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https://statistics.laerd.com/statistical-guides/measures-of-spread-standard-deviation.php
 
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.
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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.
  
  
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* 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.
 
* The higher the value the more widely distributed are the variable data values around the mean.

Revision as of 20:55, 13 December 2020

Standard Deviation

https://statistics.laerd.com/statistical-guides/measures-of-spread-standard-deviation.php

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.



Population standard deviation ()



Sample standard deviation formula ()

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 higher the value the more widely distributed are the variable data values around the mean.
  • Assuming the frequency distributions approximately normal, about 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 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).