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Paper multi regression

*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: Sat, 11 Dec 2010 16:45:31 +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/11/t1292085918sf9f0vzzyzblrj7.htm/, Retrieved Sat, 11 Dec 2010 17:45:30 +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/11/t1292085918sf9f0vzzyzblrj7.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 «
17080 9 57 0 0 17240 8 68 0 0 17200 7 55 0 0 15580 9 53 0 1 18950 9 57 0 1 23360 8 61 0 0 19190 6 58 0 0 14150 6 56 0 0 19870 8 59 0 0 19420 9 60 0 0 16020 6 53 0 0 17350 8 54 0 1 21930 8 59 0 0 21180 8 61 0 1 22580 8 58 0 1 19320 7 53 0 1 14720 5 50 0 1 18780 6 53 0 0 23520 9 59 0 1 26040 9 62 0 1 14000 5 49 0 0 12560 5 53 0 0 25780 7 63 0 1 29880 9 65 0 0 14040 3 52 0 1 23480 9 60 0 1 17550 5 52 0 1 29800 8 60 0 0 21000 9 60 0 0 12820 5 49 0 0 30000 9 66 0 1 26730 8 60 0 0 20930 7 58 0 0 16120 5 52 0 0 21750 8 59 0 0 27250 9 59 0 1 20710 8 55 0 1 15470 8 57 0 1 20040 8 57 0 1 31350 9 60 0 0 24200 8 59 0 1 17760 5 51 0 1 19310 8 57 0 0 13430 5 50 0 0 20760 9 57 0 0 16240 7 54 0 1 13440 8 53 0 0 16500 6 55 0 1 27320 8 61 0 1 20170 5 55 0 1 27970 9 62 0 0 35560 9 62 0 1 17030 8 55 0 1 16340 6 54 0 1 25700 9 57 0 1 30160 9 63 0 0 24190 7 60 0 0 15690 4 50 0 0 16980 8 58 0 0 21230 8 60 0 1 24810 8 60 0 0 14810 6 51 0 0 15770 4 49 0 0 19400 8 59 0 1 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!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time56 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
FEV[t] = -43911.5173625923 + 672.489601226765Age[t] + 1025.40345016719Ht[t] -784.919499820661Smoker[t] + 1577.98544463906gender[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-43911.51736259232244.896032-19.560600
Age672.48960122676595.1548617.067300
Ht1025.4034501671947.60254721.540900
Smoker-784.919499820661596.543657-1.31580.1887150.094358
gender1577.98544463906334.8483464.71253e-061e-06


Multiple Linear Regression - Regression Statistics
Multiple R0.876902166040816
R-squared0.768957408807075
Adjusted R-squared0.767526804527243
F-TEST (value)537.505318310097
F-TEST (DF numerator)4
F-TEST (DF denominator)646
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4146.98896816715
Sum Squared Residuals11109596306.3566


