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*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Mon, 15 Dec 2008 06:37:07 -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/Dec/15/t12293483159ssqg3mi9zww53a.htm/, Retrieved Mon, 15 Dec 2008 14:38:35 +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/2008/Dec/15/t12293483159ssqg3mi9zww53a.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},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

6340,5 0 7901,5 0 8191,1 0 7181,7 0 7594,4 0 7384,7 0 7876,7 0 8463,4 0 8317,2 0 7778,7 0 8532,8 0 7272,2 0 6680,1 0 8427,6 0 8752,8 0 7952,7 0 8694,3 0 7787 0 8474,2 0 9154,7 0 8557,2 0 7951,1 0 9156,7 0 7865,7 0 7337,4 0 9131,7 0 8814,6 0 8598,8 0 8439,6 0 7451,8 0 8016,2 0 9544,1 0 8270,7 0 8102,2 0 9369 0 7657,7 0 7816,6 0 9391,3 0 9445,4 0 9533,1 0 10068,7 0 8955,5 0 10423,9 0 11617,2 0 9391,1 0 10872 0 10230,4 0 9221 0 9428,6 0 10934,5 0 10986 0 11724,6 0 11180,9 0 11163,2 0 11240,9 0 12107,1 0 10762,3 0 11340,4 0 11266,8 0 9542,7 0 9227,7 0 10571,9 1 10774,4 1 10392,8 1 9920,2 1 9884,9 1 10174,5 1 11395,4 1 10760,2 1 10570,1 1 10536 1 9902,6 1 8889 1 10837,3 1 11624,1 1 10509 1 10984,9 1 10649,1 1 10855,7 1 11677,4 1 10760,2 1 10046,2 1 10772,8 1 9987,7 1 8638,7 1 11063,7 1 11855,7 1 10684,5 1 11337,4 1 10478 1 11123,9 1 12909,3 1 11339,9 1 10462,2 1 12733,5 1 10519,2 1 10414,9 1 12476,8 1 12384,6 1 12266,7 1 12919,9 1 11497,3 1 12142 1 13919,4 1 12656,8 1 12034,1 1 13199,7 1 10881,3 1 11301,2 1 13643,9 1 12517 1 13981,1 1 14275,7 1 13435 1 13565,7 1 16216,3 1 12970 1 14079,9 1 14235 1 12213,4 1 12581 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

y[t] = + 5824.1816 -1487.68506666667x[t] -269.96818989899M1[t] + 1602.27771717172M2[t] + 1631.76694545455M3[t] + 1312.63617373737M4[t] + 1504.67540202020M5[t] + 764.66463030303M6[t] + 1218.32385858586M7[t] + 2462.32308686869M8[t] + 1073.39231515152M9[t] + 951.461543434344M10[t] + 1563.98077171717M11[t] + 67.0607717171717t + 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)	5824.1816	213.350643	27.2986	0	0
x	-1487.68506666667	207.085308	-7.1839	0	0
M1	-269.96818989899	245.790179	-1.0984	0.274509	0.137255
M2	1602.27771717172	253.189344	6.3284	0	0
M3	1631.76694545455	252.857172	6.4533	0	0
M4	1312.63617373737	252.559594	5.1973	1e-06	0
M5	1504.67540202020	252.296735	5.9639	0	0
M6	764.66463030303	252.068701	3.0336	0.003034	0.001517
M7	1218.32385858586	251.875588	4.837	4e-06	2e-06
M8	2462.32308686869	251.717477	9.7821	0	0
M9	1073.39231515152	251.594433	4.2664	4.3e-05	2.2e-05
M10	951.461543434344	251.506507	3.783	0.000256	0.000128
M11	1563.98077171717	251.453737	6.2198	0	0
t	67.0607717171717	2.974406	22.5459	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.96152441237668
R-squared	0.92452919559632
Adjusted R-squared	0.915359845528582
F-TEST (value)	100.828214515372
F-TEST (DF numerator)	13
F-TEST (DF denominator)	107
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	562.228310907063
Sum Squared Residuals	33822772.0736388

