DEFINITION of 'Nonlinearity'
A relationship which cannot be explained as a linear combination of its variable inputs. Nonlinearity is a common issue when examining causeeffect relations. Such instances require complex modeling and hypothesis to offer explanations to nonlinear events. Nonlinearity without explanation can lead to random, unforecasted outcomes such as chaos.
Next Up
BREAKING DOWN 'Nonlinearity'
Nonlinear regression is a common form of regression analysis used in the financial industry to model nonlinear data against independent variables in an attempt to explain their relationship. Although the models parameters are nonlinear in nature, nonlinear regression can fit data using methods of successive approximations to offer explanatory outputs.
RELATED TERMS

Nonlinear Regression
A form of regression analysis in which data is fit to a model ... 
Sum Of Squares
A statistical technique used in regression analysis. The sum ... 
Common Gap
A price gap found on a price chart for an asset. These gaps are ... 
Regression
A statistical measure that attempts to determine the strength ... 
Least Squares Method
A statistical technique to determine the line of best fit for ... 
Stepwise Regression
The stepbystep iterative construction of a regression model ...
Related Articles

Markets
Explaining Linear Relationships
A linear relationship describes the proportionality between an independent variable and a dependent variable. 
Markets
Understanding Regression
Regression is a statistical analysis that attempts to predict the effect of one or more variables on another variable. 
Markets
How Do Companies Forecast Oil Prices?
Read about the different forecasting methods that businesses use to predict future crude oil prices, and why it's so difficult to guess correctly. 
Investing
The Linear Regression Of Time and Price
This investment strategy can help investors be successful by identifying price trends while eliminating human bias. 
Trading
Bet Smarter With The Monte Carlo Simulation
This technique can reduce uncertainty in estimating future outcomes. 
Professionals
Advertising From TV To Internet: An Industry History
Changes in television consumption habits have forced advertising to target audiences through new means, namely Internet advertising. 
Trading
What's a Sensitivity Analysis?
Sensitivity analysis is used in financial modeling to determine how one variable (the target variable) may be affected by changes in another variable (the input variable). 
Markets
What's a Regressive Tax?
A regressive tax is a levy in a tax system where the tax rate does not change based on the level of income. 
Investing
The Basics Of Business Forecasting
Discover the methods behind financial forecasts and the risks inherent when we seek to predict the future. 
Markets
How To Buy Oil Options
Crude oil options are the most widely traded energy derivative in the New York Mercantile Exchange.
RELATED FAQS

What is a "non linear" exposure in Value at Risk (VaR)?
Learn about nonlinearity and value at risk and what a nonlinear exposure is in the value at risk of a portfolio of nonlinear ... Read Answer >> 
What is the difference between linear regression and multiple regression?
Learn the difference between linear regression and multiple regression and how multiple regression encompasses not only linear ... Read Answer >> 
What are some of the more common types of regressions investors can use?
Learn about the most common types of regressions investors use to model asset prices including linear regressions and multiple ... Read Answer >> 
What is the difference between work in progress and work in process?
Learn how financial institutions can use Bayesian analysis to model credit default risk, and understand how derivatives have ... Read Answer >> 
How can I use a regression to see the correlation between prices and interest rates?
Learn how to use linear regression to calculate the correlation between stock prices and interest rates by taking the square ... Read Answer >> 
How can I run linear regressions in MATLAB?
Learn how to run linear regressions in MATLAB by loading data, specifying dependent and independent variables and using the ... Read Answer >>