The actual or observed data point is below the line of best fit.
Step-by-step explanation:
Since we know that residual is the difference between the observed value of the dependent variable (y) and the predicted value (ŷ) of a regression model. Residual tells us that how far the data falls from the regression line.
If we have a positive value for residual, this means dependent variable's actual value is greater than its predicted value.
If we have a negative value for residual, this means dependent variable's actual value is less than its predicted value.
For data points above the line, the residual is positive, and for data points below the line, the residual is negative.
Since our given residual value is -4.5, therefore, observed or actual value of dependent variable is 4.5 less than its predicted value and it is below line of best fit.
The residual value is the difference between the observed value (from the scatter plot) and the predicted value (from the line of best fit).
Step-by-step explanation:
The residual value is the difference between the observed value (from the scatter plot) and the predicted value (from the line of best fit).
Residual Value = Observed Value - Predicted Value
<em>Since the residual value of -4.5 is negative, we can say the predicted value is larger than the observed value. In other words, the line of best fit is "above" the scatter plot point in that specific point.</em>
Simple, we know > sign is greater than sign and < sign means less than sign. We divide 1/2 and that gets us 0.5. 3.5 is greater than 1/2 so the answer would be 3.5>1/2
