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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: Fri, 20 Nov 2009 06:54:09 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii.htm/, Retrieved Fri, 20 Nov 2009 14:57:57 +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/2009/Nov/20/t1258725465rmtllii0syw32ii.htm/},
    year = {2009},
}
@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 = {2009},
    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:

Rob_WS7_3

Dataseries X:

» Textbox « » Textfile « » CSV «

106370 100.3 109375 101.9 116476 102.1 123297 103.2 114813 103.7 117925 106.2 126466 107.7 131235 109.9 120546 111.7 123791 114.9 129813 116 133463 118.3 122987 120.4 125418 126 130199 128.1 133016 130.1 121454 130.8 122044 133.6 128313 134.2 131556 135.5 120027 136.2 123001 139.1 130111 139 132524 139.6 123742 138.7 124931 140.9 133646 141.3 136557 141.8 127509 142 128945 144.5 137191 144.6 139716 145.5 129083 146.8 131604 149.5 139413 149.9 143125 150.1 133948 150.9 137116 152.8 144864 153.1 149277 154 138796 154.9 143258 156.9 150034 158.4 154708 159.7 144888 160.2 148762 163.2 156500 163.7 161088 164.4 152772 163.7 158011 165.5 163318 165.6 169969 166.8 162269 167.5 165765 170.6 170600 170.9 174681 172 166364 171.8 170240 173.9 176150 174 182056 173.8 172218 173.9 177856 176 182253 176.6 188090 178.2 176863 179.2 183273 181.3 187969 181.8 194650 182.9 183036 183.8 189516 186.3 193805 187.4 200499 189.2 188142 189.7 193732 191.9 1971 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

HFCE[t] = + 207810.550167275 -916.412977675751RPI[t] -12387.1281946683Q1[t] -7662.91380445677Q2[t] -3693.15077859989Q3[t] + 2256.38373280118t + 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)	207810.550167275	15140.955004	13.7251	0	0
RPI	-916.412977675751	142.774914	-6.4186	0	0
Q1	-12387.1281946683	1503.800975	-8.2372	0	0
Q2	-7662.91380445677	1498.259582	-5.1145	2e-06	1e-06
Q3	-3693.15077859989	1513.506414	-2.4401	0.016785	0.008392
t	2256.38373280118	172.293592	13.0962	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.98769504249566
R-squared	0.975541496970502
Adjusted R-squared	0.974085633694937
F-TEST (value)	670.07768747493
F-TEST (DF numerator)	5
F-TEST (DF denominator)	84
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5019.13495884401
Sum Squared Residuals	2116104121.74756

