In regression analysis, the relationship between the total sum of squares ($SS_{Total}$), the sum of squares due to regression ($SS_{Regression}$), and the sum of squares due to error ($SS_{Error}$) is given by:

$SS_{Total} = SS_{Regression} + SS_{Error}$

You are given:

• $SS_{Total} = 2130$
• $SS_{Error} = 230$

Rearranging the equation to solve for $SS_{Regression}$:

$SS_{Regression} = SS_{Total} - SS_{Error}$

Substitute the values:

$SS_{Regression} = 2130 - 230 = 1900$

Final Answer:

The sum of squares due to regression ($SS_{Regression}$) is 1900.

---

Would you like me to explain the concepts of these sums of squares further or assist with any related topics?

Expanding Questions:

1. What does the sum of squares due to regression represent in a regression analysis?
2. How is the coefficient of determination ($R^2$) related to these sums of squares?
3. Can you calculate $R^2$ with the given data? What does it indicate?
4. How does the sum of squares due to error measure the quality of a regression model?
5. How would these values change if we added more predictors to the model?

Tip:

Always compare $SS_{Regression}$ and $SS_{Error}$ to evaluate the explanatory power of the regression model.