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The variance is a measure of variability. It is calculated by taking the average of squared deviations from the mean. Variance tells you the degree of spread in your data set. The more spread the data, the larger the variance is in relation to the mean.
In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) is obtained as the square root of the variance. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value.
Table of Content. What is Variance? Types of Variance. Variance Solved Example. Variance Formula for Grouped and Ungrouped Data. How to Calculate Variance? Variance and Standard Deviation. Variance of Binomial Distribution. Variance of Poisson Distribution. Variance of Uniform Distribution. Variance and Covariance.
In statistics, the variance is used to understand how different numbers correlate to each other within a data set, instead of using more comprehensive mathematical methods such as organising numbers of the data set into quartiles.
Variance Formulas. There are two formulas for the variance. The correct formula depends on whether you are working with the entire population or using a sample to estimate the population value. In other words, decide which formula to use depending on whether you are performing descriptive or inferential statistics.
Variance is a measurement of the spread between numbers in a data set. Investors use the variance equation to evaluate a portfolio’s asset allocation.
Variance is a measure of the variability of data and describes how the data points are spread out with respect to the mean. There can be two types of variance - sample variance and population variance.