# read in some data and do OLS

# R  BATCH ols1.R ols1.out

# read in the data as a matrix

dta <- matrix(scan("exampledta.txt"),byrow=T,ncol=4)

# define y & X (including column of 1s to X)

y <- dta[,1]

N <- nrow(dta)

X <- cbind( rep(1,N), dta[,2:4])

# compute the OLS estimates

bols <- (solve(t(X) %*% X)) %*% (t(X) %*% y)

# compute the residuals

e <- y - X %*% bols

# compute s^2

ssq <- as.numeric(t(e) %*% e)/(nrow(X) - ncol(X))

# compute the variance covariance matrix for the OLS estimates

varcovbols <- ssq*(solve(t(X) %*% X))

# compute the standard errors

sebols <- sqrt(diag(varcovbols)) 

# print the coefficients and standard errors 

print(cbind(bols,sebols),digits=3)