Nemsova W72

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Tue, 08 Jan 2008 06:44:26 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg.htm/, Retrieved Tue, 08 Jan 2008 14:44:28 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

209 71 3 283 3810 216 71 3 469 3558 216 71 1 536 3397 223 72 4 781 3407 214 72 3 1056 3646 214 72 4 1032 3614 214 72 2 942 3621 214 72 4 918 3607 215 72 -1 903 3642 220 72 -3 781 3600 205 72 -4 763 3758 217 72 -2 719 3510 220 72 -1 768 3576 220 72 -1 768 3579 219 72 0 762 3599 223 72 1 734 3552 224 72 3 715 3520 204 72 3 719 3732 206 72 5 711 3556 204 72 6 725 3596 205 72 6 719 3587 210 72 4 724 3560 210 72 5 760 3594 209 72 7 767 3599 221 72 7 791 3539 223 72 7 758 3563 223 72 10 768 3579 223 72 6 762 3586 225 72 2 757 3561 214 72 1 747 3650 201 72 1 744 3672 194 72 3 715 3675 210 72 8 703 3543 211 72 6 796 3615 210 72 7 831 3736 229 72 3 827 3549 226 72 -1 795 3600 226 72 -1 852 3674 226 72 -3 882 3660 226 66 -6 894 3696 226 66 -4 905 3712 226 66 -1 922 3719 224 66 2 1012 3747 224 66 4 991 3695 224 66 3 918 3680 224 72 5 901 3638 226 72 4 901 3583 225 72 8 901 3614 226 72 2 901 3610 183 72 5 901 3929 208 72 3 901 3676 197 72 3 901 3809 213 72 7 901 3635 214 72 4 901 3633 213 72 5 901 3633 213 72 6 901 3630 213 72 7 901 3634 178 72 6 901 3943 200 72 7 901 3620 198 72 5 901 3657 197 72 5 901 3669 198 72 4 901 3653 198 72 4 901 3658 200 72 4 901 3527 230 72 4 901 3432 230 72 5 901 3468 231 72 4 901 3572 231 72 3 901 3577 231 72 2 901 3584 230 72 2 901 3592 231 72 1 901 3584 231 72 3 901 3585 229 72 3 901 3593 222 72 6 766 3547 218 72 8 766 3569 218 72 4 766 3570 218 72 5 766 3573 235 72 5 766 3459 218 72 8 766 3667 218 72 11 766 3567 233 72 8 766 3482 232 72 8 766 3535 232 72 8 766 3533 232 72 8 766 3532 233 72 9 766 3523 232 72 10 766 3535 228 72 9 766 3589 210 72 6 766 3673 212 72 7 766 3562 212 72 6 766 3588 211 72 11 766 3580 224 72 4 766 3476 220 72 3 766 3601 218 72 3 766 3593 218 72 5 766 3601 220 72 5 766 3574 211 72 7 766 3755 220 72 9 766 3460 219 72 8 766 3471 220 72 9 766 3449 223 72 11 766 3551 223 72 8 766 3553 213 72 10 766 3649 214 72 11 766 3598 214 72 12 701 3573 214 72 11 701 3573 215 72 11 701 3568 227 72 7 701 3439 224 72 8 701 3501 224 72 11 701 3505 224 72 10 701 3508 224 72 9 701 3508 224 72 11 701 3506 218 72 12 701 3609 217 72 13 701 3564 231 72 14 701 3446 221 72 16 701 3540 220 72 16 701 3521 221 72 14 701 3522 220 72 15 701 3534 223 72 15 701 3524 225 72 11 701 3482 231 72 8 701 3469 229 72 9 701 3544 227 72 10 701 3528 227 72 10 701 3516 219 72 13 701 3571 218 72 15 701 3541 226 72 15 701 3472 227 72 15 701 3496 227 72 17 701 3486 227 72 18 701 3502 227 72 13 701 3525 227 72 11 701 3521 228 72 8 813 3551 183 72 9 813 3845 162 72 12 813 3835 169 72 16 813 3775 169 72 13 813 3795 168 72 13 813 3807 180 72 18 813 3677 181 72 15 813 3747 183 72 11 813 3713 184 72 15 813 3705 182 72 19 813 3735 182 72 14 813 3726 182 72 19 813 3732 189 67 25 813 3649 189 67 19 813 3681 213 67 12 813 3573 216 67 12 813 3582 215 67 13 813 3595 215 67 18 813 3598 215 67 21 813 3596 215 67 22 813 3597 214 67 22 813 3609 214 67 23 813 3614 214 67 23 813 3621 214 67 25 813 3613 214 67 25 813 3619 214 67 24 813 3619 214 67 18 813 3603 214 67 17 813 3620 214 67 14 813 