(a) For fx(x) = 1-x| |x|≤1 " otherwise sketch the PDF, then calculate E[X] and P[|X| > 1/2].

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Calculate each of the requested quantities. All of these problems are carefully chosen so that they can be completed without integration, so we are expecting exact answers. For the Gaussian problems, you will sometimes need to lookup values for the standard normal CDF Φ(z). 

(a) For fx (x):
=
{}
1−|x| |x|≤1
otherwise'
sketch the PDF, then calculate E[X] and P[|X| > 1/2].
(b) For fx(x) from part (a), determine and sketch the conditional PDF fx|Â(x) given the event
{|X| > 1/2}. Using your sketch, calculate P[X > 0||X|>1/2].
(c) Let X be Exponential (2). Calculate E[X – 1] and E[(X − 1)²].
(d) Let X be Uniform(-2, 3). Determine P[X – 1 > 0] and P[X² – 1 > 0].
(e) Let X be Gaussian (2, 1) and let Y
well as P[Y > 1].
= 3X 2. Determine the mean and variance of Y as
(f) Let X be Gaussian (1,4). Determine (up to two decimal places) the maximum value of a
such that P[X ≤ a] ≤ 0.1. Determine the minimum value of b such that P[X ≥ b] ≤ 0.2.
Transcribed Image Text:(a) For fx (x): = {} 1−|x| |x|≤1 otherwise' sketch the PDF, then calculate E[X] and P[|X| > 1/2]. (b) For fx(x) from part (a), determine and sketch the conditional PDF fx|Â(x) given the event {|X| > 1/2}. Using your sketch, calculate P[X > 0||X|>1/2]. (c) Let X be Exponential (2). Calculate E[X – 1] and E[(X − 1)²]. (d) Let X be Uniform(-2, 3). Determine P[X – 1 > 0] and P[X² – 1 > 0]. (e) Let X be Gaussian (2, 1) and let Y well as P[Y > 1]. = 3X 2. Determine the mean and variance of Y as (f) Let X be Gaussian (1,4). Determine (up to two decimal places) the maximum value of a such that P[X ≤ a] ≤ 0.1. Determine the minimum value of b such that P[X ≥ b] ≤ 0.2.
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