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Q

*Unverified author*
R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Fri, 21 Nov 2008 06:19:35 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp.htm/, Retrieved Fri, 21 Nov 2008 13:20:40 +0000
 
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/Nov/21/t12272736288o3d0d7gjuatfgp.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},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
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Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1687 0 -183.9235445 1508 0 -177.0726091 1507 0 -228.6351091 1385 0 -237.4476091 1632 0 -127.7601091 1511 0 -193.0101091 1559 0 -220.6351091 1630 0 -164.5101091 1579 0 -268.3226091 1653 0 -333.6976091 2152 0 -34.26010911 2148 0 -154.8851091 1752 0 -97.74528053 1765 0 101.1056549 1717 0 2.543154874 1558 0 -43.26934513 1575 0 -163.5818451 1520 0 -162.8318451 1805 0 46.54315487 1800 0 26.66815487 1719 0 -107.1443451 2008 0 42.48065487 2242 0 76.91815487 2478 0 196.2931549 2030 0 201.4329835 1655 0 12.28391886 1693 0 -0.278581137 1623 0 42.90891886 1805 0 87.59641886 1746 0 84.34641886 1795 0 57.72141886 1926 0 173.8464189 1619 0 -185.9660811 1992 0 47.65891886 2233 0 89.09641886 2192 0 -68.52858114 2080 0 272.6112475 1768 0 146.4621829 1835 0 162.8996829 1569 0 10.08718285 1976 0 279.7746829 1853 0 212.5246829 1965 0 248.8996829 1689 0 -41.97531715 1778 0 -5.787817149 1976 0 52.83718285 2397 0 274.2746829 2654 0 414.6496829 2097 0 310.7895114 1963 0 362.6404468 1677 0 26.07794684 1941 0 403.2654468 2003 0 327.9529468 1813 0 193.7029468 2012 0 317.0779468 1912 0 202.2029468 2084 0 321.3904468 2080 0 178.0154468 2118 0 16.45294684 2150 0 -68.17205316 1608 0 -157.0322246 1503 0 -76.18128917 1548 0 -81.74378917 1382 0 -134.5562892 1731 0 77.13121083 1798 0 199.8812108 1779 0 105.2562108 1887 0 198.3812108 2004 0 262.5687108 2077 0 196.1937108 2092 0 11.63121083 2051 0 -145.9937892 1577 0 -166.8539606 1356 0 -202.0030252 1652 0 43.43447482 1382 0 -113.3780252 1519 0 -113.6905252 1421 0 -155.9405252 1442 0 -210.5655252 1543 0 -124.4405252 1656 0 -64.25302518 1561 0 -298.6280252 1905 0 -154.1905252 2199 0 23.18447482 1473 0 -249.6756966 1655 0 118.1752388 1407 0 -180.3872612 1395 0 -79.19976119 1530 0 -81.51226119 1309 0 -246.7622612 1526 0 -105.3872612 1327 0 -319.2622612 1627 0 -72.07476119 1748 0 -90.44976119 1958 0 -80.01226119 2274 0 119.3627388 1648 0 -53.49743261 1401 0 -114.6464972 1411 0 -155.2089972 1403 0 -50.02149721 1394 0 -196.3339972 1520 0 -14.58399721 1528 0 -82.20899721 1643 0 17.91600279 1515 0 -162.8964972 1685 0 -132.2714972 2000 0 -16.83399721 2215 0 81.54100279 1956 0 275.6808314 1462 0 -32.46823322 1563 0 17.96926678 1459 0 27.15676678 1446 0 -123.1557332 1622 0 108.5942668 1657 0 67.96926678 1638 0 34.09426678 1643 0 -13.71823322 1683 0 -113.0932332 2050 0 54.34426678 2262 0 149.7192668 1813 0 153.8590954 1445 0 -28.28996923 1762 0 238.1475308 1461 0 50.33503077 1556 0 8.022530771 1431 0 -61.22746923 1427 0 -140.8524692 1554 0 -28.72746923 1645 0 9.460030771 1653 0 -121.9149692 2016 0 41.52253077 2207 0 115.8975308 1665 0 27.03735936 1361 0 -91.11170524 1506 0 3.325794759 1360 0 -29.48670524 1453 0 -73.79920524 1522 0 50.95079476 1460 0 -86.67420524 1552 0 -9.54920524 1548 0 -66.36170524 1827 0 73.26329476 1737 0 -216.2992052 1941 0 -128.9242052 1474 0 -142.7843767 1458 0 27.06655875 1542 0 60.50405875 1404 0 35.69155875 1522 0 16.37905875 1385 0 -64.87094125 1641 0 115.5040587 1510 0 -30.37094125 1681 0 87.81655875 1938 0 205.4415587 1868 0 -64.12094125 1726 0 -322.7459413 1456 0 -139.6061127 1445 0 35.24482274 1456 0 -4.317677263 1365 0 17.86982274 1487 0 2.557322737 1558 0 129.3073227 1488 0 -16.31767726 1684 0 164.8073227 1594 0 21.99482274 1850 0 138.6198227 1998 0 87.05732274 2079 0 51.43232274 1494 0 -80.42784867 1057 1 -105.1918797 1218 1 5.245620328 1168 1 68.43312033 1236 1 -0.879379672 1076 1 -105.1293797 1174 1 -82.75437967 1139 1 -132.6293797 1427 1 102.5581203 1487 1 23.18312033 1483 1 -180.3793797 1513 1 -267.0043797 1357 1 30.13544892 1165 1 23.98638432 1282 1 90.42388432 1110 1 31.61138432 1297 1 81.29888432 1185 1 25.04888432 1222 1 -13.57611568 1284 1 33.54888432 1444 1 140.7363843 1575 1 132.3613843 1737 1 94.79888432 1763 1 4.173884316
 
