
Calculate the coefficient of correlation between $x$ and $y$ for the data
x 1 2 3 4 5 6 7 8 9 10 y 3 10 5 1 2 9 4 8 7 6
A) 0.12
B) 0.19
C) 0.22
D) 0.62
| x | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| y | 3 | 10 | 5 | 1 | 2 | 9 | 4 | 8 | 7 | 6 |
Answer
572.7k+ views
Hint: To calculate the coefficient to correlation, we will be using Karl Pearson’s coefficient of correlation which is given by ${r_{xy}} = \dfrac{{n\sum {{x_i}{y_i}} - \sum {{x_i}} \sum {{y_i}} }}{{\sqrt {n\sum {{x_i}^2} - {{\left( {\sum {{x_i}} } \right)}^2}} \sqrt {n\sum {{y_i}^2} - {{\left( {\sum {{y_i}} } \right)}^2}} }}$. Since there are ten terms, $n = 10$. A table is drawn to find the required terms to be substituted in Karl Pearson’s coefficient of correlation equation and do further simplification.
Complete step-by-step answer:
We need to find the correlation coefficient between $x$ and $y$.
Karl Pearson’s coefficient of correlation is given as
${r_{xy}} = \dfrac{{n\sum {{x_i}{y_i}} - \sum {{x_i}} \sum {{y_i}} }}{{\sqrt {n\sum {{x_i}^2} - {{\left( {\sum {{x_i}} } \right)}^2}} \sqrt {n\sum {{y_i}^2} - {{\left( {\sum {{y_i}} } \right)}^2}} }}$
Where $n$ is the number of terms.
${x_i}$ is the sum of values of $x$.
${y_i}$ is the sum of values of $y$.
Now, find the values,
Substitute the values in the formula,
$ \Rightarrow {r_{xy}} = \dfrac{{10 \times 321 - 55 \times 55}}{{\sqrt {10 \times 385 - {{\left( {55} \right)}^2}} \sqrt {10 \times 385 - {{\left( {55} \right)}^2}} }}$
Simplify the terms,
$ \Rightarrow {r_{xy}} = \dfrac{{3210 - 3025}}{{\sqrt {3850 - 3025} \times \sqrt {3850 - 3025} }}$
Subtract the values,
$ \Rightarrow {r_{xy}} = \dfrac{{185}}{{\sqrt {825} \times \sqrt {825} }}$
We know that,
$\sqrt a \times \sqrt a = {\left( {\sqrt a } \right)^2} = a$
Use this formula in the denominator,
$ \Rightarrow {r_{xy}} = \dfrac{{185}}{{825}}$
Divide numerator by the denominator,
$\therefore {r_{xy}} = 0.22$
Hence, option (C) is correct.
Note: The degree of correlation can be determined as follows:
There will be a perfect correlation between two variables if the value of the correlation coefficient is near $ \pm 1$, that is, as one variable increases, the other variable tends to also increase (if positive) or decrease (if negative).
There will be a high degree of correlation or strong correlation between two variables if the value of the correlation coefficient lies between $ \pm 0.5$ and $ \pm 1$.
There will be a moderate degree of correlation or medium correlation between two variables if the value of the correlation coefficient lies between $ \pm 0.3$ and $ \pm 0.49$.
There will be a low degree of correlation or a small correlation between two variables if the value of the correlation coefficient lies below $ \pm 0.29$.
There will be no correlation between the two variables if the value of the correlation coefficient is zero.
Complete step-by-step answer:
We need to find the correlation coefficient between $x$ and $y$.
Karl Pearson’s coefficient of correlation is given as
${r_{xy}} = \dfrac{{n\sum {{x_i}{y_i}} - \sum {{x_i}} \sum {{y_i}} }}{{\sqrt {n\sum {{x_i}^2} - {{\left( {\sum {{x_i}} } \right)}^2}} \sqrt {n\sum {{y_i}^2} - {{\left( {\sum {{y_i}} } \right)}^2}} }}$
Where $n$ is the number of terms.
${x_i}$ is the sum of values of $x$.
${y_i}$ is the sum of values of $y$.
Now, find the values,
| $x$ | $y$ | ${x^2}$ | ${y^2}$ | $xy$ |
| 1 | 3 | 1 | 9 | 3 |
| 2 | 10 | 4 | 100 | 20 |
| 3 | 5 | 9 | 25 | 15 |
| 4 | 1 | 16 | 1 | 4 |
| 5 | 2 | 25 | 4 | 10 |
| 6 | 9 | 36 | 81 | 54 |
| 7 | 4 | 49 | 16 | 28 |
| 8 | 8 | 64 | 64 | 64 |
| 9 | 7 | 81 | 49 | 63 |
| 10 | 6 | 100 | 36 | 60 |
| $\sum {{x_i}} = 55$ | $\sum {{y_i}} = 55$ | $\sum {{x_i}^2} = 385$ | $\sum {{y_i}^2} = 385$ | $\sum {{x_i}{y_i}} = 321$ |
Substitute the values in the formula,
$ \Rightarrow {r_{xy}} = \dfrac{{10 \times 321 - 55 \times 55}}{{\sqrt {10 \times 385 - {{\left( {55} \right)}^2}} \sqrt {10 \times 385 - {{\left( {55} \right)}^2}} }}$
Simplify the terms,
$ \Rightarrow {r_{xy}} = \dfrac{{3210 - 3025}}{{\sqrt {3850 - 3025} \times \sqrt {3850 - 3025} }}$
Subtract the values,
$ \Rightarrow {r_{xy}} = \dfrac{{185}}{{\sqrt {825} \times \sqrt {825} }}$
We know that,
$\sqrt a \times \sqrt a = {\left( {\sqrt a } \right)^2} = a$
Use this formula in the denominator,
$ \Rightarrow {r_{xy}} = \dfrac{{185}}{{825}}$
Divide numerator by the denominator,
$\therefore {r_{xy}} = 0.22$
Hence, option (C) is correct.
Note: The degree of correlation can be determined as follows:
There will be a perfect correlation between two variables if the value of the correlation coefficient is near $ \pm 1$, that is, as one variable increases, the other variable tends to also increase (if positive) or decrease (if negative).
There will be a high degree of correlation or strong correlation between two variables if the value of the correlation coefficient lies between $ \pm 0.5$ and $ \pm 1$.
There will be a moderate degree of correlation or medium correlation between two variables if the value of the correlation coefficient lies between $ \pm 0.3$ and $ \pm 0.49$.
There will be a low degree of correlation or a small correlation between two variables if the value of the correlation coefficient lies below $ \pm 0.29$.
There will be no correlation between the two variables if the value of the correlation coefficient is zero.
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