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	106370	105763.584044531	606.415955469496
2	109375	111277.921403262	-1902.92140326173
3	116476	117320.785566385	-844.785566384756
4	123297	122262.265802343	1034.73419765748
5	114813	111673.314851638	3139.68514836246
6	117925	116362.880530461	1562.11946953911
7	126466	121214.407822605	5251.59217739472
8	131235	125147.833783120	6087.16621688032
9	120546	113367.545961436	7178.45403856375
10	123791	117415.622555887	6375.37744411349
11	129813	122633.715039101	7179.28496089875
12	133463	126475.499701848	6987.5002981519
13	122987	114420.287986862	8566.71201313807
14	125418	116268.973434890	9149.02656510961
15	130199	120570.652940429	9628.34705957062
16	133016	124687.361496479	8328.63850352105
17	121454	113915.127950239	7538.87204976118
18	122044	118329.769735759	3714.23026424060
19	128313	124006.068707812	4306.93129218798
20	131556	128764.266348235	2791.7336517654
21	120027	117992.032801995	2034.96719800550
22	123001	122315.033289747	685.966710252511
23	130111	128632.821346173	1478.17865382688
24	132524	134032.508070969	-1508.50807096875
25	123742	124726.535289010	-984.535289009851
26	124931	129691.024861136	-4760.02486113585
27	133646	135550.606428724	-1904.60642872361
28	136557	141041.934451287	-4484.93445128681
29	127509	130727.907393885	-3218.90739388459
30	128945	135417.473072708	-6472.47307270788
31	137191	141551.978533598	-4360.97853359837
32	139716	146676.741365091	-6960.74136509126
33	129083	135354.660032246	-6271.6600322457
34	131604	139860.943115534	-8256.94311553384
35	139413	145720.524683122	-6307.5246831216
36	143125	151486.776598988	-8361.77659898753
37	133948	140622.901754980	-6674.90175497984
38	137116	145862.315220409	-8746.31522040857
39	144864	151813.538085764	-6949.53808576393
40	149277	156938.300917257	-7661.30091725682
41	138796	145982.784775482	-7186.78477548156
42	143258	151130.556943143	-7872.55694314272
43	150034	155982.084235287	-5948.08423528716
44	154708	160740.281875710	-6032.28187570976
45	144888	150151.330925005	-5263.33092500481
46	148762	154382.690114990	-5620.69011499023
47	156500	160150.630384810	-3650.63038481041
48	161088	165458.675811838	-4370.67581183844
49	152772	155969.420434344	-3197.4204343444
50	158011	161300.475197541	-3289.47519754071
51	163318	167434.980658431	-4116.9806584312
52	169969	172284.819596621	-2315.81959662135
53	162269	161512.586050381	756.413949618739
54	165765	165652.303942599	112.696057400897
55	170600	171603.526807954	-1003.52680795443
56	174681	176545.007043912	-1864.00704391218
57	166364	166597.545177580	-233.545177580242
58	170240	171653.676047474	-1413.67604747384
59	176150	177788.181508364	-1638.18150836433
60	182056	183920.998615301	-1864.99861530054
61	172218	173698.612855666	-1480.61285566590
62	177856	178754.743725559	-898.74372555949
63	182253	184431.042697612	-2178.04269761211
64	188090	188914.316444732	-824.316444731976
65	176863	177867.159005189	-1004.15900518914
66	183273	182923.289875083	349.710124917284
67	187969	188691.230144903	-722.230144902905
68	194650	193632.710380861	1017.28961913935
69	183036	182677.194239085	358.805760914604
70	189516	187366.759917909	2149.24008209132
71	193805	192584.852401123	1220.14759887658
72	200499	196884.843552708	3614.15644729185
73	188142	186295.892602003	1846.10739799680
74	193732	191260.382174129	2471.6178258708
75	197126	196845.039848414	280.960151585752
76	205140	201786.520084372	3353.47991562801
77	191751	191197.569133667	553.430866332951
78	196700	195062.363132582	1637.63686741785
79	199784	199730.607829191	53.3921708085906
80	207360	203755.675087473	3604.32491252657
81	196101	192158.669861325	3942.33013867486
82	200824	196115.105158008	4708.89484199219
83	205743	201608.121534525	4134.87846547471
84	212489	205083.341006202	7405.6589937982
85	200810	193761.259673356	7048.74032664374
86	203683	196892.923290131	6790.07670986924
87	207286	201194.602795670	6091.39720433025
88	210910	208885.321964655	2024.67803534524
89	194915	202970.077200096	-8055.07720009614
90	217920	208392.77326106	9527.22673893995

