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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 12:40:47 -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/t11954144533bv9lfcry22ydox.htm/, Retrieved Sun, 18 Nov 2007 20:34:23 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1687 0 1508 0 1507 0 1385 0 1632 0 1511 0 1559 0 1630 0 1579 0 1653 0 2152 0 2148 0 1752 0 1765 0 1717 0 1558 0 1575 0 1520 0 1805 0 1800 0 1719 0 2008 0 2242 0 2478 0 2030 0 1655 0 1693 0 1623 0 1805 0 1746 0 1795 0 1926 0 1619 0 1992 0 2233 0 2192 0 2080 0 1768 0 1835 0 1569 0 1976 0 1853 0 1965 0 1689 0 1778 0 1976 0 2397 0 2654 0 2097 0 1963 0 1677 0 1941 0 2003 0 1813 0 2012 0 1912 0 2084 0 2080 0 2118 0 2150 0 1608 0 1503 0 1548 0 1382 0 1731 0 1798 0 1779 0 1887 0 2004 0 2077 0 2092 0 2051 0 1577 0 1356 0 1652 0 1382 0 1519 0 1421 0 1442 0 1543 0 1656 0 1561 0 1905 0 2199 0 1473 0 1655 0 1407 0 1395 0 1530 0 1309 0 1526 0 1327 0 1627 0 1748 0 1958 0 2274 0 1648 0 1401 0 1411 0 1403 0 1394 0 1520 0 1528 0 1643 0 1515 0 1685 0 2000 0 2215 0 1956 0 1462 0 1563 0 1459 0 1446 0 1622 0 1657 0 1638 0 1643 0 1683 0 2050 0 2262 0 1813 0 1445 0 1762 0 1461 0 1556 0 1431 0 1427 0 1554 0 1645 0 1653 0 2016 0 2207 0 1665 0 1361 0 1506 0 1360 0 1453 0 1522 0 1460 0 1552 0 1548 0 1827 0 1737 0 1941 0 1474 0 1458 0 1542 0 1404 0 1522 0 1385 0 1641 0 1510 0 1681 0 1938 0 1868 0 1726 0 1456 0 1445 0 1456 0 1365 0 1487 0 1558 0 1488 0 1684 0 1594 0 1850 0 1998 0 2079 0 1494 0 1057 1 1218 1 1168 1 1236 1 1076 1 1174 1 1139 1 1427 1 1487 1 1483 1 1513 1 1357 1 1165 1 1282 1 1110 1 1297 1 1185 1 1222 1 1284 1 1444 1 1575 1 1737 1 1763 1

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

Doden[t] = + 1846.02995173261 -251.176611180784Inv.wet[t] -1.50915849932545t + 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)	1846.02995173261	38.78143	47.6009	0	0
Inv.wet	-251.176611180784	67.520939	-3.72	0.000263	0.000131
t	-1.50915849932545	0.395585	-3.815	0.000184	9.2e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.505523756005193
R-squared	0.255554267885598
Adjusted R-squared	0.247676535270630
F-TEST (value)	32.4400789384575
F-TEST (DF numerator)	2
F-TEST (DF denominator)	189
p-value	7.73159314348959e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	251.198646170456
Sum Squared Residuals	11926043.6093574

