FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

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 15:03:14 +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/t1258034663fagi4z9rmty7nh6.htm/, Retrieved Wed, 11 Sep 2024 07:11:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55996, Retrieved Wed, 11 Sep 2024 07:11:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact762

Tree of Dependent Computations

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]
- RMPD    [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D      [Multiple Regression] [] [2009-11-14 11:27:12] [e2a6b1b31bd881219e1879835b4c60d0]
-             [Multiple Regression] [Regressiemodel me...] [2009-11-14 11:29:26] [e2a6b1b31bd881219e1879835b4c60d0]
-    D      [Multiple Regression] [Multiple regressi...] [2009-11-14 12:18:51] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-   PD        [Multiple Regression] [fixed seasonal ef...] [2009-11-21 00:06:45] [3dd791303389e75e672968b227170a72]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-20 12:20:57] [73863f7f907331e734eff34b7de6fc83]
-    D      [Multiple Regression] [WS 7 2] [2009-11-14 13:20:47] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD      [] [] [-0001-11-30 00:00:00] [ca7a691f2b8ebdc7b81799394c1aa70d]
-    D      [Multiple Regression] [] [2009-11-17 18:34:34] [96d96f181930b548ce74f8c3116c4873]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-17 18:53:04] [eaf42bcf5162b5692bb3c7f9d4636222]
-    D      [Multiple Regression] [] [2009-11-18 12:06:02] [6ba840d2473f9a55d7b3e13093db69b8]
-    D        [Multiple Regression] [] [2009-12-15 14:57:54] [6ba840d2473f9a55d7b3e13093db69b8]
-   PD          [Multiple Regression] [] [2009-12-21 11:18:14] [6ba840d2473f9a55d7b3e13093db69b8]
-    D          [Multiple Regression] [] [2009-12-21 11:50:01] [6ba840d2473f9a55d7b3e13093db69b8]
-    D      [Multiple Regression] [] [2009-11-18 14:23:39] [ee35698a38947a6c6c039b1e3deafc05]
-    D      [Multiple Regression] [] [2009-11-18 14:33:37] [94b62ad0aa784646217b93aa983cee13]
-    D      [Multiple Regression] [SHW WS7 - Fixed S...] [2009-11-18 14:34:12] [253127ae8da904b75450fbd69fe4eb21]
-    D      [Multiple Regression] [] [2009-11-18 14:44:06] [7e8bf94ac9834384fa22d029eca19fa6]
-    D      [Multiple Regression] [WS7 Include dummies] [2009-11-18 15:42:15] [445b292c553470d9fed8bc2796fd3a00]
F    D      [Multiple Regression] [] [2009-11-18 16:31:11] [90f6d58d515a4caed6fb4b8be4e11eaa]
-   PD      [Multiple Regression] [] [2009-11-18 17:10:28] [7369a9baefff1ba9d2171738b4c9faa6]
-    D      [Multiple Regression] [] [2009-11-18 17:28:08] [e48499dbc39d7bbb032694d069ad98a5]
-    D      [Multiple Regression] [SHw WS7] [2009-11-18 17:51:18] [af2352cd9a951bedd08ebe247d0de1a2]
-    D      [Multiple Regression] [ws7_2] [2009-11-18 17:59:26] [8b1aef4e7013bd33fbc2a5833375c5f5]
-             [Multiple Regression] [] [2009-11-19 13:59:48] [08fc5c07292c885b941f0cb515ce13f3]
-    D          [Multiple Regression] [] [2009-11-20 16:55:24] [4d62210f0915d3a20cbf115865da7cd4]
- R PD        [Multiple Regression] [Multiple_Regressi...] [2009-12-29 14:44:45] [2663058f2a5dda519058ac6b2228468f]
- R PD        [Multiple Regression] [Multiple_Regressi...] [2009-12-29 14:48:15] [2663058f2a5dda519058ac6b2228468f]
-    D        [Multiple Regression] [Paper Multiple re...] [2010-12-04 13:03:59] [814f53995537cd15c528d8efbf1cf544]
-   PD      [Multiple Regression] [] [2009-11-18 18:37:15] [7369a9baefff1ba9d2171738b4c9faa6]
-    D      [Multiple Regression] [] [2009-11-18 18:48:31] [539cd8be0bf6326526ff2d448281a204]
-    D      [Multiple Regression] [] [2009-11-18 18:51:31] [539cd8be0bf6326526ff2d448281a204]
-   PD      [Multiple Regression] [Grondstofprijsind...] [2009-11-18 19:41:50] [016baa4dcb32aa0a4ae1d7f97a4b0730]
-   PD        [Multiple Regression] [] [2009-11-21 14:55:39] [6998f38352c0f6bc3cf32a17448703fc]
F R  D      [Multiple Regression] [Model 2] [2009-11-18 20:35:45] [1f74ef2f756548f1f3a7b6136ea56d7f]
-    D        [Multiple Regression] [model 2 ws 7] [2009-11-20 14:15:33] [134dc66689e3d457a82860db6471d419]
-   PD        [Multiple Regression] [model 3 ws 7] [2009-11-20 14:17:31] [134dc66689e3d457a82860db6471d419]
- R PD      [Multiple Regression] [Model 3] [2009-11-18 21:10:22] [1f74ef2f756548f1f3a7b6136ea56d7f]
-   PD      [Multiple Regression] [multiple regressi...] [2009-11-18 21:40:19] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [M2] [2009-11-19 07:34:35] [3b0db66ac8145b1be856a517e2900332]
-    D      [Multiple Regression] [Multivariate regr...] [2009-11-19 08:45:43] [21324e9cdf3569788a3d630236984d87]
-    D        [Multiple Regression] [] [2010-12-07 12:28:40] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:16:37] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 16:44:08] [3931071255a6f7f4a767409781cc5f7d]
- R P             [Multiple Regression] [] [2011-11-27 16:47:29] [3931071255a6f7f4a767409781cc5f7d]
-    D        [Multiple Regression] [] [2010-12-21 20:19:59] [f47feae0308dca73181bb669fbad1c56]
-   PD        [Multiple Regression] [] [2010-12-21 20:26:33] [f47feae0308dca73181bb669fbad1c56]
-   PD        [Multiple Regression] [] [2010-12-21 20:36:54] [f47feae0308dca73181bb669fbad1c56]
-    D          [Multiple Regression] [] [2010-12-28 18:23:34] [f47feae0308dca73181bb669fbad1c56]

[Truncated]
Feedback Forum

Post a new message

Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=0

As an alternative you can also use a QR Code:  
