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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: Wed, 18 Nov 2009 05:11:35 -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/Nov/18/t1258546390qzn3ifrvav1yr41.htm/, Retrieved Wed, 18 Nov 2009 13:13:24 +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/Nov/18/t1258546390qzn3ifrvav1yr41.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 «

149657 0 142773 0 133639 0 128332 0 120297 0 118632 0 155276 0 169316 0 167395 0 157939 0 149601 0 146310 0 141579 0 136473 0 129818 0 124226 0 116428 0 116440 0 147747 0 160069 0 163129 0 151108 0 141481 0 139174 0 134066 0 130104 0 123090 0 116598 0 109627 0 105428 0 137272 0 159836 0 155283 0 141514 0 131852 0 130691 0 128461 0 123066 0 117599 0 111599 0 105395 0 102334 0 131305 0 149033 0 144954 0 132404 0 122104 0 118755 0 116222 1 110924 1 103753 1 99983 1 93302 1 91496 1 119321 1 139261 1 133739 1 123913 1 113438 1 109416 1 109406 1 105645 1 101328 1 97686 1 93093 1 91382 1 122257 1 139183 1 139887 1 131822 1 116805 1 113706 1 113012 1 110452 1 107005 1 102841 1 98173 1 98181 1 137277 1 147579 1 146571 1 138920 1 130340 1 128140 1 127059 1 122860 1 117702 1 113537 1 108366 1 111078 1 150739 1 159129 1 157928 1 147768 1 137507 1 136919 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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 135682.416666667 -13898.7583333333X[t] -628.084027777662M1[t] -5258.07638888886M2[t] -11287.8187500000M3[t] -16163.6861111111M4[t] -22413.1784722222M5[t] -23611.2958333334M6[t] + 9682.21180555554M7[t] + 24974.3444444445M8[t] + 23174.9770833334M9[t] + 12753.3597222222M10[t] + 2486.49236111111M11[t] -15.6326388888899t + 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)	135682.416666667	3993.160784	33.9787	0	0
X	-13898.7583333333	3857.759822	-3.6028	0.000538	0.000269
M1	-628.084027777662	4675.032978	-0.1343	0.893456	0.446728
M2	-5258.07638888886	4663.966549	-1.1274	0.262869	0.131434
M3	-11287.8187500000	4653.93139	-2.4254	0.017486	0.008743
M4	-16163.6861111111	4644.934187	-3.4799	0.000806	0.000403
M5	-22413.1784722222	4636.980981	-4.8336	6e-06	3e-06
M6	-23611.2958333334	4630.077151	-5.0995	2e-06	1e-06
M7	9682.21180555554	4624.227399	2.0938	0.039367	0.019683
M8	24974.3444444445	4619.435728	5.4064	1e-06	0
M9	23174.9770833334	4615.705434	5.0209	3e-06	1e-06
M10	12753.3597222222	4613.039091	2.7646	0.007037	0.003518
M11	2486.49236111111	4611.438545	0.5392	0.591209	0.295605
t	-15.6326388888899	70.152684	-0.2228	0.824216	0.412108

Multiple Linear Regression - Regression Statistics
Multiple R	0.895559876202289
R-squared	0.80202749186346
Adjusted R-squared	0.770641606427178
F-TEST (value)	25.5537634422238
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9221.80981332463
Sum Squared Residuals	6973425651.1167

