I would like to fit data in g~t scatterplot, where
g <- c(1.038459504,1.019448815,1.017729187,1.010076583,1.00895011,1.007841198,+ +1.006566597,1.009939696,1.003751382) t <- c(3,4,5,6,7,8,9,10,11)
g=exp(a+b*t) curve. How can I estimate a and b using linear regression?
The solution from handbook is
R^2 = 0.88644 and
g = 1 + exp(-2.390289 - 0.326016*t), which gives fairly good estimations (you can see on this graph that this curve seems to be good at fitting data above).
I have not a clue how they compute a and b and why it is g=1+exp(a+b*t) now. Any idea? I suppose it has something to do with transformation into (ln g) = a + b*t in order to regress the data.
PS: I did ask quite a similar question yesterday, but now it should be more clear what I want to figure out.