LINEAR AND NON LINEAR RELATIONSHIP
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Linear relationship is a statistical term used to describe the relationship between a variable and a constant.
A nonlinear relationship is a relationship in which is extremely hard (or impossible) to calculate or predict because there is not a proportional increase and/or decrease between the independent and dependent variables.
A nonlinear relationship is a relationship in which is extremely hard (or impossible) to calculate or predict because there is not a proportional increase and/or decrease between the independent and dependent variables.
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A model is linear when each term is either a constant or the product of a parameter and a predictor variable. A linear equation is constructed by adding the results for each term. This constrains the equation to just one basic form:
Response = constant + parameter * predictor + ... + parameter * predictor
Nonlinear Regression Equations
While a linear equation has one basic form, nonlinear equations can take many different forms. The easiest way to determine whether an equation is nonlinear is to focus on the term “nonlinear” itself. Literally, it’s not linear. If the equation doesn’t meet the criteria above for a linear equation, it’s nonlinear.
Response = constant + parameter * predictor + ... + parameter * predictor
Nonlinear Regression Equations
While a linear equation has one basic form, nonlinear equations can take many different forms. The easiest way to determine whether an equation is nonlinear is to focus on the term “nonlinear” itself. Literally, it’s not linear. If the equation doesn’t meet the criteria above for a linear equation, it’s nonlinear.
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