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	149657	135038.699999999	14618.3000000008
2	142773	130393.075	12379.9250000000
3	133639	124347.7	9291.29999999995
4	128332	119456.200000000	8875.79999999988
5	120297	113191.075	7105.92500000001
6	118632	111977.325	6654.67499999994
7	155276	145255.2	10020.8000000000
8	169316	160531.7	8784.30000000005
9	167395	158716.7	8678.30000000001
10	157939	148279.45	9659.54999999998
11	149601	137996.95	11604.0500000000
12	146310	135494.825	10815.175
13	141579	134851.108333333	6727.89166666652
14	136473	130205.483333333	6267.51666666664
15	129818	124160.108333333	5657.89166666665
16	124226	119268.608333333	4957.39166666666
17	116428	113003.483333333	3424.51666666664
18	116440	111789.733333333	4650.26666666665
19	147747	145067.608333333	2679.39166666665
20	160069	160344.108333333	-275.108333333362
21	163129	158529.108333333	4599.89166666664
22	151108	148091.858333333	3016.14166666665
23	141481	137809.358333333	3671.64166666665
24	139174	135307.233333333	3866.76666666666
25	134066	134663.516666667	-597.516666666795
26	130104	130017.891666667	86.1083333333254
27	123090	123972.516666667	-882.516666666673
28	116598	119081.016666667	-2483.01666666666
29	109627	112815.891666667	-3188.89166666668
30	105428	111602.141666667	-6174.14166666667
31	137272	144880.016666667	-7608.01666666667
32	159836	160156.516666667	-320.516666666682
33	155283	158341.516666667	-3058.51666666668
34	141514	147904.266666667	-6390.26666666667
35	131852	137621.766666667	-5769.76666666667
36	130691	135119.641666667	-4428.64166666666
37	128461	134475.925	-6014.92500000012
38	123066	129830.3	-6764.29999999999
39	117599	123784.925	-6185.925
40	111599	118893.425	-7294.42499999998
41	105395	112628.3	-7233.3
42	102334	111414.55	-9080.54999999999
43	131305	144692.425	-13387.425
44	149033	159968.925	-10935.925
45	144954	158153.925	-13199.925
46	132404	147716.675	-15312.675
47	122104	137434.175	-15330.175
48	118755	134932.05	-16177.0500000000
49	116222	120389.575000000	-4167.57500000013
50	110924	115743.95	-4819.95000000001
51	103753	109698.575	-5945.575
52	99983	104807.075	-4824.07499999999
53	93302	98541.95	-5239.95000000001
54	91496	97328.2	-5832.20000000001
55	119321	130606.075	-11285.075
56	139261	145882.575	-6621.57500000002
57	133739	144067.575	-10328.5750000000
58	123913	133630.325	-9717.32500000001
59	113438	123347.825	-9909.82500000001
60	109416	120845.7	-11429.7
61	109406	120201.983333333	-10795.9833333334
62	105645	115556.358333333	-9911.35833333332
63	101328	109510.983333333	-8182.98333333333
64	97686	104619.483333333	-6933.48333333331
65	93093	98354.3583333333	-5261.35833333333
66	91382	97140.6083333333	-5758.60833333333
67	122257	130418.483333333	-8161.48333333333
68	139183	145694.983333333	-6511.98333333334
69	139887	143879.983333333	-3992.98333333334
70	131822	133442.733333333	-1620.73333333333
71	116805	123160.233333333	-6355.23333333333
72	113706	120658.108333333	-6952.10833333332
73	113012	120014.391666667	-7002.39166666677
74	110452	115368.766666667	-4916.76666666665
75	107005	109323.391666667	-2318.39166666665
76	102841	104431.891666667	-1590.89166666663
77	98173	98166.7666666667	6.23333333334313
78	98181	96953.0166666666	1227.98333333335
79	137277	130230.891666667	7046.10833333335
80	147579	145507.391666667	2071.60833333333
81	146571	143692.391666667	2878.60833333334
82	138920	133255.141666667	5664.85833333335
83	130340	122972.641666667	7367.35833333335
84	128140	120470.516666667	7669.48333333335
85	127059	119826.8	7232.19999999991
86	122860	115181.175	7678.82500000003
87	117702	109135.8	8566.20000000003
88	113537	104244.3	9292.70000000004
89	108366	97979.175	10386.8250000000
90	111078	96765.425	14312.5750000000
91	150739	130043.3	20695.7000000000
92	159129	145319.8	13809.2000000000
93	157928	143504.8	14423.2000000000
94	147768	133067.55	14700.4500000000
95	137507	122785.05	14721.9500000000
96	136919	120282.925	16636.0750000000

