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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
If variables have zero correlation, they do not have a linear relationship. Zero correlation shows that two things were not found to be related.
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.
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Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the 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.
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.
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.
The three different types of correlation are positive correlation (both variables move in the same direction), negative correlation (variables move in opposite directions), and no correlation (variables show no relationship).
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
Positive correlation is a relationship between two variables in which both variables move in tandem that is in the same direction.
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.