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6340.5	5621.27418181818	719.22581818182
2	7901.5	7560.58086060606	340.919139393937
3	8191.1	7657.13086060606	533.969139393939
4	7181.7	7405.06086060606	-223.360860606060
5	7594.4	7664.16086060606	-69.7608606060595
6	7384.7	6991.21086060606	393.489139393940
7	7876.7	7511.93086060606	364.769139393939
8	8463.4	8822.99086060606	-359.590860606061
9	8317.2	7501.12086060606	816.079139393939
10	7778.7	7446.25086060606	332.44913939394
11	8532.8	8125.83086060606	406.969139393939
12	7272.2	6628.91086060606	643.289139393939
13	6680.1	6426.00344242424	254.096557575758
14	8427.6	8365.31012121212	62.2898787878808
15	8752.8	8461.86012121212	290.939878787878
16	7952.7	8209.79012121212	-257.090121212121
17	8694.3	8468.89012121212	225.409878787878
18	7787	7795.94012121212	-8.94012121212112
19	8474.2	8316.66012121212	157.53987878788
20	9154.7	9627.72012121212	-473.020121212121
21	8557.2	8305.85012121212	251.349878787880
22	7951.1	8250.98012121212	-299.880121212121
23	9156.7	8930.56012121212	226.139878787879
24	7865.7	7433.64012121212	432.059878787878
25	7337.4	7230.7327030303	106.667296969696
26	9131.7	9170.03938181818	-38.3393818181808
27	8814.6	9266.58938181818	-451.989381818182
28	8598.8	9014.51938181818	-415.719381818183
29	8439.6	9273.61938181818	-834.019381818182
30	7451.8	8600.66938181818	-1148.86938181818
31	8016.2	9121.38938181818	-1105.18938181818
32	9544.1	10432.4493818182	-888.349381818182
33	8270.7	9110.57938181818	-839.879381818181
34	8102.2	9055.70938181818	-953.509381818182
35	9369	9735.28938181818	-366.289381818182
36	7657.7	8238.36938181818	-580.669381818182
37	7816.6	8035.46196363636	-218.861963636364
38	9391.3	9974.76864242424	-583.468642424243
39	9445.4	10071.3186424242	-625.918642424243
40	9533.1	9819.24864242424	-286.148642424242
41	10068.7	10078.3486424242	-9.64864242424192
42	8955.5	9405.39864242424	-449.898642424243
43	10423.9	9926.11864242424	497.781357575757
44	11617.2	11237.1786424242	380.021357575758
45	9391.1	9915.30864242424	-524.208642424242
46	10872	9860.43864242424	1011.56135757576
47	10230.4	10540.0186424242	-309.618642424243
48	9221	9043.09864242424	177.901357575757
49	9428.6	8840.19122424242	588.408775757575
50	10934.5	10779.4979030303	155.002096969697
51	10986	10876.0479030303	109.952096969696
52	11724.6	10623.9779030303	1100.62209696970
53	11180.9	10883.0779030303	297.822096969697
54	11163.2	10210.1279030303	953.072096969697
55	11240.9	10730.8479030303	510.052096969696
56	12107.1	12041.9079030303	65.1920969696974
57	10762.3	10720.0379030303	42.2620969696963
58	11340.4	10665.1679030303	675.232096969696
59	11266.8	11344.7479030303	-77.9479030303037
60	9542.7	9847.8279030303	-305.127903030303
61	9227.7	9644.92048484849	-417.220484848485
