To find the predicted GPA for a student who sleeps 2.5 hours per day, we can substitute $x = 2.5$ into the least-squares regression line equation:

$\hat{y} = 0.375x + 1.33$

Substitute $x = 2.5$:

$\hat{y} = 0.375(2.5) + 1.33$

Calculating this:

$\hat{y} = 0.9375 + 1.33 = 2.2675$

Rounding to the hundredths place, the predicted GPA is:

$\hat{y} = 2.27$

The correct answer is:

2.27

Would you like further details on the calculations or have any questions?

Here are 5 related questions for more practice:

1. What would be the predicted GPA if the student sleeps 4 hours per day?
2. How would the equation change if the slope were 0.5 instead of 0.375?
3. If a student's GPA is actually 3.0 when they sleep 2.5 hours, what is the residual of the prediction?
4. How does increasing the intercept in a regression line equation affect predictions?
5. What does the slope of 0.375 in this context represent?

Tip: In least-squares regression, the residual for a data point is the difference between its actual and predicted values, indicating how well the model fits that point.