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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 02:14:24 -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/t1195376876b26l8ldz6hn7vjo.htm/, Retrieved Sun, 18 Nov 2007 10:08:06 +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] = + 2165.22639318386 -395.811145503876gordel[t] + 1.00000000001762et[t] -442.550696587991M1[t] -617.8124999965M2[t] -567.250000002188M3[t] -680.4374999915M4[t] -543.125M5[t] -598.87499999275M6[t] -523.249999992751M7[t] -508.374999989M8[t] -455.56249999475M9[t] -316.187499988375M10[t] -116.624999999000M11[t] + 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)	2165.22639318386	21.083338	102.6985	0	0
gordel	-395.811145503876	18.65458	-21.2179	0	0
et	1.00000000001762	0.041217	24.2616	0	0
M1	-442.550696587991	29.656345	-14.9226	0	0
M2	-617.8124999965	29.633418	-20.8485	0	0
M3	-567.250000002188	29.633418	-19.1422	0	0
M4	-680.4374999915	29.633418	-22.9618	0	0
M5	-543.125	29.633418	-18.3281	0	0
M6	-598.87499999275	29.633418	-20.2094	0	0
M7	-523.249999992751	29.633418	-17.6574	0	0
M8	-508.374999989	29.633418	-17.1555	0	0
M9	-455.56249999475	29.633418	-15.3733	0	0
M10	-316.187499988375	29.633418	-10.67	0	0
M11	-116.624999999000	29.633418	-3.9356	0.000119	5.9e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.960178790197478
R-squared	0.921943309145093
Adjusted R-squared	0.916242539588274
F-TEST (value)	161.722606036986
F-TEST (DF numerator)	13
F-TEST (DF denominator)	178
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	83.8159633601225
Sum Squared Residuals	1250470.59708940

