Explain the difference between Variance and R squared error
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Give proper example or explanation in simple terms
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Statistics
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Variance is a measure of how far observed values differ from the average of predicted values, i.e., their difference from the predicted value mean. The goal is to have a value that is low. What low means is quantified by the r2 score.
R2 is the total variance explained by the model. If R2 is 100% means two variables are perfectly correlated, with no variance at all.
Variance expresses the difference between predicted and actual values, while R-square expresses the variance of the target variable explained by its predictors.
The variance is the deviation of the observations from their mean value.
Considering this aspect in regression analysis, the variance is the mean squared error that measures the squared and thus, the summed difference between the actual values and the values predicted through the formed regression equation.
R-squared error is completely different in concept as compared to variance.
The R-squared error gives the variability in the response variable due to the considered independent variables in the regression equation. This is basically the term that explains the variation existing in the dependent variable due to one or more independent variables in the model.