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Session – 9 
Measures of Dispersions 
1 
Standard Deviation 
Standard deviation is the root of sum of the squares of deviations divided by 
their numbers. It is also called ‘Mean error deviation’. It is also called mean square 
error deviation (or) Root mean square deviation. It is a second moment of dispersion. 
Since the sum of squares of deviations from the mean is a minimum, the deviations 
are taken only from the mean (But not from median and mode). 
The standard deviation is Root Mean Square (RMS) average of all the 
deviations from the mean. It is denoted by sigma (). 
Characteristics of standard deviation 
1. Standard deviation and coefficient of variation possesses all these properties 
which a good measure of dispersion should possess. 
2. The process of squaring the deviation eliminates negative sign and makes 
mathematical computations easy. 
Merits 
1. It is based on all observations. 
2. It can be smoothly handled algebraically. 
3. It is a well defined and definite measure of dispersion. 
4. It is of great importance when we are making comparison between variability of 
two series. 
Merits 
1. It is difficult to calculate and understand. 
2. It gives more weightage to extreme values as the deviation is squared. 
3. It is not useful in economic studies. 
Standard deviation 
If the variant xi takes the values of x1, x2 ………….. xn the standard deviation 
denoted by  and it is defined by 
  
 x x 
= N 
2 
i   
The quantity 2 is called variance.
2
3 
Alternate Expressions 
For raw data 
 
2 
2 x =  
 2 
x 
n 
2 
For a grouped data 2 fx =  
 x 
2 
n 
 
For a grouped data with step deviation method  = 
2 2 
fd 
  
 
N 
fd 
N 
 
 
 
 
Coefficient of variance 
It is defined as the ratio to be equal to standard deviation divided by mean. 
 
The percentage form of CV is given by CV = x 100 
x
Problems 
1. Ten students of a class have obtained the following marks in a particular subject 
out of 100. Calculate SD and CV for the given data below. 
4 
Sl. No. 
(x) 
marks 
d = (x1 = 38.5) 
d = (x1 - x ) 
(x1 - x )2 
1. 5 - 33.5 1122.25 
2. 10 - 28.4 812.25 
3. 20 - 18.5 342.25 
4. 25 - 13.5 182.25 
5. 40 1.5 2.25 
6. 42 3.5 12.25 
7. 45 6.5 42.25 
8. 48 9.5 90.25 
9. 70 31.5 992.25 
10. 80 41.5 1722.25 
x = 385 (x1 - x )2 = 
d2 = 5320.50 
x 
N 
x 
 
 
= 
385 
10 
= 38.5 
  
 x x 
= N 
2 
i   
 = 
5320.5 
10 
= 23.066 
 
CV = x 100 
x 
23 
CV = x 100 
38.5 
CV = 59.9%
2. Compute standard deviation and coefficient of varience for following data of 100 
15.5 25.5   
25.5 10 = 25.5 + 6.5 
  = 12.359 
5 
students marks. 
Class f Class 
Mid 
point 
x 
d fd fd2 
1 – 10 3 0.5 – 10.5 5.5 -2 -6 12 
11 – 20 16 10.5 – 20.5 15.5 -1 -16 16 
21 – 30 26 20.5 – 30.5 25.5 0 0 0 
31 – 40 31 30.5 – 40.5 35.5 1 31 31 
41 – 50 16 40.5 – 50.5 45.5 2 32 64 
51 – 60 8 50.5 – 60.5 55.5 3 24 72 
N = f = 
100 fd = 65 fd2= 195 
a = 25.5 
x a x 25.5 
 
d = d 
10 
h 
 
 
 
10 
d = 1 
10 
10 
 
 
 
fd 
N 
x a h 
 
   
 
 
   
 
65 
100 
x 32 
 = h 
2 2 
fd 
  
 
N 
fd 
N 
 
 
 
 
 = 10 
2 
65 
100 
195 
100 
 
 
 
 
CV = x 100 
x 
12.359 
CV = x 100 
32 
= 38.62%
3. The AM and SD of a set of nine items are 43 and 5 respectively if an item of value 
20835  2 = 7.64 is modified SD. 
6 
63 is added, find the mean and SD. 
 
