Covariance is a measure of the degree of dispersion when a random variable or set of data is measured in probability theory and statistical variance. Variance in probability theory is used to measure the degree of deviation between a random variable and its mathematical expectation (i.e., the mean). The variance (sample variance) in statistics is the average of the squared values of the difference between each sample value and the mean of all sample values. The study of variance, i.e., the degree of deviation, is important in many practical problems.

II. Calculation of variance.

The variance is the average of the squared difference between the actual value and the expected value, and the standard deviation is the arithmetic square root of the variance. [5] In practice, we use the following formula to calculate the variance.

The variance is the average of the sum of the squares of the differences of the individual data from the mean,, where x denotes the mean of the sample, n denotes the number of samples, xi denotes the individuals, and s^2 is the variance.

When the variance is used as an estimate of the variance of the sample X, it is found that its mathematical expectation is not the variance of X, but how many times the variance of X, its mathematical expectation is the variance of X. Using it as an estimate of the variance of X is "unbiased", so we always use the sample to estimate the variance of X, and call it the "sample variance". "sample variance".

The variance is the degree of deviation from the center, which is used to measure the magnitude of the fluctuations in a batch of data (i.e., the magnitude of the deviation from the mean of this batch of data) and is called the variance of this batch of data, and is denoted as S2. In the case of the same size of the sample, the larger the variance, the greater the fluctuations in the data, the more unstable it is.