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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 07:18:19 -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/t1258726770xo4nb4k2xa59zyu.htm/, Retrieved Fri, 20 Nov 2009 15:19:42 +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/t1258726770xo4nb4k2xa59zyu.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_5

Dataseries X:

» Textbox « » Textfile « » CSV «

103,7 114813 123297 116476 109375 106370 106,2 117925 114813 123297 116476 109375 107,7 126466 117925 114813 123297 116476 109,9 131235 126466 117925 114813 123297 111,7 120546 131235 126466 117925 114813 114,9 123791 120546 131235 126466 117925 116 129813 123791 120546 131235 126466 118,3 133463 129813 123791 120546 131235 120,4 122987 133463 129813 123791 120546 126 125418 122987 133463 129813 123791 128,1 130199 125418 122987 133463 129813 130,1 133016 130199 125418 122987 133463 130,8 121454 133016 130199 125418 122987 133,6 122044 121454 133016 130199 125418 134,2 128313 122044 121454 133016 130199 135,5 131556 128313 122044 121454 133016 136,2 120027 131556 128313 122044 121454 139,1 123001 120027 131556 128313 122044 139 130111 123001 120027 131556 128313 139,6 132524 130111 123001 120027 131556 138,7 123742 132524 130111 123001 120027 140,9 124931 123742 132524 130111 123001 141,3 133646 124931 123742 132524 130111 141,8 136557 133646 124931 123742 132524 142 127509 136557 1336 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

HFCE[t] = + 73767.229794668 -441.574474829804RPI[t] + 0.0134539688684464`HFCE-1`[t] + 0.670282928478978`HFCE-2`[t] -0.112605495888278`HFCE-3`[t] + 0.269398473310232`HFCE-4`[t] -11927.4055660515Q1[t] -10325.7832993243Q2[t] + 473.109328445136Q3[t] + 720.105157172006t + 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)	73767.229794668	12600.382939	5.8544	0	0
RPI	-441.574474829804	84.869548	-5.203	2e-06	1e-06
`HFCE-1`	0.0134539688684464	0.17706	0.076	0.93963	0.469815
`HFCE-2`	0.670282928478978	0.213132	3.1449	0.002371	0.001186
`HFCE-3`	-0.112605495888278	0.204417	-0.5509	0.583344	0.291672
`HFCE-4`	0.269398473310232	0.190312	1.4156	0.160989	0.080494
Q1	-11927.4055660515	2951.295999	-4.0414	0.000126	6.3e-05
Q2	-10325.7832993243	5126.094707	-2.0144	0.047511	0.023755
Q3	473.109328445136	3355.910076	0.141	0.88826	0.44413
t	720.105157172006	129.288081	5.5698	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998054720351583
R-squared	0.996113224816076
Adjusted R-squared	0.995652948807453
F-TEST (value)	2164.16499264645
F-TEST (DF numerator)	9
F-TEST (DF denominator)	76
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2000.54464928762
Sum Squared Residuals	304165595.928294

