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

Title produced by software: Multiple Regression

Date of computation: Sun, 18 Nov 2007 07:22:40 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/18/t1195395372reham8zpg0vyxqd.htm/, Retrieved Sun, 18 Nov 2007 15:16:22 +0100

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

Text written by user:

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 compuational 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

Const[t] = + 1846.02995172578 -251.176611181584Inv.wet[t] + 1.00000000001661et[t] -1.50915849924538t + 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)	1846.02995172578	31.428327	58.7378	0	0
Inv.wet	-251.176611181584	54.718719	-4.5903	8e-06	4e-06
et	1.00000000001661	0.100108	9.9892	0	0
t	-1.50915849924538	0.320581	-4.7076	5e-06	2e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.716713275798988
R-squared	0.513677919706516
Adjusted R-squared	0.505917460978429
F-TEST (value)	66.1916953243184
F-TEST (DF numerator)	3
F-TEST (DF denominator)	188
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	203.570453314249
Sum Squared Residuals	7790894.73896291

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1660.59724872346	26.4027512765405
2	1508	1665.93902562435	-157.939025624350
3	1507	1612.86736712425	-105.867367124248
4	1385	1602.54570862486	-217.545708624856
5	1632	1710.72405012743	-78.724050127432
6	1511	1643.96489162710	-132.964891627103
7	1559	1614.8307331274	-55.8307331273989
8	1630	1669.44657462909	-39.4465746290856
9	1579	1564.12491612812	14.8750838718838
10	1653	1497.24075762779	155.759242372215
11	2152	1795.16909912351	356.830900876487
12	2148	1673.03494063226	474.965059367736
13	1752	1728.66561070397	23.3343892960326
14	1765	1926.00738763802	-161.007387638024
15	1717	1825.93572911114	-108.935729111142
16	1558	1778.61407060714	-220.614070607136
17	1575	1656.79241213589	-81.7924121358925
18	1520	1656.03325363666	-136.033253636660
19	1805	1863.89909511089	-58.8990951108913
20	1800	1842.51493661132	-42.5149366113159
21	1719	1707.19327813985	11.8067218601517
22	2008	1855.30911961309	152.690880386912
23	2242	1888.23746111441	353.762538885586
24	2478	2006.10330264715	471.896697352849
25	2030	2009.73397274799	20.2660272520087
26	1655	1819.07574960560	-164.075749605605
27	1693	1805.00409110915	-112.004091109151
28	1623	1846.68243260762	-223.682432607623
29	1805	1889.86077410912	-84.8607741091193
30	1746	1885.10161560982	-139.10161560982
31	1795	1856.96745711013	-61.9674571101324
32	1926	1971.58329865282	-45.5832986528155
33	1619	1610.26164014759	8.73835985240528
34	1992	1842.37748161223	149.622518387771
35	2233	1882.30582311367	350.694176886328
36	2192	1723.17166461181	468.828335388191
37	2080	2062.80233475823	17.1976652417712
38	1768	1935.14411165689	-167.144111656888
39	1835	1950.07245315792	-115.072453157916
40	1569	1795.75079460613	-226.750794606133
41	1976	2063.92913616137	-87.9291361613663
42	1853	1995.16997766100	-142.169977661004
43	1965	2030.03581916236	-65.0358191623626
44	1689	1737.65166060829	-48.6516606082868
45	1778	1772.33000211064	5.66999788935767
46	1976	1829.44584361137	146.554156388629
47	2397	2049.37418516580	347.625814834197
48	2654	2188.24002666889	465.759973331112
49	2097	2082.87069666792	14.1293033320818
50	1963	2133.21247356953	-170.212473569534
51	1677	1795.1408151047	-118.140815104699
52	1941	2170.81915657172	-229.819156571718
53	2003	2093.99749807122	-90.9974980712217
54	1813	1958.23833956975	-145.238339569747
55	2012	2080.10418107255	-68.1041810725504
56	1912	1963.72002257140	-51.7200225713973
57	2084	2081.39836407413	2.60163592586877
58	2080	1936.51420557250	143.485794427495
59	2118	1773.44254711058	344.557452889424
60	2150	1687.30838860993	462.691611390074
61	1608	1596.93905866920	11.0609413307955
62	1503	1676.2808356013	-173.280835601302
63	1548	1669.20917710196	-121.209177101964
64	1382	1614.88751857184	-232.887518571842
65	1731	1825.06586010611	-94.0658601061117
66	1798	1946.30670157890	-148.306701578905
67	1779	1850.17254307809	-71.1725430780881
68	1887	1941.78838458039	-54.7883845803892
69	2004	2004.46672608221	-0.466726082209838
70	2077	1936.58256758186	140.417432418138
71	2092	1750.51090910955	341.489090890448
72	2051	1591.37675057769	459.623249422311
73	1577	1569.00742067810	7.99257932190315
74	1356	1532.34919757827	-176.349197578268
75	1652	1776.27753910310	-124.277539103098
76	1382	1617.95588058125	-235.955880581249
77	1519	1616.134222082	-97.1342220819982
78	1421	1572.37506358205	-151.375063582051
79	1442	1516.2409050819	-74.2409050818986
80	1543	1600.85674658408	-57.8567465840835
81	1656	1659.53508810584	-3.53508810583768
82	1561	1423.6509295827	137.3490704173
83	1905	1566.57927108585	338.420728914147
84	2199	1742.44511260955	456.554887390446
85	1473	1468.07578268578	4.92421731422319
86	1655	1834.41755959264	-179.417559592640