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1844.52079323326	-157.520793233258
2	1508	1843.01163473395	-335.011634733954
3	1507	1841.50247623463	-334.502476234629
4	1385	1839.99331773530	-454.993317735303
5	1632	1838.48415923598	-206.484159235977
6	1511	1836.97500073665	-325.975000736652
7	1559	1835.46584223733	-276.465842237326
8	1630	1833.956683738	-203.956683738001
9	1579	1832.44752523868	-253.447525238675
10	1653	1830.93836673935	-177.93836673935
11	2152	1829.42920824002	322.570791759975
12	2148	1827.9200497407	320.079950259301
13	1752	1826.41089124137	-74.4108912413737
14	1765	1824.90173274205	-59.9017327420482
15	1717	1823.39257424272	-106.392574242723
16	1558	1821.88341574340	-263.883415743397
17	1575	1820.37425724407	-245.374257244072
18	1520	1818.86509874475	-298.865098744746
19	1805	1817.35594024542	-12.3559402454209
20	1800	1815.84678174610	-15.8467817460955
21	1719	1814.33762324677	-95.33762324677
22	2008	1812.82846474744	195.171535252555
23	2242	1811.31930624812	430.680693751881
24	2478	1809.81014774879	668.189852251206
25	2030	1808.30098924947	221.699010750532
26	1655	1806.79183075014	-151.791830750143
27	1693	1805.28267225082	-112.282672250817
28	1623	1803.77351375149	-180.773513751492
29	1805	1802.26435525217	2.7356447478336
30	1746	1800.75519675284	-54.755196752841
31	1795	1799.24603825352	-4.24603825351549
32	1926	1797.73687975419	128.26312024581
33	1619	1796.22772125486	-177.227721254865
34	1992	1794.71856275554	197.281437244461
35	2233	1793.20940425621	439.790595743786
36	2192	1791.70024575689	400.299754243112
37	2080	1790.19108725756	289.808912742437
38	1768	1788.68192875824	-20.6819287582373
39	1835	1787.17277025891	47.8272297410881
40	1569	1785.66361175959	-216.663611759586
41	1976	1784.15445326026	191.845546739739
42	1853	1782.64529476094	70.3547052390645
43	1965	1781.13613626161	183.86386373839
44	1689	1779.62697776228	-90.6269777622846
45	1778	1778.11781926296	-0.117819262959129
46	1976	1776.60866076363	199.391339236366
47	2397	1775.09950226431	621.900497735692
48	2654	1773.59034376498	880.409656235017
49	2097	1772.08118526566	324.918814734343
50	1963	1770.57202676633	192.427973233668
51	1677	1769.06286826701	-92.0628682670064
52	1941	1767.55370976768	173.446290232319
53	2003	1766.04455126836	236.955448731644
54	1813	1764.53539276903	48.46460723097
55	2012	1763.02623426970	248.973765730295
56	1912	1761.51707577038	150.482924229621
57	2084	1760.00791727105	323.992082728946
58	2080	1758.49875877173	321.501241228272
59	2118	1756.98960027240	361.010399727597
60	2150	1755.48044177308	394.519558226923
61	1608	1753.97128327375	-145.971283273752
62	1503	1752.46212477443	-249.462124774426
63	1548	1750.9529662751	-202.952966275101
64	1382	1749.44380777578	-367.443807775775
65	1731	1747.93464927645	-16.9346492764500
66	1798	1746.42549077712	51.5745092228754
67	1779	1744.9163322778	34.0836677222009
68	1887	1743.40717377847	143.592826221526
69	2004	1741.89801527915	262.101984720852
70	2077	1740.38885677982	336.611143220177
71	2092	1738.87969828050	353.120301719503
72	2051	1737.37053978117	313.629460218828
73	1577	1735.86138128185	-158.861381281846
74	1356	1734.35222278252	-378.352222782521
75	1652	1732.84306428320	-80.8430642831955
76	1382	1731.33390578387	-349.33390578387
77	1519	1729.82474728454	-210.824747284545
78	1421	1728.31558878522	-307.315588785219
79	1442	1726.80643028589	-284.806430285894
80	1543	1725.29727178657	-182.297271786568
81	1656	1723.78811328724	-67.7881132872428
82	1561	1722.27895478792	-161.278954787917
83	1905	1720.76979628859	184.230203711408
84	2199	1719.26063778927	479.739362210734
85	1473	1717.75147928994	-244.751479289941
86	1655	1716.24232079062	-61.2423207906155
87	1407	1714.73316229129	-307.73316229129
88	1395	1713.22400379196	-318.224003791965
89	1530	1711.71484529264	-181.714845292639
90	1309	1710.20568679331	-401.205686793314
91	1526	1708.69652829399	-182.696528293988
92	1327	1707.18736979466	-380.187369794663
93	1627	1705.67821129534	-78.6782112953373
94	1748	1704.16905279601	43.8309472039881
95	1958	1702.65989429669	255.340105703314
96	2274	1701.15073579736	572.849264202639
97	1648	1699.64157729804	-51.6415772980355
98	1401	1698.13241879871	-297.13241879871
99	1411	1696.62326029938	-285.623260299385
100	1403	1695.11410180006	-292.114101800059
101	1394	1693.60494330073	-299.604943300734
102	1520	1692.09578480141	-172.095784801408
103	1528	1690.58662630208	-162.586626302083
104	1643	1689.07746780276	-46.0774678027573
105	1515	1687.56830930343	-172.568309303432
106	1685	1686.05915080411	-1.05915080410641
107	2000	1684.54999230478	315.450007695219
108	2215	1683.04083380546	531.959166194544
109	1956	1681.53167530613	274.46832469387
110	1462	1680.02251680680	-218.022516806805
111	1563	1678.51335830748	-115.513358307479