 
x 
x i N 
xi = x x N 
xi = 43 x 9 
x = 387 for 9 items 
x = 387 + 63 for 10 item 
x = 450 
Modified mean 
450 
10 
x 
 
 
x  
N 
x = 45 
x = 43  = 5 for 9 items 
 
2 
2 x =  
 2 
x 
N 
2 
x  
 
25 =  43 
2 
9 
x2 
25 = 1849 
9 
 
 
25 + 1849 = 
x2 
9 
x2 
9 
= 1874 
x2 = 1874 
x2 = 16866 for 9 items 
If 63 is added 
x2 = 16866 + (63)2 
= 20835 for 10 items 
 
x 2 
2  
 2 
Modified = x 
N 
2 =  2 45 
10
4. The mean of 5 observations is 4.4. and variance is 8.24 and if the 3 items of the 
five observations are 1, 2 and 6. Find the values of other two observations. 
7 
w.k.t. 
x 
N 
x 
 
 
x 
N 
4.4 
 
 
x = 22 
 
2 
2 x =  
 2 
x 
N 
2 
x  
 
8.24 =  4.4 
2 
5 
x2 
8.24 = 19.36 
9 
 
 
8.24 + 19.36 = 
x2 
5 
x2 = 138 
x2 = 12 + 22 + 62 + x1 
2 + x2 
2 
138 = 1 + 4 + 36 + x1 
2 + x2 
2 
97 = x1 
2 + x2 
2 
x1 
2 + x2 
2 = 97 ---- (1) 
x = 1 + 2 + 6 + x1 + x2 
22 = 9 + x1 + x2 
x1 + x2 = - 13 ---- (2)  put (2) in (1) 
x2 = 13 – x1 
by (1) & (2) 
x1 
2 + (13 – x1)2 = 97 
x1 
2 + 169 + x1 
2 – 26x1 = 97 
2 x1 
2 – 26x1 + 72 = 0 
x1 
2 – 13x1 + 36 = 0
- (-13)  169  4 x 36 
8 
x1 = 
- b  b2  49 
2a 
x1 = 
2 
x1 = 
13  5 
2 
x1 = 
5 
2 
13  
2 
x1 = 6.5  2.5 
x1 = 9 or x1 = 4 
x1 = 9 x2 = 4
5. The mean and S.D. of the frequency distribution of a continuous random variable 
x are 40.604 and 7.92 respectively. Change of origin and scale is given below. 
Determine the actual class interval. 
d -3 -2 -1 0 1 2 3 4 
f 3 15 45 57 50 36 25 9 
d f fd fd2 MV CI 
-3 3 -9 27 22.5 20-25 
-2 15 -30 60 29.5 25-30 
-1 45 -45 45 32.5 30-35 
0 57 0 0 37.5 35-40 
1 50 50 50 42.5 40-45 
2 36 72 144 47.5 50-55 
3 25 75 225 52.5 55-60 
4 9 36 144 57.5 
N = 240 fd = 149 fd2 = 695 
9 
fd 
N 
x a h 
 
  
149 
240 
40.604  a  h 
40.604 = a + 0.62h ----- (1) 
 = h 
2 2 
fd 
  
 
N 
fd 
N 
 
 
 
 
7.92 = h 
2 
149 
240 
695 
240 
 
 
  
 
= h 2.895  0.620 
7.92 = h x 1.584 
h = 4.998 
h = 5 
Put h = 5 in equation (1) 
40.604 = a + 0.62 x 5 
a = 37.5
10 
Combined Standard Deviation 
Suppose we have different samples of various sizes n1, n2, n3 …….. having 
means x1, x2, x3 and standard deviation 1, 2, 3 ……. then combine standard 
deviation can be computed by the following formula. 
2 (n1 + n2) = n1 (1 
2 + d1 
2) + n2 (2 
2 + d2 
2) 
d1 = x x 1  
d2 = x x 2  
1. The mean’s of two samples of sizes 50 and 100 respectively are 54.1 and 50.3 and 
there standard deviations are 8 and 7 respectively obtain the SD for combined 
group. 
n1 = 50 
1 x = 54.1 
1 = 8 
n2 = 100 
2 x = 50.3 
2 = 7 
 
