Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
0 0 1 40 2898 1 0 1 26 994 0 2 0 38 3977 0 2 1 37 3040 0 2 0 38 3523 0 5 1 40 3100 1 6 0 40 3670 1 7 0 41 3097 1 7 1 39 3040 1 7 1 39 3239 0 8 0 38 2955 0 8 0 38 2200 1 8 0 40 3182 0 8 0 40 3510 1 8 1 39 3381 1 8 0 40 3530 1 8 1 38 2985 1 8 0 39 3374 0 8 0 42 3765 1 8 0 39 2715 1 8 0 39 3640 1 8 0 42 3040 1 8 0 39 3670 1 9 1 38 2775 1 9 0 38 2680 1 9 0 37 2699 1 9 1 38 2557 1 9 0 40 3835 0 9 1 41 3381 1 9 0 36 2766 1 9 0 41 3125 1 9 1 38 2557 1 9 0 38 2950 0 9 0 40 3779 0 9 0 41 2760 1 9 0 39 3011 1 9 1 37 2415 1 9 0 38 3125 0 9 1 38 3154 0 9 0 39 3170 0 9 0 41 2784 1 9 1 40 2475 1 9 0 40 2642 0 9 1 40 4034 0 9 0 41 3988 1 9 0 40 3370 0 9 0 40 2955 1 9 0 40 2443 0 9 0 38 4000 1 9 0 37 3630 1 9 0 39 2920 1 9 1 35 2727 0 9 0 38 3800 0 9 0 40 3771 1 9 0 37 3075 1 9 1 37 2415 1 9 0 37 2890 0 9 0 37 3892 1 9 1 40 3125 0 9 1 42 3892 0 9 0 39 3296 0 9 0 38 3120 0 9 1 38 1710 1 10 0 41 3068 1 10 0 41 3779 1 10 1 40 2898 0 10 0 40 3381 1 10 1 40 3324 1 10 1 38 2727 0 10 1 37 2642 1 10 1 34 2244 0 10 0 36 2528 0 10 1 39 3352 0 10 1 39 3551 0 10 0 40 3267 1 10 0 40 3210 1 10 0 40 3395 0 10 1 40 3068 1 10 1 39 3420 1 10 0 38 2985 1 10 0 33 2147 1 10 0 39 2863 1 10 0 38 3125 0 10 0 40 4290 1 10 1 34 2585 1 10 0 38 2869 1 10 1 36 3097 1 10 0 39 2756 0 10 1 35 3125 0 10 1 41 3921 1 10 0 40 3438 1 10 0 40 2980 1 10 0 36 2869 0 10 1 41 4830 0 10 1 40 3409 1 10 0 41 3807 1 10 1 39 3220 0 10 1 38 2756 0 10 1 38 3097 1 10 0 39 3615 0 10 0 40 2926 1 10 0 40 4050 1 10 0 38 2840 1 10 0 41 2671 1 10 0 38 3540 1 10 0 38 2900 0 10 0 40 3779 1 10 0 38 2740 1 10 0 35 2320 1 10 0 38 2905 1 10 1 39 3070 0 10 1 41 3352 1 10 0 36 2581 1 10 0 41 4091 1 10 1 43 3235 1 10 0 41 3722 1 10 1 40 3438 0 10 0 40 3475 1 10 0 31 3438 1 10 0 39 3409 0 10 0 38 3040 0 10 0 38 3800 1 10 1 36 3182 1 10 1 39 3494 1 10 1 35 2131 1 10 0 35 1790 0 10 0 40 3864 1 10 0 39 2955 0 10 0 40 3949 1 10 0 35 2273 1 10 0 39 2620 1 10 0 36 2205 1 10 0 41 2860 1 10 0 40 3381 1 10 0 40 3190 1 10 0 40 3267 0 10 1 40 3665 1 10 0 38 3160 1 10 0 39 3216 1 10 1 37 2415 1 10 0 40 3200 0 10 0 40 3352 1 10 1 40 3470 1 10 0 38 3580 0 10 0 41 3750 1 10 0 42 3035 1 10 0 38 