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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 17 Dec 2008 08:25:56 -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/17/t1229528000c65vpxi8jk930cs.htm/, Retrieved Wed, 17 Dec 2008 16:33:31 +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/17/t1229528000c65vpxi8jk930cs.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 «

6392,3 0 8686,4 0 9244,7 0 8182,7 0 7451,4 0 7988,8 0 8243,5 0 8843 0 9092,7 0 8246,7 0 9311,7 0 8341,2 0 7116,7 0 9635,7 0 9815,4 0 8611,3 0 8297,8 0 8715,1 0 8919,9 0 10085,8 0 9511,7 0 8991,3 0 10311,2 0 8895,4 0 7449,8 0 10084 0 9859,4 0 9100,1 0 8920,8 0 8502,7 0 8599,6 0 10394,4 0 9290,4 0 8742,2 0 10217,3 0 8639 0 8139,6 0 10779,1 0 10427,7 0 10349,1 0 10036,4 0 9492,1 0 10638,8 0 12054,5 0 10324,7 0 11817,3 0 11008,9 0 9996,6 0 9419,5 0 11958,8 0 12594,6 0 11890,6 0 10871,7 0 11835,7 0 11542,2 0 13093,7 0 11180,2 0 12035,7 0 12112 0 10875,2 0 9897,3 0 11672,1 1 12385,7 1 11405,6 1 9830,9 1 11025,1 1 10853,8 1 12252,6 1 11839,4 1 11669,1 1 11601,4 1 11178,4 1 9516,4 1 12102,8 1 12989 1 11610,2 1 10205,5 1 11356,2 1 11307,1 1 12648,6 1 11947,2 1 11714,1 1 12192,5 1 11268,8 1 9097,4 1 12639,8 1 13040,1 1 11687,3 1 11191,7 1 11391,9 1 11793,1 1 13933,2 1 12778,1 1 11810,3 1 13698,4 1 11956,6 1 10723,8 1 13938,9 1 13979,8 1 13807,4 1 12973,9 1 12509,8 1 12934,1 1 14908,3 1 13772,1 1 13012,6 1 14049,9 1 11816,5 1 11593,2 1 14466,2 1 13615,9 1 14733,9 1 13880,7 1 13527,5 1 13584 1 16170,2 1 13260,6 1 14741,9 1 15486,5 1 13154,5 1 12621,2 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

y[t] = + 6977.32040000001 -1116.87460000000x[t] -1075.56453484848M1[t] + 1619.51407575757M2[t] + 1754.82866818181M3[t] + 1033.88326060606M4[t] + 198.607853030304M5[t] + 403.482445454546M6[t] + 547.067037878787M7[t] + 2080.35163030303M8[t] + 878.096222727272M9[t] + 792.970815151514M10[t] + 1450.29540757576M11[t] + 63.5354075757575t + 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)	6977.32040000001	193.856184	35.9923	0	0
x	-1116.87460000000	188.16333	-5.9357	0	0
M1	-1075.56453484848	223.331627	-4.816	5e-06	2e-06
M2	1619.51407575757	230.054709	7.0397	0	0
M3	1754.82866818181	229.752888	7.6379	0	0
M4	1033.88326060606	229.482501	4.5053	1.7e-05	8e-06
M5	198.607853030304	229.24366	0.8664	0.388231	0.194115
M6	403.482445454546	229.036462	1.7617	0.080984	0.040492
M7	547.067037878787	228.860995	2.3904	0.018578	0.009289
M8	2080.35163030303	228.71733	9.0957	0	0
M9	878.096222727272	228.605529	3.8411	0.000208	0.000104
M10	792.970815151514	228.525637	3.4699	0.000752	0.000376
M11	1450.29540757576	228.477689	6.3476	0	0
t	63.5354075757575	2.702626	23.5088	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.970403731723856
R-squared	0.941683402543585
Adjusted R-squared	0.934598208460095
F-TEST (value)	132.908624865753
F-TEST (DF numerator)	13
F-TEST (DF denominator)	107
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	510.85590017062
Sum Squared Residuals	27924191.3290873

