The coefficient of determination is a measure used in statistical analysis that assesses how well a model explains and predicts future outcomes. What does the coefficient of determination tell you? In anoverfittingcondition, an incorrectly high value of R-squared is obtained, even when the model actually has a decreased ability to predict. Every predictor added to a model increases R-squared and never decreases it. The adjusted R-squared compares the descriptive power of regression models that include diverse numbers of predictors. With a multiple regression made up of several independent variables, the R-Squared must be adjusted. The result is a linear regression equation that can be used to make predictions about data. Linear regression is the most widely used statistical technique it is a way to model a relationship between two sets of variables. More advanced regression techniques (like multiple regression) use multiple independent variables. However, many people just call them the independent and dependent variables. Technically, in regression analysis, the independent variable is usually called the predictor variable and the dependent variable is called the criterion variable. Simple linear regression plots one independent variable X against one dependent variable Y.

The X variable is sometimes called the independent variable and the Y variable is called the dependent variable.

Find Equation, Coefficient, Slope What is the Difference Between R-Squared and Adjusted R-Squared?

Simple linear regression equation