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Browse CoursesRegression analysis is a statistical method used to examine the relationship between two or more variables, with the goal of predicting one variable based on the others.
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Regression Analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It helps understand and quantify the influence of independent variables on the dependent variable.
The primary purpose of Regression Analysis is to examine the strength and nature of the relationship between variables. It is widely used for making predictions, understanding trends, and identifying patterns in data.
Common types include Linear Regression, where the relationship is modeled as a linear equation, and Multiple Regression, which involves multiple independent variables. Other types include Polynomial Regression, Ridge Regression, and Logistic Regression for categorical outcomes.
The goodness of fit is often measured using metrics like R-squared, which indicates the proportion of variance in the dependent variable explained by the independent variables. Another metric is the Mean Squared Error (MSE), quantifying the average squared difference between predicted and observed values.
Regression Analysis finds application in diverse fields such as economics, finance, biology, psychology, and marketing. It is used to analyze and model relationships between variables, enabling better decision-making and predictions in various disciplines.