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0114374532063558	0.0228749064127116	0.988562546793644
18	0.0047812303222279	0.0095624606444558	0.995218769677772
19	0.00190076203595300	0.00380152407190601	0.998099237964047
20	0.00135609798275482	0.00271219596550965	0.998643902017245
21	0.000457089306114144	0.000914178612228288	0.999542910693886
22	0.000157312197360885	0.00031462439472177	0.99984268780264
23	8.86230267848214e-05	0.000177246053569643	0.999911376973215
24	4.36422064330728e-05	8.72844128661456e-05	0.999956357793567
25	2.78345214218111e-05	5.56690428436222e-05	0.999972165478578
26	1.07306623397564e-05	2.14613246795128e-05	0.99998926933766
27	4.31099631415487e-06	8.62199262830974e-06	0.999995689003686
28	1.52422585146163e-06	3.04845170292327e-06	0.999998475774148
29	5.3757564382403e-07	1.07515128764806e-06	0.999999462424356
30	4.22177611021321e-07	8.44355222042642e-07	0.999999577822389
31	1.29000709516074e-06	2.58001419032148e-06	0.999998709992905
32	7.88804929085506e-06	1.57760985817101e-05	0.999992111950709
33	5.35945646235033e-06	1.07189129247007e-05	0.999994640543538
34	6.33997704182202e-06	1.26799540836440e-05	0.999993660022958
35	1.15581817076452e-05	2.31163634152903e-05	0.999988441818292
36	1.56168324166436e-05	3.12336648332871e-05	0.999984383167583
37	1.03441869014352e-05	2.06883738028704e-05	0.999989655813099
38	6.55582412887952e-06	1.31116482577590e-05	0.999993444175871
39	6.94131684383077e-06	1.38826336876615e-05	0.999993058683156
40	5.76943268076598e-06	1.15388653615320e-05	0.99999423056732
41	7.9469684321937e-06	1.58939368643874e-05	0.999992053031568
42	5.37487911006237e-06	1.07497582201247e-05	0.99999462512089
43	3.60864935193846e-06	7.21729870387693e-06	0.999996391350648
44	1.94759840748235e-06	3.8951968149647e-06	0.999998052401593
45	2.60559910207784e-06	5.21119820415568e-06	0.999997394400898
46	4.00576148215528e-06	8.01152296431056e-06	0.999995994238518
47	8.78417916030895e-06	1.75683583206179e-05	0.99999121582084
48	2.46502682070830e-05	4.93005364141661e-05	0.999975349731793
49	9.78593663348462e-05	0.000195718732669692	0.999902140633665
50	0.0003912750500684	0.0007825501001368	0.999608724949932
51	0.00100785511788390	0.00201571023576781	0.998992144882116
52	0.00503929276699768	0.0100785855339954	0.994960707233002
53	0.0204517954028686	0.0409035908057372	0.979548204597131
54	0.0560944737929038	0.112188947585808	0.943905526207096
55	0.045508215005928	0.091016430011856	0.954491784994072
56	0.128526252363628	0.257052504727256	0.871473747636372
57	0.142882648694496	0.285765297388991	0.857117351305504
58	0.124968764126850	0.249937528253699	0.87503123587315
59	0.133031603002597	0.266063206005194	0.866968396997403
60	0.12334960275207	0.24669920550414	0.87665039724793
61	0.124532501858131	0.249065003716263	0.875467498141869
62	0.130069536267681	0.260139072535362	0.869930463732319
63	0.182430737822284	0.364861475644568	0.817569262177716
64	0.333149339976696	0.666298679953392	0.666850660023304
65	0.654603323581501	0.690793352836998	0.345396676418499
66	0.765011327169156	0.469977345661688	0.234988672830844
67	0.937246988386276	0.125506023227448	0.0627530116137239
68	0.936414803345777	0.127170393308446	0.0635851966542232
69	0.973450540444756	0.0530989191104873	0.0265494595552436
70	0.998099060504617	0.00380187899076514	0.00190093949538257
71	0.996515772589026	0.00696845482194854	0.00348422741097427
72	0.995797465974107	0.00840506805178578	0.00420253402589289
73	0.996774906196644	0.00645018760671275	0.00322509380335638
74	0.995475432532907	0.00904913493418667	0.00452456746709334
75	0.992052687588997	0.0158946248220052	0.00794731241100259
76	0.984901610031857	0.0301967799362856	0.0150983899681428
77	0.971074379889858	0.0578512402202836	0.0289256201101418
78	0.960189960716985	0.079620078566031	0.0398100392830155
79	0.968523926860718	0.0629521462785631	0.0314760731392816

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	39	0.619047619047619	NOK
5% type I error level	44	0.698412698412698	NOK
10% type I error level	49	0.777777777777778	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/105z1v1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/105z1v1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/1p8pr1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/1p8pr1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/2bv751258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/2bv751258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/337pj1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/337pj1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/4nwpi1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/4nwpi1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/5ltab1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/5ltab1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/6xwir1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/6xwir1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/7ch001258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/7ch001258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/8wkyy1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/8wkyy1258546290.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/94rma1258546290.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546390qzn3ifrvav1yr41/94rma1258546290.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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