3609 214 67 16 813 3591 227 72 18 817 3515 228 72 18 815 3558 228 72 17 823 3556 227 72 21 855 3572 224 72 20 852 3585 233 72 20 864 3516 234 72 20 870 3541 234 72 22 864 3536 234 72 23 813 3526 234 72 22 833 3527 233 73 23 821 3537 225 72 24 765 3567 205 72 21 726 3646 218 72 22 758 3535 219 72 23 802 3558 219 72 22 958 3599 219 72 20 966 3597 218 72 21 982 3609 225 72 22 910 3535 227 72 25 968 3552 222 72 22 1000 3582 222 72 22 933 3535 221 72 20 977 3557 221 72 21 978 3561 221 72 23 992 3566 221 72 17 963 3559 221 72 18 763 3511 221 72 16 781 3525 220 72 18 770 3530 221 72 18 779 3527 221 72 24 776 3525 221 72 21 814 3537 221 72 22 846 3579 228 72 16 933 3520 230 72 15 877 3496 230 72 17 879 3504 231 72 19 786 3480 231 72 21 786 3475 219 72 20 771 3564 218 70 15 771 3555 217 70 17 745 3553 228 70 16 752 3498 219 70 19 745 3593 217 70 23 755 3556 217 70 26 753 3561 217 70 27 872 3609 217 70 27 802 3575 227 70 30 718 3473 228 70 31 763 3523 227 70 29 757 3542 227 70 28 815 3557 227 70 25 816 3564 227 70 23 761 3543 227 70 21 869 3572 227 70 20 862 3546 227 70 21 826 3503 227 70 23 797 3499 227 70 23 900 3510 228 70 22 831 3508 227 70 17 769 3487 215 70 17 775 3579 218 67 18 867 3562 218 67 20 873 3574 217 67 21 958 3609 217 67 20 975 3593 218 67 20 987 3555 201 67 23 939 3747 220 67 23 942 3521 210 66 25 771 3705 223 65 23 833 3508 211 65 22 899 3705 211 65 22 767 3645 212 65 19 781 3640 221 63 22 772 3566 222 63 22 780 3619 224 63 25 760 3585 224 63 27 790 3602 237 63 22 777 3510 236 63 22 788 3565 236 63 19 803 3570 237 63 20 821 3568 232 63 20 804 3588 229 63 22 802 3597 229 63 25 767 3567 228 63 24 803 3635 228 63 23 829 3666 227 63 23 761 3630 225 63 22 771 3617 226 63 18 790 3588 226 63 13 775 3581 237 63 16 796 3546 234 63 17 802 3605 233 63 16 808 3602 233 63 16 784 3562 234 63 16 789 3561 234 63 12 793 3571 229 61 9 777 3622 229 61 10 791 3573 233 61 11 790 3557 235 61 15 802 3572 235 61 15 787 3566 235 61 13 786 3564 235 61 12 844 3582 235 61 12 768 3567 235 61 14 804 3574 235 61 14 774 3567 235 61 13 821 3573 235 61 13 790 3569 230 61 17 764 3603 229 61 16 781 3601 230 61 12 798 3592 230 61 9 772 3584 240 61 11 780 3514 242 61 12 815 3552 242 61 14 845 3563 242 61 13 820 3555 236 61 14 794 3576 236 61 14 774 3566 236 61 15 880 3604 230 61 12 761 3590 230 61 12 957 3694 229 61 14 1029 3717 217 61 14 977 3744 217 61 16 967 3693 217 61 16 891 3661 218 61 15 1029 3707 234 61 14 1029 3601 234 61 11 1052 3639 234 61 9 993 3610 229 61 9 967 3604 229 61 9 1008 3591 221 61 10 1118 3770 220 61 9 936 3705 220 61 8 881 3676 220 61 7 898 3682 220 61 5 901 3683 228 61 6 934 3668 229 61 6 956 3672 230 61 7 944 3648 231 61 8 958 3640 232 61 4 914 3627 231 61 4 970 3634 231 61 4 952 3616 232 61 6 1007 3627 232 61 6 1024 3610 232 61 10 1008 3593 232 61 8 1033 3601 231 61 10 1024 3603 241 65 9 1031 3462 232 65 9 1039 3607 232 65 12 994 3590 227 65 10 983 3603 227 65 7 941 3550 227 65 5 957 3555 227 65 7 974 3558 227 65 10 1014 3578 227 65 9 972 3560 223 65 3 1027 3658 222 65 -1 1013 3592 222 65 3 1007 3617 222 65 5 1017 3637 222 65 4 995 3631 222 65 2 1039 3652 222 65 0 992 3636 221 61 0 927 3640 220 61 1 881 3643 193 61 0 1023 3978 193 58 1 991 3822 208 58 -2 978 3723 208 58 -2 985 3765 208 58 1 1063 3774 209 