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WS6Q1season

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

Title produced by software: Multiple Regression

Date of computation: Thu, 15 Nov 2007 06:30:10 -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/15/t11951330744dyr3ydse79dyom.htm/, Retrieved Thu, 15 Nov 2007 14:24:47 +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

y[t] = + 2165.22639318886 -395.811145510835x[t] -442.550696594425M1[t] -617.812500000002M2[t] -567.25M3[t] -680.4375M4[t] -543.125000000001M5[t] -598.874999999998M6[t] -523.250000000001M7[t] -508.375M8[t] -455.5625M9[t] -316.187500000000M10[t] -116.625000000000M11[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)	2165.22639318886	43.631881	49.6249	0	0
x	-395.811145510835	38.605577	-10.2527	0	0
M1	-442.550696594425	61.373686	-7.2108	0	0
M2	-617.812500000002	61.326238	-10.0742	0	0
M3	-567.25	61.326238	-9.2497	0	0
M4	-680.4375	61.326238	-11.0954	0	0
M5	-543.125000000001	61.326238	-8.8563	0	0
M6	-598.874999999998	61.326238	-9.7654	0	0
M7	-523.250000000001	61.326238	-8.5322	0	0
M8	-508.375	61.326238	-8.2897	0	0
M9	-455.5625	61.326238	-7.4285	0	0
M10	-316.187500000000	61.326238	-5.1558	1e-06	0
M11	-116.625000000000	61.326238	-1.9017	0.058815	0.029407

Multiple Linear Regression - Regression Statistics
Multiple R	0.814751285561214
R-squared	0.663819657323651
Adjusted R-squared	0.641282427647024
F-TEST (value)	29.4543591580865
F-TEST (DF numerator)	12
F-TEST (DF denominator)	179
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	173.456794605829
Sum Squared Residuals	5385619.46749226