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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2165.22639318886 -395.811145510835X[t] -442.550696594425M1[t] -617.812500000002M2[t] -567.25M3[t] -680.4375M4[t] -543.125000000001M5[t] -598.874999999998M6[t] -523.250000000001M7[t] -508.375M8[t] -455.5625M9[t] -316.187500000000M10[t] -116.625000000000M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2165.22639318886 -395.811145510835X[t] -442.550696594425M1[t] -617.812500000002M2[t] -567.25M3[t] -680.4375M4[t] -543.125000000001M5[t] -598.874999999998M6[t] -523.250000000001M7[t] -508.375M8[t] -455.5625M9[t] -316.187500000000M10[t] -116.625000000000M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2165.22639318886 -395.811145510835X[t] -442.550696594425M1[t] -617.812500000002M2[t] -567.25M3[t] -680.4375M4[t] -543.125000000001M5[t] -598.874999999998M6[t] -523.250000000001M7[t] -508.375M8[t] -455.5625M9[t] -316.187500000000M10[t] -116.625000000000M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2165.22639318886 -395.811145510835X[t] -442.550696594425M1[t] -617.812500000002M2[t] -567.25M3[t] -680.4375M4[t] -543.125000000001M5[t] -598.874999999998M6[t] -523.250000000001M7[t] -508.375M8[t] -455.5625M9[t] -316.187500000000M10[t] -116.625000000000M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2165.2263931888643.63188149.624900
X-395.81114551083538.605577-10.252700
M1-442.55069659442561.373686-7.210800
M2-617.81250000000261.326238-10.074200
M3-567.2561.326238-9.249700
M4-680.437561.326238-11.095400
M5-543.12500000000161.326238-8.856300
M6-598.87499999999861.326238-9.765400
M7-523.25000000000161.326238-8.532200
M8-508.37561.326238-8.289700
M9-455.562561.326238-7.428500
M10-316.18750000000061.326238-5.15581e-060
M11-116.62500000000061.326238-1.90170.0588150.029407

\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) & 2165.22639318886 & 43.631881 & 49.6249 & 0 & 0 \tabularnewline
X & -395.811145510835 & 38.605577 & -10.2527 & 0 & 0 \tabularnewline
M1 & -442.550696594425 & 61.373686 & -7.2108 & 0 & 0 \tabularnewline
M2 & -617.812500000002 & 61.326238 & -10.0742 & 0 & 0 \tabularnewline
M3 & -567.25 & 61.326238 & -9.2497 & 0 & 0 \tabularnewline
M4 & -680.4375 & 61.326238 & -11.0954 & 0 & 0 \tabularnewline
M5 & -543.125000000001 & 61.326238 & -8.8563 & 0 & 0 \tabularnewline
M6 & -598.874999999998 & 61.326238 & -9.7654 & 0 & 0 \tabularnewline
M7 & -523.250000000001 & 61.326238 & -8.5322 & 0 & 0 \tabularnewline
M8 & -508.375 & 61.326238 & -8.2897 & 0 & 0 \tabularnewline
M9 & -455.5625 & 61.326238 & -7.4285 & 0 & 0 \tabularnewline
M10 & -316.187500000000 & 61.326238 & -5.1558 & 1e-06 & 0 \tabularnewline
M11 & -116.625000000000 & 61.326238 & -1.9017 & 0.058815 & 0.029407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&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]2165.22639318886[/C][C]43.631881[/C][C]49.6249[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-395.811145510835[/C][C]38.605577[/C][C]-10.2527[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-442.550696594425[/C][C]61.373686[/C][C]-7.2108[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-617.812500000002[/C][C]61.326238[/C][C]-10.0742[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-567.25[/C][C]61.326238[/C][C]-9.2497[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-680.4375[/C][C]61.326238[/C][C]-11.0954[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-543.125000000001[/C][C]61.326238[/C][C]-8.8563[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-598.874999999998[/C][C]61.326238[/C][C]-9.7654[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-523.250000000001[/C][C]61.326238[/C][C]-8.5322[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-508.375[/C][C]61.326238[/C][C]-8.2897[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-455.5625[/C][C]61.326238[/C][C]-7.4285[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-316.187500000000[/C][C]61.326238[/C][C]-5.1558[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-116.625000000000[/C][C]61.326238[/C][C]-1.9017[/C][C]0.058815[/C][C]0.029407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=2

As an alternative you can also use a QR Code:  
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)2165.2263931888643.63188149.624900
X-395.81114551083538.605577-10.252700
M1-442.55069659442561.373686-7.210800
M2-617.81250000000261.326238-10.074200
M3-567.2561.326238-9.249700
M4-680.437561.326238-11.095400
M5-543.12500000000161.326238-8.856300
M6-598.87499999999861.326238-9.765400
M7-523.25000000000161.326238-8.532200
M8-508.37561.326238-8.289700
M9-455.562561.326238-7.428500
M10-316.18750000000061.326238-5.15581e-060
M11-116.62500000000061.326238-1.90170.0588150.029407






Multiple Linear Regression - Regression Statistics
Multiple R0.814751285561214
R-squared0.663819657323651
Adjusted R-squared0.641282427647024
F-TEST (value)29.4543591580865
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.456794605829
Sum Squared Residuals5385619.46749226

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.814751285561214 \tabularnewline
R-squared & 0.663819657323651 \tabularnewline
Adjusted R-squared & 0.641282427647024 \tabularnewline
F-TEST (value) & 29.4543591580865 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 179 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 173.456794605829 \tabularnewline
Sum Squared Residuals & 5385619.46749226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.814751285561214[/C][/ROW]
[ROW][C]R-squared[/C][C]0.663819657323651[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.641282427647024[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.4543591580865[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]179[/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]173.456794605829[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5385619.46749226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.814751285561214
R-squared0.663819657323651
Adjusted R-squared0.641282427647024
F-TEST (value)29.4543591580865
F-TEST (DF numerator)12
F-TEST (DF denominator)179
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation173.456794605829
Sum Squared Residuals5385619.46749226






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871722.67569659441-35.6756965944053
215081547.41389318886-39.413893188857
315071597.97639318886-90.9763931888592
413851484.78889318885-99.7888931888466
516321622.101393188869.8986068111442
615111566.35139318886-55.3513931888552
715591641.97639318885-82.9763931888532
816301656.85139318886-26.8513931888614
915791709.66389318885-130.663893188851
