The Coefficient of Determination, denoted as R^2, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It is commonly used in the context of regression analysis to determine how well the model fits the data.
In the field of statistics, a dependent variable is the subject of an equation whose value depends on independent variables. Typically denoted as 'Y', the dependent variable is influenced or predicted by the independent variables, often denoted as 'X'.
The Law of Large Numbers (LLN) is a mathematical principle that states that as the number of exposures increases, the results become more predictable and closer to the expected outcomes.
