# Correlation

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### Understanding the Correlation Coefficient

In statistical data analysis we sometimes use a correlation coefficient to quantify the linear relationship between two variables.

The most commonly used correlation statistic is the Pearson correlation coefficient. This statistic measures both the **strength** and **direction** of the linear relationship between two variables.

### Correlation Example

Suppose we want to look at the relationship between age and height in children. We select a group of children for study, and for each child we record their age in years and their height in inches. We could plot these values on a graph so that the child's age would be on the horizontal axis and the child's height would be on the vertical axis. Each dot on the plot represents a single child's age and height. This is called a scatter plot.

Since older children are generally taller than younger children, we would expect the dots on the plot to roughly approximate a straight line (a linear relationship between the variables) and that the line will slope upward (since age and height tend to increase at the same time).

### Correlation Coefficient

The Pearson correlation coefficient is a number between -1

and +1 that measures both the strength and direction of the linear relationship between two variables.

The magnitude of the number represents the strength of the correlation. A correlation coefficient of zero represents no linear relationship (the scatter plot does not resemble a straight line at all), while a correlation coefficient of -1 or +1 means that the relationship is perfectly linear (all of the dots fall exactly on a straight line).

The sign (+/-) of the correlation coefficient indicates the direction of the correlation. A positive (+) correlation coefficient means that as values on one variable increase, values on the other variable tend to also increase; a negative (-) correlation coefficient means that as values on one variable increase, values on the other tend to decrease, that is, they tend to go in opposite directions.

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Category: Forex