# ch5sol - Chapter 5 Properties of a Random Sample 5.1 Let X...

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Unformatted text preview: Chapter 5 Properties of a Random Sample 5.1 Let X = # color blind people in a sample of size n . Then X binomial( n,p ), where p = . 01. The probability that a sample contains a color blind person is P ( X &gt; 0) = 1- P ( X = 0), where P ( X = 0) = ( n ) ( . 01) ( . 99) n = . 99 n . Thus, P ( X &gt; 0) = 1- . 99 n &gt; . 95 n &gt; log( . 05) / log( . 99) 299 . 5.3 Note that Y i Bernoulli with p i = P ( X i ) = 1- F ( ) for each i . Since the Y i s are iid Bernoulli, n i =1 Y i binomial( n,p = 1- F ( )). 5.5 Let Y = X 1 + + X n . Then X = (1 /n ) Y , a scale transformation. Therefore the pdf of X is f X ( x ) = 1 1 /n f Y x 1 /n = nf Y ( nx ). 5.6 a. For Z = X- Y , set W = X . Then Y = W- Z , X = W , and | J | = 1- 1 1 = 1 . Then f Z,W ( z,w ) = f X ( w ) f Y ( w- z ) 1, thus f Z ( z ) = R - f X ( w ) f Y ( w- z ) dw . b. For Z = XY , set W = X . Then Y = Z/W and | J | = 1 1 /w- z/w 2 =- 1 /w . Then f Z,W ( z,w ) = f X ( w ) f Y ( z/w ) |- 1 /w | , thus f Z ( z ) = R - |- 1 /w | f X ( w ) f Y ( z/w ) dw . c. For Z = X/Y , set W = X . Then Y=W/Z and | J | = 1- w/z 2 1 /z = w/z 2 . Then f Z,W ( z,w ) = f X ( w ) f Y ( w/z ) | w/z 2 | , thus f Z ( z ) = R - | w/z 2 | f X ( w ) f Y ( w/z ) dw . 5.7 It is, perhaps, easiest to recover the constants by doing the integrations. We have Z - B 1+ ( ) 2 d = B, Z - D 1+ ( - z ) 2 d = D and Z - &quot; A 1+ ( ) 2- C 1+ ( - z ) 2 # d = Z - &quot; A 1+ ( ) 2- C ( - z ) 1+ ( - z ) 2 # d- Cz Z - 1 1+ ( - z ) 2 d = A 2 2 log 1+ 2- C 2 2 log &quot; 1+ - z 2 # -- Cz. The integral is finite and equal to zero if A = M 2 2 , C = M 2 2 for some constant M . Hence f Z ( z ) = 1 2 B- D- 2 Mz = 1 ( + ) 1 1+( z/ ( + )) 2 , if B = + , D = + ) , M =- 2 2 z ( + ) 1 1+ ( z + ) 2 . 5-2 Solutions Manual for Statistical Inference 5.8 a. 1 2 n ( n- 1) n X i =1 n X j =1 ( X i- X j ) 2 = 1 2 n ( n- 1) n X i =1 n X j =1 ( X i- X + X- X j ) 2 = 1 2 n ( n- 1) n X i =1 n X j =1 h ( X i- X ) 2- 2( X i- X )( X j- X ) + ( X j- X ) 2 i = 1 2 n ( n- 1) n X i =1 n ( X i- X ) 2- 2 n X i =1 ( X i- X ) n X j =1 ( X j- X ) | {z } =0 + n n X j =1 ( X j- X ) 2 = n 2 n ( n- 1) n X i =1 ( X i- X ) 2 + n 2 n ( n- 1) n X j =1 ( X j- X ) 2 = 1 n- 1 n X i =1 ( X i- X ) 2 = S 2 . b. Although all of the calculations here are straightforward, there is a tedious amount of book- keeping needed. It seems that induction is the easiest route. (Note: Without loss of generality we can assume 1 = 0, so E X i = 0.) (i) Prove the equation for n = 4. We have S 2 = 1 24 4 i =1 4 j =1 ( X i- X j ) 2 , and to calculate Var( S 2 ) we need to calculate E( S 2 ) 2 and E( S 2 ). The latter expectation is straightforward and we get E(...
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## This note was uploaded on 04/18/2010 for the course STAT 622 taught by Professor Peruggia,m during the Spring '08 term at Ohio State.

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ch5sol - Chapter 5 Properties of a Random Sample 5.1 Let X...

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