Numerical Measures of Central Tendency and Variability
Measures of Variability
Standard Deviation
The standard deviation is simply the square root of the variance. This makes the standard deviations of the two quiz distributions 
and 
. The standard deviation is an especially useful measure of variability when the distribution is normal or approximately normal (see Chapter on Normal Distributions) because the proportion of the distribution within a given number of standard deviations from the mean can be calculated. For example, 
of the distribution is within one standard deviation of the mean, and approximately 
of the distribution is within two standard deviations of the mean. Therefore, if you had a normal distribution with a mean of 
and a standard deviation of 
, then of the distribution would be between 
and 
. Similarly, about of the distribution would be between 
and 
. The symbol for the population standard deviation is σ; the symbol for an estimate computed in a sample is 
. Figure 2 shows two normal distributions. The red distribution has a mean of 
and a standard deviation of 
; the blue distribution has a mean of 
and a standard deviation of . For the red distribution, of the distribution is between 
and 
; for the blue distribution, is between and 
.
Figure 2. Normal distributions with standard deviations of and .
R code
q1=c(9,9,9,8,8,8,8,7,7,7,7,7,6,6,6,6,6,6,5,5)
IQR(q1, type = 6)
[1] 2
x=c(1,2,4,5)
var(x)
[1] 3.333333
sd(q1)
[1] 1.256562
q2=c(10,10,9,9,9,8,8,8,7,7,7,6,6,6,5,5,4,4,3,3)
sd(q2)
[1] 2.202869