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*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: Mon, 06 Dec 2010 15:16:44 +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/06/t1291648490q79r1taey3chjgn.htm/, Retrieved Mon, 06 Dec 2010 16:14:53 +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/06/t1291648490q79r1taey3chjgn.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 «
3484.74 13830.14 9349.44 7977 -5.6 6 1 2.77 3411.13 14153.22 9327.78 8241 -6.2 3 1 2.76 3288.18 15418.03 9753.63 8444 -7.1 2 1.2 2.76 3280.37 16666.97 10443.5 8490 -1.4 2 1.2 2.46 3173.95 16505.21 10853.87 8388 -0.1 2 0.8 2.46 3165.26 17135.96 10704.02 8099 -0.9 -8 0.7 2.47 3092.71 18033.25 11052.23 7984 0 0 0.7 2.71 3053.05 17671 10935.47 7786 0.1 -2 0.9 2.8 3181.96 17544.22 10714.03 8086 2.6 3 1.2 2.89 2999.93 17677.9 10394.48 9315 6 5 1.3 3.36 3249.57 18470.97 10817.9 9113 6.4 8 1.5 3.31 3210.52 18409.96 11251.2 9023 8.6 8 1.9 3.5 3030.29 18941.6 11281.26 9026 6.4 9 1.8 3.51 2803.47 19685.53 10539.68 9787 7.7 11 1.9 3.71 2767.63 19834.71 10483.39 9536 9.2 13 2.2 3.71 2882.6 19598.93 10947.43 9490 8.6 12 2.1 3.71 2863.36 17039.97 10580.27 9736 7.4 13 2.2 4.21 2897.06 16969.28 10582.92 9694 8.6 15 2.7 4.21 3012.61 16973.38 10654.41 9647 6.2 13 2.8 4.21 3142.95 16329.89 11014.51 9753 6 16 2.9 4.5 3032.93 16153.34 10967.87 10070 6.6 10 3.4 4.51 3045.78 15311.7 10433.56 10137 5.1 14 3 4.51 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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
BEL_20[t] = -1770.68500382969 + 0.0955963133515418Nikkei[t] + 0.337085778067862DJ_Indust[t] -0.0386263036901069Goudprijs[t] -5.28252498489441Conjunct_Seizoenzuiver[t] + 0.206420080151677Cons_vertrouw[t] -25.1205354165253Alg_consumptie_index_BE[t] + 22.2266428401259Gem_rente_kasbon_1j[t] + 6.5800042346992t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-1770.68500382969370.631391-4.77755e-062e-06
Nikkei0.09559631335154180.0191844.98322e-061e-06
DJ_Indust0.3370857780678620.0425597.920400
Goudprijs-0.03862630369010690.020299-1.90290.0593920.029696
Conjunct_Seizoenzuiver-5.282524984894417.911318-0.66770.5055650.252782
Cons_vertrouw0.2064200801516777.0212710.02940.9765940.488297
Alg_consumptie_index_BE-25.120535416525329.578485-0.84930.3973730.198687
Gem_rente_kasbon_1j22.226642840125944.5554260.49890.6187730.309387
t6.58000423469922.6836612.45190.0156150.007808


Multiple Linear Regression - Regression Statistics
Multiple R0.930603564293447
R-squared0.866022993875667
Adjusted R-squared0.857309042257825
F-TEST (value)99.3834980793776
F-TEST (DF numerator)8
F-TEST (DF denominator)123
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation284.425783203429
Sum Squared Residuals9950457.21655872


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
13484.742468.714555550451016.02544444955
23411.132491.00918289771920.120817102292
33288.182753.73094448663534.449055513375
43280.373073.6951787986206.674821201404
53173.953210.2622287939-36.312228793896
63165.263242.6864447545-77.4264447545041
73092.713459.09421113822-366.384211138219
83053.053395.36952165827-342.319521658273
93181.963285.88768465306-103.927684653061
102999.933140.44624024474-140.516240244737
113249.573365.74299722522-116.172997225225
123210.523498.57959835751-288.059598357507
133030.293580.56164121704-550.271641217043
142803.473374.36675514193-570.896755141931
152767.633370.88135323919-603.251353239194
162882.63518.59490158866-635.99490158866
172863.363162.42799676657-299.067996766571
182897.063146.27942214748-249.219422147481
193012.613188.91823607622-176.308236076219
203142.953256.88260724284-113.932607242838
213032.933201.87282664477-168.942826644766
223045.782964.0965672481581.6834327518455
233110.523001.80809449923108.711905500765
243013.242998.4684099989414.7715900010552
252987.12972.6241174215614.4758825784437