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1722.67569659441	-35.6756965944053
2	1508	1547.41389318886	-39.413893188857
3	1507	1597.97639318886	-90.9763931888592
4	1385	1484.78889318885	-99.7888931888466
5	1632	1622.10139318886	9.8986068111442
6	1511	1566.35139318886	-55.3513931888552
7	1559	1641.97639318885	-82.9763931888532
8	1630	1656.85139318886	-26.8513931888614
9	1579	1709.66389318885	-130.663893188851
10	1653	1849.03889318885	-196.038893188855
11	2152	2048.60139318886	103.398606811145
12	2148	2165.22639318885	-17.226393188854
13	1752	1722.67569659443	29.3243034055713
14	1765	1547.41389318885	217.586106811146
15	1717	1597.97639318885	119.023606811146
16	1558	1484.78889318885	73.211106811145
17	1575	1622.10139318885	-47.1013931888544
18	1520	1566.35139318885	-46.3513931888545
19	1805	1641.97639318885	163.023606811145
20	1800	1656.85139318885	143.148606811146
21	1719	1709.66389318885	9.33610681114532
22	2008	1849.03889318885	158.961106811145
23	2242	2048.60139318885	193.398606811146
24	2478	2165.22639318885	312.773606811145
25	2030	1722.67569659443	307.324303405571
26	1655	1547.41389318885	107.586106811146
27	1693	1597.97639318885	95.0236068111458
28	1623	1484.78889318885	138.211106811145
29	1805	1622.10139318885	182.898606811146
30	1746	1566.35139318885	179.648606811145
31	1795	1641.97639318885	153.023606811145
32	1926	1656.85139318885	269.148606811146
33	1619	1709.66389318885	-90.6638931888547
34	1992	1849.03889318885	142.961106811145
35	2233	2048.60139318885	184.398606811146
36	2192	2165.22639318885	26.7736068111454
37	2080	1722.67569659443	357.324303405571
38	1768	1547.41389318885	220.586106811146
39	1835	1597.97639318885	237.023606811146
40	1569	1484.78889318885	84.211106811145
41	1976	1622.10139318885	353.898606811146
42	1853	1566.35139318885	286.648606811145
43	1965	1641.97639318885	323.023606811145
44	1689	1656.85139318885	32.148606811146
45	1778	1709.66389318885	68.3361068111453
46	1976	1849.03889318885	126.961106811145
47	2397	2048.60139318885	348.398606811146
48	2654	2165.22639318885	488.773606811145
49	2097	1722.67569659443	374.324303405571
50	1963	1547.41389318885	415.586106811145
51	1677	1597.97639318885	79.0236068111458
52	1941	1484.78889318886	456.211106811145
53	2003	1622.10139318885	380.898606811146
54	1813	1566.35139318885	246.648606811145
55	2012	1641.97639318885	370.023606811145
56	1912	1656.85139318885	255.148606811146
57	2084	1709.66389318885	374.336106811145
58	2080	1849.03889318885	230.961106811145
59	2118	2048.60139318885	69.3986068111455
60	2150	2165.22639318885	-15.2263931888546
61	1608	1722.67569659443	-114.675696594429
62	1503	1547.41389318885	-44.4138931888543
63	1548	1597.97639318885	-49.9763931888542
64	1382	1484.78889318885	-102.788893188855
65	1731	1622.10139318885	108.898606811146
66	1798	1566.35139318885	231.648606811145
67	1779	1641.97639318885	137.023606811145
68	1887	1656.85139318885	230.148606811146
69	2004	1709.66389318885	294.336106811145
70	2077	1849.03889318885	227.961106811145
71	2092	2048.60139318885	43.3986068111455
72	2051	2165.22639318885	-114.226393188855
73	1577	1722.67569659443	-145.675696594429
74	1356	1547.41389318885	-191.413893188854
75	1652	1597.97639318885	54.0236068111458
76	1382	1484.78889318885	-102.788893188855
77	1519	1622.10139318885	-103.101393188854
78	1421	1566.35139318885	-145.351393188855
79	1442	1641.97639318885	-199.976393188855
80	1543	1656.85139318885	-113.851393188854
81	1656	1709.66389318885	-53.6638931888547
82	1561	1849.03889318885	-288.038893188855
83	1905	2048.60139318885	-143.601393188855
84	2199	2165.22639318885	33.7736068111454
85	1473	1722.67569659443	-249.675696594429
86	1655	1547.41389318885	107.586106811146
87	1407	1597.97639318885	-190.976393188854
88	1395	1484.78889318885	-89.788893188855
89	1530	1622.10139318885	-92.1013931888544
90	1309	1566.35139318885	-257.351393188855
91	1526	1641.97639318885	-115.976393188855
92	1327	1656.85139318885	-329.851393188854
93	1627	1709.66389318885	-82.6638931888547
94	1748	1849.03889318885	-101.038893188855
95	1958	2048.60139318885	-90.6013931888545
96	2274	2165.22639318885	108.773606811145
97	1648	1722.67569659443	-74.6756965944287
98	1401	1547.41389318885	-146.413893188854
99	1411	1597.97639318885	-186.976393188854
100	1403	1484.78889318885	-81.788893188855
101	1394	1622.10139318885	-228.101393188854
102	1520	1566.35139318885	-46.3513931888545
103	1528	1641.97639318885	-113.976393188855
104	1643	1656.85139318885	-13.851393188854
105	1515	1709.66389318885	-194.663893188855
106	1685	1849.03889318885	-164.038893188855
107	2000	2048.60139318885	-48.6013931888545
108	2215	2165.22639318885	49.7736068111454
109	1956	1722.67569659443	233.324303405571
110	1462	1547.41389318885	-85.4138931888543
111	1563	1597.97639318885	-34.9763931888542