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1538.75215209260	148.247847907395
2	1508	1370.34128408424	137.658715915758
3	1507	1369.34128407764	137.658715922356
4	1385	1247.34128408817	137.658715911826
5	1632	1494.34128408161	137.658715918392
6	1511	1373.34128408771	137.658715912292
7	1559	1421.34128408722	137.658715912777
8	1630	1492.34128409196	137.65871590804
9	1579	1441.34128408438	137.65871591562
10	1653	1515.34128408960	137.658715910397
11	2152	2014.34128407426	137.658715925745
12	2148	2010.34128408113	137.65871591887
13	1752	1624.93041606415	127.069583935854
14	1765	1648.51954808914	116.480451910859
15	1717	1600.51954805572	116.480451944283
16	1558	1441.51954806160	116.480451938403
17	1575	1458.51954808098	116.480451919023
18	1520	1403.51954808824	116.480451911760
19	1805	1688.51954806193	116.480451938070
20	1800	1683.51954806533	116.480451934670
21	1719	1602.51954808722	116.480451912778
22	2008	1891.51954806623	116.480451933767
23	2242	2125.51954805621	116.480451943785
24	2478	2361.51954808732	116.480451912681
25	2030	1924.10868009942	105.891319900582
26	1655	1559.69781204758	95.3021879524242
27	1693	1597.69781204467	95.302187955333
28	1623	1527.69781205312	95.3021879468843
29	1805	1709.69781204540	95.3021879545969
30	1746	1650.69781205260	95.302187947404
31	1795	1699.69781205213	95.3021879478735
32	1926	1830.69781209792	95.302187902077
33	1619	1523.69781208583	95.3021879141673
34	1992	1896.69781205632	95.3021879436756
35	2233	2137.69781204643	95.3021879535703
36	2192	2096.69781204265	95.302187957348
37	2080	1995.28694410067	84.7130558993275
38	1768	1693.87607608994	74.1239239100598
39	1835	1760.87607608454	74.1239239154574
40	1569	1494.87607604254	74.1239239574626
41	1976	1901.87607608879	74.1239239112103
42	1853	1778.87607609485	74.1239239051454
43	1965	1890.87607609550	74.1239239045046
44	1689	1614.87607604412	74.1239239558802
45	1778	1703.87607604001	74.1239239599924
46	1976	1901.87607604642	74.1239239535843
47	2397	2322.87607608969	74.1239239103072
48	2654	2579.87607609117	74.1239239088338
49	2097	2033.46520800135	63.5347919986547
50	1963	1910.05433999375	52.9456600062504
51	1677	1624.05434002213	52.9456599778686
52	1941	1888.05433999947	52.9456600005342
53	2003	1950.05433998964	52.9456600103614
54	1813	1760.05433999452	52.945660005477
55	2012	1959.05433999670	52.9456600033032
56	1912	1859.05433999842	52.9456600015772
57	2084	2031.05433999477	52.945660005227
58	2080	2027.05433999862	52.9456600013785
59	2118	2065.05434002515	52.9456599748505
60	2150	2097.05434002266	52.9456599773418
61	1608	1565.6434719931	42.3565280068985
62	1503	1471.23260401602	31.7673959839831
63	1548	1516.23260401023	31.7673959897685
64	1382	1350.23260398999	31.7673960100116
65	1731	1699.23260401522	31.7673959847813
66	1798	1766.23260399463	31.7673960053684
67	1779	1747.23260399296	31.7673960070358
68	1887	1855.23260399836	31.7673960016445
69	2004	1972.23260399374	31.7673960062635
70	2077	2045.23260399894	31.7673960010581
71	2092	2060.23260401506	31.7673959849355
72	2051	2019.23260398129	31.7673960187132
73	1577	1555.82173599293	21.1782640070715
74	1356	1345.4108679838	10.5891320162003
75	1652	1641.41086800244	10.5891319975626
76	1382	1371.41086799036	10.5891320096383
77	1519	1508.41086798186	10.5891320181439
78	1421	1410.41086798836	10.5891320116383
79	1442	1431.4108679874	10.5891320126011
80	1543	1532.41086799267	10.5891320073333
81	1656	1645.41086800798	10.5891319920226
82	1561	1550.41086799022	10.5891320097778
83	1905	1894.41086798214	10.5891320178575
84	2199	2188.41086800427	10.5891319957320
85	1473	1472.99999999147	8.5309714847881e-09
86	1655	1665.58913198944	-10.5891319894418
87	1407	1417.58913197849	-10.5891319784932
88	1395	1405.58913200096	-10.5891320009639
89	1530	1540.58913199242	-10.5891319924232
90	1309	1319.58913198676	-10.5891319867612
91	1526	1536.58913198925	-10.5891319892523
92	1327	1337.58913198923	-10.5891319892336
93	1627	1637.58913199784	-10.5891319978396
94	1748	1758.58913200389	-10.5891320038908
95	1958	1968.58913199345	-10.5891319934496
96	2274	2284.58913198596	-10.5891319859629
97	1648	1669.17826398493	-21.178263984926
98	1401	1432.76739598534	-31.767395985339
99	1411	1442.76739597894	-31.767395978937
100	1403	1434.76739598148	-31.7673959814781
101	1394	1425.7673959804	-31.7673959803999
