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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: Sat, 05 Dec 2009 12:08:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd.htm/, Retrieved Sat, 05 Dec 2009 20:09:34 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd.htm/},
    year = {2009},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

8823 0 9051 8776 0 8823 8255 0 8776 7969 0 8255 8758 0 7969 8693 0 8758 8271 0 8693 7790 0 8271 7769 0 7790 8170 0 7769 8209 0 8170 9395 0 8209 9260 0 9395 9018 0 9260 8501 0 9018 8500 0 8501 9649 0 8500 9319 0 9649 8830 0 9319 8436 0 8830 8169 0 8436 8269 0 8169 7945 0 8269 9144 0 7945 8770 0 9144 8834 0 8770 7837 0 8834 7792 0 7837 8616 0 7792 8518 0 8616 7940 0 8518 7545 0 7940 7531 0 7545 7665 0 7531 7599 0 7665 8444 0 7599 8549 0 8444 7986 0 8549 7335 0 7986 7287 0 7335 7870 0 7287 7839 0 7870 7327 0 7839 7259 0 7327 6964 0 7259 7271 0 6964 6956 0 7271 7608 0 6956 7692 0 7608 7255 0 7692 6804 0 7255 6655 0 6804 7341 0 6655 7602 0 7341 7086 0 7602 6625 0 7086 6272 0 6625 6576 0 6272 6491 0 6576 7649 0 6491 7400 0 7649 6913 0 7400 6532 0 6913 6486 0 6532 7295 0 6486 7556 0 7295 7088 1 7556 6952 1 7088 6773 1 6952 6917 1 6773 7371 1 6917 8221 1 7371 7953 1 8221 8027 1 7953 7287 1 8027 8076 1 7287 8933 1 8076 9433 1 8933 9479 1 943 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 1029.25177948533 + 293.630335878267X[t] + 1.00160644084244Y1[t] -1166.49099523471M1[t] -1299.84636877925M2[t] -1732.48748327642M3[t] -1001.05820969392M4[t] -266.302441146827M5[t] -1044.60240661893M6[t] -1571.60816034634M7[t] -1468.87276527106M8[t] -1262.17642796682M9[t] -931.311419086338M10[t] -1030.21523527544M11[t] + 0.0556754590232193t + 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)	1029.25177948533	163.088154	6.311	0	0
X	293.630335878267	106.535185	2.7562	0.007259	0.003629
Y1	1.00160644084244	0.015669	63.9209	0	0
M1	-1166.49099523471	136.138675	-8.5684	0	0
M2	-1299.84636877925	135.996499	-9.5579	0	0
M3	-1732.48748327642	135.623453	-12.7742	0	0
M4	-1001.05820969392	134.858196	-7.423	0	0
M5	-266.302441146827	134.959846	-1.9732	0.05197	0.025985
M6	-1044.60240661893	136.424166	-7.657	0	0
M7	-1571.60816034634	135.982433	-11.5574	0	0
M8	-1468.87276527106	135.278237	-10.8582	0	0
M9	-1262.17642796682	134.953683	-9.3527	0	0
M10	-931.311419086338	134.881544	-6.9047	0	0
M11	-1030.21523527544	139.14898	-7.4037	0	0
t	0.0556754590232193	1.63011	0.0342	0.97284	0.48642

Multiple Linear Regression - Regression Statistics
Multiple R	0.994108696621259
R-squared	0.988252100698017
Adjusted R-squared	0.986170194492603
F-TEST (value)	474.686178526083
F-TEST (DF numerator)	14
F-TEST (DF denominator)	79
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	260.288261028821
Sum Squared Residuals	5352248.32752319

