I have a data set of paired measurements $(x_1,y_1),(x_2,y_2),...,(x_n,y_n)$. I need to fit a linear regression line $y=ax+b$ to this data. Therefore, I have to estimate the parameters $a$ and $b$.
How can I then calculate the confidence interval for these estimated parameters?
I referred to the wiki article which says http://en.wikipedia.org/wiki/Simple_linear_regression:
Under the first assumption above, that of the normality of the error terms, the estimator of the slope coefficient will itself be normally distributed with mean $\beta$ and variance $\sigma^2/\sum(x_i-\bar > x)^2$.
I didn't get how this formula was derived.