Under the :
Here’s a concise paper-style explanation, including the formula, its derivation, and its role in variance estimation. 1. Definition of SXX In the context of simple linear regression:
[ \mathrmVar(S_xx) = 2(n-1)\sigma_x^4 ] The variance of the slope estimator (\hat\beta_1) in simple linear regression is: sxx variance formula
[ \mathrmVar(\hat\beta 1) = \frac\sigma^2S xx ]
It seems you’re looking for a paper or derivation related to the term — a common notation in statistics, particularly in simple linear regression and sum of squares decomposition . Under the : Here’s a concise paper-style explanation,
[ S_xx = \sum_i=1^n (x_i - \barx)^2 ]
where (s_x^2) is the sample variance of (x). including the formula
[ \mathrmVar(S_xx) = 2(n-1)\sigma_x^4 ] We know: