# State the null hypothesis i.e. "There is no relationship between the two sets of data." # Rank both sets of data from the highest to the lowest. Make sure to check for tied ranks. # Subtract the two sets of ranks to get the difference d. # Square the values of d. # Add the squared values of d to get Sigma d2. # Use the formula Rs = 1-(6Sigma d2/n3-n) where n is the number of ranks you have. # If the Rs value...
... is -1, there is a perfect negative correlation.
...falls between -1 and -0.5, there is a strong negative correlation.
...falls between -0.5 and 0, there is a weak negative correlation.
... is 0, there is no correlation
...falls between 0 and 0.5, there is a weak positive correlation.
...falls between 0.5 and 1, there is a strong positive correlation
...is 1, there is a perfect positive correlation
between the 2 sets of data. # If the Rs value is 0, state that null hypothesis is accepted. Otherwise, say it is rejected. (sourced from http://www.revision-notes.co.uk/revision/181.html)
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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.
positive correlation-negative correlation and no correlation
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
No, The correlation can not be over 1. An example of a strong correlation would be .99
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.