1. LINEAR AND NONLINEAR MODELS
The decisive criterion is position of parameters, not type of regression line!
Examples of linear models (paramerers are in linear position):
y = a + bx přímka
y = a + bx + cx parabola
y = a + (b/x) - hyperbola
curves !!
Examples of nonlinear models (paramerers are in nonlinear position):
y = axb
y = aebx
Pros – they can model complicated real processes, e.g. growth, with real prediction.
Cons – relatively complicated calculus

2. NONLINEAR MODEL ESTIMATION OF PARAMETERS
(arbitrally selected)
1. aproximation
2. estimation of param
(first computed)
2. aproximation
3. estimstion of param
(second computed)

3. NONLINEAR MODEL ESTIMATION OF PARAMETERS
local min. (there are not optimal solution)
global minimum (optimal solution)

4. ESTIMATION OF REGRESSION MODEL QUALITY
Akaike information criterion (AIC)
RSS residual sum of squares
m number of parameters
The AIC is smaller, the model is better.