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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 12 Nov 2009 15:10:54 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/12/t1258035104fui5g595g77aupf.htm/, Retrieved Thu, 31 Oct 2024 23:37:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56003, Retrieved Thu, 31 Oct 2024 23:37:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact704
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D    [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D      [Multiple Regression] [WS 7 4] [2009-11-14 13:58:56] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD        [Multiple Regression] [ws7 4] [2009-11-18 20:49:31] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D          [Multiple Regression] [WS 75] [2009-11-18 20:57:24] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D            [Multiple Regression] [Paper hypothese t...] [2010-12-19 12:54:13] [a9e130f95bad0a0597234e75c6380c5a]
-    D              [Multiple Regression] [Multiple Regression] [2010-12-22 14:47:15] [a9e130f95bad0a0597234e75c6380c5a]
-    D              [Multiple Regression] [] [2011-12-20 17:24:43] [06f5daa9a1979410bf169cb7a41fb3eb]
-    D          [Multiple Regression] [WS7: Correctie] [2009-11-27 15:55:13] [8cf9233b7464ea02e32be3b30fdac052]
-   PD      [Multiple Regression] [] [2009-11-17 12:48:49] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D      [Multiple Regression] [] [2009-11-17 16:58:20] [1eab65e90adf64584b8e6f0da23ff414]
-    D      [Multiple Regression] [] [2009-11-17 17:00:20] [1eab65e90adf64584b8e6f0da23ff414]
-    D      [Multiple Regression] [Multiple regressi...] [2009-11-17 17:36:08] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-   PD        [Multiple Regression] [autoregressions m...] [2009-11-21 00:12:58] [3dd791303389e75e672968b227170a72]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-20 13:25:49] [73863f7f907331e734eff34b7de6fc83]
-    D          [Multiple Regression] [Multiple Regressi...] [2009-12-20 14:49:21] [73863f7f907331e734eff34b7de6fc83]
-    D          [Multiple Regression] [Multiple Regressi...] [2009-12-20 15:10:27] [73863f7f907331e734eff34b7de6fc83]
-    D      [Multiple Regression] [] [2009-11-18 12:18:41] [6ba840d2473f9a55d7b3e13093db69b8]
-    D        [Multiple Regression] [] [2009-12-15 15:08:14] [6ba840d2473f9a55d7b3e13093db69b8]
-    D          [Multiple Regression] [] [2009-12-21 09:27:51] [6ba840d2473f9a55d7b3e13093db69b8]
-    D      [Multiple Regression] [] [2009-11-18 14:48:54] [ee35698a38947a6c6c039b1e3deafc05]
-    D        [Multiple Regression] [] [2009-11-18 15:05:53] [ee35698a38947a6c6c039b1e3deafc05]
-    D          [Multiple Regression] [] [2009-11-18 15:17:01] [ee35698a38947a6c6c039b1e3deafc05]
-    D          [Multiple Regression] [] [2009-11-18 15:17:01] [ee35698a38947a6c6c039b1e3deafc05]
-    D      [Multiple Regression] [SHW WS7] [2009-11-18 14:48:40] [253127ae8da904b75450fbd69fe4eb21]
-    D      [Multiple Regression] [] [2009-11-18 15:02:28] [94b62ad0aa784646217b93aa983cee13]
-    D      [Multiple Regression] [WS7 Y(t-4)] [2009-11-18 16:26:23] [445b292c553470d9fed8bc2796fd3a00]
-    D      [Multiple Regression] [WS7 (t-1)] [2009-11-18 16:31:41] [445b292c553470d9fed8bc2796fd3a00]
-    D      [Multiple Regression] [] [2009-11-18 16:49:45] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Multiple Regression] [] [2009-11-20 13:30:42] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D          [Multiple Regression] [] [2009-11-20 13:35:13] [90f6d58d515a4caed6fb4b8be4e11eaa]
-   PD      [Multiple Regression] [] [2009-11-18 17:29:14] [03c44f58d7d4de05d4cfabfda8c46d2c]
-   PD      [Multiple Regression] [] [2009-11-18 17:25:36] [96d96f181930b548ce74f8c3116c4873]
-   P         [Multiple Regression] [] [2009-11-19 18:27:34] [96d96f181930b548ce74f8c3116c4873]
-   PD      [Multiple Regression] [Berekening 4 TVD] [2009-11-18 17:29:00] [42ad1186d39724f834063794eac7cea3]
-    D        [Multiple Regression] [Berekening 5 TVD] [2009-11-18 17:46:23] [42ad1186d39724f834063794eac7cea3]
-               [Multiple Regression] [BDM 6] [2009-11-18 18:00:53] [f5d341d4bbba73282fc6e80153a6d315]
-               [Multiple Regression] [TG 6] [2009-11-18 18:08:13] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   P           [Multiple Regression] [review WS 7 2 maa...] [2009-11-27 10:24:38] [12f02da0296cb21dc23d82ae014a8b71]
-             [Multiple Regression] [BDM 5] [2009-11-18 17:59:47] [f5d341d4bbba73282fc6e80153a6d315]
-             [Multiple Regression] [TG 5] [2009-11-18 18:07:12] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   PD        [Multiple Regression] [review Ws 7 model...] [2009-11-27 10:16:15] [12f02da0296cb21dc23d82ae014a8b71]
-   PD      [Multiple Regression] [] [2009-11-18 17:51:43] [7369a9baefff1ba9d2171738b4c9faa6]
- R  D      [Multiple Regression] [] [2009-11-18 17:51:37] [c0117c881d5fcd069841276db0c34efe]
-   PD      [Multiple Regression] [] [2009-11-18 18:01:24] [7369a9baefff1ba9d2171738b4c9faa6]
- R  D      [Multiple Regression] [] [2009-11-18 18:46:54] [c0117c881d5fcd069841276db0c34efe]
-    D      [Multiple Regression] [WS7_4] [2009-11-18 19:13:17] [8b1aef4e7013bd33fbc2a5833375c5f5]
-             [Multiple Regression] [] [2009-11-19 14:06:12] [08fc5c07292c885b941f0cb515ce13f3]
-    D          [Multiple Regression] [] [2009-11-20 17:36:35] [4d62210f0915d3a20cbf115865da7cd4]
-    D            [Multiple Regression] [verbetering] [2009-11-26 18:08:09] [42ad1186d39724f834063794eac7cea3]

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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 522.918085028905 -80.7982303933677X[t] + 0.389747913943074Y1[t] + 0.172724627545124Y2[t] + 0.0359567363109739Y3[t] + 0.0354648834548642Y4[t] + 188.662307824867M1[t] + 104.987349633496M2[t] + 181.667324587968M3[t] + 176.541027973435M4[t] + 208.395663285797M5[t] + 324.64288106546M6[t] + 458.312477153942M7[t] + 471.425528086186M8[t] -54.2481744163299M9[t] -111.187507110586M10[t] + 86.6003491334288M11[t] -0.767119250424237t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  522.918085028905 -80.7982303933677X[t] +  0.389747913943074Y1[t] +  0.172724627545124Y2[t] +  0.0359567363109739Y3[t] +  0.0354648834548642Y4[t] +  188.662307824867M1[t] +  104.987349633496M2[t] +  181.667324587968M3[t] +  176.541027973435M4[t] +  208.395663285797M5[t] +  324.64288106546M6[t] +  458.312477153942M7[t] +  471.425528086186M8[t] -54.2481744163299M9[t] -111.187507110586M10[t] +  86.6003491334288M11[t] -0.767119250424237t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  522.918085028905 -80.7982303933677X[t] +  0.389747913943074Y1[t] +  0.172724627545124Y2[t] +  0.0359567363109739Y3[t] +  0.0354648834548642Y4[t] +  188.662307824867M1[t] +  104.987349633496M2[t] +  181.667324587968M3[t] +  176.541027973435M4[t] +  208.395663285797M5[t] +  324.64288106546M6[t] +  458.312477153942M7[t] +  471.425528086186M8[t] -54.2481744163299M9[t] -111.187507110586M10[t] +  86.6003491334288M11[t] -0.767119250424237t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 522.918085028905 -80.7982303933677X[t] + 0.389747913943074Y1[t] + 0.172724627545124Y2[t] + 0.0359567363109739Y3[t] + 0.0354648834548642Y4[t] + 188.662307824867M1[t] + 104.987349633496M2[t] + 181.667324587968M3[t] + 176.541027973435M4[t] + 208.395663285797M5[t] + 324.64288106546M6[t] + 458.312477153942M7[t] + 471.425528086186M8[t] -54.2481744163299M9[t] -111.187507110586M10[t] + 86.6003491334288M11[t] -0.767119250424237t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)522.918085028905164.861573.17190.0017970.000899
X-80.798230393367738.149531-2.11790.0356340.017817
Y10.3897479139430740.0767195.08021e-060
Y20.1727246275451240.0821272.10310.0369250.018463
Y30.03595673631097390.0819030.4390.6612060.330603
Y40.03546488345486420.0751740.47180.6376970.318849
M1188.66230782486756.5212583.33790.0010370.000519
M2104.98734963349663.6515671.64940.1009110.050455
M3181.66732458796863.2337672.87290.0045850.002293
M4176.54102797343568.2895052.58520.0105710.005286
M5208.39566328579762.6406013.32680.0010760.000538
M6324.6428810654666.3752764.8912e-061e-06
M7458.31247715394265.9366286.950800
M8471.42552808618673.3054386.43100
M9-54.248174416329980.31085-0.67550.500290.250145
M10-111.18750711058677.361225-1.43730.1524850.076242
M1186.600349133428862.448111.38680.1673320.083666
t-0.7671192504242370.256233-2.99380.0031660.001583

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 522.918085028905 & 164.86157 & 3.1719 & 0.001797 & 0.000899 \tabularnewline
X & -80.7982303933677 & 38.149531 & -2.1179 & 0.035634 & 0.017817 \tabularnewline
Y1 & 0.389747913943074 & 0.076719 & 5.0802 & 1e-06 & 0 \tabularnewline
Y2 & 0.172724627545124 & 0.082127 & 2.1031 & 0.036925 & 0.018463 \tabularnewline
Y3 & 0.0359567363109739 & 0.081903 & 0.439 & 0.661206 & 0.330603 \tabularnewline
Y4 & 0.0354648834548642 & 0.075174 & 0.4718 & 0.637697 & 0.318849 \tabularnewline
M1 & 188.662307824867 & 56.521258 & 3.3379 & 0.001037 & 0.000519 \tabularnewline
M2 & 104.987349633496 & 63.651567 & 1.6494 & 0.100911 & 0.050455 \tabularnewline
M3 & 181.667324587968 & 63.233767 & 2.8729 & 0.004585 & 0.002293 \tabularnewline
M4 & 176.541027973435 & 68.289505 & 2.5852 & 0.010571 & 0.005286 \tabularnewline
M5 & 208.395663285797 & 62.640601 & 3.3268 & 0.001076 & 0.000538 \tabularnewline
M6 & 324.64288106546 & 66.375276 & 4.891 & 2e-06 & 1e-06 \tabularnewline
M7 & 458.312477153942 & 65.936628 & 6.9508 & 0 & 0 \tabularnewline
M8 & 471.425528086186 & 73.305438 & 6.431 & 0 & 0 \tabularnewline
M9 & -54.2481744163299 & 80.31085 & -0.6755 & 0.50029 & 0.250145 \tabularnewline
M10 & -111.187507110586 & 77.361225 & -1.4373 & 0.152485 & 0.076242 \tabularnewline
M11 & 86.6003491334288 & 62.44811 & 1.3868 & 0.167332 & 0.083666 \tabularnewline
t & -0.767119250424237 & 0.256233 & -2.9938 & 0.003166 & 0.001583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]522.918085028905[/C][C]164.86157[/C][C]3.1719[/C][C]0.001797[/C][C]0.000899[/C][/ROW]
[ROW][C]X[/C][C]-80.7982303933677[/C][C]38.149531[/C][C]-2.1179[/C][C]0.035634[/C][C]0.017817[/C][/ROW]
[ROW][C]Y1[/C][C]0.389747913943074[/C][C]0.076719[/C][C]5.0802[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]0.172724627545124[/C][C]0.082127[/C][C]2.1031[/C][C]0.036925[/C][C]0.018463[/C][/ROW]
[ROW][C]Y3[/C][C]0.0359567363109739[/C][C]0.081903[/C][C]0.439[/C][C]0.661206[/C][C]0.330603[/C][/ROW]
[ROW][C]Y4[/C][C]0.0354648834548642[/C][C]0.075174[/C][C]0.4718[/C][C]0.637697[/C][C]0.318849[/C][/ROW]
[ROW][C]M1[/C][C]188.662307824867[/C][C]56.521258[/C][C]3.3379[/C][C]0.001037[/C][C]0.000519[/C][/ROW]
[ROW][C]M2[/C][C]104.987349633496[/C][C]63.651567[/C][C]1.6494[/C][C]0.100911[/C][C]0.050455[/C][/ROW]
[ROW][C]M3[/C][C]181.667324587968[/C][C]63.233767[/C][C]2.8729[/C][C]0.004585[/C][C]0.002293[/C][/ROW]
[ROW][C]M4[/C][C]176.541027973435[/C][C]68.289505[/C][C]2.5852[/C][C]0.010571[/C][C]0.005286[/C][/ROW]
[ROW][C]M5[/C][C]208.395663285797[/C][C]62.640601[/C][C]3.3268[/C][C]0.001076[/C][C]0.000538[/C][/ROW]
[ROW][C]M6[/C][C]324.64288106546[/C][C]66.375276[/C][C]4.891[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M7[/C][C]458.312477153942[/C][C]65.936628[/C][C]6.9508[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]471.425528086186[/C][C]73.305438[/C][C]6.431[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-54.2481744163299[/C][C]80.31085[/C][C]-0.6755[/C][C]0.50029[/C][C]0.250145[/C][/ROW]
[ROW][C]M10[/C][C]-111.187507110586[/C][C]77.361225[/C][C]-1.4373[/C][C]0.152485[/C][C]0.076242[/C][/ROW]
[ROW][C]M11[/C][C]86.6003491334288[/C][C]62.44811[/C][C]1.3868[/C][C]0.167332[/C][C]0.083666[/C][/ROW]
[ROW][C]t[/C][C]-0.767119250424237[/C][C]0.256233[/C][C]-2.9938[/C][C]0.003166[/C][C]0.001583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)522.918085028905164.861573.17190.0017970.000899
X-80.798230393367738.149531-2.11790.0356340.017817
Y10.3897479139430740.0767195.08021e-060
Y20.1727246275451240.0821272.10310.0369250.018463
Y30.03595673631097390.0819030.4390.6612060.330603
Y40.03546488345486420.0751740.47180.6376970.318849
M1188.66230782486756.5212583.33790.0010370.000519
M2104.98734963349663.6515671.64940.1009110.050455
M3181.66732458796863.2337672.87290.0045850.002293
M4176.54102797343568.2895052.58520.0105710.005286
M5208.39566328579762.6406013.32680.0010760.000538
M6324.6428810654666.3752764.8912e-061e-06
M7458.31247715394265.9366286.950800
M8471.42552808618673.3054386.43100
M9-54.248174416329980.31085-0.67550.500290.250145
