Data Analysis (Regression Analysis)

This text references MATLAB source code designed for regression analysis. We will now expand on MATLAB's applications and the significance of regression analysis. MATLAB is a highly popular mathematical software extensively used in scientific computing and engineering fields. It offers robust data analysis and visualization capabilities, making it an ideal tool for conducting regression analysis. Regression analysis is a statistical method used to study relationships between independent and dependent variables. Through regression analysis, we can predict how dependent variables change in response to variations in independent variables. This technique finds wide applications across numerous domains including economics, medical research, and social sciences. Consequently, mastering MATLAB and regression analysis skills is essential for professionals working in related fields.

Key MATLAB functions for regression analysis include: - fitlm() for linear regression modeling with customizable formula specifications - regstats() for comprehensive regression diagnostics and statistical outputs - polyfit() for polynomial curve fitting with degree optimization - stepwisefit() for automated variable selection using stepwise regression algorithms Implementation typically involves data preprocessing, model specification, parameter estimation using least squares methods, and validation through residual analysis. The code structure generally follows: data import → exploratory analysis → model fitting → diagnostic checking → prediction generation. Advanced implementations may incorporate cross-validation techniques and regularization methods like ridge or lasso regression to handle multicollinearity and overfitting issues.