Most statistic and econometric programs and spreadsheets can give you the correlation coefficient between two variables.
The correlation coefficient is bound between -1 and 1 and tells you the linear relationship between these two variables. A coefficient close to 1 means a strong and positive associantion between the two variables (when one of them grows, the other does, also, and when one of them decreases, the other one does the same).
A coefficient close to -1 means strong negative association between the two variables, this is, observations with a large value in one of the variables tend to have a small value in the other variable or vice-versa.
A coeffcient close to 0 means no linear relation between the two variables.
Yo have to be careful with the following matters:
1) Association does not mean necessarily a causal relation between both variables. For example, there might be a third variable you have not cosidered and this third variable might be the explanation for the behaviour of the other two.
2) Even if there is a causal relationship between the variables, the correlation coefficient does not tell you which variable is the cause and which is the effect.
3) If the coefficient is clse to 0, it does not necessarily mean that there is no relation between the two variables. It means there is'nt a LINEAR relationship, but there might be another type of functional relationship (for example, quadratic or exponential).
The correlation coeffient shows how strong the linear relationship between two variables are. If the correlation is positive, that means both the variables are moving in same direction. Negative correlation implies, when one variable increases the other variable decreases. If correlation is +/- 0.8 and above, high degree of correlation or the association between the dependent variables are strong. correlation between +/- 0.5 to+/_0.8, sufficient degree of correlation and less than +/-0.5, weak correlation.
I think this will give you some idea.
Most statistic and econometric programs and spreadsheets can give you the correlation coefficient between two variables.
The correlation coefficient is bound between -1 and 1 and tells you the linear relationship between these two variables. A coefficient close to 1 means a strong and positive associantion between the two variables (when one of them grows, the other does, also, and when one of them decreases, the other one does the same).
A coefficient close to -1 means strong negative association between the two variables, this is, observations with a large value in one of the variables tend to have a small value in the other variable or vice-versa.
A coeffcient close to 0 means no linear relation between the two variables.
Yo have to be careful with the following matters:
1) Association does not mean necessarily a causal relation between both variables. For example, there might be a third variable you have not cosidered and this third variable might be the explanation for the behaviour of the other two.
2) Even if there is a causal relationship between the variables, the correlation coefficient does not tell you which variable is the cause and which is the effect.
3) If the coefficient is clse to 0, it does not necessarily mean that there is no relation between the two variables. It means there is'nt a LINEAR relationship, but there might be another type of functional relationship (for example, quadratic or exponential).
The two above (Romani and Krishna) have explained the correlation coefficient well. Now, if one wants to establish a relationship between two variables based on both strength and cause/effect, which approach can one use? Thank you.
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