M10-111.18750711058677.361225-1.43730.1524850.076242
M1186.600349133428862.448111.38680.1673320.083666
t-0.7671192504242370.256233-2.99380.0031660.001583







Multiple Linear Regression - Regression Statistics
Multiple R0.909113674036662
R-squared0.82648767232044
Adjusted R-squared0.809136439552483
F-TEST (value)47.6327926305489
F-TEST (DF numerator)17
F-TEST (DF denominator)170
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation127.324879886795
Sum Squared Residuals2755976.25649176

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.909113674036662 \tabularnewline
R-squared & 0.82648767232044 \tabularnewline
Adjusted R-squared & 0.809136439552483 \tabularnewline
F-TEST (value) & 47.6327926305489 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 170 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 127.324879886795 \tabularnewline
Sum Squared Residuals & 2755976.25649176 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.909113674036662[/C][/ROW]
[ROW][C]R-squared[/C][C]0.82648767232044[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.809136439552483[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]47.6327926305489[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]170[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]127.324879886795[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2755976.25649176[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.909113674036662
R-squared0.82648767232044
Adjusted R-squared0.809136439552483
F-TEST (value)47.6327926305489
F-TEST (DF numerator)17
F-TEST (DF denominator)170
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation127.324879886795
Sum Squared Residuals2755976.25649176







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116321624.962164870317.03783512969226
215111609.33124673722-98.3312467372175
315591676.32540114441-117.325401144406
416301672.79480330307-42.7948033030729
515791744.25426449686-165.254264496858
616531849.55534041559-196.555340415592
721522006.74544956455145.254550435451
821482227.04142591574-79.0414259157364
917521786.08329108286-34.0832910828553
1017651593.91257950137171.087420498627
1117171745.15423676709-28.1542367670882
1215581626.94356155909-68.9435615590894
1315751731.00139341833-156.001393418334
1415201624.45693487588-104.456934875882
1518051674.45053850205130.549461497950
1618001765.1077716438534.8922283561497
1717191842.09834950806-123.098349508062
1820081933.4423451288074.5576548712037
1922422174.9189823683367.0810176316715
2024782325.29352321490152.706476785095
2120301938.7696132321291.2303867678834
2216551765.88233555681-110.882335556812
2316931756.15154417936-63.1515441793628
2416231611.0838558239611.9161441760431
2518051748.8881823647156.111817635295
2617461711.3565260166334.6434739833669
2717951794.540831040770.459168959230808
2819261801.61589410062124.384105899383
2916191896.55705498525-277.557054985246
3019921914.6809220977777.3190779022267
3122332146.3810219262786.6189780737299
3221922310.68966862782-118.689668627819
3320801812.41996106494267.580038935061
3417681725.8740080088942.1259919911072
3518351789.0210482910645.9789517089369
3615691668.39539165884-99.395391658839
3719761748.99961649398227.000383506021
3818531768.5842467949384.4157532050709
3919651859.6686878276105.331312172398
4016891881.38264181579-192.382641815791
4117781834.25642091437-56.2564209143736
4219761936.4170603839739.5829396160272
4323972155.91012375939241.089876240609
4426542359.95124516331294.048754836687
4520972016.6685139073080.3314860927041
4619631808.42253608640154.577463913597
4716771881.18103223541-204.181032235406
4819411648.28713329548292.712866704525
4920031865.10438492295137.895615077045
5018131835.39005884965-22.3900588496519
5120121847.30935953032164.690640469680
5219121897.7501461898314.2498538101749
5320841919.60211461405164.397885385949
5420802085.26345425645-5.26345425644624
5521182249.77741355291-131.777413552913
5621502278.88093775439-128.88093775439
5716081777.43171810334-169.431718103336
5815031515.23358132895-12.2335813289512
5915481580.21232036230-32.2123203622983
6013821473.89374740365-91.8937474036534
6117311581.86596635789149.134033642112
6217981602.66786308097195.332136919032
6317791760.6018255603018.3981744397026
6418871765.53747969497121.462520305033
6520041850.22234819798153.777651802018
6620772031.65018163545.3498183649984
6720922216.42253234963-124.422532349626
6820512255.26072611289-204.260726112894
6915771722.20534241639-145.205342416386
7013561475.80495707021-119.804957070208
7116521503.87767868906148.122321310936
7213821475.20589691184-93.2058969118374
7315191584.23884499268-65.2388449926761
7414211509.36203702842-88.3620370284215
7514421571.53215783841-129.532157838408
7615431552.24698900862-9.2469890086225
7716561627.661190432128.3388095679010
7815611801.90752350292-240.907523502917
7919051921.67822434894-16.6782243489416
8021992059.33366324247139.666336757527
8114731707.48764194518-234.487641945176
8216551426.60519833885228.394801661146
8314071591.93317645622-184.933176456224
8413951423.66619280166-28.666192801664
8515301544.84531939797-14.8453193979652
8613091508.48385299161-199.483852991607
8715261512.3534725002913.6465274997104
8813271557.29179207403-230.29179207403
8916271545.1420379802781.8579620197308
9017481743.139182346914.86081765309311
9119581975.55903421944-17.5590342194350
9222742094.38121684803179.618783151966
9316481742.36313781567-94.3631378156688
9414011507.09763957023-106.097639570226
9514111518.53497917642-107.534979176420
9614031381.0999931694221.9000068305806
9713941536.52211379621-142.522113796213
9815201438.7902492583381.2097507416726
9915281562.32381541536-34.3238154153577
10016431580.7043562381962.2956437618072
10115151662.20604424803-147.206044248034
10216851752.41797116605-67.4179711660492
10320001933.8875847920566.1124152079459
10422152097.84329539812117.156704601882
10519561711.17967291030244.820327089704
10614621607.01970830184-145.019708301844
10715631585.67143386851-22.6714338685113
10814591450.654694023878.34530597612882
10914461588.51325437786-142.513254377861
11016221467.15307073082154.846929269182
11116571609.2585917833647.7414082166392
11216381643.25010190300-5.25010190300413
11316431678.84511166992-35.8451116699208
11416831800.49248710446-117.492487104458
11520501950.4065965689899.5934034310243
11622622112.20494866562149.795051334379
11718131733.3962168473979.603783152614
11814451551.92529014615-106.925290146154
11917621548.60387736679213.396122633205
12014611512.59781545509-51.5978154550942
12115561608.77677723080-52.7767772308038
12214311507.71784702171-76.7178470217067
12314271551.74044352525-124.740443525246
12415541515.4384175910038.5615824089955
12516451594.2075921028750.7924078971276
12616531762.51384112206-109.513841122060
12720161918.6768883559497.3231116440577
12822072081.65921302252125.340786977475
12916651695.87424091647-30.8742409164725
13013611473.25083782429-112.250837824287
13115061477.9129501792428.0870498207617
13213601381.83588420274-21.8358842027445
13314531507.72013366447-54.7201336644692
13415221428.7392187926093.2607812073961
13514601547.50079551997-87.500795519974
13615521527.5271117836724.4728882163302
13715481589.54175798733-41.541757987332
13818271719.57128990206107.428710097945
13917371961.63173318646-224.631733186458
14019411990.20946603109-49.2094660310937
14114741537.62207214042-63.6220721404223
14214581339.79776461945118.202235380549
14315421454.0634686228887.9365313771192
14414041387.1142713370916.8857286629086
14515221518.595708146783.40429185321661
14613851458.76081366389-73.7608136638874
14716411499.67673180735141.323268192647
14815101569.24424890606-59.2442489060646
14916811592.7510762660888.2489237339183
15019381756.59737733347181.402622666532
15118682023.56965707282-155.569657072819
15217262054.52616623431-328.526166234309
15314561475.95579307600-19.9557930759967
15414451295.08801076141149.911989238589
15514561433.5974728664522.4025271335545
15613651333.8729283784231.1270716215838
15714871478.229985054808.7700149451982
15815581425.62462238887132.375377611128
15914881547.40003525709-59.4000352570887
16016841527.64713140737156.352868592634
16115941629.91415873356-35.91415873356
16218501744.17200719029105.827992809706
16319981965.8697119938332.1302880061727
16420792083.83085047994-4.83085047993892
16514941620.53593961774-126.535939617738
16610571282.42002959262-225.420029592616
16712181216.238319471701.76168052830480
16811681097.9775678133870.0224321866176
16912361257.73397513645-21.7339751364453
17010761181.44940494181-105.449404941814
17111741210.65987850872-36.659878508723
17211391215.99763169937-76.9976316993702
17314271247.02951853790179.970481462103
17414871466.5610331243620.4389668756409
17514831674.81015033969-191.810150339689
17615131705.07483715508-192.074837155084
17713571202.00684492392154.993155076085
17811651090.6655232925274.334476707483
17912821186.8464614675195.1535385324919
18011101107.371066165462.62893383453497
18112971236.0021797738160.9978202261871
18211851192.13200682666-7.13200682666083
18312221254.65743423875-32.6574342387546
18412841244.4634826405539.5365173594474
18514441308.71095932536135.289040674639
18615751494.6179832898580.3820167101494
18717371709.7548956007827.2451043992194
18817631815.81881613375-52.8188161337465

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1632 & 1624.96216487031 & 7.03783512969226 \tabularnewline
2 & 1511 & 1609.33124673722 & -98.3312467372175 \tabularnewline
3 & 1559 & 1676.32540114441 & -117.325401144406 \tabularnewline
4 & 1630 & 1672.79480330307 & -42.7948033030729 \tabularnewline
5 & 1579 & 1744.25426449686 & -165.254264496858 \tabularnewline
6 & 1653 & 1849.55534041559 & -196.555340415592 \tabularnewline
7 & 2152 & 2006.74544956455 & 145.254550435451 \tabularnewline
8 & 2148 & 2227.04142591574 & -79.0414259157364 \tabularnewline
9 & 1752 & 1786.08329108286 & -34.0832910828553 \tabularnewline
10 & 1765 & 1593.91257950137 & 171.087420498627 \tabularnewline
11 & 1717 & 1745.15423676709 & -28.1542367670882 \tabularnewline
12 & 1558 & 1626.94356155909 & -68.9435615590894 \tabularnewline
13 & 1575 & 1731.00139341833 & -156.001393418334 \tabularnewline
14 & 1520 & 1624.45693487588 & -104.456934875882 \tabularnewline
15 & 1805 & 1674.45053850205 & 130.549461497950 \tabularnewline
16 & 1800 & 1765.10777164385 & 34.8922283561497 \tabularnewline
17 & 1719 & 1842.09834950806 & -123.098349508062 \tabularnewline
18 & 2008 & 1933.44234512880 & 74.5576548712037 \tabularnewline
19 & 2242 & 2174.91898236833 & 67.0810176316715 \tabularnewline
20 & 2478 & 2325.29352321490 & 152.706476785095 \tabularnewline
21 & 2030 & 1938.76961323212 & 91.2303867678834 \tabularnewline
22 & 1655 & 1765.88233555681 & -110.882335556812 \tabularnewline
23 & 1693 & 1756.15154417936 & -63.1515441793628 \tabularnewline
24 & 1623 & 1611.08385582396 & 11.9161441760431 \tabularnewline
25 & 1805 & 1748.88818236471 & 56.111817635295 \tabularnewline
26 & 1746 & 1711.35652601663 & 34.6434739833669 \tabularnewline
27 & 1795 & 1794.54083104077 & 0.459168959230808 \tabularnewline
28 & 1926 & 1801.61589410062 & 124.384105899383 \tabularnewline
29 & 1619 & 1896.55705498525 & -277.557054985246 \tabularnewline
30 & 1992 & 1914.68092209777 & 77.3190779022267 \tabularnewline
31 & 2233 & 2146.38102192627 & 86.6189780737299 \tabularnewline
32 & 2192 & 2310.68966862782 & -118.689668627819 \tabularnewline
33 & 2080 & 1812.41996106494 & 267.580038935061 \tabularnewline
34 & 1768 & 1725.87400800889 & 42.1259919911072 \tabularnewline
35 & 1835 & 1789.02104829106 & 45.9789517089369 \tabularnewline
36 & 1569 & 1668.39539165884 & -99.395391658839 \tabularnewline
37 & 1976 & 1748.99961649398 & 227.000383506021 \tabularnewline
38 & 1853 & 1768.58424679493 & 84.4157532050709 \tabularnewline
39 & 1965 & 1859.6686878276 & 105.331312172398 \tabularnewline
40 & 1689 & 1881.38264181579 & -192.382641815791 \tabularnewline
41 & 1778 & 1834.25642091437 & -56.2564209143736 \tabularnewline
42 & 1976 & 1936.41706038397 & 39.5829396160272 \tabularnewline
43 & 2397 & 2155.91012375939 & 241.089876240609 \tabularnewline
44 & 2654 & 2359.95124516331 & 294.048754836687 \tabularnewline
45 & 2097 & 2016.66851390730 & 80.3314860927041 \tabularnewline
46 & 1963 & 1808.42253608640 & 154.577463913597 \tabularnewline
47 & 1677 & 1881.18103223541 & -204.181032235406 \tabularnewline
48 & 1941 & 1648.28713329548 & 292.712866704525 \tabularnewline
49 & 2003 & 1865.10438492295 & 137.895615077045 \tabularnewline
50 & 1813 & 1835.39005884965 & -22.3900588496519 \tabularnewline
51 & 2012 & 1847.30935953032 & 164.690640469680 \tabularnewline
