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Toon Wouters

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 25 Nov 2008 01:47:13 -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/Nov/25/t12276029442wpi0rhxd0oln4k.htm/, Retrieved Tue, 25 Nov 2008 08:49:14 +0000

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/Nov/25/t12276029442wpi0rhxd0oln4k.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},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

124 0 113 0 109 0 109 0 106 0 101 0 98 0 93 0 91 0 122 0 139 0 140 0 132 0 117 0 114 0 113 0 110 0 107 0 103 0 98 0 98 0 137 0 148 0 147 0 139 0 130 0 128 0 127 0 123 0 118 0 114 0 108 0 111 0 151 0 159 0 158 0 148 0 138 0 137 0 136 0 133 0 126 0 120 0 114 0 116 0 153 0 162 0 161 0 149 0 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 0 157 0 157 0 147 0 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 0 150 0 149 0 134 0 123 0 116 0 117 0 111 0 105 0 102 0 95 0 93 0 124 1 130 1 124 1 115 1 106 1 105 1 105 1 101 1 95 1 93 1 84 1 87 1 116 1 120 1 117 1 109 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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 125.222222222222 -17.0347222222222X[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)	125.222222222222	1.983441	63.1338	0	0
X	-17.0347222222222	4.883658	-3.4881	0.000739	0.000369

Multiple Linear Regression - Regression Statistics
Multiple R	0.336945361646795
R-squared	0.113532176735289
Adjusted R-squared	0.104200936490398
F-TEST (value)	12.1668903335161
F-TEST (DF numerator)	1
F-TEST (DF denominator)	95
p-value	0.000738802148248396
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	17.8509729942686
Sum Squared Residuals	30272.4375

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	124	125.222222222223	-1.22222222222251
2	113	125.222222222222	-12.2222222222222
3	109	125.222222222222	-16.2222222222222
4	109	125.222222222222	-16.2222222222222
5	106	125.222222222222	-19.2222222222222
6	101	125.222222222222	-24.2222222222222
7	98	125.222222222222	-27.2222222222222
8	93	125.222222222222	-32.2222222222222
9	91	125.222222222222	-34.2222222222222
10	122	125.222222222222	-3.22222222222222
11	139	125.222222222222	13.7777777777778
12	140	125.222222222222	14.7777777777778
13	132	125.222222222222	6.77777777777778
14	117	125.222222222222	-8.22222222222222
15	114	125.222222222222	-11.2222222222222
16	113	125.222222222222	-12.2222222222222
17	110	125.222222222222	-15.2222222222222
18	107	125.222222222222	-18.2222222222222
19	103	125.222222222222	-22.2222222222222
20	98	125.222222222222	-27.2222222222222
21	98	125.222222222222	-27.2222222222222
22	137	125.222222222222	11.7777777777778
23	148	125.222222222222	22.7777777777778
24	147	125.222222222222	21.7777777777778
25	139	125.222222222222	13.7777777777778
26	130	125.222222222222	4.77777777777778
27	128	125.222222222222	2.77777777777778
28	127	125.222222222222	1.77777777777778
29	123	125.222222222222	-2.22222222222222
30	118	125.222222222222	-7.22222222222222
31	114	125.222222222222	-11.2222222222222
32	108	125.222222222222	-17.2222222222222
33	111	125.222222222222	-14.2222222222222
34	151	125.222222222222	25.7777777777778
35	159	125.222222222222	33.7777777777778
36	158	125.222222222222	32.7777777777778
37	148	125.222222222222	22.7777777777778
38	138	125.222222222222	12.7777777777778
39	137	125.222222222222	11.7777777777778
40	136	125.222222222222	10.7777777777778
41	133	125.222222222222	7.77777777777778
42	126	125.222222222222	0.77777777777778
43	120	125.222222222222	-5.22222222222222
44	114	125.222222222222	-11.2222222222222
45	116	125.222222222222	-9.22222222222222
46	153	125.222222222222	27.7777777777778
47	162	125.222222222222	36.7777777777778
48	161	125.222222222222	35.7777777777778
49	149	125.222222222222	23.7777777777778
50	139	125.222222222222	13.7777777777778
51	135	125.222222222222	9.77777777777778
52	130	125.222222222222	4.77777777777778
53	127	125.222222222222	1.77777777777778
54	122	125.222222222222	-3.22222222222222
55	117	125.222222222222	-8.22222222222222
56	112	125.222222222222	-13.2222222222222
57	113	125.222222222222	-12.2222222222222
58	149	125.222222222222	23.7777777777778
59	157	125.222222222222	31.7777777777778
60	157	125.222222222222	31.7777777777778
61	147	125.222222222222	21.7777777777778
62	137	125.222222222222	11.7777777777778
63	132	125.222222222222	6.77777777777778
64	125	125.222222222222	-0.22222222222222
65	123	125.222222222222	-2.22222222222222
66	117	125.222222222222	-8.22222222222222
67	114	125.222222222222	-11.2222222222222
68	111	125.222222222222	-14.2222222222222
69	112	125.222222222222	-13.2222222222222
70	144	125.222222222222	18.7777777777778
71	150	125.222222222222	24.7777777777778
72	149	125.222222222222	23.7777777777778
73	134	125.222222222222	8.77777777777778
74	123	125.222222222222	-2.22222222222222
75	116	125.222222222222	-9.22222222222222
76	117	125.222222222222	-8.22222222222222
77	111	125.222222222222	-14.2222222222222
78	105	125.222222222222	-20.2222222222222
79	102	125.222222222222	-23.2222222222222
80	95	125.222222222222	-30.2222222222222
81	93	125.222222222222	-32.2222222222222
82	124	108.1875	15.8125
83	130	108.1875	21.8125
84	124	108.1875	15.8125
85	115	108.1875	6.8125
86	106	108.1875	-2.1875
87	105	108.1875	-3.1875
88	105	108.1875	-3.1875
89	101	108.1875	-7.1875
90	95	108.1875	-13.1875
91	93	108.1875	-15.1875
92	84	108.1875	-24.1875
93	87	108.1875	-21.1875
94	116	108.1875	7.8125
95	120	108.1875	11.8125
96	117	108.1875	8.8125
97	109	108.1875	0.8125

