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4 11 8 7 18 12 20 9 5 4 2 0 1 1.5 9 15 18 18 23 20 25 9 6 6 2 1 2 1.8 4 19 18 20 23 20 19 8 5 5 1 1 2 2.1 5 16 12 9 22 14 18 8 4 6 2 0 2 2.1 4 24 24 19 22 25 24 8 5 5 0 0 0 1.9 4 15 16 12 19 15 20 8 7 4 0 2 2 1.6 9 17 19 16 25 20 20 7 3 0 0 1 1 2.1 8 19 16 17 28 21 24 8 4 5 2 1 0 2.1 11 19 15 9 16 15 21 9 4 3 2 2 2 2.2 4 28 28 28 28 28 28 8 7 5 0 1 0 1.5 4 26 21 20 21 11 10 7 6 2 2 1 1 1.9 6 15 18 16 22 22 22 9 6 3 3 0 1 2.2 4 26 22 22 24 22 19 7 2 4 0 1 1 1.6 8 16 19 17 24 27 27 8 4 6 0 1 1 1.5 4 24 22 12 26 24 23 8 4 3 2 0 2 1.9 4 25 25 18 28 23 24 8 5 4 1 0 0 0.1 11 22 20 20 24 24 24 8 3 1 2 0 1 2.2 4 15 16 12 20 21 25 8 4 5 1 1 1 1.8 4 21 19 16 26 20 24 6 7 4 1 1 2 1.6 6 22 18 16 21 19 21 9 5 4 1 0 2 2.2 6 27 26 21 28 25 28 7 2 4 0 0 2 2.1 4 26 24 15 27 16 28 7 3 3 2 0 1 1.9 8 26 20 17 23 24 22 8 6 6 1 0 2 1.6 5 22 19 17 24 21 26 7 6 5 1 0 2 1.9 4 21 19 17 24 22 26 8 2 5 1 0 2 2.2 9 22 23 18 22 25 21 8 7 6 2 2 0 1.8 4 20 18 15 21 23 26 3 2 4 0 0 0 2.4 7 21 16 20 25 20 23 9 10 6 1 2 2 2.4 10 20 18 13 20 21 20 8 4 5 2 0 1 2.5 4 22 21 21 21 22 24 8 4 6 3 0 2 1.9 4 21 20 12 26 25 25 6 2 5 2 0 1 2.1 7 8 15 6 23 23 24 5 4 4 1 0 2 1.9 12 22 19 13 21 19 20 8 4 4 0 1 2 2.1 4 18 27 6 27 27 23 8 6 6 2 1 2 1.9 7 20 19 19 27 21 24 8 7 6 2 1 1 1.5 5 24 7 12 25 19 25 9 2 4 2 0 1 1.9 8 17 20 14 23 25 23 7 6 6 3 0 1 2.1 5 20 20 13 25 16 21 7 3 6 3 1 1 1.5 4 23 19 12 23 24 23 3 3 3 1 0 0 2.1 9 20 19 17 19 24 21 7 2 4 0 1 0 2.1 7 22 20 19 22 18 18 8 5 5 2 1 1 1.8 4 19 18 10 24 28 24 8 7 6 2 1 2 2.4 4 15 14 10 19 15 18 7 6 6 2 1 1 2.1 4 20 17 11 21 17 21 8 4 6 2 1 2 1.9 4 22 17 11 27 18 23 8 6 6 1 1 2 2.1 4 17 8 10 25 26 25 9 4 6 3 0 2 1.9 7 14 9 7 25 18 22 6 3 5 2 0 2 2.4 4 24 22 22 23 22 22 9 5 5 2 1 1 2.1 7 17 20 12 17 19 23 8 2 3 0 0 2 2.2 4 23 20 18 28 17 24 8 3 5 0 1 2 2.2 4 25 22 20 25 26 25 8 5 1 0 0 2 1.8 4 16 22 9 20 21 22 7 7 5 3 1 2 2.1 4 18 22 16 25 26 24 8 4 6 2 2 1 2.4 8 20 16 14 21 21 21 7 3 6 0 0 1 2.2 4 18 14 11 24 12 24 7 2 4 2 2 2 2.1 4 23 24 20 28 20 25 9 5 6 0 0 1 1.5 4 24 21 17 20 20 23 7 4 6 0 1 0 1.9 4 23 20 14 19 24 27 9 6 6 2 2 2 1.8 7 13 