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The Normal Distribution
The central limit theorem states that the sum (and mean) of a large number of independent random variables is approximately normally distributed.
The normal distribution is completely described by two parameters—its mean, μ, and variance, σ2, indicated as X ~ N(μ, σ2).
The normal distribution has a skewness of 0 (it is symmetric), a kurtosis of 3; excess kurtosis 0. The mean, median, and the mode are all equal.
A linear combination of two or more normal random variables is also normally distributed.
The normal density with μ = 0 and σ = 1 is called the standard normal distribution (or unit normal distribution).
Approximately 68% of all observations fall in the interval μ ± σ.
Approximately 90% fall in μ ± 1.65σ.
Approximately 95% fall in μ ± 1.96σ.
Approximately 99% fall in μ ± 2.58σ.
N(x) is a conventional notation for the cdf of a standard normal variable.
P(Z ≤ 1.282) = N(1.282) = 0.90 or 90 percent
P(Z ≤ 1.65) = N(1.65) = 0.95 or 95 percent
P(Z ≤ 2.327) = N(2.327) = 0.99 or 99 percent
P(Z ≥ x) = 1.0 − N(x)
N(−x) = 1.0 − N(x)
P(Z ≥ −x) = N(x)
[Practice Problems] A portfolio has an expected mean return of 8% and standard deviation of 14%. The probability that its return falls between 8 and 11 percent is closest to:
A. 8.3%
B. 14.8%.
C. 58.3%.
[Solutions] A
P(8% ≤ Portfolio return ≤ 11%) = N(Z corresponding to 11%) – N(Z corresponding to 8%). For the first term, Z = (11% – 8%)/14% = 0.21 approximately, N(0.21) = 0.5832. 8 percent is the mean, N(Z corresponding to 8%) must equal 50%. So P(8% ≤ Portfolio return ≤ 11%) = 0.5832 – 0.50 = 0.0832 or approximately 8.3 percent.
Distribution of the Sample Mean:[Practicewhen the sample size is large.
Distribution of the Sample Mean:The Central:[PracticeB.C.when the sample size is large.
The Normal Distribution:μ;indicated as X ~ Nμ:Approximately 99% fall in μ ± 2.58σ.[PracticemeanNZ corresponding to 8% must equal 50%. So P8% ≤ Portfolio return ≤ 11% = 0.5832 – 0.50 = 0.0832 or approximately 8.3 percent.
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