3438 1 10 0 38 3068 0 10 0 39 3438 1 10 1 38 3750 1 10 1 42 3409 1 10 1 39 1790 1 10 0 40 3170 0 10 0 38 3800 1 10 1 40 2825 1 10 0 37 2756 1 10 0 40 3040 0 10 1 39 2585 1 10 1 38 2756 0 10 1 41 3523 1 10 1 26 909 0 10 1 40 3779 1 10 1 38 2415 0 10 0 40 3665 1 10 0 39 3295 1 10 1 39 2699 0 10 1 39 3267 1 10 0 39 3460 1 11 0 28 1250 1 11 0 38 3730 1 11 1 40 3523 1 11 1 40 3011 1 11 1 39 3740 1 11 1 43 3949 0 11 1 37 2330 0 11 0 40 3182 1 11 0 39 3040 1 11 0 37 2557 0 11 0 38 3147 1 11 1 36 2671 1 11 1 40 2557 1 11 0 38 3200 1 11 0 41 3203 1 11 1 36 2756 1 11 0 32 1619 1 11 1 35 2330 1 11 1 39 3381 0 11 1 38 2983 0 11 1 38 2600 1 11 0 40 3585 0 11 0 40 3693 1 11 0 38 3020 1 11 1 34 1800 1 11 0 38 2290 1 11 0 40 3750 0 11 0 40 3551 0 11 0 37 3779 0 11 0 38 3125 1 11 0 38 3470 1 11 0 41 3210 1 11 1 40 3100 1 11 1 38 3182 1 11 0 37 3355 1 11 0 42 2750 1 11 0 39 4006 1 11 0 38 3438 0 11 0 40 3068 1 11 1 40 2650 1 11 0 39 3005 1 11 0 39 2993 1 11 0 40 3494 1 11 0 41 2795 1 11 0 41 3260 1 11 1 41 2959 1 11 0 38 4020 0 11 0 37 2949 1 11 0 38 3900 1 11 1 39 2926 1 11 1 38 3239 1 11 0 41 3409 1 11 0 42 3630 1 11 0 41 3466 1 11 0 40 2330 0 11 1 40 3949 1 11 1 38 2813 1 11 0 37 3137 0 11 0 40 3296 1 11 0 41 3267 1 11 0 38 2940 1 11 0 40 3323 1 11 1 37 3409 1 11 1 41 3296 0 11 1 40 3807 1 11 0 38 3296 1 11 1 38 2983 1 11 1 30 1680 1 11 0 39 3551 1 11 0 37 2385 1 11 0 40 3356 1 11 0 40 3466 0 11 0 41 2699 0 11 0 40 4233 1 11 1 40 2890 1 11 1 37 2443 0 11 1 39 3580 0 11 1 42 3551 1 11 1 31 2030 1 11 0 36 3125 1 11 0 37 2700 1 11 1 33 2216 0 11 1 40 3182 1 11 1 39 3040 1 11 1 39 3011 1 11 1 39 2800 1 11 0 38 3425 1 11 1 37 2760 1 11 0 41 3665 1 11 0 36 1733 1 11 0 40 2850 1 11 1 38 2485 1 11 0 40 3381 1 11 0 39 3065 1 11 0 39 3167 1 11 0 38 2635 0 11 1 40 3665 1 11 1 39 3300 0 11 0 40 3636 1 11 1 38 2813 1 11 1 42 3920 1 11 0 39 2671 1 11 0 40 3182 1 11 1 40 3409 1 11 0 37 3600 1 11 0 40 2557 1 11 0 40 3890 1 11 0 39 4148 1 11 1 32 2030 1 11 1 39 2890 1 11 1 36 2373 1 11 0 41 2977 1 11 1 38 2840 1 11 0 41 2658 0 11 1 40 2983 1 11 0 39 3700 0 12 1 40 3608 0 12 0 40 3559 1 12 0 40 3540 1 12 1 39 3440 1 12 0 39 3324 1 12 0 38 3494 1 12 0 41 3636 1 12 1 39 2813 1 12 0 39 