1016531849.03889318885-196.038893188855
1121522048.60139318886103.398606811145
1221482165.22639318885-17.226393188854
1317521722.6756965944329.3243034055713
1417651547.41389318885217.586106811146
1517171597.97639318885119.023606811146
1615581484.7888931888573.211106811145
1715751622.10139318885-47.1013931888544
1815201566.35139318885-46.3513931888545
1918051641.97639318885163.023606811145
2018001656.85139318885143.148606811146
2117191709.663893188859.33610681114532
2220081849.03889318885158.961106811145
2322422048.60139318885193.398606811146
2424782165.22639318885312.773606811145
2520301722.67569659443307.324303405571
2616551547.41389318885107.586106811146
2716931597.9763931888595.0236068111458
2816231484.78889318885138.211106811145
2918051622.10139318885182.898606811146
3017461566.35139318885179.648606811145
3117951641.97639318885153.023606811145
3219261656.85139318885269.148606811146
3316191709.66389318885-90.6638931888547
3419921849.03889318885142.961106811145
3522332048.60139318885184.398606811146
3621922165.2263931888526.7736068111454
3720801722.67569659443357.324303405571
3817681547.41389318885220.586106811146
3918351597.97639318885237.023606811146
4015691484.7888931888584.211106811145
4119761622.10139318885353.898606811146
4218531566.35139318885286.648606811145
4319651641.97639318885323.023606811145
4416891656.8513931888532.148606811146
4517781709.6638931888568.3361068111453
4619761849.03889318885126.961106811145
4723972048.60139318885348.398606811146
4826542165.22639318885488.773606811145
4920971722.67569659443374.324303405571
5019631547.41389318885415.586106811145
5116771597.9763931888579.0236068111458
5219411484.78889318886456.211106811145
5320031622.10139318885380.898606811146
5418131566.35139318885246.648606811145
5520121641.97639318885370.023606811145
5619121656.85139318885255.148606811146
5720841709.66389318885374.336106811145
5820801849.03889318885230.961106811145
5921182048.6013931888569.3986068111455
6021502165.22639318885-15.2263931888546
6116081722.67569659443-114.675696594429
6215031547.41389318885-44.4138931888543
6315481597.97639318885-49.9763931888542
6413821484.78889318885-102.788893188855
6517311622.10139318885108.898606811146
6617981566.35139318885231.648606811145
6717791641.97639318885137.023606811145
6818871656.85139318885230.148606811146
6920041709.66389318885294.336106811145
7020771849.03889318885227.961106811145
7120922048.6013931888543.3986068111455
7220512165.22639318885-114.226393188855
7315771722.67569659443-145.675696594429
7413561547.41389318885-191.413893188854
7516521597.9763931888554.0236068111458
7613821484.78889318885-102.788893188855
7715191622.10139318885-103.101393188854
7814211566.35139318885-145.351393188855
7914421641.97639318885-199.976393188855
8015431656.85139318885-113.851393188854
8116561709.66389318885-53.6638931888547
8215611849.03889318885-288.038893188855
8319052048.60139318885-143.601393188855
8421992165.2263931888533.7736068111454
8514731722.67569659443-249.675696594429
8616551547.41389318885107.586106811146
8714071597.97639318885-190.976393188854
8813951484.78889318885-89.788893188855
8915301622.10139318885-92.1013931888544
9013091566.35139318885-257.351393188855
9115261641.97639318885-115.976393188855
9213271656.85139318885-329.851393188854
9316271709.66389318885-82.6638931888547
9417481849.03889318885-101.038893188855
9519582048.60139318885-90.6013931888545
9622742165.22639318885108.773606811145
9716481722.67569659443-74.6756965944287
9814011547.41389318885-146.413893188854
9914111597.97639318885-186.976393188854
10014031484.78889318885-81.788893188855
10113941622.10139318885-228.101393188854
10215201566.35139318885-46.3513931888545
10315281641.97639318885-113.976393188855
10416431656.85139318885-13.851393188854
10515151709.66389318885-194.663893188855
10616851849.03889318885-164.038893188855
10720002048.60139318885-48.6013931888545
10822152165.2263931888549.7736068111454
10919561722.67569659443233.324303405571
11014621547.41389318885-85.4138931888543
11115631597.97639318885-34.9763931888542
11214591484.78889318885-25.7888931888550
11314461622.10139318885-176.101393188854
11416221566.3513931888555.6486068111455
11516571641.9763931888515.0236068111454
11616381656.85139318885-18.851393188854
11716431709.66389318885-66.6638931888547
11816831849.03889318885-166.038893188855
11920502048.601393188851.39860681114553
12022622165.2263931888596.7736068111454
12118131722.6756965944390.3243034055712
12214451547.41389318885-102.413893188854
12317621597.97639318885164.023606811146
12414611484.78889318885-23.7888931888550
12515561622.10139318885-66.1013931888544
12614311566.35139318885-135.351393188855
12714271641.97639318885-214.976393188855
12815541656.85139318885-102.851393188854
12916451709.66389318885-64.6638931888547
13016531849.03889318885-196.038893188855
13120162048.60139318885-32.6013931888545
13222072165.2263931888541.7736068111454
13316651722.67569659443-57.6756965944287
13413611547.41389318885-186.413893188854
13515061597.97639318885-91.9763931888542
13613601484.78889318885-124.788893188855
13714531622.10139318885-169.101393188854
13815221566.35139318885-44.3513931888545
13914601641.97639318885-181.976393188855
14015521656.85139318885-104.851393188854
14115481709.66389318885-161.663893188855
14218271849.03889318885-22.0388931888545
14317372048.60139318885-311.601393188855
14419412165.22639318885-224.226393188855
14514741722.67569659443-248.675696594429
14614581547.41389318885-89.4138931888543
14715421597.97639318885-55.9763931888542
14814041484.78889318885-80.788893188855
14915221622.10139318885-100.101393188854
15013851566.35139318885-181.351393188855
15116411641.97639318885-0.9763931888546
15215101656.85139318885-146.851393188854
15316811709.66389318885-28.6638931888547
15419381849.0388931888588.9611068111455
15518682048.60139318885-180.601393188855
15617262165.22639318885-439.226393188855
15714561722.67569659443-266.675696594429
15814451547.41389318885-102.413893188854
15914561597.97639318885-141.976393188854
16013651484.78889318885-119.788893188855
