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Sagot :
Sure! Let's analyze the provided data to determine the best-fitting function.
The data points given are:
[tex]\[ \begin{array}{|c|c|} \hline x & y \\ \hline -6 & -3 \\ \hline -5 & -4 \\ \hline -4 & -5 \\ \hline -3 & -6 \\ \hline -2 & -7 \\ \hline \end{array} \][/tex]
To model this data, we look for a potential linear relationship [tex]\( y = mx + b \)[/tex].
The linear function is determined by the coefficients [tex]\( m \)[/tex] (slope) and [tex]\( b \)[/tex] (y-intercept). From the analysis:
The slope [tex]\( m = -1.0000000000000004 \approx -1 \)[/tex]
The intercept [tex]\( b = -8.999999999999998 \approx -9 \)[/tex]
Thus, the linear function that best models the data is:
[tex]\[ y = -1x - 9 \][/tex]
So, the function that models the data is:
[tex]\[ y = -x - 9 \][/tex]
The data points given are:
[tex]\[ \begin{array}{|c|c|} \hline x & y \\ \hline -6 & -3 \\ \hline -5 & -4 \\ \hline -4 & -5 \\ \hline -3 & -6 \\ \hline -2 & -7 \\ \hline \end{array} \][/tex]
To model this data, we look for a potential linear relationship [tex]\( y = mx + b \)[/tex].
The linear function is determined by the coefficients [tex]\( m \)[/tex] (slope) and [tex]\( b \)[/tex] (y-intercept). From the analysis:
The slope [tex]\( m = -1.0000000000000004 \approx -1 \)[/tex]
The intercept [tex]\( b = -8.999999999999998 \approx -9 \)[/tex]
Thus, the linear function that best models the data is:
[tex]\[ y = -1x - 9 \][/tex]
So, the function that models the data is:
[tex]\[ y = -x - 9 \][/tex]
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