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	114813	112839.054216257	1973.94578374277
2	117925	118524.632622351	-599.632622351276
3	126466	124881.373552473	1584.62644752682
4	131235	129150.648371672	2084.35162832833
5	120546	120301.557427003	244.442572997204
6	123791	124139.620852688	-348.620852687663
7	129813	129815.807372434	-2.80737243414336
8	133463	133791.671277259	-328.671277258834
9	122987	122497.610137536	489.389862463521
10	125418	124848.197163124	569.802836876374
11	130199	129662.018736778	536.981263222051
12	133016	132882.606442646	133.393557354398
13	121454	121512.764045843	-58.7640458432724
14	122044	124447.255974463	-2403.25597446259
15	128313	128930.220116044	-617.220116043573
16	131556	131143.819228906	412.180771094216
17	120027	120691.829196334	-664.829196333544
18	123001	123204.628618729	-203.628618728678
19	130111	128403.78347815	1707.21652185008
20	132524	132646.801780971	-122.801780971092
21	123742	123194.310704238	547.689295761865
22	124931	126044.380219188	-1113.38021918774
23	133646	133160.026388937	485.973611063157
24	136557	135739.412701887	817.58729811272
25	127509	127824.732295891	-315.732295890568
26	128945	130210.943515295	-1265.94351529532
27	137191	137660.396911539	-469.396911539018
28	139716	140386.516908107	-670.516908107316
29	129083	131567.075102973	-2484.07510297302
30	131604	133704.271076842	-2100.27107684168
31	139413	139890.56908265	-477.569082649891
32	143125	143721.660704888	-596.660704888409
33	133948	134296.888815235	-348.888815235051
34	137116	137944.065128989	-828.065128989324
35	144864	144907.767387548	-43.7673875478447
36	149277	149018.431625836	258.568374163670
37	138796	139837.434683540	-1041.43468353954
38	143258	144073.947654751	-815.94765475105
39	150034	149555.751281003	478.248718996623
40	154708	154679.740477500	28.2595224995953
41	144888	144530.362683653	357.63731634734
42	148762	148967.192264256	-205.19226425627
43	156500	155034.471096884	1465.52890311603
44	161088	160038.102103136	1049.89789686351
45	152772	151306.553237399	1465.44676260102
46	158011	155969.129835776	2041.87016422364
47	163318	163508.354054245	-190.354054244632
48	169969	168980.900487616	988.099512383786
49	162269	158280.913897731	3988.08610226872
50	165765	164401.997881678	1363.00211832212
51	170600	171355.138394751	-755.138394750844
52	174681	176183.592922932	-1502.59292293248
53	166364	165892.293957053	471.706042947255
54	170240	170307.612445926	-67.6124459258823
55	176150	177102.855840294	-952.855840293737
56	182056	182151.651229665	-95.6512296654724
57	172218	172263.978616167	-45.9786161666594
58	177856	177863.420474643	-7.42047464301231
59	182253	183526.180519338	-1273.18051933804
60	188090	189603.749692135	-1513.74969213537
61	176863	177695.427694989	-832.427694989112
62	183273	183874.984693894	-601.984693893728
63	187969	188261.435551479	-292.435551478773
64	194650	195219.093658299	-569.093658298867
65	183036	183105.572931722	-69.572931722097
66	189516	189843.318824504	-327.318824503875
67	193805	193691.878386681	113.121613319474
68	200499	200652.829039166	-153.829039166426
69	188142	188331.168258343	-189.168258342689
70	193732	195264.792202733	-1532.79220273261
71	197126	198668.878256605	-1542.87825660458
72	205140	205417.505996586	-277.505996585815
73	191751	192413.757059351	-662.757059350805
74	196700	199549.397881239	-2849.39788123889
75	199784	201421.802595861	-1637.80259586087
76	207360	207766.848630034	-406.848630034271
77	196101	193857.848122702	2243.15187729816
78	200824	200634.942705422	189.057294577589
79	205743	204295.23416856	1447.76583143992
80	212489	209890.693371321	2598.30662867898
81	200810	197932.235176257	2877.7648237428
82	203683	203482.412094146	200.587905854467
83	207286	206850.056827748	435.943172251792
84	210910	213042.723349435	-2132.72334943486
85	194915	202860.631739784	-7945.63173978427
86	217920	207013.855869365	10906.1441306354

