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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, 21 Dec 2010 16:41:23 +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/Dec/21/t1292949564ofl41eif4goe3sq.htm/, Retrieved Tue, 21 Dec 2010 17:39:27 +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/Dec/21/t1292949564ofl41eif4goe3sq.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 «

320 101 196 324 112 178 343 103 201 295 104 181 301 120 193 367 88 181 196 77 115 182 70 104 342 97 200 361 148 244 333 106 172 330 127 182 345 93 192 323 80 183 365 116 214 323 109 120 316 122 181 358 124 199 235 87 99 169 64 74 430 122 202 409 96 185 407 89 148 341 109 193 326 98 184 374 94 183 364 114 213 349 89 189 300 130 204 385 140 206 304 74 97 196 55 86 443 140 220 414 103 205 325 91 174 388 120 216 356 89 182 386 97 213 444 154 227 387 81 209 327 110 219 448 116 221 225 73 114 182 73 97 460 174 205 411 103 215 342 130 224 361 91 189 377 136 182 331 106 201 428 136 198 340 122 173 352 131 238 461 135 258 221 75 122 198 68 101 422 143 259 329 115 243 320 93 188 375 128 173 364 152 224 351 125 215 380 107 196 319 116 159 322 220 187 386 137 208 221 34 131 187 51 93 343 153 210 342 145 228 365 116 176 313 145 195 356 98 188 337 118 188 389 139 190 326 140 188 343 113 176 357 149 225 220 79 93 218 47 79 391 166 235 425 180 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	7 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

Vl[t] = + 95.3492814718785 + 0.191650587366588Br[t] + 1.21026270515463Wa[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)	95.3492814718785	16.299779	5.8497	0	0
Br	0.191650587366588	0.157215	1.219	0.225563	0.112781
Wa	1.21026270515463	0.122326	9.8937	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.841400321668874
R-squared	0.707954501304484
Adjusted R-squared	0.70239172990076
F-TEST (value)	127.266509788723
F-TEST (DF numerator)	2
F-TEST (DF denominator)	105
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	41.3646763682078
Sum Squared Residuals	179658.82735989

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	320	351.917481006211	-31.9174810062108
2	324	332.24090877446	-8.24090877445972
3	343	358.352095706717	-15.3520957067169
4	295	334.338492190991	-39.3384921909909
5	301	351.928054050712	-50.9280540507119
6	367	331.272082793126	35.7279172068745
7	196	249.286587791888	-53.2865877918877
8	182	234.632143923621	-52.6321439236207
9	342	355.991929477363	-13.9919294773627
10	361	419.017668459862	-58.0176684598622
11	333	323.829429019333	9.17057098066752
12	330	339.956718405577	-9.95671840557708
13	345	345.543225486659	-0.543225486659366
14	323	332.159403504502	-9.1594035045021
15	365	376.576968509493	-11.5769685094927
16	323	261.470720113392	61.5292798866083
17	316	337.78820276359	-21.7882027635895
18	358	359.956232631106	-1.95623263110596
19	235	231.83889038308	3.16110961692039
20	169	197.174359244782	-28.1743592447824
21	430	363.203719571837	66.7962804281633
22	409	337.646338312677	71.3536616873233
23	407	291.525064110389	115.474935889611
24	341	349.819897589679	-8.8198975896794
25	326	336.819376782255	-10.8193767822553
26	374	334.842511727634	39.1574882723657
27	364	374.983404629605	-10.9834046296049
28	349	341.145835021729	7.85416497827086
29	300	367.157449681079	-67.1574496810786
