Question

6.15 Some nonlinear regression models can be converted to linear models by applying transformations to the predictor variables. Show how the nonlinear regression equation
y = αXβ can be converted to a linear regression equation solvable by the method of
least squares. ?

Answer 1

Answer:

In regression analysis, curve fitting is the process of specifying the model that provides the best fit to the specific curves in your dataset. Curved relationships between variables are not as straightforward to fit and interpret as linear relationships.

For linear relationships, as you increase the independent variable by one unit, the mean of the dependent variable always changes by a specific amount. This relationship holds true regardless of where you are in the observation space.

Unfortunately, the real world isn’t always nice and neat like this. Sometimes your data have curved relationships between variables. In a curved relationship, the change in the dependent variable associated with a one unit shift in the independent variable varies based on the location in the observation space. In other words, the effect of the independent variable is not a constant value.