True. The sum of squares due to regression (ssr) represents the amount of variation in the dependent variable that is explained by the independent variable(s) in a regression model. On the other hand, the sum of squares total (sst) represents the total variation in the dependent variable.
In fact, the coefficient of determination (R-squared) in a regression model is defined as the ratio of ssr to sst. It represents the proportion of the total variation in the dependent variable that is explained by the independent variable(s) in the model. Therefore, R-squared values range from 0 to 1, where 0 indicates that the model explains none of the variations and 1 indicates that the model explains all of the variations.
Understanding the relationship between SSR and sst is important in evaluating the performance of a regression model and determining how well it fits the data. If SSR is small relative to sst, it may indicate that the model is not a good fit for the data and that there are other variables or factors that should be included in the model. On the other hand, if ssr is large relative to sst, it suggests that the model is a good fit and that the independent variable(s) have a strong influence on the dependent variable.
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