3239 0 12 1 39 3068 1 12 0 41 3210 0 12 0 42 3892 0 12 0 39 3864 1 12 0 40 2784 0 12 0 40 3551 1 12 0 40 3608 1 12 0 39 3130 0 12 1 40 3466 1 12 0 40 3608 1 12 0 37 2225 1 12 0 36 2585 1 12 0 40 2917 1 12 0 36 2415 0 12 0 40 4489 0 12 0 40 3977 1 12 0 38 3080 1 12 0 40 3722 0 12 1 40 3097 1 12 0 40 2960 0 12 0 39 3807 0 12 0 40 3210 1 12 0 40 3210 1 12 1 20 284 1 12 0 40 3805 0 12 1 41 3267 0 12 1 41 3722 0 12 0 40 3864 0 12 1 42 4318 0 12 1 42 3807 1 12 1 22 455 0 12 0 40 3722 1 12 0 38 3324 1 12 0 40 3665 1 12 0 41 3240 1 12 0 40 3182 1 12 1 40 3040 0 12 1 40 3665 0 12 0 40 4148 0 12 0 38 3636 0 12 1 33 1591 0 12 0 39 4135 0 12 0 40 3352 1 12 1 38 3011 1 12 1 34 2301 1 12 0 40 2740 1 12 0 41 2450 0 12 1 40 2472 0 12 0 40 4148 0 12 0 40 3352 0 12 0 38 4034 0 12 0 42 3807 0 12 0 39 2926 1 12 0 40 2699 1 12 0 39 2990 0 12 0 38 2898 0 12 1 41 3864 1 12 0 40 2970 1 12 0 42 3655 1 12 0 35 2890 1 12 1 38 3980 0 12 1 40 2756 0 12 0 40 4148 0 12 0 40 3494 1 12 1 37 2830 1 12 0 39 2983 1 12 0 40 3430 1 12 0 35 2159 0 12 1 42 3125 0 12 0 40 3239 0 12 0 38 4262 1 12 0 38 3459 1 12 0 28 1190 0 12 0 40 3182 1 12 0 35 3352 0 12 0 39 2898 0 12 0 40 3154 1 12 1 28 1165 0 12 1 39 2102 1 12 0 40 3110 0 12 1 34 2367 1 12 0 39 3892 1 12 1 40 3011 1 12 0 37 3125 1 12 0 41 3490 1 12 1 38 2955 1 12 0 41 3977 0 12 0 42 3665 1 12 0 39 3355 1 12 0 40 3182 0 12 0 40 3239 1 12 0 40 2955 1 12 0 39 3182 1 12 0 38 2784 0 12 0 40 3802 1 12 1 40 3270 1 12 0 39 2225 0 12 1 36 3182 1 12 0 41 3210 0 12 0 39 3779 1 12 0 36 2415 0 12 0 40 4675 1 12 0 38 3352 1 12 1 38 2900 0 12 0 41 3864 1 12 0 40 2841 0 12 0 40 2557 0 12 0 40 3494 1 12 0 40 3438 1 12 0 36 2445 1 12 0 42 3714 1 12 0 42 2935 0 12 0 41 3381 0 12 0 41 3864 1 12 1 40 3240 1 12 0 40 2980 1 12 1 38 2710 1 12 0 40 3570 1 12 0 42 3864 0 12 1 39 2500 1 12 0 28 1160 1 12 0 39 2443 1 12 0 43 3040 0 12 0 41 3864 1 12 0 42 3205 0 12 0 33 1875 1 12 0 39 3835 0 12 1 39 2841 1 12 0 36 2926 1 12 0 40 2983 0 12 1 40 3267 1 12 1 38 2890 1 12 0 38 2443 0 12 0 38 3352 0 12 0 39 3438 1 12 1 38 3120 0 12 0 40 4176 0 12 0 42 4148 1 12 0 40 3270 0 12 0 40 3523 0 12 0 42 3020 0 12 0 38 3620 1 12 0 42 3300 0 12 0 37 3779 0 12 1 41 3665 0 12 1 40 