Table of content:

Micro-Syllabus of Unit 3 : Model Building through Regression (5 Hrs.) 5 marks fix

Introduction, Linear Regression Model: Preliminary Considerations, Maximum a Posteriori Estimation of the Parameter Vector, Relationship Between Regularized Least-Squares Estimation and Map Estimation, Computer Experiment: Pattern Classification, The Minimum-Description-Length Principle, Finite Sample-Size Considerations, The instrumental- Variables Method

🗒️Note:→

# Linear Regression Model:

           **Regression** is a statistical procedure that determines the equation for the straight line that best fits a specific set of data.

Any straight line can be represented by an equation of the form y = mx + c, where m and c are constants.

The value of m is called the slope constant and determines the direction and degree to which the line is tilted.

The value of c is called the y-intercept and determines the point where the line crosses the y-axis.