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Multiple Regression Final m/v

*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, 23 Nov 2010 23:46:10 +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/24/t1290556050buxxdees1gdynww.htm/, Retrieved Wed, 24 Nov 2010 00:47:30 +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/24/t1290556050buxxdees1gdynww.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 «

7 7 1 7 7 1 7 7 4 5 2 5 6 2 5 6 5 6 1 5 5 1 5 5 7 7 1 7 7 1 7 6 6 6 2 5 6 1 4 5 5 4 1 7 7 1 4 7 5 6 2 5 6 2 5 6 5 6 1 6 7 1 6 7 6 7 1 7 5 1 6 7 5 6 2 5 6 3 6 6 6 7 1 5 6 1 5 7 7 7 1 7 7 1 6 7 6 7 1 3 7 2 7 7 3 7 1 7 7 1 6 7 6 6 1 6 6 1 5 6 3 3 1 5 5 1 4 4 4 6 1 5 6 1 4 5 6 7 1 7 6 1 6 7 6 7 1 3 6 1 6 6 5 5 1 7 6 1 5 6 6 6 1 7 7 1 7 7 6 6 1 7 6 1 6 6 3 4 1 7 7 1 4 7 4 5 3 4 3 3 4 5 5 6 2 6 7 2 6 6 4 4 1 5 5 1 6 7 5 7 1 7 7 0 5 7 6 6 2 6 6 2 5 5 2 5 1 4 5 1 2 6 5 7 1 5 7 1 5 5 3 6 1 7 7 2 5 7 7 7 1 7 7 1 7 7 6 5 1 7 6 1 6 5 6 6 1 7 6 2 7 5 6 5 1 7 6 1 6 5 7 3 1 6 5 1 6 6 5 6 1 3 6 1 5 7 5 5 1 4 6 2 4 5 7 6 1 5 6 1 5 6 5 4 3 7 7 3 6 7 5 5 1 5 5 1 6 6 2 6 3 6 7 2 4 7 5 4 4 5 3 6 5 1 6 7 1 6 7 1 6 6 5 6 1 7 7 1 5 7 1 4 1 7 7 1 6 6 5 7 1 7 6 1 5 6 5 3 2 7 7 1 6 7 5 7 1 6 7 1 5 7 6 4 1 7 6 1 5 4 6 5 1 6 7 1 7 6 6 7 1 7 6 1 5 6 5 6 2 7 6 2 5 6 6 6 1 6 6 2 6 6 5 6 4 6 6 4 3 6 5 6 1 5 7 1 6 7 6 6 2 5 6 2 5 6 6 6 1 6 7 1 6 7 4 6 2 5 6 2 4 5 5 6 1 6 6 2 6 6 4 5 1 3 5 1 6 5 6 7 1 7 7 2 5 6 6 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Q1_2[t] = + 0.683448246864695 + 0.198926748843929Q1_3[t] -0.0928214103551924Q1_5[t] + 0.0904997968487101Q1_7[t] -0.075972113093604Q1_8[t] + 0.116383027938451Q1_12[t] + 0.580006004424285Q1_16[t] -0.00989775749926517Q1_22[t] + 0.124131261356080Q1_2v[t] -0.261297359223972Q1_3v[t] + 0.227200441858429Q1_5v[t] -0.128064165925015Q1_7v[t] + 0.308882429796853Q1_8v[t] -0.158991872319213Q1_12v[t] + 0.0889780949233021Q1_16v[t] -0.102927253819690Q1_22v[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)	0.683448246864695	0.926271	0.7378	0.462638	0.231319
Q1_3	0.198926748843929	0.106626	1.8657	0.065539	0.032769
Q1_5	-0.0928214103551924	0.14066	-0.6599	0.511102	0.255551
Q1_7	0.0904997968487101	0.086763	1.0431	0.299875	0.149938
Q1_8	-0.075972113093604	0.111699	-0.6802	0.498257	0.249129
Q1_12	0.116383027938451	0.125318	0.9287	0.355672	0.177836
Q1_16	0.580006004424285	0.101982	5.6873	0	0
Q1_22	-0.00989775749926517	0.11257	-0.0879	0.930143	0.465071
Q1_2v	0.124131261356080	0.112668	1.1017	0.273681	0.136841
Q1_3v	-0.261297359223972	0.107925	-2.4211	0.017601	0.008801
Q1_5v	0.227200441858429	0.239237	0.9497	0.344964	0.172482
Q1_7v	-0.128064165925015	0.129997	-0.9851	0.327356	0.163678
Q1_8v	0.308882429796853	0.161301	1.9149	0.058862	0.029431
Q1_12v	-0.158991872319213	0.142698	-1.1142	0.268339	0.13417
Q1_16v	0.0889780949233021	0.161312	0.5516	0.582676	0.291338
Q1_22v	-0.102927253819690	0.153545	-0.6703	0.504459	0.252229

Multiple Linear Regression - Regression Statistics
Multiple R	0.673124912686542
R-squared	0.453097148079265
Adjusted R-squared	0.356584880093253
F-TEST (value)	4.69471039831883
F-TEST (DF numerator)	15
F-TEST (DF denominator)	85
p-value	1.81266670795655e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.90392117898313
Sum Squared Residuals	69.4512473142114