2869 1 12 0 38 3154 0 12 1 39 2810 1 12 1 34 2131 0 12 0 40 4233 0 12 0 38 3860 1 12 0 40 3920 0 12 0 42 3693 1 12 0 40 3580 1 12 0 40 3608 1 12 1 37 2330 0 12 1 40 2813 1 12 0 37 3068 1 12 0 39 3250 1 12 0 39 3175 1 12 0 39 2940 0 12 0 41 3267 1 12 0 40 3645 0 12 0 41 4205 0 12 0 34 1818 0 12 0 35 2983 1 12 0 40 2756 0 12 0 39 2642 0 12 0 41 3182 0 12 0 41 3125 0 12 0 38 3949 0 12 0 39 3466 1 12 1 38 3250 0 12 0 40 3750 0 12 0 40 3466 0 12 1 40 2557 0 12 0 40 3722 0 12 0 40 3551 1 12 0 39 3097 1 12 0 35 1763 1 12 0 40 3610 1 12 0 39 3551 0 12 1 40 2460 1 12 0 36 2358 0 12 0 40 3779 1 12 1 40 2671 1 12 0 38 3210 0 12 0 40 4375 1 12 0 40 4160 1 12 0 40 2841 0 12 0 40 3352 1 12 1 38 2790 1 12 0 42 2540 1 12 0 39 2890 1 12 1 40 3182 0 12 0 38 2557 0 12 0 40 3551 1 12 1 40 3324 1 12 0 39 2620 1 12 0 38 3068 1 12 0 38 3150 1 12 0 40 3636 1 12 0 40 3130 0 12 0 35 2784 1 12 0 40 3520 0 12 0 40 4205 1 12 0 38 3210 0 12 0 42 3523 1 12 0 38 3125 1 12 1 39 3097 1 12 1 39 3125 1 12 0 40 2930 0 12 0 39 3665 0 12 0 40 3693 1 12 0 41 3835 1 12 0 40 3636 1 12 0 43 4165 1 12 1 38 3035 1 12 0 37 2000 1 12 1 39 2740 0 12 0 38 3608 0 12 0 39 3750 0 12 0 40 4091 1 12 0 40 3267 1 12 0 41 3097 1 12 1 40 3381 1 12 0 39 3040 0 12 0 42 3779 0 12 1 40 3523 0 12 0 40 3551 0 12 0 40 3636 0 12 1 38 3154 1 12 1 38 2841 1 12 0 37 2960 1 12 0 40 3097 1 12 0 39 3600 0 12 1 38 3409 1 12 0 39 2880 1 12 0 39 2690 0 12 0 41 3090 0 12 0 40 3409 1 12 1 32 2159 1 12 0 40 3921 0 12 0 40 2940 1 12 0 40 3400 1 12 0 40 3239 0 12 0 40 3636 1 12 0 40 4347 1 12 1 38 2977 1 12 0 42 3636 0 12 0 39 2671 0 12 0 40 3949 0 12 0 37 3608 1 12 0 40 3710 0 12 1 35 2216 1 12 0 36 2500 1 12 0 29 1450 0 12 0 40 3296 1 12 0 38 3510 1 12 0 41 3950 0 12 0 39 4176 1 12 1 40 3636 1 12 0 40 3125 0 12 0 38 3845 1 12 1 41 3523 0 12 0 39 2784 0 12 0 40 3494 0 12 0 42 4233 0 12 0 40 3779 1 12 0 39 3720 1 12 0 38 3302 0 12 0 41 3693 1 12 0 35 2580 1 12 0 38 2597 0 12 0 38 2926 0 12 0 41 4505 0 12 0 41 3977 1 12 0 41 3182 1 12 1 36 2160 1 12 0 39 3402 0 12 0 40 3239 0 12 0 39 3608 0 12 0 38 3267 0 12 0 39 3995 1 12 1 39 3068 0 12 0 40 4065 1 12 0 38 3346 0 12 0 40 3154 1 12 1 33 2515 0 12 0 39 3182 0 12 1 36 2443 0 12 0 40 