To estimate the salary for an employee with 20 years of experience, we can plug the value of Years of Experience = 20 into the regression equation:

$\text{Estimated Salary} = 29,586.67 + 2415.82 \times (\text{Years of Experience})$

Substitute 20 for Years of Experience:

$\text{Estimated Salary} = 29,586.67 + 2415.82 \times 20$

Now, calculate:

$\text{Estimated Salary} = 29,586.67 + 48,316.40 = 77,903.07$

So, the estimated salary for an employee with 20 years of experience is $77,903.07.

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Would you like to dive deeper into the interpretation of this regression equation or have any other questions about regression?

Here are a few related questions:

1. How can we interpret the slope (2415.82) in this context?
2. What does the intercept (29,586.67) represent in this equation?
3. How can we evaluate the accuracy of a regression model?
4. What is the difference between simple linear regression and multiple regression?
5. How would you predict salary for an employee with 0 years of experience?

Tip: The slope of a regression equation gives you the change in the dependent variable (salary) for a one-unit change in the independent variable (years of experience).