You can use the fact that a residual value is obtained by subtracting the prediction by the line of best fit from the actual data value we have.
The residual value 1.3 when referring to the line of best fit of a data set means:
Option B: A data point is 1.3 units below the line of best fit
<h3>What is a residual value and how is it calculated?</h3>
First of all, this whole story starts with data. We get data and we try to fit a line which can best imitate the way data is lying on the coordinate plane.
Remember that on a 2d coordinate cartesian plane, we have (x,y) called as point on plane and x is abscissa and y is called ordinate.
Let the best fit line be denoted by y = mx + c (assuming we're working with 2d data) with slope m and y-intercept c.
Now, this line is used to predict where can the next data point may lie.
When this best fit line is used to predict already present data point, we get the error that best fit line made when predicting the real data.
This is measured by "residual value"
For data point with ordinate b, we suppose get prediction as y
Then we have the residual value as y - b.
Remember, the prediction is before the real data point's ordinate.
<h3>How to know what does 1.3 residual value mean?</h3>
Let the real value be b and the predicted value be y from which this residual was calculated.
Then we have:
y - b = 1.3
y = 1.3 + b
Thus, we see that prediction is bigger than the real data point's y-ordinate. Since y axis has increasing value as we go higher and higher vertically, thus this prediction value's ordinate is higher than that of real value's ordinate.
The prediction, since shows the height of best fit line on that input point, thus we have:
Option B: A data point is 1.3 units below the line of best fit.
Learn more about line of best fit here:
brainly.com/question/2396661
