Here are the essential concepts you must grasp in order to answer the question correctly.
Correlation Coefficient (r)
The correlation coefficient, denoted as r, measures the strength and direction of a linear relationship between two variables. Its value ranges from -1 to 1, where values close to 1 indicate a strong positive correlation, values close to -1 indicate a strong negative correlation, and values around 0 suggest no linear correlation. Understanding r is crucial for interpreting the relationship between the variables in a dataset.
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Coefficient of Determination (r^2)
The coefficient of determination, represented as r^2, quantifies the proportion of variance in the dependent variable that can be explained by the independent variable in a regression model. It is calculated by squaring the correlation coefficient (r). An r^2 value closer to 1 indicates that a large proportion of the variance is explained by the model, while a value closer to 0 suggests that the model explains little of the variance.
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Coefficient of Determination
Explained vs. Unexplained Variation
Explained variation refers to the portion of the total variation in the dependent variable that is accounted for by the regression model, as indicated by r^2. Conversely, unexplained variation is the portion that remains after accounting for the model, representing the variability that cannot be predicted by the independent variable. Understanding these concepts helps in assessing the effectiveness of the regression model in capturing the underlying data patterns.
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