20 8 24 24 27 7 4 5 3 0 2 1.8 12 20 18 16 21 22 23 6 4 2 0 0 1 1.6 4 20 14 11 24 21 18 3 2 2 1 0 0 1.2 4 19 19 10 23 20 20 9 9 6 2 1 2 1.8 4 22 24 15 18 23 23 9 8 6 2 2 1 1.5 5 22 19 15 27 19 24 7 8 5 0 1 2 2.1 15 15 16 10 25 24 26 6 3 6 3 1 2 2.4 5 17 16 10 20 21 20 9 2 5 2 0 1 2.4 10 19 16 18 21 16 23 8 4 4 0 1 2 1.5 9 20 14 10 23 17 22 8 2 5 3 0 2 1.8 8 22 22 22 27 23 23 7 2 4 2 1 2 2.1 4 21 21 16 24 20 17 9 1 5 2 0 2 2.2 5 21 15 10 27 19 20 5 4 4 3 1 0 2.1 4 16 14 7 24 18 22 6 5 6 0 1 1 1.9 9 20 15 16 23 18 18 8 8 5 1 1 2 2.1 4 21 14 16 24 21 19 8 4 4 2 0 1 1.9 10 20 20 16 21 20 19 8 6 5 1 1 1 1.6 4 23 21 22 23 17 16 8 5 5 2 1 2 2.4 7 15 17 13 22 20 24 9 6 6 2 1 2 1.9 4 18 14 5 27 25 26 7 3 4 0 0 0 1.9 6 22 19 18 24 15 14 8 8 6 3 1 2 1.9 7 16 16 10 25 17 25 9 4 2 0 1 0 2.1 5 17 13 8 19 17 23 9 6 5 2 1 1 1.8 4 24 26 16 24 24 18 8 4 6 1 0 1 2.1 4 13 13 8 25 21 22 4 3 5 0 0 0 2.4 4 19 18 16 23 22 26 7 8 5 0 2 2 2.1 4 20 15 14 23 18 25 8 6 3 1 2 2 2.2 4 22 18 15 25 22 26 6 3 3 0 0 0 2.1 4 19 21 9 26 20 26 7 5 5 2 1 2 2.2 6 21 17 21 26 21 24 7 4 6 1 0 2 1.6 10 15 18 7 16 21 22 3 3 2 2 0 0 2.4 7 21 20 17 23 20 21 8 7 6 1 0 1 2.1 4 24 18 18 26 18 22 8 2 4 1 1 1 1.9 4 22 25 16 25 25 28 8 4 5 3 0 2 2.4 7 20 20 16 23 23 22 8 6 6 2 1 1 2.1 4 21 19 14 26 21 26 5 6 5 0 0 2 1.8 8 19 18 15 22 20 20 6 6 5 2 1 1 2.1 11 14 12 8 20 21 24 6 4 6 1 1 2 1.8 6 25 22 22 27 20 21 7 6 5 0 0 2 1.9 14 11 16 5 20 22 23 7 5 6 1 1 2 1.9 5 17 18 13 22 15 23 7 5 5 0 0 2 2.4 4 22 23 22 24 24 23 8 6 4 0 0 2 1.8 8 20 20 18 21 22 22 9 8 5 1 1 2 1.8 9 22 20 15 24 21 23 8 5 5 2 2 1 2.1 4 15 16 11 26 17 21 8 6 5 2 1 2 2.1 4 23 22 19 24 23 27 7 4 5 2 1 2 2.4 5 20 19 19 24 22 23 9 3 4 2 1 2 1.9 4 22 23 21 27 23 26 7 3 5 3 0 1 1.8 5 16 6 4 25 16 27 6 2 0 0 0 0 1.8 4 25 19 17 27 18 27 7 4 5 0 0 1 2.2 4 18 24 10 19 25 23 8 5 6 0 0 1 2.4 7 19 19 13 22 18 23 6 3 1 0 1 0 1.8 10 25 15 15 22 14 23 2 4 1 0 1 0 2.4 4 21 18 11 25 20 28 4 5 3 3 0 0 1.8 5 22 18 20 23 19 24 8 3 3 2 0 2 1.9 4 21 22 13 24 18 20 6 5 6 0 0 1 2.4 4 22 23 18 24 22 23 8 4 4 2 0 1 2.1 4 23 18 20 23 21 22 6 4 5 2 0 2 1.9 6 20 17 15 22 14 15 7 6 6 0 2 2 2.1 4 6 6 4 24 5 27 7 3 6 2 