A genetic test examines the genetic information contained inside a person's cells, called DNA, to determine if that person has or will develop a certain disease or could pass a disease to his or her offspring.
Variance is non-negative because the squares are positive or zero. The variance of a constant random variable is zero, and if the variance of a variable in a data set is 0, then all the entries have the same value. Variance is invariant with respect to changes in a location parameter.That is, if a constant is added to all values of the variable, the variance is unchanged.ere Cov(â, â) is the covariance, which is zero for independent random variables (if it exists). The formula states that the variance of a sum is equal to the sum of all elements in the covariance matrix of the components.
Consider first the case where the target is a constantâsay, the parameter âand denote the mean of the estimator as . The mean squared error can then be decomposed as. The mean squared error thus comprises the variance of the estimator and the squared bias. The two components can be associated with an estimatorâs precision (small variance) and its accuracy (small bias). If is an unbiased estimator of âthat is, if âthen the mean squared error is simply the variance of the estimator. By choosing an estimator that has minimum variance, you also choose an estimator that has minimum mean squared error among all unbiased estimators.