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The seatbelt law

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 03:29:04 -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/t119512222707uum1ref5calo3.htm/, Retrieved Thu, 15 Nov 2007 11:23:58 +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

Slacht[t] = + 1717.75147928992 -396.055827109141wet[t] + 1.00000000002839V3[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)	1717.75147928992	16.512653	104.0264	0	0
wet	-396.055827109141	47.709356	-8.3014	0	0
V3	1.00000000002839	0.105564	9.473	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.675537295805097
R-squared	0.456350638023663
Adjusted R-squared	0.450597734722326
F-TEST (value)	79.3252752775477
F-TEST (DF numerator)	2
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	214.664492264491
Sum Squared Residuals	8709279.56120347

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1533.82793478468	153.172065215319
2	1508	1540.67887018490	-32.6788701848964
3	1507	1489.11637018343	17.8836298165677
4	1385	1480.30387018318	-95.3038701831818
5	1632	1589.99137018630	42.0086298137040
6	1511	1524.74137018444	-13.7413701844435
7	1559	1497.11637018366	61.8836298163408
8	1630	1553.24137018525	76.7586298147474
9	1579	1449.42887018231	129.571129817695
10	1653	1384.05387018045	268.946129819551
11	2152	1683.49137017895	468.508629821049
12	2148	1562.86637018553	585.133629814474
13	1752	1620.00619875715	131.993801242852
14	1765	1818.85713419279	-53.8571341927937
15	1717	1720.29463416400	-3.29463416399540
16	1558	1674.48213415869	-116.482134158695
17	1575	1554.16963418528	20.8303658147210
18	1520	1554.9196341853	-34.9196341853003
19	1805	1764.29463416124	40.7053658387554
20	1800	1744.41963416068	55.5803658393197
21	1719	1610.60713418688	108.392865813119
22	2008	1760.23213416113	247.767865838871
23	2242	1794.66963416211	447.330365837893
24	2478	1914.04463419550	563.955365804504
25	2030	1919.18446279564	110.815537204358
26	1655	1730.03539815027	-75.0353981502719
27	1693	1717.47289815292	-24.4728981529153
28	1623	1760.66039815114	-137.660398151141
29	1805	1805.34789815241	-0.347898152410115
30	1746	1802.09789815232	-56.0978981523178
31	1795	1775.47289815156	19.5271018484381
32	1926	1891.59789819486	34.4021018051411
33	1619	1531.78539818464	87.2146018153565
34	1992	1765.41039815128	226.589601848724
35	2233	1806.84789815245	426.152101847547
36	2192	1649.22289814798	542.777101852022
37	2080	1990.36272679766	89.6372732023371
38	1768	1864.21366219408	-96.2136621940814
39	1835	1880.65116219455	-45.6511621945481
40	1569	1727.83866214021	-158.838662140210
41	1976	1997.52616219787	-21.5261621978663
42	1853	1930.27616219596	-77.276162195957
43	1965	1966.65116219699	-1.65116219698961
44	1689	1675.77616213873	13.2238378612685
45	1778	1711.96366214076	66.0363378592411
46	1976	1770.58866214142	205.411337858577
47	2397	1992.02616219771	404.97383780229
48	2654	2132.40116220170	521.598837798305
49	2097	2028.54099069875	68.4590093012532
50	1963	2080.39192610022	-117.391926100219
51	1677	1743.82942613066	-66.8294261306636
52	1941	2121.01692610137	-180.016926101372
53	2003	2045.70442609923	-42.7044260992341
54	1813	1911.45442609542	-98.4544260954226
55	2012	2034.82942609893	-22.8294260989253
56	1912	1919.95442609566	-7.95442609566395
57	2084	2039.14192609905	44.8580739009522
58	2080	1895.76692609498	184.233073905023
59	2118	1734.20442613039	383.79557386961
60	2150	1649.57942612799	500.420573872012
61	1608	1560.71925468546	47.2807453145351
62	1503	1641.57019011776	-138.570190117760
63	1548	1636.00769011760	-88.0076901176024
64	1382	1583.19519008610	-201.195190086103
65	1731	1794.88269012211	-63.882690122113
66	1798	1917.63269009560	-119.632690095598
67	1779	1823.00769009291	-44.0076900929115
68	1887	1916.13269009556	-29.1326900955554
69	2004	1980.32019009738	23.6798099026222
70	2077	1913.94519009549	163.054809904507
71	2092	1729.38269012025	362.617309879747
72	2051	1571.75769008578	479.242309914222
73	1577	1550.89751868519	26.1024813148139
74	1356	1515.74845408419	-159.748454084188
75	1652	1761.18595411116	-109.185954111156
76	1382	1604.37345408670	-222.373454086704
77	1519	1604.06095408670	-85.0609540866954
78	1421	1561.81095408550	-140.810954085496
79	1442	1507.18595408395	-65.1859540839451
80	1543	1593.31095408639	-50.3109540863902
81	1656	1653.4984541081	2.50154589190100
82	1561	1419.12345408144	141.876545918555
83	1905	1563.56095408555	341.439045914454
84	2199	1740.93595411058	458.064045889419
85	1473	1468.07578268283	4.92421731716533
86	1655	1835.92671809328	-180.926718093278
87	1407	1537.36421808480	-130.364218084802
88	1395	1638.55171809767	-243.551718097675
89	1530	1636.23921809761	-106.239218097609
90	1309	1470.98921808292	-161.989218082917
91	1526	1612.36421808693	-86.3642180869312
92	1327	1398.48921808086	-71.489218080859
93	1627	1645.67671809788	-18.6767180978769
94	1748	1627.30171809736	120.698281902645
95	1958	1637.73921809765	320.260781902348
96	2274	1837.11421809331	436.885781906688
97	1648	1664.25404667840	-16.2540466784044
98	1401	1603.10498208667	-202.104982086668
99	1411	1562.54248208552	-151.542482085517
100	1403	1667.72998207850	-264.729982078503
101	1394	1521.41748208435	-127.417482084349
102	1520	1703.16748207951	-183.167482079509
103	1528	1635.54248207759	-107.542482077589