87	1407	1534.34590108844	-127.345901088437
88	1395	1634.02424260087	-239.024242600872
89	1530	1630.20258410159	-100.202584101588
90	1309	1463.44342558960	-154.443425589598
91	1526	1603.3092670927	-77.3092670927007
92	1327	1387.92510858990	-60.9251085899035
93	1627	1633.60345010476	-6.60345010476319
94	1748	1613.71929160521	134.280708394787
95	1958	1622.64763310614	335.352366893859
96	2274	1820.51347460021	453.486525399794
97	1648	1646.14414468809	1.85585531190981
98	1401	1583.48592159783	-182.485921597829
99	1411	1541.41426309791	-130.414263097910
100	1403	1645.09260459041	-242.092604590412
101	1394	1497.27094609874	-103.270946098737
102	1520	1677.51178759251	-157.511787592509
103	1528	1608.37762909214	-80.377629092141
104	1643	1706.99347059456	-63.9934705945585
105	1515	1524.67181210231	-9.67181210231035
106	1685	1553.78765360357	131.212346396427
107	2000	1667.71599509625	332.284004903755
108	2215	1764.58183659863	450.418163401366
109	1956	1957.21250671261	-1.21250671261230
110	1462	1647.55428358825	-185.554283588249
111	1563	1696.48262508984	-133.482625089842
112	1459	1704.16096659075	-245.160966590749
113	1446	1552.33930810901	-106.339308109007
114	1622	1782.58014961361	-160.580149613611
115	1657	1740.44599109369	-83.4459910936905
116	1638	1705.06183259388	-67.0618325938826
117	1643	1655.74017409384	-12.7401740938432
118	1683	1554.85601561295	128.143984387053
119	2050	1720.78435709648	329.215642903517
120	2262	1814.65019861882	447.349801381179
121	1813	1817.28086871964	-4.28086871964461
122	1445	1633.62264558737	-188.622645587374
123	1762	1898.55098712255	-136.550987122554
124	1461	1709.22932859019	-248.229328590189
125	1556	1665.40767009124	-109.407670091241
126	1431	1594.64851158985	-163.648511589846
127	1427	1513.51435311928	-86.514353119278
128	1554	1624.13019459189	-70.1301945918947
129	1645	1660.80853609428	-15.8085360942835
130	1653	1527.92437762186	125.075622378144
131	2016	1689.85271909533	326.147280904675
132	2207	1762.71856062732	444.281439372685
133	1665	1672.34923068659	-7.34923068659388
134	1361	1552.69100758539	-191.691007585386
135	1506	1645.61934908671	-139.619349086709
136	1360	1611.29769058792	-251.297690587919
137	1453	1565.47603208794	-112.476032087938
138	1522	1688.71687359076	-166.716873590764
139	1460	1549.58271508923	-89.5827150892332
140	1552	1625.19855659127	-73.1985565912686
141	1548	1566.87689809108	-18.8768980910798
142	1827	1704.99273959415	122.007260405847
143	1737	1413.9210811301	323.078918869901
144	1941	1499.78692263230	441.213077367695
145	1474	1484.41759263283	-10.4175926328291
146	1458	1652.75936958640	-194.759369586404
147	1542	1684.68771108771	-142.687711087714
148	1404	1658.36605258806	-254.366052588057
149	1522	1637.54439408849	-115.544394088491
150	1385	1554.78523558790	-169.785235587896
151	1641	1733.65107704165	-92.6510770416462
152	1510	1586.26691858998	-76.2669185899782
153	1681	1702.94526009270	-21.9452600926956
154	1938	1819.06110154540	118.938898454596
155	1868	1547.98944309168	320.010556908318
156	1726	1287.85528453814	438.144715461859
157	1456	1469.48595464194	-13.4859546419372
158	1445	1642.82773158560	-197.827731585596
159	1456	1601.75607308269	-145.756073082693
160	1365	1622.43441458682	-257.434414586816
161	1487	1605.61275608432	-118.612756084317
162	1558	1730.85359755018	-172.853597550176
163	1488	1583.71943908851	-95.7194390885124
164	1684	1763.33528055227	-79.335280552275
165	1594	1619.01362209066	-25.0136220906579
166	1850	1734.12946355335	115.870536446651
167	1998	1681.05780509325	316.942194906752
168	2079	1643.92364659341	435.076353406589
169	1494	1510.55431668198	-16.5543166819755
170	1057	1233.10451597073	-176.104515970735
171	1218	1342.03285750132	-124.032857501324
172	1168	1403.71119900513	-235.711199005128
173	1236	1332.88954050273	-96.889540502731
174	1076	1227.13038197375	-151.130381973754
175	1174	1247.99622350488	-73.9962235048806
176	1139	1196.61206497481	-57.6120649748069
177	1427	1430.29040647947	-3.29040647946732
178	1487	1349.40624800890	137.593751991096
179	1483	1144.33458947628	338.665410523722
180	1513	1056.20043097559	456.799569024406
181	1357	1351.83110110128	5.16889889871692
182	1165	1344.17287800194	-179.172878001936
183	1282	1409.10121950379	-127.101219503794
184	1110	1348.77956100357	-238.779561003571
185	1297	1396.95790250515	-99.9579025051512
186	1185	1339.19874400497	-154.198744004972
187	1222	1299.06458550508	-77.0645855050848
188	1284	1344.68042700662	-60.6804270066221
189	1444	1450.35876848916	-6.35876848915676
190	1575	1440.47460998977	134.525390010228
191	1737	1401.40295150990	335.597048490097
192	1763	1309.26879300515	453.731206994847

Charts produced by software:

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Parameters:

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')

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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