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Variance
Consider the example of investment opportunities. The expected gains were Rs.114 and Rs.81 respectively. The fact is that an investor also looks at the dispersion before coming to a decision.
The dispersion of opportunity 1 is far greater than that of opportunity 2. This might alarm the investor.
In this example, it might be worthwhile to compute the coefficient of variation.
For opportunity 1, this works out to be
= (42/114) x 100 = 36.84%
For opportunity 2, this works out to be
= (29.14/81) x 100 = 35.97%
The investor may regard both opportunities homogeneous in this regard and therefore find opportunity 1 more attractive (because of the higher expected returns).
Compute the linear convolution of the discrete-time signal x(n) ={3, 2, 2,1} and the impulse response function of a filter h(n) = {2, 1, 3} using the DFT and the IDFT.
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