The regression model provided is:

$\widehat{\text{final}^{1.19}} = 136.19258 + (-61.01581) \log(\text{finalfirst}) + 0.13017 \cdot \text{atndrte} + 4.02086 \cdot \text{finalfirst}$

where:

• $\text{final}^{1.19}$ is the dependent variable (response variable),
• $\log(\text{finalfirst})$ is the logarithm of the initial final-first score,
• $\text{atndrte}$ represents the attendance rate,
• $\text{finalfirst}$ is the initial final-first score.

Would you like a detailed breakdown of the interpretation of each coefficient?

Related Questions:

1. How do we interpret the coefficients in this model?
2. Why is the response variable transformed using an exponent of 1.19?
3. What does the multiple R-squared value tell us about the model?
4. How does attendance rate ($\text{atndrte}$) impact the final score?
5. What is the significance of the p-values in this regression output?

Tip:

When a model involves transformations (like exponentiation or logarithms), interpreting coefficients requires reversing the transformation to understand their effect in the original scale.