regressie master 1 juli 2005

*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, 12 Dec 2008 11:24:20 -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/Dec/12/t12291063880xk5fp64wskjjz1.htm/, Retrieved Fri, 12 Dec 2008 19:26:38 +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/2008/Dec/12/t12291063880xk5fp64wskjjz1.htm/}, year = {2008}, } @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 = {2008}, 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 «

8310 0 7649 0 7279 0 6857 0 6496 0 6280 0 8962 0 11205 0 10363 0 9175 0 8234 0 8121 0 7438 0 6876 0 6489 0 6319 0 5952 0 6055 0 9107 0 11493 0 10213 0 9238 0 8218 0 7995 0 7581 0 7051 0 6668 0 6433 0 6135 0 6365 0 10095 0 12029 0 12184 0 11331 0 9961 0 9739 0 9080 0 8507 0 8097 0 7772 0 7440 0 7902 0 13539 0 14992 0 15436 0 14156 0 12846 0 12302 0 11691 0 10648 0 10064 0 10016 0 9691 0 10260 0 16882 0 18573 0 18227 0 16346 0 14694 0 14453 0 13949 0 13277 0 12726 0 12279 0 11819 0 12207 0 18637 0 20519 0 19974 0 17802 0 15997 0 15430 0 14452 0 13614 0 13080 0 12290 0 11890 0 12292 0 18700 1 20388 1 19170 1 17530 1 15564 1 15163 1 13406 1 12763 1 12083 1 12054 1 11770 1 12266 1 17549 1 18655 1 17279 1 14788 1 13138 1 12494 1 11767 1 10928 1 10104 1 9760 1 9536 1 9978 1 14846 1 15565 1 13587 1 11804 1 10611 1 10915 1 9988 1 9376 1 9319 1 8852 1 8392 1 9050 1 13250 1 14037 1 12486 1 11182 1 10287 1

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 computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation NWWZm[t] = + 7816.03693677434 -3678.67929976539Dummy[t] -764.203148248423M1[t] -1549.10260281939M2[t] -2114.70205739036M3[t] -2530.00151196134M4[t] -2968.70096653232M5[t] -2702.90042110329M6[t] + 2468.56805430227M7[t] + 3969.8685997313M8[t] + 3028.56914516032M9[t] + 1384.26969058934M10[t] -83.5297639816325M11[t] + 87.599454570976t + 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) 7816.03693677434 873.7644 8.9452 0 0 Dummy -3678.67929976539 766.875765 -4.797 5e-06 3e-06 M1 -764.203148248423 1033.345158 -0.7395 0.461228 0.230614 M2 -1549.10260281939 1033.071836 -1.4995 0.136742 0.068371 M3 -2114.70205739036 1032.907154 -2.0473 0.043122 0.021561 M4 -2530.00151196134 1032.851164 -2.4495 0.015959 0.007979 M5 -2968.70096653232 1032.903884 -2.8741 0.004905 0.002452 M6 -2702.90042110329 1033.065298 -2.6164 0.010196 0.005098 M7 2468.56805430227 1033.636785 2.3882 0.018716 0.009358 M8 3969.8685997313 1033.368442 3.8417 0.000209 0.000105 M9 3028.56914516032 1033.20871 2.9312 0.004144 0.002072 M10 1384.26969058934 1033.15764 1.3398 0.183189 0.091594 M11 -83.5297639816325 1033.215247 -0.0808 0.93572 0.46786 t 87.599454570976 10.596418 8.2669 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.813439715126318 R-squared 0.661684170144785 Adjusted R-squared 0.619797448353187 F-TEST (value) 15.7969910712256 F-TEST (DF numerator) 13 F-TEST (DF denominator) 105 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2247.70458376204 Sum Squared Residuals 530478469.065812 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8310 7139.43324309698 1170.56675690302 2 7649 6442.1332430969 1206.86675690310 3 7279 5964.1332430969 1314.86675690310 4 6857 5636.43324309691 1220.56675690309 5 6496 5285.33324309691 1210.66675690309 6 6280 5638.73324309691 641.266756903087 7 8962 10897.8011730735 -1935.80117307345 8 11205 12486.7011730735 -1281.70117307345 9 10363 11633.0011730734 -1270.00117307344 10 9175 10076.3011730735 -901.301173073462 11 8234 8696.10117307344 -462.101173073444 12 8121 8867.23039162606 -746.230391626054 13 7438 8190.62669794862 -752.626697948617 14 6876 7493.32669794863 -617.326697948635 15 6489 7015.32669794863 -526.326697948625 16 6319 6687.62669794862 -368.626697948623 17 5952 6336.52669794862 -384.526697948623 18 6055 