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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: Mon, 19 Nov 2007 11:20:25 -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/19/t1195496111qbfprbx46qkbpup.htm/, Retrieved Mon, 19 Nov 2007 19:15:11 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1687 0 -183,923544 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	6 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 1717.75147928696 -396.055827106182X[t] + 1.00000000006915T[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)	1717.75147928696	16.512653	104.0264	0	0
X	-396.055827106182	47.709356	-8.3014	0	0
T	1.00000000006915	0.105564	9.473	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.675537295812174
R-squared	0.456350638033224
Adjusted R-squared	0.450597734731988
F-TEST (value)	79.3252752806048
F-TEST (DF numerator)	2
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	214.664492262603
Sum Squared Residuals	8709279.5610503

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1687	1533.82793527424	153.172064725760
2	1508	1540.67887017472	-32.6788701747198
3	1507	1489.11637017115	17.8836298288453
4	1385	1480.30387017054	-95.3038701705447
5	1632	1589.99137017813	42.0086298218703
6	1511	1524.74137017362	-13.7413701736176
7	1559	1497.11637017171	61.8836298282927
8	1630	1553.24137017559	76.7586298244116
9	1579	1449.42887016841	129.571129831590
10	1653	1384.05387016389	268.946129836111
11	2152	1683.49137017460	468.508629825405
12	2148	1562.86637017625	585.133629823746
13	1752	1620.00619875021	131.993801249795
14	1765	1818.85713419396	-53.8571341939561
15	1717	1720.29463416114	-3.29463416114038
16	1558	1674.48213415397	-116.482134153972
17	1575	1554.16963417565	20.8303658243474
18	1520	1554.91963417570	-34.9196341757045
19	1805	1764.29463416018	40.7053658398169
20	1800	1744.41963415881	55.5803658411913
21	1719	1610.60713417956	108.392865820445
22	2008	1760.23213415990	247.767865840098
23	2242	1794.66963416228	447.330365837716
24	2478	1914.04463420054	563.955365799462
25	2030	1919.18446280089	110.815537199106
26	1655	1730.03539814781	-75.035398147814
27	1693	1717.47289814995	-24.4728981499453
28	1623	1760.66039814993	-137.660398149932
29	1805	1805.34789815302	-0.347898153021946
30	1746	1802.09789815280	-56.0978981527972
31	1795	1775.47289815096	19.5271018490440
32	1926	1891.59789819899	34.4021018010137
33	1619	1531.78539817410	87.2146018258953
34	1992	1765.41039815026	226.58960184974
35	2233	1806.84789815313	426.152101846874
36	2192	1649.22289814223	542.777101857774
37	2080	1990.36272680582	89.637273194184
38	1768	1864.21366219709	-96.2136621970926
39	1835	1880.65116219823	-45.6511621982293
40	1569	1727.83866213766	-158.838662137662
41	1976	1997.52616220631	-21.5261622063113
42	1853	1930.27616220166	-77.276162201661
43	1965	1966.65116220418	-1.65116220417629
44	1689	1675.77616213406	13.2238378659381
45	1778	1711.96366213756	66.0363378624357
46	1976	1770.58866214062	205.411337859382
47	2397	1992.02616220593	404.973837794069
48	2654	2132.40116221564	521.598837784362
49	2097	2028.54099070846	68.459009291544
50	1963	2080.39192611204	-117.391926112042
51	1677	1743.82942612877	-66.8294261287678
52	1941	2121.01692611485	-180.016926114851
53	2003	2045.70442610964	-42.7044261096429
54	1813	1911.45442610036	-98.4544261003594
55	2012	2034.82942610889	-22.8294261088909
56	1912	1919.95442610095	-7.95442610094718
57	2084	2039.14192610919	44.8580738908109
58	2080	1895.76692609927	184.233073900725
59	2118	1734.20442612810	383.795573871898
60	2150	1649.57942612225	500.42057387775
61	1608	1560.71925467611	47.2807453238945
62	1503	1641.57019011170	-138.570190111696
63	1548	1636.00769011131	-88.0076901113118
64	1382	1583.19519007766	-201.195190077660
65	1731	1794.8826901223	-63.8826901222983
66	1798	1917.63269010079	-119.632690100787
67	1779	1823.00769009424	-44.0076900942431
68	1887	1916.13269010068	-29.1326901006829
69	2004	1980.32019010512	23.6798098948785
70	2077	1913.94519010053	163.054809899468
71	2092	1729.38269011777	362.617309882231
72	2051	1571.75769007687	479.242309923131
73	1577	1550.89751867543	26.1024813245737
74	1356	1515.74845407300	-159.748454072996
75	1652	1761.18595410997	-109.185954109968
76	1382	1604.37345407912	-222.373454079124
77	1519	1604.06095407910	-85.0609540791027
78	1421	1561.81095407618	-140.810954076181
79	1442	1507.18595407240	-65.1859540724036
80	1543	1593.31095407836	-50.3109540783593
81	1656	1653.49845410252	2.50154589747867
82	1561	1419.12345406631	141.876545933686
83	1905	1563.56095407630	341.439045923698
84	2199	1740.93595410857	458.064045891432
85	1473	1468.0757826697	4.92421733030091
86	1655	1835.92671809514	-180.926718095137
87	1407	1537.36421807449	-130.364218074491
88	1395	1638.55171809149	-243.551718091488
89	1530	1636.23921809133	-106.239218091328
90	1309	1470.9892180699	-161.989218069901
91	1526	1612.36421807968	-86.3642180796768
92	1327	1398.48921806489	-71.489218064887
93	1627	1645.67671809198	-18.6767180919804
94	1748	1627.30171809071	120.698281909290
95	1958	1637.73921809143	320.260781908568
96	2274	1837.11421809522	436.885781904781
97	1648	1664.25404667327	-16.2540466732651
98	1401	1603.10498207904	-202.104982079037
99	1411	1562.54248207623	-151.542482076232
100	1403	1667.72998207351	-264.729982073506