n x n x 
1 1 2 2 
 
(n n ) 
x 
1 2 
 
 
(50 x 54.1) (100 x 50.3) 
50 100 
x 
 
 
x  51.56 
2 (n1 + n2) = n1 (1 
2 + d1 
2) + n2 (2 
2 + d2 
2) 
d1 = x x 1  
d2 = x x 2  
d1 = 94.1 – 51.56 
d1 = 2.54 d1 
2 = 6.45 
d2 = 50.3 – 51.56 
d2 = - 1.26 d2 
2 = 1.56 
2 150 = 50 (82 + 6.45) + 100 (72 + 1.58) 
32 = (64 + 6.45) + 2 (49 + 1.58) 
32 = 70.45 + 2 x 50.58 
 = 7.56
2. The mean wage is Rs. 75 per day, SD wage is Rs. 5 per day for a group of 1000 
workers and the same is Rs. 60 and Rs. 4.5 for the other group of 1500 workers. 
Find mean and standard deviation for the entire group. 
We have by data, 1 x = 75, 1 = 5, n1 = 1000 
2 x = 60, 2 = 450, n2 = 1500 
Let x and  be the mean and SD of the entire group. 
 
x  
22 
Consider 
 
n x n x 
1 1 2 2 
n n 
1 2 
x 
 
 
1000 x 75 1500 x 60 
i.e., 60 
 
1000 1500 
 
Also we have, 
(n1 + n2) 2 = n1 (1 
2 + d1 
2) + n2 (2 
2 + d2 
2), 
where d1 = 1 x - x = 75 – 66 = 9; d2 = 2 x - x = 60 – 66 = -6 
 (1000 + 1500) 2 = 1000 (52 + 92) + 1500 (4.52 + (-6)2) 
 2 = 76.15 or  = 8.73
3. The runs scored by 3 batsman are 50, 48 and 12. Arithmtic mean’s respectively. 
The SD of there runs are 15, 12 and 2 respectively. Who is t he most consistent of 
the three batsman? If the one of these three is to be selected who is to be selected? 
A B C 
AM ( x ) 50 48 12 
SD() 15 12 2 
23 
CVA = 
A 
x 
A 
 
x 100 
CVA = 
15 
50 
x 100 
CVA = 30% 
CVB = 
B 
x 
B 
 
x 100 
CVB = 
12 
48 
x 100 
CVB = 25% 
CVC = 
C 
x 
C 
 
x 100 
CVC = 
2 
12 
x 100 
CVC = 16.66% 
Evaluation Criteria 
1. Less CV indicates more constant player and hence more consistent player is 
(Player C) 
2. Highest rune scorer = x A = 50
4. The coefficient of variation of the two series are 75% and 90% with SD 15 and 18 
24 
respectively compute there mean. 
CVA = 75% 
CVB = 80% 
A = 15 
B = 18 
 
CV= x 100 
x 
15 
75 = x 100 
x 
A 
18 
90 = x 100 
x 
A 
x A = 20 x A = 20 
5. Goals scored by two teams A & B in a foot ball season are as shown below. By 
calculating CV in each, find which team may be considered as more consistent. 
No. of goals 
x 
No. of matches Team (A) 
fx 
Team (B) 
A-team B-team fx 
0 27 17 0 0 
1 9 9 9 9 
2 8 6 16 12 
3 5 5 15 15 
4 4 3 16 12 
N = f = 53 f = 40 fx = 56 fx2 = 48 
Team (A) 
fx2 
Team (B) 
fx2 
0 0 
9 9 
32 24 
45 45 
64 48 
fx2 = 150 fx2 = 126 
x A = 
fx 
N 
56 
= 
53 
= 1.056
150 2   = 1.30 
126 2   = 1.30 
1.30 
1.30 
26 
x B = 
fx 
N 
48 
= 
40 
= 1.2 
 2 
2 
2 
A 
x 
fx  
N 
 
  = 1.056 1.715 
53 
  
A 
 2 
2 
2 
B 
x 
fx  
N 
 
  = 1.2 1.95 
40 
  
B 
CVA = 
A 
x 
A 
 
x 100 = x 100 
1.056 
= 123.8% 
CVB = 
B 
x 
B 
 
x 100 = x 100 
1.2 
= 109% 
Since, CVB < CVA, team B is more consistent player 
6. The prices of x and y share A & B respectively state which share more stable in its 
value. 
Price A 
(x) 
(xi = 53) 
(xi = x ) 
(xi = x )2 
Price - A 
(4) 
(xi = 105) 
(xi = x ) 
(xi = x )2 
55 2 4 108 3 9 
54 1 1 107 2 4 
52 -1 1 105 0 0 
53 0 0 105 0 0 
56 3 9 106 1 1 
58 5 25 107 2 4 
52 -1 1 104 -1 1 
50 -3 9 103 -2 4 
51 -2 4 104 -1 1 
49 -4 16 101 -4 16 
x = 530 (xi= x )2 = 70 x = 1050 x(xi= x )2 = 40
2.64 
2 
27 
x A = 
x 
N 
= 
530 
10 
= 53 
x B = 
x 
N 
= 
1050 
10 
= 105 
2.64 
70 
     