3807 1 12 1 38 3130 0 12 1 40 3722 1 12 0 40 2727 0 12 1 40 3011 1 12 0 29 793 1 12 0 41 3494 1 12 0 35 2386 0 12 0 40 3466 0 12 0 38 2760 0 12 0 41 4659 0 12 0 39 3608 1 12 0 38 2557 0 12 0 40 2671 0 12 0 38 3740 1 12 1 33 2614 0 12 0 40 3949 1 12 0 39 3630 0 12 0 39 2898 1 12 0 40 3940 1 12 0 23 701 0 12 0 40 3977 1 12 1 34 2472 1 12 1 37 3011 1 12 0 39 3352 0 12 0 41 3494 0 12 0 40 4091 1 12 0 38 3530 1 12 1 36 3010 0 12 0 40 3068 1 12 0 42 3636 1 12 1 41 3665 1 12 0 42 3430 1 12 0 38 3182 1 12 0 39 3300 1 12 0 37 2557 1 12 0 37 2550 0 12 1 40 3097 0 12 0 40 3154 1 12 0 40 3625 1 12 1 40 3330 0 12 0 40 4347 0 12 0 39 3296 1 12 0 39 3154 1 12 0 41 4050 0 12 0 36 3239 0 12 0 40 3608 1 12 0 36 2500 0 12 0 41 3438 0 12 0 40 3977 1 12 1 38 2780 1 12 1 36 2189 1 12 1 34 1760 1 12 0 35 2360 0 12 1 40 3608 1 12 0 40 3892 1 12 0 39 2870 1 12 1 38 2926 0 12 0 40 3905 1 12 0 39 3020 0 12 0 39 3779 0 12 0 40 3381 1 12 0 39 3280 1 12 0 41 3154 0 12 0 40 3494 1 12 1 40 3523 0 12 0 32 1861 1 12 1 40 3360 0 12 0 40 3977 1 12 1 38 3296 0 12 0 38 3523 1 12 0 40 3693 1 12 0 39 3921 1 12 1 26 653 1 12 1 40 3494 1 12 0 41 3500 1 12 0 38 2863 0 12 0 40 3494 1 12 0 38 3335 0 12 0 40 3750 0 12 0 42 3636 1 12 1 39 4190 1 12 0 39 3540 1 12 1 40 3381 1 12 0 39 3580 1 12 0 32 2813 0 12 0 41 3494 1 12 0 42 4065 0 12 0 38 3520 1 12 0 39 3551 0 12 0 40 4063 1 12 0 39 3210 1 12 0 40 3640 0 12 1 40 2770 1 12 1 41 3750 1 12 0 41 3695 1 12 0 41 3305 0 12 0 40 3892 0 12 1 42 3210 1 12 1 40 2851 1 12 0 38 3175 1 12 0 40 3949 0 12 1 37 2727 0 12 0 42 4262 1 12 0 40 3523 0 12 0 36 2955 0 12 0 40 3239 0 12 0 40 3665 0 12 1 39 2960 1 12 0 40 3353 0 12 1 40 3864 1 12 0 39 3125 1 12 0 40 3182 0 12 0 40 3438 0 12 0 40 3921 1 12 0 39 3728 0 12 0 38 2639 1 12 1 40 3324 1 12 0 40 2869 1 12 1 38 3352 1 12 0 38 3040 1 12 0 40 3381 1 12 0 40 4517 0 12 0 40 4375 0 12 0 40 4233 0 12 0 40 2983 1 12 0 33 1720 1 12 1 39 3555 0 12 0 40 3494 1 12 0 39 1932 1 12 1 39 3068 0 12 1 40 2756 1 12 0 40 3864 1 12 1 43 4690 0 12 0 41 3977 0 12 0 39 3864 0 12 1 42 3636 1 12 1 35 3154 0 12 0 39 3352 1 12 0 38 2443 1 12 1 34 2220 1 12 0 39 3240 1 12 0 41 3087 1 12 1 37 2955 1 12 0 39 2931 1 12 0 38 3350 0 12 1 41 3324 