Regression & Correlation
Last updated
Last updated
Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables (here, "independent" not in the statistical sense of being uncorrelated). The goal of regression is to model the expected value of the dependent variable based on the values of the independent variables.
Linear regression, the most common type, fits a straight line (hyperplane to be precise) to the data points that best represents the relationship between the variables. This line (hyperplane), known as the regression line, can be used to make predictions. Regression analysis helps in understanding how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held constant.
Correlation is a statistical measure that describes the strength and direction of a relationship between two variables. The correlation coefficient, typically represented by r, ranges from -1 to 1. A value of 1 indicates a perfect positive correlation, meaning that as one variable increases, the other also increases proportionally. A value of -1 indicates a perfect negative correlation, where one variable increases as the other decreases. A value of 0 indicates no correlation, implying that there is no linear relationship between the variables. Correlation is useful for identifying and quantifying the degree to which two variables are related, but it does not imply causation.