52 & 1912 & 1897.75014618983 & 14.2498538101749 \tabularnewline
53 & 2084 & 1919.60211461405 & 164.397885385949 \tabularnewline
54 & 2080 & 2085.26345425645 & -5.26345425644624 \tabularnewline
55 & 2118 & 2249.77741355291 & -131.777413552913 \tabularnewline
56 & 2150 & 2278.88093775439 & -128.88093775439 \tabularnewline
57 & 1608 & 1777.43171810334 & -169.431718103336 \tabularnewline
58 & 1503 & 1515.23358132895 & -12.2335813289512 \tabularnewline
59 & 1548 & 1580.21232036230 & -32.2123203622983 \tabularnewline
60 & 1382 & 1473.89374740365 & -91.8937474036534 \tabularnewline
61 & 1731 & 1581.86596635789 & 149.134033642112 \tabularnewline
62 & 1798 & 1602.66786308097 & 195.332136919032 \tabularnewline
63 & 1779 & 1760.60182556030 & 18.3981744397026 \tabularnewline
64 & 1887 & 1765.53747969497 & 121.462520305033 \tabularnewline
65 & 2004 & 1850.22234819798 & 153.777651802018 \tabularnewline
66 & 2077 & 2031.650181635 & 45.3498183649984 \tabularnewline
67 & 2092 & 2216.42253234963 & -124.422532349626 \tabularnewline
68 & 2051 & 2255.26072611289 & -204.260726112894 \tabularnewline
69 & 1577 & 1722.20534241639 & -145.205342416386 \tabularnewline
70 & 1356 & 1475.80495707021 & -119.804957070208 \tabularnewline
71 & 1652 & 1503.87767868906 & 148.122321310936 \tabularnewline
72 & 1382 & 1475.20589691184 & -93.2058969118374 \tabularnewline
73 & 1519 & 1584.23884499268 & -65.2388449926761 \tabularnewline
74 & 1421 & 1509.36203702842 & -88.3620370284215 \tabularnewline
75 & 1442 & 1571.53215783841 & -129.532157838408 \tabularnewline
76 & 1543 & 1552.24698900862 & -9.2469890086225 \tabularnewline
77 & 1656 & 1627.6611904321 & 28.3388095679010 \tabularnewline
78 & 1561 & 1801.90752350292 & -240.907523502917 \tabularnewline
79 & 1905 & 1921.67822434894 & -16.6782243489416 \tabularnewline
80 & 2199 & 2059.33366324247 & 139.666336757527 \tabularnewline
81 & 1473 & 1707.48764194518 & -234.487641945176 \tabularnewline
82 & 1655 & 1426.60519833885 & 228.394801661146 \tabularnewline
83 & 1407 & 1591.93317645622 & -184.933176456224 \tabularnewline
84 & 1395 & 1423.66619280166 & -28.666192801664 \tabularnewline
85 & 1530 & 1544.84531939797 & -14.8453193979652 \tabularnewline
86 & 1309 & 1508.48385299161 & -199.483852991607 \tabularnewline
87 & 1526 & 1512.35347250029 & 13.6465274997104 \tabularnewline
88 & 1327 & 1557.29179207403 & -230.29179207403 \tabularnewline
89 & 1627 & 1545.14203798027 & 81.8579620197308 \tabularnewline
90 & 1748 & 1743.13918234691 & 4.86081765309311 \tabularnewline
91 & 1958 & 1975.55903421944 & -17.5590342194350 \tabularnewline
92 & 2274 & 2094.38121684803 & 179.618783151966 \tabularnewline
93 & 1648 & 1742.36313781567 & -94.3631378156688 \tabularnewline
94 & 1401 & 1507.09763957023 & -106.097639570226 \tabularnewline
95 & 1411 & 1518.53497917642 & -107.534979176420 \tabularnewline
96 & 1403 & 1381.09999316942 & 21.9000068305806 \tabularnewline
97 & 1394 & 1536.52211379621 & -142.522113796213 \tabularnewline
98 & 1520 & 1438.79024925833 & 81.2097507416726 \tabularnewline
99 & 1528 & 1562.32381541536 & -34.3238154153577 \tabularnewline
100 & 1643 & 1580.70435623819 & 62.2956437618072 \tabularnewline
101 & 1515 & 1662.20604424803 & -147.206044248034 \tabularnewline
102 & 1685 & 1752.41797116605 & -67.4179711660492 \tabularnewline
103 & 2000 & 1933.88758479205 & 66.1124152079459 \tabularnewline
104 & 2215 & 2097.84329539812 & 117.156704601882 \tabularnewline
105 & 1956 & 1711.17967291030 & 244.820327089704 \tabularnewline
106 & 1462 & 1607.01970830184 & -145.019708301844 \tabularnewline
107 & 1563 & 1585.67143386851 & -22.6714338685113 \tabularnewline
108 & 1459 & 1450.65469402387 & 8.34530597612882 \tabularnewline
109 & 1446 & 1588.51325437786 & -142.513254377861 \tabularnewline
110 & 1622 & 1467.15307073082 & 154.846929269182 \tabularnewline
111 & 1657 & 1609.25859178336 & 47.7414082166392 \tabularnewline
112 & 1638 & 1643.25010190300 & -5.25010190300413 \tabularnewline
113 & 1643 & 1678.84511166992 & -35.8451116699208 \tabularnewline
114 & 1683 & 1800.49248710446 & -117.492487104458 \tabularnewline
115 & 2050 & 1950.40659656898 & 99.5934034310243 \tabularnewline
116 & 2262 & 2112.20494866562 & 149.795051334379 \tabularnewline
117 & 1813 & 1733.39621684739 & 79.603783152614 \tabularnewline
118 & 1445 & 1551.92529014615 & -106.925290146154 \tabularnewline
119 & 1762 & 1548.60387736679 & 213.396122633205 \tabularnewline
120 & 1461 & 1512.59781545509 & -51.5978154550942 \tabularnewline
121 & 1556 & 1608.77677723080 & -52.7767772308038 \tabularnewline
122 & 1431 & 1507.71784702171 & -76.7178470217067 \tabularnewline
123 & 1427 & 1551.74044352525 & -124.740443525246 \tabularnewline
124 & 1554 & 1515.43841759100 & 38.5615824089955 \tabularnewline
125 & 1645 & 1594.20759210287 & 50.7924078971276 \tabularnewline
126 & 1653 & 1762.51384112206 & -109.513841122060 \tabularnewline
127 & 2016 & 1918.67688835594 & 97.3231116440577 \tabularnewline
128 & 2207 & 2081.65921302252 & 125.340786977475 \tabularnewline
129 & 1665 & 1695.87424091647 & -30.8742409164725 \tabularnewline
130 & 1361 & 1473.25083782429 & -112.250837824287 \tabularnewline
131 & 1506 & 1477.91295017924 & 28.0870498207617 \tabularnewline
132 & 1360 & 1381.83588420274 & -21.8358842027445 \tabularnewline
133 & 1453 & 1507.72013366447 & -54.7201336644692 \tabularnewline
134 & 1522 & 1428.73921879260 & 93.2607812073961 \tabularnewline
135 & 1460 & 1547.50079551997 & -87.500795519974 \tabularnewline
136 & 1552 & 1527.52711178367 & 24.4728882163302 \tabularnewline
137 & 1548 & 1589.54175798733 & -41.541757987332 \tabularnewline
138 & 1827 & 1719.57128990206 & 107.428710097945 \tabularnewline
139 & 1737 & 1961.63173318646 & -224.631733186458 \tabularnewline
140 & 1941 & 1990.20946603109 & -49.2094660310937 \tabularnewline
141 & 1474 & 1537.62207214042 & -63.6220721404223 \tabularnewline
142 & 1458 & 1339.79776461945 & 118.202235380549 \tabularnewline
143 & 1542 & 1454.06346862288 & 87.9365313771192 \tabularnewline
144 & 1404 & 1387.11427133709 & 16.8857286629086 \tabularnewline
145 & 1522 & 1518.59570814678 & 3.40429185321661 \tabularnewline
146 & 1385 & 1458.76081366389 & -73.7608136638874 \tabularnewline
147 & 1641 & 1499.67673180735 & 141.323268192647 \tabularnewline
148 & 1510 & 1569.24424890606 & -59.2442489060646 \tabularnewline
149 & 1681 & 1592.75107626608 & 88.2489237339183 \tabularnewline
150 & 1938 & 1756.59737733347 & 181.402622666532 \tabularnewline
151 & 1868 & 2023.56965707282 & -155.569657072819 \tabularnewline
152 & 1726 & 2054.52616623431 & -328.526166234309 \tabularnewline
153 & 1456 & 1475.95579307600 & -19.9557930759967 \tabularnewline
154 & 1445 & 1295.08801076141 & 149.911989238589 \tabularnewline
155 & 1456 & 1433.59747286645 & 22.4025271335545 \tabularnewline
156 & 1365 & 1333.87292837842 & 31.1270716215838 \tabularnewline
157 & 1487 & 1478.22998505480 & 8.7700149451982 \tabularnewline
158 & 1558 & 1425.62462238887 & 132.375377611128 \tabularnewline
159 & 1488 & 1547.40003525709 & -59.4000352570887 \tabularnewline
160 & 1684 & 1527.64713140737 & 156.352868592634 \tabularnewline
161 & 1594 & 1629.91415873356 & -35.91415873356 \tabularnewline
162 & 1850 & 1744.17200719029 & 105.827992809706 \tabularnewline
163 & 1998 & 1965.86971199383 & 32.1302880061727 \tabularnewline
164 & 2079 & 2083.83085047994 & -4.83085047993892 \tabularnewline
165 & 1494 & 1620.53593961774 & -126.535939617738 \tabularnewline
166 & 1057 & 1282.42002959262 & -225.420029592616 \tabularnewline
167 & 1218 & 1216.23831947170 & 1.76168052830480 \tabularnewline
168 & 1168 & 1097.97756781338 & 70.0224321866176 \tabularnewline
169 & 1236 & 1257.73397513645 & -21.7339751364453 \tabularnewline
170 & 1076 & 1181.44940494181 & -105.449404941814 \tabularnewline
171 & 1174 & 1210.65987850872 & -36.659878508723 \tabularnewline
172 & 1139 & 1215.99763169937 & -76.9976316993702 \tabularnewline
173 & 1427 & 1247.02951853790 & 179.970481462103 \tabularnewline
174 & 1487 & 1466.56103312436 & 20.4389668756409 \tabularnewline
175 & 1483 & 1674.81015033969 & -191.810150339689 \tabularnewline
176 & 1513 & 1705.07483715508 & -192.074837155084 \tabularnewline
177 & 1357 & 1202.00684492392 & 154.993155076085 \tabularnewline
178 & 1165 & 1090.66552329252 & 74.334476707483 \tabularnewline
179 & 1282 & 1186.84646146751 & 95.1535385324919 \tabularnewline
180 & 1110 & 1107.37106616546 & 2.62893383453497 \tabularnewline
181 & 1297 & 1236.00217977381 & 60.9978202261871 \tabularnewline
182 & 1185 & 1192.13200682666 & -7.13200682666083 \tabularnewline
183 & 1222 & 1254.65743423875 & -32.6574342387546 \tabularnewline
184 & 1284 & 1244.46348264055 & 39.5365173594474 \tabularnewline
185 & 1444 & 1308.71095932536 & 135.289040674639 \tabularnewline
186 & 1575 & 1494.61798328985 & 80.3820167101494 \tabularnewline
187 & 1737 & 1709.75489560078 & 27.2451043992194 \tabularnewline
188 & 1763 & 1815.81881613375 & -52.8188161337465 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1632[/C][C]1624.96216487031[/C][C]7.03783512969226[/C][/ROW]
[ROW][C]2[/C][C]1511[/C][C]1609.33124673722[/C][C]-98.3312467372175[/C][/ROW]
[ROW][C]3[/C][C]1559[/C][C]1676.32540114441[/C][C]-117.325401144406[/C][/ROW]
[ROW][C]4[/C][C]1630[/C][C]1672.79480330307[/C][C]-42.7948033030729[/C][/ROW]
[ROW][C]5[/C][C]1579[/C][C]1744.25426449686[/C][C]-165.254264496858[/C][/ROW]
[ROW][C]6[/C][C]1653[/C][C]1849.55534041559[/C][C]-196.555340415592[/C][/ROW]
[ROW][C]7[/C][C]2152[/C][C]2006.74544956455[/C][C]145.254550435451[/C][/ROW]
[ROW][C]8[/C][C]2148[/C][C]2227.04142591574[/C][C]-79.0414259157364[/C][/ROW]
[ROW][C]9[/C][C]1752[/C][C]1786.08329108286[/C][C]-34.0832910828553[/C][/ROW]
[ROW][C]10[/C][C]1765[/C][C]1593.91257950137[/C][C]171.087420498627[/C][/ROW]
[ROW][C]11[/C][C]1717[/C][C]1745.15423676709[/C][C]-28.1542367670882[/C][/ROW]
[ROW][C]12[/C][C]1558[/C][C]1626.94356155909[/C][C]-68.9435615590894[/C][/ROW]
[ROW][C]13[/C][C]1575[/C][C]1731.00139341833[/C][C]-156.001393418334[/C][/ROW]
[ROW][C]14[/C][C]1520[/C][C]1624.45693487588[/C][C]-104.456934875882[/C][/ROW]
[ROW][C]15[/C][C]1805[/C][C]1674.45053850205[/C][C]130.549461497950[/C][/ROW]
[ROW][C]16[/C][C]1800[/C][C]1765.10777164385[/C][C]34.8922283561497[/C][/ROW]
[ROW][C]17[/C][C]1719[/C][C]1842.09834950806[/C][C]-123.098349508062[/C][/ROW]
[ROW][C]18[/C][C]2008[/C][C]1933.44234512880[/C][C]74.5576548712037[/C][/ROW]
[ROW][C]19[/C][C]2242[/C][C]2174.91898236833[/C][C]67.0810176316715[/C][/ROW]
[ROW][C]20[/C][C]2478[/C][C]2325.29352321490[/C][C]152.706476785095[/C][/ROW]
[ROW][C]21[/C][C]2030[/C][C]1938.76961323212[/C][C]91.2303867678834[/C][/ROW]
[ROW][C]22[/C][C]1655[/C][C]1765.88233555681[/C][C]-110.882335556812[/C][/ROW]
[ROW][C]23[/C][C]1693[/C][C]1756.15154417936[/C][C]-63.1515441793628[/C][/ROW]
[ROW][C]24[/C][C]1623[/C][C]1611.08385582396[/C][C]11.9161441760431[/C][/ROW]
[ROW][C]25[/C][C]1805[/C][C]1748.88818236471[/C][C]56.111817635295[/C][/ROW]
[ROW][C]26[/C][C]1746[/C][C]1711.35652601663[/C][C]34.6434739833669[/C][/ROW]
[ROW][C]27[/C][C]1795[/C][C]1794.54083104077[/C][C]0.459168959230808[/C][/ROW]
[ROW][C]28[/C][C]1926[/C][C]1801.61589410062[/C][C]124.384105899383[/C][/ROW]
[ROW][C]29[/C][C]1619[/C][C]1896.55705498525[/C][C]-277.557054985246[/C][/ROW]
[ROW][C]30[/C][C]1992[/C][C]1914.68092209777[/C][C]77.3190779022267[/C][/ROW]
[ROW][C]31[/C][C]2233[/C][C]2146.38102192627[/C][C]86.6189780737299[/C][/ROW]
[ROW][C]32[/C][C]2192[/C][C]2310.68966862782[/C][C]-118.689668627819[/C][/ROW]
[ROW][C]33[/C][C]2080[/C][C]1812.41996106494[/C][C]267.580038935061[/C][/ROW]
[ROW][C]34[/C][C]1768[/C][C]1725.87400800889[/C][C]42.1259919911072[/C][/ROW]
[ROW][C]35[/C][C]1835[/C][C]1789.02104829106[/C][C]45.9789517089369[/C][/ROW]
[ROW][C]36[/C][C]1569[/C][C]1668.39539165884[/C][C]-99.395391658839[/C][/ROW]
[ROW][C]37[/C][C]1976[/C][C]1748.99961649398[/C][C]227.000383506021[/C][/ROW]
[ROW][C]38[/C][C]1853[/C][C]1768.58424679493[/C][C]84.4157532050709[/C][/ROW]
[ROW][C]39[/C][C]1965[/C][C]1859.6686878276[/C][C]105.331312172398[/C][/ROW]
[ROW][C]40[/C][C]1689[/C][C]1881.38264181579[/C][C]-192.382641815791[/C][/ROW]
[ROW][C]41[/C][C]1778[/C][C]1834.25642091437[/C][C]-56.2564209143736[/C][/ROW]
[ROW][C]42[/C][C]1976[/C][C]1936.41706038397[/C][C]39.5829396160272[/C][/ROW]
[ROW][C]43[/C][C]2397[/C][C]2155.91012375939[/C][C]241.089876240609[/C][/ROW]
[ROW][C]44[/C][C]2654[/C][C]2359.95124516331[/C][C]294.048754836687[/C][/ROW]
[ROW][C]45[/C][C]2097[/C][C]2016.66851390730[/C][C]80.3314860927041[/C][/ROW]
