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A linear correlation coefficient is a value that denotes the strength of the linear relationship between two variables; x and y. Two variables have a linear relationship when their equation can be graphed to form a line.
The correlation coefficient, expressed as r, can indicate the strength and direction of the relationship between x and y. The value of r can range from -1 to +1. The sign of the linear correlation coefficient indicates the direction of the relationship between the two variables; a positive value indicates a positive relationship while a negative value indicates a negative relationship. The closer the value of r is to -1 or +1, the stronger the relationship is between the two variables. The closer the value of r is to 0, the weaker the relationship is between the two variables. A value of 0 indicates that there is no relationship between the two variables.
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Although there are many different types of correlation coefficients, Pearson’s correlation coefficient is the most widely used. For this reason, when people refer to correlation coefficients, they’re often referring to Pearson’s correlation coefficient or Pearson’s R.
Before we can delve into the correlation coefficient formula and its application, we must first consider the different requirements for Pearson’s correlation coefficient:
If your data meets the aforementioned requirements, you can use Pearson’s correlation coefficient formula to calculate your correlation coefficient (r).
pxy =Cov(x,y)xy
Where,
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These are a few strengths of Pearson’s method of studying correlation:
These are a few weaknesses of Pearson’s method of studying correlation:
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A linear relationship, or linear association, describes the straight-line relationship between two variables. Any equation that forms a straight line when graphed depicts a linear relationship.
A linear correlation coefficient is a value that reflects the strength of the linear relationship between two variables, x and y.
The linear correlation coefficient between two variables can be calculated using Pearson’s correlation coefficient formula, which is;
pxy =Cov(x,y)xy
The value of the correlation coefficient, r, indicates the strength and direction of the relationship between x and y. A value above 0 indicates a positive relationship while a value below 0 indicates a negative relationship. The closer the value is to -1 or +1, the stronger the relationship between the variables. The closer the value is to 0, the weaker the relationship between the variables.
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