I wanted to send out a quick thanks to all that replied to my query about
estimating the confidence interval around the x-intercept of a linear
regression. The method I was able to implement in the most straightforward way
was taken from Section 3.2 of Draper and Smith (1998). Applied Regressio
(Ted Harding) wrote:
> On 24-Mar-09 03:31:32, Kevin J Emerson wrote:
...
> When I have time for it (not today) I'll see if I can implement
> this neatly in R. It's basically a question of solving
>
> (N-2)*(1 - R(X0))/R(X0) = qf(P,1,(N-1))
>
> for X0 (two solutions, maybe one, if any exist).
On 24-Mar-09 03:31:32, Kevin J Emerson wrote:
> Hello all,
>
> This is something that I am sure has a really suave solution in R,
> but I can't quite figure out the best (or even a basic) way to do it.
>
> I have a simple linear regression that is fit with lm for which I
> would like to estimate
I should probably also add the health warning that the delta-method approach to
ratios of coefficients is REALLY BAD unless the denominator is well away from
zero.
When the denominator is near zero nothing really works (Fieller's method is
valid but only because 'valid' means less than you mi
On Mon, 23 Mar 2009, Kevin J Emerson wrote:
> Now, it is simple enough to calculate the x-intercept itself ( - intercept /
> slope ), but it is a whole separate process to generate the confidence
> interval of it. I can't figure out how to propagate the error of the slope
> and intercept into
Hello all,
This is something that I am sure has a really suave solution in R, but I can't
quite figure out the best (or even a basic) way to do it.
I have a simple linear regression that is fit with lm for which I would like to
estimate the x intercept with some measure of error around it (conf
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