[ROW][C]46[/C][C]1963[/C][C]1808.42253608640[/C][C]154.577463913597[/C][/ROW]
[ROW][C]47[/C][C]1677[/C][C]1881.18103223541[/C][C]-204.181032235406[/C][/ROW]
[ROW][C]48[/C][C]1941[/C][C]1648.28713329548[/C][C]292.712866704525[/C][/ROW]
[ROW][C]49[/C][C]2003[/C][C]1865.10438492295[/C][C]137.895615077045[/C][/ROW]
[ROW][C]50[/C][C]1813[/C][C]1835.39005884965[/C][C]-22.3900588496519[/C][/ROW]
[ROW][C]51[/C][C]2012[/C][C]1847.30935953032[/C][C]164.690640469680[/C][/ROW]
[ROW][C]52[/C][C]1912[/C][C]1897.75014618983[/C][C]14.2498538101749[/C][/ROW]
[ROW][C]53[/C][C]2084[/C][C]1919.60211461405[/C][C]164.397885385949[/C][/ROW]
[ROW][C]54[/C][C]2080[/C][C]2085.26345425645[/C][C]-5.26345425644624[/C][/ROW]
[ROW][C]55[/C][C]2118[/C][C]2249.77741355291[/C][C]-131.777413552913[/C][/ROW]
[ROW][C]56[/C][C]2150[/C][C]2278.88093775439[/C][C]-128.88093775439[/C][/ROW]
[ROW][C]57[/C][C]1608[/C][C]1777.43171810334[/C][C]-169.431718103336[/C][/ROW]
[ROW][C]58[/C][C]1503[/C][C]1515.23358132895[/C][C]-12.2335813289512[/C][/ROW]
[ROW][C]59[/C][C]1548[/C][C]1580.21232036230[/C][C]-32.2123203622983[/C][/ROW]
[ROW][C]60[/C][C]1382[/C][C]1473.89374740365[/C][C]-91.8937474036534[/C][/ROW]
[ROW][C]61[/C][C]1731[/C][C]1581.86596635789[/C][C]149.134033642112[/C][/ROW]
[ROW][C]62[/C][C]1798[/C][C]1602.66786308097[/C][C]195.332136919032[/C][/ROW]
[ROW][C]63[/C][C]1779[/C][C]1760.60182556030[/C][C]18.3981744397026[/C][/ROW]
[ROW][C]64[/C][C]1887[/C][C]1765.53747969497[/C][C]121.462520305033[/C][/ROW]
[ROW][C]65[/C][C]2004[/C][C]1850.22234819798[/C][C]153.777651802018[/C][/ROW]
[ROW][C]66[/C][C]2077[/C][C]2031.650181635[/C][C]45.3498183649984[/C][/ROW]
[ROW][C]67[/C][C]2092[/C][C]2216.42253234963[/C][C]-124.422532349626[/C][/ROW]
[ROW][C]68[/C][C]2051[/C][C]2255.26072611289[/C][C]-204.260726112894[/C][/ROW]
[ROW][C]69[/C][C]1577[/C][C]1722.20534241639[/C][C]-145.205342416386[/C][/ROW]
[ROW][C]70[/C][C]1356[/C][C]1475.80495707021[/C][C]-119.804957070208[/C][/ROW]
[ROW][C]71[/C][C]1652[/C][C]1503.87767868906[/C][C]148.122321310936[/C][/ROW]
[ROW][C]72[/C][C]1382[/C][C]1475.20589691184[/C][C]-93.2058969118374[/C][/ROW]
[ROW][C]73[/C][C]1519[/C][C]1584.23884499268[/C][C]-65.2388449926761[/C][/ROW]
[ROW][C]74[/C][C]1421[/C][C]1509.36203702842[/C][C]-88.3620370284215[/C][/ROW]
[ROW][C]75[/C][C]1442[/C][C]1571.53215783841[/C][C]-129.532157838408[/C][/ROW]
[ROW][C]76[/C][C]1543[/C][C]1552.24698900862[/C][C]-9.2469890086225[/C][/ROW]
[ROW][C]77[/C][C]1656[/C][C]1627.6611904321[/C][C]28.3388095679010[/C][/ROW]
[ROW][C]78[/C][C]1561[/C][C]1801.90752350292[/C][C]-240.907523502917[/C][/ROW]
[ROW][C]79[/C][C]1905[/C][C]1921.67822434894[/C][C]-16.6782243489416[/C][/ROW]
[ROW][C]80[/C][C]2199[/C][C]2059.33366324247[/C][C]139.666336757527[/C][/ROW]
[ROW][C]81[/C][C]1473[/C][C]1707.48764194518[/C][C]-234.487641945176[/C][/ROW]
[ROW][C]82[/C][C]1655[/C][C]1426.60519833885[/C][C]228.394801661146[/C][/ROW]
[ROW][C]83[/C][C]1407[/C][C]1591.93317645622[/C][C]-184.933176456224[/C][/ROW]
[ROW][C]84[/C][C]1395[/C][C]1423.66619280166[/C][C]-28.666192801664[/C][/ROW]
[ROW][C]85[/C][C]1530[/C][C]1544.84531939797[/C][C]-14.8453193979652[/C][/ROW]
[ROW][C]86[/C][C]1309[/C][C]1508.48385299161[/C][C]-199.483852991607[/C][/ROW]
[ROW][C]87[/C][C]1526[/C][C]1512.35347250029[/C][C]13.6465274997104[/C][/ROW]
[ROW][C]88[/C][C]1327[/C][C]1557.29179207403[/C][C]-230.29179207403[/C][/ROW]
[ROW][C]89[/C][C]1627[/C][C]1545.14203798027[/C][C]81.8579620197308[/C][/ROW]
[ROW][C]90[/C][C]1748[/C][C]1743.13918234691[/C][C]4.86081765309311[/C][/ROW]
[ROW][C]91[/C][C]1958[/C][C]1975.55903421944[/C][C]-17.5590342194350[/C][/ROW]
[ROW][C]92[/C][C]2274[/C][C]2094.38121684803[/C][C]179.618783151966[/C][/ROW]
[ROW][C]93[/C][C]1648[/C][C]1742.36313781567[/C][C]-94.3631378156688[/C][/ROW]
[ROW][C]94[/C][C]1401[/C][C]1507.09763957023[/C][C]-106.097639570226[/C][/ROW]
[ROW][C]95[/C][C]1411[/C][C]1518.53497917642[/C][C]-107.534979176420[/C][/ROW]
[ROW][C]96[/C][C]1403[/C][C]1381.09999316942[/C][C]21.9000068305806[/C][/ROW]
[ROW][C]97[/C][C]1394[/C][C]1536.52211379621[/C][C]-142.522113796213[/C][/ROW]
[ROW][C]98[/C][C]1520[/C][C]1438.79024925833[/C][C]81.2097507416726[/C][/ROW]
[ROW][C]99[/C][C]1528[/C][C]1562.32381541536[/C][C]-34.3238154153577[/C][/ROW]
[ROW][C]100[/C][C]1643[/C][C]1580.70435623819[/C][C]62.2956437618072[/C][/ROW]
[ROW][C]101[/C][C]1515[/C][C]1662.20604424803[/C][C]-147.206044248034[/C][/ROW]
[ROW][C]102[/C][C]1685[/C][C]1752.41797116605[/C][C]-67.4179711660492[/C][/ROW]
[ROW][C]103[/C][C]2000[/C][C]1933.88758479205[/C][C]66.1124152079459[/C][/ROW]
[ROW][C]104[/C][C]2215[/C][C]2097.84329539812[/C][C]117.156704601882[/C][/ROW]
[ROW][C]105[/C][C]1956[/C][C]1711.17967291030[/C][C]244.820327089704[/C][/ROW]
[ROW][C]106[/C][C]1462[/C][C]1607.01970830184[/C][C]-145.019708301844[/C][/ROW]
[ROW][C]107[/C][C]1563[/C][C]1585.67143386851[/C][C]-22.6714338685113[/C][/ROW]
[ROW][C]108[/C][C]1459[/C][C]1450.65469402387[/C][C]8.34530597612882[/C][/ROW]
[ROW][C]109[/C][C]1446[/C][C]1588.51325437786[/C][C]-142.513254377861[/C][/ROW]
[ROW][C]110[/C][C]1622[/C][C]1467.15307073082[/C][C]154.846929269182[/C][/ROW]
[ROW][C]111[/C][C]1657[/C][C]1609.25859178336[/C][C]47.7414082166392[/C][/ROW]
[ROW][C]112[/C][C]1638[/C][C]1643.25010190300[/C][C]-5.25010190300413[/C][/ROW]
[ROW][C]113[/C][C]1643[/C][C]1678.84511166992[/C][C]-35.8451116699208[/C][/ROW]
[ROW][C]114[/C][C]1683[/C][C]1800.49248710446[/C][C]-117.492487104458[/C][/ROW]
[ROW][C]115[/C][C]2050[/C][C]1950.40659656898[/C][C]99.5934034310243[/C][/ROW]
[ROW][C]116[/C][C]2262[/C][C]2112.20494866562[/C][C]149.795051334379[/C][/ROW]
[ROW][C]117[/C][C]1813[/C][C]1733.39621684739[/C][C]79.603783152614[/C][/ROW]
[ROW][C]118[/C][C]1445[/C][C]1551.92529014615[/C][C]-106.925290146154[/C][/ROW]
[ROW][C]119[/C][C]1762[/C][C]1548.60387736679[/C][C]213.396122633205[/C][/ROW]
[ROW][C]120[/C][C]1461[/C][C]1512.59781545509[/C][C]-51.5978154550942[/C][/ROW]
[ROW][C]121[/C][C]1556[/C][C]1608.77677723080[/C][C]-52.7767772308038[/C][/ROW]
[ROW][C]122[/C][C]1431[/C][C]1507.71784702171[/C][C]-76.7178470217067[/C][/ROW]
[ROW][C]123[/C][C]1427[/C][C]1551.74044352525[/C][C]-124.740443525246[/C][/ROW]
[ROW][C]124[/C][C]1554[/C][C]1515.43841759100[/C][C]38.5615824089955[/C][/ROW]
[ROW][C]125[/C][C]1645[/C][C]1594.20759210287[/C][C]50.7924078971276[/C][/ROW]
[ROW][C]126[/C][C]1653[/C][C]1762.51384112206[/C][C]-109.513841122060[/C][/ROW]
[ROW][C]127[/C][C]2016[/C][C]1918.67688835594[/C][C]97.3231116440577[/C][/ROW]
[ROW][C]128[/C][C]2207[/C][C]2081.65921302252[/C][C]125.340786977475[/C][/ROW]
[ROW][C]129[/C][C]1665[/C][C]1695.87424091647[/C][C]-30.8742409164725[/C][/ROW]
[ROW][C]130[/C][C]1361[/C][C]1473.25083782429[/C][C]-112.250837824287[/C][/ROW]
[ROW][C]131[/C][C]1506[/C][C]1477.91295017924[/C][C]28.0870498207617[/C][/ROW]
[ROW][C]132[/C][C]1360[/C][C]1381.83588420274[/C][C]-21.8358842027445[/C][/ROW]
[ROW][C]133[/C][C]1453[/C][C]1507.72013366447[/C][C]-54.7201336644692[/C][/ROW]
[ROW][C]134[/C][C]1522[/C][C]1428.73921879260[/C][C]93.2607812073961[/C][/ROW]
[ROW][C]135[/C][C]1460[/C][C]1547.50079551997[/C][C]-87.500795519974[/C][/ROW]
[ROW][C]136[/C][C]1552[/C][C]1527.52711178367[/C][C]24.4728882163302[/C][/ROW]
[ROW][C]137[/C][C]1548[/C][C]1589.54175798733[/C][C]-41.541757987332[/C][/ROW]
[ROW][C]138[/C][C]1827[/C][C]1719.57128990206[/C][C]107.428710097945[/C][/ROW]
[ROW][C]139[/C][C]1737[/C][C]1961.63173318646[/C][C]-224.631733186458[/C][/ROW]
[ROW][C]140[/C][C]1941[/C][C]1990.20946603109[/C][C]-49.2094660310937[/C][/ROW]
[ROW][C]141[/C][C]1474[/C][C]1537.62207214042[/C][C]-63.6220721404223[/C][/ROW]
[ROW][C]142[/C][C]1458[/C][C]1339.79776461945[/C][C]118.202235380549[/C][/ROW]
[ROW][C]143[/C][C]1542[/C][C]1454.06346862288[/C][C]87.9365313771192[/C][/ROW]
[ROW][C]144[/C][C]1404[/C][C]1387.11427133709[/C][C]16.8857286629086[/C][/ROW]
[ROW][C]145[/C][C]1522[/C][C]1518.59570814678[/C][C]3.40429185321661[/C][/ROW]
[ROW][C]146[/C][C]1385[/C][C]1458.76081366389[/C][C]-73.7608136638874[/C][/ROW]
[ROW][C]147[/C][C]1641[/C][C]1499.67673180735[/C][C]141.323268192647[/C][/ROW]
[ROW][C]148[/C][C]1510[/C][C]1569.24424890606[/C][C]-59.2442489060646[/C][/ROW]
[ROW][C]149[/C][C]1681[/C][C]1592.75107626608[/C][C]88.2489237339183[/C][/ROW]
[ROW][C]150[/C][C]1938[/C][C]1756.59737733347[/C][C]181.402622666532[/C][/ROW]
[ROW][C]151[/C][C]1868[/C][C]2023.56965707282[/C][C]-155.569657072819[/C][/ROW]
[ROW][C]152[/C][C]1726[/C][C]2054.52616623431[/C][C]-328.526166234309[/C][/ROW]
[ROW][C]153[/C][C]1456[/C][C]1475.95579307600[/C][C]-19.9557930759967[/C][/ROW]
[ROW][C]154[/C][C]1445[/C][C]1295.08801076141[/C][C]149.911989238589[/C][/ROW]
[ROW][C]155[/C][C]1456[/C][C]1433.59747286645[/C][C]22.4025271335545[/C][/ROW]
[ROW][C]156[/C][C]1365[/C][C]1333.87292837842[/C][C]31.1270716215838[/C][/ROW]
[ROW][C]157[/C][C]1487[/C][C]1478.22998505480[/C][C]8.7700149451982[/C][/ROW]
[ROW][C]158[/C][C]1558[/C][C]1425.62462238887[/C][C]132.375377611128[/C][/ROW]
[ROW][C]159[/C][C]1488[/C][C]1547.40003525709[/C][C]-59.4000352570887[/C][/ROW]
[ROW][C]160[/C][C]1684[/C][C]1527.64713140737[/C][C]156.352868592634[/C][/ROW]
[ROW][C]161[/C][C]1594[/C][C]1629.91415873356[/C][C]-35.91415873356[/C][/ROW]
[ROW][C]162[/C][C]1850[/C][C]1744.17200719029[/C][C]105.827992809706[/C][/ROW]
[ROW][C]163[/C][C]1998[/C][C]1965.86971199383[/C][C]32.1302880061727[/C][/ROW]
[ROW][C]164[/C][C]2079[/C][C]2083.83085047994[/C][C]-4.83085047993892[/C][/ROW]
[ROW][C]165[/C][C]1494[/C][C]1620.53593961774[/C][C]-126.535939617738[/C][/ROW]
[ROW][C]166[/C][C]1057[/C][C]1282.42002959262[/C][C]-225.420029592616[/C][/ROW]
[ROW][C]167[/C][C]1218[/C][C]1216.23831947170[/C][C]1.76168052830480[/C][/ROW]
[ROW][C]168[/C][C]1168[/C][C]1097.97756781338[/C][C]70.0224321866176[/C][/ROW]
[ROW][C]169[/C][C]1236[/C][C]1257.73397513645[/C][C]-21.7339751364453[/C][/ROW]
[ROW][C]170[/C][C]1076[/C][C]1181.44940494181[/C][C]-105.449404941814[/C][/ROW]
[ROW][C]171[/C][C]1174[/C][C]1210.65987850872[/C][C]-36.659878508723[/C][/ROW]
[ROW][C]172[/C][C]1139[/C][C]1215.99763169937[/C][C]-76.9976316993702[/C][/ROW]
[ROW][C]173[/C][C]1427[/C][C]1247.02951853790[/C][C]179.970481462103[/C][/ROW]
[ROW][C]174[/C][C]1487[/C][C]1466.56103312436[/C][C]20.4389668756409[/C][/ROW]
[ROW][C]175[/C][C]1483[/C][C]1674.81015033969[/C][C]-191.810150339689[/C][/ROW]
[ROW][C]176[/C][C]1513[/C][C]1705.07483715508[/C][C]-192.074837155084[/C][/ROW]
[ROW][C]177[/C][C]1357[/C][C]1202.00684492392[/C][C]154.993155076085[/C][/ROW]
[ROW][C]178[/C][C]1165[/C][C]1090.66552329252[/C][C]74.334476707483[/C][/ROW]
[ROW][C]179[/C][C]1282[/C][C]1186.84646146751[/C][C]95.1535385324919[/C][/ROW]
[ROW][C]180[/C][C]1110[/C][C]1107.37106616546[/C][C]2.62893383453497[/C][/ROW]
[ROW][C]181[/C][C]1297[/C][C]1236.00217977381[/C][C]60.9978202261871[/C][/ROW]
[ROW][C]182[/C][C]1185[/C][C]1192.13200682666[/C][C]-7.13200682666083[/C][/ROW]
[ROW][C]183[/C][C]1222[/C][C]1254.65743423875[/C][C]-32.6574342387546[/C][/ROW]
[ROW][C]184[/C][C]1284[/C][C]1244.46348264055[/C][C]39.5365173594474[/C][/ROW]
[ROW][C]185[/C][C]1444[/C][C]1308.71095932536[/C][C]135.289040674639[/C][/ROW]
[ROW][C]186[/C][C]1575[/C][C]1494.61798328985[/C][C]80.3820167101494[/C][/ROW]
[ROW][C]187[/C][C]1737[/C][C]1709.75489560078[/C][C]27.2451043992194[/C][/ROW]
[ROW][C]188[/C][C]1763[/C][C]1815.81881613375[/C][C]-52.8188161337465[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116321624.962164870317.03783512969226
215111609.33124673722-98.3312467372175
315591676.32540114441-117.325401144406
416301672.79480330307-42.7948033030729
515791744.25426449686-165.254264496858
616531849.55534041559-196.555340415592
721522006.74544956455145.254550435451
821482227.04142591574-79.0414259157364
917521786.08329108286-34.0832910828553
1017651593.91257950137171.087420498627
1117171745.15423676709-28.1542367670882
1215581626.94356155909-68.9435615590894
1315751731.00139341833-156.001393418334
1415201624.45693487588-104.456934875882
1518051674.45053850205130.549461497950
1618001765.1077716438534.8922283561497
1717191842.09834950806-123.098349508062
1820081933.4423451288074.5576548712037
1922422174.9189823683367.0810176316715
2024782325.29352321490152.706476785095
2120301938.7696132321291.2303867678834
2216551765.88233555681-110.882335556812
2316931756.15154417936-63.1515441793628
2416231611.0838558239611.9161441760431
2518051748.8881823647156.111817635295
2617461711.3565260166334.6434739833669
2717951794.540831040770.459168959230808
2819261801.61589410062124.384105899383
2916191896.55705498525-277.557054985246
3019921914.6809220977777.3190779022267
3122332146.3810219262786.6189780737299
