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Standard deviation involves adding and subtracting values at various steps in the calculation process. For example, the differences between each data point and the mean are calculated, …

It's a way of quantifying how spread out the data is from its mean. A small standard deviation means that the data points are generally close to the mean, while a large standard deviation …

The range is easy to calculate—it's the difference between the largest and smallest data points in a set. Standard deviation is the square root of the variance. Standard deviation is a measure …

The formula we use for standard deviation depends on whether the data is being considered a population of its own, or the data is a sample representing a larger population.

Sal explains a different variance formula and why it works! For a population, the variance is calculated as σ² = ( Σ (x-μ)² ) / N. Another equivalent formula is σ² = ( (Σ x²) / N ) - μ². If we …

A standard deviation close to 0 indicates that the data points tend to be close to the mean (shown by the dotted line). The further the data points are from the mean, the greater the standard …

The standard deviation (sigma) is a measure of how much the data is spread out around the mean, not a better guess as to what the mean should be. A small value of sigma indicates that …

When we think about population parameters to get the population standard deviation, we just take the square root of the population variance. So if we want to get an estimate of the sample …

Even when we subtract two random variables, we still add their variances; subtracting two variables increases the overall variability in the outcomes. We can find the standard deviation …