Can anyone explain the theory (or the formula) about computing Sum Sq (bold highligh below) related to regression items? The Wikipedia link gives an introduction on how to calculate the total, model, and regression sum of squares. Is it similar to the Sum Sq computation? Is the regression sum of squares equal to (0.000437+ 0.002545+ 0.060984+ 0.062330+ 0.060480)?
TraingData <- data.frame(x1 = c(3.532,2.868,2.868,3.532,2.868,2.536,3.864),
x2 = c(1.992,1.992,1.328,1.328,1.328,1.66,1.66),
y = c(9.040330254,8.900894412,8.701929163,9.057944749,
8.701929163,8.74317832,9.10859913)
)
lm.sol <- lm(y~1+x1+x2+I(x1^2)+I(x2^2)+I(x1*x2), data=TraingData)
anova(lm.sol)
Analysis of Variance Table
Response: y
Df **Sum Sq** Mean Sq F value Pr(>F)
x1 1 0.000437 0.000437 0.1055 0.8001
x2 1 0.002545 0.002545 0.6141 0.5768
I(x1^2) 1 0.060984 0.060984 14.7162 0.1623
I(x2^2) 1 0.062330 0.062330 15.0409 0.1607
I(x1 * x2) 1 0.060480 0.060480 14.5945 0.1630
Residuals 1 0.004144 0.004144