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.106933696614810	0.213867393229621	0.89306630338519
6	0.0829679742606318	0.165935948521264	0.917032025739368
7	0.0745519049806427	0.149103809961285	0.925448095019357
8	0.093439687255375	0.18687937451075	0.906560312744625
9	0.112785215885878	0.225570431771756	0.887214784114122
10	0.129420855579957	0.258841711159915	0.870579144420043
11	0.360120954827694	0.720241909655388	0.639879045172306
12	0.529960581259632	0.940078837480736	0.470039418740368
13	0.535380187893541	0.929239624212918	0.464619812106459
14	0.450490731710692	0.900981463421383	0.549509268289308
15	0.370152191013573	0.740304382027147	0.629847808986427
16	0.298417720709079	0.596835441418157	0.701582279290921
17	0.241423412243086	0.482846824486172	0.758576587756914
18	0.201379239181494	0.402758478362988	0.798620760818506
19	0.183192760377751	0.366385520755503	0.816807239622249
20	0.196328966441856	0.392657932883712	0.803671033558144
21	0.21034395244885	0.4206879048977	0.78965604755115
22	0.276752229534997	0.553504459069994	0.723247770465003
23	0.468411751599984	0.936823503199968	0.531588248400016
24	0.606103968106738	0.787792063786525	0.393896031893262
25	0.63152997547295	0.7369400490541	0.36847002452705
26	0.59447616806057	0.811047663878859	0.405523831939429
27	0.547454466510766	0.905091066978469	0.452545533489234
28	0.495996782458927	0.991993564917854	0.504003217541073
29	0.437319469808482	0.874638939616965	0.562680530191518
30	0.381638545593218	0.763277091186435	0.618361454406782
31	0.337446298342959	0.674892596685919	0.66255370165704
32	0.320500839208021	0.641001678416042	0.679499160791979
33	0.292015862272763	0.584031724545526	0.707984137727237
34	0.414775399881214	0.829550799762427	0.585224600118786
35	0.624938928191031	0.750122143617939	0.375061071808969
36	0.772594006568261	0.454811986863478	0.227405993431739
37	0.807415005266064	0.385169989467872	0.192584994733936
38	0.790259025267535	0.41948194946493	0.209740974732465
39	0.767690119229584	0.464619761540832	0.232309880770416
40	0.739627790187533	0.520744419624933	0.260372209812467
41	0.700362500002746	0.599274999994507	0.299637499997253
42	0.647924868045464	0.704150263909072	0.352075131954536
43	0.597189917700464	0.805620164599071	0.402810082299536
44	0.563146849483356	0.873706301033288	0.436853150516644
45	0.52197069332025	0.9560586133595	0.47802930667975
46	0.602231591417582	0.795536817164835	0.397768408582418
47	0.762782339763582	0.474435320472837	0.237217660236418
48	0.872591744554357	0.254816510891286	0.127408255445643
49	0.893614640390502	0.212770719218996	0.106385359609498
50	0.88197058520033	0.236058829599340	0.118029414799670
51	0.860270067430234	0.279459865139532	0.139729932569766
52	0.827469823940496	0.345060352119009	0.172530176059504
53	0.787288325827415	0.425423348345169	0.212711674172585
54	0.742651456172374	0.514697087655252	0.257348543827626
55	0.7023476850037	0.595304629992599	0.297652314996299
56	0.677522435476446	0.644955129047109	0.322477564523554
57	0.648381409459677	0.703237181080646	0.351618590540323
58	0.688640040178014	0.622719919643973	0.311359959821986
59	0.799418094806652	0.401163810386695	0.200581905193348
60	0.892463295162848	0.215073409674304	0.107536704837152
61	0.918527035562598	0.162945928874805	0.0814729644374025
