A chemical model is determined by two parameter k1 and k2 you run an experiment and acquire N = 50 data points. From the data analysis, you obtain the following parameter estimates k1 = 1.9, k2 = 0.4 as well as the following error-covariance matrix.

C = (1.6 0.08

0.08 0.9)

You also know that the residual sum of squares for this model is equal to 500.

1) Compute a 95% confidence interval for k1.

2) Compute the correlation between k1 and k2.

3) Assume that you are given a second model with 4 parameters and a residual sum of squares equal to 490. Compute Akaike information criterion(AIC) values for both models and use these results to decide which of the 2 models provides a better fit for the data.