16114871622.10139318885-135.101393188854
16215581566.35139318885-8.3513931888545
16314881641.97639318885-153.976393188855
16416841656.8513931888527.148606811146
16515941709.66389318885-115.663893188855
16618501849.038893188850.9611068111455
16719982048.60139318885-50.6013931888545
16820792165.22639318885-86.2263931888546
16914941722.67569659443-228.675696594429
17010571151.60274767802-94.6027476780183
17112181202.1652476780215.8347523219817
17211681088.9777476780279.022252321981
17312361226.290247678029.70975232198158
17410761170.54024767802-94.5402476780185
17511741246.16524767802-72.1652476780186
17611391261.04024767802-122.040247678018
17714271313.85274767802113.147252321981
17814871453.2277476780233.7722523219814
17914831652.79024767802-169.790247678018
18015131769.41524767802-256.415247678019
18113571326.8645510835930.1354489164072
18211651151.6027476780213.3972523219817
18312821202.1652476780279.8347523219817
18411101088.9777476780221.0222523219809
18512971226.2902476780270.7097523219816
18611851170.5402476780214.4597523219815
18712221246.16524767802-24.1652476780187
18812841261.0402476780222.9597523219819
18914441313.85274767802130.147252321981
19015751453.22774767802121.772252321981
19117371652.7902476780284.2097523219815
19217631769.41524767802-6.41524767801867

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1722.67569659441 & -35.6756965944053 \tabularnewline
2 & 1508 & 1547.41389318886 & -39.413893188857 \tabularnewline
3 & 1507 & 1597.97639318886 & -90.9763931888592 \tabularnewline
4 & 1385 & 1484.78889318885 & -99.7888931888466 \tabularnewline
5 & 1632 & 1622.10139318886 & 9.8986068111442 \tabularnewline
6 & 1511 & 1566.35139318886 & -55.3513931888552 \tabularnewline
7 & 1559 & 1641.97639318885 & -82.9763931888532 \tabularnewline
8 & 1630 & 1656.85139318886 & -26.8513931888614 \tabularnewline
9 & 1579 & 1709.66389318885 & -130.663893188851 \tabularnewline
10 & 1653 & 1849.03889318885 & -196.038893188855 \tabularnewline
11 & 2152 & 2048.60139318886 & 103.398606811145 \tabularnewline
12 & 2148 & 2165.22639318885 & -17.226393188854 \tabularnewline
13 & 1752 & 1722.67569659443 & 29.3243034055713 \tabularnewline
14 & 1765 & 1547.41389318885 & 217.586106811146 \tabularnewline
15 & 1717 & 1597.97639318885 & 119.023606811146 \tabularnewline
16 & 1558 & 1484.78889318885 & 73.211106811145 \tabularnewline
17 & 1575 & 1622.10139318885 & -47.1013931888544 \tabularnewline
18 & 1520 & 1566.35139318885 & -46.3513931888545 \tabularnewline
19 & 1805 & 1641.97639318885 & 163.023606811145 \tabularnewline
20 & 1800 & 1656.85139318885 & 143.148606811146 \tabularnewline
21 & 1719 & 1709.66389318885 & 9.33610681114532 \tabularnewline
22 & 2008 & 1849.03889318885 & 158.961106811145 \tabularnewline
23 & 2242 & 2048.60139318885 & 193.398606811146 \tabularnewline
24 & 2478 & 2165.22639318885 & 312.773606811145 \tabularnewline
25 & 2030 & 1722.67569659443 & 307.324303405571 \tabularnewline
26 & 1655 & 1547.41389318885 & 107.586106811146 \tabularnewline
27 & 1693 & 1597.97639318885 & 95.0236068111458 \tabularnewline
28 & 1623 & 1484.78889318885 & 138.211106811145 \tabularnewline
29 & 1805 & 1622.10139318885 & 182.898606811146 \tabularnewline
30 & 1746 & 1566.35139318885 & 179.648606811145 \tabularnewline
31 & 1795 & 1641.97639318885 & 153.023606811145 \tabularnewline
32 & 1926 & 1656.85139318885 & 269.148606811146 \tabularnewline
33 & 1619 & 1709.66389318885 & -90.6638931888547 \tabularnewline
34 & 1992 & 1849.03889318885 & 142.961106811145 \tabularnewline
35 & 2233 & 2048.60139318885 & 184.398606811146 \tabularnewline
36 & 2192 & 2165.22639318885 & 26.7736068111454 \tabularnewline
37 & 2080 & 1722.67569659443 & 357.324303405571 \tabularnewline
38 & 1768 & 1547.41389318885 & 220.586106811146 \tabularnewline
39 & 1835 & 1597.97639318885 & 237.023606811146 \tabularnewline
40 & 1569 & 1484.78889318885 & 84.211106811145 \tabularnewline
41 & 1976 & 1622.10139318885 & 353.898606811146 \tabularnewline
42 & 1853 & 1566.35139318885 & 286.648606811145 \tabularnewline
43 & 1965 & 1641.97639318885 & 323.023606811145 \tabularnewline
44 & 1689 & 1656.85139318885 & 32.148606811146 \tabularnewline
45 & 1778 & 1709.66389318885 & 68.3361068111453 \tabularnewline
46 & 1976 & 1849.03889318885 & 126.961106811145 \tabularnewline
47 & 2397 & 2048.60139318885 & 348.398606811146 \tabularnewline
48 & 2654 & 2165.22639318885 & 488.773606811145 \tabularnewline
49 & 2097 & 1722.67569659443 & 374.324303405571 \tabularnewline
50 & 1963 & 1547.41389318885 & 415.586106811145 \tabularnewline
51 & 1677 & 1597.97639318885 & 79.0236068111458 \tabularnewline
52 & 1941 & 1484.78889318886 & 456.211106811145 \tabularnewline
53 & 2003 & 1622.10139318885 & 380.898606811146 \tabularnewline
54 & 1813 & 1566.35139318885 & 246.648606811145 \tabularnewline
55 & 2012 & 1641.97639318885 & 370.023606811145 \tabularnewline
56 & 1912 & 1656.85139318885 & 255.148606811146 \tabularnewline
57 & 2084 & 1709.66389318885 & 374.336106811145 \tabularnewline
58 & 2080 & 1849.03889318885 & 230.961106811145 \tabularnewline
59 & 2118 & 2048.60139318885 & 69.3986068111455 \tabularnewline
60 & 2150 & 2165.22639318885 & -15.2263931888546 \tabularnewline
61 & 1608 & 1722.67569659443 & -114.675696594429 \tabularnewline
62 & 1503 & 1547.41389318885 & -44.4138931888543 \tabularnewline
63 & 1548 & 1597.97639318885 & -49.9763931888542 \tabularnewline
64 & 1382 & 1484.78889318885 & -102.788893188855 \tabularnewline
65 & 1731 & 1622.10139318885 & 108.898606811146 \tabularnewline
66 & 1798 & 1566.35139318885 & 231.648606811145 \tabularnewline
67 & 1779 & 1641.97639318885 & 137.023606811145 \tabularnewline
68 & 1887 & 1656.85139318885 & 230.148606811146 \tabularnewline
69 & 2004 & 1709.66389318885 & 294.336106811145 \tabularnewline
70 & 2077 & 1849.03889318885 & 227.961106811145 \tabularnewline
71 & 2092 & 2048.60139318885 & 43.3986068111455 \tabularnewline
72 & 2051 & 2165.22639318885 & -114.226393188855 \tabularnewline
73 & 1577 & 1722.67569659443 & -145.675696594429 \tabularnewline
74 & 1356 & 1547.41389318885 & -191.413893188854 \tabularnewline
