Nonlinear Regression

What is Nonlinear Regression?

Nonlinear regression is a statistical modeling technique used to describe relationships between variables when the data cannot be accurately represented by a straight line. Unlike linear regression, which assumes a linear relationship between independent and dependent variables, nonlinear regression fits data using curved or complex mathematical functions.

Key takeaways

• Models curved relationships: Suitable when data does not follow a straight line.
• Flexible: Can use many types of functions depending on the pattern.
• Requires iteration: Uses algorithms to estimate best-fit parameters.
• Common in sciences and business: Widely used in forecasting, growth modeling, and machine learning.
• Sensitive to initial values: Parameter estimation may fail if starting values are poor.

When nonlinear regression is used

• Growth curves (population, revenue, biological processes)
• Diminishing returns models
• Demand forecasting with saturation limits
• Chemical reaction rates
• Machine learning and predictive analytics

Common nonlinear regression models

1. Exponential model

Used for growth or decay:
y = a·e^(bx)

2. Logistic model

Used for saturation and S-curve patterns:
y = L / (1 + e^(−k(x−x₀)))

3. Polynomial model

Models curves using power terms:
y = a + bx + cx² + …

4. Power model

Used for elasticity or scaling:
y = axᵇ

5. Michaelis–Menten model

Common in biology and chemistry.

How nonlinear regression works

1. Choose a nonlinear function: Based on theory or pattern in the data.
2. Provide initial parameter estimates: Needed for iterative solving.
3. Use optimization algorithms: Such as Newton–Raphson or Levenberg–Marquardt.
4. Minimize error: Typically using least squares.
5. Evaluate model fit: Using R², residual plots, or AIC.

Advantages

• Captures complex relationships
• More accurate than linear models for non-linear data
• Highly flexible

Disadvantages

• Computationally intensive
• Requires good initial guesses
• Risk of overfitting
• Interpretation can be difficult

Nonlinear vs. linear regression

Applications in business

• Customer lifetime value modeling
• Marketing response curves
• Revenue forecasting
• Pricing elasticity
• Operations modeling

Related concepts

• Linear regression
• Curve fitting
• Machine learning models
• Optimization algorithms
• Residual analysis

Sources

• Penn State Statistics – Nonlinear Regression
• MIT OpenCourseWare – Regression Analysis
• Springer – Nonlinear Statistical Models

Frequently Asked Questions (FAQ)