Finite Math Examples

$\begin{array}{c} x & y \\ 50 & 45 \\ 160 & 154 \\ 135 & 128 \\ 220 & 217 \\ 155 & 159 \\ 110 & 186 \\ 170 & 167 \\ 216 & 216 \\ 165 & 159 \\ 115 & 115 \end{array}$

Step 1

The linear correlation coefficient measures the relationship between the paired values in a sample.

$r = \frac{n (\sum_{}^{} x y) - \sum_{}^{} x \sum_{}^{} y}{\sqrt{n (\sum_{}^{} x^{2}) - {(\sum_{}^{} x)}^{2}} \cdot \sqrt{n (\sum_{}^{} y^{2}) - {(\sum_{}^{} y)}^{2}}}$

Step 2

Sum up the $x$ values.

$\sum_{}^{} x = 50 + 160 + 135 + 220 + 155 + 110 + 170 + 216 + 165 + 115$

Step 3

Simplify the expression.

$\sum_{}^{} x = 1496$

Step 4

Sum up the $y$ values.

$\sum_{}^{} y = 45 + 154 + 128 + 217 + 159 + 186 + 167 + 216 + 159 + 115$

Step 5

Simplify the expression.

$\sum_{}^{} y = 1546$

Step 6

Sum up the values of $x \cdot y$ .

$\sum_{}^{} x y = 50 \cdot 45 + 160 \cdot 154 + 135 \cdot 128 + 220 \cdot 217 + 155 \cdot 159 + 110 \cdot 186 + 170 \cdot 167 + 216 \cdot 216 + 165 \cdot 159 + 115 \cdot 115$

Step 7

Simplify the expression.

$\sum_{}^{} x y = 251521$

Step 8

Sum up the values of $x^{2}$ .

$\sum_{}^{} x^{2} = {(50)}^{2} + {(160)}^{2} + {(135)}^{2} + {(220)}^{2} + {(155)}^{2} + {(110)}^{2} + {(170)}^{2} + {(216)}^{2} + {(165)}^{2} + {(115)}^{2}$

Step 9

Simplify the expression.

$\sum_{}^{} x^{2} = 246856$

Step 10

Sum up the values of $y^{2}$ .

$\sum_{}^{} y^{2} = {(45)}^{2} + {(154)}^{2} + {(128)}^{2} + {(217)}^{2} + {(159)}^{2} + {(186)}^{2} + {(167)}^{2} + {(216)}^{2} + {(159)}^{2} + {(115)}^{2}$

Step 11

Simplify the expression.

$\sum_{}^{} y^{2} = 262142$

Step 12

Fill in the computed values.

$r = \frac{10 (251521) - 1496 \cdot 1546}{\sqrt{10 (246856) - {(1496)}^{2}} \cdot \sqrt{10 (262142) - {(1546)}^{2}}}$

Step 13

Simplify the expression.

$r = 0.87645404$