0 12 0 42 3438 1 12 0 42 3438 1 12 0 39 3430 0 12 0 39 3750 1 12 0 40 3520 1 12 1 40 3480 0 12 1 40 2955 0 12 0 43 3722 0 12 0 41 2898 0 12 0 38 3665 1 12 0 41 3324 1 12 0 40 2841 1 12 1 41 2690 1 12 1 40 3780 0 12 0 28 1165 1 12 0 40 3381 0 12 1 40 1900 1 12 0 38 2205 1 12 0 43 2790 1 12 0 38 3380 1 12 0 38 3060 0 12 0 40 3068 1 12 0 39 3280 1 12 1 39 2443 0 12 0 38 3750 0 12 0 39 3125 1 12 0 37 3799 1 12 0 39 3665 0 12 0 39 3409 0 12 0 40 3011 1 12 0 41 3930 0 12 0 42 3267 0 12 1 39 3693 1 12 0 40 3438 0 12 0 37 2557 1 12 0 37 2637 1 12 0 42 4034 0 12 0 38 3381 1 12 1 36 2960 0 12 0 38 3210 1 12 0 39 3572 1 12 1 39 3007 0 12 0 42 3977 0 12 1 41 3693 1 12 0 40 3011 0 12 0 39 2955 1 12 0 41 3296 1 12 0 40 3605 1 12 0 38 3722 1 12 1 38 3267 0 12 0 40 3608 1 12 0 39 2983 1 12 0 34 2386 0 12 1 26 682 0 12 0 35 2784 0 12 1 38 2528 1 12 0 30 1446 1 12 0 39 3480 1 12 0 38 2727 1 12 0 40 3267 1 12 0 38 2955 1 12 0 40 3068 0 12 0 34 2330 0 12 1 40 3154 1 12 0 37 3779 0 12 0 40 3714 1 12 0 39 3135 1 12 0 36 2188 1 12 0 38 4040 0 12 1 33 1750 0 12 0 42 4205 1 12 0 39 3335 1 12 0 27 1051 0 12 0 40 2898 0 12 0 42 3580 1 12 0 36 2860 1 12 0 41 3355 0 12 1 40 2699 1 12 1 39 2000 0 13 0 40 3154 0 13 0 38 3494 1 13 0 37 3210 1 13 1 24 521 1 13 0 39 2898 0 13 1 39 2386 1 13 0 38 3154 1 13 0 39 3220 1 13 0 40 3510 0 13 0 41 3608 1 13 0 38 3225 0 13 0 40 3494 1 13 0 38 2671 0 13 1 40 2841 1 13 0 41 3466 0 13 0 40 3949 1 13 1 39 3095 1 13 0 40 2784 1 13 0 37 3523 1 13 0 40 4240 1 13 0 41 3550 1 13 0 39 3296 1 13 1 40 3170 1 13 0 37 3693 1 13 0 39 3551 1 13 0 39 3100 1 13 1 27 1095 1 13 0 40 3523 0 13 0 41 3750 1 13 1 40 3182 1 13 1 38 2617 1 13 0 34 1421 1 13 1 38 2869 0 13 0 41 3939 1 13 0 40 3494 1 13 0 40 3608 1 13 0 38 3210 1 13 0 33 1920 0 13 0 38 2898 1 13 0 40 2813 1 13 0 39 2825 1 13 0 42 4460 1 13 0 39 2975 1 13 1 39 2810 0 13 0 40 3864 1 13 1 39 2869 1 13 0 40 3977 0 13 0 41 3665 1 13 0 40 3494 1 13 0 41 3180 1 13 0 40 3040 0 13 0 39 4091 1 13 0 38 3182 1 13 0 40 3296 1 13 0 40 4091 0 13 0 41 3779 0 13 1 20 410 1 13 0 40 3750 1 13 0 40 3010 1 13 0 40 3324 1 13 0 37 2926 1 13 0 38 3580 1 13 0 40 3636 1 13 0 40 3324 1 13 0 39 3324 1 14 0 40 3352 0 14 0 40 3352 0 