6689.92669794862 -634.926697948623 19 9107 11948.9946279252 -2841.99462792516 20 11493 13537.8946279252 -2044.89462792516 21 10213 12684.1946279252 -2471.19462792516 22 9238 11127.4946279252 -1889.49462792516 23 8218 9747.29462792516 -1529.29462792516 24 7995 9918.42384647777 -1923.42384647777 25 7581 9241.82015280033 -1660.82015280033 26 7051 8544.52015280034 -1493.52015280034 27 6668 8066.52015280034 -1398.52015280034 28 6433 7738.82015280033 -1305.82015280033 29 6135 7387.72015280033 -1252.72015280033 30 6365 7741.12015280033 -1376.12015280033 31 10095 13000.1880827769 -2905.18808277688 32 12029 14589.0880827769 -2560.08808277688 33 12184 13735.3880827769 -1551.38808277688 34 11331 12178.6880827769 -847.688082776873 35 9961 10798.4880827769 -837.488082776876 36 9739 10969.6173013295 -1230.61730132948 37 9080 10293.0136076520 -1213.01360765204 38 8507 9595.71360765205 -1088.71360765205 39 8097 9117.71360765205 -1020.71360765205 40 7772 8790.01360765205 -1018.01360765205 41 7440 8438.91360765205 -998.913607652048 42 7902 8792.31360765205 -890.313607652047 43 13539 14051.3815376286 -512.381537628587 44 14992 15640.2815376286 -648.281537628586 45 15436 14786.5815376286 649.418462371412 46 14156 13229.8815376286 926.118462371414 47 12846 11849.6815376286 996.318462371412 48 12302 12020.8107561812 281.189243818804 49 11691 11344.2070625038 346.792937496247 50 10648 10646.9070625038 1.09293749623943 51 10064 10168.9070625038 -104.907062503762 52 10016 9841.20706250376 174.792937496239 53 9691 9490.10706250376 200.89293749624 54 10260 9843.50706250376 416.492937496240 55 16882 15102.5749924803 1779.42500751970 56 18573 16691.4749924803 1881.52500751970 57 18227 15837.7749924803 2389.2250075197 58 16346 14281.0749924803 2064.92500751970 59 14694 12900.8749924803 1793.1250075197 60 14453 13072.0042110329 1380.99578896709 61 13949 12395.4005173555 1553.59948264453 62 13277 11698.1005173555 1578.89948264453 63 12726 11220.1005173555 1505.89948264453 64 12279 10892.4005173555 1386.59948264453 65 11819 10541.3005173555 1277.69948264453 66 12207 10894.7005173555 1312.29948264453 67 18637 16153.768447332 2483.23155266799 68 20519 17742.668447332 2776.33155266799 69 19974 16888.968447332 3085.03155266799 70 17802 15332.268447332 2469.73155266799 71 15997 13952.068447332 2044.93155266799 72 15430 14123.1976658846 1306.80233411538 73 14452 13446.5939722072 1005.40602779282 74 13614 12749.2939722072 864.706027792816 75 13080 12271.2939722072 808.706027792814 76 12290 11943.5939722072 346.406027792815 77 11890 11592.4939722072 297.506027792815 78 12292 11945.8939722072 346.106027792815 79 18700 13526.2826024183 5173.71739758166 80 20388 15115.1826024183 5272.81739758166 81 19170 14261.4826024183 4908.51739758167 82 17530 12704.7826024183 4825.21739758167 83 15564 11324.5826024183 4239.41739758166 84 15163 11495.7118209709 3667.28817902906 85 13406 10819.1081272935 2586.8918727065 86 12763 10121.8081272935 2641.19187270649 87 12083 9643.80812729351 2439.19187270649 88 12054 9316.10812729351 2737.89187270649 89 11770 8965.0081272935 2804.99187270649 90 12266 9318.4081272935 2947.59187270649 91 17549 14577.4760572700 2971.52394272995 92 18655 16166.3760572700 2488.62394272995 93 17279 15312.6760572700 1966.32394272995 94 14788 13755.9760572700 1032.02394272995 95 13138 12375.7760572700 762.223942729952 96 12494 12546.9052758227 -52.9052758226561 97 11767 11870.3015821452 -103.301582145214 98 10928 11173.0015821452 -245.001582145221 99 10104 10695.0015821452 -591.001582145223 100 9760 10367.3015821452 -607.301582145221 101 9536 10016.2015821452 -480.201582145221 102 9978 10369.6015821452 -391.601582145221 103 14846 15628.6695121218 -782.66951212176 104 15565 17217.5695121218 -1652.56951212176 105 13587 16363.8695121218 -2776.86951212176 106 11804 14807.1695121218 -3003.16951212176 107 10611 13426.9695121218 -2815.96951212176 108 10915 13598.0987306744 -2683.09873067437 109 9988 12921.4950369969 -2933.49503699693 110 9376 12224.1950369969 -2848.19503699693 111 9319 11746.1950369969 -2427.19503699693 112 8852 11418.4950369969 -2566.49503699693 113 8392 11067.3950369969 -2675.39503699693 114 9050 11420.7950369969 -2370.79503699693 115 13250 16679.8629669735 -3429.86296697347 116 14037 18268.7629669735 -4231.76296697347 117 12486 17415.0629669735 -4929.06296697347 118 11182 15858.3629669735 -4676.36296697347 119 10287 14478.1629669735 -4191.16296697347 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000193360457003266 0.000386720914006531 0.999806639542997 18 7.43633602070481e-05 0.000148726720414096 0.999925636639793 19 6.55516804436995e-05 0.000131103360887399 0.999934448319556 20 3.39654362305023e-05 6.79308724610045e-05 0.99996603456377 21 5.24860541334412e-06 1.04972108266882e-05 0.999994751394587 22 1.06178968403694e-06 2.12357936807389e-06 0.999998938210316 23 1.73065401566452e-07 3.46130803132905e-07 0.999999826934598 24 2.42042642816732e-08 4.84085285633465e-08 0.999999975795736 25 3.19008377876306e-09 6.38016755752613e-09 0.999999996809916 26 4.3944843215048e-10 8.7889686430096e-10 0.999999999560552 27 5.69488007043488e-11 1.13897601408698e-10 0.999999999943051 28 7.75886050600847e-12 1.55177210120169e-11 0.99999999999224 29 1.15389571285764e-12 2.30779142571528e-12 0.999999999998846 30 4.26218080433194e-13 8.52436160866388e-13 0.999999999999574 31 1.90810674152520e-11 3.81621348305041e-11 0.999999999980919 32 2.12625095138846e-11 4.25250190277692e-11 0.999999999978737 33 1.91982382107480e-09 3.83964764214961e-09 0.999999998080176 34 2.76043272840395e-08 5.52086545680791e-08 0.999999972395673 35 5.1549637526799e-08 1.03099275053598e-07 0.999999948450363 36 7.39639704807198e-08 1.47927940961440e-07 0.99999992603603 37 5.07383054602123e-08 1.01476610920425e-07 0.999999949261695 38 3.42843116590167e-08 6.85686233180334e-08 0.999999965715688 39 2.30097276720339e-08 4.60194553440679e-08 0.999999976990272 40 1.61414788075985e-08 3.22829576151971e-08 0.99999998385852 41 1.28990314155169e-08 2.57980628310338e-08 0.999999987100969 42 1.84014763084534e-08 3.68029526169068e-08 0.999999981598524 43 5.86759133835745e-06 1.17351826767149e-05 0.999994132408662 44 5.90544509016369e-05 0.000118108901803274 0.999940945549098 45 0.000922974736337982 0.00184594947267596 0.999077025263662 46 0.00376658337896534 0.00753316675793067 0.996233416621035 47 0.00912129718573013 0.0182425943714603 0.99087870281427 48 0.0182190123368438 0.0364380246736876 0.981780987663156 49 0.0266723326047081 0.0533446652094162 0.973327667395292 50 0.0421213311530515 0.084242662306103 0.957878668846949 51 0.0753809414904305 0.150761882980861 0.92461905850957 52 0.136290739453435 0.272581478906871 0.863709260546565 53 0.268353881998550 0.536707763997099 0.73164611800145 54 0.541870755562340 0.916258488875319 0.458129244437660 55 0.875748028762247 0.248503942475505 0.124251971237753 56 0.9711185525278 0.0577628949443992 0.0288814474721996 57 0.990295760859816 0.0194084782803674 0.0097042391401837 58 0.995554578890175 0.00889084221965029 0.00444542110982514 59 0.998269009984355 0.00346198003128969 0.00173099001564484 60 0.99937377225769 0.00125245548461877 0.000626227742309385 61 0.999595086633175 0.000809826733650242 0.000404913366825121 62 0.999757994140558 0.000484011718883099 0.000242005859441549 63 0.999883458788565 0.000233082422870496 0.000116541211435248 64 0.999961196053902 7.76078921968571e-05 3.88039460984286e-05 65 0.999994700992162 1.05980156763187e-05 5.29900783815933e-06 66 0.999999896147057 2.07705886784182e-07 1.03852943392091e-07 