The following relationship is thought to exist between a response and a predictor :
= .
For a sample (
, ), . . . , ( , ), use the method of least squares to determine an estimator for a.
The following relationship is thought to exist between a responsey and a predictor x" alt="x" />:
= ae^x" alt="ae^x" />.
For a sample (x_{1}" alt="x_{1}" />, ), . . . , ( x_{n}" alt="x_{n}" />, ), use the method of least squares to determine an estimator for a.
If you correctly inputted the correct data into the formula then your line will be correct (you inputted ln y and x, yes?).
From my first post,
By the way, things don't look so good ..... Your model predicts that the line of best fit of ln y versus x should have a gradient close to 1 ........ So either:
1. You've made a mistake.
2. Perhaps you should be using a model ........ In which case your best estimate for b is b = 0.45646 (assuming your line is correct).
3. Your data is terrible.
4. Your model is terrible (see 2.)