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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: Tue, 17 Nov 2009 02:50:15 -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/17/t1258451631r7nelll34v3xky4.htm/, Retrieved Tue, 17 Nov 2009 10:54:03 +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/17/t1258451631r7nelll34v3xky4.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

108.8235294 111.7647059 105.8823529 100 111.7647059 108.8235294 111.7647059 105.8823529 117.6470588 111.7647059 108.8235294 111.7647059 111.7647059 117.6470588 111.7647059 108.8235294 120.5882353 111.7647059 117.6470588 111.7647059 102.9411765 120.5882353 111.7647059 117.6470588 114.7058824 102.9411765 120.5882353 111.7647059 114.7058824 114.7058824 102.9411765 120.5882353 117.6470588 114.7058824 114.7058824 102.9411765 111.7647059 117.6470588 114.7058824 114.7058824 97.05882353 111.7647059 117.6470588 114.7058824 94.11764706 97.05882353 111.7647059 117.6470588 82.35294118 94.11764706 97.05882353 111.7647059 82.35294118 82.35294118 94.11764706 97.05882353 85.29411765 82.35294118 82.35294118 94.11764706 85.29411765 85.29411765 82.35294118 82.35294118 73.52941176 85.29411765 85.29411765 82.35294118 61.76470588 73.52941176 85.29411765 85.29411765 32.35294118 61.76470588 73.52941176 85.29411765 20.58823529 32.35294118 61.76470588 73.52941176 50 20.58823529 32.35294118 61.76470588 70.58823529 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

x[t] = + 7.53923727288653 + 0.826888794540061y0[t] + 0.069050797530038y1[t] -0.0217694593981442y2[t] + 2.53073445567753M1[t] + 1.28398056947326M2[t] + 6.14157249017446M3[t] -0.388146101513589M4[t] + 4.19313623427351M5[t] + 1.84968635378636M6[t] -2.19663690001674M7[t] -0.985667763809098M8[t] + 3.69010557347512M9[t] + 9.24640884194022M10[t] + 4.94710351660652M11[t] -0.0551774169632081t + 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)	7.53923727288653	6.662794	1.1315	0.260866	0.130433
y0	0.826888794540061	0.106434	7.769	0	0
y1	0.069050797530038	0.137621	0.5017	0.617085	0.308543
y2	-0.0217694593981442	0.107522	-0.2025	0.840015	0.420007
M1	2.53073445567753	5.31012	0.4766	0.634824	0.317412
M2	1.28398056947326	5.35786	0.2396	0.811157	0.405579
M3	6.14157249017446	5.335442	1.1511	0.252778	0.126389
M4	-0.388146101513589	5.338231	-0.0727	0.9422	0.4711
M5	4.19313623427351	5.307446	0.79	0.4316	0.2158
M6	1.84968635378636	5.374224	0.3442	0.731525	0.365762
M7	-2.19663690001674	5.299369	-0.4145	0.679499	0.339749
M8	-0.985667763809098	5.381707	-0.1832	0.855096	0.427548
M9	3.69010557347512	5.439081	0.6784	0.499251	0.249626
M10	9.24640884194022	5.488148	1.6848	0.095532	0.047766
M11	4.94710351660652	5.450876	0.9076	0.366551	0.183276
t	-0.0551774169632081	0.041184	-1.3398	0.183725	0.091863

Multiple Linear Regression - Regression Statistics
Multiple R	0.903067366036784
R-squared	0.815530667600615
Adjusted R-squared	0.78444033067937
F-TEST (value)	26.2310012807659
F-TEST (DF numerator)	15
F-TEST (DF denominator)	89
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.8315560323775
Sum Squared Residuals	10441.7119413455

