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insens350 [35]
3 years ago
14

What does a residual value of 1.3 mean when referring to the line of best fit of a data set?

Mathematics
2 answers:
suter [353]3 years ago
8 0
<span>A data point is 1.3 units above the line of best fit</span>
Mashutka [201]3 years ago
3 0
<h2>Answer:</h2>

The residual value of 1.3 mean when referring to the line of best fit of a data set is:

         The point lie 1.3 units above the line of best fit.

<h2>Step-by-step explanation:</h2>

<u>Residual value--</u>

In statistics the residual value is the difference between the Measured value (i.e. the value on the scatter plot or the data point) and the  predicted value ( i.e. the value that lie on the line of best fit)

Hence, if the Residual value is positive then the point lie above the line of best fit and if the Residual value is negative then the data point lie below the line of best fit or trend line.

Hence, here the residual value is 1.3, this means that the data point lie 1.3 units above the line of best fit.

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Step-by-step explanation:

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The distance between two points, 'A', and 'B', on the coordinate plane given their coordinates, (x₁, y₁), and (x₂, y₂) can be found using following formula;

l = \sqrt{\left (y_{2}-y_{1}  \right )^{2}+\left (x_{2}-x_{1}  \right )^{2}}

Substituting the known 'x', and 'y', values for the coordinates of the points, we have;

l_{(2, \, -2), \ (6, \, 3) } = \sqrt{\left (3-(-2)  \right )^{2}+\left (6-2  \right )^{2}} = \sqrt{5^2 + 4^2} = \sqrt{41}

Therefore, the distance between the points, (2, -2), and (6, 3) = √(41) ≈ 6.4.

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