a. Conjecture what the image of a point with coordinates
b. Suppose a point Pwith coordinates
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A PROBLEM SOLVING APPROACH TO MATHEMATIC
- 5. Let X be a positive random variable with finite variance, and let A = (0, 1). Prove that P(X AEX) 2 (1-A)² (EX)² EX2arrow_forward6. Let, for p = (0, 1), and xe R. X be a random variable defined as follows: P(X=-x) = P(X = x)=p. P(X=0)= 1-2p. Show that there is equality in Chebyshev's inequality for X. This means that Chebyshev's inequality, in spite of being rather crude, cannot be improved without additional assumptions.arrow_forward4. Prove that, for any random variable X, the minimum of EIX-al is attained for a = med (X).arrow_forward
- 8. Recall, from Sect. 2.16.4, the likelihood ratio statistic, Ln, which was defined as a product of independent, identically distributed random variables with mean 1 (under the so-called null hypothesis), and the, sometimes more convenient, log-likelihood, log L, which was a sum of independent, identically distributed random variables, which, however, do not have mean log 1 = 0. (a) Verify that the last claim is correct, by proving the more general statement, namely that, if Y is a non-negative random variable with finite mean, then E(log Y) log(EY). (b) Prove that, in fact, there is strict inequality: E(log Y) < log(EY), unless Y is degenerate. (c) Review the proof of Jensen's inequality, Theorem 5.1. Generalize with a glimpse on (b).arrow_forward2. Derive the component transformation equations for tensors shown be- low where [C] = [BA] is the direction cosine matrix from frame A to B. B[T] = [C]^[T][C]T 3. The transport theorem for vectors shows that the time derivative can be constructed from two parts: the first is an explicit frame-dependent change of the vector whereas the second is an active rotational change of the vector. The same holds true for tensors. Starting from the previous result, derive a version of transport theorem for tensors. [C] (^[T])[C] = dt d B dt B [T] + [WB/A]B[T] – TWB/A] (10 pt) (7pt)arrow_forwardUse the graph of the function y = f (x) to find the value, if possible. f(x) 8 7 6 Q5 y 3 2 1 x -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 -1 -2 -3 -4 -5 -6 -7 -8+ Olim f(z) x-1+ O Limit does not exist.arrow_forward
- 3. Prove that, for any random variable X, the minimum of E(X - a)² is attained for a = EX. Provedarrow_forwardShade the areas givenarrow_forward7. Cantelli's inequality. Let X be a random variable with finite variance, o². (a) Prove that, for x ≥ 0, P(X EX2x)≤ 02 x² +0² 202 P(|X - EX2x)<≤ (b) Find X assuming two values where there is equality. (c) When is Cantelli's inequality better than Chebyshev's inequality? (d) Use Cantelli's inequality to show that med (X) - EX ≤ o√√3; recall, from Proposition 6.1, that an application of Chebyshev's inequality yields the bound o√√2. (e) Generalize Cantelli's inequality to moments of order r 1.arrow_forward
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