62	0.913824480960823	0.172351038078355	0.0861755190391774
63	0.898662078409297	0.202675843181407	0.101337921590703
64	0.870751969544788	0.258496060910424	0.129248030455212
65	0.836244544049073	0.327510911901855	0.163755455950927
66	0.796956753836044	0.406086492327911	0.203043246163956
67	0.757225789992394	0.485548420015212	0.242774210007606
68	0.722500020618907	0.554999958762187	0.277499979381093
69	0.682170386277379	0.635659227445243	0.317829613722621
70	0.728818525809086	0.542362948381828	0.271181474190914
71	0.849572052108692	0.300855895782617	0.150427947891308
72	0.949636713002442	0.100726573995116	0.0503632869975581
73	0.968558940111003	0.0628821197779946	0.0314410598889973
74	0.968615596711254	0.0627688065774916	0.0313844032887458
75	0.961925850187563	0.0761482996248748	0.0380741498124374
76	0.959418739111125	0.0811625217777501	0.0405812608888751
77	0.95157911256903	0.0968417748619405	0.0484208874309703
78	0.938064340183986	0.123871319632028	0.0619356598160139
79	0.9213697340067	0.157260531986600	0.0786302659932998
80	0.89996704951643	0.200065900967140	0.100032950483570
81	0.87387080231339	0.252258395373219	0.126129197686609
82	0.869729135395286	0.260541729209429	0.130270864604714
83	0.915796580826744	0.168406838346512	0.084203419173256
84	0.933422597869502	0.133154804260996	0.0665774021304978
85	0.917372534738728	0.165254930522544	0.0826274652612718
86	0.872182250337194	0.255635499325611	0.127817749662806
87	0.806802674509293	0.386394650981414	0.193197325490707
88	0.718993815707956	0.562012368584087	0.281006184292044
89	0.608781682186047	0.782436635627905	0.391218317813953
90	0.509318467117219	0.981363065765563	0.490681532882781
91	0.424682317322951	0.849364634645902	0.575317682677049
92	0.539107910405546	0.921784179188908	0.460892089594454

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	0	0	OK
10% type I error level	5	0.0568181818181818	OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/10ef241227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/10ef241227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/1vwrk1227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/1vwrk1227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/2bmg81227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/2bmg81227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/3nmb81227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/3nmb81227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/4qqxj1227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/4qqxj1227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/5lah81227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/5lah81227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/615d01227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/615d01227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/778711227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/778711227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/8x7ha1227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/8x7ha1227602826.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/9p0pw1227602826.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276029442wpi0rhxd0oln4k/9p0pw1227602826.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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