Q: What implies a stronger linear relationship a correlation of plus 4 or a correlation of -6 and why?

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A negative correlation is a measure of the linear component of a relationship where one variable increase as the other decrease.

Correlation * * * * * That is simply not true. Consider the coordinates of a circle. There is obviously a very strong relationship between the x coordinate and the y coordinate. But the correlation is not just small, but 0. The correlation between two variables is a measure of the linear relationship between them. But there can be non-linear relationships which will not necessarily be reflected by any correlation.

Some people will give the answer "correlation". But that is not correct for the following reason: Consider the coordinates of a circle. There is obviously a very strong relationship between the x coordinate and the y coordinate. The correlation between the two is not just small, but 0. The correlation between two variables is a measure of the linear relationship between them. But there can be non-linear relationships which will not necessarily be reflected by any correlation.

When x and y values of points agree in a linear relationship

It is a measure of the strength of a linear relationship between one dependent variable and one or more explanatory variables.It is very important to recognise that a high level of correlation does not imply causation. Also, it does not provide information on non-linear relationships.

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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.

Correlation between two variables implies a linear relationship between them. The existence of correlation implies no causal relationship: the two could be causally related to a third variable. For example, my age is correlated with the number of TV sets in the UK but obviously there is no causal link between them - they are both linked to time.

Pearson's correlation coefficient, also known as the product moment correlation coefficient (PMCC), and denoted by r, is a measure of linear agreement between two random variable. It can take any value from -1 to +1. +1 indicates a perfect positive linear relationship between the two variables, a value of 0 implies no linear relationship whereas a value of -1 shows a perfect negative linear relationship. A low (or 0) correlation does not imply that the variables are unrelated: it simply means a there is no linear relationship: a symmetric relationship will give a very low or zero value for r.The browser which we are compelled to use is not suited for any serious mathematical answer and I suggest that you look up Wikipedia for the formula to calculate r.

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.

If variables have zero correlation, they do not have a linear relationship. Zero correlation shows that two things were not found to be related.

It means that here is no linear relationship between the two variables. There may be a perfect non-linear relationship, though.

A negative correlation is a measure of the linear component of a relationship where one variable increase as the other decrease.

Correlation. It will merely determine whether or not there is a linear relationship between the variables. However, the absence of correlation is not absence of a relation - only that the relationship is not linear.For example, if you take any set of points that are symmetrically placed about a vertical axis - such as from a circle, ellipse or parabola, or parts of a sine or cosine curve - then the correlation will be 0. But, the fact that these are well-defined curves clearly implies a very definite [non-linear] relationship.

The correlation coefficient is a statistical measure that quantifies the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation.

The Correlation Coefficient computed from the sample data measures the strength and direction of a linear relationship between two variables. The symbol for the sample correlation coefficient is r. The symbol for the population correlation is p (Greek letter rho).

Correlation * * * * * That is simply not true. Consider the coordinates of a circle. There is obviously a very strong relationship between the x coordinate and the y coordinate. But the correlation is not just small, but 0. The correlation between two variables is a measure of the linear relationship between them. But there can be non-linear relationships which will not necessarily be reflected by any correlation.

Nothing happens. It simply means that there is no linear relationship between the two variables. It is possible that there is a non-linear relationship or that there is none.