8042 b p x 50 6 p x 44 or x 56 p x 445 or

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Unformatted text preview: 1 − Φ(1)))K =P = (2Φ(1) − 1)K = (0.6826)K, so K = 1/0.6826 ≈ 1.46. The same reasoning can be used to find FX . For 0 ≤ v ≤ 4, FX (v ) = P {0 ≤ X ≤ v } = P {0 ≤ Z ≤ v }K = P = Φ v−2 2 − Φ(−1) K = Φ v−2 2 −1 ≤ Z −2 v−2 ≤ 2 2 K − 0.1587 K. Thus, 0 FX (v ) = Φ 1 v −2 2 − 0.1587 K if v ≤ 0 if 0 < v ≤ 4 if v ≥ 4. Finally, as for finding E [X ], since the pdf fX (u) is symmetric about the point u = 2, and has bounded support (so the mean exists), it follows that E [X ] = 2. 3.6. LINEAR SCALING OF PDFS AND THE GAUSSIAN DISTRIBUTION 3.6.3 93 The central limit theorem and the Gaussian approximation The Gaussian distribution arises frequently in practice, because of the phenomenon known as the central limit theorem (CLT), and the associated Gaussian approximation. There are many mathematical formulations of the CLT which differ in various details, but the main idea is the following: If many independent random variables are added together, and if each of them is small in magnitude compared to the sum, then the sum h...
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This note was uploaded on 02/09/2014 for the course ISYE 2027 taught by Professor Zahrn during the Spring '08 term at Georgia Institute of Technology.

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