| Schiphol: MR - Model 2 | *The author of this computation has been verified* | R Software Module: /rwasp_multipleregression.wasp (opens new window with default values) | Title produced by software: Multiple Regression | Date of computation: Fri, 10 Dec 2010 17:21:25 +0000 | | Cite this page as follows: | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi.htm/, Retrieved Fri, 10 Dec 2010 18:22:52 +0100 | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | Original text written by user: | | | IsPrivate? | No (this computation is public) | | User-defined keywords: | | | Dataseries X: | » Textbox « » Textfile « » CSV « | 1149822 1
1086979 2
1276674 3
1522522 4
1742117 5
1737275 6
1979900 7
2061036 8
1867943 9
1707752 10
1298756 11
1281814 12
1281151 13
1164976 14
1454329 15
1645288 16
1817743 17
1895785 18
2236311 19
2295951 20
2087315 21
1980891 22
1465446 23
1445026 24
1488120 25
1338333 26
1715789 27
1806090 28
2083316 29
2092278 30
2430800 31
2424894 32
2299016 33
2130688 34
1652221 35
1608162 36
1647074 37
1479691 38
1884978 39
2007898 40
2208954 41
2217164 42
2534291 43
2560312 44
2429069 45
2315077 46
1799608 47
1772590 48
1744799 49
1659093 50
2099821 51
2135736 52
2427894 53
2468882 54
2703217 55
2766841 56
2655236 57
2550373 58
2052097 59
1998055 60
1920748 61
1876694 62
2380930 63
2467402 64
2770771 65
2781340 66
3143926 67
3172235 68
2952540 69
2920877 70
2384552 71
2248987 72
2208616 73
2178756 74
2632870 75
2706905 76
3029745 77
3015402 78
3391414 79
3507805 80
3177852 81
3142961 82
2545815 83
2414007 84
2372578 85
2332664 86
2 etc... | | 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!
Multiple Linear Regression - Estimated Regression Equation | Passagiers[t] = + 1239719.44871795 + 11148.1183835657t -72174.3525266157M1[t] -158838.839331234M2[t] + 297898.515969410M3[t] + 429204.502849003M4[t] + 782780.2265707M5[t] + 773356.002923976M6[t] + 1211480.77927725M7[t] + 1183314.60826211M8[t] + 885381.174089069M9[t] + 797206.634652871M10[t] + 110446.229494676M11[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) | 1239719.44871795 | 72513.567327 | 17.0964 | 0 | 0 | t | 11148.1183835657 | 281.517471 | 39.6001 | 0 | 0 | M1 | -72174.3525266157 | 90752.168564 | -0.7953 | 0.42733 | 0.213665 | M2 | -158838.839331234 | 90748.238717 | -1.7503 | 0.081502 | 0.040751 | M3 | 297898.515969410 | 90745.182052 | 3.2828 | 0.001201 | 0.000601 | M4 | 429204.502849003 | 90742.998657 | 4.7299 | 4e-06 | 2e-06 | M5 | 782780.2265707 | 90741.688594 | 8.6265 | 0 | 0 | M6 | 773356.002923976 | 90741.251903 | 8.5227 | 0 | 0 | M7 | 1211480.77927725 | 90741.688594 | 13.3509 | 0 | 0 | M8 | 1183314.60826211 | 90742.998657 | 13.0403 | 0 | 0 | M9 | 885381.174089069 | 90745.182052 | 9.7568 | 0 | 0 | M10 | 797206.634652871 | 90748.238717 | 8.7848 | 0 | 0 | M11 | 110446.229494676 | 91959.740693 | 1.201 | 0.231074 | 0.115537 |
Multiple Linear Regression - Regression Statistics | Multiple R | 0.956090738463255 | R-squared | 0.914109500175212 | Adjusted R-squared | 0.909270598776632 | F-TEST (value) | 188.908478367328 | F-TEST (DF numerator) | 12 | F-TEST (DF denominator) | 213 | p-value | 0 | Multiple Linear Regression - Residual Statistics | Residual Standard Deviation | 275877.929356579 | Sum Squared Residuals | 16211138595993.7 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error | 1 | 1149822 | 1178693.21457489 | -28871.2145748946 | 2 | 1086979 | 1103176.84615384 | -16197.8461538373 | 3 | 1276674 | 1571062.31983806 | -294388.319838056 | 4 | 1522522 | 1713516.42510121 | -190994.425101213 | 5 | 1742117 | 2078240.26720648 | -336123.267206477 | 6 | 1737275 | 2079964.16194332 | -342689.161943320 | 7 | 1979900 | 2529237.05668016 | -549337.056680164 | 8 | 2061036 | 2512219.00404858 | -451183.004048583 | 9 | 1867943 | 2225433.68825912 | -357490.688259116 | 10 | 1707752 | 2148407.26720647 | -440655.267206474 | 11 | 1298756 | 1472794.98043185 | -174038.980431851 | 12 | 1281814 | 1373496.86932074 | -91682.8693207373 | 13 | 1281151 | 1312470.63517769 | -31319.6351776881 | 14 | 1164976 | 1236954.26675664 | -71978.2667566359 | 15 | 1454329 | 1704839.74044085 | -250510.740440846 | 16 | 1645288 | 1847293.84570400 | -202005.845704004 | 17 | 1817743 | 2212017.68780927 | -394274.687809267 | 18 | 1895785 | 2213741.58254611 | -317956.582546109 | 19 | 2236311 | 2663014.47728295 | -426703.477282951 | 20 | 2295951 | 2645996.42465137 | -350045.424651372 | 21 | 2087315 | 2359211.1088619 | -271896.108861898 | 22 | 1980891 | 2282184.68780927 | -301293.687809267 | 23 | 1465446 | 1606572.40103464 | -141126.401034638 | 24 | 1445026 | 1507274.28992353 | -62248.289923527 | 25 | 1488120 | 1446248.05578048 | 41871.9442195228 | 26 | 1338333 | 1370731.68735942 | -32398.6873594247 | 27 | 1715789 | 1838617.16104363 | -122828.161043635 | 28 | 1806090 | 1981071.26630679 | -174981.266306793 | 29 | 2083316 | 2345795.10841206 | -262479.108412056 | 30 | 2092278 | 2347519.0031489 | -255241.003148898 | 31 | 2430800 | 2796791.89788574 | -365991.89788574 | 32 | 2424894 | 2779773.84525416 | -354879.845254161 | 33 | 2299016 | 2492988.52946469 | -193972.529464687 | 34 | 2130688 | 2415962.10841206 | -285274.108412056 | 35 | 1652221 | 1740349.82163743 | -88128.8216374267 | 36 | 1608162 | 1641051.71052632 | -32889.7105263158 | 37 | 1647074 | 1580025.47638327 | 67048.5236167338 | 38 | 1479691 | 1504509.10796221 | -24818.1079622138 | 39 | 1884978 | 1972394.58164642 | -87416.5816464239 | 40 | 2007898 | 2114848.68690958 | -106950.686909582 | 41 | 2208954 | 2479572.52901484 | -270618.529014845 | 42 | 2217164 | 2481296.42375169 | -264132.423751687 | 43 | 2534291 | 2930569.31848853 | -396278.318488529 | 44 | 2560312 | 2913551.26585695 | -353239.26585695 | 45 | 2429069 | 2626765.95006748 | -197696.950067476 | 46 | 2315077 | 2549739.52901484 | -234662.529014845 | 47 | 1799608 | 1874127.24224022 | -74519.242240216 | 48 | 1772590 | 1774829.13112911 | -2239.1311291052 | 49 | 1744799 | 1713802.89698606 | 30996.1030139447 | 50 | 1659093 | 1638286.52856500 | 20806.4714349972 | 51 | 2099821 | 2106172.00224921 | -6351.00224921276 | 52 | 2135736 | 2248626.10751237 | -112890.107512371 | 53 | 2427894 | 2613349.94961763 | -185455.949617634 | 54 | 2468882 | 2615073.84435448 | -146191.844354476 | 55 | 2703217 | 3064346.73909132 | -361129.739091318 | 56 | 2766841 | 3047328.68645974 | -280487.686459739 | 57 | 2655236 | 2760543.37067027 | -105307.370670265 | 58 | 2550373 | 2683516.94961763 | -133143.949617634 | 59 | 2052097 | 2007904.66284301 | 44192.3371569951 | 60 | 1998055 | 1908606.55173189 | 89448.448268106 | 61 | 1920748 | 1847580.31758884 | 73167.6824111557 | 62 | 1876694 | 1772063.94916779 | 104630.050832208 | 63 | 2380930 | 2239949.422852 | 140980.577147998 | 64 | 2467402 | 2382403.52811516 | 84998.4718848403 | 65 | 2770771 | 2747127.37022042 | 23643.6297795772 | 66 | 2781340 | 2748851.26495726 | 32488.7350427350 | 67 | 3143926 | 3198124.15969411 | -54198.1596941069 | 68 | 3172235 | 3181106.10706253 | -8871.10706252829 | 69 | 2952540 | 2894320.79127305 | 58219.2087269459 | 70 | 2920877 | 2817294.37022042 | 103582.629779577 | 71 | 2384552 | 2141682.08344579 | 242869.916554206 | 72 | 2248987 | 2042383.97233468 | 206603.027665317 | 73 | 2208616 | 1981357.73819163 | 227258.261808367 | 74 | 2178756 | 1905841.36977058 | 272914.630229419 | 75 | 2632870 | 2373726.84345479 | 259143.156545209 | 76 | 2706905 | 2516180.94871795 | 190724.051282051 | 77 | 3029745 | 2880904.79082321 | 148840.209176788 | 78 | 3015402 | 2882628.68556005 | 132773.314439946 | 79 | 3391414 | 3331901.5802969 | 59512.4197031043 | 80 | 3507805 | 3314883.52766532 | 192921.472334683 | 81 | 3177852 | 3028098.21187584 | 149753.788124157 | 82 | 3142961 | 2951071.79082321 | 191889.209176788 | 83 | 2545815 | 2275459.50404858 | 270355.495951417 | 84 | 2414007 | 2176161.39293747 | 237845.607062528 | 85 | 2372578 | 2115135.15879442 | 257442.841205578 | 86 | 2332664 | 2039618.79037337 | 293045.20962663 | 87 | 2825328 | 2507504.26405758 | 317823.73594242 | 88 | 2901478 | 2649958.36932074 | 251519.630679262 | 89 | 3263955 | 3014682.211426 | 249272.788573999 | 90 | 3226738 | 3016406.10616284 | 210331.893837157 | 91 | 3610786 | 3465679.00089969 | 145106.999100315 | 92 | 3709274 | 3448660.94826811 | 260613.051731894 | 93 | 3467185 | 3161875.63247863 | 305309.367521368 | 94 | 3449646 | 3084849.211426 | 364796.788573999 | 95 | 2802951 | 2409236.92465137 | 393714.075348628 | 96 | 2462530 | 2309938.81354026 | 152591.186459739 | 97 | 2490645 | 2248912.57939721 | 241732.420602789 | 98 | 2561520 | 2173396.21097616 | 388123.789023841 | 99 | 3067554 | 2641281.68466037 | 426272.315339631 | 100 | 3226951 | 2783735.78992353 | 443215.210076473 | 101 | 3546493 | 3148459.63202879 | 398033.36797121 | 102 | 3492787 | 3150183.52676563 | 342603.473234368 | 103 | 3952263 | 3599456.42150247 | 352806.578497526 | 104 | 3932072 | 3582438.36887090 | 349633.631129105 | 105 | 3720284 | 3295653.05308142 | 424630.946918579 | 106 | 3651555 | 3218626.63202879 | 432928.36797121 | 107 | 2914972 | 2543014.34525416 | 371957.654745839 | 108 | 2713514 | 2443716.23414305 | 269797.76585695 | 109 | 2703997 | 2382690 | 321307 | 110 | 2591373 | 2307173.63157895 | 284199.368421052 | 111 | 3163748 | 2775059.10526316 | 388688.894736842 | 112 | 3355137 | 2917513.21052632 | 437623.789473684 | 113 | 3613702 | 3282237.05263158 | 331464.947368421 | 114 | 3686773 | 3283960.94736842 | 402812.052631579 | 115 | 4098716 | 3733233.84210526 | 365482.157894737 | 116 | 4063517 | 3716215.78947368 | 347301.210526316 | 117 | 3551489 | 3429430.47368421 | 122058.526315790 | 118 | 3226663 | 3352404.05263158 | -125741.052631579 | 119 | 2656842 | 2676791.76585695 | -19949.76585695 | 120 | 2597484 | 2577493.65474584 | 19990.3452541609 | 121 | 2572399 | 2516467.42060279 | 55931.5793972106 | 122 | 2596631 | 2440951.05218174 | 155679.947818263 | 123 | 3165225 | 2908836.52586595 | 256388.474134053 | 124 | 3303145 | 3051290.63112911 | 251854.368870895 | 125 | 3698247 | 3416014.47323437 | 282232.526765632 | 126 | 3668631 | 3417738.36797121 | 250892.63202879 | 127 | 4130433 | 3867011.26270805 | 263421.737291948 | 128 | 4131400 | 3849993.21007647 | 281406.789923527 | 129 | 3864358 | 3563207.894287 | 301150.105713001 | 130 | 3721110 | 3486181.47323437 | 234928.526765632 | 131 | 2892532 | 2810569.18645974 | 81962.813540261 | 132 | 2843451 | 2711271.07534863 | 132179.924651372 | 133 | 2747502 | 2650244.84120558 | 97257.1587944216 | 134 | 2668775 | 2574728.47278453 | 94046.527215474 | 135 | 3018602 | 3042613.94646874 | -24011.9464687359 | 136 | 3013392 | 3185068.05173189 | -171676.051731894 | 137 | 3393657 | 3549791.89383716 | -156134.893837157 | 138 | 3544233 | 3551515.788574 | -7282.78857399904 | 139 | 4075832 | 4000788.68331084 | 75043.316689159 | 140 | 4032923 | 3983770.63067926 | 49152.3693207378 | 141 | 3734509 | 3696985.31488979 | 37523.6851102118 | 142 | 3761285 | 3619958.89383716 | 141326.106162843 | 143 | 2970090 | 2944346.60706253 | 25743.3929374720 | 144 | 2847849 | 2845048.49595142 | 2800.50404858279 | 145 | 2741680 | 2784022.26180837 | -42342.2618083675 | 146 | 2830639 | 2708505.89338731 | 122133.106612685 | 147 | 3257673 | 3176391.36707152 | 81281.6329284751 | 148 | 3480085 | 3318845.47233468 | 161239.527665317 | 149 | 3843271 | 3683569.31443995 | 159701.685560054 | 150 | 3796961 | 3685293.20917679 | 111667.790823212 | 151 | 4337767 | 4134566.10391363 | 203200.89608637 | 152 | 4243630 | 