10 
A A 
2 
40 
     
10 
B B 
CVA = 
A  
x 
x 100 = x 100 
53 
= 4.98% 
CVB = 
B  
x 
x 100 = x 100 
105 
= 1.903% 
Since, CVB is less share B is more stable. 
7. A student while computing the coefficient of variation obtained the mean and SD 
of 100 observations as 40 and 5.1 respectively. It was later discovered that he had 
wrongly copied an observation as 50 instead of 40. Calculate the correct 
coefficient of variation. 
>> 
x 
n 
x 
 
 i.e. 
x 
100 
40 
 
 
 x (incorrect) = 4000 
Now correct x = 4000 – 50 + 40 = 3990 
 3990 
correct 
x  = 39.9 
100 
 
x 2 
2 
Let us consider 2  
 x 
n 
  
2 
x 
 
 
 5.1  2  
 40 
2 
100 
x 
 x 
 
i.e.     1626.01 
100 
or 
100 
40 5.1 
2 2 
2  2  
 
 x2 (incorrect) = 100 x 1626.01 = 162601 
Now correct x2 = 162601 – (50)2 + (40)2 = 161701
 correct 2 = correct  2 
161701 2   
 
5  
28 
2 
correct x 
x  
 
n 
i.e., correct 2 = 39.9 25 
100 
Now correct efficient of variation = x 100 
x 
x 100 12.56% 
39.9 
Hence correct C.V. = 12.53%
8. The mean and SD of 21 observations are 30 and 5 respectively. It was 
subsequently noted that one of the observations 10 was incorrect. Omit it and 
determine the mean and SD of the rest. 
 
 
30   
 
 
50   
29 
>> 
x 
n 
x 
 
 x 
i.e. or x 630 
21 
 incorrect x = 630 
Now omitting the incorrect value 10, 
New x = 630 – 10 = 620 
n = 21 – 1 = 20 
620 
x   
New 31 
20 
 
x 2 
2 
Next consider 2  
 x 
n 
  
2 
x 
 
 
 5  2  
 30 
2 
100 
i.e. 
x 
21 
900 25 
 2 
  
incorrect x2  925 x 21  19425 
Again omitting the incorrect value 10. 
New x = 19425 –(10)2 = 19325, n = 20 
 
x 
2 
2 
Hence new 2 new  
 new x 
20 
  
19325  2  
(31) 5.25 
20 
 New  = 5.25 = 2.29 
9. The mean of 200 items was 50. Later on it was discovered that two items were 
misread as 92 and 8 instead of 192 and 88. Find out the correct mean. 
>> 
x 
n 
x 
 
 x 
i.e. or x 10000 
200 
 incorrect x = 10000 
Correct x = 10000 – 92 – 8 + 192 + 88 = 10180 
 10180 
Correct mean = 
200 
= 50.9
10. Find the missing frequencies in the following data given that the median is 137.2. 
Class 100- 
 , h = 10 f = f1, c = 192 
30 
110 
110- 
120 
120- 
130 
130- 
140 
140- 
150 
150- 
100 
106- 
170 
170- 
180 
Frequency 15 44 133 F1 125 F2 35 16 N=600 
>> We prepare the table with the column of cumulative frequencies and use 
the formula for median. 
Class Frequency cf 
100-110 15 15 
110-120 44 59 
120-130 133 192 
130-140 f1 192 + f1  Median class 
140-150 125 317 + f1 
150-160 f2 317 + f1 + f2 
160-170 35 352 + f1 + f2 
170-180 16 368 + f1 + f2 
N = 600 
 
  c 
2 
h 
Median = 1 +  
 
N 
f 
We can take the median class as 130-140 since median is given to be 137.2 
130 
 
