Respuesta :
Answer:
The residual value is -1.8 when x = 3
Step-by-step explanation:
We are given the following table
x | y
0 | -3
2 | -1
3 | -1
5 | 5
6 | 6
Residual value:
A residual value basically shows the position of a data point with respect to the line of best fit.
The residual value is calculated as,
Residual value = Observed value - Predicted value
Where observed values are already given in the question and the predicted values are calculated by using the equation of line of best fit.
[tex]y = 1.6x - 4[/tex]
When we substitute x = 3 in the above equation then we would get the predicted value.
[tex]y = 1.6x - 4 \\\\y = 1.6(3) - 4 \\\\y = 4.8 - 4 \\\\y = 0.8[/tex]
So the predicted value is 0.8
From the given table, the observed value corresponding to x = 3 is -1
So the residual value is,
Residual value = Observed value - Predicted value
Residual value = -1 - 0.8
Residual value = -1.8
Therefore, the residual value is -1.8 when x = 3
Note: A residual value closer to 0 is desired which means that the regression line best fits the data.
Answer:
–1.8
Step-by-step explanation:
Given the following table:
x y
0 -3
2 -1
3 -1
5 5
6 6
We are also given y = 1.6x – 4
Therefore, when x = 3, we have
Predicted y = 1.6(3) – 4 = 0.8
Since the observed y = - 1 when x = 3 on the table, the residual value can be estimated as follows:
Residual value = Observed value of y - Predicted value of y = -1 - 0.8 = - 1.8.
Therefore, the residual value when x = 3 is –1.8.
