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correlation is used when there is metric data and chi square is used when there is categorized data.

sayan chakrabortty

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Q: What is the difference between correlation coefficient and chi square?
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What is coefficient of determination?

The coefficient of determination R2 is the square of the correlation coefficient. It is used generally to determine the goodness of fit of a model. See: http://en.wikipedia.org/wiki/Coefficient_of_determination for more details.


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It defined a direct correlation between matter and energy.


How can you tell if it appears to have a good r-square?

r^2 , the square of the correlation coefficient represents the percentage of variation explained by the independent variable of the dependent variable. It varies between 0 and 100 percent. The user has to make his/her own judgment as to whether the obtained value of r^2 is good enough for him/her.


What does the value of r square interpret?

R2 refers to the fraction of variance. it is the square of the correlation coefficient between two dependent variables. It is a statistical term that tells us how good one variable is at predicting another. If R2 is 1.0, then given the value of one variable you can perfectly predict the value of the other variable. If R2 is 0.0, then knowing either variable does not help you predict the other variable. In turn, the higher the R2 value the more correlation there is between the two variables.


What is difference between centimeter square and square centimeter?

No mathematical difference.


What's the difference between square kilometers and kilometers squared?

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What is the difference between a square pyramid and a pyramid?

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How do you calculate Spearman's rank correlation?

# 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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There isn't a significant difference, just which ever you prefer.


Is there a difference between a perfect square and a square?

No, but a perfect square is usually the square of a whole number.