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

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

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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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Given co-efficient of determination, r2 = 0.81. co-efficient of correlation, r = square root of 0.81 = +0.9, if the data have move in the same direction.(Let x and y as variables then x and y have linear relationship and x increase or decrease and y also have increase or decrease) = -0.9, if the data have move in the opposite direction.(Let x and y as variables then x and y have linear relationship and x decrease or increase and y is also increase or decrease)

The coefficient, also commonly known as R-square, is used as a guideline to measure the accuracy of the model.

The larger the difference, the larger the value of chi-square and the greater the likelihood of rejecting the null hypothesis

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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.

It defined a direct correlation between matter and energy.

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.

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.

No mathematical difference.

There is no difference between square meters and meters square. for simplicity we use these in different form.

the difference between them is that the bottom face is different one of them is a rectangle and one of them is a square

It is the same as the difference between a blue square and a square.

the difference between them is that the bottom face is different one of them is a rectangle and one of them is a square

# 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)

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

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