101	1394	1521.41748207339	-127.417482073388
102	1520	1703.16748207596	-183.167482075956
103	1528	1635.54248207128	-107.542482071280
104	1643	1735.66748207820	-92.6674820782034
105	1515	1554.8549820757	-39.8549820757
106	1685	1585.47998207782	99.5200179221822
107	2000	1700.9174820758	299.082517924200
108	2215	1799.29248208260	415.707517917397
109	1956	1993.43231070603	-37.4323107060282
110	1462	1685.28324606472	-223.283246064719
111	1563	1735.72074606821	-172.720746068207
112	1459	1744.90824606884	-285.908246068843
113	1446	1594.59574607845	-148.595746078448
114	1622	1826.34574609447	-204.345746094474
115	1657	1785.72074607166	-128.720746071665
116	1638	1751.84574606932	-113.845746069322
117	1643	1704.03324606602	-61.0332460660159
118	1683	1604.65824607914	78.341753920856
119	2050	1772.09574607072	277.904253929277
120	2262	1867.47074609732	394.529253902682
121	1813	1871.61057469760	-58.6105746976041
122	1445	1689.46151005501	-244.461510055008
123	1762	1955.89901010343	-193.899010103433
124	1461	1768.08651006045	-307.086510060445
125	1556	1725.77401005852	-169.774010058519
126	1431	1656.52401005273	-225.524010052731
127	1427	1576.89901007722	-149.899010077224
128	1554	1689.02401005498	-135.024010054978
129	1645	1727.21151005862	-82.2115100586187
130	1653	1595.83651007853	57.1634899214661
131	2016	1759.27401005984	256.725989940164
132	2207	1833.64901009498	373.350989905021
133	1665	1744.78883864883	-79.7888386488342
134	1361	1626.63977404066	-265.639774040664
135	1506	1721.07727404619	-215.077274046195
136	1360	1688.26477404493	-328.264774044926
137	1453	1643.95227404186	-190.952274041861
138	1522	1768.70227405049	-246.702274050488
139	1460	1631.07727404097	-171.077274040971
140	1552	1708.20227404630	-156.202274046304
141	1548	1651.38977404238	-103.389774042375
142	1827	1791.01477405203	35.9852259479692
143	1737	1501.45227407201	235.547725927993
144	1941	1588.82727407805	352.172725921951
145	1474	1574.96710257709	-100.967102577091
146	1458	1744.81803803884	-286.818038038836
147	1542	1778.25553804115	-236.255538041148
148	1404	1753.44303803943	-349.443038039433
149	1522	1734.13053803810	-212.130538038097
150	1385	1652.88053803248	-267.880538032479
151	1641	1833.25553799495	-192.255537994952
152	1510	1687.38053803486	-177.380538034864
153	1681	1805.56803804304	-124.568038043037
154	1938	1923.19303800117	14.8069619988289
155	1868	1653.63053803253	214.369461967470
156	1726	1395.00553796465	330.994462035354
157	1456	1578.14536657731	-122.145366577311
158	1445	1752.9963020294	-307.996302029402
159	1456	1713.43380202367	-257.433802023666
160	1365	1735.6213020282	-370.6213020282
161	1487	1720.30880202414	-233.308802024141
162	1558	1847.05880199591	-289.058801995906
163	1488	1701.43380202584	-213.433802025836
164	1684	1882.55880199836	-198.558801998361
165	1594	1739.74630202849	-145.746302028486
166	1850	1856.37130199655	-6.37130199655028
167	1998	1804.80880203298	193.191197967015
168	2079	1769.18380203052	309.816197969479
169	1494	1637.32363061140	-143.323630611403
170	1057	1216.50377247351	-159.503772473508
171	1218	1326.94127250915	-108.941272509145
172	1168	1390.12877251551	-222.128772515515
173	1236	1320.81627250872	-84.8162725087218
174	1076	1216.56627247351	-140.566272473513
175	1174	1238.94127250506	-64.94127250506
176	1139	1189.06627247161	-50.0662724716111
177	1427	1424.25377248787	2.74622751212534
178	1487	1344.87877251239	142.121227487614
179	1483	1141.31627246831	341.683727531691
180	1513	1054.69127246232	458.308727537681
181	1357	1351.83110110287	5.16889889713347
182	1165	1345.68203650244	-180.682036502441
183	1282	1412.11953650704	-130.119536507036
184	1110	1353.30703650297	-243.307036502969
185	1297	1402.99453650640	-105.994536506405
186	1185	1346.74453650251	-161.744536502515
187	1222	1308.11953649984	-86.1195364998438
188	1284	1355.24453650310	-71.2445365031026
189	1444	1462.43203649051	-18.4320364905147
190	1575	1454.05703648994	120.942963510064
191	1737	1416.49453650734	320.505463492662
192	1763	1325.86953649707	437.130463502929

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/1ae7p1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/1ae7p1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/2hrrv1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/2hrrv1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/3mh1d1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/3mh1d1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/4joz71195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/4joz71195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/532cn1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/532cn1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/6pwv11195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/6pwv11195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/7tems1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/7tems1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/80fua1195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/80fua1195496414.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/95kt51195496414.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/19/t1195496111qbfprbx46qkbpup/95kt51195496414.ps (open in new window)

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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