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We are given the linear regression equation \( y = ax + b \), where:

- \( a = -1.131 \)
- \( b = 28.508 \)
- \( x \) is the number of hours of TV watched per day
- \( y \) is the predicted number of sit-ups a person can do

To predict the number of sit-ups for someone who watches 6 hours of TV per day, we substitute \( x = 6 \) into the equation.

\[
y = (-1.131)(6) + 28.508
\]
\[
y = -6.786 + 28.508 = 21.722
\]

Thus, the predicted number of sit-ups is approximately **21.7**.

Would you like further details or have any questions?

Here are some follow-up questions:
1. How does the value of the slope \(a = -1.131\) affect the relationship between TV watching and sit-ups?
2. What does the \( r^2 \) value of 0.561001 tell us about the strength of this regression model?
3. How would the prediction change if the person watched 8 hours of TV?
4. Can you explain what the \( r = -0.749 \) value represents?
5. What are some limitations of this linear regression model?

**Tip:** The slope in a regression model shows how much the dependent variable changes for each unit increase in the independent variable.

Use the given regression equation to predict the number of sit-ups a person who watches 6 hours of TV can do, rounded to one decimal place.

Learn how to use linear regression to predict the number of sit-ups based on the number of TV hours watched. In this example, a person watches 6 hours of TV per day, and we calculate the predicted sit-ups using the equation y = -1.131x + 28.508. Suitable for students in Grades 10-12.

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