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What is Effect of linear transformation on correlation?
The correlation remains the same.
Does correlation demonstrate causation?
NO. correlation just (implies) a relationship ... for example, both may be caused by the same thing.
What does Correlation of plus 1.00 mean?
It mean that there is no correlation between the two variables. The variables are the same.
Does the strength of correlation depend on the direction?
Absolutely not. The simplest way to demonstrate this is to consider a measure of agreement - disagreement. If we scored it so that "strongly agree" is 5 and "strongly disagree" is 1, we would get one value of the correlation. If we reverse-scored it, we would get exactly the same value, but with the opposite sign. The strength of the correlation is the same, but the direction of the relation has switched. Another consideration is the fact that the actual strength of the correlation is based on the square of its value. 0.20 squared is 0.04; 0.40 squared is 0.16. A correlation of 0.40 is four times as strong as a correlation of 0.20. But when you square something, you automatically lose the sign. The square of a negative number is positive. So by definition, correlations of the same size but different signs are equal in strength.
When the units of measurement are changed what happens to the correlation of the data?
My understanding is, nothing changes. The correlation will be the same regardless of what units are used. Others may have a deeper level of understanding and suggest something else.