3221922310.68966862782-118.689668627819
3320801812.41996106494267.580038935061
3417681725.8740080088942.1259919911072
3518351789.0210482910645.9789517089369
3615691668.39539165884-99.395391658839
3719761748.99961649398227.000383506021
3818531768.5842467949384.4157532050709
3919651859.6686878276105.331312172398
4016891881.38264181579-192.382641815791
4117781834.25642091437-56.2564209143736
4219761936.4170603839739.5829396160272
4323972155.91012375939241.089876240609
4426542359.95124516331294.048754836687
4520972016.6685139073080.3314860927041
4619631808.42253608640154.577463913597
4716771881.18103223541-204.181032235406
4819411648.28713329548292.712866704525
4920031865.10438492295137.895615077045
5018131835.39005884965-22.3900588496519
5120121847.30935953032164.690640469680
5219121897.7501461898314.2498538101749
5320841919.60211461405164.397885385949
5420802085.26345425645-5.26345425644624
5521182249.77741355291-131.777413552913
5621502278.88093775439-128.88093775439
5716081777.43171810334-169.431718103336
5815031515.23358132895-12.2335813289512
5915481580.21232036230-32.2123203622983
6013821473.89374740365-91.8937474036534
6117311581.86596635789149.134033642112
6217981602.66786308097195.332136919032
6317791760.6018255603018.3981744397026
6418871765.53747969497121.462520305033
6520041850.22234819798153.777651802018
6620772031.65018163545.3498183649984
6720922216.42253234963-124.422532349626
6820512255.26072611289-204.260726112894
6915771722.20534241639-145.205342416386
7013561475.80495707021-119.804957070208
7116521503.87767868906148.122321310936
7213821475.20589691184-93.2058969118374
7315191584.23884499268-65.2388449926761
7414211509.36203702842-88.3620370284215
7514421571.53215783841-129.532157838408
7615431552.24698900862-9.2469890086225
7716561627.661190432128.3388095679010
7815611801.90752350292-240.907523502917
7919051921.67822434894-16.6782243489416
8021992059.33366324247139.666336757527
8114731707.48764194518-234.487641945176
8216551426.60519833885228.394801661146
8314071591.93317645622-184.933176456224
8413951423.66619280166-28.666192801664
8515301544.84531939797-14.8453193979652
8613091508.48385299161-199.483852991607
8715261512.3534725002913.6465274997104
8813271557.29179207403-230.29179207403
8916271545.1420379802781.8579620197308
9017481743.139182346914.86081765309311
9119581975.55903421944-17.5590342194350
9222742094.38121684803179.618783151966
9316481742.36313781567-94.3631378156688
9414011507.09763957023-106.097639570226
9514111518.53497917642-107.534979176420
9614031381.0999931694221.9000068305806
9713941536.52211379621-142.522113796213
9815201438.7902492583381.2097507416726
9915281562.32381541536-34.3238154153577
10016431580.7043562381962.2956437618072
10115151662.20604424803-147.206044248034
10216851752.41797116605-67.4179711660492
10320001933.8875847920566.1124152079459
10422152097.84329539812117.156704601882
10519561711.17967291030244.820327089704
10614621607.01970830184-145.019708301844
10715631585.67143386851-22.6714338685113
10814591450.654694023878.34530597612882
10914461588.51325437786-142.513254377861
11016221467.15307073082154.846929269182
11116571609.2585917833647.7414082166392
11216381643.25010190300-5.25010190300413
11316431678.84511166992-35.8451116699208
11416831800.49248710446-117.492487104458
11520501950.4065965689899.5934034310243
11622622112.20494866562149.795051334379
11718131733.3962168473979.603783152614
11814451551.92529014615-106.925290146154
11917621548.60387736679213.396122633205
12014611512.59781545509-51.5978154550942
12115561608.77677723080-52.7767772308038
12214311507.71784702171-76.7178470217067
12314271551.74044352525-124.740443525246
12415541515.4384175910038.5615824089955
12516451594.2075921028750.7924078971276
12616531762.51384112206-109.513841122060
12720161918.6768883559497.3231116440577
12822072081.65921302252125.340786977475
12916651695.87424091647-30.8742409164725
13013611473.25083782429-112.250837824287
13115061477.9129501792428.0870498207617
13213601381.83588420274-21.8358842027445
13314531507.72013366447-54.7201336644692
13415221428.7392187926093.2607812073961
13514601547.50079551997-87.500795519974
13615521527.5271117836724.4728882163302
13715481589.54175798733-41.541757987332
13818271719.57128990206107.428710097945
13917371961.63173318646-224.631733186458
14019411990.20946603109-49.2094660310937
14114741537.62207214042-63.6220721404223
14214581339.79776461945118.202235380549
14315421454.0634686228887.9365313771192
14414041387.1142713370916.8857286629086
14515221518.595708146783.40429185321661
14613851458.76081366389-73.7608136638874
14716411499.67673180735141.323268192647
14815101569.24424890606-59.2442489060646
14916811592.7510762660888.2489237339183
15019381756.59737733347181.402622666532
15118682023.56965707282-155.569657072819
15217262054.52616623431-328.526166234309
15314561475.95579307600-19.9557930759967
15414451295.08801076141149.911989238589
15514561433.5974728664522.4025271335545
15613651333.8729283784231.1270716215838
15714871478.229985054808.7700149451982
15815581425.62462238887132.375377611128
15914881547.40003525709-59.4000352570887
16016841527.64713140737156.352868592634
16115941629.91415873356-35.91415873356
16218501744.17200719029105.827992809706
16319981965.8697119938332.1302880061727
16420792083.83085047994-4.83085047993892
16514941620.53593961774-126.535939617738
16610571282.42002959262-225.420029592616
16712181216.238319471701.76168052830480
16811681097.9775678133870.0224321866176
16912361257.73397513645-21.7339751364453
17010761181.44940494181-105.449404941814
17111741210.65987850872-36.659878508723
17211391215.99763169937-76.9976316993702
17314271247.02951853790179.970481462103
17414871466.5610331243620.4389668756409
17514831674.81015033969-191.810150339689
17615131705.07483715508-192.074837155084
17713571202.00684492392154.993155076085
17811651090.6655232925274.334476707483
17912821186.8464614675195.1535385324919
18011101107.371066165462.62893383453497
18112971236.0021797738160.9978202261871
18211851192.13200682666-7.13200682666083
18312221254.65743423875-32.6574342387546
18412841244.4634826405539.5365173594474
18514441308.71095932536135.289040674639
18615751494.6179832898580.3820167101494
18717371709.7548956007827.2451043992194
18817631815.81881613375-52.8188161337465







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.8016492668011580.3967014663976840.198350733198842
220.7228217753356120.5543564493287770.277178224664388
230.6785323575887340.6429352848225330.321467642411266
240.5679789227661470.8640421544677050.432021077233853
250.4490784278374440.8981568556748880.550921572162556
260.3686115901088470.7372231802176950.631388409891153
270.3344753676919530.6689507353839060.665524632308047
280.2534019305198960.5068038610397910.746598069480105
290.3846916391049420.7693832782098830.615308360895058
300.3034946659246770.6069893318493550.696505334075323
310.3156191791871610.6312383583743220.684380820812839
320.4108515903987280.8217031807974570.589148409601272
330.3675715824295520.7351431648591050.632428417570448
340.3084593527086260.6169187054172520.691540647291374
350.2451489067367430.4902978134734860.754851093263257
360.2898301591335210.5796603182670420.710169840866479
370.3175729398729160.6351458797458320.682427060127084
380.2748645219073150.549729043814630.725135478092685
390.2312150726641150.4624301453282310.768784927335885
400.3948002311630540.7896004623261070.605199768836946
410.3387799568673540.6775599137347090.661220043132646
420.2910086162552530.5820172325105050.708991383744747
430.2741187046947510.5482374093895020.725881295305249
440.372191420537360.744382841074720.62780857946264
450.3300695948350560.6601391896701120.669930405164944
460.3042311984943520.6084623969887030.695768801505648
470.3820380020461890.7640760040923790.61796199795381
480.4841639967727970.9683279935455940.515836003227203
490.4755416042674040.9510832085348090.524458395732596
500.4426660845942390.8853321691884780.557333915405761
510.4378597840646750.875719568129350.562140215935325
520.3965443556270430.7930887112540860.603455644372957
530.4639825133802650.927965026760530.536017486619735
540.4228629944859520.8457259889719030.577137005514048
550.6614372024275550.6771255951448910.338562797572445
560.8400852024202990.3198295951594020.159914797579701
570.9508020218811350.09839595623772960.0491979781188648
580.9500359044238080.09992819115238360.0499640955761918
590.936445100935460.1271097981290790.0635548990645394
600.9357217396241350.1285565207517300.0642782603758652
610.9341030660988460.1317938678023070.0658969339011536
620.949431706552320.1011365868953600.0505682934476802
630.9451727729023040.1096544541953920.0548272270976961
640.9477000952913480.1045998094173040.0522999047086518
650.965892226528350.06821554694329850.0341077734716493
660.9688295602158070.06234087956838670.0311704397841934
670.9814587322191560.03708253556168740.0185412677808437
680.9864258616237350.02714827675252930.0135741383762647
690.9861492419969650.02770151600607030.0138507580030352
700.9847405393831280.03051892123374420.0152594606168721
710.987304944556210.02539011088758010.0126950554437900
720.9861942947899930.02761141042001430.0138057052100071
730.9852918095984910.02941638080301720.0147081904015086
740.981995498947730.03600900210454080.0180045010522704
750.9808923586563040.03821528268739190.0191076413436960
760.9748087723939330.05038245521213390.0251912276060670
770.9692512258716540.0614975482566910.0307487741283455
780.9829441039034690.03411179219306290.0170558960965314
790.9775147104450060.04497057910998890.0224852895549944
800.98001176091570.03997647816859810.0199882390842990
810.9881644371108360.02367112577832730.0118355628891637
820.9939947513514320.01201049729713630.00600524864856816
830.9951508600403350.009698279919329370.00484913995966469
840.9933259220417750.01334815591644990.00667407795822497
850.9914255843432310.01714883131353710.00857441565676853
860.9948703755188820.01025924896223570.00512962448111783
870.993018938022790.01396212395442070.00698106197721033
880.9967868761003820.006426247799236310.00321312389961816
890.9963754773614720.00724904527705540.0036245226385277
900.995376381020420.009247237959157950.00462361897957897
910.9937424339516550.01251513209669080.00625756604834539
920.9961200102397230.007759979520553170.00387998976027658
930.995293580438050.009412839123902630.00470641956195132
940.9944862877970780.01102742440584410.00551371220292203
950.994102965765670.01179406846865960.00589703423432981
960.9923674283481860.01526514330362750.00763257165181375
970.9934006227634080.01319875447318350.00659937723659175
980.991774920359590.01645015928082140.00822507964041072
990.989027569114610.02194486177078140.0109724308853907
1000.98634436050740.02731127898519880.0136556394925994
1010.9890796445574930.0218407108850150.0109203554425075
1020.988050850058130.0238982998837390.0119491499418695
1030.9845631232457920.0308737535084160.015436876754208
1040.9829051660127480.03418966797450380.0170948339872519
1050.9944455094897330.01110898102053480.00555449051026739
1060.9937231782098410.01255364358031830.00627682179015915
1070.9912654227447440.01746915451051180.00873457725525589
1080.988246372239390.02350725552121910.0117536277606096
1090.9894139433418850.02117211331622940.0105860566581147
1100.990865121378140.01826975724372210.00913487862186103
1110.9890050695458280.02198986090834390.0109949304541720
1120.985051717409920.02989656518015850.0149482825900792
1130.9798696205286630.04026075894267430.0201303794713371
1140.9813057461082310.03738850778353740.0186942538917687
1150.9812145637441040.03757087251179150.0187854362558958
1160.989104710543480.02179057891303870.0108952894565194
1170.9924981558433490.01500368831330230.00750184415665114
1180.9908077448497140.01838451030057230.00919225515028616
1190.9976444863598440.004711027280311840.00235551364015592
1200.9966494417671960.006701116465607070.00335055823280353
1210.9955071549400870.008985690119825010.00449284505991251
1220.993945671845680.01210865630864040.00605432815432019
1230.9927710325482350.01445793490353000.00722896745176501
1240.990126738860970.01974652227805830.00987326113902914
1250.9869431026202150.02611379475957020.0130568973797851
1260.98989508081680.02020983836639880.0101049191831994
1270.9937900620505420.01241987589891640.00620993794945819
1280.9992684640227580.001463071954483230.000731535977241616
1290.9996382527493880.0007234945012246580.000361747250612329
1300.9995328192553450.0009343614893092450.000467180744654623
1310.999323176376330.001353647247337800.000676823623668898