62	10571.9	10096.5420969697	475.357903030303
63	10774.4	10193.0920969697	581.307903030303
64	10392.8	9941.0220969697	451.777903030302
65	9920.2	10200.1220969697	-279.922096969697
66	9884.9	9527.1720969697	357.727903030303
67	10174.5	10047.8920969697	126.607903030303
68	11395.4	11358.9520969697	36.4479030303027
69	10760.2	10037.0820969697	723.117903030303
70	10570.1	9982.2120969697	587.887903030303
71	10536	10661.7920969697	-125.792096969697
72	9902.6	9164.8720969697	737.727903030304
73	8889	8961.96467878788	-72.9646787878792
74	10837.3	10901.2713575758	-63.9713575757584
75	11624.1	10997.8213575758	626.278642424242
76	10509	10745.7513575758	-236.751357575758
77	10984.9	11004.8513575758	-19.9513575757583
78	10649.1	10331.9013575758	317.198642424243
79	10855.7	10852.6213575758	3.07864242424267
80	11677.4	12163.6813575758	-486.281357575758
81	10760.2	10841.8113575758	-81.6113575757573
82	10046.2	10786.9413575758	-740.741357575757
83	10772.8	11466.5213575758	-693.721357575758
84	9987.7	9969.60135757576	18.0986424242433
85	8638.7	9766.69393939394	-1127.99393939394
86	11063.7	11706.0006181818	-642.300618181818
87	11855.7	11802.5506181818	53.1493818181822
88	10684.5	11550.4806181818	-865.980618181818
89	11337.4	11809.5806181818	-472.180618181819
90	10478	11136.6306181818	-658.630618181818
91	11123.9	11657.3506181818	-533.450618181819
92	12909.3	12968.4106181818	-59.1106181818184
93	11339.9	11646.5406181818	-306.640618181819
94	10462.2	11591.6706181818	-1129.47061818182
95	12733.5	12271.2506181818	462.249381818183
96	10519.2	10774.3306181818	-255.130618181817
97	10414.9	10571.4232	-156.523200000001
98	12476.8	12510.7298787879	-33.9298787878791
99	12384.6	12607.2798787879	-222.679878787878
100	12266.7	12355.2098787879	-88.5098787878782
101	12919.9	12614.3098787879	305.590121212121
102	11497.3	11941.3598787879	-444.059878787879
103	12142	12462.0798787879	-320.079878787879
104	13919.4	13773.1398787879	146.260121212121
105	12656.8	12451.2698787879	205.530121212120
106	12034.1	12396.3998787879	-362.299878787879
107	13199.7	13075.9798787879	123.720121212122
108	10881.3	11579.0598787879	-697.75987878788
109	11301.2	11376.1524606061	-74.9524606060604
110	13643.9	13315.4591393939	328.440860606061
111	12517	13412.0091393939	-895.009139393939
112	13981.1	13159.9391393939	821.16086060606
113	14275.7	13419.0391393939	856.66086060606
114	13435	12746.0891393939	688.910860606061
115	13565.7	13266.8091393939	298.890860606062
116	16216.3	14577.8691393939	1638.43086060606
117	12970	13255.9991393939	-285.999139393939
118	14079.9	13201.1291393939	878.77086060606
119	14235	13880.7091393939	354.290860606061
120	12213.4	12383.7891393939	-170.389139393939
121	12581	12180.8817212121	400.118278787879

