The data used were obtained adding ε ∈ N(µ = 0, 0.01) to a linear model y = θ 1 x + θ 2 with θ 1 = -820.000 and θ 2 = -0.333 (Fig. ). The least-squares estimated parameters are θ 1 = -787.551 and θ 2 = -0.334. The non-linear F-Test based confidence interval differs much from the smaller linear t-distribution bases interval. Monte Carlo simulation with T = 50000 steps was performed as bootstrapping and using SE y and σ f it as input standard deviation. The BS confidence interval is the smallest and the interval using SE y is the widest, since SE y > σ f it . However, the obtained confidence intervals after Monte Carlo simulations are very close to the one calculated with the linear approach, being only slightly smaller. Using SE y as ε for Monte Carlo simulation recovers the linear approach best.