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write the complete equation for the attached model

The regression model from the output is:

\[
\text{final} = 43.70187 + 0.06904 \times (\text{finalfirst})^2 + 0.13017 \times \text{attndrt} - 1.86883 \times \text{finalfirst}
\]

Where:

- **final** is the dependent variable.
- **finalfirst** is the independent variable related to the first exam.
- **attndrt** represents attendance rate.
- \( (\text{finalfirst})^2 \) indicates that the square of the variable **finalfirst** is included.

The numbers correspond to the coefficient estimates from the model, and the significance of each term is indicated by asterisks (e.g., **finalfirst^2** is significant with **p < 0.01**).

Would you like further details on how to interpret these results, or do you have other questions? Here are a few related questions:

1. What does the **Adjusted R-squared** value of 0.1853 indicate about the model fit?
2. How do I interpret the p-value for the coefficients in terms of statistical significance?
3. Why is it important to include interaction terms, like \( \text{finalfirst}^2 \), in regression models?
4. What does the residual standard error of 9.803 tell us about the model’s predictions?
5. Can this model be improved further, and if so, how?

**Tip**: Always check the significance codes in regression output to identify the most statistically relevant predictors for your model!

Write the complete equation for the attached model.

Understand the complete equation for a regression model that includes transformations, attendance rate, and statistical significance analysis. Suitable for university-level statistics and data science students.

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