# Inverse linear model doesn't seem exact inverse [duplicate]

This question already has an answer here:

I'm dealing with a quantity that diminishes over time from 100% to 0%. I'm trying to plot the values, a lm abline, and large indicative points where the graph intersects y==100% and y==0%. I'm finding the results of lm(y~x) don't appear to be the inverse of lm(x~y) and I'm confused why this should be for a deterministic calculation. This illustrates in R:

set.seed(100)
d = data.frame(t = sort(sample(1:60,10)), q = sort(sample(1:100,10), dec=T))

# linear model - dependant y
fit = lm(q ~ t, d)

# predicting x where y == c(100,0) - i.e. dependant x