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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, 22 Nov 2010 10:46:43 +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/Nov/22/t12904227988tjk1t2dmqppl2z.htm/, Retrieved Mon, 22 Nov 2010 11:46:51 +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/Nov/22/t12904227988tjk1t2dmqppl2z.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 «

9 15 6 25 68 0 14 10 8 23 48 0 8 10 7 17 44 0 8 12 9 19 67 1 14 9 8 29 46 1 15 18 11 23 54 1 9 14 9 23 61 0 11 11 11 21 52 0 14 11 12 26 46 1 14 9 6 24 55 0 6 17 8 25 52 0 10 21 12 26 76 0 9 16 9 23 49 0 11 21 7 29 30 1 14 14 8 24 75 1 8 24 20 20 51 1 11 7 8 23 50 1 10 9 6 29 38 0 16 18 16 24 47 0 8 14 6 22 52 1 11 13 6 22 66 0 11 13 6 22 66 1 7 18 11 17 33 0 13 14 12 24 48 0 10 12 8 21 57 0 9 12 8 24 64 1 9 9 7 23 58 1 15 11 9 21 59 1 13 8 9 24 42 0 16 5 4 24 39 0 11 9 6 19 59 0 6 11 8 26 37 1 14 11 8 24 49 1 4 15 4 28 80 1 12 16 14 22 62 0 10 12 8 23 44 0 14 14 10 24 53 1 9 13 6 23 58 1 10 10 8 23 69 1 14 18 10 30 63 1 14 17 11 20 36 1 10 12 8 23 38 0 9 13 8 21 46 0 14 13 10 27 56 0 8 11 8 12 37 1 9 13 10 15 51 0 8 12 7 22 44 1 10 12 8 27 58 1 9 12 8 21 37 0 9 12 7 21 65 0 9 13 6 21 48 0 9 17 9 21 53 1 11 18 5 18 51 1 15 7 5 24 39 1 8 17 7 24 64 1 12 14 7 28 47 1 8 12 7 25 47 1 14 9 9 14 64 0 11 9 5 30 59 0 10 13 8 19 54 1 12 10 8 29 55 0 9 12 9 2 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	9 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

intrinsic[t] = + 54.3924753054961 -0.606890253799587Doubts[t] + 0.0642016921348219Parentalexpectations[t] -0.429106442448299Parentalcriticism[t] + 0.396721573130468organization[t] -1.23923857358372geslacht[t] + 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)	54.3924753054961	10.04849	5.413	1e-06	0
Doubts	-0.606890253799587	0.455825	-1.3314	0.186836	0.093418
Parentalexpectations	0.0642016921348219	0.397934	0.1613	0.872234	0.436117
Parentalcriticism	-0.429106442448299	0.549566	-0.7808	0.437219	0.21861
organization	0.396721573130468	0.330639	1.1999	0.233733	0.116867
geslacht	-1.23923857358372	2.426081	-0.5108	0.610899	0.305449

Multiple Linear Regression - Regression Statistics
Multiple R	0.226823336640143
R-squared	0.0514488260445678
Adjusted R-squared	-0.00783562232764679
F-TEST (value)	0.867830054208296
F-TEST (DF numerator)	5
F-TEST (DF denominator)	80
p-value	0.506544612128841
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.0635117234388
Sum Squared Residuals	9792.10333237338

