Why_the_value_of_correlation_coefficient_always_lies_between_-1_and_1
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 can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
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.
Although Spearman's rank correlation coefficient puts a numerical value between the linear association between two variables, it can only be used for data that has not been grouped.
The observed relationship indicates that the a one-to-one correspondence exists between the variables of interest. In effect, the value of the obtained r-value is -1 or 1.
Why the value of correlation coefficient is always between -1 and 1?
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.
The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.
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 can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
It will be invaluable if (when) you need to calculate sample correlation coefficient, but otherwise, it has pretty much no value.
A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.
1.
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.
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.
Although Spearman's rank correlation coefficient puts a numerical value between the linear association between two variables, it can only be used for data that has not been grouped.
FALSE: THE RANGE IS -1 to +1