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6392.3	5965.29127272723	427.008727272767
2	8686.4	8723.9052909091	-37.5052909090933
3	9244.7	8922.7552909091	321.944709090906
4	8182.7	8265.34529090909	-82.645290909089
5	7451.4	7493.60529090908	-42.2052909090857
6	7988.8	7762.0152909091	226.784709090905
7	8243.5	7969.1352909091	274.364709090907
8	8843	9565.9552909091	-722.955290909096
9	9092.7	8427.2352909091	665.46470909091
10	8246.7	8405.6452909091	-158.945290909096
11	9311.7	9126.50529090909	185.194709090911
12	8341.2	7739.7452909091	601.454709090909
13	7116.7	6727.71616363637	388.983836363634
14	9635.7	9486.33018181818	149.369818181822
15	9815.4	9685.18018181818	130.219818181817
16	8611.3	9027.77018181818	-416.470181818184
17	8297.8	8256.03018181818	41.7698181818158
18	8715.1	8524.44018181818	190.659818181818
19	8919.9	8731.56018181818	188.339818181817
20	10085.8	10328.3801818182	-242.580181818183
21	9511.7	9189.66018181818	322.039818181818
22	8991.3	9168.07018181818	-176.770181818183
23	10311.2	9888.93018181818	422.269818181818
24	8895.4	8502.17018181818	393.229818181816
25	7449.8	7490.14105454546	-40.3410545454595
26	10084	10248.7550727273	-164.755072727273
27	9859.4	10447.6050727273	-588.205072727274
28	9100.1	9790.19507272727	-690.095072727273
29	8920.8	9018.45507272727	-97.6550727272747
30	8502.7	9286.86507272727	-784.165072727272
31	8599.6	9493.98507272727	-894.385072727273
32	10394.4	11090.8050727273	-696.405072727273
33	9290.4	9952.08507272727	-661.685072727274
34	8742.2	9930.49507272727	-1188.29507272727
35	10217.3	10651.3550727273	-434.055072727274
36	8639	9264.59507272727	-625.595072727274
37	8139.6	8252.56594545455	-112.965945454550
38	10779.1	11011.1799636364	-232.079963636364
39	10427.7	11210.0299636364	-782.329963636363
40	10349.1	10552.6199636364	-203.519963636364
41	10036.4	9780.87996363637	255.520036363635
42	9492.1	10049.2899636364	-557.189963636363
43	10638.8	10256.4099636364	382.390036363636
44	12054.5	11853.2299636364	201.270036363637
45	10324.7	10714.5099636364	-389.809963636363
46	11817.3	10692.9199636364	1124.38003636364
47	11008.9	11413.7799636364	-404.879963636364
48	9996.6	10027.0199636364	-30.4199636363639
49	9419.5	9014.99083636364	404.50916363636
50	11958.8	11773.6048545455	185.195145454546
51	12594.6	11972.4548545455	622.145145454547
52	11890.6	11315.0448545455	575.555145454546
53	10871.7	10543.3048545455	328.395145454546
54	11835.7	10811.7148545455	1023.98514545455
55	11542.2	11018.8348545455	523.365145454547
56	13093.7	12615.6548545455	478.045145454547
57	11180.2	11476.9348545455	-296.734854545454
58	12035.7	11455.3448545455	580.355145454546
59	12112	12176.2048545455	-64.204854545455
60	10875.2	10789.4448545455	85.7551454545451
61	9897.3	9777.41572727273	119.884272727268
