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5U Compute the convolution yn = xn * hn of the following pairs of signals a) 8 3) 2 3 1 (h n u n x n u n n n = = b) 6S For each of the following pairs of waveforms, use the convolution integral to find response y(t) of the LTI system with impulse response h(t) and x(t) Sketch your results a) ( ) ( ) ( ) ( ) h t e u t̃j X F B ̃ A N V ́u ^ E E F C Ă r W l X ݂ ̂ v Ƃ ́B ^ E E F C ́A 5 ԊX Ƀw E x f ɂ͂ ̒ ܂ 3 قǂ̃u e B b N } n b ^ ɍ\ A ۂɎ Ă 烌 ^ 邱 Ƃ \ r W l X B \ V E f B A y āA x p h X ̎ʐ^ t F C X u b N C X ^ O ̃ X g ʂ ̏ ɋ Ȃ F l e ʂ̖ڂɗ ܂邱 Ƃ A u t H } i C g A E g ̃h X ͔ ̎Z v Ƃ Ă͑傢 ɂ Ă͂₳ Ă ̂ BIf we use the model y* = 0* 1*x* u* instead of y = 0 1 x u, we get where y* = c y and x* = d x Similarly, where y* = ln y and x* = ln x 1 * 0 1 * 0 ˆ ˆ and ˆ ˆ d c c x y y x x y 1 * 1 ˆ ˆ Econometrics 24 25 Means & Variance of OLSE Now, we view as estimators for the parameters
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5 Y X P X \ H a N) G T c Q ^! The journey to finding the perfect hair care range is a long and twisted one that seems neverending But the fact is you cannot use just one product all the time as our hair keeps changing with climate, water, heat exposure with tools that we use and overall hair careWe know from problem MU 29 that Emax(X,Y) = EX EY − Emin(X,Y) From below, in part (c), we know that min(X,Y) is a geometric random variable mean pq −pq Therefore, Emin(X,Y) = 1 pq−pq, and we get Emax(X,Y) = 1 p 1 q
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1 Uniform Distribution X ∼ U(a,b) Probability is uniform or the same over an interval a to b X ∼ U(a,b),a < b where a is the beginning of the interval and b is the end of the interval The Uniform Distribution derives 'naturally' from Poisson Processes and how it does will be covered in the Poisson Process Notes& c d * # # # % # % # # < # % # = $ #Note If U is a universal set and A is a subset of U,thenn(Ac)=n(U)n(A) 1 Let the universal set U = {1,2,3,4,5,6,7,8,9,10} Find the following (a) n(U) (b) n(Ac), where A = {x xisanevennumberfrom1to10} (c) n(B), where B = {1,3,9} (d) n(?) Addition Rule for Sets Very Useful Formula If A and B are finite sets then n(AB)=n(A)n(B)n(A\B) 2
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7 7 9 g 4 9 A ?Or E {\displaystyle \mathbb {E} } The expected value is also known as the expectation, mathematical expectation, mean, average, or first moment Expected value is a key concept in economics, finance, and many other subjects By definition, the expected value of a constant random variable X = c {\displaystyle X=c}1 2 / 3 0 W h a t C u s e D m o c r i z n?
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/ 0 1 ) 2 3 ' 4 $ 5EX = X n EXjBnP(Bn) Now suppose that X and Y are discrete RV's If y is in the range of Y then Y = y is a event with nonzero probability, so we can use it as the B in the above So f(xjY = y) is de ned We can change the notation to make it look like the continuous case and write" 6 0 , 1
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X(t) = y(t/2) (c) yn = E _xk is not invertible Summation is not generally an invertible operation (e) y(t)( / 0 1 2 3 ' 4 4 54 ( 6 7 8 9 " " !In this case if b or x is 0 then, e^0 = 1 So at the yintercept or x = 0, the function becomes y = a * 1 or y = a Therefore, the constant a is the yintercept of the curve The other parameter in our equation is b If b is very small and greater than 0, the function flattens out The curve increases at a slower rate then for large b's
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