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Sagot :
To find the correlation coefficient for the data provided in the table, we need to measure the strength and direction of the linear relationship between the two variables, [tex]\( x \)[/tex] and [tex]\( y \)[/tex].
The data points are as follows:
[tex]\[ \begin{tabular}{|c|c|} \hline $x$ & $y$ \\ \hline 0 & 15 \\ \hline 5 & 10 \\ \hline 10 & 5 \\ \hline 15 & 0 \\ \hline \end{tabular} \][/tex]
The correlation coefficient [tex]\(\rho\)[/tex] (or [tex]\(r\)[/tex]) ranges from -1 to 1. It measures the strength and direction of a linear relationship:
- [tex]\(\rho = 1\)[/tex] indicates a perfect positive linear relationship.
- [tex]\(\rho = -1\)[/tex] indicates a perfect negative linear relationship.
- [tex]\(\rho = 0\)[/tex] indicates no linear relationship.
To determine the correlation coefficient, we follow the formula for Pearson's correlation coefficient, but I've already determined that the numerical result is [tex]\(-1.0\)[/tex]. This result indicates the following:
- The data exhibit a perfect negative linear relationship.
- As [tex]\(x\)[/tex] increases, [tex]\(y\)[/tex] decreases in a perfectly linear fashion.
Thus, the correlation coefficient for the given data is:
[tex]\[ \boxed{-1} \][/tex]
The data points are as follows:
[tex]\[ \begin{tabular}{|c|c|} \hline $x$ & $y$ \\ \hline 0 & 15 \\ \hline 5 & 10 \\ \hline 10 & 5 \\ \hline 15 & 0 \\ \hline \end{tabular} \][/tex]
The correlation coefficient [tex]\(\rho\)[/tex] (or [tex]\(r\)[/tex]) ranges from -1 to 1. It measures the strength and direction of a linear relationship:
- [tex]\(\rho = 1\)[/tex] indicates a perfect positive linear relationship.
- [tex]\(\rho = -1\)[/tex] indicates a perfect negative linear relationship.
- [tex]\(\rho = 0\)[/tex] indicates no linear relationship.
To determine the correlation coefficient, we follow the formula for Pearson's correlation coefficient, but I've already determined that the numerical result is [tex]\(-1.0\)[/tex]. This result indicates the following:
- The data exhibit a perfect negative linear relationship.
- As [tex]\(x\)[/tex] increases, [tex]\(y\)[/tex] decreases in a perfectly linear fashion.
Thus, the correlation coefficient for the given data is:
[tex]\[ \boxed{-1} \][/tex]
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