IE383_Assignment6_sol - IE 383 Assignment 6 solution...

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IE 383 Assignment 6 solution Problem 1 a) Linear model = + + Y a bt ε t Y Y*t t^2 1 8025 8025 1 2 7200 14400 4 3 7145 21435 9 4 6775 27100 16 5 6952 34760 25 6 6542 39252 36 7 6525 45675 49 8 6121 48968 64 Sum 36 55285 239615 204 According to method of least squares, it follows that So the fitted regression equation is: From this equation we can predict that b) = . α 0 4 = = a1 Y1 8025 = - - =- b1 Y8 Y18 1 272 = - - = Y1 a1 b11 αα 8433 = - * Y1 a1 2 b1 - = 1 αα 8841
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t ( ) Y t * ( ) α Y t (1- )* α ( - ) Y t 1 ( ) Y t * α ( ) Y t (1- )* - α Yt 1 Yt 1 8025 8433 8841 2 7200 2880 5059.800 7939.80 0 3175.920 5304.600 8480.520 3 7145 2858 4763.880 7621.88 0 3048.752 5088.312 8137.064 4 6775 2710 4573.128 7283.12 8 2913.251 4882.238 7795.490 5 6952 2781 4369.877 7150.67 7 2860.271 4677.294 7537.564 6 6542 2617 4290.406 6907.20 6 2762.882 4522.539 7285.421 7 6525 2610 4144.324 6754.32 4 2701.729 4371.253 7072.982 8 6121 2448 4052.594 6500.99 4 2600.398 4243.789 6844.187 = - - = . Y8 a8 b81 αα 6500 994
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This note was uploaded on 09/23/2009 for the course IE 383 taught by Professor Leyla,o during the Spring '08 term at Purdue University-West Lafayette.

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IE383_Assignment6_sol - IE 383 Assignment 6 solution...

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