The relationship between two random independently distributed variables is considered to be theoretically the weakest when the correlation coefficient is zero and theoretically the strongest when the correlation coefficient is one, indicating a positive relationship between two variables and negative one, indicating a negative relationationship between two variables.
I state that this is a theoretical result as if variables are not random, independently distributed, then a high correlation coefficient can result. For example, let us say that we obtained the following data on age and frequency of accidents:
Age 18 1 in 18 people have accidents in a year
Age 25 1 in 25 people have accident in a year
Age 30 1 in 30 people have accidents in a year
Age 35 1 in 6 people have accidents.
Age 40 1 in 400 people have accidents
If I selectively calculated a correlation coefficient this data including only the three groups ages 18, 25 and 30, you can see I will have a correlation coefficient of 1, however the data was not a random sample of all ages.
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0
Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the variables).
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
The correlation coefficient is a measure of linear association between two (or more) variables. It does not measure non-linear relationships nor does it say anything about causality.
False.
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
0
Yes, but not a linear correlation.
Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the variables).
The correlation remains the same.
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
Correlation has no effect on linear transformations.
-0.9
If the two variables increase together and decrease together AND in a linear fashion, the correlation is positive. If one increases when the other decreases, again, in a linear fashion, the correlation is negative.
If variables have zero correlation, they do not have a linear relationship. Zero correlation shows that two things were not found to be related.
Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.Very few people will assume, given NO correlation, that there is also a casual relationship.I will assume that you meant the fallacy in assuming that if "there is no correlation between two events there is also nocausal relationship".Correlation is a measure of linear relationship. If there is a non-linear relationship it is possible for the correlation to be low. Or, in the extreme case of a relationship that is symmetric about a specific value of the explanatory variable, for the correlation to be zero.
The closer the correlation is to 1 or -1, the more linear the data is