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.0305171520858574	0.0610343041717148	0.969482847914143
14	0.00732516862210036	0.0146503372442007	0.9926748313779
15	0.0103530625475485	0.0207061250950970	0.989646937452451
16	0.0039317189757869	0.0078634379515738	0.996068281024213
17	0.00126719315037428	0.00253438630074856	0.998732806849626
18	0.000467388481958151	0.000934776963916302	0.999532611518042
19	0.000221245378027514	0.000442490756055028	0.999778754621973
20	0.000139424518874187	0.000278849037748375	0.999860575481126
21	7.12040120366285e-05	0.000142408024073257	0.999928795987963
22	0.000127302778497150	0.000254605556994299	0.999872697221503
23	0.000204194467820281	0.000408388935640562	0.99979580553218
24	8.97737506206766e-05	0.000179547501241353	0.99991022624938
25	3.58912272030788e-05	7.17824544061576e-05	0.999964108772797
26	1.72735113587092e-05	3.45470227174184e-05	0.999982726488641
27	6.40407928285482e-06	1.28081585657096e-05	0.999993595920717
28	2.76212777338138e-06	5.52425554676275e-06	0.999997237872227
29	1.99996741565744e-06	3.99993483131488e-06	0.999998000032584
30	6.8434914807691e-07	1.36869829615382e-06	0.999999315650852
31	3.88086036132218e-07	7.76172072264436e-07	0.999999611913964
32	1.39539040085575e-07	2.79078080171151e-07	0.99999986046096
33	8.14889789967227e-08	1.62977957993445e-07	0.999999918511021
34	6.73928396325658e-08	1.34785679265132e-07	0.99999993260716
35	2.5478464957233e-08	5.0956929914466e-08	0.999999974521535
36	1.22514834096202e-08	2.45029668192404e-08	0.999999987748517
37	4.33732763848020e-09	8.67465527696041e-09	0.999999995662672
38	8.06331936646638e-09	1.61266387329328e-08	0.99999999193668
39	2.83936756510294e-09	5.67873513020588e-09	0.999999997160632
40	1.31809478193916e-09	2.63618956387831e-09	0.999999998681905
41	5.60446912367825e-10	1.12089382473565e-09	0.999999999439553
42	9.10468538416632e-10	1.82093707683326e-09	0.999999999089531
43	4.26005726385211e-10	8.52011452770423e-10	0.999999999573994
44	1.78772658407056e-10	3.57545316814113e-10	0.999999999821227
45	1.32181621919898e-10	2.64363243839797e-10	0.999999999867818
46	3.92912885768024e-10	7.85825771536048e-10	0.999999999607087
47	1.94104394184173e-09	3.88208788368345e-09	0.999999998058956
48	8.22246261121178e-10	1.64449252224236e-09	0.999999999177754
49	3.80872175639238e-09	7.61744351278476e-09	0.999999996191278
50	1.39889164001503e-09	2.79778328003006e-09	0.999999998601108
51	7.63274295594752e-09	1.52654859118950e-08	0.999999992367257
52	1.09370600915712e-08	2.18741201831425e-08	0.99999998906294
53	5.29512931113407e-09	1.05902586222681e-08	0.99999999470487
54	1.98658545888064e-09	3.97317091776128e-09	0.999999998013415
55	2.1348051419923e-09	4.2696102839846e-09	0.999999997865195
56	1.26466853567520e-09	2.52933707135040e-09	0.999999998735331
57	9.13036013538756e-10	1.82607202707751e-09	0.999999999086964
58	8.30567299684864e-10	1.66113459936973e-09	0.999999999169433
59	2.13362858721561e-09	4.26725717443122e-09	0.999999997866371
60	3.39786929324157e-09	6.79573858648314e-09	0.99999999660213
61	1.95948170464652e-09	3.91896340929303e-09	0.999999998040518
62	1.78157744706822e-09	3.56315489413645e-09	0.999999998218423
63	8.2182147429345e-10	1.6436429485869e-09	0.999999999178179
64	8.54556776645712e-10	1.70911355329142e-09	0.999999999145443
65	9.3364544849719e-10	1.86729089699438e-09	0.999999999066355
66	8.34068859069626e-09	1.66813771813925e-08	0.999999991659311
67	7.59749745876459e-09	1.51949949175292e-08	0.999999992402502
68	4.759787966876e-08	9.519575933752e-08	0.99999995240212
69	1.32789889669839e-05	2.65579779339678e-05	0.999986721011033
70	0.000198414591093837	0.000396829182187674	0.999801585408906
71	0.00253889960830893	0.00507779921661786	0.997461100391691
72	0.00199796468042651	0.00399592936085302	0.998002035319574
73	0.00158316704442400	0.00316633408884799	0.998416832955576

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	58	0.950819672131147	NOK
5% type I error level	60	0.98360655737705	NOK
10% type I error level	61	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/10of4p1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/10of4p1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/13h3v1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/13h3v1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/2xdp81258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/2xdp81258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/3efnk1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/3efnk1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/4v21p1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/4v21p1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/5px7k1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/5px7k1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/64emw1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/64emw1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/7novu1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/7novu1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/8udbc1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/8udbc1258726692.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/9k2rs1258726692.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726770xo4nb4k2xa59zyu/9k2rs1258726692.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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