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
11708020588.8857079811-3508.8857079811
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3642592031042.5883551815-5122.58835518155
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3803183030490.00636070971339.99363929033
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3863171028086.28561143493623.71438856511
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3922971032387.5675576351-2677.56755763509
3932891025362.98910987373547.01089012625
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3984273041935.7743614261794.225638573897
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4142491023984.6718107661925.328189233853
4153060028405.86136372122194.13863627876
4162545031042.5883551815-5592.58835518155
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4183305029464.60291054253585.39708945751
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4222988036842.0952072442-6962.09520724424
4232498026588.0607055724-1608.06070557240
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4262704027380.4579135541-340.457913554052
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4292287025362.9891098737-2492.98910987375
4302434031482.0717142228-7142.07171422279
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4323086033773.3625649037-2913.36256490368
4332696032067.9918053487-5107.99180534874
4342868027733.3717624945946.62823750553
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4373255031834.9855631632715.014436836792
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4404111039884.96746109171225.03253890827
4411916028285.9537569663-9125.95375696635
4421858023864.7642040113-5284.76420401126
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4522606030137.0925117693-4077.09251176925
4533169027301.36611161424388.63388838577
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4614877041263.28476019937506.71523980067
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4702538038306.9820164527-12926.9820164527
4712758032067.9918053487-4487.99180534874
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4751858024890.1676541784-6310.16765417844
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4783501030809.5821129964200.41788700398
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4813186032075.4695135097-215.469513509732
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4832081028086.2856114349-7276.28561143489
4842974027060.88216126772679.1178387323
4853297030697.15221440212272.84778559787
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4874448037161.67095953067318.3290404694
4883984039212.477859865627.522140135037
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4912304033653.4549581488-10613.4549581488
4923680035783.3536604231016.64633957701
4933102028326.76956178142693.23043821859
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4952677032747.9591147365-5977.9591147365
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4973681033213.30286227083596.69713772919
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5033297030697.15221440212272.84778559787
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5052216026940.9745545128-4780.97455451281
5063247032740.4814065755-270.481406575504
5074324034791.28830690998448.71169309013
5082362026035.4787111005-2415.47871110052
5092563028086.2856114349-2456.28561143489
5103206030689.67450624111370.32549375887
5113585038859.5640109246-3009.56401092455
5124720039565.39170880547634.60829119462
5133331032507.47516439802.524835610025
5145083042288.68821036658541.31178963348
5153498033333.87920586241646.12079413756
5162417026707.9683123273-2537.96831232728
5172364026940.9745545128-3300.97455451281
5182341026940.9745545128-3530.97455451281
5192759026948.4522626738641.547737326191
5202953031402.9799122830-1872.97991228296
5213231030336.76065730071973.23934269928
5223078034791.2883069099-4011.28830690987
5233369037867.4986574114-4177.49865741143
5243529038539.9882586382-3249.98825863820
5252866027733.3717624945926.628237505531
5262891029078.350964948-168.350964948006
5273022027060.88216126773159.1178387323
5283127027966.378004683303.62199532
5292866026035.47871110052624.52128889948
5302605028758.7752126617-2708.77521266165
5313056029431.26481388841128.73518611158
5322569028758.7752126617-3068.77521266165
5332501027060.8821612677-2050.8821612677
5343320032740.4814065755459.518593424495
5352123031715.0779564083-10485.0779564083
5363780038859.5640109246-1059.56401092455
5373847032740.48140657555729.51859342449
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5393924035463.77790813663776.22209186336
5402132024890.1676541784-3570.16765417844
5412752036489.1813583038-8969.18135830383
5422449029431.2648138884-4941.26481388842
5433456028991.78145484725568.21854515282
5443073030490.0063607097239.993639290329
5452688026388.3925600409491.607439959067
5463329034118.7987056831-828.798705683106
5474271041935.7743614261774.225638573897
5483530031362.16410746793937.8358925321
5492928032740.4814065755-3460.48140657550
5502689027060.8821612677-170.882161267701
5512332024184.3399562976-864.33995629761
5522934031129.1578652824-1789.15786528238
5532276033973.0307104352-11213.0307104351
5543110031042.588355181557.4116448184488
5552894033765.8848567427-4825.88485674269
5564637038107.98260775808262.01739224205
5572435029464.6029105425-5114.60291054249
5582838027413.7960102081966.203989791882
5593035027733.37176249452616.62823750553
5604831038539.98825863829770.0117413618
5612812027613.4641557396506.535844260419
5622714031834.9855631632-4694.98556316321
5633086026388.39256004094471.60743995907
5643519031834.98556316323355.01443683679
5654232039212.4778598653107.52214013504
5662770027966.37800468-266.378004679997
5673341031834.98556316321575.01443683679
5683090031042.5883551815-142.588355181551
5692531028958.4433581931-3648.44335819312
5702822037514.584808471-9294.584808471
5713038028679.68341072181700.31658927817
5722935033412.9710078023-4062.97100780227
5732568028439.1994603753-2759.1994603753
5742387027613.4641557396-3743.46415573958
5752499032387.5675576351-7397.56755763509
5764130033765.88485674277534.11514325731
5773001029784.1786628288225.821337171161
5783132024337.58565970666982.41434029344
5793577030456.66826405565313.33173594439
5803222026707.96831232735512.03168767272
5813280032740.481406575559.5185934244951
5822659030689.6745062411-4099.67450624114
5832822027060.88216126771159.11783873230
5842140026035.4787111005-4635.47871110052
5854203038539.98825863823490.01174136180
5862997032154.5613154496-2184.56131544956
5873120025250.55921127995949.44078872014
5882562028086.2856114349-2466.28561143489
5893082030809.58211299610.4178870039768
5903806036808.75711059021251.24288940982
5913339035031.7722572564-1641.7722572564
5923152027620.94186390063899.05813609942
5932458025010.0752609333-430.075260933331
5942391025915.5711043456-2005.57110434563
5953141027380.45791355414029.54208644595
5962579028758.7752126617-2968.77521266165
5973104032428.3833624501-1388.38336245015
5984045036376.75145970994073.24854029007
5994763036023.837610769511606.1623892305
6002100023864.7642040113-2864.76420401126
6013069029352.17301194861337.82698805141
6022785035816.6917570771-7966.69175707706
6034284039532.05361215133307.94638784869
6044506039772.53756249785287.46243750216
6052906035869.9231705238-6809.92317052382
6065102044272.81891739286747.18108260724
6073519035757.4932719299-567.493271929924
6083688037368.8168132231-488.816813223056
6094429040877.03281460493412.96718539515
6104279037481.24671181695308.75328818305
6114500039532.05361215135467.94638784868
6122635031801.6474665091-5451.64746650914
6132679033067.5348670228-6277.53486702285
6142198028965.9210663541-6985.92106635411
6153345035757.4932719299-2307.49327192992
6163082034844.5197203566-4024.51972035663
6173387035550.3474182375-1680.34741823747
6183082037015.9029642826-6195.90296428264
6192903030663.8141177481-1633.81411774807
6203004031016.7279666885-976.727966688481
6215793038506.650161984119423.3498380159
6223985040557.4570623185-707.4570623185
6234220039498.71551549722701.28448450275
6244724041902.4362647725337.56373522796
6253731036455.8432616498854.15673835024
6263406039066.709864617-5006.70986461701
6273500031095.81976862833904.18023137169
6283674036575.7508684047164.249131595348
6295633043953.243165106412376.7568348936
6303122031016.7279666885203.272033311518
6313330036143.7452175244-2843.74521752441
6322608029638.4106675809-3558.41066758088
6333645044306.1570140468-7856.15701404683
6343799035670.92376182912319.0762381709
6354086037688.39256550943171.60743449060
6362887030776.2440163420-1906.24401634196
6374070039419.62371355741280.37628644257
6383960040877.0328146048-1277.03281460485
6394299036102.92941270936887.07058729065
6402981034524.9439680703-4714.94396807028
6412264029991.3245165213-7351.32451652129
6424404041790.00636617812249.99363382186
6432278026915.1141660197-4135.11416601974
6444504042255.35011371252784.64988628754
6455638040877.032814604915502.9671853951
6464872041470.43061389187249.5693861082
6474270036343.41336305596356.58663694413
6483727036696.3272119963573.672788003715
6492853029717.5024695207-1187.50246952071
6502795030663.8141177481-2713.81411774807
6513211034877.8578170107-2767.8578170107


