1. Let's first define the terms SSR and SST. SSR stands for the Sum of Squares due to Regression, and SST stands for the Total Sum of Squares. 2. The relationship between SSR, SST, and SSE (Sum of Squares due to Error) is given by the equation: $$ SST = SSR + SSE $$ 3. To find SSR and SST, you typically need data points and a regression model. SSR measures how well the regression model explains the variation in the dependent variable. 4. SST measures the total variation in the dependent variable. 5. If you have the observed values $y_i$, the predicted values from the regression $\\hat{y}_i$, and the mean of observed values $\bar{y}$, then: - $$ SST = \sum (y_i - \bar{y})^2 $$ - $$ SSR = \sum (\hat{y}_i - \bar{y})^2 $$ 6. Without specific data or a regression model, we cannot compute numerical values for SSR and SST. 7. Please provide the data or regression equation to proceed with calculations.