This project presents a multiple linear regression analysis using the Ordinary Least Squares (OLS) method in R. The study examines the impact of study hours and attendance rate on student exam scores. The analysis includes manual matrix-based estimation, hypothesis testing (F-test and t-test), coefficient of determination (R-square and Adjusted R-square), model validation using the function in R, and prediction.

This project presents a comprehensive implementation of multiple linear regression analysis using R. The analysis includes manual computation of regression coefficients using matrix algebra, followed by validation with built-in R functions. Statistical tests such as F-test, t-test, coefficient of determination (R²), and classical assumption tests (normality, heteroskedasticity, autocorrelation, and multicollinearity) are conducted. The objective is to evaluate the influence of independent variables on academic performance while ensuring model validity and reliability.

This project demonstrates several visualization techniques used in exploratory data analysis (EDA) to analyze numerical and categorical variables using R. The visualizations help identify patterns, distributions, and insights within the dataset.