58 2 996 3762 208 58 3 1017 3782 208 58 4 1020 3794 201 58 8 1008 3850 224 58 9 1014 3648 229 58 7 849 3586 229 58 5 1053 3695 195 58 2 1011 4000 194 58 1 917 3814 194 58 -2 903 3815 194 58 -2 908 3800 188 58 -2 982 3949 188 58 1 1007 3874 189 58 3 997 3858 188 58 5 1009 3877 183 58 4 969 3919 178 58 3 951 3923 177 58 3 954 3905 185 58 3 949 3810 185 58 3 945 3839 185 58 2 958 3859 185 58 4 986 3855 169 58 4 1043 4143 193 58 4 1044 3701 196 58 2 1054 3800 208 58 -2 1053 3748 209 58 0 1033 3763 209 58 -2 1051 3767 229 58 -4 1034 3645 228 58 -2 1087 3681 228 58 -3 1091 3669 241 58 -4 1125 3619 240 58 -5 1161 3697 240 58 -7 1098 3666 242 58 -7 1079 3616 243 58 -5 1034 3597 239 58 -5 1094 3665 238 58 -6 1066 3622 239 58 -5 1022 3600 239 58 -5 1037 3632 239 58 -8 1027 3652 239 58 -6 993 3637 239 58 -3 1031 3639 239 58 -3 986 3652 235 58 -3 988 3640 235 58 -3 1022 3606 236 58 -3 1017 3593 236 58 -2 983 3583 236 58 0 1086 3621

Text written by user:

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation SEC[t] = + 5062.21461870763 -4.88046360527164Pull[t] -7.31041697649466Cullet[t] -1.60162109871740Temp[t] + 0.152671025334581Boost[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5062.21461870763 67.803628 74.6599 0 0 Pull -4.88046360527164 0.165141 -29.5534 0 0 Cullet -7.31041697649466 0.555976 -13.1488 0 0 Temp -1.60162109871740 0.30794 -5.2011 0 0 Boost 0.152671025334581 0.026698 5.7184 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.88538171738402 R-squared 0.783900785477878 Adjusted R-squared 0.78162605690396 F-TEST (value) 344.612888968991 F-TEST (DF numerator) 4 F-TEST (DF denominator) 380 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 47.6361713199635 Sum Squared Residuals 862297.830849468 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3810 3561.55915674826 248.440843251738 2 3558 3555.7927222236 2.2072777763973 3 3397 3569.22492311845 -172.224923118453 4 3407 3560.35079881588 -153.350798815878 5 3646 3647.86112432905 -1.86112432904975 6 3614 3642.5953986223 -28.5953986223024 7 3621 3632.05824853962 -11.0582485396249 8 3607 3625.19090173416 -18.1909017341601 9 3642 3626.02847824246 15.9715217575432 10 3600 3586.20353732271 13.7964626772855 11 3758 3658.26403404448 99.735965955516 12 3510 3589.77770346907 -79.777703469068 13 3576 3581.01557179593 -5.0155717959301 14 3579 3581.01557179593 -2.01557179593010 15 3599 3583.37838815048 15.6216118495231 16 3552 3557.98012392130 -5.98012392130463 17 3520 3546.99566863724 -26.9956686372411 18 3732 3645.21562484401 86.7843751559878 19 3556 3631.03008723336 -75.0300872333576 20 3596 3641.32678769987 -45.3267876998676 21 3587 3635.53029794259 -48.5302979425884 22 3560 3615.09457724034 -55.0945772403379 23 3594 3618.98911305367 -24.9891130536655 24 3599 3621.73503163884 -22.7350316388444 25 3539 3566.83357298361 -27.8335729836147 26 3563 3552.03450193703 10.9654980629698 27 3579 3548.75634889422 30.2436511057762 28 3586 3554.24680713709 31.7531928629141 29 3561 3550.12900919474 10.8709908052607 30 3650 3603.8890196981 46.1109803019011 31 3672 3666.87703349063 5.12296650937348 32 3675 3693.40957679539 -18.4095767953904 33 3543 3605.48200131344 -62.4820013134421 34 3615 3618.00318526172 -3.00318526172135 35 3736 3626.62551365499 109.374486345014 