Implementation of Regression Analysis, Stepwise Regression, and Prediction

MATLAB serves as a powerful computational tool for implementing regression analysis, stepwise regression, and predictive modeling, particularly valuable for mathematical modeling applications. These analytical methods enable you to understand relationships within data and forecast future trends. The stepwise regression approach, implemented using functions like stepwiselm(), helps identify the most relevant variables by iteratively adding or removing predictors based on statistical significance, thereby enhancing model precision. MATLAB's comprehensive statistical toolbox provides essential functions such as fitlm() for linear regression and predict() for generating forecasts, with algorithms handling parameter estimation through ordinary least squares or maximum likelihood methods. Furthermore, MATLAB's visualization capabilities through functions like plot() and scatter() allow for effective presentation of analytical results, facilitating clear communication and sharing of research findings. The platform's integrated environment supports complete workflow implementation from data preprocessing using table arrays to model validation through residual analysis and cross-validation techniques. In summary, MATLAB offers a robust framework for advanced data analysis and mathematical modeling, combining computational efficiency with extensive analytical capabilities.