Learn the meaning of variance in statistics, how to calculate variance , examples of variance , and its importance in data analysis with solved problems. 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. Variance is the expected value of the squared variation of a random variable from its mean value, in probability and statistics. Informally, variance estimates how far a set of numbers (random) are spread out from their mean value. The value of variance is equal to the square of standard deviation, which is another central tool. Variance is symbolically represented by σ2, s2, or Var (X). The formula for variance is given by: Sample variance formula The population variance is written as σ 2, where σ is the population standard deviation. Population variance formula The mean is the average of the data, whereas the variance is a measure of how far each value in the data set is from the mean. The mean is a measure of centre and the variance is a measure of spread.