62	11672.1	11419.1551454545	252.944854545454
63	12385.7	11618.0051454545	767.694854545455
64	11405.6	10960.5951454545	445.004854545454
65	9830.9	10188.8551454545	-357.955145454546
66	11025.1	10457.2651454545	567.834854545454
67	10853.8	10664.3851454545	189.414854545453
68	12252.6	12261.2051454545	-8.60514545454526
69	11839.4	11122.4851454545	716.914854545453
70	11669.1	11100.8951454545	568.204854545455
71	11601.4	11821.7551454545	-220.355145454546
72	11178.4	10434.9951454545	743.404854545453
73	9516.4	9422.96601818182	93.433981818177
74	12102.8	12181.5800363636	-78.7800363636379
75	12989	12380.4300363636	608.569963636364
76	11610.2	11723.0200363636	-112.820036363636
77	10205.5	10951.2800363636	-745.780036363637
78	11356.2	11219.6900363636	136.509963636364
79	11307.1	11426.8100363636	-119.710036363636
80	12648.6	13023.6300363636	-375.030036363635
81	11947.2	11884.9100363636	62.2899636363642
82	11714.1	11863.3200363636	-149.220036363636
83	12192.5	12584.1800363636	-391.680036363636
84	11268.8	11197.4200363636	71.3799636363621
85	9097.4	10185.3909090909	-1087.99090909091
86	12639.8	12944.0049272727	-304.204927272728
87	13040.1	13142.8549272727	-102.754927272726
88	11687.3	12485.4449272727	-798.144927272728
89	11191.7	11713.7049272727	-522.004927272727
90	11391.9	11982.1149272727	-590.214927272727
91	11793.1	12189.2349272727	-396.134927272726
92	13933.2	13786.0549272727	147.145072727275
93	12778.1	12647.3349272727	130.765072727273
94	11810.3	12625.7449272727	-815.444927272727
95	13698.4	13346.6049272727	351.795072727273
96	11956.6	11959.8449272727	-3.24492727272725
97	10723.8	10947.8158	-224.015800000005
98	13938.9	13706.4298181818	232.470181818182
99	13979.8	13905.2798181818	74.5201818181821
100	13807.4	13247.8698181818	559.530181818182
101	12973.9	12476.1298181818	497.770181818182
102	12509.8	12744.5398181818	-234.739818181818
103	12934.1	12951.6598181818	-17.559818181817
104	14908.3	14548.4798181818	359.820181818183
105	13772.1	13409.7598181818	362.340181818183
106	13012.6	13388.1698181818	-375.569818181816
107	14049.9	14109.0298181818	-59.1298181818175
108	11816.5	12722.2698181818	-905.769818181818
109	11593.2	11710.2406909091	-117.040690909093
110	14466.2	14468.8547090909	-2.65470909090794
111	13615.9	14667.7047090909	-1051.80470909091
112	14733.9	14010.2947090909	723.605290909092
113	13880.7	13238.5547090909	642.145290909092
114	13527.5	13506.9647090909	20.5352909090921
115	13584	13714.0847090909	-130.084709090908
116	16170.2	15310.9047090909	859.295290909094
117	13260.6	14172.1847090909	-911.584709090908
118	14741.9	14150.5947090909	591.305290909093
119	15486.5	14871.4547090909	615.045290909093
120	13154.5	13484.6947090909	-330.194709090909
121	12621.2	12472.6655818182	148.534418181817

