# Assertion True False 1. The CDF Fy(x) of a random variable X is continuous from the right 2. The pdf f(x) of a random variable X may take some negative values for the parameter X = x 3. The central movements E(X - u)", k odd integer is always zero for a symmetric pdf f(x) The Expectation operator is nonlinear; i.e., E(aX + b) + aE (X) + b, where a and b are constants 5. A function Y = g(X) of a single random variable X is measurable; i.e., Y is a random variable 6. If random variable X~U (0,1), the Y = aX is X-U(0, a) 7. If random variable X -N(0,1), the Y = aX is X-N(0, a') 8. The transformation to generate random variable Y with DCF Fy(y) from X~U(0,1)is Y = Fy-1(x) 9. If Y = ax + b then fr(y) = =fx( ) , ifa> 0 10 The characteristic function O(y) = E(e/2 )*) is linear; i.e., if Y = aX + b then O(y) = a@ (y) +b 11. The CDF Fy (x) is non-decreasing and is monotone for all types of random variables. 12. The density function fx(x) for a discrete RV may be expressed as an impulse train: fx(x) = ME-. po(x - 13. The density function for a mixed-type random variable is: fx(x) = fx (x) + Zie_.. p.6(x -[) 14. A real-valued random variable X(@) is a function with domain Q and range the real line R 15. For an event A = (@ ( A ) c n, the mapping X(A) is a Borel set B C R such that P(A) = P(B) 16. The random variable can be defined for events that contain continuous or discrete elementary outcomes 17. The CDF Fx(x) of discrete R.V. X satisfies: Fx(-co) = 1, Fx(co) = 1 and P(X = x) is defined for all XER. 18. If X'(@) is a continuous random variable, then P(X = x) = 0, for all x E R. 19, Event (w: X(@)