Gitarchana Roy
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Gitarchana Roy's post in Non Linear Regression was marked as the answerLinear Regression
Nonlinear Regression
Represents relationship between variables with a straight line
Represents relationship between variables with a curved line
Example: Defects vs. Rework
Example: Growth of Business i.e., Revenue with employee strength
Form of linear model is typically either the constant or a parameter multiplied by an independent variable. Simple Addition.
Rational function which is the ratio of 2 polynomial functions.
R-squared value is valid
R-squared value is invalid
Might not capture true relationships if they are complex.
Explains complex relationships
Data set must be homogeneous. Might be overlooked while creating models.
Better fit and prediction accuracy
Easy to understand.
Difficult to interpret and comprehend results.
Governing Criteria:
If better model fit is essential, then nonlinear regression should be selected. If simple, easy to understand models need to be created then Linear models should be created. If prediction accuracy is important, then Nonlinear regression should be selected.