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0133604220550558	0.0267208441101116	0.986639577944944
10	0.00258489005530117	0.00516978011060233	0.997415109944699
11	0.00107740855493654	0.00215481710987308	0.998922591445063
12	0.00107590494660891	0.00215180989321783	0.998924095053391
13	0.000753547309656478	0.00150709461931296	0.999246452690344
14	0.000406935468693978	0.000813870937387956	0.999593064531306
15	0.000200740005016254	0.000401480010032508	0.999799259994984
16	0.000282888439568416	0.000565776879136832	0.999717111560432
17	0.00376915584763046	0.00753831169526092	0.99623084415237
18	0.0468723965891504	0.0937447931783007	0.95312760341085
19	0.188796242868056	0.377592485736112	0.811203757131944
20	0.486944526160188	0.973889052320376	0.513055473839812
21	0.782389426643155	0.435221146713689	0.217610573356845
22	0.877325863503558	0.245348272992885	0.122674136496442
23	0.944084890838707	0.111830218322586	0.0559151091612931
24	0.975849267294388	0.0483014654112234	0.0241507327056117
25	0.985113945156926	0.0297721096861472	0.0148860548430736
26	0.983781281676798	0.0324374366464043	0.0162187183232021
27	0.98707745758639	0.0258450848272185	0.0129225424136092
28	0.98457887750454	0.0308422449909189	0.0154211224954594
29	0.986755718647863	0.0264885627042738	0.0132442813521369
30	0.980169919975476	0.0396601600490475	0.0198300800245237
31	0.978572332461795	0.0428553350764093	0.0214276675382047
32	0.968758023500476	0.0624839529990472	0.0312419764995236
33	0.95786689854204	0.0842662029159206	0.0421331014579603
34	0.943282981050768	0.113434037898464	0.0567170189492318
35	0.924536042773884	0.150927914452232	0.0754639572261158
36	0.9041995991623	0.191600801675401	0.0958004008377004
37	0.883785164751393	0.232429670497214	0.116214835248607
38	0.885472091837342	0.229055816325316	0.114527908162658
39	0.86848857822009	0.26302284355982	0.13151142177991
40	0.865658568146349	0.268682863707302	0.134341431853651
41	0.852328612874887	0.295342774250226	0.147671387125113
42	0.890770336345988	0.218459327308025	0.109229663654012
43	0.88618244424751	0.227635111504981	0.113817555752490
44	0.898723248840586	0.202553502318828	0.101276751159414
45	0.902801431539164	0.194397136921672	0.0971985684608359
46	0.939205942379239	0.121588115241522	0.0607940576207611
47	0.945632665526299	0.108734668947402	0.0543673344737011
48	0.959703844454563	0.0805923110908738	0.0402961555454369
49	0.96766842015986	0.0646631596802807	0.0323315798401403
50	0.983110948950965	0.0337781020980708	0.0168890510490354
51	0.98397355275316	0.0320528944936781	0.0160264472468391
52	0.987861426590954	0.0242771468180915	0.0121385734090458
53	0.993574852376297	0.0128502952474059	0.00642514762370294
54	0.995551657913243	0.0088966841735144	0.0044483420867572
55	0.99483411637496	0.0103317672500815	0.00516588362504075
56	0.994620599382428	0.0107588012351437	0.00537940061757184
57	0.99385177049673	0.0122964590065392	0.00614822950326962
58	0.994213870117966	0.0115722597640684	0.00578612988203419
59	0.99198275370299	0.0160344925940212	0.0080172462970106
60	0.99068137959589	0.0186372408082188	0.00931862040410941
61	0.986191645385102	0.0276167092297954	0.0138083546148977
62	0.984810936432748	0.0303781271345039	0.0151890635672520
63	0.978466308160133	0.0430673836797343	0.0215336918398671
64	0.974201367551695	0.0515972648966101	0.0257986324483050
65	0.962729039579407	0.0745419208411854	0.0372709604205927
66	0.960634887671608	0.0787302246567842	0.0393651123283921
67	0.9447543777205	0.110491244558999	0.0552456222794994
68	0.928464043808275	0.143071912383451	0.0715359561917254
69	0.899112137580236	0.201775724839528	0.100887862419764
70	0.880062089339664	0.239875821320672	0.119937910660336
71	0.837340867627117	0.325318264745766	0.162659132372883
72	0.798752430984234	0.402495138031532	0.201247569015766
73	0.744109526034257	0.511780947931486	0.255890473965743
74	0.690172853696265	0.61965429260747	0.309827146303735
75	0.599847699673499	0.800304600653001	0.400152300326501
76	0.520758562522424	0.958482874955152	0.479241437477576
77	0.422470366305636	0.844940732611272	0.577529633694364
78	0.369946444425491	0.739892888850982	0.630053555574509
79	0.290864993224098	0.581729986448197	0.709135006775902
80	0.200078067964217	0.400156135928433	0.799921932035784
81	0.131274864127197	0.262549728254395	0.868725135872803

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.123287671232877	NOK
5% type I error level	31	0.424657534246575	NOK
10% type I error level	39	0.534246575342466	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/10ebvq1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/10ebvq1258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/15pf31258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/15pf31258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/2icme1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/2icme1258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/3y2421258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/3y2421258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/44b151258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/44b151258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/557vq1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/557vq1258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/6u6k11258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/6u6k11258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/7xf6n1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/7xf6n1258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/81aqo1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/81aqo1258725244.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/9vtnw1258725244.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725465rmtllii0syw32ii/9vtnw1258725244.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Quarterly 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')

}

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

Software written by Ed van Stee & Patrick Wessa

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