What's not true about a correlation coefficient \( r \) ? \( r<0 \) when scatter plot shows a falling trend. the closer \( r \) is to \( \pm 1 \), the more closely the scattered points line up. \( r>0 \) when scatter plot shows a rising trend \( r \) is the slope of the best-fit line. \( -1 \leq r \leq 1 \)

Did you know that while a correlation coefficient \( r \) measures the strength and direction of a linear relationship between two variables, it does not provide any information about causation? Just because two variables have a strong correlation does not mean that one causes the other to change. Spurious correlations can mislead you into thinking there’s a relationship when there isn’t! One common mistake many make is assuming that \( r \) is the same as the slope of the best-fit line. While they are related—both give insights into the relationship between variables—they represent different concepts. The slope indicates how much \( y \) changes for a given change in \( x \), while \( r \) only assesses strength and direction! So remember, they’re not interchangeable!