112	1459	1677.00419980815	-218.004199808154
113	1446	1675.49504130883	-229.495041308828
114	1622	1673.98588280950	-51.9858828095028
115	1657	1672.47672431018	-15.4767243101773
116	1638	1670.96756581085	-32.9675658108519
117	1643	1669.45840731153	-26.4584073115264
118	1683	1667.9492488122	15.0507511877990
119	2050	1666.44009031288	383.559909687125
120	2262	1664.93093181355	597.06906818645
121	1813	1663.42177331422	149.578226685775
122	1445	1661.9126148149	-216.912614814899
123	1762	1660.40345631557	101.596543684426
124	1461	1658.89429781625	-197.894297816248
125	1556	1657.38513931692	-101.385139316923
126	1431	1655.87598081760	-224.875980817597
127	1427	1654.36682231827	-227.366822318272
128	1554	1652.85766381895	-98.8576638189464
129	1645	1651.34850531962	-6.34850531962096
130	1653	1649.83934682030	3.16065317970451
131	2016	1648.33018832097	367.66981167903
132	2207	1646.82102982164	560.178970178355
133	1665	1645.31187132232	19.6881286776809
134	1361	1643.80271282299	-282.802712822994
135	1506	1642.29355432367	-136.293554323668
136	1360	1640.78439582434	-280.784395824343
137	1453	1639.27523732502	-186.275237325017
138	1522	1637.76607882569	-115.766078825692
139	1460	1636.25692032637	-176.256920326366
140	1552	1634.74776182704	-82.747761827041
141	1548	1633.23860332772	-85.2386033277155
142	1827	1631.72944482839	195.27055517161
143	1737	1630.22028632906	106.779713670935
144	1941	1628.71112782974	312.288872170261
145	1474	1627.20196933041	-153.201969330414
146	1458	1625.69281083109	-167.692810831088
147	1542	1624.18365233176	-82.1836523317628
148	1404	1622.67449383244	-218.674493832437
149	1522	1621.16533533311	-99.1653353331119
150	1385	1619.65617683379	-234.656176833786
151	1641	1618.14701833446	22.8529816655390
152	1510	1616.63785983514	-106.637859835136
153	1681	1615.12870133581	65.8712986641899
154	1938	1613.61954283648	324.380457163515
155	1868	1612.11038433716	255.889615662841
156	1726	1610.60122583783	115.398774162166
157	1456	1609.09206733851	-153.092067338508
158	1445	1607.58290883918	-162.582908839183
159	1456	1606.07375033986	-150.073750339857
160	1365	1604.56459184053	-239.564591840532
161	1487	1603.05543334121	-116.055433341206
162	1558	1601.54627484188	-43.546274841881
163	1488	1600.03711634256	-112.037116342556
164	1684	1598.52795784323	85.47204215677
165	1594	1597.01879934390	-3.01879934390459
166	1850	1595.50964084458	254.490359155421
167	1998	1594.00048234525	403.999517654746
168	2079	1592.49132384593	486.508676154072
169	1494	1590.98216534660	-96.9821653466028
170	1057	1338.29639566649	-281.296395666493
171	1218	1336.78723716717	-118.787237167168
172	1168	1335.27807866784	-167.278078667842
173	1236	1333.76892016852	-97.7689201685167
174	1076	1332.25976166919	-256.259761669191
175	1174	1330.75060316987	-156.750603169866
176	1139	1329.24144467054	-190.241444670540
177	1427	1327.73228617121	99.2677138287851
178	1487	1326.22312767189	160.776872328111
179	1483	1324.71396917256	158.286030827436
180	1513	1323.20481067324	189.795189326761
181	1357	1321.69565217391	35.3043478260870
182	1165	1320.18649367459	-155.186493674588
183	1282	1318.67733517526	-36.6773351752621
184	1110	1317.16817667594	-207.168176675937
185	1297	1315.65901817661	-18.6590181766112
186	1185	1314.14985967729	-129.149859677286
187	1222	1312.64070117796	-90.6407011779603
188	1284	1311.13154267863	-27.1315426786349
189	1444	1309.62238417931	134.377615820691
190	1575	1308.11322567998	266.886774320016
191	1737	1306.60406718066	430.395932819342
192	1763	1305.09490868133	457.905091318667

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/18ffo1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/18ffo1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/2r2cm1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/2r2cm1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/3n3f41195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/3n3f41195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/4g6bh1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/4g6bh1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/5vkbc1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/5vkbc1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/6felg1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/6felg1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/7cfed1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/7cfed1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/8pole1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/8pole1195414842.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/9xwrt1195414842.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/18/t11954144533bv9lfcry22ydox/9xwrt1195414842.ps (open in new window)

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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