To calculate the residual of a data point, you need to determine the difference between the actual value and the predicted value from the line of best fit.
Given your line of best fit: \( y = -0.9x + 18 \) and the data point \( (1, 16.88) \):
- First, substitute \( x = 1 \) into the line of best fit to find the predicted \( y \):
\[ y = -0.9(1) + 18 \]
\[ y = -0.9 + 18 = 17.1 \]
- Next, compare the actual \( y \) value (which is \( 16.88 \)) with the predicted \( y \) value \( 17.1 \):
The residual is calculated as:
\[ \text{Residual} = \text{Actual} - \text{Predicted} \]
Substituting the values:
\[ \text{Residual} = 16.88 - 17.1 = -0.22 \]
Thus, the residual of the data point rounded to the nearest hundredth is \( \boxed{-0.22} \).