130 130 
l  
2 
 137.2 = 130 + 
10 
1 f 
(300 - 192) 
i.e., 137-2 – 130 = 
1080 
1 f 
i.e., 7.2 f1 = 1080 or f1 150 
But the last cumulative frequency must be equal to N = 600 
i.e. 368 + f1 + f2 = 600 
368 + 150 + f2 = 600  f2 = 82 
Thus f1 = 150, f2 = 82
Relationship between various measures of dispersion 
We have some of following relationships among the various methods of 
31 
measures of dispersion 
1. Mean  QD covers 50% of observations of the distribution 
2. Mean  MD covers 57.5% of observations 
3. Mean  1  includes 68.27% of observations 
4. Mean  2  includes 95.45% of observations 
5. Mean  3  includes 99.73% of observations 
2 
6. QD =    
3 
6745 
4 
2 
7. MD =    
5 
x 
A 
8. QD = 
5 
MD 
6 
9. Combining the results we get 3 QD = 2 SD and 5 MD = 4 SD that is also equal 
to 6 QD. 
10. Range = 6 times SD. 
SOURCES AND REFERENCES 
1. Statistics for Management, Richard I Levin, PHI / 2000. 
2. Statistics, RSN Pillai and Bagavathi, S. Chands, Delhi. 
3. An Introduction to Statistical Method, C.B. Gupta, & Vijaya Gupta, Vikasa 
Publications, 23e/2006. 
4. Business Statistics, C.M. Chikkodi and Salya Prasad, Himalaya Publications, 
2000. 
5. Statistics, D.C. Sancheti and Kappor, Sultan Chand and Sons, New Delhi, 2004. 
6. Fundamentals of Statistics, D.N. Elhance and Veena and Aggarwal, KITAB 
Publications, Kolkata, 2003. 
7. Business Statistics, Dr. J.S. Chandan, Prof. Jagit Singh and Kanna, Vikas 
Publications, 2006.

