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Detailed explanation-1: -Hence, the value of the sum of squares due to regression (SSR) when the total sum of squares (SST) is 25.32 and the sum of squares due to error (SSE) is 6.89 is 18.43.

Detailed explanation-2: -What is the SSR? The second term is the sum of squares due to regression, or SSR. It is the sum of the differences between the predicted value and the mean of the dependent variable. Think of it as a measure that describes how well our line fits the data.

Detailed explanation-3: -SSR is the additional amount of explained variability in Y due to the regression model compared to the baseline model. The difference between SST and SSR is remaining unexplained variability of Y after adopting the regression model, which is called as sum of squares of errors (SSE).

Detailed explanation-4: -The sum of squares measures the deviation of data points away from the mean value. A higher sum of squares indicates higher variability while a lower result indicates low variability from the mean. To calculate the sum of squares, subtract the data points from the mean, square the differences, and add them together.

Detailed explanation-5: -The Total SS (TSS or SST) tells you how much variation there is in the dependent variable. Total SS = (Yi – mean of Y)2. Note: Sigma () is a mathematical term for summation or “adding up.” It’s telling you to add up all the possible results from the rest of the equation.