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This test develops calculation and interpretation of the mean, variance and standard deviation. Tasks compare data spread, examine the effect of outliers and assess whether a sample reliably represents a population.
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Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
Adding the same constant to all values leaves spread and variance unchanged. Multiplying all values by k multiplies standard deviation by |k| and variance by k². A larger standard deviation means greater spread.
Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
Adding the same constant to all values leaves spread and variance unchanged. Multiplying all values by k multiplies standard deviation by |k| and variance by k². A larger standard deviation means greater spread.
Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
Adding the same constant to all values leaves spread and variance unchanged. Multiplying all values by k multiplies standard deviation by |k| and variance by k². A larger standard deviation means greater spread.
Mean μ = sum of values / count. Population variance = sum of squared deviations (x − μ)² / count. Standard deviation is the square root of variance. It is zero only when all values are equal.
A larger random, properly selected sample generally reduces sampling error. Selection must represent the population; even a large biased sample can give a misleading estimate.
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Variance is the mean of the squared deviations of data values from their mean. Standard deviation is the square root of variance, so it has the same units as the original data.
Find the mean, each deviation, its square and the mean of those squares. A small standard deviation indicates values clustered closer to the mean. Compare both centre and spread, and check whether the required formula is for a complete population or a sample.
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https://www.catestia.com/en/mathematics/10th-grade/variance-and-standard-deviation-test/96/