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S7 pn

  • 1. Session – 9 Measures of Dispersions 1 Standard Deviation Standard deviation is the root of sum of the squares of deviations divided by their numbers. It is also called ‘Mean error deviation’. It is also called mean square error deviation (or) Root mean square deviation. It is a second moment of dispersion. Since the sum of squares of deviations from the mean is a minimum, the deviations are taken only from the mean (But not from median and mode). The standard deviation is Root Mean Square (RMS) average of all the deviations from the mean. It is denoted by sigma (). Characteristics of standard deviation 1. Standard deviation and coefficient of variation possesses all these properties which a good measure of dispersion should possess. 2. The process of squaring the deviation eliminates negative sign and makes mathematical computations easy. Merits 1. It is based on all observations. 2. It can be smoothly handled algebraically. 3. It is a well defined and definite measure of dispersion. 4. It is of great importance when we are making comparison between variability of two series. Merits 1. It is difficult to calculate and understand. 2. It gives more weightage to extreme values as the deviation is squared. 3. It is not useful in economic studies. Standard deviation If the variant xi takes the values of x1, x2 ………….. xn the standard deviation denoted by  and it is defined by    x x = N 2 i   The quantity 2 is called variance.
  • 2. 2
  • 3. 3 Alternate Expressions For raw data  2 2 x =   2 x n 2 For a grouped data 2 fx =   x 2 n  For a grouped data with step deviation method  = 2 2 fd    N fd N     Coefficient of variance It is defined as the ratio to be equal to standard deviation divided by mean.  The percentage form of CV is given by CV = x 100 x
  • 4. Problems 1. Ten students of a class have obtained the following marks in a particular subject out of 100. Calculate SD and CV for the given data below. 4 Sl. No. (x) marks d = (x1 = 38.5) d = (x1 - x ) (x1 - x )2 1. 5 - 33.5 1122.25 2. 10 - 28.4 812.25 3. 20 - 18.5 342.25 4. 25 - 13.5 182.25 5. 40 1.5 2.25 6. 42 3.5 12.25 7. 45 6.5 42.25 8. 48 9.5 90.25 9. 70 31.5 992.25 10. 80 41.5 1722.25 x = 385 (x1 - x )2 = d2 = 5320.50 x N x   = 385 10 = 38.5    x x = N 2 i    = 5320.5 10 = 23.066  CV = x 100 x 23 CV = x 100 38.5 CV = 59.9%
  • 5. 2. Compute standard deviation and coefficient of varience for following data of 100 15.5 25.5   25.5 10 = 25.5 + 6.5   = 12.359 5 students marks. Class f Class Mid point x d fd fd2 1 – 10 3 0.5 – 10.5 5.5 -2 -6 12 11 – 20 16 10.5 – 20.5 15.5 -1 -16 16 21 – 30 26 20.5 – 30.5 25.5 0 0 0 31 – 40 31 30.5 – 40.5 35.5 1 31 31 41 – 50 16 40.5 – 50.5 45.5 2 32 64 51 – 60 8 50.5 – 60.5 55.5 3 24 72 N = f = 100 fd = 65 fd2= 195 a = 25.5 x a x 25.5  d = d 10 h    10 d = 1 10 10    fd N x a h           65 100 x 32  = h 2 2 fd    N fd N      = 10 2 65 100 195 100     CV = x 100 x 12.359 CV = x 100 32 = 38.62%
  • 6. 3. The AM and SD of a set of nine items are 43 and 5 respectively if an item of value 20835  2 = 7.64 is modified SD. 6 63 is added, find the mean and SD.   x x i N xi = x x N xi = 43 x 9 x = 387 for 9 items x = 387 + 63 for 10 item x = 450 Modified mean 450 10 x   x  N x = 45 x = 43  = 5 for 9 items  2 2 x =   2 x N 2 x   25 =  43 2 9 x2 25 = 1849 9   25 + 1849 = x2 9 x2 9 = 1874 x2 = 1874 x2 = 16866 for 9 items If 63 is added x2 = 16866 + (63)2 = 20835 for 10 items  x 2 2   2 Modified = x N 2 =  2 45 10
  • 7. 4. The mean of 5 observations is 4.4. and variance is 8.24 and if the 3 items of the five observations are 1, 2 and 6. Find the values of other two observations. 7 w.k.t. x N x   x N 4.4   x = 22  2 2 x =   2 x N 2 x   8.24 =  4.4 2 5 x2 8.24 = 19.36 9   8.24 + 19.36 = x2 5 x2 = 138 x2 = 12 + 22 + 62 + x1 2 + x2 2 138 = 1 + 4 + 36 + x1 2 + x2 2 97 = x1 2 + x2 2 x1 2 + x2 2 = 97 ---- (1) x = 1 + 2 + 6 + x1 + x2 22 = 9 + x1 + x2 x1 + x2 = - 13 ---- (2)  put (2) in (1) x2 = 13 – x1 by (1) & (2) x1 2 + (13 – x1)2 = 97 x1 2 + 169 + x1 2 – 26x1 = 97 2 x1 2 – 26x1 + 72 = 0 x1 2 – 13x1 + 36 = 0
