Related Formula
σ = √((Σ aᵢ²)/(n) - ((Σ aᵢ)/(n))²)$$\sigma = \sqrt{\frac{\sum a_i^2}{n} - \left(\frac{\sum a_i}{n}\right)^2}$$
(Σi=1ⁿ aᵢ)² = Σi=1ⁿ aᵢ² + 2 Σk < j ak aⱼ$$\left(\sum_{i=1}^n a_i\right)^2 = \sum_{i=1}^n a_i^2 + 2 \sum_{k < j} a_k a_j$$
Core Logic
Given:
Σ aᵢ = 50$\sum a_i = 50$
Σk < j ak aⱼ = 1100$\sum_{k < j} a_k a_j = 1100$
Number of observations, n = 10$n = 10$.
To find the standard deviation, we need the sum of squares, Σ aᵢ²$\sum a_i^2$. We use the algebraic identity for the square of a sum of n$n$ terms.
Step 1: Finding the Sum of Squares
Substitute the known values into the identity:
(Σ aᵢ)² = Σ aᵢ² + 2 Σk < j ak aⱼ$$(\sum a_i)^2 = \sum a_i^2 + 2 \sum_{k < j} a_k a_j$$
(50)² = Σ aᵢ² + 2(1100)$$(50)^2 = \sum a_i^2 + 2(1100)$$
2500 = Σ aᵢ² + 2200$$2500 = \sum a_i^2 + 2200$$
Σ aᵢ² = 2500 - 2200 = 300$$\sum a_i^2 = 2500 - 2200 = 300$$
Step 2: Calculating Standard Deviation
Now, apply the variance formula:
σ² = (Σ aᵢ²)/(n) - ((Σ aᵢ)/(n))²$$\sigma^2 = \frac{\sum a_i^2}{n} - \left(\frac{\sum a_i}{n}\right)^2$$
σ² = (300)/(10) - ((50)/(10))²$$\sigma^2 = \frac{300}{10} - \left(\frac{50}{10}\right)^2$$
σ² = 30 - (5)²$$\sigma^2 = 30 - (5)^2$$
σ² = 30 - 25 = 5$$\sigma^2 = 30 - 25 = 5$$
The standard deviation σ$\sigma$ is the square root of variance:
σ = √(5)$$\sigma = \sqrt{5}$$
Pattern Recognition
Whenever you see pairwise products Σ aᵢ aⱼ$\sum a_i a_j$ in a statistics problem, immediately bridge it to Σ aᵢ²$\sum a_i^2$ using the multinomial expansion identity. The variance formula handles the rest organically.
Chapter Mix
Class 11 Maths: Statistics