Standard deviation (σ) is the square root of variance
Standard deviation
Standard deviation (σ) is the square root of variance
Standard deviation measures the variation of values around their mean. It is denoted by the Greek letter σ (sigma) and is expressed in the same units as the data. Standard deviation is a crucial concept in statistics as it quantifies the amount of dispersion or spread in a dataset.
Example
If the mean of a dataset is 10 and the variance is 4, the standard deviation (σ) would be √4 = 2.
Remember this
Understanding standard deviation is essential for interpreting data variability and making informed decisions based on statistical analysis.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Related concepts
Mean squared error
Mean squared error (MSE) formula: MSE = (1/n) * Σ(y_i - ŷ_i)²
Expected value
Expected value formula: E[X] = Σ [x * P(x)]
Pearson correlation coefficient
Pearson correlation coefficient formula: r = Σ[(xi - x̄)(yi - ȳ)] / [√(Σ(xi - x̄)²) * √(Σ(yi - ȳ)²)]
Mahalanobis distance
Mahalanobis distance formula: D^2 = (x - μ)'Σ^(-1)(x - μ)
Precision and recall
Precision = Relevant retrieved instances / All retrieved instances
Batch normalization
Batch normalization formula: Y = (X - μ) / σ * γ + β
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