2 2 2.7 8 15 22 9 19 25 23 7 4 6 1 2 1 2.1 5 18 20 18 25 21 23 9 3 6 3 2 2 2.1 4 24 16 12 26 11 20 7 10 6 3 2 2 2.1 17 22 16 17 18 20 18 6 4 6 0 0 1 2.1 4 21 17 12 24 9 22 8 8 5 3 1 2 2.1 4 23 20 16 28 15 20 8 3 6 2 2 2 2.1 8 20 23 17 23 23 21 9 5 5 2 0 2 2.1 4 20 18 14 19 21 25 7 4 6 0 0 1 2.1 7 18 13 13 19 9 19 6 3 5 2 0 2 2.4 4 25 22 20 27 24 25 8 5 5 3 0 2 1.95 4 16 20 16 24 16 24 6 3 6 2 0 2 2.1 5 20 20 15 26 20 22 6 3 4 0 0 2 2.1 7 14 13 10 21 15 28 9 4 5 0 0 2 1.95 4 22 16 16 25 18 22 6 3 6 0 0 1 2.1 4 26 25 21 28 22 21 9 6 6 1 1 1 2.4 7 20 16 15 19 21 23 8 6 5 2 2 2 2.1 11 17 15 16 20 21 19 8 4 6 3 2 2 2.25 7 22 19 19 26 21 21 9 4 6 0 0 0 2.4 4 22 19 9 27 20 25 6 4 6 2 0 1 2.25 4 20 24 19 23 24 23 4 3 4 2 1 2 2.55 4 17 9 7 18 15 28 8 2 6 1 2 0 1.95 4 22 22 23 23 24 14 5 5 5 2 0 2 2.4 4 17 15 14 21 18 23 7 4 6 2 2 2 2.1 4 22 22 10 23 24 24 9 4 6 2 0 0 2.1 6 21 22 16 22 24 25 9 4 5 2 0 2 2.4 8 25 24 12 21 15 15 8 3 4 2 1 1 2.1 23 11 12 10 14 19 23 6 4 5 2 2 1 2.1 4 19 21 7 24 20 26 8 2 6 1 1 2 2.25 8 24 25 20 26 26 21 3 0 0 0 0 0 2.25 6 17 26 9 24 26 26 8 4 6 2 1 2 2.4 4 22 19 14 26 18 15 7 3 4 0 0 0 2.1 4 22 21 12 22 23 23 7 6 6 0 2 2 2.1 7 17 14 10 20 13 15 9 4 4 2 0 1 2.4 4 26 28 19 20 16 16 4 4 6 0 0 1 2.1 4 19 16 16 20 19 20 7 2 4 0 0 0 1.95 4 20 21 11 18 22 20 6 4 5 0 1 0 2.1 4 19 16 15 18 21 20 3 2 1 0 1 0 2.25 10 21 16 14 25 11 21 8 4 5 3 2 2 2.25 6 24 25 11 28 23 28 8 3 5 0 0 1 2.4 5 21 21 14 23 18 19 9 6 5 2 2 0 2.25 5 19 22 15 20 19 21 8 6 5 3 0 2 2.25 4 13 9 7 22 15 22 8 4 5 0 0 2 2.1 4 24 20 22 27 8 27 9 5 6 2 2 1 2.1 5 28 19 19 24 15 20 8 4 5 0 1 2 2.1 5 27 24 22 23 21 17 9 6 6 3 2 2 2.7 5 22 22 11 20 25 26 7 6 5 2 1 2 2.1 5 23 22 19 22 14 21 7 9 6 2 1 2 2.1 4 19 12 9 21 21 24 6 4 5 2 1 0 2.25 6 18 17 11 24 18 21 8 8 6 3 1 2 2.7 4 23 18 17 26 18 25 6 5 5 3 0 2 2.4 4 21 10 12 24 12 22 7 4 5 3 0 0 2.1 4 22 22 17 18 24 17 8 4 6 2 2 1 2.1 9 17 24 10 17 17 14 8 7 6 3 2 1 2.4 18 15 18 17 23 20 23 7 4 6 1 2 2 1.95 6 21 18 13 21 24 28 9 8 6 2 1 2 2.7 5 20 23 11 21 22 24 9 4 6 3 2 1 2.1 4 26 21 19 24 15 22 9 3 6 2 0 1 2.25 11 19 21 21 22 22 24 6 5 6 2 1 2 2.1 4 28 28 24 24 26 25 8 8 6 2 2 2 