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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.

Q: Which correlation coefficient indicates the weakest relationship between variables?

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partial correlation is the relation between two variable after controlling for other variables and multiple correlation is correlation between dependent and group of independent variables.

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.

Size of variables

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.

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A coefficient of zero means there is no correlation between two variables. A coefficient of -1 indicates strong negative correlation, while +1 suggests strong positive correlation.

The correlation coefficient is zero when there is no linear relationship between two variables, meaning they are not related in a linear fashion. This indicates that changes in one variable do not predict or explain changes in the other variable.

"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".

If the correlation coefficient is 0, then the two tings vary separately. They are not related.

The weakest correlation coefficient is 0, which means there is absolutely no relationship between the two variables you are correlating.

Correlation coefficient is a measure of the strength and direction of a relationship between two variables. It quantifies how closely the two variables are related and ranges from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.

A correlation coefficient represents the strength and direction of the relationship between two variables. It measures how closely the two variables are related to each other.

The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.

A correlation coefficient is a statistic that measures the strength and direction of a relationship between two variables. It ranges from -1 to 1, with 1 indicating a perfect positive relationship, -1 indicating a perfect negative relationship, and 0 indicating no relationship between the variables.

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The value of a correlation coefficient reflects the strength and direction of the relationship between two variables. A correlation coefficient ranges from -1 to 1, with 1 indicating a perfect positive relationship, 0 indicating no relationship, and -1 indicating a perfect negative relationship.

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).