102	1520	1551.76739598085	-31.7673959808525
103	1528	1559.76739597966	-31.7673959796607
104	1643	1674.76739598518	-31.7673959851752
105	1515	1546.76739598624	-31.7673959862392
106	1685	1716.76739599315	-31.7673959931538
107	2000	2031.76739597456	-31.7673959745629
108	2215	2246.76739597530	-31.7673959752963
109	1956	1998.35652800073	-42.3565280007267
110	1462	1514.94565996679	-52.9456599667872
111	1563	1615.94565996199	-52.9456599619887
112	1459	1511.94565997284	-52.9456599728381
113	1446	1498.94565998169	-52.9456599816894
114	1622	1674.94565999302	-52.9456599930232
115	1657	1709.94565997231	-52.9456599723072
116	1638	1690.94565997546	-52.9456599754603
117	1643	1695.94565996887	-52.9456599688679
118	1683	1735.94565999349	-52.9456599934918
119	2050	2102.94565996582	-52.9456599658171
120	2262	2314.9456599865	-52.9456599864978
121	1813	1876.53479199858	-63.53479199858
122	1445	1519.12392395686	-74.1239239568608
123	1762	1836.12392398587	-74.1239239858686
124	1461	1535.12392396325	-74.1239239632467
125	1556	1630.123923955	-74.1239239550008
126	1431	1505.12392396003	-74.1239239600306
127	1427	1501.12392398863	-74.1239239886275
128	1554	1628.12392396435	-74.1239239643534
129	1645	1719.12392396028	-74.1239239602762
130	1653	1727.12392399334	-74.1239239933363
131	2016	2090.12392395559	-74.1239239555913
132	2207	2281.1239239859	-74.1239239859018
133	1665	1749.71305595635	-84.7130559563452
134	1361	1456.30218794575	-95.3021879457539
135	1506	1601.30218794073	-95.3021879407305
136	1360	1455.30218795184	-95.30218795184
137	1453	1548.30218794256	-95.302187942559
138	1522	1617.30218795201	-95.3021879520074
139	1460	1555.30218794958	-95.3021879495821
140	1552	1647.30218795469	-95.3021879546914
141	1548	1643.30218794794	-95.3021879479402
142	1827	1922.30218795678	-95.3021879567757
143	1737	1832.30218798105	-95.302187981048
144	1941	2036.30218798159	-95.3021879815876
145	1474	1579.89131989335	-105.891319893353
146	1458	1574.48045193784	-116.480451937836
147	1542	1658.48045193274	-116.480451932738
148	1404	1520.48045194299	-116.480451942989
149	1522	1638.48045193415	-116.480451934148
150	1385	1501.48045193997	-116.480451939966
151	1641	1757.48045189314	-116.480451893145
152	1510	1626.48045194432	-116.480451944324
153	1681	1797.48045194066	-116.480451940657
154	1938	2054.48045189910	-116.480451899105
155	1868	1984.48045193373	-116.480451933730
156	1726	1842.48045187817	-116.480451878172
157	1456	1583.06958389341	-127.069583893409
158	1445	1582.65871592798	-137.658715927980
159	1456	1593.65871591860	-137.658715918596
160	1365	1502.65871593267	-137.658715932674
161	1487	1624.65871592090	-137.658715920905
162	1558	1695.65871589339	-137.658715893388
163	1488	1625.65871593082	-137.658715930822
164	1684	1821.65871589776	-137.658715897764
165	1594	1731.65871592950	-137.658715929497
166	1850	1987.65871589793	-137.658715897927
167	1998	2135.65871592639	-137.658715926394
168	2079	2216.65871592477	-137.658715924766
169	1494	1642.24784792445	-148.247847924451
170	1057	1046.41086798163	10.5891320183705
171	1218	1207.41086800589	10.5891319941119
172	1168	1157.41086801969	10.5891319803108
173	1236	1225.41086800797	10.5891319920322
174	1076	1065.41086798538	10.5891320146191
175	1174	1163.41086801578	10.5891319842250
176	1139	1128.41086798865	10.5891320113538
177	1427	1416.41086798704	10.5891320129593
178	1487	1476.41086802202	10.5891319779832
179	1483	1472.41086797780	10.5891320221953
180	1513	1502.41086797528	10.5891320247218
181	1357	1357.00000001252	-1.25235146697378e-08
182	1165	1175.58913200391	-10.5891320039058
183	1282	1292.58913199939	-10.5891319993891
184	1110	1120.58913200904	-10.5891320090404
185	1297	1307.58913200142	-10.5891320014160
186	1185	1195.58913200767	-10.5891320076747
187	1222	1232.58913200699	-10.5891320069938
188	1284	1294.58913201157	-10.5891320115745
189	1444	1454.58913198771	-10.5891319877134
190	1575	1585.58913199394	-10.5891319939408
191	1737	1747.58913200265	-10.5891320026538
192	1763	1773.58913199606	-10.5891319960567

Charts produced by software:

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Parameters:

par1 = 1 ; par2 = Include Monthly 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')

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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