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	68	57.2368890768943	10.7631109231057
2	48	52.2297733160645	-4.22977331606453
3	44	53.9198918425276	-9.91989184252756
4	67	52.7442869145778	14.2557130854222
5	46	53.3066624891288	-7.30666248912881
6	54	49.6099386984149	4.39006130158509
7	61	55.0919249111535	5.90807508884654
8	52	52.0338832959923	-0.0338832959922875
9	46	50.5284753842138	-4.52847538421385
10	55	53.4205060819568	1.57949391804322
11	52	58.3277503376659	-6.32775033766592
12	76	54.8372918943441	21.1627081056559
13	49	55.2203282954231	-6.2203282954231
14	30	56.3268599985937	-26.3268599985937
15	75	51.6440630841506	23.3559369158494
16	51	49.1912579263949	1.80874207360515
17	50	52.6186004274751	-2.61860042747511
18	38	57.8316749628075	-19.8316749628075
19	47	48.493476379088	-1.49347637908802
20	52	55.3501743455838	-3.35017434558375
21	66	54.7045404656339	11.2954595343661
22	66	53.4653018920502	12.5346981079498
23	33	53.3239698636125	-20.3239698636125
24	48	51.7737661417407	-3.77376614174068
25	57	53.9922945692716	3.00770543072841
26	64	54.5501109688789	9.44988903112114
27	58	54.3898907617922	3.61010923820777
28	59	49.2252965921068	9.77470340789318
29	42	52.6758753162767	-10.6758753162767
30	39	52.8081316907149	-13.8081316907149
31	59	53.2575689777032	5.7424310222968
32	37	57.1000231844037	-20.1000231844037
33	49	51.4514580077461	-2.45145800774610
34	80	61.0804793765963	18.9195206234037
35	62	50.8574037486524	11.1425962513476
36	44	54.7857377155325	-10.7857377155325
37	53	50.785850199254	2.21414980074603
38	58	55.0758039727798	2.92419602722019
39	69	53.4180957576792	15.5819042423208
40	63	53.4229864065761	9.57701359342393
41	36	48.9624625406883	-12.9624625406883
42	38	54.7857377155325	-16.7857377155325
43	46	54.663386515206	-8.663386515206
44	56	53.1510518000943	2.84894819990572
45	37	50.332140652978	-13.332140652978
46	51	51.4248441915266	-0.424844191526596
47	44	54.7926645188658	-10.7926645188658
48	58	55.1333854344707	2.86661456552932
49	37	54.5991848230712	-17.5991848230712
50	65	55.0282912655195	9.97170873448052
51	48	55.5215994001026	-7.5215994001026
52	53	53.2518482677133	-0.251848267713267
53	51	52.6285305026507	-1.62853050265070
54	39	51.8750803127521	-12.8750803127521
55	64	55.9071161258009	8.09288387419915
56	47	54.8738363267199	-7.87383632671991
57	47	55.9828292382572	-8.98282923825722
58	64	48.1659710233072	15.8340289766928
59	59	58.0506127245866	0.949387275413355
60	54	52.0238145415618	1.97618545843824
61	55	55.8238832624465	-0.823883262446517
62	72	54.517726099561	17.4822739004390
63	58	54.4883196010217	3.51168039897835
64	59	53.0874181544603	5.9125818455397
65	36	49.0384371267561	-13.0384371267561
66	62	57.2659641014803	4.7340358985197
67	63	58.746195078119	4.25380492188101
68	50	54.8656352921107	-4.86563529211069
69	70	56.405576923873	13.5944230761269
70	59	53.2084951235109	5.79150487648912
71	73	53.3294290999909	19.6705709000091
72	62	54.8178611112388	7.18213888876115
73	41	53.3266867607795	-12.3266867607795
74	56	54.0655719154058	1.9344280845942
75	52	54.4359608031616	-2.43596080316155
76	54	51.6371362808173	2.36286371918271
77	73	51.6712507316822	21.3287492683178
78	40	49.9375424884691	-9.93754248846911
79	41	54.429934061263	-13.4299340612630
80	54	53.6688503422753	0.331149657724668
81	42	49.0305426650362	-7.03054266503625
82	70	54.3011986620595	15.6988013379405
83	51	52.3148484571957	-1.31484845719565
84	60	56.9367540652163	3.06324593478370
85	49	54.2263601689933	-5.22636016899328
86	52	56.1597384302183	-4.15973843021834