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0852093028729346	0.170418605745869	0.914790697127065
18	0.0370583930767375	0.074116786153475	0.962941606923262
19	0.0120676527766171	0.0241353055532343	0.987932347223383
20	0.00374582093373057	0.00749164186746113	0.99625417906627
21	0.00268717085074131	0.00537434170148262	0.997312829149259
22	0.00199595076160714	0.00399190152321429	0.998004049238393
23	0.000685927510573451	0.00137185502114690	0.999314072489426
24	0.000233358306272453	0.000466716612544906	0.999766641693728
25	7.15700204451277e-05	0.000143140040890255	0.999928429979555
26	2.41349627778659e-05	4.82699255557319e-05	0.999975865037222
27	5.1781805424263e-05	0.000103563610848526	0.999948218194576
28	2.59425307891369e-05	5.18850615782738e-05	0.99997405746921
29	4.90459237865506e-05	9.80918475731012e-05	0.999950954076213
30	0.000681408865112608	0.00136281773022522	0.999318591134887
31	0.00263890372279001	0.00527780744558003	0.99736109627721
32	0.00178058942882193	0.00356117885764386	0.998219410571178
33	0.00393839918139157	0.00787679836278313	0.996061600818608
34	0.00339038057207703	0.00678076114415406	0.996609619427923
35	0.00186734772245009	0.00373469544490018	0.99813265227755
36	0.00160498763949627	0.00320997527899254	0.998395012360504
37	0.00117463261392068	0.00234926522784137	0.99882536738608
38	0.000796643518457057	0.00159328703691411	0.999203356481543
39	0.000533402277599423	0.00106680455519885	0.9994665977224
40	0.00193477529586634	0.00386955059173268	0.998065224704134
41	0.00678495756686226	0.0135699151337245	0.993215042433138
42	0.00781169548393606	0.0156233909678721	0.992188304516064
43	0.0423184067178742	0.0846368134357485	0.957681593282126
44	0.149520927539097	0.299041855078193	0.850479072460903
45	0.132829699650940	0.265659399301879	0.86717030034906
46	0.402512569444737	0.805025138889474	0.597487430555263
47	0.358696046760925	0.71739209352185	0.641303953239075
48	0.317891700343391	0.635783400686781	0.68210829965661
49	0.34915528946778	0.69831057893556	0.65084471053222
50	0.328410037712944	0.656820075425887	0.671589962287056
51	0.301282567832435	0.60256513566487	0.698717432167565
52	0.535488271563309	0.929023456873382	0.464511728436691
53	0.513687999824726	0.972624000350548	0.486312000175274
54	0.641089345896906	0.717821308206189	0.358910654103094
55	0.629471859646969	0.741056280706062	0.370528140353031
56	0.590309382142378	0.819381235715245	0.409690617857622
57	0.53398039753979	0.93203920492042	0.46601960246021
58	0.559050200581731	0.881899598836537	0.440949799418269
59	0.501090847799139	0.997818304401723	0.498909152200861
60	0.456678397630379	0.913356795260757	0.543321602369621
61	0.42869541761736	0.85739083523472	0.57130458238264
62	0.400335502309669	0.800671004619338	0.599664497690331
63	0.387919572917516	0.775839145835032	0.612080427082484
64	0.365219937120055	0.73043987424011	0.634780062879945
65	0.341207193979708	0.682414387959416	0.658792806020292
66	0.30827953763072	0.61655907526144	0.69172046236928
67	0.275816769644267	0.551633539288535	0.724183230355733
68	0.228533280381652	0.457066560763304	0.771466719618348
69	0.262782964397913	0.525565928795827	0.737217035602087
70	0.320845267670625	0.641690535341249	0.679154732329375
71	0.282315605573552	0.564631211147105	0.717684394426448
72	0.407136549696344	0.814273099392687	0.592863450303656
73	0.411457397370661	0.822914794741322	0.588542602629339
74	0.381709491469744	0.763418982939488	0.618290508530256
75	0.548731998371607	0.902536003256786	0.451268001628393
76	0.515912949316389	0.968174101367221	0.484087050683611
77	0.464110503339099	0.928221006678198	0.535889496660901
78	0.532262795839487	0.935474408321025	0.467737204160513
79	0.56416257318042	0.871674853639159	0.435837426819579
80	0.531110111409272	0.937779777181457	0.468889888590728
81	0.545052237247095	0.90989552550581	0.454947762752905
82	0.550611849587831	0.898776300824339	0.449388150412169
83	0.523444738871213	0.953110522257575	0.476555261128787
84	0.652725502046894	0.694548995906213	0.347274497953106
85	0.682552935600104	0.634894128799791	0.317447064399896
86	0.63964074104099	0.72071851791802	0.36035925895901
87	0.77304476169783	0.453910476604338	0.226955238302169
88	0.780494132503232	0.439011734993537	0.219505867496768
89	0.750375968792938	0.499248062414124	0.249624031207062
90	0.703277313589682	0.593445372820636	0.296722686410318
91	0.639557044345688	0.720885911308623	0.360442955654312
92	0.591783225372508	0.816433549254983	0.408216774627492
93	0.524597743113188	0.950804513773623	0.475402256886812
94	0.616887335534961	0.766225328930077	0.383112664465039
95	0.645151543476907	0.709696913046187	0.354848456523093
96	0.686670108147853	0.626659783704294	0.313329891852147
97	0.634823718596281	0.730352562807438	0.365176281403719
98	0.542166246572355	0.91566750685529	0.457833753427645
99	0.739513214866717	0.520973570266566	0.260486785133283
100	0.650588054691839	0.698823890616323	0.349411945308161
101	0.541932452234195	0.91613509553161	0.458067547765805
102	0.469770542090316	0.939541084180633	0.530229457909684
103	0.3312278283969	0.6624556567938	0.6687721716031
104	0.422128649551563	0.844257299103127	0.577871350448437

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	21	0.238636363636364	NOK
5% type I error level	24	0.272727272727273	NOK
10% type I error level	26	0.295454545454545	NOK

Charts produced by software:

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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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