30	385	371.494480965054	13.5055190349463
31	304	226.926907337005	77.0730926629953
32	196	209.972656420339	-13.9726564203387
33	443	388.438158837219	54.5618411627815
34	414	363.193146527335	50.8068534726646
35	325	323.375195619143	1.62480438085708
36	388	379.764096269268	8.23590373073174
37	356	332.673996085647	23.3260039143532
38	386	371.725344644373	14.2746553556271
39	444	399.593105996433	44.4068940035669
40	387	363.817884425889	23.182115574111
41	327	381.478378511066	-54.4783785110663
42	448	385.048807445575	62.951192554425
43	225	247.309722737267	-22.3097227372668
44	182	226.735256749638	-44.7352567496381
45	460	376.800338230363	83.1996617696369
46	411	375.295773578882	35.7042264211184
47	342	391.362703784171	-49.3627037841711
48	361	341.529136196462	19.4708638035377
49	377	341.681573691876	35.3184263081236
50	331	358.927047468817	-27.9270474688166
51	428	361.04577697435	66.9542230256496
52	340	328.106101122353	11.8938988776475
53	352	408.498032243702	-56.4980322437025
54	461	433.469888696261	27.5301113037386
55	221	257.375125553237	-36.375125553237
56	198	230.618054633424	-32.6180546334237
57	422	436.213356100349	-14.2133561003487
58	329	411.48293637161	-82.4829363716102
59	320	340.702174666041	-20.7021746660409
60	375	329.256004646552	45.743995353448
61	364	395.579016706236	-31.5790167062361
62	351	379.512086500947	-28.5120865009466
63	380	353.06738453041	26.9326154695899
64	319	310.012519725988	8.98748027401178
65	322	363.831536556443	-41.8315365564429
66	386	373.340054613263	12.6599453867368
67	221	260.409815817599	-39.4098158175985
68	187	217.677893006955	-30.6778930069547
69	343	378.826989421438	-35.8269894214379
70	342	399.078513415288	-57.0785134152885
71	365	330.586985713617	34.4130142863831
72	313	359.139844145186	-46.1398441451858
73	356	341.660427602874	14.3395723971262
74	337	345.493439350206	-8.49343935020555
75	389	351.938627095213	37.0613729047869
76	326	349.70975227227	-23.7097522722705
77	343	330.012033951517	12.9879660484829
78	357	396.214327649291	-39.2143276492909
79	220	223.044109453219	-3.04410945321914
80	218	199.967612785324	18.0323872146764
81	391	411.575014686069	-20.5750146860692
82	425	428.781275371057	-3.78127537105692
83	332	354.731880635754	-22.7318806357543
84	298	359.452213094463	-61.4522130944626
85	360	372.562879219296	-12.5628792192956
86	336	308.106586896824	27.8934131031764
87	325	382.982943162548	-57.9829431625477
88	393	340.410951805767	52.5890481942334
89	301	346.653915918906	-45.6539159189064
90	426	497.897540971282	-71.8975409712821
91	265	277.729300906048	-12.7293009060478
92	210	210.10394782675	-0.103947826750139
93	429	442.509185435995	-13.5091854359948
94	440	405.009108579151	34.9908914208486
95	357	378.564406608615	-21.5644066086149
96	431	421.650990546541	9.34900945345935
97	442	397.524162627353	44.4758373726468
98	422	385.957274245954	36.0427257540458
99	544	476.233530640409	67.766469359591
100	420	391.181626241306	28.8183737586942
101	396	371.998500501697	24.0014994983029
102	482	422.094650902229	59.9053490977711
103	261	306.190081023158	-45.1900810231578
104	211	226.86654815605	-15.8665481560496
105	448	525.371428104108	-77.3714281041079
106	468	413.339083064321	54.660916935679
107	464	408.600683602663	55.3993163973373
108	425	367.490964719358	57.509035280642

