I have two columns of data: Observed (Obs) and Predicted (Pred), each column having 23 data. I have plotted Observed on y-axis and Predicted on x-axis (as pointed by Pineiro et al., 2008). On deriving a best fit linear model for the data, I have obtained: $$Obs = 0.21 + 1.09 * Pred $$ It is well known that in the ideal case, the equation should have been: $$Obs = 0.00 + 1.00 * Pred $$ Pineiro et al. (2008) [Link available on top], on page 4 [Eq. (9)], suggest:
We tested the hypothesis of slope = 1 and intercept = 0 to assess statistically the significance of regression parameters. This test can be performed easily with statistical computer packages with the model:
$$ Pred - Obs = a + b* Pred + \epsilon $$ The significance of the regression parameters of this models corresponds to the tests: b = 1 and a = 0.
Please help on how do I conduct these tests so that I can compare the slope ($b$) with 1 and intercept ($a$) with 0. Also I am not able to get the basic concept behind proposing the model shown above.