The line of best fit before the transformation is y = 0.6366x + 2.3220 and after the transformation is y = 0.0411x + 0.5134
A mathematical notion called the line of the best fit connects points spread throughout a graph. It's a type of linear regression that uses scatter data to figure out the best way to define the dots' relationship.
[tex]\rm m = \dfrac{n\sum xy-\sum x \sum y}{n\sum x^2 - (\sum x)^2} \\\\\rm c = \dfrac{\sum y -m \sum x}{n}[/tex]
We have data as shown in the table.
Let's suppose the line of best fit is:
y = mx + c
Before the transformation, we can calculate the line of best fit.
m = 0.6366
c = 2.3220
y = 0.6366x + 2.3220
After transformation:
Line of best fit after transformation:
y = Mx + C
M = 0.0411
C = 0.5134
Line of best after transformation
[tex]\rm Transformation = \ log_1_0(y \ value)[/tex]
y = 0.0411x + 0.5134
Thus, the line of best fit before the transformation is y = 0.6366x + 2.3220 and after the transformation is y = 0.0411x + 0.5134
Learn more about the line of best fit here:
brainly.com/question/14279419
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