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.178367104242464	0.356734208484929	0.821632895757536
7	0.264889983666971	0.529779967333941	0.73511001633303
8	0.161242903482076	0.322485806964153	0.838757096517924
9	0.0997668422396185	0.199533684479237	0.900233157760381
10	0.0618554345571666	0.123710869114333	0.938144565442833
11	0.114955549052564	0.229911098105129	0.885044450947436
12	0.126909862414416	0.253819724828832	0.873090137585584
13	0.0798923629246993	0.159784725849399	0.9201076370753
14	0.0490688602130293	0.0981377204260585	0.95093113978697
15	0.028511886586214	0.057023773172428	0.971488113413786
16	0.22736777569516	0.454735551390321	0.77263222430484
17	0.169829611803814	0.339659223607628	0.830170388196186
18	0.126982734402079	0.253965468804158	0.873017265597921
19	0.0890475938387092	0.178095187677418	0.91095240616129
20	0.0709763516597057	0.141952703319411	0.929023648340294
21	0.197114743850081	0.394229487700163	0.802885256149918
22	0.387227229017626	0.774454458035252	0.612772770982374
23	0.81860437697514	0.362791246049719	0.18139562302486
24	0.770823782327467	0.458352435345067	0.229176217672534
25	0.718587605903623	0.562824788192753	0.281412394096377
26	0.709610817885058	0.580778364229885	0.290389182114942
27	0.653064090235906	0.693871819528188	0.346935909764094
28	0.592164845368996	0.815670309262007	0.407835154631004
29	0.656035305014402	0.687929389971196	0.343964694985598
30	0.614872469404862	0.770255061190276	0.385127530595138
31	0.717257855302392	0.565484289395216	0.282742144697608
32	0.684334089159993	0.631331821680013	0.315665910840007
33	0.733076187014933	0.533847625970133	0.266923812985067
34	0.753678201302695	0.49264359739461	0.246321798697305
35	0.703793586681551	0.592412826636898	0.296206413318449
36	0.652107377625319	0.695785244749363	0.347892622374681
37	0.610354177330851	0.779291645338299	0.389645822669149
38	0.55773661750788	0.884526764984241	0.44226338249212
39	0.565531493655849	0.868937012688303	0.434468506344151
40	0.523910851835629	0.952178296328742	0.476089148164371
41	0.565796194577909	0.868407610844183	0.434203805422091
42	0.633153954277438	0.733692091445124	0.366846045722562
43	0.595040548615848	0.809918902768304	0.404959451384152
44	0.600225876148447	0.799548247703107	0.399774123851553
45	0.714680781204777	0.570638437590447	0.285319218795223
46	0.702658370637508	0.594683258724985	0.297341629362492
47	0.732634635430662	0.534730729138676	0.267365364569338
48	0.697153335242221	0.605693329515559	0.302846664757779
49	0.67485996153034	0.650280076939321	0.32514003846966
50	0.646293908979692	0.707412182040617	0.353706091020308
51	0.710169736711389	0.579660526577221	0.289830263288611
52	0.664587731939944	0.670824536120112	0.335412268060056
53	0.71376795537924	0.572464089241519	0.286232044620759
54	0.686263013493456	0.627473973013089	0.313736986506544
55	0.66877729904103	0.66244540191794	0.33122270095897
56	0.643838929525913	0.712322140948175	0.356161070474087
57	0.600982855037455	0.798034289925089	0.399017144962545
58	0.744087299558117	0.511825400883765	0.255912700441883
59	0.708272653402395	0.58345469319521	0.291727346597605
60	0.711310826732087	0.577378346535827	0.288689173267913
61	0.703830200609558	0.592339598780884	0.296169799390442
62	0.681944314036576	0.636111371926848	0.318055685963424
63	0.649758939075887	0.700482121848226	0.350241060924113
64	0.598059392922957	0.803881214154087	0.401940607077043
65	0.62047029413832	0.75905941172336	0.37952970586168
66	0.569333457266423	0.861333085467153	0.430666542733577
67	0.565809600451912	0.868380799096176	0.434190399548088
68	0.545037383893465	0.909925232213069	0.454962616106535
69	0.531944096060261	0.936111807879478	0.468055903939739
70	0.592399895233467	0.815200209533065	0.407600104766533
71	0.567012760212893	0.865974479574214	0.432987239787107
72	0.58238422948132	0.83523154103736	0.41761577051868
73	0.525626672832481	0.948746654335037	0.474373327167519
74	0.470017643597326	0.940035287194653	0.529982356402674
75	0.452605308905919	0.905210617811838	0.547394691094081
76	0.412336105589534	0.824672211179068	0.587663894410466
77	0.355615479599126	0.711230959198253	0.644384520400874
78	0.353684851883457	0.707369703766914	0.646315148116543
79	0.297610807487944	0.595221614975888	0.702389192512056
80	0.248479763204546	0.496959526409093	0.751520236795454
81	0.212093403031013	0.424186806062027	0.787906596968987
82	0.16812282898735	0.336245657974701	0.83187717101265
83	0.145218540155482	0.290437080310965	0.854781459844518
84	0.204484786958319	0.408969573916638	0.795515213041681
85	0.176798403209576	0.353596806419152	0.823201596790424
86	0.146058672035621	0.292117344071242	0.853941327964379
87	0.176769570564857	0.353539141129714	0.823230429435143
88	0.18613053188091	0.37226106376182	0.81386946811909
89	0.204995341258425	0.409990682516849	0.795004658741575
90	0.442495044986576	0.884990089973152	0.557504955013424
91	0.367281477287565	0.73456295457513	0.632718522712435
92	0.294950441810013	0.589900883620026	0.705049558189987
93	0.235118632304188	0.470237264608375	0.764881367695812
94	0.186215468322783	0.372430936645565	0.813784531677217
95	0.177110520462384	0.354221040924768	0.822889479537616
96	0.126003991318328	0.252007982636656	0.873996008681672
97	0.0895645472459554	0.179129094491911	0.910435452754045
98	0.0601385226473053	0.120277045294611	0.939861477352695
99	0.0631614979139151	0.12632299582783	0.936838502086085
100	0.0375959860910968	0.0751919721821936	0.962404013908903
101	0.0198914472179327	0.0397828944358654	0.980108552782067
102	0.0177968076166918	0.0355936152333836	0.982203192383308

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	2	0.0206185567010309	OK
10% type I error level	5	0.0515463917525773	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/10nc4z1292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/10nc4z1292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/1yt651292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/1yt651292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/292o81292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/292o81292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/392o81292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/392o81292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/492o81292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/492o81292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/592o81292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/592o81292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/62u5t1292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/62u5t1292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/7dlmw1292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/7dlmw1292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/8dlmw1292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/8dlmw1292949673.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/9dlmw1292949673.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292949564ofl41eif4goe3sq/9dlmw1292949673.ps (open in new window)

Parameters (Session):

par2 = blue ; par3 = FALSE ; par4 = Unknown ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = Unknown ;

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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