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* The higher the value the more widely distributed are the variable data values around the mean. | * The higher the value the more widely distributed are the variable data values around the mean. | ||
| − | * Assuming the frequency distributions approximately normal, about 68% of all observations are within < | + | * Assuming the frequency distributions approximately normal, about 68% of all observations are within <math> +/-\ 1 </math> standard deviation. |
* Approximately <math>95%</math> of all observations fall within two standard deviations of the mean (if data is normally distributed). | * Approximately <math>95%</math> of all observations fall within two standard deviations of the mean (if data is normally distributed). | ||
Revision as of 20:52, 13 December 2020
Standard Deviation
https://statistics.laerd.com/statistical-guides/measures-of-spread-standard-deviation.php
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population. However, as we are often presented with data from a sample only, we can estimate the population standard deviation from a sample standard deviation. These two standard deviations - sample and population standard deviations - are calculated differently. In statistics, we are usually presented with having to calculate sample standard deviations, and so this is what this article will focus on, although the formula for a population standard deviation will also be shown.
- Population standard deviation ()
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle \mu: \text{population mean};\ \ \ N: \text{Number of scores in the population}}
- Sample standard deviation formula (Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle s} )
Sometimes our data is only a sample of the whole population. In this case, we can still estimate the Standard deviation; but when we use a sample as an estimate of the whole population, the Standard deviation formula changes to this:
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle s = \sqrt{\frac{\sum_{i=1}^{n}(x_{i} - \bar{x})^2}{n -1}}}
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle \bar{x}: \text{Sample mean};\ \ \ n: \text{Number of scores in the sample}}
- The Standard Deviation is the square root of the variance.
- This measure is the most widely used to express deviation from the mean in a variable.
- The higher the value the more widely distributed are the variable data values around the mean.
- Assuming the frequency distributions approximately normal, about 68% of all observations are within Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle +/-\ 1 } standard deviation.
- Approximately Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle 95%} of all observations fall within two standard deviations of the mean (if data is normally distributed).