  • 8. - (-13)  169  4 x 36 8 x1 = - b  b2  49 2a x1 = 2 x1 = 13  5 2 x1 = 5 2 13  2 x1 = 6.5  2.5 x1 = 9 or x1 = 4 x1 = 9 x2 = 4
  • 9. 5. The mean and S.D. of the frequency distribution of a continuous random variable x are 40.604 and 7.92 respectively. Change of origin and scale is given below. Determine the actual class interval. d -3 -2 -1 0 1 2 3 4 f 3 15 45 57 50 36 25 9 d f fd fd2 MV CI -3 3 -9 27 22.5 20-25 -2 15 -30 60 29.5 25-30 -1 45 -45 45 32.5 30-35 0 57 0 0 37.5 35-40 1 50 50 50 42.5 40-45 2 36 72 144 47.5 50-55 3 25 75 225 52.5 55-60 4 9 36 144 57.5 N = 240 fd = 149 fd2 = 695 9 fd N x a h    149 240 40.604  a  h 40.604 = a + 0.62h ----- (1)  = h 2 2 fd    N fd N     7.92 = h 2 149 240 695 240      = h 2.895  0.620 7.92 = h x 1.584 h = 4.998 h = 5 Put h = 5 in equation (1) 40.604 = a + 0.62 x 5 a = 37.5
  • 10. 10 Combined Standard Deviation Suppose we have different samples of various sizes n1, n2, n3 …….. having means x1, x2, x3 and standard deviation 1, 2, 3 ……. then combine standard deviation can be computed by the following formula. 2 (n1 + n2) = n1 (1 2 + d1 2) + n2 (2 2 + d2 2) d1 = x x 1  d2 = x x 2  1. The mean’s of two samples of sizes 50 and 100 respectively are 54.1 and 50.3 and there standard deviations are 8 and 7 respectively obtain the SD for combined group. n1 = 50 1 x = 54.1 1 = 8 n2 = 100 2 x = 50.3 2 = 7  n x n x 1 1 2 2  (n n ) x 1 2   (50 x 54.1) (100 x 50.3) 50 100 x   x  51.56 2 (n1 + n2) = n1 (1 2 + d1 2) + n2 (2 2 + d2 2) d1 = x x 1  d2 = x x 2  d1 = 94.1 – 51.56 d1 = 2.54 d1 2 = 6.45 d2 = 50.3 – 51.56 d2 = - 1.26 d2 2 = 1.56 2 150 = 50 (82 + 6.45) + 100 (72 + 1.58) 32 = (64 + 6.45) + 2 (49 + 1.58) 32 = 70.45 + 2 x 50.58  = 7.56
  • 11. 2. The mean wage is Rs. 75 per day, SD wage is Rs. 5 per day for a group of 1000 workers and the same is Rs. 60 and Rs. 4.5 for the other group of 1500 workers. Find mean and standard deviation for the entire group. We have by data, 1 x = 75, 1 = 5, n1 = 1000 2 x = 60, 2 = 450, n2 = 1500 Let x and  be the mean and SD of the entire group.  x  22 Consider  n x n x 1 1 2 2 n n 1 2 x   1000 x 75 1500 x 60 i.e., 60  1000 1500  Also we have, (n1 + n2) 2 = n1 (1 2 + d1 2) + n2 (2 2 + d2 2), where d1 = 1 x - x = 75 – 66 = 9; d2 = 2 x - x = 60 – 66 = -6  (1000 + 1500) 2 = 1000 (52 + 92) + 1500 (4.52 + (-6)2)  2 = 76.15 or  = 8.73
  • 12. 3. The runs scored by 3 batsman are 50, 48 and 12. Arithmtic mean’s respectively. The SD of there runs are 15, 12 and 2 respectively. Who is t he most consistent of the three batsman? If the one of these three is to be selected who is to be selected? A B C AM ( x ) 50 48 12 SD() 15 12 2 23 CVA = A x A  x 100 CVA = 15 50 x 100 CVA = 30% CVB = B x B  x 100 CVB = 12 48 x 100 CVB = 25% CVC = C x C  x 100 CVC = 2 12 x 100 CVC = 16.66% Evaluation Criteria 1. Less CV indicates more constant player and hence more consistent player is (Player C) 2. Highest rune scorer = x A = 50
  • 13. 4. The coefficient of variation of the two series are 75% and 90% with SD 15 and 18 24 respectively compute there mean. CVA = 75% CVB = 80% A = 15 B = 18  CV= x 100 x 15 75 = x 100 x A 18 90 = x 100 x A x A = 20 x A = 20 5. Goals scored by two teams A & B in a foot ball season are as shown below. By calculating CV in each, find which team may be considered as more consistent. No. of goals x No. of matches Team (A) fx Team (B) A-team B-team fx 0 27 17 0 0 1 9 9 9 9 2 8 6 16 12 3 5 5 15 15 4 4 3 16 12 N = f = 53 f = 40 fx = 56 fx2 = 48 Team (A) fx2 Team (B) fx2 0 0 9 9 32 24 45 45 64 48 fx2 = 150 fx2 = 126 x A = fx N 56 = 53 = 1.056
  • 14. 150 2   = 1.30 126 2   = 1.30 1.30 1.30 26 x B = fx N 48 = 40 = 1.2  2 2 2 A x fx  N    = 1.056 1.715 53   A  2 2 2 B x fx  N    = 1.2 1.95 40   B CVA = A x A  x 100 = x 100 1.056 = 123.8% CVB = B x B  x 100 = x 100 1.2 = 109% Since, CVB < CVA, team B is more consistent player 6. The prices of x and y share A & B respectively state which share more stable in its value. Price A (x) (xi = 53) (xi = x ) (xi = x )2 Price - A (4) (xi = 105) (xi = x ) (xi = x )2 55 2 4 108 3 9 54 1 1 107 2 4 52 -1 1 105 0 0 53 0 0 105 0 0 56 3 9 106 1 1 58 5 25 107 2 4 52 -1 1 104 -1 1 50 -3 9 103 -2 4 51 -2 4 104 -1 1 49 -4 16 101 -4 16 x = 530 (xi= x )2 = 70 x = 1050 x(xi= x )2 = 40
