In economics, explained variation and total variation are terms used in statistical analysis to understand the relationship between variables.
Explained variation is the part of the total variation in the dependent variable that can be attributed to the independent variable(s) being considered. It represents the portion of the variation in the dependent variable that is already explained by the independent variable(s) in the statistical model. Explained variation is typically measured using the coefficient of determination (R-squared) in regression analysis.
On the other hand, total variation is the overall variability of the dependent variable, regardless of whether it can be explained by the independent variable(s) or not. It represents the total amount of variation that exists in the dependent variable, including both the variation that can be explained and the random or unexplained variation. Total variation is typically measured using the sum of squared residuals (SSR) in regression analysis.
The difference between explained variation and total variation can be seen as the unexplained or residual variation. If the explained variation is high, close to 1 or 100%, it means that the independent variable(s) can account for most of the variation in the dependent variable. Conversely, if the explained variation is low, close to 0 or 0%, it means that the independent variable(s) have little influence on the dependent variable, and most of the variation is due to other factors or random errors.
Understanding the relationship between explained variation and total variation is crucial in empirical analysis, as it provides insights into the effectiveness and significance of the independent variable(s) in explaining the behavior of the dependent variable.
Difference between explained variation and total variation in economics
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