Curve fitting models

• Linear Regression (y=mx+b) : This method creates a straight line from the data. When the independent variable (x) and the dependent variable (y) have a linear relationship, it makes logic.
• Logarithmic Regression (y=a+bln(x)): This method involves fitting the data to a logarithmic function. When the predicted growth of the relationship between the variables is logarithmic, then it is suitable.
• Piecewise Regression: Combining various regression models in various data regions is known as piecewise regression. It enables the fitting of several functions to various data segments. When there are distinct patterns in many areas, it is helpful.
• Regression using Nonlinear Least Squares (NLS): It is created according to the particular model form. A generic technique for fitting a variety of nonlinear models to data is NLS regression.

The curve fitting group models use statistical regression analysis to study the relationship between software complexity and the number of faults in a program, the number of changes, or failure rate. This group of models finds a relationship between input and output variables by using the methods linear regression, nonlinear regression, or time series analysis. The dependent variables, for example, are the number of errors in a program. The independent variables are the number of modules changed in the maintenance phase, the time between failures, programmers’ skill, program size, etc. Models included in this group are: Estimation of errors, Estimation of complexity, Estimation of failure rate. These are explained as following below.