75 & 1652 & 1597.97639318885 & 54.0236068111458 \tabularnewline
76 & 1382 & 1484.78889318885 & -102.788893188855 \tabularnewline
77 & 1519 & 1622.10139318885 & -103.101393188854 \tabularnewline
78 & 1421 & 1566.35139318885 & -145.351393188855 \tabularnewline
79 & 1442 & 1641.97639318885 & -199.976393188855 \tabularnewline
80 & 1543 & 1656.85139318885 & -113.851393188854 \tabularnewline
81 & 1656 & 1709.66389318885 & -53.6638931888547 \tabularnewline
82 & 1561 & 1849.03889318885 & -288.038893188855 \tabularnewline
83 & 1905 & 2048.60139318885 & -143.601393188855 \tabularnewline
84 & 2199 & 2165.22639318885 & 33.7736068111454 \tabularnewline
85 & 1473 & 1722.67569659443 & -249.675696594429 \tabularnewline
86 & 1655 & 1547.41389318885 & 107.586106811146 \tabularnewline
87 & 1407 & 1597.97639318885 & -190.976393188854 \tabularnewline
88 & 1395 & 1484.78889318885 & -89.788893188855 \tabularnewline
89 & 1530 & 1622.10139318885 & -92.1013931888544 \tabularnewline
90 & 1309 & 1566.35139318885 & -257.351393188855 \tabularnewline
91 & 1526 & 1641.97639318885 & -115.976393188855 \tabularnewline
92 & 1327 & 1656.85139318885 & -329.851393188854 \tabularnewline
93 & 1627 & 1709.66389318885 & -82.6638931888547 \tabularnewline
94 & 1748 & 1849.03889318885 & -101.038893188855 \tabularnewline
95 & 1958 & 2048.60139318885 & -90.6013931888545 \tabularnewline
96 & 2274 & 2165.22639318885 & 108.773606811145 \tabularnewline
97 & 1648 & 1722.67569659443 & -74.6756965944287 \tabularnewline
98 & 1401 & 1547.41389318885 & -146.413893188854 \tabularnewline
99 & 1411 & 1597.97639318885 & -186.976393188854 \tabularnewline
100 & 1403 & 1484.78889318885 & -81.788893188855 \tabularnewline
101 & 1394 & 1622.10139318885 & -228.101393188854 \tabularnewline
102 & 1520 & 1566.35139318885 & -46.3513931888545 \tabularnewline
103 & 1528 & 1641.97639318885 & -113.976393188855 \tabularnewline
104 & 1643 & 1656.85139318885 & -13.851393188854 \tabularnewline
105 & 1515 & 1709.66389318885 & -194.663893188855 \tabularnewline
106 & 1685 & 1849.03889318885 & -164.038893188855 \tabularnewline
107 & 2000 & 2048.60139318885 & -48.6013931888545 \tabularnewline
108 & 2215 & 2165.22639318885 & 49.7736068111454 \tabularnewline
109 & 1956 & 1722.67569659443 & 233.324303405571 \tabularnewline
110 & 1462 & 1547.41389318885 & -85.4138931888543 \tabularnewline
111 & 1563 & 1597.97639318885 & -34.9763931888542 \tabularnewline
112 & 1459 & 1484.78889318885 & -25.7888931888550 \tabularnewline
113 & 1446 & 1622.10139318885 & -176.101393188854 \tabularnewline
114 & 1622 & 1566.35139318885 & 55.6486068111455 \tabularnewline
115 & 1657 & 1641.97639318885 & 15.0236068111454 \tabularnewline
116 & 1638 & 1656.85139318885 & -18.851393188854 \tabularnewline
117 & 1643 & 1709.66389318885 & -66.6638931888547 \tabularnewline
118 & 1683 & 1849.03889318885 & -166.038893188855 \tabularnewline
119 & 2050 & 2048.60139318885 & 1.39860681114553 \tabularnewline
120 & 2262 & 2165.22639318885 & 96.7736068111454 \tabularnewline
121 & 1813 & 1722.67569659443 & 90.3243034055712 \tabularnewline
122 & 1445 & 1547.41389318885 & -102.413893188854 \tabularnewline
123 & 1762 & 1597.97639318885 & 164.023606811146 \tabularnewline
124 & 1461 & 1484.78889318885 & -23.7888931888550 \tabularnewline
125 & 1556 & 1622.10139318885 & -66.1013931888544 \tabularnewline
126 & 1431 & 1566.35139318885 & -135.351393188855 \tabularnewline
127 & 1427 & 1641.97639318885 & -214.976393188855 \tabularnewline
128 & 1554 & 1656.85139318885 & -102.851393188854 \tabularnewline
129 & 1645 & 1709.66389318885 & -64.6638931888547 \tabularnewline
130 & 1653 & 1849.03889318885 & -196.038893188855 \tabularnewline
131 & 2016 & 2048.60139318885 & -32.6013931888545 \tabularnewline
132 & 2207 & 2165.22639318885 & 41.7736068111454 \tabularnewline
133 & 1665 & 1722.67569659443 & -57.6756965944287 \tabularnewline
134 & 1361 & 1547.41389318885 & -186.413893188854 \tabularnewline
135 & 1506 & 1597.97639318885 & -91.9763931888542 \tabularnewline
136 & 1360 & 1484.78889318885 & -124.788893188855 \tabularnewline
137 & 1453 & 1622.10139318885 & -169.101393188854 \tabularnewline
138 & 1522 & 1566.35139318885 & -44.3513931888545 \tabularnewline
139 & 1460 & 1641.97639318885 & -181.976393188855 \tabularnewline
140 & 1552 & 1656.85139318885 & -104.851393188854 \tabularnewline
141 & 1548 & 1709.66389318885 & -161.663893188855 \tabularnewline
142 & 1827 & 1849.03889318885 & -22.0388931888545 \tabularnewline
143 & 1737 & 2048.60139318885 & -311.601393188855 \tabularnewline
144 & 1941 & 2165.22639318885 & -224.226393188855 \tabularnewline
145 & 1474 & 1722.67569659443 & -248.675696594429 \tabularnewline
146 & 1458 & 1547.41389318885 & -89.4138931888543 \tabularnewline
147 & 1542 & 1597.97639318885 & -55.9763931888542 \tabularnewline
148 & 1404 & 1484.78889318885 & -80.788893188855 \tabularnewline
149 & 1522 & 1622.10139318885 & -100.101393188854 \tabularnewline
150 & 1385 & 1566.35139318885 & -181.351393188855 \tabularnewline
151 & 1641 & 1641.97639318885 & -0.9763931888546 \tabularnewline
152 & 1510 & 1656.85139318885 & -146.851393188854 \tabularnewline
153 & 1681 & 1709.66389318885 & -28.6638931888547 \tabularnewline
154 & 1938 & 1849.03889318885 & 88.9611068111455 \tabularnewline
155 & 1868 & 2048.60139318885 & -180.601393188855 \tabularnewline
156 & 1726 & 2165.22639318885 & -439.226393188855 \tabularnewline
157 & 1456 & 1722.67569659443 & -266.675696594429 \tabularnewline
158 & 1445 & 1547.41389318885 & -102.413893188854 \tabularnewline
159 & 1456 & 1597.97639318885 & -141.976393188854 \tabularnewline
160 & 1365 & 1484.78889318885 & -119.788893188855 \tabularnewline
161 & 1487 & 1622.10139318885 & -135.101393188854 \tabularnewline
162 & 1558 & 1566.35139318885 & -8.3513931888545 \tabularnewline
163 & 1488 & 1641.97639318885 & -153.976393188855 \tabularnewline
164 & 1684 & 1656.85139318885 & 27.148606811146 \tabularnewline
165 & 1594 & 1709.66389318885 & -115.663893188855 \tabularnewline
166 & 1850 & 1849.03889318885 & 0.9611068111455 \tabularnewline
