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.
A type of correlation coefficient is the Pearson correlation coefficient, which measures the strength and direction of the linear relationship between two continuous variables. Its value ranges from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation. Other types include the Spearman rank correlation coefficient, which assesses the relationship between ranked variables, and the Kendall tau coefficient, which measures the ordinal association between two quantities.
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.