```
X <- cbind(Ag, Mining, Constr, Manuf, Manuf_nd, Transp, Comm,
Energy, TradeW, TradeR, RE, Services, Govt)
#Descriptive statistics
summary(X)
# Principal component analysis should not be applied if the data is not highly correlated.
pca1 <- princomp(X, scores=TRUE, cor=T)
pca1_rot <- prcomp(X,center = T,scale. = T)
summary(pca1)
# Loadings of principal components
loadings(pca1)
#pca1$loadings:
sum(pca1$loadings[2,]^2);sum(pca1$lodings[1,]^2)
```

The above generates:

sum(pca1$loadings[2,]^2)

[1] 1

sum(pca1$loadings[1,]^2)

[1] 1

The PCA’s try to capture the maximum variance across the variables,and if we have normalized the data then the variances will be 1.Is this somehow related to the fact that the sum of the squared coefficients of each PCA comes out to be 1??