36 3549 3539.69250544836 9.30749455164396 37 3600 3555.85490784833 44.145092151666 38 3674 3564.55715629241 109.442843707595 39 3660 3572.34052924988 87.6594707501227 40 3696 3622.83994670901 73.1600532909875 41 3712 3621.31608579026 90.683914209742 42 3719 3619.10662992479 99.8933700752062 43 3747 3637.8030861193 109.196913880703 44 3695 3631.39375238984 63.6062476101638 45 3680 3621.85038863913 58.1496113608709 46 3638 3572.18923715204 65.8107628479615 47 3583 3564.02993104021 18.9700689597874 48 3614 3562.50391025061 51.4960897493854 49 3610 3567.23317323765 42.7668267623526 50 3929 3772.28824496818 156.711755031824 51 3676 3653.47989703382 22.5201029661805 52 3809 3707.16499669181 101.835003308192 53 3635 3622.67109461259 12.3289053874083 54 3633 3622.59549430347 10.4045056965277 55 3633 3625.87433681003 7.12566318997349 56 3630 3624.27271571131 5.72728428869089 57 3634 3622.67109461259 11.3289053874083 58 3943 3795.08894189582 147.911058104184 59 3620 3686.11712148112 -66.117121481123 60 3657 3699.0812908891 -42.0812908891011 61 3669 3703.96175449437 -34.9617544943727 62 3653 3700.68291198782 -47.6829119878185 63 3658 3700.68291198782 -42.6829119878185 64 3527 3690.92198477728 -163.921984777275 65 3432 3544.50807661913 -112.508076619126 66 3468 3542.90645552041 -74.9064555204086 67 3572 3539.62761301385 32.3723869861456 68 3577 3541.22923411257 35.7707658874282 69 3584 3542.83085521129 41.1691447887108 70 3592 3547.71131881656 44.2886811834392 71 3584 3544.43247631001 39.5675236899934 72 3585 3541.22923411257 43.7707658874282 73 3593 3550.99016132311 42.0098386768849 74 3547 3559.73795484370 -12.7379548436959 75 3569 3576.05656706735 -7.05656706734764 76 3570 3582.46305146222 -12.4630514622172 77 3573 3580.8614303635 -7.86143036349984 78 3459 3497.89354907388 -38.8935490738820 79 3667 3576.05656706735 90.9434329326524 80 3567 3571.25170377120 -4.25170377119545 81 3482 3502.84961298827 -20.8496129882730 82 3535 3507.73007659354 27.2699234064553 83 3533 3507.73007659354 25.2699234064553 84 3532 3507.73007659354 24.2699234064553 85 3523 3501.24799188956 21.7520081104444 86 3535 3504.52683439611 30.4731656038901 87 3589 3525.65030991591 63.3496900840862 88 3673 3618.30351810696 54.6964818930444 89 3562 3606.94096979769 -44.9409697976949 90 3588 3608.54259089641 -20.5425908964123 91 3580 3605.41494900810 -25.4149490080969 92 3476 3553.18026983059 -77.1802698305874 93 3601 3574.30374535039 26.6962546496086 94 3593 3584.06467256093 8.93532743906537 95 3601 3580.8614303635 20.1385696365002 96 3574 3571.10050315296 2.89949684704344 97 3755 3611.82143340297 143.178566597033 98 3460 3564.69401875809 -104.694018758087 99 3471 3571.17610346208 -100.176103462076 100 3449 3564.69401875809 -115.694018758087 101 3551 3546.84938574484 4.15061425516274 102 3553 3551.65424904099 1.34575095901055 103 3649 3597.25564289627 51.7443571037289 104 3598 3590.77355819228 7.22644180771799 105 3573 3579.24832044682 -6.24832044681682 106 3573 3580.84994154553 -7.84994154553423 107 3568 3575.96947794026 -7.96947794026258 108 3439 3523.81039907187 -84.8103990718725 109 3501 3536.85016878897 -35.85016878897 110 3505 3532.04530549282 -27.0453054928178 111 3508 3533.64692659154 -25.6469265915352 112 3508 3535.24854769025 -27.2485476902526 