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Consider the table showing the given, predicted, and residual values for a data set:

[tex]\[
\begin{tabular}{|c|c|c|c|}
\hline
$x$ & Given & Predicted & Residual \\
\hline
1 & -2.5 & -2.2 & -0.3 \\
\hline
2 & 1.5 & 1.2 & 0.3 \\
\hline
3 & 3 & 3.7 & -0.7 \\
\hline
4 & 5 & 4.9 & 0.1 \\
\hline
\end{tabular}
\][/tex]

Which point would be on the residual plot of the data?

A. [tex]$(1, -0.3)$[/tex]
B. [tex]$(2, 0.3)$[/tex]
C. [tex]$(3, -0.7)$[/tex]
D. [tex]$(4, 0.1)$[/tex]


Sagot :

To determine which point would be on the residual plot of the data, let's understand what a residual plot represents. A residual plot shows the residuals on the y-axis and the corresponding x-values on the x-axis. The residual is calculated as the difference between the given value and the predicted value.

From the provided data table:

[tex]\[ \begin{array}{|c|c|c|c|} \hline x & \text{Given} & \text{Predicted} & \text{Residual} \\ \hline 1 & -2.5 & -2.2 & -0.3 \\ \hline 2 & 1.5 & 1.2 & 0.3 \\ \hline 3 & 3 & 3.7 & -0.7 \\ \hline 4 & 5 & 4.9 & 0.1 \\ \hline \end{array} \][/tex]

The residuals corresponding to each x value are as follows:
- For [tex]\(x = 1\)[/tex], the residual is [tex]\(-0.3\)[/tex]
- For [tex]\(x = 2\)[/tex], the residual is [tex]\(0.3\)[/tex]
- For [tex]\(x = 3\)[/tex], the residual is [tex]\(-0.7\)[/tex]
- For [tex]\(x = 4\)[/tex], the residual is [tex]\(0.1\)[/tex]

The point on a residual plot would be [tex]\((x, \text{residual})\)[/tex]. Now, we can check each of the given points to see which one matches:

1. [tex]\((1, -2.2)\)[/tex] - This point is not correct, because -2.2 is not a residual.
2. [tex]\((2, 1.5)\)[/tex] - This point is not correct, because 1.5 is not a residual.
3. [tex]\((3, 3.7)\)[/tex] - This point is not correct, because 3.7 is not a residual.
4. [tex]\((4, 0.1)\)[/tex] - This point is correct, because it matches the residual value for [tex]\(x = 4\)[/tex].

Therefore, the point [tex]\((4, 0.1)\)[/tex] would be on the residual plot of the data.