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
y[t] = + 1717.75147928992 -396.055827109141x[t] + 1.00000000002839t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)1717.7514792899216.512653104.026400
x-396.05582710914147.709356-8.301400
t1.000000000028390.1055649.47300


Multiple Linear Regression - Regression Statistics
Multiple R0.675537295805097
R-squared0.456350638023663
Adjusted R-squared0.450597734722326
F-TEST (value)79.3252752775477
F-TEST (DF numerator)2
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation214.664492264491
Sum Squared Residuals8709279.56120347


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
116871533.82793478468153.172065215319
215081540.67887018490-32.6788701848964
315071489.1163701834317.8836298165677
413851480.30387018318-95.3038701831818
516321589.9913701863042.0086298137040
615111524.74137018444-13.7413701844435
715591497.1163701836661.8836298163408
816301553.2413701852576.7586298147474
915791449.42887018231129.571129817695
1016531384.05387018045268.946129819551
1121521683.49137017895468.508629821049
1221481562.86637018553585.133629814474
1317521620.00619875715131.993801242852
1417651818.85713419279-53.8571341927937
1517171720.29463416400-3.29463416399540
1615581674.48213415869-116.482134158695
1715751554.1696341852820.8303658147210
1815201554.9196341853-34.9196341853003
1918051764.2946341612440.7053658387554
2018001744.4196341606855.5803658393197
2117191610.60713418688108.392865813119
2220081760.23213416113247.767865838871
2322421794.66963416211447.330365837893
2424781914.04463419550563.955365804504
2520301919.18446279564110.815537204358
2616551730.03539815027-75.0353981502719
2716931717.47289815292-24.4728981529153
2816231760.66039815114-137.660398151141
2918051805.34789815241-0.347898152410115
3017461802.09789815232-56.0978981523178
3117951775.4728981515619.5271018484381
3219261891.5978981948634.4021018051411
3316191531.7853981846487.2146018153565
3419921765.41039815128226.589601848724
3522331806.84789815245426.152101847547
3621921649.22289814798542.777101852022
3720801990.3627267976689.6372732023371
3817681864.21366219408-96.2136621940814
3918351880.65116219455-45.6511621945481
4015691727.83866214021-158.838662140210
4119761997.52616219787-21.5261621978663
4218531930.27616219596-77.276162195957
4319651966.65116219699-1.65116219698961
4416891675.7761621387313.2238378612685
4517781711.9636621407666.0363378592411
4619761770.58866214142205.411337858577
4723971992.02616219771404.97383780229
4826542132.40116220170521.598837798305
4920972028.5409906987568.4590093012532
5019632080.39192610022-117.391926100219
5116771743.82942613066-66.8294261306636
5219412121.01692610137-180.016926101372
5320032045.70442609923-42.7044260992341
5418131911.45442609542-98.4544260954226
5520122034.82942609893-22.8294260989253
5619121919.95442609566-7.95442609566395
5720842039.1419260990544.8580739009522
5820801895.76692609498184.233073905023
5921181734.20442613039383.79557386961
6021501649.57942612799500.420573872012
6116081560.7192546854647.2807453145351
6215031641.57019011776-138.570190117760
6315481636.00769011760-88.0076901176024
6413821583.19519008610-201.195190086103
6517311794.88269012211-63.882690122113
6617981917.63269009560-119.632690095598
6717791823.00769009291-44.0076900929115
6818871916.13269009556-29.1326900955554
6920041980.3201900973823.6798099026222
7020771913.94519009549163.054809904507
7120921729.38269012025362.617309879747
7220511571.75769008578479.242309914222
7315771550.8975186851926.1024813148139
7413561515.74845408419-159.748454084188
7516521761.18595411116-109.185954111156
7613821604.37345408670-222.373454086704
7715191604.06095408670-85.0609540866954
7814211561.81095408550-140.810954085496
7914421507.18595408395-65.1859540839451
8015431593.31095408639-50.3109540863902
8116561653.49845410812.50154589190100
8215611419.12345408144141.876545918555
8319051563.56095408555341.439045914454
8421991740.93595411058458.064045889419
8514731468.075782682834.92421731716533
8616551835.92671809328-180.926718093278
8714071537.36421808480-130.364218084802
8813951638.55171809767-243.551718097675
8915301636.23921809761-106.239218097609
9013091470.98921808292-161.989218082917
9115261612.36421808693-86.3642180869312
9213271398.48921808086-71.489218080859
9316271645.67671809788-18.6767180978769
9417481627.30171809736120.698281902645
9519581637.73921809765320.260781902348
9622741837.11421809331436.885781906688
9716481664.25404667840-16.2540466784044
9814011603.10498208667-202.104982086668
9914111562.54248208552-151.542482085517
10014031667.72998207850-264.729982078503
10113941521.41748208435-127.417482084349
10215201703.16748207951-183.167482079509
10315281635.54248207759-107.542482077589
10416431735.66748208043-92.6674820804318
10515151554.85498208530-39.8549820852984
10616851585.4799820861799.5200179138321
