Answer :
To find the linear function that best fits the given data points, we'll use the method of linear regression to determine the slope (m) and the intercept (b) of the line [tex]\( y = mx + b \)[/tex].
We are given the following data points:
[tex]\[ \begin{array}{|c|c|c|c|c|c|} \hline x & 1 & 3 & 5 & 7 & 9 \\ \hline y & 2 & 3 & 4 & 5 & 6 \\ \hline \end{array} \][/tex]
Using linear regression, the best fit line through these points is determined by calculating the slope (m) and the y-intercept (b) such that [tex]\( y = mx + b \)[/tex].
For the given data:
- The slope [tex]\( m \approx 0.5 \)[/tex]
- The intercept [tex]\( b \approx 1.5 \)[/tex]
Therefore, the linear function that best fits the data is:
[tex]\[ y = 0.5x + 1.5 \][/tex]
This is the equation of the linear fit to the given data points.
We are given the following data points:
[tex]\[ \begin{array}{|c|c|c|c|c|c|} \hline x & 1 & 3 & 5 & 7 & 9 \\ \hline y & 2 & 3 & 4 & 5 & 6 \\ \hline \end{array} \][/tex]
Using linear regression, the best fit line through these points is determined by calculating the slope (m) and the y-intercept (b) such that [tex]\( y = mx + b \)[/tex].
For the given data:
- The slope [tex]\( m \approx 0.5 \)[/tex]
- The intercept [tex]\( b \approx 1.5 \)[/tex]
Therefore, the linear function that best fits the data is:
[tex]\[ y = 0.5x + 1.5 \][/tex]
This is the equation of the linear fit to the given data points.
