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To solve this problem, we use the linear regression equation given:

\[
y = ax + b
\]

Where:
- \( a = -1.106 \) (slope)
- \( b = 21.761 \) (y-intercept)
- \( x = 13.5 \) (hours of TV watched)

We plug the value of \( x \) into the equation to predict the number of sit-ups (\( y \)):

\[
y = (-1.106 \times 13.5) + 21.761
\]

Now let's calculate that:

\[
y = -14.931 + 21.761 = 6.83
\]

So, a person who watches 13.5 hours of TV can do approximately **6.8 sit-ups**.

Would you like more details on any part of this solution?

Here are 5 related questions:
1. What is the significance of the slope (\( a \)) in a regression equation?
2. How can the correlation coefficient \( r \) be interpreted in this context?
3. What does the \( r^2 \) value represent in regression analysis?
4. How would the prediction change if someone watched only 5 hours of TV?
5. What impact does the negative sign of \( a \) have on the relationship between TV watching and sit-ups?

**Tip:** The closer the \( r \)-value is to -1 or 1, the stronger the relationship between the variables. A negative \( r \)-value indicates a negative relationship, where one variable decreases as the other increases.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). Predict the number of sit-ups a person who watches 13.5 hours of TV can do.

Learn how to predict the number of sit-ups a person can do based on hours of TV watched per day using linear regression. This example uses a regression equation with slope (-1.106) and y-intercept (21.761) to make a prediction.

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