1320.998910651746340.002178696507321710.00108934825366086
1330.9983242829969340.003351434006131370.00167571700306568
1340.9988108505381470.002378298923705310.00118914946185266
1350.9981330765327480.00373384693450340.0018669234672517
1360.9977032221621970.004593555675605560.00229677783780278
1370.9963996511423260.007200697715347080.00360034885767354
1380.9950807480887380.009838503822524640.00491925191126232
1390.9954273539756710.009145292048657340.00457264602432867
1400.994639726173890.01072054765221770.00536027382610884
1410.99199433414740.01601133170519920.00800566585259961
1420.991150431206840.01769913758631820.0088495687931591
1430.991268340550110.01746331889978090.00873165944989044
1440.9875382763183240.02492344736335170.0124617236816759
1450.9851346396522060.02973072069558820.0148653603477941
1460.9781627644157360.04367447116852730.0218372355842636
1470.990834749581750.01833050083650010.00916525041825007
1480.9858162179653030.02836756406939410.0141837820346971
1490.9843032058840680.03139358823186480.0156967941159324
1500.9982785513035540.00344289739289210.00172144869644605
1510.9970684058115040.005863188376991590.00293159418849580
1520.9981092763578130.003781447284373380.00189072364218669
1530.9968034485494280.006393102901144480.00319655145057224
1540.9952663686729860.009467262654027150.00473363132701357
1550.9955492616512730.008901476697454170.00445073834872708
1560.9918215639787250.01635687204255090.00817843602127547
1570.9852645985170040.02947080296599200.0147354014829960
1580.9752156235517640.04956875289647270.0247843764482364
1590.9566488579750460.08670228404990790.0433511420249539
1600.9800517422216550.03989651555669050.0199482577783453
1610.9755125167426580.04897496651468340.0244874832573417
1620.952897259310870.0942054813782590.0471027406891295
1630.9350419532825260.1299160934349490.0649580467174743
1640.9595678882478080.08086422350438360.0404321117521918
1650.9137467658777670.1725064682444670.0862532341222334
1660.8268312710697080.3463374578605840.173168728930292
1670.7572851461861840.4854297076276310.242714853813816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.801649266801158 & 0.396701466397684 & 0.198350733198842 \tabularnewline
22 & 0.722821775335612 & 0.554356449328777 & 0.277178224664388 \tabularnewline
23 & 0.678532357588734 & 0.642935284822533 & 0.321467642411266 \tabularnewline
24 & 0.567978922766147 & 0.864042154467705 & 0.432021077233853 \tabularnewline
25 & 0.449078427837444 & 0.898156855674888 & 0.550921572162556 \tabularnewline
26 & 0.368611590108847 & 0.737223180217695 & 0.631388409891153 \tabularnewline
27 & 0.334475367691953 & 0.668950735383906 & 0.665524632308047 \tabularnewline
28 & 0.253401930519896 & 0.506803861039791 & 0.746598069480105 \tabularnewline
29 & 0.384691639104942 & 0.769383278209883 & 0.615308360895058 \tabularnewline
30 & 0.303494665924677 & 0.606989331849355 & 0.696505334075323 \tabularnewline
31 & 0.315619179187161 & 0.631238358374322 & 0.684380820812839 \tabularnewline
32 & 0.410851590398728 & 0.821703180797457 & 0.589148409601272 \tabularnewline
33 & 0.367571582429552 & 0.735143164859105 & 0.632428417570448 \tabularnewline
34 & 0.308459352708626 & 0.616918705417252 & 0.691540647291374 \tabularnewline
35 & 0.245148906736743 & 0.490297813473486 & 0.754851093263257 \tabularnewline
36 & 0.289830159133521 & 0.579660318267042 & 0.710169840866479 \tabularnewline
37 & 0.317572939872916 & 0.635145879745832 & 0.682427060127084 \tabularnewline
38 & 0.274864521907315 & 0.54972904381463 & 0.725135478092685 \tabularnewline
39 & 0.231215072664115 & 0.462430145328231 & 0.768784927335885 \tabularnewline
40 & 0.394800231163054 & 0.789600462326107 & 0.605199768836946 \tabularnewline
41 & 0.338779956867354 & 0.677559913734709 & 0.661220043132646 \tabularnewline
42 & 0.291008616255253 & 0.582017232510505 & 0.708991383744747 \tabularnewline
43 & 0.274118704694751 & 0.548237409389502 & 0.725881295305249 \tabularnewline
44 & 0.37219142053736 & 0.74438284107472 & 0.62780857946264 \tabularnewline
45 & 0.330069594835056 & 0.660139189670112 & 0.669930405164944 \tabularnewline
46 & 0.304231198494352 & 0.608462396988703 & 0.695768801505648 \tabularnewline
47 & 0.382038002046189 & 0.764076004092379 & 0.61796199795381 \tabularnewline
48 & 0.484163996772797 & 0.968327993545594 & 0.515836003227203 \tabularnewline
49 & 0.475541604267404 & 0.951083208534809 & 0.524458395732596 \tabularnewline
50 & 0.442666084594239 & 0.885332169188478 & 0.557333915405761 \tabularnewline
51 & 0.437859784064675 & 0.87571956812935 & 0.562140215935325 \tabularnewline
52 & 0.396544355627043 & 0.793088711254086 & 0.603455644372957 \tabularnewline
53 & 0.463982513380265 & 0.92796502676053 & 0.536017486619735 \tabularnewline
54 & 0.422862994485952 & 0.845725988971903 & 0.577137005514048 \tabularnewline
55 & 0.661437202427555 & 0.677125595144891 & 0.338562797572445 \tabularnewline
56 & 0.840085202420299 & 0.319829595159402 & 0.159914797579701 \tabularnewline
57 & 0.950802021881135 & 0.0983959562377296 & 0.0491979781188648 \tabularnewline
58 & 0.950035904423808 & 0.0999281911523836 & 0.0499640955761918 \tabularnewline
59 & 0.93644510093546 & 0.127109798129079 & 0.0635548990645394 \tabularnewline
60 & 0.935721739624135 & 0.128556520751730 & 0.0642782603758652 \tabularnewline
61 & 0.934103066098846 & 0.131793867802307 & 0.0658969339011536 \tabularnewline
62 & 0.94943170655232 & 0.101136586895360 & 0.0505682934476802 \tabularnewline
63 & 0.945172772902304 & 0.109654454195392 & 0.0548272270976961 \tabularnewline
64 & 0.947700095291348 & 0.104599809417304 & 0.0522999047086518 \tabularnewline
65 & 0.96589222652835 & 0.0682155469432985 & 0.0341077734716493 \tabularnewline
66 & 0.968829560215807 & 0.0623408795683867 & 0.0311704397841934 \tabularnewline
67 & 0.981458732219156 & 0.0370825355616874 & 0.0185412677808437 \tabularnewline
68 & 0.986425861623735 & 0.0271482767525293 & 0.0135741383762647 \tabularnewline
69 & 0.986149241996965 & 0.0277015160060703 & 0.0138507580030352 \tabularnewline
70 & 0.984740539383128 & 0.0305189212337442 & 0.0152594606168721 \tabularnewline
71 & 0.98730494455621 & 0.0253901108875801 & 0.0126950554437900 \tabularnewline
72 & 0.986194294789993 & 0.0276114104200143 & 0.0138057052100071 \tabularnewline
73 & 0.985291809598491 & 0.0294163808030172 & 0.0147081904015086 \tabularnewline
74 & 0.98199549894773 & 0.0360090021045408 & 0.0180045010522704 \tabularnewline
75 & 0.980892358656304 & 0.0382152826873919 & 0.0191076413436960 \tabularnewline
76 & 0.974808772393933 & 0.0503824552121339 & 0.0251912276060670 \tabularnewline
77 & 0.969251225871654 & 0.061497548256691 & 0.0307487741283455 \tabularnewline
78 & 0.982944103903469 & 0.0341117921930629 & 0.0170558960965314 \tabularnewline
79 & 0.977514710445006 & 0.0449705791099889 & 0.0224852895549944 \tabularnewline
80 & 0.9800117609157 & 0.0399764781685981 & 0.0199882390842990 \tabularnewline
81 & 0.988164437110836 & 0.0236711257783273 & 0.0118355628891637 \tabularnewline
82 & 0.993994751351432 & 0.0120104972971363 & 0.00600524864856816 \tabularnewline
83 & 0.995150860040335 & 0.00969827991932937 & 0.00484913995966469 \tabularnewline
84 & 0.993325922041775 & 0.0133481559164499 & 0.00667407795822497 \tabularnewline
85 & 0.991425584343231 & 0.0171488313135371 & 0.00857441565676853 \tabularnewline
86 & 0.994870375518882 & 0.0102592489622357 & 0.00512962448111783 \tabularnewline
87 & 0.99301893802279 & 0.0139621239544207 & 0.00698106197721033 \tabularnewline
88 & 0.996786876100382 & 0.00642624779923631 & 0.00321312389961816 \tabularnewline
89 & 0.996375477361472 & 0.0072490452770554 & 0.0036245226385277 \tabularnewline
90 & 0.99537638102042 & 0.00924723795915795 & 0.00462361897957897 \tabularnewline
91 & 0.993742433951655 & 0.0125151320966908 & 0.00625756604834539 \tabularnewline
92 & 0.996120010239723 & 0.00775997952055317 & 0.00387998976027658 \tabularnewline
93 & 0.99529358043805 & 0.00941283912390263 & 0.00470641956195132 \tabularnewline
94 & 0.994486287797078 & 0.0110274244058441 & 0.00551371220292203 \tabularnewline
95 & 0.99410296576567 & 0.0117940684686596 & 0.00589703423432981 \tabularnewline
96 & 0.992367428348186 & 0.0152651433036275 & 0.00763257165181375 \tabularnewline
97 & 0.993400622763408 & 0.0131987544731835 & 0.00659937723659175 \tabularnewline
98 & 0.99177492035959 & 0.0164501592808214 & 0.00822507964041072 \tabularnewline
99 & 0.98902756911461 & 0.0219448617707814 & 0.0109724308853907 \tabularnewline
100 & 0.9863443605074 & 0.0273112789851988 & 0.0136556394925994 \tabularnewline
101 & 0.989079644557493 & 0.021840710885015 & 0.0109203554425075 \tabularnewline
102 & 0.98805085005813 & 0.023898299883739 & 0.0119491499418695 \tabularnewline
103 & 0.984563123245792 & 0.030873753508416 & 0.015436876754208 \tabularnewline
104 & 0.982905166012748 & 0.0341896679745038 & 0.0170948339872519 \tabularnewline
105 & 0.994445509489733 & 0.0111089810205348 & 0.00555449051026739 \tabularnewline
106 & 0.993723178209841 & 0.0125536435803183 & 0.00627682179015915 \tabularnewline
107 & 0.991265422744744 & 0.0174691545105118 & 0.00873457725525589 \tabularnewline
108 & 0.98824637223939 & 0.0235072555212191 & 0.0117536277606096 \tabularnewline
109 & 0.989413943341885 & 0.0211721133162294 & 0.0105860566581147 \tabularnewline
110 & 0.99086512137814 & 0.0182697572437221 & 0.00913487862186103 \tabularnewline
111 & 0.989005069545828 & 0.0219898609083439 & 0.0109949304541720 \tabularnewline
112 & 0.98505171740992 & 0.0298965651801585 & 0.0149482825900792 \tabularnewline
113 & 0.979869620528663 & 0.0402607589426743 & 0.0201303794713371 \tabularnewline
114 & 0.981305746108231 & 0.0373885077835374 & 0.0186942538917687 \tabularnewline
115 & 0.981214563744104 & 0.0375708725117915 & 0.0187854362558958 \tabularnewline
116 & 0.98910471054348 & 0.0217905789130387 & 0.0108952894565194 \tabularnewline
117 & 0.992498155843349 & 0.0150036883133023 & 0.00750184415665114 \tabularnewline
118 & 0.990807744849714 & 0.0183845103005723 & 0.00919225515028616 \tabularnewline
119 & 0.997644486359844 & 0.00471102728031184 & 0.00235551364015592 \tabularnewline
120 & 0.996649441767196 & 0.00670111646560707 & 0.00335055823280353 \tabularnewline
121 & 0.995507154940087 & 0.00898569011982501 & 0.00449284505991251 \tabularnewline
122 & 0.99394567184568 & 0.0121086563086404 & 0.00605432815432019 \tabularnewline
123 & 0.992771032548235 & 0.0144579349035300 & 0.00722896745176501 \tabularnewline
124 & 0.99012673886097 & 0.0197465222780583 & 0.00987326113902914 \tabularnewline
125 & 0.986943102620215 & 0.0261137947595702 & 0.0130568973797851 \tabularnewline
126 & 0.9898950808168 & 0.0202098383663988 & 0.0101049191831994 \tabularnewline
127 & 0.993790062050542 & 0.0124198758989164 & 0.00620993794945819 \tabularnewline
128 & 0.999268464022758 & 0.00146307195448323 & 0.000731535977241616 \tabularnewline
129 & 0.999638252749388 & 0.000723494501224658 & 0.000361747250612329 \tabularnewline
130 & 0.999532819255345 & 0.000934361489309245 & 0.000467180744654623 \tabularnewline
131 & 0.99932317637633 & 0.00135364724733780 & 0.000676823623668898 \tabularnewline
132 & 0.99891065174634 & 0.00217869650732171 & 0.00108934825366086 \tabularnewline
133 & 0.998324282996934 & 0.00335143400613137 & 0.00167571700306568 \tabularnewline
134 & 0.998810850538147 & 0.00237829892370531 & 0.00118914946185266 \tabularnewline
135 & 0.998133076532748 & 0.0037338469345034 & 0.0018669234672517 \tabularnewline
136 & 0.997703222162197 & 0.00459355567560556 & 0.00229677783780278 \tabularnewline
137 & 0.996399651142326 & 0.00720069771534708 & 0.00360034885767354 \tabularnewline
138 & 0.995080748088738 & 0.00983850382252464 & 0.00491925191126232 \tabularnewline
139 & 0.995427353975671 & 0.00914529204865734 & 0.00457264602432867 \tabularnewline
140 & 0.99463972617389 & 0.0107205476522177 & 0.00536027382610884 \tabularnewline
141 & 0.9919943341474 & 0.0160113317051992 & 0.00800566585259961 \tabularnewline
142 & 0.99115043120684 & 0.0176991375863182 & 0.0088495687931591 \tabularnewline
143 & 0.99126834055011 & 0.0174633188997809 & 0.00873165944989044 \tabularnewline
144 & 0.987538276318324 & 0.0249234473633517 & 0.0124617236816759 \tabularnewline
145 & 0.985134639652206 & 0.0297307206955882 & 0.0148653603477941 \tabularnewline
146 & 0.978162764415736 & 0.0436744711685273 & 0.0218372355842636 \tabularnewline
147 & 0.99083474958175 & 0.0183305008365001 & 0.00916525041825007 \tabularnewline
148 & 0.985816217965303 & 0.0283675640693941 & 0.0141837820346971 \tabularnewline
149 & 0.984303205884068 & 0.0313935882318648 & 0.0156967941159324 \tabularnewline
