Standard deviation measures how spread out data points are from the mean average.
5. Calculating Z-Scores & Percentiles
6. Standard Error of the Mean & Central Limit Theorem
The Central Limit Theorem states that as sample size n increases, the sampling distribution of the mean approaches a normal distribution with Standard Error SE = σ / √n.
7. Skewness, Kurtosis & Non-Normal Distributions
Real-world financial distributions often exhibit "heavy tails" (high kurtosis) or right skewness (income distributions).
8. Chebyshev's Inequality for Arbitrary Distributions
For ANY dataset (even non-normal, skewed distributions), Chebyshev's inequality guarantees that at least $1 - 1/k^2$ of the data lies within $k$ standard deviations of the mean ($k > 1$). At least 75% of data lies within $pm 2sigma$, and at least 88.9% lies within $pm 3sigma$.
9. Coefficient of Variation (CV) & Relative Risk
The Coefficient of Variation $CV = (sigma / mu) imes 100%$ measures relative variability, allowing comparison of risk between assets with different price scales (e.g. Bitcoin vs S&P 500).
10. Calculating Sample Variance & Bessel's Correction
11. Standard Deviation in Portfolio Finance (Sharpe Ratio)
Investors use standard deviation $\sigma$ as a measure of portfolio risk. The Sharpe Ratio $SR = (R_p - R_f) / \sigma_p$ measures excess return per unit of risk.
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