# Variance and Standard Deviation in Data Science

## FAQs

A statistical measurement of the dispersion between values in a data collection is known as variance. Variance expresses how far each number in the set deviates from the mean, and thus from every other number in the set. This symbol is frequently used to represent variation: σ2.

The standard deviation is a measure of the degree of uncertainty in a data set. A low standard deviation indicates that the majority of the data points are near to the mean (average). The numbers are spread out over a larger range when the standard deviation is high.

The following descriptive statistics are widely used to measure variability: The difference between the highest and lowest numbers is called the range. The range of a distribution's middle half is known as the interquartile range. The average distance from the mean is referred to as the standard deviation. Variance is defined as the average of squared deviations from the mean.

The standard deviation is the average difference between the mean and each member of the data collection. This comes in handy for calculating the dispersion of the data obtained. It's also simple to compute and automate.

To obtain the squared differences, remove the mean from each value and then square the results to get the variance. The average of those squared differences is then calculated. The variance is the end result. The standard deviation is a metric for determining how evenly distributed the numbers in a distribution are.

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