150 & 0.998278551303554 & 0.0034428973928921 & 0.00172144869644605 \tabularnewline
151 & 0.997068405811504 & 0.00586318837699159 & 0.00293159418849580 \tabularnewline
152 & 0.998109276357813 & 0.00378144728437338 & 0.00189072364218669 \tabularnewline
153 & 0.996803448549428 & 0.00639310290114448 & 0.00319655145057224 \tabularnewline
154 & 0.995266368672986 & 0.00946726265402715 & 0.00473363132701357 \tabularnewline
155 & 0.995549261651273 & 0.00890147669745417 & 0.00445073834872708 \tabularnewline
156 & 0.991821563978725 & 0.0163568720425509 & 0.00817843602127547 \tabularnewline
157 & 0.985264598517004 & 0.0294708029659920 & 0.0147354014829960 \tabularnewline
158 & 0.975215623551764 & 0.0495687528964727 & 0.0247843764482364 \tabularnewline
159 & 0.956648857975046 & 0.0867022840499079 & 0.0433511420249539 \tabularnewline
160 & 0.980051742221655 & 0.0398965155566905 & 0.0199482577783453 \tabularnewline
161 & 0.975512516742658 & 0.0489749665146834 & 0.0244874832573417 \tabularnewline
162 & 0.95289725931087 & 0.094205481378259 & 0.0471027406891295 \tabularnewline
163 & 0.935041953282526 & 0.129916093434949 & 0.0649580467174743 \tabularnewline
164 & 0.959567888247808 & 0.0808642235043836 & 0.0404321117521918 \tabularnewline
165 & 0.913746765877767 & 0.172506468244467 & 0.0862532341222334 \tabularnewline
166 & 0.826831271069708 & 0.346337457860584 & 0.173168728930292 \tabularnewline
167 & 0.757285146186184 & 0.485429707627631 & 0.242714853813816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]21[/C][C]0.801649266801158[/C][C]0.396701466397684[/C][C]0.198350733198842[/C][/ROW]
[ROW][C]22[/C][C]0.722821775335612[/C][C]0.554356449328777[/C][C]0.277178224664388[/C][/ROW]
[ROW][C]23[/C][C]0.678532357588734[/C][C]0.642935284822533[/C][C]0.321467642411266[/C][/ROW]
[ROW][C]24[/C][C]0.567978922766147[/C][C]0.864042154467705[/C][C]0.432021077233853[/C][/ROW]
[ROW][C]25[/C][C]0.449078427837444[/C][C]0.898156855674888[/C][C]0.550921572162556[/C][/ROW]
[ROW][C]26[/C][C]0.368611590108847[/C][C]0.737223180217695[/C][C]0.631388409891153[/C][/ROW]
[ROW][C]27[/C][C]0.334475367691953[/C][C]0.668950735383906[/C][C]0.665524632308047[/C][/ROW]
[ROW][C]28[/C][C]0.253401930519896[/C][C]0.506803861039791[/C][C]0.746598069480105[/C][/ROW]
[ROW][C]29[/C][C]0.384691639104942[/C][C]0.769383278209883[/C][C]0.615308360895058[/C][/ROW]
[ROW][C]30[/C][C]0.303494665924677[/C][C]0.606989331849355[/C][C]0.696505334075323[/C][/ROW]
[ROW][C]31[/C][C]0.315619179187161[/C][C]0.631238358374322[/C][C]0.684380820812839[/C][/ROW]
[ROW][C]32[/C][C]0.410851590398728[/C][C]0.821703180797457[/C][C]0.589148409601272[/C][/ROW]
[ROW][C]33[/C][C]0.367571582429552[/C][C]0.735143164859105[/C][C]0.632428417570448[/C][/ROW]
[ROW][C]34[/C][C]0.308459352708626[/C][C]0.616918705417252[/C][C]0.691540647291374[/C][/ROW]
[ROW][C]35[/C][C]0.245148906736743[/C][C]0.490297813473486[/C][C]0.754851093263257[/C][/ROW]
[ROW][C]36[/C][C]0.289830159133521[/C][C]0.579660318267042[/C][C]0.710169840866479[/C][/ROW]
[ROW][C]37[/C][C]0.317572939872916[/C][C]0.635145879745832[/C][C]0.682427060127084[/C][/ROW]
[ROW][C]38[/C][C]0.274864521907315[/C][C]0.54972904381463[/C][C]0.725135478092685[/C][/ROW]
[ROW][C]39[/C][C]0.231215072664115[/C][C]0.462430145328231[/C][C]0.768784927335885[/C][/ROW]
[ROW][C]40[/C][C]0.394800231163054[/C][C]0.789600462326107[/C][C]0.605199768836946[/C][/ROW]
[ROW][C]41[/C][C]0.338779956867354[/C][C]0.677559913734709[/C][C]0.661220043132646[/C][/ROW]
[ROW][C]42[/C][C]0.291008616255253[/C][C]0.582017232510505[/C][C]0.708991383744747[/C][/ROW]
[ROW][C]43[/C][C]0.274118704694751[/C][C]0.548237409389502[/C][C]0.725881295305249[/C][/ROW]
[ROW][C]44[/C][C]0.37219142053736[/C][C]0.74438284107472[/C][C]0.62780857946264[/C][/ROW]
[ROW][C]45[/C][C]0.330069594835056[/C][C]0.660139189670112[/C][C]0.669930405164944[/C][/ROW]
[ROW][C]46[/C][C]0.304231198494352[/C][C]0.608462396988703[/C][C]0.695768801505648[/C][/ROW]
[ROW][C]47[/C][C]0.382038002046189[/C][C]0.764076004092379[/C][C]0.61796199795381[/C][/ROW]
[ROW][C]48[/C][C]0.484163996772797[/C][C]0.968327993545594[/C][C]0.515836003227203[/C][/ROW]
[ROW][C]49[/C][C]0.475541604267404[/C][C]0.951083208534809[/C][C]0.524458395732596[/C][/ROW]
[ROW][C]50[/C][C]0.442666084594239[/C][C]0.885332169188478[/C][C]0.557333915405761[/C][/ROW]
[ROW][C]51[/C][C]0.437859784064675[/C][C]0.87571956812935[/C][C]0.562140215935325[/C][/ROW]
[ROW][C]52[/C][C]0.396544355627043[/C][C]0.793088711254086[/C][C]0.603455644372957[/C][/ROW]
[ROW][C]53[/C][C]0.463982513380265[/C][C]0.92796502676053[/C][C]0.536017486619735[/C][/ROW]
[ROW][C]54[/C][C]0.422862994485952[/C][C]0.845725988971903[/C][C]0.577137005514048[/C][/ROW]
[ROW][C]55[/C][C]0.661437202427555[/C][C]0.677125595144891[/C][C]0.338562797572445[/C][/ROW]
[ROW][C]56[/C][C]0.840085202420299[/C][C]0.319829595159402[/C][C]0.159914797579701[/C][/ROW]
[ROW][C]57[/C][C]0.950802021881135[/C][C]0.0983959562377296[/C][C]0.0491979781188648[/C][/ROW]
[ROW][C]58[/C][C]0.950035904423808[/C][C]0.0999281911523836[/C][C]0.0499640955761918[/C][/ROW]
[ROW][C]59[/C][C]0.93644510093546[/C][C]0.127109798129079[/C][C]0.0635548990645394[/C][/ROW]
[ROW][C]60[/C][C]0.935721739624135[/C][C]0.128556520751730[/C][C]0.0642782603758652[/C][/ROW]
[ROW][C]61[/C][C]0.934103066098846[/C][C]0.131793867802307[/C][C]0.0658969339011536[/C][/ROW]
[ROW][C]62[/C][C]0.94943170655232[/C][C]0.101136586895360[/C][C]0.0505682934476802[/C][/ROW]
[ROW][C]63[/C][C]0.945172772902304[/C][C]0.109654454195392[/C][C]0.0548272270976961[/C][/ROW]
[ROW][C]64[/C][C]0.947700095291348[/C][C]0.104599809417304[/C][C]0.0522999047086518[/C][/ROW]
[ROW][C]65[/C][C]0.96589222652835[/C][C]0.0682155469432985[/C][C]0.0341077734716493[/C][/ROW]
[ROW][C]66[/C][C]0.968829560215807[/C][C]0.0623408795683867[/C][C]0.0311704397841934[/C][/ROW]
[ROW][C]67[/C][C]0.981458732219156[/C][C]0.0370825355616874[/C][C]0.0185412677808437[/C][/ROW]
[ROW][C]68[/C][C]0.986425861623735[/C][C]0.0271482767525293[/C][C]0.0135741383762647[/C][/ROW]
[ROW][C]69[/C][C]0.986149241996965[/C][C]0.0277015160060703[/C][C]0.0138507580030352[/C][/ROW]
[ROW][C]70[/C][C]0.984740539383128[/C][C]0.0305189212337442[/C][C]0.0152594606168721[/C][/ROW]
[ROW][C]71[/C][C]0.98730494455621[/C][C]0.0253901108875801[/C][C]0.0126950554437900[/C][/ROW]
[ROW][C]72[/C][C]0.986194294789993[/C][C]0.0276114104200143[/C][C]0.0138057052100071[/C][/ROW]
[ROW][C]73[/C][C]0.985291809598491[/C][C]0.0294163808030172[/C][C]0.0147081904015086[/C][/ROW]
[ROW][C]74[/C][C]0.98199549894773[/C][C]0.0360090021045408[/C][C]0.0180045010522704[/C][/ROW]
[ROW][C]75[/C][C]0.980892358656304[/C][C]0.0382152826873919[/C][C]0.0191076413436960[/C][/ROW]
[ROW][C]76[/C][C]0.974808772393933[/C][C]0.0503824552121339[/C][C]0.0251912276060670[/C][/ROW]
[ROW][C]77[/C][C]0.969251225871654[/C][C]0.061497548256691[/C][C]0.0307487741283455[/C][/ROW]
[ROW][C]78[/C][C]0.982944103903469[/C][C]0.0341117921930629[/C][C]0.0170558960965314[/C][/ROW]
[ROW][C]79[/C][C]0.977514710445006[/C][C]0.0449705791099889[/C][C]0.0224852895549944[/C][/ROW]
[ROW][C]80[/C][C]0.9800117609157[/C][C]0.0399764781685981[/C][C]0.0199882390842990[/C][/ROW]
[ROW][C]81[/C][C]0.988164437110836[/C][C]0.0236711257783273[/C][C]0.0118355628891637[/C][/ROW]
[ROW][C]82[/C][C]0.993994751351432[/C][C]0.0120104972971363[/C][C]0.00600524864856816[/C][/ROW]
[ROW][C]83[/C][C]0.995150860040335[/C][C]0.00969827991932937[/C][C]0.00484913995966469[/C][/ROW]
[ROW][C]84[/C][C]0.993325922041775[/C][C]0.0133481559164499[/C][C]0.00667407795822497[/C][/ROW]
[ROW][C]85[/C][C]0.991425584343231[/C][C]0.0171488313135371[/C][C]0.00857441565676853[/C][/ROW]
[ROW][C]86[/C][C]0.994870375518882[/C][C]0.0102592489622357[/C][C]0.00512962448111783[/C][/ROW]
[ROW][C]87[/C][C]0.99301893802279[/C][C]0.0139621239544207[/C][C]0.00698106197721033[/C][/ROW]
[ROW][C]88[/C][C]0.996786876100382[/C][C]0.00642624779923631[/C][C]0.00321312389961816[/C][/ROW]
[ROW][C]89[/C][C]0.996375477361472[/C][C]0.0072490452770554[/C][C]0.0036245226385277[/C][/ROW]
[ROW][C]90[/C][C]0.99537638102042[/C][C]0.00924723795915795[/C][C]0.00462361897957897[/C][/ROW]
[ROW][C]91[/C][C]0.993742433951655[/C][C]0.0125151320966908[/C][C]0.00625756604834539[/C][/ROW]
[ROW][C]92[/C][C]0.996120010239723[/C][C]0.00775997952055317[/C][C]0.00387998976027658[/C][/ROW]
[ROW][C]93[/C][C]0.99529358043805[/C][C]0.00941283912390263[/C][C]0.00470641956195132[/C][/ROW]
[ROW][C]94[/C][C]0.994486287797078[/C][C]0.0110274244058441[/C][C]0.00551371220292203[/C][/ROW]
[ROW][C]95[/C][C]0.99410296576567[/C][C]0.0117940684686596[/C][C]0.00589703423432981[/C][/ROW]
[ROW][C]96[/C][C]0.992367428348186[/C][C]0.0152651433036275[/C][C]0.00763257165181375[/C][/ROW]
[ROW][C]97[/C][C]0.993400622763408[/C][C]0.0131987544731835[/C][C]0.00659937723659175[/C][/ROW]
[ROW][C]98[/C][C]0.99177492035959[/C][C]0.0164501592808214[/C][C]0.00822507964041072[/C][/ROW]
[ROW][C]99[/C][C]0.98902756911461[/C][C]0.0219448617707814[/C][C]0.0109724308853907[/C][/ROW]
[ROW][C]100[/C][C]0.9863443605074[/C][C]0.0273112789851988[/C][C]0.0136556394925994[/C][/ROW]
[ROW][C]101[/C][C]0.989079644557493[/C][C]0.021840710885015[/C][C]0.0109203554425075[/C][/ROW]
[ROW][C]102[/C][C]0.98805085005813[/C][C]0.023898299883739[/C][C]0.0119491499418695[/C][/ROW]
[ROW][C]103[/C][C]0.984563123245792[/C][C]0.030873753508416[/C][C]0.015436876754208[/C][/ROW]
[ROW][C]104[/C][C]0.982905166012748[/C][C]0.0341896679745038[/C][C]0.0170948339872519[/C][/ROW]
[ROW][C]105[/C][C]0.994445509489733[/C][C]0.0111089810205348[/C][C]0.00555449051026739[/C][/ROW]
[ROW][C]106[/C][C]0.993723178209841[/C][C]0.0125536435803183[/C][C]0.00627682179015915[/C][/ROW]
[ROW][C]107[/C][C]0.991265422744744[/C][C]0.0174691545105118[/C][C]0.00873457725525589[/C][/ROW]
[ROW][C]108[/C][C]0.98824637223939[/C][C]0.0235072555212191[/C][C]0.0117536277606096[/C][/ROW]
[ROW][C]109[/C][C]0.989413943341885[/C][C]0.0211721133162294[/C][C]0.0105860566581147[/C][/ROW]
[ROW][C]110[/C][C]0.99086512137814[/C][C]0.0182697572437221[/C][C]0.00913487862186103[/C][/ROW]
[ROW][C]111[/C][C]0.989005069545828[/C][C]0.0219898609083439[/C][C]0.0109949304541720[/C][/ROW]
[ROW][C]112[/C][C]0.98505171740992[/C][C]0.0298965651801585[/C][C]0.0149482825900792[/C][/ROW]
[ROW][C]113[/C][C]0.979869620528663[/C][C]0.0402607589426743[/C][C]0.0201303794713371[/C][/ROW]
[ROW][C]114[/C][C]0.981305746108231[/C][C]0.0373885077835374[/C][C]0.0186942538917687[/C][/ROW]
[ROW][C]115[/C][C]0.981214563744104[/C][C]0.0375708725117915[/C][C]0.0187854362558958[/C][/ROW]
[ROW][C]116[/C][C]0.98910471054348[/C][C]0.0217905789130387[/C][C]0.0108952894565194[/C][/ROW]
[ROW][C]117[/C][C]0.992498155843349[/C][C]0.0150036883133023[/C][C]0.00750184415665114[/C][/ROW]
[ROW][C]118[/C][C]0.990807744849714[/C][C]0.0183845103005723[/C][C]0.00919225515028616[/C][/ROW]
[ROW][C]119[/C][C]0.997644486359844[/C][C]0.00471102728031184[/C][C]0.00235551364015592[/C][/ROW]
[ROW][C]120[/C][C]0.996649441767196[/C][C]0.00670111646560707[/C][C]0.00335055823280353[/C][/ROW]
[ROW][C]121[/C][C]0.995507154940087[/C][C]0.00898569011982501[/C][C]0.00449284505991251[/C][/ROW]
[ROW][C]122[/C][C]0.99394567184568[/C][C]0.0121086563086404[/C][C]0.00605432815432019[/C][/ROW]
[ROW][C]123[/C][C]0.992771032548235[/C][C]0.0144579349035300[/C][C]0.00722896745176501[/C][/ROW]
[ROW][C]124[/C][C]0.99012673886097[/C][C]0.0197465222780583[/C][C]0.00987326113902914[/C][/ROW]
[ROW][C]125[/C][C]0.986943102620215[/C][C]0.0261137947595702[/C][C]0.0130568973797851[/C][/ROW]
[ROW][C]126[/C][C]0.9898950808168[/C][C]0.0202098383663988[/C][C]0.0101049191831994[/C][/ROW]
[ROW][C]127[/C][C]0.993790062050542[/C][C]0.0124198758989164[/C][C]0.00620993794945819[/C][/ROW]
[ROW][C]128[/C][C]0.999268464022758[/C][C]0.00146307195448323[/C][C]0.000731535977241616[/C][/ROW]
[ROW][C]129[/C][C]0.999638252749388[/C][C]0.000723494501224658[/C][C]0.000361747250612329[/C][/ROW]
[ROW][C]130[/C][C]0.999532819255345[/C][C]0.000934361489309245[/C][C]0.000467180744654623[/C][/ROW]
[ROW][C]131[/C][C]0.99932317637633[/C][C]0.00135364724733780[/C][C]0.000676823623668898[/C][/ROW]
[ROW][C]132[/C][C]0.99891065174634[/C][C]0.00217869650732171[/C][C]0.00108934825366086[/C][/ROW]
[ROW][C]133[/C][C]0.998324282996934[/C][C]0.00335143400613137[/C][C]0.00167571700306568[/C][/ROW]
[ROW][C]134[/C][C]0.998810850538147[/C][C]0.00237829892370531[/C][C]0.00118914946185266[/C][/ROW]
[ROW][C]135[/C][C]0.998133076532748[/C][C]0.0037338469345034[/C][C]0.0018669234672517[/C][/ROW]
[ROW][C]136[/C][C]0.997703222162197[/C][C]0.00459355567560556[/C][C]0.00229677783780278[/C][/ROW]
