We would need to have the list of correlation coefficients to respond to this question.
-.21
None of them.
Assuming that all of these coefficients are based on samples of the same size then the weakest correlation is -0.01 because its absolute value (0.01) is the smallest.
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 correlation coefficient has a range of -1 to 1. Any number outside of this range has been incorrectly calculated. I note that is you meant to ask - Is r= -0.626 is a very strong correlation coefficient? then the answer No, this value is not a strong indicator that a linear relationship exists. Please see related link. The diagrams showing x-y graphs and the correlation coefficients is a good way to gain a "feel" of the coefficients and strength of relationships.
We would need to have the list of correlation coefficients to respond to this question.
The possible range of correlation coefficients depends on the type of correlation being measured. Here are the types for the most common correlation coefficients: Pearson Correlation Coefficient (r) Spearman's Rank Correlation Coefficient (ρ) Kendall's Rank Correlation Coefficient (τ) All of these correlation coefficients ranges from -1 to +1. In all the three cases, -1 represents negative correlation, 0 represents no correlation, and +1 represents positive correlation. It's important to note that correlation coefficients only measure the strength and direction of a linear relationship between variables. They do not capture non-linear relationships or establish causation. For better understanding of correlation analysis, you can get professional help from online platforms like SPSS-Tutor, Silverlake Consult, etc.
-.21
None of them.
Assuming that all of these coefficients are based on samples of the same size then the weakest correlation is -0.01 because its absolute value (0.01) is the smallest.
Correlation coefficients measure the strength and direction of a relationship between two variables. They range from -1 to 1: a value of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. They are commonly used in statistics to quantify the relationship between variables.
No, The correlation can not be over 1. An example of a strong correlation would be .99
A correlation coefficient of 1 or -1 would be the highest possible statistical relationship. However, the calculation of correlation coefficients between non independent values or small sets of data may show high coefficients when no relationship exists.
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. .
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. .
8.7.4 Properties of Regression Coefficients:(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, providedr > 0.(d) Regression coefficients are independent of the changes of origin but not of scale.