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5563146790669320.8873706418661360.443685320933068
90.4065640724455390.8131281448910790.59343592755446
100.2682019483898930.5364038967797860.731798051610107
110.1663742128754820.3327484257509640.833625787124518
120.09947359050324440.1989471810064890.900526409496756
130.09330633377154950.1866126675430990.90669366622845
140.0742221084043750.148444216808750.925777891595625
150.07859223850855540.1571844770171110.921407761491445
160.05259950373114760.1051990074622950.947400496268852
170.0348586388579420.0697172777158840.965141361142058
180.02675244669602680.05350489339205360.973247553303973
190.02588096093516690.05176192187033370.974119039064833
200.03194714234973870.06389428469947740.968052857650261
210.01971675077041170.03943350154082340.980283249229588
220.01688054851070900.03376109702141810.98311945148929
230.01625635907736500.03251271815472990.983743640922635
240.05658476036613550.1131695207322710.943415239633865
250.04029447793754560.08058895587509120.959705522062454
260.02761420512815870.05522841025631740.972385794871841
270.01962469683442270.03924939366884540.980375303165577
280.1151632902047480.2303265804094970.884836709795252
290.08808537027745710.1761707405549140.911914629722543
300.06580482967391470.1316096593478290.934195170326085
310.06036545937294770.1207309187458950.939634540627052
320.08190856045799830.1638171209159970.918091439542002
330.06343754530080240.1268750906016050.936562454699198
340.04913858722508090.09827717445016180.95086141277492
350.03649001267461990.07298002534923990.96350998732538
360.04103357241318120.08206714482636230.958966427586819
370.03048772367583260.06097544735166520.969512276324167
380.04582099394744110.09164198789488210.95417900605256
390.03475852118691720.06951704237383450.965241478813083
400.1059798262499870.2119596524999730.894020173750013
410.08924416792969460.1784883358593890.910755832070305
420.0798230687163580.1596461374327160.920176931283642
430.0636621206685390.1273242413370780.936337879331461
440.04981680452472760.09963360904945530.950183195475272
450.03828964691036530.07657929382073050.961710353089635
460.03211118419827490.06422236839654990.967888815801725
470.03310711933328050.0662142386665610.96689288066672
480.02702816140796290.05405632281592580.972971838592037
490.0260501634124180.0521003268248360.973949836587582
500.02162437836751060.04324875673502130.97837562163249
510.02166229355586840.04332458711173680.978337706444132
520.08469397761332280.1693879552266460.915306022386677
530.07708425740502520.1541685148100500.922915742594975
540.06453189013697490.1290637802739500.935468109863025
550.06309808874951260.1261961774990250.936901911250487
560.06812200528546950.1362440105709390.93187799471453
570.05842251466588340.1168450293317670.941577485334117
580.05597675507373910.1119535101474780.94402324492626
590.05804216796830590.1160843359366120.941957832031694
600.05203857815730130.1040771563146030.947961421842699
610.04456610539838810.08913221079677620.955433894601612
620.03590123528347190.07180247056694390.964098764716528
630.03680726123020150.0736145224604030.963192738769798
640.03567551894025560.07135103788051110.964324481059744
650.0345251502251490.0690503004502980.965474849774851
660.0289623454367980.0579246908735960.971037654563202
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6430.2426153898696360.4852307797392720.757384610130364


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level5100.80188679245283NOK
5% type I error level5540.871069182389937NOK
10% type I error level5840.918238993710692NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/10jh151292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/10jh151292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/1n73f1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/1n73f1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/2n73f1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/2n73f1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/3n73f1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/3n73f1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/4xy2i1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/4xy2i1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/5xy2i1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/5xy2i1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/6xy2i1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/6xy2i1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/7q8jl1292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/7q8jl1292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/8jh151292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/8jh151292085873.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/9jh151292085873.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292085918sf9f0vzzyzblrj7/9jh151292085873.ps (open in new window)


 
Parameters (Session):
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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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