262995.552971.0138249534524.5361750465484
272833.182685.70549232266147.474507677338
282848.962821.5485637437327.4114362562711
292794.832968.75357735117-173.92357735117
302845.262950.12693347642-104.866933476424
312915.022788.78620736817126.233792631829
322892.632705.2557797642187.374220235804
332604.422137.94410699397466.475893006025
342641.652255.80599253319385.844007466809
352659.812443.11883260993216.691167390067
362638.532525.71096542434112.819034575655
372720.252455.15855377993265.091446220066
382745.882407.8514146226338.028585377399
392735.72749.16151724167-13.4615172416718
402811.72651.81789514717159.882104852827
412799.432659.40358717499140.026412825012
422555.282413.38016399667141.899836003335
432304.982085.8981706409219.081829359097
442214.952059.51215995094155.437840049061
452065.811837.88605229075227.923947709246
461940.491762.70251523031177.78748476969
4720421966.3537228347975.6462771652112
481995.371915.3874471118479.9825528881582
491946.811890.2858957592356.5241042407695
501765.91688.0469392848277.8530607151772
511635.251717.51575976165-82.2657597616549
521833.421852.419929748-18.9999297480023
531910.431970.61333655813-60.1833365581281
541959.672206.478234715-246.808234715003
551969.62296.17238305763-326.572383057631
562061.412330.07375851178-268.663758511778
572093.482444.64333478101-351.163334781011
582120.882538.54779191912-417.667791919117
592174.562491.93479481776-317.374794817759
602196.722631.99491748651-435.274917486515
612350.442844.37128760197-493.931287601966
622440.252856.95039631174-416.700396311742
632408.642831.347824097-422.707824097003
642472.812878.05973887039-405.249738870391
652407.62687.67136954634-280.071369546336
662454.622841.86431968284-387.244319682844
672448.052742.69319306098-294.64319306098
682497.842682.54772946135-184.707729461347
692645.642755.61628880492-109.976288804916
702756.762662.3642148662694.3957851337415
712849.272817.0763576954132.1936423045897
722921.442948.03235238953-26.5923523895317
732981.852941.5293355516340.320664448373
743080.583025.742122773154.8378772268966
753106.223030.9104758362675.3095241637409
763119.312880.96614117017238.34385882983
773061.262898.33861547214162.921384527856
783097.312933.05115452246164.258845477541
793161.692986.84847084399174.841529156007
803257.163035.62329450069221.536705499311
813277.013066.5122513349210.497748665096
823295.323027.53637079026267.783629209742
833363.993231.40060970402132.589390295977
843494.173368.55170897259125.618291027413
853667.033412.46641404139254.563585958608
863813.063441.6408431349371.419156865098
873917.963537.76692973619380.193070263807
883895.513607.5488274552287.961172544804
893801.063526.55618788865274.503812111354
903570.123362.9455446265207.1744553735
913701.613363.08191816122338.528081838776
923862.273520.23986375816342.03013624184
933970.13678.02100558691292.07899441309
944138.523886.06779144087252.452208559133
954199.753899.28611587892300.46388412108
964290.894038.38464818643252.505351813567
974443.914137.56686536448306.34313463552
984502.644205.11896320073297.521036799268
994356.984053.49884470001303.481155299992
1004591.274236.3311205109354.938879489104
1014696.964495.28120707454201.678792925455
1024621.44569.7245684713651.6754315286372
1034562.844648.93764585881-86.0976458588118
1044202.524379.94304627355-177.423046273549
1054296.494433.63150010792-137.141500107917
1064435.234591.57555783358-156.345557833586
1074105.184185.89231052842-80.7123105284217
1084116.684266.55060215488-149.870602154877
1093844.493728.43351691644116.056483083563
1103720.983642.662563839278.317436160804
1113674.43459.58790269405214.812097305948
1123857.623783.0864690387474.5335309612638
1133801.063876.9142213784-75.8542213784019
1143504.373641.43386318247-137.063863182466
1153032.63294.42288067018-261.822880670183
1163047.033409.52552472784-362.495524727842
1172962.343203.53675335188-241.196753351877