167 & 1998 & 2048.60139318885 & -50.6013931888545 \tabularnewline
168 & 2079 & 2165.22639318885 & -86.2263931888546 \tabularnewline
169 & 1494 & 1722.67569659443 & -228.675696594429 \tabularnewline
170 & 1057 & 1151.60274767802 & -94.6027476780183 \tabularnewline
171 & 1218 & 1202.16524767802 & 15.8347523219817 \tabularnewline
172 & 1168 & 1088.97774767802 & 79.022252321981 \tabularnewline
173 & 1236 & 1226.29024767802 & 9.70975232198158 \tabularnewline
174 & 1076 & 1170.54024767802 & -94.5402476780185 \tabularnewline
175 & 1174 & 1246.16524767802 & -72.1652476780186 \tabularnewline
176 & 1139 & 1261.04024767802 & -122.040247678018 \tabularnewline
177 & 1427 & 1313.85274767802 & 113.147252321981 \tabularnewline
178 & 1487 & 1453.22774767802 & 33.7722523219814 \tabularnewline
179 & 1483 & 1652.79024767802 & -169.790247678018 \tabularnewline
180 & 1513 & 1769.41524767802 & -256.415247678019 \tabularnewline
181 & 1357 & 1326.86455108359 & 30.1354489164072 \tabularnewline
182 & 1165 & 1151.60274767802 & 13.3972523219817 \tabularnewline
183 & 1282 & 1202.16524767802 & 79.8347523219817 \tabularnewline
184 & 1110 & 1088.97774767802 & 21.0222523219809 \tabularnewline
185 & 1297 & 1226.29024767802 & 70.7097523219816 \tabularnewline
186 & 1185 & 1170.54024767802 & 14.4597523219815 \tabularnewline
187 & 1222 & 1246.16524767802 & -24.1652476780187 \tabularnewline
188 & 1284 & 1261.04024767802 & 22.9597523219819 \tabularnewline
189 & 1444 & 1313.85274767802 & 130.147252321981 \tabularnewline
190 & 1575 & 1453.22774767802 & 121.772252321981 \tabularnewline
191 & 1737 & 1652.79024767802 & 84.2097523219815 \tabularnewline
192 & 1763 & 1769.41524767802 & -6.41524767801867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&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]1687[/C][C]1722.67569659441[/C][C]-35.6756965944053[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1547.41389318886[/C][C]-39.413893188857[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1597.97639318886[/C][C]-90.9763931888592[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1484.78889318885[/C][C]-99.7888931888466[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1622.10139318886[/C][C]9.8986068111442[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1566.35139318886[/C][C]-55.3513931888552[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1641.97639318885[/C][C]-82.9763931888532[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1656.85139318886[/C][C]-26.8513931888614[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1709.66389318885[/C][C]-130.663893188851[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1849.03889318885[/C][C]-196.038893188855[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]2048.60139318886[/C][C]103.398606811145[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2165.22639318885[/C][C]-17.226393188854[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1722.67569659443[/C][C]29.3243034055713[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1547.41389318885[/C][C]217.586106811146[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1597.97639318885[/C][C]119.023606811146[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1484.78889318885[/C][C]73.211106811145[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1622.10139318885[/C][C]-47.1013931888544[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1566.35139318885[/C][C]-46.3513931888545[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1641.97639318885[/C][C]163.023606811145[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1656.85139318885[/C][C]143.148606811146[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1709.66389318885[/C][C]9.33610681114532[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1849.03889318885[/C][C]158.961106811145[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2048.60139318885[/C][C]193.398606811146[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]2165.22639318885[/C][C]312.773606811145[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1722.67569659443[/C][C]307.324303405571[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1547.41389318885[/C][C]107.586106811146[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1597.97639318885[/C][C]95.0236068111458[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1484.78889318885[/C][C]138.211106811145[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1622.10139318885[/C][C]182.898606811146[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1566.35139318885[/C][C]179.648606811145[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1641.97639318885[/C][C]153.023606811145[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1656.85139318885[/C][C]269.148606811146[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1709.66389318885[/C][C]-90.6638931888547[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1849.03889318885[/C][C]142.961106811145[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2048.60139318885[/C][C]184.398606811146[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2165.22639318885[/C][C]26.7736068111454[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1722.67569659443[/C][C]357.324303405571[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1547.41389318885[/C][C]220.586106811146[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1597.97639318885[/C][C]237.023606811146[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1484.78889318885[/C][C]84.211106811145[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1622.10139318885[/C][C]353.898606811146[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1566.35139318885[/C][C]286.648606811145[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1641.97639318885[/C][C]323.023606811145[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1656.85139318885[/C][C]32.148606811146[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1709.66389318885[/C][C]68.3361068111453[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1849.03889318885[/C][C]126.961106811