113 3506 3532.04530549282 -26.0453054928178 114 3609 3559.72646602573 49.2735339742697 115 3564 3563.00530853228 0.994691467715487 116 3446 3493.07719695976 -47.0771969597642 117 3540 3538.67859081505 1.32140918495423 118 3521 3543.55905442032 -22.5590544203174 119 3522 3541.88183301248 -19.8818330124806 120 3534 3545.16067551903 -11.1606755190348 121 3524 3530.51928470322 -6.51928470321988 122 3482 3527.16484188755 -45.1648418875462 123 3469 3502.68692355207 -33.6869235520685 124 3544 3510.84622966389 33.1537703361056 125 3528 3519.00553577572 8.9944642242797 126 3516 3519.00553577572 -3.00553577572031 127 3571 3553.24438132174 17.7556186782588 128 3541 3554.92160272958 -13.9216027295781 129 3472 3515.87789388740 -43.877893887405 130 3496 3510.99743028213 -14.9974302821333 131 3486 3507.7941880847 -21.7941880846985 132 3502 3506.19256698598 -4.19256698598112 133 3525 3514.20067247957 10.7993275204319 134 3521 3517.403914677 3.59608532299709 135 3551 3534.42746920536 16.5725307946434 136 3845 3752.44671034386 92.553289656137 137 3835 3850.13158275842 -15.1315827584152 138 3775 3809.56185312664 -34.5618531266441 139 3795 3814.3667164228 -19.3667164227963 140 3807 3819.24718002807 -12.2471800280680 141 3677 3752.67351127122 -75.6735112712213 142 3747 3752.5979109621 -5.59791096210186 143 3713 3749.24346814643 -36.2434681464282 144 3705 3737.95652014629 -32.9565201462869 145 3735 3741.31096296196 -6.31096296196064 146 3726 3749.31906845555 -23.3190684555476 147 3732 3741.31096296196 -9.31096296196064 148 3649 3734.09007601523 -85.0900760152281 149 3681 3743.69980260753 -62.6998026075325 150 3573 3637.78002377203 -64.7800237720349 151 3582 3623.13863295622 -41.13863295622 152 3595 3626.41747546277 -31.4174754627742 153 3598 3618.40936996919 -20.4093699691872 154 3596 3613.60450667303 -17.6045066730350 155 3597 3612.00288557432 -15.0028855743176 156 3609 3616.88334917959 -7.88334917958928 157 3614 3615.28172808087 -1.28172808087188 158 3621 3615.28172808087 5.71827191912812 159 3613 3612.07848588344 0.921514116562918 160 3619 3612.07848588344 6.92151411656292 161 3619 3613.68010698215 5.31989301784553 162 3603 3623.28983357446 -20.2898335744589 163 3620 3624.89145467318 -4.89145467317626 164 3609 3629.69631796933 -20.6963179693284 165 3591 3626.49307577189 -35.4930757718937 166 3515 3523.90240592479 -8.90240592479255 167 3558 3518.71660026885 39.2833997311483 168 3556 3521.53958957025 34.4604104297542 169 3572 3524.89904159135 47.1009584086456 170 3585 3540.68404042988 44.315959570117 171 3516 3498.59192028645 17.4080797135468 172 3541 3494.62748283319 46.3725171668109 173 3536 3490.50821448375 45.4917855162532 174 3526 3481.12037109297 44.8796289070342 175 3527 3485.77541269837 41.2245873016252 176 3537 3479.91178592442 57.0882140755806 177 3567 3516.11471322563 50.8852867743668 178 3646 3612.57467863917 33.4253213608305 179 3535 3552.41250348263 -17.4125034826274 180 3558 3552.64794389336 5.35205610664004 181 3599 3578.06624494427 20.9337550557280 182 3597 3582.49085534438 14.5091446556165 183 3609 3588.21243425629 20.787565743709 184 3535 3541.45525409658 -6.4552540965823 185 3552 3535.74438305929 16.2556169407075 186 3582 3569.83703719251 12.1629628074905 187 3535 3559.60807849509 -24.6080784950926 188 