1182197.822276.99463047801-79.1746304780064
1192014.452133.83760186707-119.387601867068
1201862.832147.64479067156-284.814790671562
1211905.412009.29424770509-103.884247705087
1221810.991627.39543520662183.594564793377
1231670.071534.5582815353135.511718464701
1241864.441918.55819287984-54.1181928798359
1252052.022123.41356931459-71.3935693145946
1262029.62244.23144403825-214.631444038255
1272070.832294.93099992802-224.100999928024
1282293.412554.60918294172-261.199182941717
1292443.272621.43149784129-178.161497841285
1302513.172628.06484580727-114.894845807275
1312466.922606.50088840556-139.58088840556
1322502.662687.96447898928-185.304478989281


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.09909768572298630.1981953714459730.900902314277014
130.04146165927848650.0829233185569730.958538340721514
140.03221303803640410.06442607607280830.967786961963596
150.02552579961503650.0510515992300730.974474200384964
160.01615202672739090.03230405345478190.98384797327261
170.009807743201380440.01961548640276090.99019225679862
180.00518315145101380.01036630290202760.994816848548986
190.00641905605982370.01283811211964740.993580943940176
200.005592166133110790.01118433226622160.99440783386689
210.003784582634915370.007569165269830730.996215417365085
220.00370321169222960.00740642338445920.99629678830777
230.002179395570719370.004358791141438740.99782060442928
240.001106454592621260.002212909185242530.998893545407379
250.0005358621029014260.001071724205802850.999464137897099
260.0002424292247361510.0004848584494723020.999757570775264
270.0001032075743486190.0002064151486972380.999896792425651
285.26061444810564e-050.0001052122889621130.999947393855519
294.29943774621196e-058.59887549242392e-050.999957005622538
302.74690990932213e-055.49381981864426e-050.999972530900907
312.18424763754517e-054.36849527509033e-050.999978157523625
321.14224795834013e-052.28449591668026e-050.999988577520417
336.45289282490208e-061.29057856498042e-050.999993547107175
344.95596221558763e-069.91192443117526e-060.999995044037784
352.98457122730728e-065.96914245461456e-060.999997015428773
361.29113565243568e-062.58227130487136e-060.999998708864348
375.86947829496836e-071.17389565899367e-060.99999941305217
384.43489869954533e-078.86979739909065e-070.99999955651013
392.3823255094251e-074.7646510188502e-070.99999976176745
402.26364511286907e-064.52729022573813e-060.999997736354887
413.69076424499174e-067.38152848998348e-060.999996309235755
421.90082341875233e-063.80164683750466e-060.999998099176581
439.28213005344426e-071.85642601068885e-060.999999071786995
447.74275363284999e-071.54855072657e-060.999999225724637
457.78365429325444e-071.55673085865089e-060.99999922163457
461.81213087436266e-063.62426174872533e-060.999998187869126
472.73541781479087e-065.47083562958174e-060.999997264582185
482.69811875237728e-065.39623750475457e-060.999997301881248
493.75995799827355e-067.51991599654709e-060.999996240042002
502.70196554626032e-065.40393109252065e-060.999997298034454
511.75920946057267e-063.51841892114534e-060.99999824079054
522.00763851042682e-064.01527702085364e-060.99999799236149
532.93883244372723e-065.87766488745445e-060.999997061167556
542.56703110884539e-065.13406221769078e-060.999997432968891
554.4837717801403e-068.9675435602806e-060.99999551622822
561.37902941357407e-052.75805882714815e-050.999986209705864
572.0407760488999e-054.08155209779979e-050.999979592239511
580.0001339388038904820.0002678776077809640.99986606119611
590.0001610733172909820.0003221466345819640.999838926682709
600.0001303213503716540.0002606427007433080.999869678649628
610.0001849222555378390.0003698445110756780.999815077744462
620.0002611077844864820.0005222155689729630.999738892215513
630.001555331986427160.003110663972854320.998444668013573
640.02353449235206820.04706898470413640.976465507647932