2.7 10 21 17 13 24 17 21 9 4 5 1 0 1 2.1 6 19 21 16 24 23 22 9 10 6 3 1 0 2.1 8 22 21 13 23 19 16 8 5 6 2 2 2 1.65 8 21 20 15 21 21 18 8 5 6 2 2 2 1.65 6 20 18 15 24 23 27 8 3 6 1 0 2 2.1 8 19 17 11 19 19 17 8 3 5 1 1 2 2.1 4 11 7 7 19 18 25 8 3 3 0 0 2 2.1 4 17 17 13 23 16 24 9 4 4 1 1 1 2.1 9 19 14 13 25 23 21 6 5 6 1 0 2 2.1 9 20 18 12 24 13 21 9 5 4 2 1 2 2.4 5 17 14 8 21 18 19 8 4 6 0 0 0 2.4 4 21 23 7 18 23 27 8 7 6 3 1 0 2.1 4 21 20 17 23 21 28 8 5 3 1 0 1 2.25 15 12 14 9 20 23 19 8 4 4 1 2 0 2.4 10 23 17 18 23 16 23 9 7 4 3 0 2 2.1 9 22 21 17 23 17 25 9 7 4 3 0 2 2.1 7 22 23 17 23 20 26 9 7 4 3 0 2 2.4 9 21 24 18 23 18 25 8 7 4 3 0 2 2.4 6 20 21 12 27 20 25 8 7 4 0 0 0 2.1 4 18 14 14 19 19 24 8 7 6 2 1 2 2.1 7 21 24 22 25 26 24 3 1 4 1 1 0 2.4 4 24 16 19 25 9 24 6 2 4 2 1 2 2.1 7 22 21 21 21 23 22 5 3 2 1 0 2 2.7 4 20 8 10 25 9 21 4 6 5 1 0 1 2.1 15 17 17 16 17 13 17 9 8 6 3 2 2 2.1 4 19 18 11 22 27 23 8 8 6 1 1 1 2.25 9 16 17 15 23 22 17 3 0 1 0 0 0 2.1 4 19 16 12 27 12 25 6 3 4 1 0 2 2.4 4 23 22 21 27 18 19 6 6 5 1 1 2 2.25 28 8 17 22 5 6 8 9 5 5 2 0 2 2.25 4 22 21 20 19 17 14 7 7 6 1 0 1 2.1 4 23 20 15 24 22 22 6 3 5 0 1 2 2.1 4 15 20 9 23 22 25 9 3 6 2 0 0 2.4 5 17 19 15 28 23 28 7 4 6 2 0 1 2.25 4 21 8 14 25 19 25 8 4 5 3 0 2 2.1 4 25 19 11 27 20 24 8 1 5 0 0 2 2.1 12 18 11 9 16 17 15 8 5 6 2 0 2 1.65 5 23 15 18 23 18 25 7 3 4 1 0 1 1.65 4 20 13 12 25 24 24 0 0 0 0 0 0 2.7 6 21 18 11 26 20 28 6 4 6 1 1 0 2.1 6 21 19 14 24 18 24 9 6 5 2 2 1 1.95 5 24 23 10 23 23 25 9 4 6 1 1 2 2.25 4 22 20 18 24 27 23 6 1 2 0 1 2 2.4 4 22 22 11 27 25 26 8 3 5 0 0 2 1.95 4 23 19 14 25 24 26 8 7 5 2 0 2 2.1 10 17 16 16 19 12 22 5 3 1 0 0 2 2.4 7 15 11 11 19 16 25 6 5 5 1 1 0 2.1 4 24 11 8 14 16 20 6 3 4 1 0 0 2.1 4 22 21 16 24 24 22 9 3 5 2 2 2 2.4 7 19 14 13 20 23 26 9 6 4 2 1 2 2.4 4 18 21 12 21 24 20 9 9 6 3 0 2 2.4 4 21 20 17 28 24 26 6 4 5 0 1 2 2.25 12 20 21 23 26 26 26 4 3 6 0 1 1 2.4 5 19 20 14 19 19 21 8 9 6 2 2 2 2.1 8 19 19 10 23 28 21 4 5 6 0 1 0 2.1 6 16 19 16 23 23 24 5 3 6 3 1 1 1.8 17 18 18 11 21 21 21 8 6 5 2 0 1 2.7 4 23 20 16 26 19 18 6 2 6 1 0 1 2.1 5 22 21 19 25 23 23 8 4 5 3 1 2 2.1 4 23 