4117548.05128205 | 126081.948717949 | 153 | 3927202 | 3830762.73549258 | 96439.264507423 | 154 | 3915296 | 3753736.31443995 | 161559.685560054 | 155 | 3087396 | 3078124.02766532 | 9271.97233468318 | 156 | 2963792 | 2978825.91655421 | -15033.9165542061 | 157 | 2955792 | 2917799.68241116 | 37992.3175888436 | 158 | 2829925 | 2842283.31399010 | -12358.3139901039 | 159 | 3281195 | 3310168.78767431 | -28973.7876743139 | 160 | 3548011 | 3452622.89293747 | 95388.1070625282 | 161 | 4059648 | 3817346.73504274 | 242301.264957265 | 162 | 3941175 | 3819070.62977958 | 122104.370220423 | 163 | 4528594 | 4268343.52451642 | 260250.475483581 | 164 | 4433151 | 4251325.47188484 | 181825.528115160 | 165 | 4145737 | 3964540.15609537 | 181196.843904634 | 166 | 4077132 | 3887513.73504274 | 189618.264957265 | 167 | 3198519 | 3211901.44826811 | -13382.4482681060 | 168 | 3078660 | 3112603.33715700 | -33943.3371569953 | 169 | 3028202 | 3051577.10301395 | -23375.1030139454 | 170 | 2858642 | 2976060.73459289 | -117418.734592893 | 171 | 3398954 | 3443946.20827710 | -44992.208277103 | 172 | 3808883 | 3586400.31354026 | 222482.686459739 | 173 | 4175961 | 3951124.15564552 | 224836.844354476 | 174 | 4227542 | 3952848.05038237 | 274693.949617634 | 175 | 4744616 | 4402120.94511921 | 342495.054880792 | 176 | 4608012 | 4385102.89248763 | 222909.107512371 | 177 | 4295049 | 4098317.57669816 | 196731.423301845 | 178 | 4201144 | 4021291.15564552 | 179852.844354476 | 179 | 3353276 | 3345678.86887089 | 7597.131129105 | 180 | 3286851 | 3246380.75775978 | 40470.2422402157 | 181 | 3169889 | 3185354.52361673 | -15465.5236167344 | 182 | 3051720 | 3109838.15519568 | -58118.1551956821 | 183 | 3695426 | 3577723.62887989 | 117702.371120108 | 184 | 3905501 | 3720177.73414305 | 185323.26585695 | 185 | 4296458 | 4084901.57624831 | 211556.423751687 | 186 | 4246247 | 4086625.47098516 | 159621.529014845 | 187 | 4921849 | 4535898.365722 | 385950.634278003 | 188 | 4821446 | 4518880.31309042 | 302565.686909582 | 189 | 4425064 | 4232094.99730094 | 192969.002699056 | 190 | 4379099 | 4155068.57624831 | 224030.423751687 | 191 | 3472889 | 3479456.28947368 | -6567.28947368386 | 192 | 3359160 | 3380158.17836257 | -20998.1783625732 | 193 | 3200944 | 3319131.94421952 | -118187.944219523 | 194 | 3153170 | 3243615.57579847 | -90445.575798471 | 195 | 3741498 | 3711501.04948268 | 29996.9505173189 | 196 | 3918719 | 3853955.15474584 | 64763.8452541611 | 197 | 4403449 | 4218678.9968511 | 184770.003148898 | 198 | 4400407 | 4220402.89158794 | 180004.108412056 | 199 | 4847473 | 4669675.78632479 | 177797.213675213 | 200 | 4716136 | 4652657.73369321 | 63478.2663067926 | 201 | 4297440 | 4365872.41790373 | -68432.4179037332 | 202 | 4272253 | 4288845.9968511 | -16592.9968511023 | 203 | 3271834 | 3613233.71007647 | -341399.710076473 | 204 | 3168388 | 3513935.59896536 | -345547.598965362 | 205 | 2911748 | 3452909.36482231 | -541161.364822312 | 206 | 2720999 | 3377392.99640126 | -656393.99640126 | 207 | 3199918 | 3845278.47008547 | -645360.47008547 | 208 | 3672623 | 3987732.57534863 | -315109.575348628 | 209 | 3892013 | 4352456.41745389 | -460443.417453891 | 210 | 3850845 | 4354180.31219073 | -503335.312190733 | 211 | 4532467 | 4803453.20692758 | -270986.206927575 | 212 | 4484739 | 4786435.154296 | -301696.154295997 | 213 | 4014972 | 4499649.83850652 | -484677.838506522 | 214 | 3983758 | 4422623.41745389 | -438865.417453891 | 215 | 3158459 | 3747011.13067926 | -588552.130679262 | 216 | 3100569 | 3647713.01956815 | -547144.019568151 | 217 | 2935404 | 3586686.7854251 | -651282.785425102 | 218 | 2855719 | 3511170.41700405 | -655451.417004049 | 219 | 3465611 | 3979055.89068826 | -513444.890688259 | 220 | 3006985 | 4121509.99595142 | -1114524.99595142 | 221 | 4095110 | 4486233.83805668 | -391123.83805668 | 222 | 4104793 | 4487957.73279352 | -383164.732793522 | 223 | 4730788 | 4937230.62753036 | -206442.627530364 | 224 | 4642726 | 4920212.57489878 | -277486.574898785 | 225 | 4246919 | 4633427.25910931 | -386508.259109311 | 226 | 4308117 | 4556400.83805668 | -248283.838056680 |
