Since the sample variance is an unbiased estimator of $\sigma^2$, this is sufficient to show that the sample variance (and therefore also ) to $\sigma^2$. This means that the sample variance converges to the true variance for IID data, so long as the underlying distribution has finite kurtosis.

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High Performance Systems Sets New Standard for Commercial Epoxy Flooring Across New Jersey with Advanced Engineering

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LONG definition: 1. continuing for a large amount of time: 2. being a distance between two points that is more than…. Learn more.