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"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
a zero correlation means that there is no relationship between the two or more variables.
A correlation
correlation
I think you're referring to Correlation. This means the relationship between two variables. There can be a positive correlation, where as one variable increases, so does the other. There can be a negative correlation, where as one variable increases, the other decreases. Lastly, there can be no correlation, where there is no relationship between the two variables.
We would need to have the list of correlation coefficients to respond to this question.
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.
A correlation coefficient represents the strength and direction of a linear relationship between two variables. A correlation coefficient close to zero indicates a weak relationship between the variables, where changes in one variable do not consistently predict changes in the other. However, it is important to note that a correlation coefficient of zero does not necessarily mean there is no relationship between the variables, as non-linear relationships may exist.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
A correlation diagram visually represents the relationship between variables in a dataset. It shows how strongly and in what direction variables are related to each other.
a zero correlation means that there is no relationship between the two or more variables.
Positive correlation is a relationship between two variables in which both variables move in tandem that is in the same direction.
Where only bivariate collinear relations exist, a matrix of correlation coefficients is a perfectly adequate diagnostic tool for identifying collinearity. However, they are incapable of diagnosing a collinear relationship involving more than two indepdendent variables. This is the advantage of auxilliary regression. They allow a researcher to detect a collinear relationship between as many independent variables as the researcher requires.
relationship between 2 variables
A correlation
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.
Correlation-apex (;