Standard Deviation

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    Education, Technical Indicators
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Hakan Kwai
Instructor

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data points. It provides insight into how spread out the data points are from the mean or average value.

 

To calculate the standard deviation, follow these steps:

 

  1. Find the mean (average) of the data set.

 

  1. Subtract the mean from each data point to get the deviation.

 

  1. Square each deviation.

 

  1. Find the average of the squared deviations.

 

  1. Take the square root of the average squared deviations to obtain the standard deviation.

 

The standard deviation allows us to understand the extent to which data points deviate from the mean. A higher standard deviation indicates that the data points are more spread out and the distribution is more heterogeneous. Conversely, a lower standard deviation suggests that the data points are closer to the mean and the distribution is more homogeneous.

 

Standard deviation is commonly used in statistical analysis and research to describe the variability and dispersion of data. It helps in comparing and interpreting different data sets, assessing the reliability of statistical results, and making informed decisions.

 

In finance, standard deviation is widely used to measure risk and volatility. It helps investors evaluate the potential fluctuations in the prices of stocks, bonds, or other financial instruments. A higher standard deviation in investment returns indicates higher volatility and potential for larger losses or gains.

 

Standard deviation is a fundamental concept in probability theory and plays a crucial role in various fields such as quality control, physics, social sciences, and engineering. It provides a measure of uncertainty or variability and helps in understanding the characteristics of a data set.

 

It is important to note that standard deviation assumes a normal distribution of the data. If the data set does not follow a normal distribution, other measures of dispersion or variability may be more appropriate.

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