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	8823	8928.35635577457	-105.356355774566
2	8776	8566.69038917697	209.309610823031
3	8255	8087.02944741923	167.970552580774
4	7969	8296.67744078184	-327.677440781836
5	8758	8745.02944270701	12.9705572929847
6	8693	8757.05263451863	-64.0526345186299
7	8271	8164.99813759548	106.001862404518
8	7790	7845.11129009427	-55.1112900942681
9	7769	7570.09060481232	198.909395187684
10	8170	7879.97755389414	290.022446105864
11	8209	8182.77359594188	26.2264040581251
12	9395	9252.1071578692	142.892842130806
13	9260	9273.57707693264	-13.5770769326362
14	9018	9005.0605093334	12.9394906666032
15	8501	8330.08631161138	170.913688388625
16	8500	8543.74073073735	-43.7407307373549
17	9649	9277.55056830263	371.449431697368
18	9319	9650.15207881752	-331.15207881752
19	8830	8792.67187507113	37.3281249288731
20	8436	8405.67739603347	30.3226039665295
21	8169	8217.79647110481	-48.7964711048116
22	8269	8281.2882357394	-12.2882357393896
23	7945	8282.60073909355	-337.600739093554
24	9144	8988.35116299507	155.648837004933
25	8770	9022.84196578946	-252.841965789461
26	8834	8514.94145882888	319.058541171120
27	7837	8146.45883200465	-309.458832004645
28	7792	7879.34215952625	-87.3421595262537
29	8616	8569.08131369446	46.9186863055372
30	8518	8616.16073093556	-98.160730935558
31	7940	7991.05322146461	-51.0532214646114
32	7545	7514.91576919198	30.084230808022
33	7531	7326.03323782248	204.966762177523
34	7665	7642.93143199019	22.0685680098086
35	7599	7678.298554333	-79.2985543329994
36	8444	8642.46343997186	-198.463439971862
37	8549	8322.38556270803	226.614437291968
38	7986	8294.25454091098	-308.254540910979
39	7335	7297.76467567854	37.2353243214649
40	7287	7377.20383173163	-90.2038317316278
41	7870	8063.93816657731	-193.938166577309
42	7839	7869.63043157538	-30.6304315753766
43	7327	7311.63055364087	15.3694463591263
44	7259	6901.59912646384	357.400873536159
45	6964	7040.24190124982	-76.2419012498176
46	7271	7075.68868554081	195.311314459193
47	6956	7284.33372214936	-328.333722149357
48	7608	7999.09860401845	-391.098604018452
49	7692	7485.71068367203	206.289316327969
50	7255	7436.54592661729	-181.545926617287
51	6804	6566.25847293099	237.741527069010
52	6655	6846.01891715257	-191.018917152571
53	7341	7431.59100147317	-90.5910014731662
54	7602	7340.448729878	261.551270121995
55	7086	7074.9179326695	11.0820673305061
56	6625	6660.88007972909	-35.8800797290919
57	6272	6405.89152326399	-133.891523263990
58	6576	6383.24513398612	192.754866013883
59	6491	6588.88535127214	-97.88535127214
60	7649	7534.019714535	114.980285465004
61	7400	7527.44465325485	-127.444653254850
62	6913	7144.74495139957	-231.744951399573
63	6532	6224.37717567115	307.622824328845
64	6486	6574.2500707517	-88.2500707517061
65	7295	7262.98761847907	32.0123815209273
66	7556	7295.04293910753	260.957060892469
67	7088	7323.14247777729	-235.142477777289
68	6952	6957.18173399732	-5.18173399732387
69	6773	7027.71527080601	-254.715270806014
70	6917	7179.34840223473	-262.348402234727
71	7371	7224.73158898596	146.268411014041
72	8221	8709.73182386289	-488.731823862889
73	7953	8394.66197880327	-441.661978803273
74	8027	7992.93175457199	34.0682454280105
75	7287	7634.46519215618	-347.465192156179
76	8076	7624.7613749743	451.238625025705
77	8933	9149.8403008051	-216.840300805099
78	9433	9229.972730594	203.027269406006
79	9479	9203.82587274683	275.174127253173
80	9199	9352.69083955987	-153.690839559876
81	9469	9278.99304888725	190.006951112745
82	10015	9880.34747225422	134.652527745777
83	10999	10328.3764482241	670.623551775885
84	13009	12344.2280967475	664.77190325246
85	13699	13191.0217230652	507.978276934848
86	13895	13748.8304691609	146.169530839074
87	13248	13512.5598925279	-264.559892527894
88	13973	13596.0054743444	376.994525655644
89	15095	15056.9815879612	38.0184120387566
90	15201	15402.5397245734	-201.539724573386
91	14823	14981.7599290343	-158.759929034297
92	14538	14705.9437649302	-167.943764930151
93	14547	14627.2379420533	-80.2379420533186
94	14407	14967.1730843604	-560.17308436041