104	1643	1735.66748208043	-92.6674820804318
105	1515	1554.85498208530	-39.8549820852984
106	1685	1585.47998208617	99.5200179138321
107	2000	1700.91748207945	299.082517920555
108	2215	1799.29248208224	415.707517917762
109	1956	1993.43231069775	-37.4323106977500
110	1462	1685.28324606900	-223.283246069001
111	1563	1735.72074607043	-172.720746070433
112	1459	1744.90824607069	-285.908246070694
113	1446	1594.59574608643	-148.595746086427
114	1622	1826.34574609301	-204.345746093006
115	1657	1785.72074607185	-128.720746071853
116	1638	1751.84574607089	-113.845746070891
117	1643	1704.03324606953	-61.0332460695337
118	1683	1604.65824608671	78.3417539132876
119	2050	1772.09574607147	277.904253928534
120	2262	1867.47074609417	394.529253905826
121	1813	1871.61057469429	-58.6105746942914
122	1445	1689.46151005912	-244.46151005912
123	1762	1955.89901009668	-193.899010096684
124	1461	1768.08651006135	-307.086510061352
125	1556	1725.77401006115	-169.774010061151
126	1431	1656.52401005819	-225.524010058185
127	1427	1576.89901008592	-149.899010085924
128	1554	1689.02401005911	-135.024010059108
129	1645	1727.21151006119	-82.2115100611918
130	1653	1595.83651008646	57.1634899135381
131	2016	1759.27401006110	256.725989938898
132	2207	1833.64901009321	373.350989906786
133	1665	1744.78883865069	-79.7888386506908
134	1361	1626.63977404734	-265.639774047336
135	1506	1721.07727404902	-215.077274049018
136	1360	1688.26477404909	-328.264774049086
137	1453	1643.95227404783	-190.952274047828
138	1522	1768.70227405137	-246.70227405137
139	1460	1631.07727404746	-171.077274047462
140	1552	1708.20227404965	-156.202274049652
141	1548	1651.38977404804	-103.389774048039
142	1827	1791.01477405200	35.9852259479968
143	1737	1501.45227408378	235.547725916218
144	1941	1588.82727408626	352.172725913737
145	1474	1574.96710258587	-100.967102585869
146	1458	1744.81803804069	-286.818038040692
147	1542	1778.25553804164	-236.255538041641
148	1404	1753.44303804094	-349.443038040937
149	1522	1734.13053804039	-212.130538040388
150	1385	1652.88053803808	-267.880538038081
151	1641	1833.25553799320	-192.255537993202
152	1510	1687.38053803906	-177.380538039061
153	1681	1805.56803804242	-124.568038042416
154	1938	1923.19303799576	14.8069620042441
155	1868	1653.63053803810	214.369461961897
156	1726	1395.00553798076	330.99446201924
157	1456	1578.14536658596	-122.145366585960
158	1445	1752.99630203092	-307.996302030924
159	1456	1713.4338020268	-257.433802026801
160	1365	1735.62130203043	-370.621302030431
161	1487	1720.30880202700	-233.308802026996
162	1558	1847.05880199359	-289.058801993594
163	1488	1701.43380202946	-213.43380202946
164	1684	1882.55880199460	-198.558801994602
165	1594	1739.74630203055	-145.746302030548
166	1850	1856.37130199386	-6.37130199385873
167	1998	1804.80880203239	193.191197967605
168	2079	1769.18380203138	309.816197968617
169	1494	1637.32363061764	-143.323630617640
170	1057	1216.50377247780	-159.503772477796
171	1218	1326.94127250893	-108.941272508932
172	1168	1390.12877251273	-222.128772512725
173	1236	1320.81627250876	-84.8162725087576
174	1076	1216.56627247780	-140.566272477798
175	1174	1238.94127250843	-64.9412725084331
176	1139	1189.06627247702	-50.0662724770171
177	1427	1424.25377248369	2.74622751630567
178	1487	1344.87877251144	142.121227488559
179	1483	1141.31627247566	341.683727524339
180	1513	1054.69127247320	458.308727526798
181	1357	1351.83110110164	5.16889889836181
182	1165	1345.68203650146	-180.682036501464
183	1282	1412.11953650335	-130.119536503350
184	1110	1353.30703650168	-243.30703650168
185	1297	1402.99453650309	-105.994536503091
186	1185	1346.74453650149	-161.744536501494
187	1222	1308.11953650040	-86.1195365003972
188	1284	1355.24453650174	-71.2445365017351
189	1444	1462.43203648478	-18.4320364847782
190	1575	1454.05703648454	120.942963515460
191	1737	1416.49453650347	320.505463496526
192	1763	1325.86953649690	437.130463503099

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/19ebb1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/19ebb1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/2pzym1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/2pzym1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/329xs1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/329xs1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/4iif81195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/4iif81195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/5jqea1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/5jqea1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/6edl21195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/6edl21195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/7fd0g1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/7fd0g1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/80moz1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/80moz1195122538.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/9t4al1195122538.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/15/t119512222707uum1ref5calo3/9t4al1195122538.ps (open in new window)

Parameters:

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')

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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