Regression analysis is a statistical technique used to examine and quantify the relationship between a dependent variable and one or more independent variables. It helps in understanding how changes in one or more predictor variables influence the outcome variable. Regression analysis is widely used in economics, business, social sciences, health studies, engineering, and research for prediction, forecasting, trend analysis, and decision-making. Unlike descriptive statistics, which summarize data, regression provides an analytical framework to estimate the nature and strength of relationships among variables. The simplest form is simple linear regression, which studies the relationship between one dependent variable and one independent variable. It is represented by the equation: Y = a + bX + e where Y is the dependent variable, X is the independent variable, a is the intercept, b is the regression coefficient or slope, and e is the random error term. The regression coefficient indicates the amount of change in the dependent variable for a one-unit change in the independent variable. Multiple regression extends this concept by including two or more independent variables, making it useful for analyzing complex real-world situations where several factors jointly influence the outcome. Regression analysis serves several important purposes. It helps estimate future values based on existing data, identifies the contribution of explanatory variables, tests research hypotheses, measures the impact of policy interventions, and supports planning and decision-making. In data analysis, regression is also useful for identifying trends, evaluating cause-and-effect relationships under appropriate assumptions, and measuring the