3557 3574.40930941252 -17.4093094125206 189 3561 3572.96035933914 -11.9603593391378 190 3566 3571.89451149639 -5.89451149638713 191 3559 3577.07677835399 -18.0767783539887 192 3511 3544.94095218835 -33.940952188355 193 3525 3550.89227284181 -25.8922728418123 194 3530 3550.89011297097 -20.8901129709687 195 3527 3547.38368859371 -20.3836885937083 196 3525 3537.3159489254 -12.3159489254002 197 3537 3547.92231118427 -10.9223111842665 198 3579 3551.20616289626 27.7938371037443 199 3520 3539.93502345577 -19.9350234557671 200 3496 3523.22613992520 -27.2261399252047 201 3504 3520.32823977844 -16.3282397784391 202 3480 3498.04612861962 -18.0461286196166 203 3475 3494.84288642218 -19.8428864221818 204 3564 3552.72000540414 11.2799945958599 205 3555 3580.22940845599 -25.2294084559881 206 3553 3577.93718320513 -24.9371832051258 207 3498 3526.9224018232 -28.9224018231972 208 3593 3564.97301379715 28.0269862028522 209 3556 3569.85416686617 -13.8541668661673 210 3561 3564.74396151935 -3.7439615193459 211 3609 3581.31019243544 27.6898075645563 212 3575 3570.62322066202 4.37677933797702 213 3473 3504.18935518505 -31.1893551850496 214 3523 3504.57746662112 18.4225333788833 215 3542 3511.74514627182 30.2548537281844 216 3557 3522.20168683994 34.7983131600612 217 3564 3527.15922116143 36.8407788385745 218 3543 3521.96555696546 21.0344430345417 219 3572 3541.65726989903 30.3427301009721 220 3546 3542.19019382040 3.80980617959676 221 3503 3535.09241580964 -32.0924158096409 222 3499 3527.46171387750 -28.4617138775033 223 3510 3543.18682948697 -33.1868294869651 224 3508 3529.37368623232 -21.3736862323248 225 3487 3532.79665176044 -45.7966517604394 226 3579 3592.27824117571 -13.2782411757065 227 3562 3612.01221452144 -50.0122145214397 228 3574 3609.72499847601 -35.7249984760124 229 3609 3625.98087813601 -16.9808781360060 230 3593 3630.17790666541 -37.1779066654113 231 3555 3627.12949536415 -72.1294953641546 232 3747 3697.96430414156 49.0356958584396 233 3521 3605.6935087174 -84.693508717403 234 3705 3632.49857421697 72.5014257830341 235 3508 3589.03181009311 -81.031810093108 236 3705 3659.27528212717 45.7247178728325 237 3645 3639.122706783 5.87729321699722 238 3640 3641.18450082857 -1.18450082856747 239 3566 3605.70225980995 -39.7022598099486 240 3619 3602.04316440735 16.9568355926464 241 3585 3584.42395339397 0.576046606033465 242 3602 3585.80084195657 16.1991580434308 243 3510 3528.37819725228 -18.3781972522753 244 3565 3534.93804213623 30.0619578637727 245 3570 3542.0329708124 27.9670291876018 246 3568 3538.29896456443 29.7010354355683 247 3588 3560.1058751601 27.8941248398980 248 3597 3571.23868172781 25.7613182721871 249 3567 3561.09033254495 5.90966745504962 250 3635 3573.06857416098 61.9314258390156 251 3666 3578.6396419184 87.3603580815991 252 3630 3573.13847580092 56.861524199079 253 3617 3586.02773436353 30.9722656364725 254 3588 3590.45450463448 -2.45450463448246 255 3581 3596.17254474805 -15.1725447480507 256 3546 3540.88867332594 5.11132667406329 257 3605 3554.84446919504 50.1555308049583 258 3602 3562.24258005104 39.7574199489618 259 3562 3558.57847544301 3.42152455699170 260 3561 3554.46136696441 6.53863303559044 261 3571 3561.47853546062 9.52146453938252 262 3622 3602.86381433076 19.1361856692361 263 3573 3603.39958758673 -30.3995875867306 264 3557 3582.12344104159 -25.1234410415921 265 3572 3567.78808174019 4.21191825980581 266 3566 3565.49801636018 0.50198363982453 267 3564 3568.54858753228 -4.54858753227568 268 3582 3579.0051281004 2.99487189960121 269 3567 3567.40213017497 -0.402130174970615 270 3574 3569.69504488958 4.30495511041925 271 3567 3565.11491412954 1.88508587045669 272 3573 3573.89207341899 -0.89207341898603 273 3569 3569.15927163361 -0.159271633614011 274 3603 3583.1856586064 19.8143413935965 275 3601 3592.26315074108 8.73684925891957 276 3592 3596.38457896137 -4.38457896136627 277 3584 3597.21999559882 -13.2199955988194 278 3514 3546.43348555134 -32.4334855513448 279 3552 3540.41442312879 11.5855768712055 280 3563 3541.7913116914 21.2086883086029 281 3555 3539.57615715675 15.42384284325 282 3576 3563.28787103096 12.7121289690367 283 3566 3560.23445052427 5.76554947572833 284 3604 3574.81595811102 29.1840418889801 285 3590 3590.73575102399 -0.735751023986765 286 3694 3620.65927198956 73.3407280104353 287 3717 3633.32880722149 83.6711927785086 288 3744 3683.95547716735 60.0445228326471 289 3693 3679.22552471657 13.7744752834278 290 3661 3667.62252679114 -6.62252679114406 291 3707 3685.41228578076 21.5877142192380 292 3601 3608.92648919513 -7.92648919513318 293 3639 3617.24278607398 21.7572139260193 294 3610 3611.43843777668 -1.43843777667525 295 3604 3631.87130914433 -27.8713091443343 296 3591 3638.13082118305 -47.1308211830522 297 3770 3692.36672171331 77.6332782866882 298 3705 3671.06267980641 33.9373201935929 299 3676 3664.26739451172 11.7326054882775 300 3682 3668.46442304113 13.5355769588722 301 3683 3672.12567831457 10.8743216854337 302 3668 3636.51849220972 31.481507790283 303 3672 3634.99679116181 37.0032088381939 304 3648 3626.6826541538 21.3173458461979 305 3640 3622.3379638045 17.6620361955028 306 3627 3617.14645947937 9.8535405206264 307 3634 3630.57650050338 3.42349949661821 308 3616 3627.82842204736 -11.8284220473593 309 3627 3628.14162263805 -1.14162263805486 310 3610 3630.73703006874 -20.7370300687427 311 3593 3621.88780926852 -28.8878092685198 312 3601 3628.90782709932 -27.9078270993192 313 3603 3629.21100927914 -26.2110092791448 314 3462 3553.83502359651 -91.8350235965092 315 3607 3598.98056424663 8.01943575336939 316 3590 3587.30550481042 2.69449518957773 317 3603 3613.23168375553 -10.2316837555349 318 3550 3611.62436398763 -61.6243639876346 319 3555 3617.27034259042 -62.2703425904227 320 3558 3616.66250782368 -58.6625078236758 321 3578 3617.96448554091 -39.9644855409069 322 3560 3613.15392357557 -53.1539235755719 323 3658 3650.68241098236 7.31758901763522 324 3592 3659.83196462782 -67.8319646278218 325 3617 3652.50945408095 -35.5094540809448 326 3637 3650.83292213686 -13.8329221368558 327 3631 3649.07578067821 -18.0757806782124 328 3652 3658.99654799037 -6.9965479903688 329 3636 3655.02425199708 -19.0242519970783 330 3640 3679.22276686158 -39.2227668615808 331 3643 3675.47874220274 -32.4787422027443 332 3978 3830.53216624131 147.467833758694 333 3822 3845.97632326137 -23.9763232613665 334 3723 3775.58950914909 -52.5895091490945 335 3765 3776.65820632644 -11.6582063264366 336 3774 3783.76168300638 -9.76168300638174 337 3762 3767.05063960498 -5.05063960497576 338 3782 3773.53557364356 