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0423450463876221	0.0846900927752443	0.957654953612378
18	0.0110243647424286	0.0220487294848573	0.988975635257571
19	0.00262511696443820	0.00525023392887639	0.997374883035562
20	0.00700635704018269	0.0140127140803654	0.992993642959817
21	0.00460369757294277	0.00920739514588554	0.995396302427057
22	0.00150330111202018	0.00300660222404035	0.99849669888798
23	0.000773942074161771	0.00154788414832354	0.999226057925838
24	0.000338900806496716	0.000677801612993432	0.999661099193503
25	0.000359675551056950	0.000719351102113899	0.999640324448943
26	0.000128511418529527	0.000257022837059055	0.99987148858147
27	0.000672102775296476	0.00134420555059295	0.999327897224703
28	0.00036910933430579	0.00073821866861158	0.999630890665694
29	0.000163003180819240	0.000326006361638479	0.99983699681918
30	0.000864357514070048	0.00172871502814010	0.99913564248593
31	0.0035266350752554	0.0070532701505108	0.996473364924745
32	0.00241466254094226	0.00482932508188452	0.997585337459058
33	0.00587370262976448	0.0117474052595290	0.994126297370236
34	0.0114900258208616	0.0229800516417232	0.988509974179138
35	0.00824089535226801	0.0164817907045360	0.991759104647732
36	0.0113954309315143	0.0227908618630286	0.988604569068486
37	0.00852028402879505	0.0170405680575901	0.991479715971205
38	0.00796793724688458	0.0159358744937692	0.992032062753115
39	0.0074758749875496	0.0149517499750992	0.99252412501245
40	0.0170647548888168	0.0341295097776336	0.982935245111183
41	0.0303301834846368	0.0606603669692737	0.969669816515363
42	0.0279070101231341	0.0558140202462682	0.972092989876866
43	0.0610817915039312	0.122163583007862	0.938918208496069
44	0.139237587664959	0.278475175329918	0.860762412335041
45	0.123424242653078	0.246848485306156	0.876575757346922
46	0.550672682597487	0.898654634805025	0.449327317402513
47	0.533962208031336	0.932075583937327	0.466037791968664
48	0.480382241235449	0.960764482470898	0.519617758764551
49	0.461153054015321	0.922306108030642	0.538846945984679
50	0.435995738973346	0.871991477946693	0.564004261026654
51	0.508389816660496	0.983220366679008	0.491610183339504
52	0.567820325390653	0.864359349218694	0.432179674609347
53	0.523485835900341	0.953028328199317	0.476514164099659
54	0.666544165582774	0.666911668834453	0.333455834417226
55	0.648385531387695	0.70322893722461	0.351614468612305
56	0.64646333193113	0.70707333613774	0.35353666806887
57	0.624300724071755	0.751398551856489	0.375699275928245
58	0.612406764365028	0.775186471269944	0.387593235634972
59	0.563669440071423	0.872661119857155	0.436330559928577
60	0.506818987221081	0.986362025557837	0.493181012778919
61	0.450543183466475	0.90108636693295	0.549456816533525
62	0.397345244514625	0.79469048902925	0.602654755485375
63	0.413332573546506	0.826665147093012	0.586667426453494
64	0.367124282706779	0.734248565413559	0.63287571729322
65	0.38203166772246	0.76406333544492	0.61796833227754
66	0.376331175737454	0.752662351474908	0.623668824262546
67	0.338749375013902	0.677498750027804	0.661250624986098
68	0.290318952451465	0.580637904902931	0.709681047548534
69	0.315631653369783	0.631263306739567	0.684368346630217
70	0.332219334907485	0.66443866981497	0.667780665092515
71	0.300536593857239	0.601073187714479	0.69946340614276
72	0.398701688012434	0.797403376024869	0.601298311987566
73	0.404807925085436	0.809615850170871	0.595192074914564
74	0.359941469829332	0.719882939658665	0.640058530170668
75	0.478346559312381	0.956693118624761	0.521653440687619
76	0.424105769924497	0.848211539848994	0.575894230075503
77	0.488048599092073	0.976097198184146	0.511951400907927
78	0.48834976704923	0.97669953409846	0.51165023295077
79	0.455533596366195	0.911067192732389	0.544466403633805
80	0.43630699219902	0.87261398439804	0.56369300780098
81	0.409999272466042	0.819998544932084	0.590000727533958
82	0.379469672971793	0.758939345943585	0.620530327028207
83	0.343104651384623	0.686209302769246	0.656895348615377
84	0.387324014741655	0.774648029483309	0.612675985258345
85	0.486393571252599	0.972787142505199	0.513606428747401
86	0.426727150327179	0.853454300654358	0.573272849672821
87	0.422454729800269	0.844909459600538	0.577545270199731
88	0.589826714373446	0.820346571253107	0.410173285626554
89	0.66320805063825	0.6735838987235	0.33679194936175
90	0.632122181346643	0.735755637306714	0.367877818653357
91	0.572050755568965	0.85589848886207	0.427949244431035
92	0.526664005984947	0.946671988030106	0.473335994015053
93	0.488860784468933	0.977721568937866	0.511139215531067
94	0.584118392666283	0.831763214667433	0.415881607333717
95	0.508449985265897	0.983100029468206	0.491550014734103
96	0.516574158341158	0.966851683317685	0.483425841658842
97	0.426415331381404	0.852830662762808	0.573584668618596
98	0.351618521524242	0.703237043048484	0.648381478475758
99	0.55244246116588	0.895115077668239	0.447557538834120
100	0.458629979723832	0.917259959447663	0.541370020276168
101	0.357010456617469	0.714020913234937	0.642989543382531
102	0.248361534643292	0.496723069286583	0.751638465356708
103	0.169901963688406	0.339803927376812	0.830098036311594
104	0.098898399851753	0.197796799703506	0.901101600148247

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	13	0.147727272727273	NOK
5% type I error level	23	0.261363636363636	NOK
10% type I error level	26	0.295454545454545	NOK

Charts produced by software:

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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')

}

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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