The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.
No, it is a linear transformation.
If variables have zero correlation, they do not have a linear relationship. Zero correlation shows that two things were not found to be related.
men and penises
No. If the correlation coefficient is close to 1 or -1, then the two variables have a high degree of statistical linear correlation. See the related link, particularly the graphs which illustrate correlation.
Correlation has no effect on linear transformations.
linear transformation can be define as the vector of 1 function present in other vector are known as linear transformation.
0
Yes, but not a linear correlation.
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, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.
No, it is a linear transformation.
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.
An affine transformation is a linear transformation between vector spaces, followed by a translation.
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.
Strengths:WeaknessesCalculating the strength of a relationship between variables.Cannot assume cause and effect, strong correlation between variables may be misleading.Useful as a pointer for further, more detailedresearch.Lack of correlation may not mean there is no relationship, it could be non-linear.