of classes. That is, we take midpoint as X here.
STEPS:-
Find Mean using the equation \( {{{\frac{Σfm}{Σf}} }} \)
Take deviations of mid points from mean, |d| (modulus) = (m - X̄), ignoring signs
Find f|d| and Σf|D|
MD = \( {{{\frac{Σf|D|}{Σf}} }} \)
Coefficient of MD = \( {{{\frac{MD}{Mean}} }} \)
Let us find MD from AM for the following series relates the marks of 20 students:
Table 6.15
Marks
No. of Students
0 - 10
2
10 - 20
2
20 - 30
5
30 - 40
5
40 - 50
3
50 - 60
2
60 - 70
1
We need to find midvalue (m), fm, |d| and f|D|. This is shown in the below given table.