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Regression is a statistical technique used to understand and quantify the relationship between two or more variables, where one variable is considered dependent and the others are independent. In environmental science research, regression analysis is used to model and predict environmental parameters such as pollution levels, temperature changes, or biodiversity trends based on influencing factors like industrial activity, climate variables, or land use. The simplest form is simple linear regression, which examines the relationship between a dependent variable (Y) and one independent variable (X) using the equation: Y = a + bX + ε Here, *a* is the intercept, *b* is the regression coefficient (slope), and *ε* is the error term. When there are multiple independent variables, the method extends to multiple regression, which helps in studying the combined effect of several predictors on the dependent variable. Regression analysis not only helps in prediction but also provides insights into the strength and direction of relationships between variables. For example, in environmental impact assessments, regression models can predict how changes in temperature might influence crop yield or how variations in rainfall might affect river discharge. The concept of regression revolves around the line of best fit, which minimizes the sum of __________ ________ __________ ________ ________ _____ ______ _________ ________.
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