Goldfeld-Quandt test for Heteroskedasticity | p-values | Alternative Hypothesis | breakpoint index | greater | 2-sided | less | 16 | 0.0016475283671912 | 0.0032950567343824 | 0.998352471632809 | 17 | 0.000297837270656921 | 0.000595674541313841 | 0.999702162729343 | 18 | 4.52151541051941e-05 | 9.04303082103882e-05 | 0.999954784845895 | 19 | 9.14089346935268e-05 | 0.000182817869387054 | 0.999908591065306 | 20 | 3.62507514556537e-05 | 7.25015029113074e-05 | 0.999963749248544 | 21 | 9.4439608754994e-06 | 1.88879217509988e-05 | 0.999990556039124 | 22 | 5.46410736664212e-06 | 1.09282147332842e-05 | 0.999994535892633 | 23 | 9.888799599554e-07 | 1.9777599199108e-06 | 0.99999901112004 | 24 | 1.69976454943561e-07 | 3.39952909887122e-07 | 0.999999830023545 | 25 | 2.83972656056530e-08 | 5.67945312113061e-08 | 0.999999971602734 | 26 | 6.26515617602578e-09 | 1.25303123520516e-08 | 0.999999993734844 | 27 | 3.97828947216972e-09 | 7.95657894433945e-09 | 0.99999999602171 | 28 | 9.07940688871085e-10 | 1.81588137774217e-09 | 0.99999999909206 | 29 | 2.15331271818467e-10 | 4.30662543636934e-10 | 0.999999999784669 | 30 | 3.97439370994918e-11 | 7.94878741989836e-11 | 0.999999999960256 | 31 | 1.30271839780629e-11 | 2.60543679561259e-11 | 0.999999999986973 | 32 | 2.9194517336554e-12 | 5.8389034673108e-12 | 0.99999999999708 | 33 | 8.22827791872584e-13 | 1.64565558374517e-12 | 0.999999999999177 | 34 | 1.66178851473403e-13 | 3.32357702946806e-13 | 0.999999999999834 | 35 | 2.78369602456118e-14 | 5.56739204912237e-14 | 0.999999999999972 | 36 | 5.23555113618661e-15 | 1.04711022723732e-14 | 0.999999999999995 | 37 | 8.86814409267795e-16 | 1.77362881853559e-15 | 1 | 38 | 4.86439470832261e-16 | 9.72878941664521e-16 | 1 | 39 | 1.36762336300917e-16 | 2.73524672601833e-16 | 1 | 40 | 2.38244711188207e-17 | 4.76489422376413e-17 | 1 | 41 | 5.98826713806307e-18 | 1.19765342761261e-17 | 1 | 42 | 2.02866885623061e-18 | 4.05733771246122e-18 | 1 | 43 | 7.5680313494751e-19 | 1.51360626989502e-18 | 1 | 44 | 3.76352914166153e-19 | 7.52705828332307e-19 | 1 | 45 | 8.41381956463514e-20 | 1.68276391292703e-19 | 1 | 46 | 2.58968012688268e-20 | 5.17936025376537e-20 | 1 | 47 | 5.35890291711913e-21 | 1.07178058342383e-20 | 1 | 48 | 1.00597249102243e-21 | 2.01194498204487e-21 | 1 | 49 | 7.93407570492202e-22 | 1.58681514098440e-21 | 1 | 50 | 1.83857898496313e-22 | 3.67715796992627e-22 | 1 | 51 | 2.78572466679663e-22 | 5.57144933359326e-22 | 1 | 52 | 9.16038309191818e-23 | 1.83207661838364e-22 | 1 | 53 | 3.84400739093731e-23 | 7.68801478187461e-23 | 1 | 54 | 2.17828636950043e-23 | 4.35657273900086e-23 | 1 | 55 | 1.77255849457070e-23 | 3.54511698914139e-23 | 1 | 56 | 1.04384874203027e-23 | 2.08769748406055e-23 | 1 | 57 | 6.85977325530242e-24 | 1.37195465106048e-23 | 1 | 58 | 1.42285694859968e-23 | 2.84571389719935e-23 | 1 | 59 | 7.74021533366078e-24 | 1.54804306673216e-23 | 1 | 60 | 2.09427883139215e-24 | 4.18855766278429e-24 | 1 | 61 | 1.44708326781953e-24 | 2.89416653563906e-24 | 1 | 62 | 3.16199474439455e-25 | 6.3239894887891e-25 | 1 | 63 | 2.40647963797932e-23 | 4.81295927595864e-23 | 1 | 64 | 5.43668101176858e-23 | 1.08733620235372e-22 | 1 | 65 | 2.35198095577538e-21 | 4.70396191155076e-21 | 1 | 66 | 2.0160189765048e-20 | 4.0320379530096e-20 | 1 | 67 | 2.55392364103195e-18 | 5.1078472820639e-18 | 1 | 68 | 4.97487320012015e-17 | 9.9497464002403e-17 | 1 | 69 | 8.2148596285197e-17 | 1.64297192570394e-16 | 1 | 70 | 1.35134830042936e-15 | 2.70269660085873e-15 | 0.999999999999999 | 71 | 2.27055190588784e-15 | 4.54110381177568e-15 | 0.999999999999998 | 72 | 9.28193827190896e-16 | 1.85638765438179e-15 | 1 | 73 | 3.37802608765385e-16 | 6.7560521753077e-16 | 1 | 74 | 1.45649894369859e-16 | 2.91299788739719e-16 | 1 | 75 | 2.16239235435942e-16 | 4.32478470871883e-16 | 1 | 76 | 1.38273635713665e-16 | 2.76547271427329e-16 | 1 | 77 | 3.85530892828254e-16 | 7.71061785656508e-16 | 1 | 78 | 5.00061510359278e-16 | 1.00012302071856e-15 | 1 | 79 | 3.99401793769995e-15 | 7.9880358753999e-15 | 0.999999999999996 | 80 | 7.32131076511417e-14 | 1.46426215302283e-13 | 0.999999999999927 | 81 | 6.14361488456342e-14 | 1.22872297691268e-13 | 0.999999999999938 | 82 | 1.17151792602901e-13 | 2.34303585205801e-13 | 0.999999999999883 | 83 | 5.3094079386996e-14 | 1.06188158773992e-13 | 0.999999999999947 | 84 | 2.52289008264674e-14 | 5.04578016529347e-14 | 0.999999999999975 | 85 | 1.74630318991638e-14 | 3.49260637983275e-14 | 0.999999999999982 | 86 | 7.69801658162775e-15 | 1.53960331632555e-14 | 0.999999999999992 | 87 | 4.10470956944565e-15 | 8.2094191388913e-15 | 0.999999999999996 | 88 | 1.87970821364950e-15 | 3.75941642729901e-15 | 0.999999999999998 | 89 | 2.4675199052421e-15 | 4.9350398104842e-15 | 0.999999999999998 | 90 | 1.8847170216896e-15 | 3.7694340433792e-15 | 0.999999999999998 | 91 | 5.86446802408789e-15 | 1.17289360481758e-14 | 0.999999999999994 | 92 | 1.78870572826095e-14 | 3.57741145652189e-14 | 0.999999999999982 | 93 | 1.70946257896933e-14 | 3.41892515793866e-14 | 0.999999999999983 | 94 | 5.0675398704284e-14 | 1.01350797408568e-13 | 0.99999999999995 | 95 | 2.54340094409637e-14 | 5.08680188819274e-14 | 0.999999999999975 | 96 | 1.22102442542060e-13 | 2.44204885084121e-13 | 0.999999999999878 | 97 | 2.86398904202154e-13 | 5.72797808404308e-13 | 0.999999999999714 | 98 | 1.43257003666156e-13 | 2.86514007332312e-13 | 0.999999999999857 | 99 | 8.43697151377797e-14 | 1.68739430275559e-13 | 0.999999999999916 | 100 | 6.88486121219237e-14 | 1.37697224243847e-13 | 0.999999999999931 | 101 | 9.06186393110551e-14 | 1.81237278622110e-13 | 0.99999999999991 | 102 | 6.01903233535873e-14 | 1.20380646707175e-13 | 0.99999999999994 | 103 | 2.35929129447682e-13 | 4.71858258895363e-13 | 0.999999999999764 | 104 | 2.47640699977145e-13 | 4.95281399954289e-13 | 0.999999999999752 | 105 | 1.91098044729223e-13 | 3.82196089458447e-13 | 0.99999999999981 | 106 | 1.76770277712854e-13 | 3.53540555425709e-13 | 0.999999999999823 | 107 | 1.06591017625593e-13 | 2.13182035251186e-13 | 0.999999999999893 | 108 | 1.42384383889681e-13 | 2.84768767779363e-13 | 0.999999999999858 | 109 | 2.66481715068067e-13 | 5.32963430136134e-13 | 0.999999999999734 | 110 | 7.55719568349101e-13 | 1.51143913669820e-12 | 0.999999999999244 | 111 | 4.53760521937554e-13 | 9.07521043875109e-13 | 0.999999999999546 | 112 | 2.60181333644732e-13 | 5.20362667289464e-13 | 0.99999999999974 | 113 | 1.20545880988086e-13 | 2.41091761976173e-13 | 0.99999999999988 | 114 | 6.34384913779502e-14 | 1.26876982755900e-13 | 0.999999999999936 | 115 | 5.50209496994878e-14 | 1.10041899398976e-13 | 0.999999999999945 | 116 | 2.71901352926156e-14 | 5.43802705852312e-14 | 0.999999999999973 | 117 | 1.72713251900780e-13 | 3.45426503801561e-13 | 0.999999999999827 | 118 | 3.19824232235643e-10 | 6.39648464471287e-10 | 0.999999999680176 | 119 | 3.23443295350543e-08 | 6.46886590701086e-08 | 0.99999996765567 | 120 | 4.79289390438178e-07 | 9.58578780876355e-07 | 0.99999952071061 | 121 | 4.93535183896339e-06 | 9.87070367792678e-06 | 0.99999506464816 | 122 | 1.24880927960895e-05 | 2.49761855921791e-05 | 0.999987511907204 | 123 | 1.24355161092002e-05 | 2.48710322184004e-05 | 0.99998756448389 | 124 | 1.14535446768825e-05 | 2.29070893537651e-05 | 0.999988546455323 | 125 | 7.71059949466936e-06 | 1.54211989893387e-05 | 0.999992289400505 | 126 | 5.60969819497748e-06 | 1.12193963899550e-05 | 0.999994390301805 | 127 | 4.35673277784235e-06 | 8.7134655556847e-06 | 0.999995643267222 | 128 | 2.92360121216239e-06 | 5.84720242432478e-06 | 0.999997076398788 | 129 | 1.89014407356609e-06 | 3.78028814713218e-06 | 0.999998109855926 | 130 | 1.42231410411682e-06 | 2.84462820823364e-06 | 0.999998577685896 | 131 | 3.70941107080531e-06 | 7.41882214161061e-06 | 0.99999629058893 | 132 | 5.73473360722198e-06 | 1.14694672144440e-05 | 0.999994265266393 | 133 | 1.50795590604574e-05 | 3.01591181209148e-05 | 0.99998492044094 | 134 | 3.59183675556261e-05 | 7.18367351112522e-05 | 0.999964081632444 | 135 | 0.000145744263375263 | 0.000291488526750526 | 0.999854255736625 | 136 | 0.00155926568687136 | 0.00311853137374272 | 0.998440734313129 | 137 | 0.00943774193902783 | 0.0188754838780557 | 0.990562258060972 | 138 | 0.0173895863540442 | 0.0347791727080885 | 0.982610413645956 | 139 | 0.0276882941880937 | 0.0553765883761874 | 0.972311705811906 | 140 | 0.0427544775302613 | 0.0855089550605225 | 0.957245522469739 | 141 | 0.0616982686534606 | 0.123396537306921 | 0.93830173134654 | 142 | 0.0685551577850411 | 0.137110315570082 | 0.931444842214959 | 143 | 0.0851774705739217 | 0.170354941147843 | 0.914822529426078 | 144 | 0.110207311790816 | 0.220414623581632 | 0.889792688209184 | 145 | 0.156552990905956 | 0.313105981811912 | 0.843447009094044 | 146 | 0.155496941247334 | 0.310993882494668 | 0.844503058752666 | 147 | 0.160011192993202 | 0.320022385986405 | 0.839988807006798 | 148 | 0.147238078219588 | 0.294476156439175 | 0.852761921780412 | 149 | 0.149191619157900 | 0.298383238315799 | 0.8508083808421 | 150 | 0.162751304724531 | 0.325502609449063 | 0.837248695275469 | 151 | 0.186783061003900 | 0.373566122007799 | 0.8132169389961 | 152 | 0.223423357337182 | 0.446846714674364 | 0.776576642662818 | 153 | 0.255151012409456 | 0.510302024818911 | 0.744848987590544 | 154 | 0.277334744519483 | 0.554669489038966 | 0.722665255480517 | 155 | 0.309348449534955 | 0.618696899069911 | 0.690651550465045 | 156 | 0.359589533868543 | 0.719179067737087 | 0.640410466131457 | 157 | 0.369284103290041 | 0.738568206580082 | 0.630715896709959 | 158 | 0.395528597306628 | 0.791057194613257 | 0.604471402693372 | 159 | 0.451836238254266 | 0.903672476508532 | 0.548163761745734 | 160 | 0.446405657219891 | 0.892811314439782 | 0.553594342780109 | 161 | 0.425672922772267 | 0.851345845544534 | 0.574327077227733 | 162 | 0.458711629442955 | 0.91742325888591 | 0.541288370557045 | 163 | 0.49780457627366 | 0.99560915254732 | 0.50219542372634 | 164 | 0.546335135556421 | 0.907329728887158 | 0.453664864443579 | 165 | 0.550958685339964 | 0.898082629320072 | 0.449041314660036 | 166 | 0.582228937454412 | 0.835542125091177 | 0.417771062545588 | 167 | 0.62052814134838 | 0.75894371730324 | 0.37947185865162 | 168 | 0.680632498109504 | 0.638735003780992 | 0.319367501890496 | 169 | 0.697963343407733 | 0.604073313184535 | 0.302036656592267 | 170 | 0.756200254576604 | 0.487599490846792 | 0.243799745423396 | 171 | 0.809357621554541 | 0.381284756890917 | 0.190642378445459 | 172 | 0.774128219984925 | 0.45174356003015 | 0.225871780015075 | 173 | 0.760721297190888 | 0.478557405618224 | 0.239278702809112 | 174 | 0.730722209793094 | 0.538555580413811 | 0.269277790206906 | 175 | 0.732758658295928 | 0.534482683408144 | 0.267241341704072 | 176 | 0.758068266863268 | 0.483863466273463 | 0.241931733136732 | 177 | 0.748127020006213 | 0.503745959987575 | 0.251872979993787 | 178 | 0.782065971399837 | 0.435868057200327 | 0.217934028600163 | 179 | 0.778255580437331 | 0.443488839125337 | 0.221744419562669 | 180 | 0.765443432932388 | 0.469113134135224 | 0.234556567067612 | 181 | 0.746787515603134 | 0.506424968793733 | 0.253212484396866 | 182 | 0.734697247149247 | 0.530605505701506 | 0.265302752850753 | 183 | 0.691453295803516 | 0.617093408392969 | 0.308546704196484 | 184 | 0.660568183007128 | 0.678863633985744 | 0.339431816992872 | 185 | 0.610603959925662 | 0.778792080148677 | 0.389396040074338 | 186 | 0.568690307586525 | 0.86261938482695 | 0.431309692413475 | 187 | 0.516057399733334 | 0.967885200533331 | 0.483942600266666 | 188 | 0.458497641358009 | 0.916995282716018 | 0.541502358641991 | 189 | 0.399609998557783 | 0.799219997115567 | 0.600390001442217 | 190 | 0.345448539894469 | 0.690897079788938 | 0.654551460105531 | 191 | 0.306593227652028 | 0.613186455304056 | 0.693406772347972 | 192 | 0.267115143277844 | 0.534230286555688 | 0.732884856722156 | 193 | 0.24737838165613 | 0.49475676331226 | 0.75262161834387 | 194 | 0.243510772316188 | 0.487021544632375 | 0.756489227683812 | 195 | 0.246835230593333 | 0.493670461186666 | 0.753164769406667 | 196 | 0.382414238028287 | 0.764828476056573 | 0.617585761971713 | 197 | 0.429580537553407 | 0.859161075106814 | 0.570419462446593 | 198 | 0.531959736497339 | 0.936080527005321 | 0.468040263502661 | 199 | 0.510301411688458 | 0.979397176623084 | 0.489698588311542 | 200 | 0.472050789385293 | 0.944101578770585 | 0.527949210614707 | 201 | 0.466894985163245 | 0.93378997032649 | 0.533105014836755 | 202 | 0.457076438079388 | 0.914152876158776 | 0.542923561920612 | 203 | 0.450290980307836 | 0.900581960615672 | 0.549709019692164 | 204 | 0.423275321475161 | 0.846550642950323 | 0.576724678524839 | 205 | 0.39237357663784 | 0.78474715327568 | 0.60762642336216 | 206 | 0.355304869034551 | 0.710609738069103 | 0.644695130965449 | 207 | 0.322867847169775 | 0.64573569433955 | 0.677132152830225 | 208 | 0.999472492180897 | 0.00105501563820622 | 0.00052750781910311 | 209 | 0.99736931373336 | 0.00526137253328173 | 0.00263068626664087 | 210 | 0.98688939992942 | 0.0262212001411588 | 0.0131106000705794 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | Description | # significant tests | % significant tests | OK/NOK | 1% type I error level | 123 | 0.63076923076923 | NOK | 5% type I error level | 126 | 0.646153846153846 | NOK | 10% type I error level | 128 | 0.656410256410256 | NOK |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/10g7m01292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/10g7m01292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/19o661292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/19o661292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/29o661292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/29o661292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/39o661292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/39o661292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/42y6r1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/42y6r1292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/52y6r1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/52y6r1292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/62y6r1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/62y6r1292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/7u7nc1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/7u7nc1292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/85g4x1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/85g4x1292001670.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/95g4x1292001670.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1292001761o6b4wus7mvyvhmi/95g4x1292001670.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; | | Parameters (R input): | par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; | | R code (references can be found in the software module): | 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')
}
| |
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Software written by Ed van Stee & Patrick Wessa
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