Regression, Curve Fitting and Goodness of Fit
Regression and Curve Fitting
Learning Objectives:
- Find equation of the best fit line for a scatter plot using concept of least square regression.
- Calculate the value of correlation coefficient and interpret it.
Linear Curve Fitting:
A straight line is described generically by f(x) = ax + b. In linear curve fitting, the goal is to identify the coefficients ‘a’ and ‘b’ such that f(x) ‘fits’ the given data well. The error between actual points and the ’best-fit’ line has to be minimized. that error such that the line will be the most accurate representation of the points. Here,
- Positive or negative error have the same value (data point is above or below the line)
- Weight greater errors more heavily.
This line later can be used for prediction of future values such as estimation of population, etc, or in case of missing scientific data, it gives reasonable value by interpolation.