10720001700.91748207945299.082517920555
10822151799.29248208224415.707517917762
10919561993.43231069775-37.4323106977500
11014621685.28324606900-223.283246069001
11115631735.72074607043-172.720746070433
11214591744.90824607069-285.908246070694
11314461594.59574608643-148.595746086427
11416221826.34574609301-204.345746093006
11516571785.72074607185-128.720746071853
11616381751.84574607089-113.845746070891
11716431704.03324606953-61.0332460695337
11816831604.6582460867178.3417539132876
11920501772.09574607147277.904253928534
12022621867.47074609417394.529253905826
12118131871.61057469429-58.6105746942914
12214451689.46151005912-244.46151005912
12317621955.89901009668-193.899010096684
12414611768.08651006135-307.086510061352
12515561725.77401006115-169.774010061151
12614311656.52401005819-225.524010058185
12714271576.89901008592-149.899010085924
12815541689.02401005911-135.024010059108
12916451727.21151006119-82.2115100611918
13016531595.8365100864657.1634899135381
13120161759.27401006110256.725989938898
13222071833.64901009321373.350989906786
13316651744.78883865069-79.7888386506908
13413611626.63977404734-265.639774047336
13515061721.07727404902-215.077274049018
13613601688.26477404909-328.264774049086
13714531643.95227404783-190.952274047828
13815221768.70227405137-246.70227405137
13914601631.07727404746-171.077274047462
14015521708.20227404965-156.202274049652
14115481651.38977404804-103.389774048039
14218271791.0147740520035.9852259479968
14317371501.45227408378235.547725916218
14419411588.82727408626352.172725913737
14514741574.96710258587-100.967102585869
14614581744.81803804069-286.818038040692
14715421778.25553804164-236.255538041641
14814041753.44303804094-349.443038040937
14915221734.13053804039-212.130538040388
15013851652.88053803808-267.880538038081
15116411833.25553799320-192.255537993202
15215101687.38053803906-177.380538039061
15316811805.56803804242-124.568038042416
15419381923.1930379957614.8069620042441
15518681653.63053803810214.369461961897
15617261395.00553798076330.99446201924
15714561578.14536658596-122.145366585960
15814451752.99630203092-307.996302030924
15914561713.4338020268-257.433802026801
16013651735.62130203043-370.621302030431
16114871720.30880202700-233.308802026996
16215581847.05880199359-289.058801993594
16314881701.43380202946-213.43380202946
16416841882.55880199460-198.558801994602
16515941739.74630203055-145.746302030548
16618501856.37130199386-6.37130199385873
16719981804.80880203239193.191197967605
16820791769.18380203138309.816197968617
16914941637.32363061764-143.323630617640
17010571216.50377247780-159.503772477796
17112181326.94127250893-108.941272508932
17211681390.12877251273-222.128772512725
17312361320.81627250876-84.8162725087576
17410761216.56627247780-140.566272477798
17511741238.94127250843-64.9412725084331
17611391189.06627247702-50.0662724770171
17714271424.253772483692.74622751630567
17814871344.87877251144142.121227488559
17914831141.31627247566341.683727524339
18015131054.69127247320458.308727526798
18113571351.831101101645.16889889836181
18211651345.68203650146-180.682036501464
18312821412.11953650335-130.119536503350
18411101353.30703650168-243.30703650168
18512971402.99453650309-105.994536503091
18611851346.74453650149-161.744536501494
18712221308.11953650040-86.1195365003972
18812841355.24453650174-71.2445365017351
18914441462.43203648478-18.4320364847782
19015751454.05703648454120.942963515460
19117371416.49453650347320.505463496526
19217631325.86953649690437.130463503099
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/1edta1227273570.png (open in new window)
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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/2a9361227273570.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/2a9361227273570.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/3m62x1227273570.png (open in new window)
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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/4yw1y1227273570.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/4yw1y1227273570.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/7uta91227273570.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/21/t12272736288o3d0d7gjuatfgp/9y2wf1227273570.png (open in new window)
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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)
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))
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$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$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()
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')
 





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Software written by Ed van Stee & Patrick Wessa


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