The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity. (c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, provided r > 0. (d) Regression coefficients are independent of the changes of origin but not of scale.
A negative correlation highlights the similarity of opposites. In a negative correlation, as the value of variable a increases, the value of variable b decreases. It can work the other way too, as the value of a decreases, the value of b increases. A negative correlation is also routinely referred to as an inverse. For instance, every time a person goes to jail, there is one less person in the work force. Therefore, incarceration rates, and work force numbers have an inverse relationship.
There appears to be a very strong negative linear relationship between the two variables. One variable increases as the other decreases following a linear relationship over the domains of measurement. A correlation coefficient can say nothing about causality. It is possible that changes in the first variable causes changes in the second or the other way around. Or, it could be that neither of them cause the other, but both are caused by something else.
A serious error. The maximum magnitude for a correlation coefficient is 1.The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them. Here they are given less than -1 value so it is not a value of correlation coefficient.
That as one variable increases so does the other
The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .
A positive correlation coefficient means that as the value of one variable increases, the value of the other variable increases; as one decreases the other decreases. A negative correlation coefficient indicates that as one variable increases, the other decreases, and vice-versa.
The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .
34.32245Correlation coefficient is less than -1 and greater than 1.Note: The Correlation coefficient is lies between -1 to 1 if it is 0 mean there is no correlation between them.
Well, friend, a correlation coefficient of 1.1 is not possible because correlation coefficients range from -1 to 1. If you meant 1.0, that would indicate a perfect positive linear relationship between two variables. It means as one variable increases, the other variable also increases proportionally.
The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
Let me rephrase: Case 1: You have x and y variables, but the values for x is a constant (vertical line) Case 1: You have x and y variables, but the values for y is a constant (horizontal line) Result is that you have zero covariance, so a correlation coefficient can not be calculated because that would cause a division by zero. If one of your x value (Case 1) or y value (case 2) is not exactly the same as the others, then a correlation coefficient can be calculated, but does it mean anything? The correlation coefficient indicates a linear relationship between two random variables, not between a constant and a random variable.
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
It means that when THIS happens, THAT usually, but not always, doesn't.