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.334579756344499	0.669159512688997	0.665420243655501
19	0.185459440997866	0.370918881995731	0.814540559002134
20	0.100598456828483	0.201196913656967	0.899401543171517
21	0.0647015879790509	0.129403175958102	0.935298412020949
22	0.0778680345428514	0.155736069085703	0.922131965457149
23	0.122656369341844	0.245312738683689	0.877343630658156
24	0.0755048142980765	0.151009628596153	0.924495185701923
25	0.0511860143737172	0.102372028747434	0.948813985626283
26	0.0504662971738615	0.100932594347723	0.949533702826139
27	0.091854675184052	0.183709350368104	0.908145324815948
28	0.0649383859503266	0.129876771900653	0.935061614049673
29	0.0437968419379526	0.0875936838759051	0.956203158062047
30	0.029081466150444	0.058162932300888	0.970918533849556
31	0.0178264903810145	0.0356529807620291	0.982173509618985
32	0.0104107295570212	0.0208214591140425	0.989589270442979
33	0.00771956872298078	0.0154391374459616	0.99228043127702
34	0.00472547119227902	0.00945094238455804	0.995274528807721
35	0.00264643513290707	0.00529287026581415	0.997353564867093
36	0.00317071883724327	0.00634143767448653	0.996829281162757
37	0.00536615410992669	0.0107323082198534	0.994633845890073
38	0.0107348834043489	0.0214697668086978	0.989265116595651
39	0.00662902224935203	0.0132580444987041	0.993370977750648
40	0.00394507984207757	0.00789015968415515	0.996054920157922
41	0.00403713163823151	0.00807426327646302	0.995962868361769
42	0.00252447477863299	0.00504894955726598	0.997475525221367
43	0.00141212510797342	0.00282425021594685	0.998587874892026
44	0.00278832474238449	0.00557664948476899	0.997211675257615
45	0.00193803705554729	0.00387607411109457	0.998061962944453
46	0.00204101527413517	0.00408203054827034	0.997958984725865
47	0.00174527679412136	0.00349055358824271	0.998254723205879
48	0.00328911316695549	0.00657822633391097	0.996710886833045
49	0.00321585018434519	0.00643170036869038	0.996784149815655
50	0.00240299673143891	0.00480599346287782	0.99759700326856
51	0.00310336480598315	0.0062067296119663	0.996896635194017
52	0.00203146906739746	0.00406293813479492	0.997968530932603
53	0.00131926202548415	0.00263852405096830	0.998680737974516
54	0.00217197289330286	0.00434394578660573	0.997828027106697
55	0.0013724841915509	0.0027449683831018	0.99862751580845
56	0.000961334815559765	0.00192266963111953	0.99903866518444
57	0.000654618966204141	0.00130923793240828	0.999345381033796
58	0.00147766827453285	0.00295533654906571	0.998522331725467
59	0.00098512919905449	0.00197025839810898	0.999014870800946
60	0.00067572197199398	0.00135144394398796	0.999324278028006
61	0.000365226545466054	0.000730453090932108	0.999634773454534
62	0.000316071698731563	0.000632143397463125	0.999683928301268
63	0.000754780895275957	0.00150956179055191	0.999245219104724
64	0.00094658413715555	0.0018931682743111	0.999053415862844
65	0.000494689391053478	0.000989378782106956	0.999505310608947
66	0.000423947507781873	0.000847895015563746	0.999576052492218
67	0.000201070677727043	0.000402141355454086	0.999798929322273
68	0.00035323267333745	0.0007064653466749	0.999646767326663
69	0.000207342922310514	0.000414685844621028	0.99979265707769
70	0.00137315662423945	0.00274631324847889	0.99862684337576
71	0.00437937915998421	0.00875875831996843	0.995620620840016
72	0.00344510571214235	0.00689021142428469	0.996554894287858
73	0.173088609373237	0.346177218746474	0.826911390626763
74	0.166179918664706	0.332359837329412	0.833820081335294
75	0.129044384970523	0.258088769941046	0.870955615029477
76	0.132355790530674	0.264711581061348	0.867644209469326

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	36	0.610169491525424	NOK
5% type I error level	42	0.711864406779661	NOK
10% type I error level	44	0.745762711864407	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/10prl21260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/10prl21260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/1jc2b1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/1jc2b1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/2mr7h1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/2mr7h1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/305hp1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/305hp1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/427uc1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/427uc1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/5jebp1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/5jebp1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/6z0u41260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/6z0u41260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/7zmwj1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/7zmwj1260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/8yyu81260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/8yyu81260040089.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/9h8ze1260040089.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260040162wbm08awg7jhvltd/9h8ze1260040089.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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