[ROW][C]137[/C][C]0.996399651142326[/C][C]0.00720069771534708[/C][C]0.00360034885767354[/C][/ROW]
[ROW][C]138[/C][C]0.995080748088738[/C][C]0.00983850382252464[/C][C]0.00491925191126232[/C][/ROW]
[ROW][C]139[/C][C]0.995427353975671[/C][C]0.00914529204865734[/C][C]0.00457264602432867[/C][/ROW]
[ROW][C]140[/C][C]0.99463972617389[/C][C]0.0107205476522177[/C][C]0.00536027382610884[/C][/ROW]
[ROW][C]141[/C][C]0.9919943341474[/C][C]0.0160113317051992[/C][C]0.00800566585259961[/C][/ROW]
[ROW][C]142[/C][C]0.99115043120684[/C][C]0.0176991375863182[/C][C]0.0088495687931591[/C][/ROW]
[ROW][C]143[/C][C]0.99126834055011[/C][C]0.0174633188997809[/C][C]0.00873165944989044[/C][/ROW]
[ROW][C]144[/C][C]0.987538276318324[/C][C]0.0249234473633517[/C][C]0.0124617236816759[/C][/ROW]
[ROW][C]145[/C][C]0.985134639652206[/C][C]0.0297307206955882[/C][C]0.0148653603477941[/C][/ROW]
[ROW][C]146[/C][C]0.978162764415736[/C][C]0.0436744711685273[/C][C]0.0218372355842636[/C][/ROW]
[ROW][C]147[/C][C]0.99083474958175[/C][C]0.0183305008365001[/C][C]0.00916525041825007[/C][/ROW]
[ROW][C]148[/C][C]0.985816217965303[/C][C]0.0283675640693941[/C][C]0.0141837820346971[/C][/ROW]
[ROW][C]149[/C][C]0.984303205884068[/C][C]0.0313935882318648[/C][C]0.0156967941159324[/C][/ROW]
[ROW][C]150[/C][C]0.998278551303554[/C][C]0.0034428973928921[/C][C]0.00172144869644605[/C][/ROW]
[ROW][C]151[/C][C]0.997068405811504[/C][C]0.00586318837699159[/C][C]0.00293159418849580[/C][/ROW]
[ROW][C]152[/C][C]0.998109276357813[/C][C]0.00378144728437338[/C][C]0.00189072364218669[/C][/ROW]
[ROW][C]153[/C][C]0.996803448549428[/C][C]0.00639310290114448[/C][C]0.00319655145057224[/C][/ROW]
[ROW][C]154[/C][C]0.995266368672986[/C][C]0.00946726265402715[/C][C]0.00473363132701357[/C][/ROW]
[ROW][C]155[/C][C]0.995549261651273[/C][C]0.00890147669745417[/C][C]0.00445073834872708[/C][/ROW]
[ROW][C]156[/C][C]0.991821563978725[/C][C]0.0163568720425509[/C][C]0.00817843602127547[/C][/ROW]
[ROW][C]157[/C][C]0.985264598517004[/C][C]0.0294708029659920[/C][C]0.0147354014829960[/C][/ROW]
[ROW][C]158[/C][C]0.975215623551764[/C][C]0.0495687528964727[/C][C]0.0247843764482364[/C][/ROW]
[ROW][C]159[/C][C]0.956648857975046[/C][C]0.0867022840499079[/C][C]0.0433511420249539[/C][/ROW]
[ROW][C]160[/C][C]0.980051742221655[/C][C]0.0398965155566905[/C][C]0.0199482577783453[/C][/ROW]
[ROW][C]161[/C][C]0.975512516742658[/C][C]0.0489749665146834[/C][C]0.0244874832573417[/C][/ROW]
[ROW][C]162[/C][C]0.95289725931087[/C][C]0.094205481378259[/C][C]0.0471027406891295[/C][/ROW]
[ROW][C]163[/C][C]0.935041953282526[/C][C]0.129916093434949[/C][C]0.0649580467174743[/C][/ROW]
[ROW][C]164[/C][C]0.959567888247808[/C][C]0.0808642235043836[/C][C]0.0404321117521918[/C][/ROW]
[ROW][C]165[/C][C]0.913746765877767[/C][C]0.172506468244467[/C][C]0.0862532341222334[/C][/ROW]
[ROW][C]166[/C][C]0.826831271069708[/C][C]0.346337457860584[/C][C]0.173168728930292[/C][/ROW]
[ROW][C]167[/C][C]0.757285146186184[/C][C]0.485429707627631[/C][C]0.242714853813816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.8016492668011580.3967014663976840.198350733198842
220.7228217753356120.5543564493287770.277178224664388
230.6785323575887340.6429352848225330.321467642411266
240.5679789227661470.8640421544677050.432021077233853
250.4490784278374440.8981568556748880.550921572162556
260.3686115901088470.7372231802176950.631388409891153
270.3344753676919530.6689507353839060.665524632308047
280.2534019305198960.5068038610397910.746598069480105
290.3846916391049420.7693832782098830.615308360895058
300.3034946659246770.6069893318493550.696505334075323
310.3156191791871610.6312383583743220.684380820812839
320.4108515903987280.8217031807974570.589148409601272
330.3675715824295520.7351431648591050.632428417570448
340.3084593527086260.6169187054172520.691540647291374
350.2451489067367430.4902978134734860.754851093263257
360.2898301591335210.5796603182670420.710169840866479
370.3175729398729160.6351458797458320.682427060127084
380.2748645219073150.549729043814630.725135478092685
390.2312150726641150.4624301453282310.768784927335885
400.3948002311630540.7896004623261070.605199768836946
410.3387799568673540.6775599137347090.661220043132646
420.2910086162552530.5820172325105050.708991383744747
430.2741187046947510.5482374093895020.725881295305249
440.372191420537360.744382841074720.62780857946264
450.3300695948350560.6601391896701120.669930405164944
460.3042311984943520.6084623969887030.695768801505648
470.3820380020461890.7640760040923790.61796199795381
480.4841639967727970.9683279935455940.515836003227203
490.4755416042674040.9510832085348090.524458395732596
500.4426660845942390.8853321691884780.557333915405761
510.4378597840646750.875719568129350.562140215935325
520.3965443556270430.7930887112540860.603455644372957
530.4639825133802650.927965026760530.536017486619735
540.4228629944859520.8457259889719030.577137005514048
550.6614372024275550.6771255951448910.338562797572445
560.8400852024202990.3198295951594020.159914797579701
570.9508020218811350.09839595623772960.0491979781188648
580.9500359044238080.09992819115238360.0499640955761918
590.936445100935460.1271097981290790.0635548990645394
600.9357217396241350.1285565207517300.0642782603758652
610.9341030660988460.1317938678023070.0658969339011536
620.949431706552320.1011365868953600.0505682934476802
630.9451727729023040.1096544541953920.0548272270976961
640.9477000952913480.1045998094173040.0522999047086518
650.965892226528350.06821554694329850.0341077734716493
660.9688295602158070.06234087956838670.0311704397841934
670.9814587322191560.03708253556168740.0185412677808437
680.9864258616237350.02714827675252930.0135741383762647
690.9861492419969650.02770151600607030.0138507580030352
700.9847405393831280.03051892123374420.0152594606168721
710.987304944556210.02539011088758010.0126950554437900
720.9861942947899930.02761141042001430.0138057052100071
730.9852918095984910.02941638080301720.0147081904015086
740.981995498947730.03600900210454080.0180045010522704
750.9808923586563040.03821528268739190.0191076413436960
760.9748087723939330.05038245521213390.0251912276060670
770.9692512258716540.0614975482566910.0307487741283455
780.9829441039034690.03411179219306290.0170558960965314
790.9775147104450060.04497057910998890.0224852895549944
800.98001176091570.03997647816859810.0199882390842990
810.9881644371108360.02367112577832730.0118355628891637
820.9939947513514320.01201049729713630.00600524864856816
830.9951508600403350.009698279919329370.00484913995966469
840.9933259220417750.01334815591644990.00667407795822497
850.9914255843432310.01714883131353710.00857441565676853
860.9948703755188820.01025924896223570.00512962448111783
870.993018938022790.01396212395442070.00698106197721033
880.9967868761003820.006426247799236310.00321312389961816
890.9963754773614720.00724904527705540.0036245226385277
900.995376381020420.009247237959157950.00462361897957897
910.9937424339516550.01251513209669080.00625756604834539
920.9961200102397230.007759979520553170.00387998976027658
930.995293580438050.009412839123902630.00470641956195132
940.9944862877970780.01102742440584410.00551371220292203
950.994102965765670.01179406846865960.00589703423432981
960.9923674283481860.01526514330362750.00763257165181375
970.9934006227634080.01319875447318350.00659937723659175
980.991774920359590.01645015928082140.00822507964041072
990.989027569114610.02194486177078140.0109724308853907
1000.98634436050740.02731127898519880.0136556394925994
1010.9890796445574930.0218407108850150.0109203554425075
1020.988050850058130.0238982998837390.0119491499418695
1030.9845631232457920.0308737535084160.015436876754208
1040.9829051660127480.03418966797450380.0170948339872519
1050.9944455094897330.01110898102053480.00555449051026739
1060.9937231782098410.01255364358031830.00627682179015915
1070.9912654227447440.01746915451051180.00873457725525589
1080.988246372239390.02350725552121910.0117536277606096
1090.9894139433418850.02117211331622940.0105860566581147
1100.990865121378140.01826975724372210.00913487862186103
1110.9890050695458280.02198986090834390.0109949304541720
1120.985051717409920.02989656518015850.0149482825900792
1130.9798696205286630.04026075894267430.0201303794713371
1140.9813057461082310.03738850778353740.0186942538917687
1150.9812145637441040.03757087251179150.0187854362558958
1160.989104710543480.02179057891303870.0108952894565194
1170.9924981558433490.01500368831330230.00750184415665114
1180.9908077448497140.01838451030057230.00919225515028616
1190.9976444863598440.004711027280311840.00235551364015592
1200.9966494417671960.006701116465607070.00335055823280353
1210.9955071549400870.008985690119825010.00449284505991251
1220.993945671845680.01210865630864040.00605432815432019
1230.9927710325482350.01445793490353000.00722896745176501
1240.990126738860970.01974652227805830.00987326113902914
1250.9869431026202150.02611379475957020.0130568973797851
1260.98989508081680.02020983836639880.0101049191831994
1270.9937900620505420.01241987589891640.00620993794945819
1280.9992684640227580.001463071954483230.000731535977241616
1290.9996382527493880.0007234945012246580.000361747250612329
1300.9995328192553450.0009343614893092450.000467180744654623
1310.999323176376330.001353647247337800.000676823623668898
1320.998910651746340.002178696507321710.00108934825366086
1330.9983242829969340.003351434006131370.00167571700306568
1340.9988108505381470.002378298923705310.00118914946185266
1350.9981330765327480.00373384693450340.0018669234672517
1360.9977032221621970.004593555675605560.00229677783780278
1370.9963996511423260.007200697715347080.00360034885767354
1380.9950807480887380.009838503822524640.00491925191126232
1390.9954273539756710.009145292048657340.00457264602432867
1400.994639726173890.01072054765221770.00536027382610884
1410.99199433414740.01601133170519920.00800566585259961
1420.991150431206840.01769913758631820.0088495687931591
1430.991268340550110.01746331889978090.00873165944989044
1440.9875382763183240.02492344736335170.0124617236816759
1450.9851346396522060.02973072069558820.0148653603477941
1460.9781627644157360.04367447116852730.0218372355842636
1470.990834749581750.01833050083650010.00916525041825007
1480.9858162179653030.02836756406939410.0141837820346971
1490.9843032058840680.03139358823186480.0156967941159324
1500.9982785513035540.00344289739289210.00172144869644605
1510.9970684058115040.005863188376991590.00293159418849580
1520.9981092763578130.003781447284373380.00189072364218669
1530.9968034485494280.006393102901144480.00319655145057224
1540.9952663686729860.009467262654027150.00473363132701357
1550.9955492616512730.008901476697454170.00445073834872708
1560.9918215639787250.01635687204255090.00817843602127547
1570.9852645985170040.02947080296599200.0147354014829960
1580.9752156235517640.04956875289647270.0247843764482364
1590.9566488579750460.08670228404990790.0433511420249539
1600.9800517422216550.03989651555669050.0199482577783453
1610.9755125167426580.04897496651468340.0244874832573417
1620.952897259310870.0942054813782590.0471027406891295
1630.9350419532825260.1299160934349490.0649580467174743
1640.9595678882478080.08086422350438360.0404321117521918
1650.9137467658777670.1725064682444670.0862532341222334
1660.8268312710697080.3463374578605840.173168728930292
1670.7572851461861840.4854297076276310.242714853813816







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.183673469387755NOK
5% type I error level920.625850340136054NOK
10% type I error level1010.687074829931973NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 27 & 0.183673469387755 & NOK \tabularnewline
5% type I error level & 92 & 0.625850340136054 & NOK \tabularnewline
10% type I error level & 101 & 0.687074829931973 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56003&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]27[/C][C]0.183673469387755[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]92[/C][C]0.625850340136054[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.687074829931973[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56003&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56003&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.183673469387755NOK
5% type I error level920.625850340136054NOK
10% type I error level1010.687074829931973NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$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')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$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()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}