145[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]2048.60139318885[/C][C]348.398606811146[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]2165.22639318885[/C][C]488.773606811145[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1722.67569659443[/C][C]374.324303405571[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1547.41389318885[/C][C]415.586106811145[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1597.97639318885[/C][C]79.0236068111458[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1484.78889318886[/C][C]456.211106811145[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1622.10139318885[/C][C]380.898606811146[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1566.35139318885[/C][C]246.648606811145[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1641.97639318885[/C][C]370.023606811145[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1656.85139318885[/C][C]255.148606811146[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1709.66389318885[/C][C]374.336106811145[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1849.03889318885[/C][C]230.961106811145[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2048.60139318885[/C][C]69.3986068111455[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2165.22639318885[/C][C]-15.2263931888546[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1722.67569659443[/C][C]-114.675696594429[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1547.41389318885[/C][C]-44.4138931888543[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1597.97639318885[/C][C]-49.9763931888542[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1484.78889318885[/C][C]-102.788893188855[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1622.10139318885[/C][C]108.898606811146[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1566.35139318885[/C][C]231.648606811145[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1641.97639318885[/C][C]137.023606811145[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1656.85139318885[/C][C]230.148606811146[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1709.66389318885[/C][C]294.336106811145[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1849.03889318885[/C][C]227.961106811145[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]2048.60139318885[/C][C]43.3986068111455[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]2165.22639318885[/C][C]-114.226393188855[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1722.67569659443[/C][C]-145.675696594429[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1547.41389318885[/C][C]-191.413893188854[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1597.97639318885[/C][C]54.0236068111458[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1484.78889318885[/C][C]-102.788893188855[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1622.10139318885[/C][C]-103.101393188854[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1566.35139318885[/C][C]-145.351393188855[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1641.97639318885[/C][C]-199.976393188855[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1656.85139318885[/C][C]-113.851393188854[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1709.66389318885[/C][C]-53.6638931888547[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1849.03889318885[/C][C]-288.038893188855[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]2048.60139318885[/C][C]-143.601393188855[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]2165.22639318885[/C][C]33.7736068111454[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.67569659443[/C][C]-249.675696594429[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1547.41389318885[/C][C]107.586106811146[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1597.97639318885[/C][C]-190.976393188854[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1484.78889318885[/C][C]-89.788893188855[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1622.10139318885[/C][C]-92.1013931888544[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1566.35139318885[/C][C]-257.351393188855[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1641.97639318885[/C][C]-115.976393188855[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1656.85139318885[/C][C]-329.851393188854[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1709.66389318885[/C][C]-82.6638931888547[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1849.03889318885[/C][C]-101.038893188855[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]2048.60139318885[/C][C]-90.6013931888545[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]2165.22639318885[/C][C]108.773606811145[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1722.67569659443[/C][C]-74.6756965944287[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1547.41389318885[/C][C]-146.413893188854[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1597.97639318885[/C][C]-186.976393188854[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1484.78889318885[/C][C]-81.788893188855[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1622.10139318885[/C][C]-228.101393188854[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1566.35139318885[/C][C]-46.3513931888545[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1641.97639318885[/C][C]-113.976393188855[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1656.85139318885[/C][C]-13.851393188854[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1709.66389318885[/C][C]-194.663893188855[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1849.03889318885[/C][C]-164.038893188855[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]2048.60139318885[/C][C]-48.6013931888545[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2165.22639318885[/C][C]49.7736068111454[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1722.67569659443[/C][C]233.324303405571[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1547.41389318885[/C][C]-85.4138931888543[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