22 17 25 23 26 9 5 5 2 1 1 2.4 5 20 19 12 24 20 23 7 4 5 2 0 1 2.55 5 24 23 17 23 18 25 4 0 0 0 0 0 2.55 6 25 16 11 22 20 20 8 2 6 1 1 2 2.1 4 25 23 19 27 28 25 8 5 6 2 1 2 2.1 4 20 18 12 26 21 26 8 3 6 2 0 1 2.1 4 23 23 8 23 25 19 4 0 0 0 0 0 2.25 6 21 20 17 22 18 21 9 5 5 3 0 2 2.25 8 23 20 13 26 24 23 8 6 5 1 0 2 2.1 10 23 23 17 22 28 24 6 3 5 0 1 1 2.1 4 11 13 7 17 9 6 3 0 0 0 0 0 1.95 5 21 21 23 25 22 22 7 3 4 0 1 0 2.4 4 27 26 18 22 26 21 8 5 6 2 1 2 2.1 4 19 18 13 28 28 28 7 4 4 0 0 2 2.4 4 21 19 17 22 18 24 7 5 5 2 0 1 2.4 16 16 18 13 21 23 14 8 7 6 3 2 1 2.4 4 22 19 13 21 22 17 8 4 5 3 1 2 2.25 7 21 18 8 24 15 20 7 8 6 2 1 2 1.95 4 22 19 16 26 24 28 7 6 6 1 1 2 2.1 4 16 13 14 26 12 19 6 4 5 1 0 1 2.1 14 18 10 13 24 12 24 8 5 5 1 1 0 2.55 5 23 21 19 27 20 21 8 5 6 0 1 2 2.1 5 24 24 15 22 25 21 7 3 6 1 0 2 2.1 5 20 21 15 23 24 26 9 6 6 0 1 2 2.1 5 20 23 8 22 23 24 9 3 4 2 0 1 1.95 7 18 18 14 23 18 26 7 6 5 3 1 1 2.25 19 4 11 7 15 20 25 7 3 2 1 0 2 2.4 16 14 16 11 20 22 23 8 7 6 2 2 2 1.95 4 22 20 17 22 20 24 8 7 6 3 0 2 2.1 4 17 20 19 25 25 24 6 6 4 3 1 2 2.1 7 23 26 17 27 28 26 9 5 6 1 1 0 1.95 9 20 21 12 24 25 23 6 5 5 1 0 1 2.1 5 18 12 12 21 14 20 5 4 4 0 0 2 2.1 14 19 15 18 17 16 16 7 4 6 1 2 2 1.95 4 20 18 16 26 24 24 9 7 6 3 0 2 2.1 16 15 14 15 20 13 20 6 2 1 0 1 0 1.95 10 24 18 20 22 19 23 7 5 5 2 0 1 2.4 5 21 16 16 24 18 23 5 4 5 2 1 0 2.4 6 19 19 12 23 16 18 9 2 6 2 2 2 2.4 4 19 7 10 22 8 21 8 5 4 2 0 0 1.95 4 27 21 28 28 27 25 4 4 3 0 0 2 2.7 4 23 24 19 21 23 23 9 7 4 3 2 2 2.1 5 23 21 18 24 20 26 8 6 5 2 2 0 1.95 4 20 20 19 28 20 26 7 4 5 0 0 0 2.1 4 17 22 8 25 26 24 8 5 6 2 2 2 1.95 5 21 17 17 24 23 23 1 0 1 0 0 0 2.1 4 23 19 16 24 24 21 8 7 6 2 1 2 2.25 4 22 20 18 21 21 23 8 4 4 2 0 2 2.7 5 16 16 12 20 15 20 9 5 4 3 0 2 2.1 8 20 20 17 26 22 23 8 6 5 2 0 1 2.4 15 16 16 13 16 25 24 9 8 3 2 1 1 1.35
Names of X columns:
AMS.A AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 Calculation Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio Probability_and_Sampling Estimation PA
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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