8.4644263564438 339 3794 3772.39196562084 21.6080343791575 340 3850 3798.31667415886 51.6833258411405 341 3648 3685.3804162909 -37.3804162909019 342 3586 3638.99062128177 -52.9906212817725 343 3695 3673.33875264746 21.6612473525381 344 4000 3837.6671954588 162.332804541203 345 3814 3829.79820378134 -15.7982037813358 346 3815 3832.4656727228 -17.4656727228039 347 3800 3833.22902784948 -33.2290278494768 348 3949 3873.80946535587 75.1905346441344 349 3874 3872.82137769308 1.17862230692204 350 3858 3863.21096163703 -5.21096163702574 351 3877 3866.72023534888 10.2797646511225 352 3919 3886.61733346057 32.3826665394301 353 3923 3909.87319412962 13.1268058703770 354 3905 3915.2116708109 -10.2116708108984 355 3810 3875.40460684205 -65.4046068420524 356 3839 3874.79392274071 -35.7939227407141 357 3859 3878.38026716878 -19.380267168781 358 3855 3879.45181368071 -24.4518136807145 359 4143 3966.24147980913 176.758520190868 360 3701 3849.26302430795 -148.263024307947 361 3800 3839.35158594291 -39.3515859429128 362 3748 3787.03983604919 -39.0398360491881 363 3763 3775.90270973979 -12.9027097397901 364 3767 3781.85403039325 -14.8540303932473 365 3645 3684.85259305456 -39.8525930545614 366 3681 3694.62137880513 -13.6213788051311 367 3669 3696.83368400519 -27.8336840051868 368 3619 3640.18009309675 -21.1800930967486 369 3697 3652.15833471278 44.8416652872174 370 3666 3645.74330231414 20.2566976858612 371 3616 3633.08162562224 -17.0816256222385 372 3597 3618.12772367948 -21.1277236794759 373 3665 3646.80983962064 18.1901603793627 374 3622 3649.01713561526 -27.0171356152581 375 3600 3635.81752579655 -35.8175257965475 376 3632 3638.10759117657 -6.10759117656617 377 3652 3641.38574421937 10.6142557806274 378 3637 3632.99168716056 4.008312839438 379 3639 3633.98832282712 5.0116771728761 380 3652 3627.11812668707 24.8818733129323 381 3640 3646.94532315882 -6.94532315882346 382 3606 3652.1361380202 -46.1361380201992 383 3593 3646.49231928825 -53.4923192882547 384 3583 3639.69988332816 -56.6998833281615 385 3621 3652.22175674019 -31.2217567401886

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/1c4su1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/1c4su1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/25nxz1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/25nxz1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/3wn5w1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/3wn5w1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/4wx1t1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/4wx1t1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/506m21199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/506m21199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/6kmav1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/6kmav1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/7bs7h1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/7bs7h1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/8o8fg1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/8o8fg1199799857.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/9br4w1199799857.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jan/08/t1199799855o1rjnvsqd221zeg/9br4w1199799857.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by