Contents
What happens to correlation when units are changed?
The correlation does not change when the units of measurement of either one of the variables change. In other words, if we change the units of measurement of the explanatory variable and/or the response variable, this has no effect on the correlation (r).
What determines the sign of correlation?
The sign of the correlation coefficient indicates whether the direction ofthe relationship is positive (direct) or negative (inverse). Variables whichhave a direct relationship (a positive correlation) increase together and decrease together.
How do you know if correlation is positive or negative?
If the correlation coefficient is greater than zero, it is a positive relationship. Conversely, if the value is less than zero, it is a negative relationship. A value of zero indicates that there is no relationship between the two variables.
When to use correlation before or after log transformation?
There is no such choice for (X,Y) or (log X, log Y). Scatter plot of the variables can be used for understanding of the relationship. Pearson correlation is for parametric testing and is more powerful than non-parametirc test. Thus, we opt to use transformation before any non-parametric procedures. Transform your data and get pearsons correlation.
What kind of correlation is used in regression?
Correlation (pearson) measures a linear relationship between two continuous variables. There is no such choice for (X,Y) or (log X, log Y). Scatter plot of the variables can be used for understanding of the relationship.
When to use scatter plot before or after log transformation?
Scatter plot of the variables can be used for understanding of the relationship. Pearson correlation is for parametric testing and is more powerful than non-parametirc test. Thus, we opt to use transformation before any non-parametric procedures.
When does the coefficient change a lot after controlling for other variables?
My general feeling about such situations is that, when the coefficient changes a lot after controlling for other variables, it is important to visualize this change, to understand what is the interaction among variables that is associated with the change in the coefficients.