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.290850461246245	0.58170092249249	0.709149538753755
10	0.277046229196993	0.554092458393986	0.722953770803007
11	0.365075122339522	0.730150244679044	0.634924877660478
12	0.427706166707516	0.855412333415033	0.572293833292484
13	0.433256262982447	0.866512525964894	0.566743737017553
14	0.778829336013884	0.442341327972232	0.221170663986116
15	0.936877341692987	0.126245316614027	0.0631226583070135
16	0.92383709685731	0.152325806285380	0.0761629031426902
17	0.886207523523924	0.227584952952152	0.113792476476076
18	0.893484034622549	0.213031930754902	0.106515965377451
19	0.868436927790866	0.263126144418267	0.131563072209134
20	0.824288736570538	0.351422526858924	0.175711263429462
21	0.79878348182297	0.402433036354060	0.201216518177030
22	0.779891643539698	0.440216712920604	0.220108356460302
23	0.91936200201199	0.161275995976019	0.0806379979880094
24	0.889963614720087	0.220072770559827	0.110036385279913
25	0.853062567723169	0.293874864553662	0.146937432276831
26	0.846120779178102	0.307758441643795	0.153879220821898
27	0.804569412113309	0.390861175773382	0.195430587886691
28	0.775360353559234	0.449279292881532	0.224639646440766
29	0.75691387127055	0.486172257458899	0.243086128729450
30	0.78949390264838	0.421012194703241	0.210506097351620
31	0.743643870670902	0.512712258658196	0.256356129329098
32	0.805506279032056	0.388987441935888	0.194493720967944
33	0.762381374563395	0.47523725087321	0.237618625436605
34	0.875414116442023	0.249171767115953	0.124585883557977
35	0.883381140594628	0.233237718810744	0.116618859405372
36	0.878532891314195	0.242934217371611	0.121467108685805
37	0.845175445146265	0.30964910970747	0.154824554853735
38	0.805650297785761	0.388699404428477	0.194349702214239
39	0.847053413393748	0.305893173212504	0.152946586606252
40	0.856496091113181	0.287007817773637	0.143503908886819
41	0.881605750274183	0.236788499451635	0.118394249725817
42	0.921510956002165	0.156978087995669	0.0784890439978344
43	0.915545387664042	0.168909224671915	0.0844546123359575
44	0.891834965623878	0.216330068752244	0.108165034376122
45	0.908877763821441	0.182244472357117	0.0911222361785586
46	0.881813522752406	0.236372954495187	0.118186477247594
47	0.88191637418152	0.236167251636962	0.118083625818481
48	0.851015165510133	0.297969668979734	0.148984834489867
49	0.932032710237971	0.135934579524058	0.067967289762029
50	0.92205583720792	0.155888325584160	0.0779441627920798
51	0.930623718915207	0.138752562169585	0.0693762810847925
52	0.905801705869968	0.188396588260064	0.094198294130032
53	0.875780319663486	0.248439360673027	0.124219680336514
54	0.888912675106945	0.222174649786109	0.111087324893055
55	0.901994458411976	0.196011083176047	0.0980055415880237
56	0.875363773120977	0.249272453758046	0.124636226879023
57	0.860140641221268	0.279718717557465	0.139859358778732
58	0.875396666356593	0.249206667286815	0.124603333643407
59	0.85728416956082	0.285431660878361	0.142715830439180
60	0.820634676511308	0.358730646977385	0.179365323488693
61	0.798243999701383	0.403512000597234	0.201756000298617
62	0.924515447536486	0.150969104927029	0.0754845524635143
63	0.907082170300367	0.185835659399266	0.0929178296996332
64	0.871197840911566	0.257604318176869	0.128802159088434
65	0.921226047290529	0.157547905418942	0.078773952709471
66	0.96715519320361	0.0656896135927802	0.0328448067963901
67	0.97517043412391	0.0496591317521787	0.0248295658760894
68	0.957289683515912	0.0854206329681767	0.0427103164840883
69	0.949522787474762	0.100954425050476	0.0504772125252382
70	0.917004697156003	0.165990605687993	0.0829953028439966
71	0.964489807487301	0.0710203850253972	0.0355101925126986
72	0.984865012086327	0.0302699758273460	0.0151349879136730
73	0.971265816725798	0.0574683665484033	0.0287341832742017
74	0.943321840930893	0.113356318138214	0.0566781590691069
75	0.976954347821199	0.0460913043576023	0.0230456521788011
76	0.941328339929482	0.117343320141037	0.0586716600705183
77	0.867300079010704	0.265399841978592	0.132699920989296

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.0434782608695652	OK
10% type I error level	7	0.101449275362319	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/10wlf71290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/10wlf71290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/172id1290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/172id1290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/20t0g1290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/20t0g1290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/30t0g1290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/30t0g1290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/40t0g1290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/40t0g1290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/5t3z11290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/5t3z11290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/6t3z11290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/6t3z11290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/7mug41290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/7mug41290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/8mug41290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/8mug41290422793.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/9mug41290422793.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904227988tjk1t2dmqppl2z/9mug41290422793.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

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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