650.08781043765528620.1756208753105720.912189562344714
660.3061824629364110.6123649258728220.693817537063589
670.6044011883816850.7911976232366310.395598811618315
680.8370155780019210.3259688439961570.162984421998079
690.964826409290660.07034718141867850.0351735907093393
700.9921604278344380.01567914433112480.00783957216556241
710.9984835402904950.003032919419009770.00151645970950489
720.9998706985325880.0002586029348237220.000129301467411861
730.9999878635584582.42728830832355e-051.21364415416177e-05
740.9999986580333482.68393330413679e-061.34196665206839e-06
750.999999776168324.47663359554303e-072.23831679777151e-07
760.9999999325869581.34826084755486e-076.7413042377743e-08
770.9999999625037217.49925573968967e-083.74962786984483e-08
780.999999974818095.03638215886201e-082.518191079431e-08
790.999999973805355.23893023009443e-082.61946511504721e-08
800.999999974335225.1329561366762e-082.5664780683381e-08
810.9999999859969122.80061753710593e-081.40030876855297e-08
820.999999993290131.34197386569586e-086.70986932847929e-09
830.9999999899379582.01240849077008e-081.00620424538504e-08
840.9999999893236172.13527664259053e-081.06763832129527e-08
850.999999992810981.43780413210656e-087.18902066053279e-09
860.9999999899674742.00650527623577e-081.00325263811789e-08
870.9999999829029033.41941941234846e-081.70970970617423e-08
880.999999985679792.86404183059871e-081.43202091529936e-08
890.9999999974062625.18747549547235e-092.59373774773618e-09
900.9999999989701842.05963242172522e-091.02981621086261e-09
910.9999999992607281.47854481760665e-097.39272408803325e-10
920.999999999430071.13985832410793e-095.69929162053963e-10
930.9999999993068211.38635747407843e-096.93178737039216e-10
940.999999999658046.83917811613035e-103.41958905806517e-10
950.9999999999169861.66028081317776e-108.30140406588878e-11
960.999999999844843.10317831194975e-101.55158915597487e-10
970.9999999996568126.86376073058275e-103.43188036529137e-10
980.9999999989738352.05233108064312e-091.02616554032156e-09
990.999999996579136.84174049060072e-093.42087024530036e-09
1000.9999999940512761.18974485227981e-085.94872426139904e-09
1010.9999999930602471.38795067949803e-086.93975339749015e-09
1020.9999999946901921.06196151243148e-085.30980756215742e-09
1030.9999999956631678.6736664673341e-094.33683323366705e-09
1040.9999999856430032.87139932394678e-081.43569966197339e-08
1050.9999999537705039.24589941915615e-084.62294970957807e-08
1060.9999998649794672.7004106504741e-071.35020532523705e-07
1070.9999995791755018.41648997106693e-074.20824498553346e-07
1080.9999993925552881.2148894230568e-066.07444711528402e-07
1090.9999978582569444.28348611108864e-062.14174305554432e-06
1100.999997126601715.74679658138078e-062.87339829069039e-06
1110.9999965196513366.96069732886509e-063.48034866443254e-06
1120.9999870703408632.58593182746013e-051.29296591373007e-05
1130.9999508893297139.82213405748964e-054.91106702874482e-05
1140.9999739799428225.2040114355683e-052.60200571778415e-05
1150.9998965906974360.0002068186051285840.000103409302564292
1160.9995858632488950.0008282735022098360.000414136751104918
1170.9986808727031420.00263825459371520.0013191272968576
1180.995092546344780.009814907310440780.00490745365522039
1190.9986042873009850.00279142539803020.0013957126990151
1200.9930404035723470.01391919285530660.0069595964276533


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level920.844036697247706NOK
5% type I error level1000.91743119266055NOK
10% type I error level1040.954128440366973NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/06/t1291648490q79r1taey3chjgn/10wpiz1291648593.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/06/t1291648490q79r1taey3chjgn/10wpiz1291648593.ps (open in new window)


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Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa


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