[ \mathrmVar(\hat\beta 1) = \frac\sigma^2S xx ]
(Sum of the squared differences between each value and the mean) sxx variance formula
[ \mathrmVar\left( \fracS_xx\sigma_x^2 \right) = 2(n-1) ] [ \mathrmVar(\hat\beta 1) = \frac\sigma^2S xx ] (Sum
(Sum of the squares minus the square of the sum divided by the sample size) 🧮 Calculating Sample Variance ( s2s squared Once you have Sxxcap S sub x x end-sub sxx variance formula
If (\sigma_x^2) is unknown, replace with (\hat\sigma x^2 = S xx/(n-1)):