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	108.8235294	107.566092217664	1.25743718233622
2	111.7647059	104.110260547263	7.65444535273689
3	117.6470588	111.013554713815	6.63350408618513
4	111.7647059	109.559828809105	2.20487709089501
5	120.5882353	109.564035366087	11.0241999339127
6	102.9411765	113.927248856071	-10.9860723560715
7	114.7058824	95.9709203917174	18.7349620082826
8	114.7058824	105.44418864151	9.26169375848992
9	117.6470588	111.261313816976	6.38574498302432
10	111.7647059	118.938354188982	-7.17364828898211
11	97.05882353	109.922910324241	-12.8640867942415
12	94.11764706	92.2902910657738	1.82735599422621
13	82.35294118	91.4464249752709	-9.09348379527088
14	82.35294118	80.5334387371093	1.81950244289065
15	85.29411765	84.5875187388735	0.706598911126541
16	85.29411765	80.6907598830159	4.60335776698413
17	73.52941176	85.4199553827698	-11.8905436227698
18	61.76470588	73.2291967920726	-11.4644909120726
19	32.35294118	58.5872303336638	-26.2342891536638
20	20.58823529	34.8665123583125	-14.2782770683125
21	50	27.9842102848179	22.0157897151821
22	70.58823529	57.6335106957213	12.9547245942787
23	76.47058824	72.5902261112744	3.88036212872557
24	79.41176471	73.2333527664178	6.17841194358217
25	73.52941176	78.0989220812656	-4.56951032126561
26	76.47058824	72.007973975433	4.46261426456703
27	73.52941176	78.7722053689506	-5.24279360895062
28	70.58823529	70.0864297115538	0.501805578446237
29	64.70588235	71.913390360985	-7.20750801098503
30	64.70588235	64.511648572953	0.194233777047068
31	64.70588235	60.0679945620729	4.63788778792709
32	61.76470588	61.3518419248102	0.412863955189781
33	50	63.5404119793233	-13.5404119793233
34	47.05882353	59.1103437866636	-12.0515202566636
35	35.29411765	51.5755006765849	-16.2813830265849
36	20.58823529	36.8981369858392	-16.3099016958392
37	41.17647059	26.4652301852713	14.7112404047287
38	47.05882353	41.4281383326729	5.63068519732714
39	44.11764706	52.8363777441776	-8.71873068417762
40	35.29411765	43.7774422791358	-8.48332462913585
41	41.17647059	40.6763233761131	0.500147213886919
42	58.82352941	42.5965038892347	16.2270255207653
43	29.41176471	53.6854230404153	-24.2736583304153
44	55.88235294	31.6114439436683	24.2709089963316
45	55.88235294	55.7051999164809	0.177153023519054
46	64.70588235	63.674419213818	1.031463136182
47	70.58823529	66.0397636732268	4.5484716167732
48	64.70588235	66.5108062140631	-1.80492386406311
49	61.76470588	64.3364092177825	-2.57170333778247
50	55.88235294	60.068215243454	-4.18586230345405
51	73.52941176	59.931543078139	13.5978686818609
52	61.76470588	67.5966489242215	-5.83194304422145
53	67.64705882	63.7412495088871	3.90580931111286
54	67.64705882	65.0101446888536	2.63691413114641
55	55.88235294	61.5709364669332	-5.68858352693322
56	52.94117647	52.8705690794532	0.0706073905468203
57	55.88235294	54.246776810246	1.63557612975403
58	55.88235294	62.2329492336114	-6.35059629361144
59	64.70588235	58.145584893991	6.56029745600893
60	67.64705882	60.3753537360986	7.27170508390143
61	58.82352941	65.892208383411	-7.06867897341103
62	41.17647059	57.3052065985106	-16.1287360085106
63	41.17647059	46.8421663428646	-5.66569575286456
64	41.17647059	39.2308103138721	1.94566027612786
65	44.11764706	44.1410821631747	-0.0234351031746805
66	32.35294118	44.1744807315322	-11.8215395515322
67	50	30.5479671784644	19.4520328215356
68	47.05882353	45.4195239470895	1.63929958291055
69	58.82352941	49.0827487741688	9.74078063583118
70	70.58823529	63.7247205774936	6.86351471250644
71	67.64705882	69.974731443895	-2.32767262389501
72	58.82352941	63.096675681252	-4.27314627125194
73	61.76470588	57.8169532546267	3.94775262537328
74	55.88235294	58.4018038962233	-2.51945095622334
75	67.64705882	58.735340714515	8.91171810548505
76	67.64705882	61.4083391854887	6.23871963451133
77	67.64705882	66.8748620715258	0.772196748474231
78	67.64705882	64.2201234870896	3.42693533291036
79	79.41176471	60.1186228163233	19.2931418936767
80	76.47058824	71.0025180070682	5.46807023293176
81	50	74.0034503859922	-24.0034503859922
82	70.58823529	57.1571415688206	13.4310937211794
83	76.47058824	68.0630524798642	8.40753576013584
84	70.58823529	69.9227077486253	0.665527541374659
85	79.41176471	67.4921994577806	11.9195652522194
86	79.41176471	72.9521089540444	6.4596557559556
87	76.47058824	78.4918508449737	-2.02126260497373
88	70.58823529	69.2828455050576	1.30538978494244
89	67.64705882	68.7418081030667	-1.09474928306669
90	67.64705882	63.5690015990042	4.07805722099579
91	70.58823529	59.3924659910184	11.1957692989816
92	50	63.0443113978172	-13.0443113978172
93	61.76470588	50.843816838413	10.920889041587
94	55.88235294	64.5873842648895	-8.70503132488945
95	64.70588235	56.6294068669222	8.07647548307782
96	64.70588235	58.2609110819302	6.4449712680698
97	52.94117647	61.4737955069276	-8.53261903692764
98	47.05882353	50.2516772752893	-3.19285374528927
99	41.17647059	49.3776777236911	-8.20120713369113
100	35.29411765	37.7786601085497	-2.48454245854970
101	41.17647059	37.1625877773905	4.01388281260949
102	47.05882353	39.3498866931887	7.70893683681133
103	23.52941176	40.6466745593912	-17.1172627993912
104	8.823529412	22.6243848622709	-13.8008554502709
105	0	13.3320711635821	-13.3320711635821