14 0 37 3125 0 14 0 41 3448 0 14 0 41 3494 1 14 0 40 3608 1 14 0 40 3779 1 14 0 40 3860 0 14 0 40 3921 0 14 0 36 2642 1 14 0 40 3285 1 14 0 40 3835 1 14 0 40 3250 0 14 0 40 3210 1 14 0 40 3892 0 14 0 42 4546 0 14 0 38 3864 1 14 0 38 3210 1 14 0 41 3420 1 14 0 40 3154 1 14 0 41 2472 0 14 0 41 3665 0 14 0 38 3580 0 14 0 40 3381 0 14 0 41 3125 0 14 0 36 2880 1 14 0 38 2600 0 14 0 40 3835 1 14 1 40 3612 1 14 0 21 320 1 14 1 40 3244 1 14 1 40 3296 0 14 0 40 3693 0 14 0 40 3679 1 14 0 40 3450 1 14 0 39 3977 1 14 0 39 3154 1 14 0 39 4034 1 14 0 42 3160 1 14 0 22 682 1 14 0 38 3045 0 14 0 40 3409 1 14 0 40 3352 1 14 1 40 3665 1 14 1 40 3020 1 14 0 40 2557 0 14 0 40 4404 1 14 0 39 2840 1 14 1 40 3500 0 14 0 40 4063 0 14 0 40 4574 1 14 0 40 4006 0 14 1 40 3636 1 14 0 42 3494 1 14 0 41 3210 1 14 0 40 3409 1 14 0 37 2585 1 14 1 38 2555 0 14 0 41 3523 1 14 0 37 3155 0 14 1 40 3381 1 14 0 40 2983 0 14 1 42 3892 1 14 0 40 3324 0 14 0 40 3296 0 14 0 37 2443 0 14 1 39 3580 0 14 0 40 2784 0 14 1 39 3835 0 14 0 40 3721 0 14 0 39 3210 0 14 0 40 3892 1 14 0 39 2841 1 14 0 38 3068 1 14 0 36 2690 1 14 0 40 3025 0 14 0 39 4063 0 14 0 39 3551 1 14 0 38 3625 1 14 1 38 3210 1 14 0 38 2955 1 14 0 39 3230 1 14 0 39 3154 0 15 0 40 3438 1 15 0 40 4404 1 15 0 39 3615 0 15 0 40 4659 1 15 1 40 2841 0 15 0 40 4006 1 15 0 39 2869 1 15 0 22 470 0 15 0 40 3296 1 15 0 40 3728 0 15 0 39 3068 0 15 0 38 2585 1 15 0 39 3296 0 15 0 40 3438 0 15 0 41 3068 1 15 0 39 3438 1 15 0 35 3040 0 15 0 39 3523 1 15 0 41 2926 1 15 0 37 2301 0 15 0 39 3409 1 15 0 40 3324 0 15 0 38 3580 1 15 0 42 3722 1 15 0 38 2869 0 15 0 42 4119 1 15 1 40 3540 0 16 0 39 3352 1 16 1 38 4347 0 16 0 38 3097 1 16 0 39 4091 1 16 0 40 3381 0 16 0 40 2727 0 16 0 40 3864 0 16 0 42 2869 1 16 0 37 3523 0 16 0 39 2528 1 16 0 38 3324 1 16 1 39 2983 0 16 0 42 4148 1 16 0 35 2760 1 16 0 41 4480 0 16 0 39 3015 1 16 0 40 2898 1 16 0 41 3720 1 16 0 38 4176 0 16 0 42 3608 0 16 0 36 2756 1 16 1 38 2950 0 16 0 42 3210 0 16 0 40 2926 1 16 0 40 3154 0 16 0 39 3090 1 16 0 39 3324 0 16 0 39 2955 1 16 0 41 4432 0 16 0 39 3864 0 16 0 37 3068 1 16 0 39 3665 0 16 0 41 3352 1 16 0 39 2815 0 16 0 40 3630 0 16 0 39 3040 1 16 0 38 3011 0 16 0 38 3523 0 16 