Correlation is not causation!
Do these correlations make sense??
Just because there is a correlation doesn't mean that the variables are necessarily related to each other.
Just because there is a correlation doesn't mean that the variables are necessarily related to each other.
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Correlation coefficient vs coefficient of determination -
When we analyze correlations, we use the r-value as a parameter. It is also called the "correlation coefficient". It is a measure of strength of the linear relationship between TWO variables. The value of 'r' range from -1 to +1. A value of 0 indicates that there is no correlation between the two variables.
When we analyze regression, we use the r-squared value as a parameter. It is also called the "coefficient of determination". Like the r-value, the r-squared is also an indicator of strength between variables. However, here the relationship could involve multiple variables. The regression analysis is used to determine the impact of one or more x-variables on the y-variable. The r-squared value is used to indicate how well the x-variable(s) can be used to predict the y-variable. So the r-squared value indicates the strength of the regression equation which is used to predict the value of the y-variable. Values of r-squared range from 0 (poor predictor) to 1 (strong predictor).
When we analyze regression, we use the r-squared value as a parameter. It is also called the "coefficient of determination". Like the r-value, the r-squared is also an indicator of strength between variables. However, here the relationship could involve multiple variables. The regression analysis is used to determine the impact of one or more x-variables on the y-variable. The r-squared value is used to indicate how well the x-variable(s) can be used to predict the y-variable. So the r-squared value indicates the strength of the regression equation which is used to predict the value of the y-variable. Values of r-squared range from 0 (poor predictor) to 1 (strong predictor).