Mean and variance are data descriptors.

Mean (μ), the average of an observed population, is computed as:

$$µ=\frac{\sum X}{N} $$Where: Σ means “the sum of”, X = all the individual items in the group, and N = the number of items in the group.

Variance (σ²), a measure of spread within the data, measures how far each number in the set is from the mean. It is computed by taking the differences between the numbers in the population and the mean, squaring the differences and dividing the sum of the squares by the number of values in the set, i.e.:

$$ σ^2 = \frac{\sum (X-μ)^2}{N} $$Consider the following populations and their descriptive statistics:

**Population I:** {1,2,3,4,5,6,7,8,9,10}

- mean (μ)= 5.5,
- variance (σ2) = 9.17,
- standard deviation (σ)= 3.03,
- relative standard deviation (σ') = σ/μ = 3.03/5.5 = 0.55

**Population II:** {10,20,30,40,50,60,70,80,90,100}

- μ = 55,
- σ2 = 917
- σ=30.28,
- σ'= 0.55

**Population III:** {11,12,13,14,15}

- μ = 13,
- σ2 = 2.5,
- σ=1.58,
- σ'= 0.12

The *relative standard deviation* (σ'), aka *relative standard
error (RSE)* and *coefficient of variation* is a *scale-invariant* measure of variability.

Note that population II differs from population I by a factor of 10.
(In general if a variable Y = βX, then the variance Var(Y) = β^{2}X). The data might
indeed be the same, if one set is recorded in millimetres and the other in centimetres. Due to the
difference in scale, the variance and the standard deviation of these populations differ substantially,
but the relative standard deviation is exactly the same. This is why, in the context of sampling,
the relative standard deviation is a more meaningful measure for spread.

Population III has much lower relative standard deviation, which is a reflection of lower spread within this data set.

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