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Prob 12.54 - data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17...

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12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls data Line Speed, x Defects, y 20 21 20 19 40 15 30 16 60 14 40 17 10 20 30 40 50 60 0 5 10 15 20 25 Line Speed, x Number of Defects, y 10 20 30 40 50 60 0 5 10 15 20 25 f(x) = -0.15x + 22.17 R² = 0.74 x y A B C D E F G H I J K L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
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12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls data A B C D E F G H I J K L 38 39 40 41 42
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12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls data M N O P Q R S T 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
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12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls data M N O P Q R S T 38 39 40 41 42
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12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls regression calcs Defects, y x^2 x*y y^2 20 21 400.00 420 441 20 19 400.00 380 361 40 15 1600.00 600 225 30 16 900.00 480 256 60 14 3600.00 840 196 40 17 1600.00 680 289 sums 210 102 8500.00 3400 1768 means 35.0000 17.0000 b1 = (E8 - (9*B9*C9)) / (D8 - (9*B9^2)) = -0.1478 b0 = C9 - (E12*B9) = 22.1739 Therefore, by regression, the single straight line which best models the data is: Y = -0.1478*x + 22.1739 where Y is the number of defects expected for line speed x. r = (E8-(6*B9*C9))/SQRT((D8-(6*B9^2))*(F8-(6*C9^2))) = -0.8597 There is a strong correlation between the DJIA and S&P indices. R^2 = r x r = 0.7391 84.14% of the variation in the S&P index can be related to the variation in the DJIA
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  • Spring '08
  • SINGER
  • Harshad number, Prime number, Errors and residuals in statistics, line speed, 12342d2d46cf9d10c59f9531bb41dfdb3a924a88.xls

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