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First, we compute the variance by taking the sum of squares and divide that by N which is the number of data points in the same. It is average squared deviation of each number from its mean. The point is a squared number is always positive and N is always positive so the variance must always be non-negative. ( It can be 0). The variance is a measure of the dispersion of a set of data points around their mean value. It would not make sense for it to be negative.
Yes, the variance of a data set is the square of the standard deviation (sigma) of the set. This means that the variance is always a positive number, even though the data might have a negative sigma value.
If the distribution is positively skewed distribution, the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency. This is true if we assume the distribution has a single mode.
The variance is standard deviation squared, or, in other terms, the standard deviation is the square root of the variance. In many cases, this means that the variance is bigger than the standard deviation - but not always, it depends on the specific values.
The sum of two decimal numbers greater than 0.5 will always be greater than 1