  • 15. 2.64 2 27 x A = x N = 530 10 = 53 x B = x N = 1050 10 = 105 2.64 70      10 A A 2 40      10 B B CVA = A  x x 100 = x 100 53 = 4.98% CVB = B  x x 100 = x 100 105 = 1.903% Since, CVB is less share B is more stable. 7. A student while computing the coefficient of variation obtained the mean and SD of 100 observations as 40 and 5.1 respectively. It was later discovered that he had wrongly copied an observation as 50 instead of 40. Calculate the correct coefficient of variation. >> x n x   i.e. x 100 40    x (incorrect) = 4000 Now correct x = 4000 – 50 + 40 = 3990  3990 correct x  = 39.9 100  x 2 2 Let us consider 2   x n   2 x    5.1  2   40 2 100 x  x  i.e.     1626.01 100 or 100 40 5.1 2 2 2  2    x2 (incorrect) = 100 x 1626.01 = 162601 Now correct x2 = 162601 – (50)2 + (40)2 = 161701
  • 16.  correct 2 = correct  2 161701 2    5  28 2 correct x x   n i.e., correct 2 = 39.9 25 100 Now correct efficient of variation = x 100 x x 100 12.56% 39.9 Hence correct C.V. = 12.53%
  • 17. 8. The mean and SD of 21 observations are 30 and 5 respectively. It was subsequently noted that one of the observations 10 was incorrect. Omit it and determine the mean and SD of the rest.   30     50   29 >> x n x   x i.e. or x 630 21  incorrect x = 630 Now omitting the incorrect value 10, New x = 630 – 10 = 620 n = 21 – 1 = 20 620 x   New 31 20  x 2 2 Next consider 2   x n   2 x    5  2   30 2 100 i.e. x 21 900 25  2   incorrect x2  925 x 21  19425 Again omitting the incorrect value 10. New x = 19425 –(10)2 = 19325, n = 20  x 2 2 Hence new 2 new   new x 20   19325  2  (31) 5.25 20  New  = 5.25 = 2.29 9. The mean of 200 items was 50. Later on it was discovered that two items were misread as 92 and 8 instead of 192 and 88. Find out the correct mean. >> x n x   x i.e. or x 10000 200  incorrect x = 10000 Correct x = 10000 – 92 – 8 + 192 + 88 = 10180  10180 Correct mean = 200 = 50.9
  • 18. 10. Find the missing frequencies in the following data given that the median is 137.2. Class 100-  , h = 10 f = f1, c = 192 30 110 110- 120 120- 130 130- 140 140- 150 150- 100 106- 170 170- 180 Frequency 15 44 133 F1 125 F2 35 16 N=600 >> We prepare the table with the column of cumulative frequencies and use the formula for median. Class Frequency cf 100-110 15 15 110-120 44 59 120-130 133 192 130-140 f1 192 + f1  Median class 140-150 125 317 + f1 150-160 f2 317 + f1 + f2 160-170 35 352 + f1 + f2 170-180 16 368 + f1 + f2 N = 600    c 2 h Median = 1 +   N f We can take the median class as 130-140 since median is given to be 137.2 130  130 130 l  2  137.2 = 130 + 10 1 f (300 - 192) i.e., 137-2 – 130 = 1080 1 f i.e., 7.2 f1 = 1080 or f1 150 But the last cumulative frequency must be equal to N = 600 i.e. 368 + f1 + f2 = 600 368 + 150 + f2 = 600  f2 = 82 Thus f1 = 150, f2 = 82
  • 19. Relationship between various measures of dispersion We have some of following relationships among the various methods of 31 measures of dispersion 1. Mean  QD covers 50% of observations of the distribution 2. Mean  MD covers 57.5% of observations 3. Mean  1  includes 68.27% of observations 4. Mean  2  includes 95.45% of observations 5. Mean  3  includes 99.73% of observations 2 6. QD =    3 6745 4 2 7. MD =    5 x A 8. QD = 5 MD 6 9. Combining the results we get 3 QD = 2 SD and 5 MD = 4 SD that is also equal to 6 QD. 10. Range = 6 times SD. SOURCES AND REFERENCES 1. Statistics for Management, Richard I Levin, PHI / 2000. 2. Statistics, RSN Pillai and Bagavathi, S. Chands, Delhi. 3. An Introduction to Statistical Method, C.B. Gupta, & Vijaya Gupta, Vikasa Publications, 23e/2006. 4. Business Statistics, C.M. Chikkodi and Salya Prasad, Himalaya Publications, 2000. 5. Statistics, D.C. Sancheti and Kappor, Sultan Chand and Sons, New Delhi, 2004. 6. Fundamentals of Statistics, D.N. Elhance and Veena and Aggarwal, KITAB Publications, Kolkata, 2003. 7. Business Statistics, Dr. J.S. Chandan, Prof. Jagit Singh and Kanna, Vikas Publications, 2006.