You must specify the expectation function that Minitab uses to perform nonlinear regression. Your choice for the function often depends on previous knowledge about the response curve's shape or the behavior of physical and chemical properties in the system. Potential nonlinear shapes include concave, convex, exponential growth or decay, sigmoidal (S), and asymptotic curves. You must specify the function that satisfies both the requirements of your previous knowledge and the nonlinear regression assumptions.
Unacceptable parameters | Example | Result |
---|---|---|
Names of constants, such as K1, K2, K3… | 1/(1 + K1 * X) | K1 acts as a number instead of a parameter, so the value of K1 is fixed. |
Names of columns, such as C1, C2, C3… | 1/(1 + C1 * X) | C1 acts as a variable instead of a parameter. |
Mathematical parameters, such as +, /, and *. | 1/(1 + B+ * X) | The symbol creates an incorrect function. |
The following examples from the expectation function catalog are acceptable functions. Thetas represent parameters and X's represent predictors. You replace the X's with the variable names. Each time you perform nonlinear regression using a new function, Minitab automatically adds the function to the catalog.
Expectation function | Model name | Model contains |
---|---|---|
1 / (1 + Theta *X ) | Convex 1 | One parameter and one predictor |
Theta1* X / ( Theta2 + X ) | Michaelis-Menten | Two parameters and one predictor |
Theta1 * cos ( X + Theta4 ) + Theta2 * cos ( 2 * X + Theta4 ) + Theta3 | Fourier 1 | Four parameters and one predictor |
Theta1 - Theta2 * ( ln ( X1 + Theta3 ) - ln ( X2 ) ) | Nernst equation | Three parameters and 2 predictors |
X1 * X2 / ( Theta1 + Theta2 * X1 + Theta3 * X1 * X2 + Theta4 * X1 * X3 ) | Enzyme reaction | Four Parameters and 3 predictors |