67 0.999999956867045 8.62659093134305e-08 4.31329546567153e-08 68 0.999999949632044 1.00735911925264e-07 5.03679559626318e-08 69 0.999999966621548 6.67569039384631e-08 3.33784519692316e-08 70 0.999999948601 1.02798002068444e-07 5.13990010342219e-08 71 0.99999988155626 2.36887482238968e-07 1.18443741119484e-07 72 0.999999721631575 5.56736850459848e-07 2.78368425229924e-07 73 0.999999467771638 1.06445672509370e-06 5.32228362546852e-07 74 0.999998966045965 2.06790807045368e-06 1.03395403522684e-06 75 0.999998196106612 3.60778677510643e-06 1.80389338755322e-06 76 0.999996384445649 7.23110870299769e-06 3.61555435149884e-06 77 0.999992761025574 1.44779488527420e-05 7.23897442637102e-06 78 0.999984980322296 3.00393554088654e-05 1.50196777044327e-05 79 0.999971123632345 5.77527353103505e-05 2.88763676551753e-05 80 0.99994275912443 0.000114481751140424 5.72408755702121e-05 81 0.999930780395824 0.000138439208351777 6.92196041758887e-05 82 0.999951823329712 9.6353340576327e-05 4.81766702881635e-05 83 0.999922082312645 0.000155835374709548 7.79176873547738e-05 84 0.99989251272324 0.000214974553518301 0.000107487276759150 85 0.999826168542217 0.000347662915565196 0.000173831457782598 86 0.999685361765616 0.000629276468768515 0.000314638234384257 87 0.99946991751246 0.00106016497508002 0.00053008248754001 88 0.998954868299619 0.00209026340076196 0.00104513170038098 89 0.997939197760042 0.00412160447991574 0.00206080223995787 90 0.995923352858205 0.00815329428358977 0.00407664714179489 91 0.994431738840018 0.0111365223199635 0.00556826115998173 92 0.996097889487339 0.00780422102532285 0.00390211051266143 93 0.999722119044784 0.00055576191043276 0.00027788095521638 94 0.999949510205763 0.000100979588474664 5.04897942373322e-05 95 0.999974392596792 5.12148064161872e-05 2.56074032080936e-05 96 0.99994510335419 0.000109793291621338 5.4896645810669e-05 97 0.999933681619227 0.000132636761545961 6.63183807729805e-05 98 0.999867919231373 0.000264161537253842 0.000132080768626921 99 0.99951297069551 0.000974058608980113 0.000487029304490057 100 0.99801114438707 0.00397771122585996 0.00198885561292998 101 0.991900823166025 0.0161983536679498 0.00809917683397489 102 0.96803805944764 0.0639238811047178 0.0319619405523589 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 72 0.837209302325581 NOK 5% type I error level 77 0.895348837209302 NOK 10% type I error level 81 0.94186046511628 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/10bp7e1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/10bp7e1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/19vdi1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/19vdi1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/2hqmu1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/2hqmu1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/3tvcc1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/3tvcc1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/4ie0z1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/4ie0z1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/5zmqe1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/5zmqe1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/6alhb1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/6alhb1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/7ikwd1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/7ikwd1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/88adm1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/88adm1229106254.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/926nl1229106254.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12291063880xk5fp64wskjjz1/926nl1229106254.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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