When we defined jointly independent random variables, we said that it is important to verify **all** the inequalities. The example below shows that having just the "product rule" does not suffice.

Though the example is not shown but the variables may independent. This can be put in other way that "if the two variables are correlated, then they are not independent" that is, they may be correlated and not independent

Step-by-step explanation:

Ok I’m sorry about my previous answer. Let me explain my work. When you simplify the squared roots and the fractions you get a number within -10 and -12, so in between those numbers. You technically solve the square root first and then bring down the negative. That means that 25 is D because 400/4 is 100 and the square root of 100 is 10 and then bring down the negative and you have -10. 24 is C because the square root of 120 is approximately 10.9 and when you bring down the negative it is -10.9. 26 is B because 23/2 is 11.5 and bring down the negative and you get -11.5. And 27 is A because it’s already a decimal so -11.75 isn’t bigger than -11.5 that’s why it’s A.