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Product moment correlation coefficient

Posted in Maths, Statistics 2

The product moment correlation coefficient (sometimes referred to as Person's product moment correlation coefficient) is a value between -1 and +1 is a measurement of correlation. A value of +1 is perfect positive correlation, a value of -1 is perfect negative correlation and a value of 0 is no correlation.

Calculating the PMCC

The product moment correlation coefficient is denoted by the letter r

Method #1

The first method involves using the following formula:

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Where:

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Example

Caclulate the product moment correlation coeffecient of the following set of bivarirate data:

5 9 15 18
103 120 143 160

Start by drawing up a table of the values you need to find, together with the x and y values youhave been given

xi yi Dynamic image 4 Dynamic image 5 Dynamic image 6
5 103
9 120
15 143
18 160
Sum: 47 526

Before you can calculate the other values for the other columns you need to work out the means for the x and y values of data:

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Now you can return to fill in the other columns:

xi yi Dynamic image 4 Dynamic image 5 Dynamic image 6
5 103 45.5625 812.25 192.375
9 120 7.5625 132.25 31.625
15 143 10.5625 132.25 37.375
18 160 39.0625 812.25 178.125
Sum: 47 526 102.75 1889 439.5

Now you have the values you an input them into the formula:

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Method #2

The second method involes the following formula:

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Where n is the number of pairs of data.

Example

Caclulate the product moment correlation coeffecient of the following set of bivarirate data:

5 9 15 18
103 120 143 160

As before draw up a table of the values you have and those you need:

xi yi Dynamic image 14 Dynamic image 15 Dynamic image 16
5 103 25 10609 515
9 120 81 14400 1080
15 143 225 20449 2145
18 160 324 25600 2880
Sum: 47 526 655 71058 6620

Next calculate the means of both the x and y sets of data:

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Now just plug all the values into the formula:

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Performing hypothesis testing

To perform a hypothesis test using the product moment coefficient look up the critical value in your table of values for the number of pairs of data and compare this to your value for r. If the the value for r is greater than the critical value you can reject the null hypothesis and accept the alternative hypothesis.