sigh1342 said:
Multiple regression analysis is a statistical technique used to examine the relationship between a dependent variable and two or more independent variables. It allows you to determine how much of the variation in the dependent variable can be explained by the independent variables.
Multiple regression analysis is typically used when you want to understand the impact of multiple independent variables on a single dependent variable. It is often used in social sciences, psychology, and business research to examine complex relationships and make predictions.
The results of a multiple regression analysis can be interpreted by looking at the coefficients, or beta values, for each independent variable. These coefficients indicate the strength and direction of the relationship between each independent variable and the dependent variable. Additionally, the overall model fit and significance of the independent variables can also be used to interpret the results.
Some common assumptions of multiple regression analysis include the assumption of linearity (that the relationship between the independent and dependent variables is linear), normality (that the residuals are normally distributed), and independence (that the observations are not influenced by each other).
While multiple regression analysis can suggest a relationship between variables, it cannot establish causation. Other methods, such as experimental designs, are better suited for determining causality. However, multiple regression analysis can be used to control for potential confounding variables and make stronger arguments for causality.
