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Explore Stack InternalIt depends on your exact definition of "correlation", but it isn't too hard to construct degenerate cases. "Independent" could mean something like "no predictive power, at all, ever" just as much as "linear correlation".
Linear correlation, for example, would not indicate dependence on y=Sin(2000*x)$y= \sin(2000x)$ if the domain of x$x$ was [0,1)$[0,1)$.
It depends on your exact definition of "correlation", but it isn't too hard to construct degenerate cases. "Independent" could mean something like "no predictive power, at all, ever" just as much as "linear correlation".
Linear correlation, for example, would not indicate dependence on y=Sin(2000*x) if the domain of x was [0,1).
It depends on your exact definition of "correlation", but it isn't too hard to construct degenerate cases. "Independent" could mean something like "no predictive power, at all, ever" just as much as "linear correlation".
Linear correlation, for example, would not indicate dependence on $y= \sin(2000x)$ if the domain of $x$ was $[0,1)$.
It depends on your exact definition of "correlation", but it isn't too hard to construct degenerate cases. "Independent" could mean something like "no predictive power, at all, ever" just as much as "linear correlation".
Linear correlation, for example, would not indicate dependence on y=Sin(2000*x) if the domain of x was [0,1).
