Relation between Standard Deviation and Variance
Variance and Standard deviation are the most common measure of the given set of data. They are used to find the deviation of the values from their mean value or the spread of all the values of the data set.
- Variance is defined as the average degree through which all the values of a given data set deviate from the mean value.
- Standard Deviation is the degree to which the values in a data set are spread out with respect to the mean value.
The relationship between Variance and Standard Deviation is discussed below.
Variance = (Standard Deviation)2
OR
ā(Variance) = Standard Deviation
Variance and Standard Deviation
Variance and Standard Deviation are the important measures used in Mathematics and Statics to find the meaning from a large set of data. The different formulas for Variance and Standard Deviation are highly used in mathematics to determine the trends of various values in mathematics. Variance is the measure of how the data points vary according to the mean while standard deviation is the measure of the central tendency of the distribution of the data.
The major difference between variance and standard deviation is in their units of measurement. Standard deviation is measured in a unit similar to the units of the mean of data, whereas the variance is measured in squared units.
Here in this article, we will learn about variance and standard deviation including their definitions, formulas, and their differences along with suitable examples in detail.
Table of Content
- Variance
- Variance Formula
- Standard Deviation
- Standard Deviation Formula
- Relation between Standard Deviation and Variance
- Differences Between Standard Deviation and Variance