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.281912121595811	0.563824243191622	0.71808787840419
20	0.18450624501164	0.36901249002328	0.81549375498836
21	0.9132219186573	0.173556162685399	0.0867780813426997
22	0.936220565501796	0.127558868996409	0.0637794344982045
23	0.906653814772452	0.186692370455096	0.0933461852275478
24	0.860546698720898	0.278906602558204	0.139453301279102
25	0.841262036971209	0.317475926057582	0.158737963028791
26	0.8093909114126	0.3812181771748	0.1906090885874
27	0.74109545406589	0.51780909186822	0.25890454593411
28	0.683409932501212	0.633180134997576	0.316590067498788
29	0.610487165534414	0.779025668931173	0.389512834465586
30	0.647512695327753	0.704974609344493	0.352487304672246
31	0.596562630194958	0.806874739610085	0.403437369805042
32	0.518677674644713	0.962644650710574	0.481322325355287
33	0.61662644631183	0.766747107376341	0.383373553688171
34	0.573743166103369	0.852513667793262	0.426256833896631
35	0.591186459533412	0.817627080933175	0.408813540466588
36	0.672702523555652	0.654594952888696	0.327297476444348
37	0.808790714569878	0.382418570860245	0.191209285430122
38	0.785094086263103	0.429811827473794	0.214905913736897
39	0.748816100835349	0.502367798329302	0.251183899164651
40	0.71022031164688	0.57955937670624	0.28977968835312
41	0.67579130411968	0.64841739176064	0.32420869588032
42	0.78906703145489	0.421865937090222	0.210932968545111
43	0.906549919956668	0.186900160086664	0.0934500800433318
44	0.977763306352532	0.0444733872949371	0.0222366936474685
45	0.968537957517603	0.0629240849647939	0.0314620424823969
46	0.957071701294215	0.0858565974115703	0.0429282987057852
47	0.959974955952985	0.0800500880940293	0.0400250440470147
48	0.947048858906828	0.105902282186344	0.052951141093172
49	0.92894337917981	0.142113241640380	0.0710566208201902
50	0.904772417437276	0.190455165125448	0.095227582562724
51	0.92632473600099	0.147350527998020	0.0736752639990101
52	0.906654996777704	0.186690006444592	0.0933450032222958
53	0.880191652925728	0.239616694148544	0.119808347074272
54	0.85051406395176	0.298971872096479	0.149485936048240
55	0.860382225049478	0.279235549901043	0.139617774950522
56	0.820527352207334	0.358945295585331	0.179472647792666
57	0.772724599008706	0.454550801982589	0.227275400991294
58	0.74529164920571	0.509416701588579	0.254708350794290
59	0.71564289548234	0.56871420903532	0.28435710451766
60	0.675933266627741	0.648133466744518	0.324066733372259
61	0.649097689772367	0.701804620455265	0.350902310227632
62	0.737764240989278	0.524471518021444	0.262235759010722
63	0.73092320337295	0.538153593254099	0.269076796627049
64	0.692225551125105	0.615548897749791	0.307774448874896
65	0.638098244514466	0.723803510971068	0.361901755485534
66	0.754558785199003	0.490882429601993	0.245441214800997
67	0.777125424470976	0.445749151058049	0.222874575529024
68	0.718650018957375	0.56269996208525	0.281349981042625
69	0.766957758849959	0.466084482300082	0.233042241150041
70	0.754312118504878	0.491375762990244	0.245687881495122
71	0.704767300863245	0.590465398273511	0.295232699136755
72	0.733531237544996	0.532937524910007	0.266468762455004
73	0.718168203006991	0.563663593986018	0.281831796993009
74	0.724858687457771	0.550282625084458	0.275141312542229
75	0.658960143174935	0.68207971365013	0.341039856825065
76	0.583644724111087	0.832710551777826	0.416355275888913
77	0.497235746448333	0.994471492896666	0.502764253551667
78	0.511643381420781	0.976713237158439	0.488356618579219
79	0.466190166954649	0.932380333909297	0.533809833045351
80	0.52571823404574	0.94856353190852	0.47428176595426
81	0.745240709302363	0.509518581395274	0.254759290697637
82	0.655194423367866	0.689611153264268	0.344805576632134
83	0.556357603076589	0.887284793846823	0.443642396923411
84	0.609691108035881	0.780617783928239	0.390308891964119
85	0.466556175127239	0.933112350254479	0.533443824872761
86	0.311222016211226	0.622444032422452	0.688777983788774

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	1	0.0147058823529412	OK
10% type I error level	4	0.0588235294117647	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258451631r7nelll34v3xky4/1022yj1258451409.png (open in new window)

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http://www.freestatistics.org/blog/date/2009/Nov/17/t1258451631r7nelll34v3xky4/8xa0a1258451409.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258451631r7nelll34v3xky4/8xa0a1258451409.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258451631r7nelll34v3xky4/9qoem1258451409.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258451631r7nelll34v3xky4/9qoem1258451409.ps (open in new window)

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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