Performing multiple linear regressions, in Excel, that have a common x-intercept? I was plotting some linear data sets in Excel, including linear trend-lines:

I was about to perform 5 separate linear regressions, so I could get the slope and y-intercept of each "independent" data set.  But then, in a flash, I realized that data might have a common point:

In fact, it might even be: the x-intercept itself.
So what I need now is way to run multiple simultaneous linear regressions, with the assumption that all of the lines intersect at a common point.
Does such a linear regression analysis method exist? Does it have a name? Does it exist in Excel?

The best I've been able to muster so far is to run five independent linear regressions, getting the slope and intercept of each data set:
Slope (m)  Intercept (b)
=========  =============
 1.15287     11484.8  
 0.86301      7173.5
 0.43212      4306.4
 0.25894      2853.6

Plotting slope against y-intercept you see some kind of correlation:

If I assume that my data sets share an x-intercept, then I can find that x-value through:
 y = mx + b
 0 = mx + b
-b = mx
 x = m / -b

Which gives:
Slope (m)  Intercept (b)  Common x-intercept (assuming their is one)
=========  =============  ============================
 1.15287     11484.8        -9961.9
 0.86301      7173.5        -8312.2
 0.43212      4306.4        -9965.6
 0.25894      2853.6       -11020.2

Which, aside from one really wonky point, converges pretty well. 
 A: It is very unlikely that Excel would be able to do this easily or reliably (you should really not use Excel for any but the simplest stats, and sometimes not even then).
If you know (or think you know) what the common x intercept is (not just estimate it from the data) then you can subtract that value from all the x variables and do a regression without intercept (because the line should go through 0,0 now).  You can compare that model to the model with each line having its own intercept (if they all go through the same fixed intercept then all the fitted intercepts should simultaneously be not significantly different from 0.
A quick way to get a feel for if your x intercepts are likely to be the same would be to reverse your x and y variables and fit the lines, this means that now the y-intercepts would be the same which is easier to test.  However this also changes the direction of the error and so answers a bit of a different question and should probably be followed up by something more formal.
You could create bootstrap estimates of the x intercept (computed as -b/m) and use that to estimate if the intercepts differ.
You could fit a nonlinear least squares model to estimate the model with a common x-intercept and compare it with a model where each gets its own intercept to see if they are significantly different (the model would be of the form slope*(x-x0) with slope and x0 as the parameters (x0 being the x-intercept).
You could fit the similar model using Bayesian techniques as well to compare.
Any of these would be doable in R or other statistical packages.