112	1459	1484.78889318885	-25.7888931888550
113	1446	1622.10139318885	-176.101393188854
114	1622	1566.35139318885	55.6486068111455
115	1657	1641.97639318885	15.0236068111454
116	1638	1656.85139318885	-18.851393188854
117	1643	1709.66389318885	-66.6638931888547
118	1683	1849.03889318885	-166.038893188855
119	2050	2048.60139318885	1.39860681114553
120	2262	2165.22639318885	96.7736068111454
121	1813	1722.67569659443	90.3243034055712
122	1445	1547.41389318885	-102.413893188854
123	1762	1597.97639318885	164.023606811146
124	1461	1484.78889318885	-23.7888931888550
125	1556	1622.10139318885	-66.1013931888544
126	1431	1566.35139318885	-135.351393188855
127	1427	1641.97639318885	-214.976393188855
128	1554	1656.85139318885	-102.851393188854
129	1645	1709.66389318885	-64.6638931888547
130	1653	1849.03889318885	-196.038893188855
131	2016	2048.60139318885	-32.6013931888545
132	2207	2165.22639318885	41.7736068111454
133	1665	1722.67569659443	-57.6756965944287
134	1361	1547.41389318885	-186.413893188854
135	1506	1597.97639318885	-91.9763931888542
136	1360	1484.78889318885	-124.788893188855
137	1453	1622.10139318885	-169.101393188854
138	1522	1566.35139318885	-44.3513931888545
139	1460	1641.97639318885	-181.976393188855
140	1552	1656.85139318885	-104.851393188854
141	1548	1709.66389318885	-161.663893188855
142	1827	1849.03889318885	-22.0388931888545
143	1737	2048.60139318885	-311.601393188855
144	1941	2165.22639318885	-224.226393188855
145	1474	1722.67569659443	-248.675696594429
146	1458	1547.41389318885	-89.4138931888543
147	1542	1597.97639318885	-55.9763931888542
148	1404	1484.78889318885	-80.788893188855
149	1522	1622.10139318885	-100.101393188854
150	1385	1566.35139318885	-181.351393188855
151	1641	1641.97639318885	-0.9763931888546
152	1510	1656.85139318885	-146.851393188854
153	1681	1709.66389318885	-28.6638931888547
154	1938	1849.03889318885	88.9611068111455
155	1868	2048.60139318885	-180.601393188855
156	1726	2165.22639318885	-439.226393188855
157	1456	1722.67569659443	-266.675696594429
158	1445	1547.41389318885	-102.413893188854
159	1456	1597.97639318885	-141.976393188854
160	1365	1484.78889318885	-119.788893188855
161	1487	1622.10139318885	-135.101393188854
162	1558	1566.35139318885	-8.3513931888545
163	1488	1641.97639318885	-153.976393188855
164	1684	1656.85139318885	27.148606811146
165	1594	1709.66389318885	-115.663893188855
166	1850	1849.03889318885	0.9611068111455
167	1998	2048.60139318885	-50.6013931888545
168	2079	2165.22639318885	-86.2263931888546
169	1494	1722.67569659443	-228.675696594429
170	1057	1151.60274767802	-94.6027476780183
171	1218	1202.16524767802	15.8347523219817
172	1168	1088.97774767802	79.022252321981
173	1236	1226.29024767802	9.70975232198158
174	1076	1170.54024767802	-94.5402476780185
175	1174	1246.16524767802	-72.1652476780186
176	1139	1261.04024767802	-122.040247678018
177	1427	1313.85274767802	113.147252321981
178	1487	1453.22774767802	33.7722523219814
179	1483	1652.79024767802	-169.790247678018
180	1513	1769.41524767802	-256.415247678019
181	1357	1326.86455108359	30.1354489164072
182	1165	1151.60274767802	13.3972523219817
183	1282	1202.16524767802	79.8347523219817
184	1110	1088.97774767802	21.0222523219809
185	1297	1226.29024767802	70.7097523219816
186	1185	1170.54024767802	14.4597523219815
187	1222	1246.16524767802	-24.1652476780187
188	1284	1261.04024767802	22.9597523219819
189	1444	1313.85274767802	130.147252321981
190	1575	1453.22774767802	121.772252321981
191	1737	1652.79024767802	84.2097523219815
192	1763	1769.41524767802	-6.41524767801867

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/1k35s1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/1k35s1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/2y8jc1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/2y8jc1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/3yxga1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/3yxga1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/4418r1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/4418r1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/5q47x1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/5q47x1195133403.ps (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/631pl1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/76xyx1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/76xyx1195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/84sj11195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/84sj11195133403.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/97l1z1195133403.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t11951330744dyr3ydse79dyom/97l1z1195133403.ps (open in new window)

Parameters:

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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