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	7	6.55870471035365	0.441295289646353
2	5	5.20280688402397	-0.202806884023974
3	6	4.61977537252105	1.38022462747895
4	5	5.0896922847082	-0.0896922847081988
5	6	5.92346305685277	0.0765369431472323
6	6	5.11406065603278	0.885939343967223
7	6	5.63695894060445	0.363041059395548
8	6	5.33338489687718	0.66661510312282
9	4	3.92190972661016	0.07809027338984
10	6	5.50236374909422	0.497636250905775
11	6	6.11138431853196	-0.111384318531962
12	3	3.50548105551907	-0.505481055519066
13	5	5.59298873631076	-0.592988736310762
14	5	5.24413792494949	-0.244137924949490
15	2	3.09630144067017	-1.09630144067017
16	3	5.22552251065442	-2.22552251065442
17	6	5.46113266977966	0.538867330220338
18	6	6.15542426851204	-0.155424268512041
19	5	4.95268302267897	0.0473169773210339
20	7	5.5970783587263	1.40292164127370
21	5	5.2682410732817	-0.268241073281701
22	5	5.2148943607927	-0.214894360792697
23	5	5.16219315114779	-0.162193151147789
24	5	6.32908536327162	-1.32908536327162
25	5	5.69927039284263	-0.699270392842633
26	6	5.48158007619131	0.518419923808686
27	5	5.02987609146532	-0.0298760914653231
28	5	4.19536747379987	0.804632526200128
29	6	5.21382354606428	0.786176453935721
30	4	4.1546369894868	-0.154636989486803
31	4	4.94180722238501	-0.941807222385013
32	6	4.92733997743747	1.07266002256253
33	3	3.03556652955895	-0.0355665295589454
34	6	4.95800170959353	1.04199829040647
35	5	4.29215877075394	0.707841229246058
36	6	6.12563144103092	-0.125631441030924
37	7	5.2964813163199	1.70351868368010
38	4	4.59423748477401	-0.594237484774013
39	5	4.48943873521316	0.510561264786845
40	4	4.89315052491175	-0.893150524911754
41	5	4.85834639432	0.141653605679998
42	3	4.85705273681687	-1.85705273681687
43	5	5.01173793183049	-0.0117379318304941
44	6	5.97838556912085	0.021614430879145
45	6	5.91535766611832	0.0846423338816765
46	4	4.14414985171917	-0.144149851719168
47	4	4.29273383980916	-0.292733839809155
48	6	5.08470430685183	0.915295693148168
49	6	5.8460116407808	0.153988359219196
50	5	5.0012968806686	-0.00129688066859569
51	6	6.00876467935651	-0.00876467935650707
52	4	3.74678879265409	0.253211207345913
53	4	4.73897302711348	-0.738973027113485
54	5	5.13605548000944	-0.136055480009441
55	3	4.07988695643614	-1.07988695643614
56	6	6.0055067618141	-0.0055067618141013
57	6	5.67351456808306	0.326485431916937
58	4	4.20304535869686	-0.203045358696862
59	5	4.72093031884291	0.279069681157086
60	5	4.77450000649779	0.225499993502207
61	4	5.18469068820968	-1.18469068820968
62	6	5.09415628144966	0.905843718550336
63	5	5.79742881096996	-0.797428810969963
64	4	4.85526770443865	-0.855267704438649
65	6	4.66986410316892	1.33013589683108
66	5	5.92044923057021	-0.920449230570208
67	6	5.40838594159228	0.591614058407717
68	5	6.19194862111634	-1.19194862111634
69	6	5.52939889578658	0.470601104213425
70	5	4.70079537516183	0.299204624838168
71	4	4.05848111772794	-0.0584811177279371
72	6	5.61194261669205	0.388057383307947
73	5	3.4976327233146	1.5023672766854
74	5	5.18323680512411	-0.183236805124113
75	3	3.96240861283944	-0.96240861283944
76	5	4.99253777823451	0.0074622217654881
77	4	4.65301480590733	-0.653014805907328
78	5	4.65495648746336	0.345043512536640
79	5	3.28163075852555	1.71836924147445
80	7	6.19194862111634	0.808051378883662
81	7	5.22398687650346	1.77601312349654
82	5	4.20168452213125	0.798315477868746
83	4	3.98018739112785	0.0198126088721492
84	6	5.36733099341657	0.632669006583427
85	5	4.8934170267239	0.106582973276100
86	5	5.35597544608194	-0.355975446081937
87	4	4.57286602021234	-0.572866020212344
88	5	5.07529374484956	-0.075293744849557
89	2	3.42809714193190	-1.42809714193190
90	7	5.89647513474844	1.10352486525156
91	4	4.63935094582333	-0.639350945823334
92	5	4.76839329766487	0.23160670233513
93	5	5.63782584778179	-0.637825847781794
94	7	6.16476161345815	0.835238386541853
95	2	5.43194755917554	-3.43194755917554
96	4	3.92583521406147	0.0741647859385272
97	6	5.76410663321951	0.23589336678049
98	5	5.32778390224284	-0.327783902242838
99	5	4.54885114897462	0.451148851025376
100	4	4.71345469906492	-0.713454699064917
101	4	4.41641927952197	-0.416419279521968