0 39 3494 0 16 1 38 3466 0 16 0 41 3921 0 16 0 42 3835 1 16 0 41 3381 0 16 0 40 3494 1 16 0 38 4100 0 16 0 39 3779 0 16 0 40 3864 0 16 0 39 3324 1 16 0 37 2479 0 16 0 39 3693 0 16 0 40 4148 0 16 0 40 3210 1 16 0 41 3458 0 16 0 40 4347 1 16 1 38 3296 1 16 0 37 2720 0 16 0 41 3636 0 16 0 35 3665 0 16 0 39 3551 1 16 0 39 4830 0 16 0 41 3864 1 16 0 40 3210 1 16 0 40 3807 0 16 0 38 4460 0 16 0 40 3608 1 16 0 38 3502 0 16 0 39 3239 0 16 0 41 3892 1 16 0 37 2995 0 16 0 39 3665 0 16 1 40 3381 0 16 1 41 3466 0 16 0 40 3892 0 16 0 41 3438 1 16 0 40 2955 0 16 0 41 3190 0 16 0 39 3060 1 16 0 39 2699 0 16 0 40 4375 0 16 0 40 3722 0 16 0 38 3580 1 16 0 39 3494 0 16 0 41 3835 1 16 0 40 3750 0 16 0 39 3152 1 17 0 38 3523 0 17 0 40 3636 0 17 0 38 3523 1 17 0 39 2727 0 17 0 38 3040 0 17 0 38 3182 1 17 0 40 3494 1 17 0 39 2813 0 17 0 40 2727 1 17 0 39 3875 0 17 0 40 3921 0 17 0 39 2642 0 17 0 39 2410 1 17 0 31 997 0 17 0 38 3409 1 17 0 39 2557 0 17 0 41 3523 0 17 0 41 3921 0 17 0 42 3486 0 17 0 39 3739 0 17 0 38 3864 0 17 0 39 3267 0 17 0 39 3523 0 17 0 37 3011 1 17 0 33 1875 0 17 0 36 2330 0 17 0 41 3351 0 17 0 40 4006 0 17 0 38 2671 0 17 0 40 2784 1 17 0 38 2700 0 17 0 38 3324 0 17 0 41 3660 0 17 0 40 3125 1 17 0 40 3466 0 17 0 37 3197 0 17 0 39 3296 1 17 0 38 3415 1 17 0 40 3320 1 17 0 40 3315 0 17 0 40 3068 0 17 0 40 4318 0 17 0 40 4034 0 17 0 40 3608 0 17 0 40 3352
Names of X columns:
black edu smoke gestate grams
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
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) 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, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) 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,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) 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,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) 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,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) 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,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) 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
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation