STA 106 Study Guide - Final Guide: Single-Stage-To-Orbit, Statistical Hypothesis Testing, White Noise
Document Summary
= sample error sum of squared errors x. We were deriving ssto = ssa + sse i j z x z }| { Y z2 = (x + y)2 = x2 + 2xy + y2 (yij y)2 = (yij yi. Reminder: s2 y = sample variance for one sample y = p(yi y)2 s2 n 1 s2 y n . We want to divide ssto or sse or ssa by what we need to so they converge to a value as nt . Ssto has a stabilized version called msto = mean squared total = sst o dfsst o nt 1. Note : sst o = (nt 1)s2 y is the overall sample variance where dfsst o = y where s2. Msa = mean squared factor a = ssa dfssa dfssa = a 1 = # groups - 1. Mse = mean squared error sse dfsse where dfsse = ni a. M st o 6= m se + m sa.