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.528924560611165	0.94215087877767	0.471075439388835
20	0.425771933664231	0.851543867328462	0.574228066335769
21	0.461811903016353	0.923623806032706	0.538188096983647
22	0.370945247380006	0.741890494760013	0.629054752619994
23	0.265945077207121	0.531890154414241	0.73405492279288
24	0.280463611133787	0.560927222267574	0.719536388866213
25	0.228421122740208	0.456842245480416	0.771578877259792
26	0.161366673334423	0.322733346668846	0.838633326665577
27	0.114106068196947	0.228212136393895	0.885893931803053
28	0.163596451299720	0.327192902599439	0.83640354870028
29	0.144686678924467	0.289373357848934	0.855313321075533
30	0.140156346836836	0.280312693673671	0.859843653163164
31	0.324557983866481	0.649115967732961	0.675442016133519
32	0.284279883672147	0.568559767344293	0.715720116327853
33	0.21963082530862	0.43926165061724	0.78036917469138
34	0.250924036101114	0.501848072202228	0.749075963898886
35	0.217468980735126	0.434937961470251	0.782531019264874
36	0.165746389678256	0.331492779356511	0.834253610321744
37	0.267483022711206	0.534966045422412	0.732516977288794
38	0.55280168599752	0.89439662800496	0.44719831400248
39	0.526745507377447	0.946508985245107	0.473254492622553
40	0.52690102899124	0.94619794201752	0.47309897100876
41	0.45546418097561	0.91092836195122	0.54453581902439
42	0.578472470614584	0.843055058770833	0.421527529385416
43	0.508981178624633	0.982037642750734	0.491018821375367
44	0.445293357363088	0.890586714726175	0.554706642636912
45	0.392664833524062	0.785329667048125	0.607335166475938
46	0.333475855674066	0.666951711348132	0.666524144325934
47	0.276028579325721	0.552057158651442	0.723971420674279
48	0.274992305443309	0.549984610886618	0.725007694556691
49	0.22061892671951	0.44123785343902	0.77938107328049
50	0.187695933870764	0.375391867741528	0.812304066129236
51	0.147317073027014	0.294634146054029	0.852682926972986
52	0.124988026324315	0.249976052648629	0.875011973675685
53	0.111910626380483	0.223821252760965	0.888089373619517
54	0.083114825104717	0.166229650209434	0.916885174895283
55	0.115939185307801	0.231878370615602	0.884060814692199
56	0.0868206378146791	0.173641275629358	0.91317936218532
57	0.0625109961237968	0.125021992247594	0.937489003876203
58	0.0459391734501822	0.0918783469003644	0.954060826549818
59	0.0326205101680775	0.065241020336155	0.967379489831923
60	0.0217643755085743	0.0435287510171486	0.978235624491426
61	0.0194493629112935	0.0388987258225871	0.980550637088706
62	0.0149079517588295	0.0298159035176590	0.98509204824117
63	0.0113650613967455	0.0227301227934910	0.988634938603254
64	0.0114200635973934	0.0228401271947869	0.988579936402607
65	0.0157300331418038	0.0314600662836076	0.984269966858196
66	0.0138807142603025	0.0277614285206049	0.986119285739698
67	0.0102552269157575	0.0205104538315149	0.989744773084243
68	0.0105440772931698	0.0210881545863397	0.98945592270683
69	0.0071879609401197	0.0143759218802394	0.99281203905988
70	0.0044095161096923	0.0088190322193846	0.995590483890308
71	0.00249422795383619	0.00498845590767237	0.997505772046164
72	0.00153316485347292	0.00306632970694584	0.998466835146527
73	0.00353505491791912	0.00707010983583824	0.99646494508208
74	0.00194807135451326	0.00389614270902653	0.998051928645487
75	0.00141272042951811	0.00282544085903623	0.998587279570482
76	0.000684504761069371	0.00136900952213874	0.99931549523893
77	0.000424482530190974	0.000848965060381949	0.999575517469809
78	0.000200609305027282	0.000401218610054564	0.999799390694973
79	0.000702923025125764	0.00140584605025153	0.999297076974874
80	0.000373741810994602	0.000747483621989205	0.999626258189005
81	0.00100657544499765	0.00201315088999531	0.998993424555002
82	0.000588796151804718	0.00117759230360944	0.999411203848195

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	13	0.203125	NOK
5% type I error level	23	0.359375	NOK
10% type I error level	25	0.390625	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/101zc01290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/101zc01290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/1uyf61290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/1uyf61290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/2uyf61290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/2uyf61290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/3n7w91290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/3n7w91290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/4n7w91290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/4n7w91290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/5n7w91290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/5n7w91290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/6yhdc1290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/6yhdc1290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/7qqcx1290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/7qqcx1290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/8qqcx1290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/8qqcx1290555959.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/9qqcx1290555959.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/24/t1290556050buxxdees1gdynww/9qqcx1290555959.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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