1597.97639318885[/C][C]-34.9763931888542[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1484.78889318885[/C][C]-25.7888931888550[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1622.10139318885[/C][C]-176.101393188854[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1566.35139318885[/C][C]55.6486068111455[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1641.97639318885[/C][C]15.0236068111454[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1656.85139318885[/C][C]-18.851393188854[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1709.66389318885[/C][C]-66.6638931888547[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1849.03889318885[/C][C]-166.038893188855[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]2048.60139318885[/C][C]1.39860681114553[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]2165.22639318885[/C][C]96.7736068111454[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1722.67569659443[/C][C]90.3243034055712[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1547.41389318885[/C][C]-102.413893188854[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1597.97639318885[/C][C]164.023606811146[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1484.78889318885[/C][C]-23.7888931888550[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1622.10139318885[/C][C]-66.1013931888544[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1566.35139318885[/C][C]-135.351393188855[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1641.97639318885[/C][C]-214.976393188855[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1656.85139318885[/C][C]-102.851393188854[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1709.66389318885[/C][C]-64.6638931888547[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1849.03889318885[/C][C]-196.038893188855[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]2048.60139318885[/C][C]-32.6013931888545[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]2165.22639318885[/C][C]41.7736068111454[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1722.67569659443[/C][C]-57.6756965944287[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1547.41389318885[/C][C]-186.413893188854[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1597.97639318885[/C][C]-91.9763931888542[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1484.78889318885[/C][C]-124.788893188855[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1622.10139318885[/C][C]-169.101393188854[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1566.35139318885[/C][C]-44.3513931888545[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1641.97639318885[/C][C]-181.976393188855[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1656.85139318885[/C][C]-104.851393188854[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1709.66389318885[/C][C]-161.663893188855[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1849.03889318885[/C][C]-22.0388931888545[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]2048.60139318885[/C][C]-311.601393188855[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]2165.22639318885[/C][C]-224.226393188855[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1722.67569659443[/C][C]-248.675696594429[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1547.41389318885[/C][C]-89.4138931888543[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1597.97639318885[/C][C]-55.9763931888542[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1484.78889318885[/C][C]-80.788893188855[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1622.10139318885[/C][C]-100.101393188854[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1566.35139318885[/C][C]-181.351393188855[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1641.97639318885[/C][C]-0.9763931888546[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1656.85139318885[/C][C]-146.851393188854[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1709.66389318885[/C][C]-28.6638931888547[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1849.03889318885[/C][C]88.9611068111455[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]2048.60139318885[/C][C]-180.601393188855[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]2165.22639318885[/C][C]-439.226393188855[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1722.67569659443[/C][C]-266.675696594429[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1547.41389318885[/C][C]-102.413893188854[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1597.97639318885[/C][C]-141.976393188854[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1484.78889318885[/C][C]-119.788893188855[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1622.10139318885[/C][C]-135.101393188854[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1566.35139318885[/C][C]-8.3513931888545[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1641.97639318885[/C][C]-153.976393188855[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1656.85139318885[/C][C]27.148606811146[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1709.66389318885[/C][C]-115.663893188855[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1849.03889318885[/C][C]0.9611068111455[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]2048.60139318885[/C][C]-50.6013931888545[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2165.22639318885[/C][C]-86.2263931888546[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1722.67569659443[/C][C]-228.675696594429[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1151.60274767802[/C][C]-94.6027476780183[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1202.16524767802[/C][C]15.8347523219817[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1088.97774767802[/C][C]79.022252321981[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1226.29024767802[/C][C]9.70975232198158[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1170.54024767802[/C][C]-94.5402476780185[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1246.16524767802[/C][C]-72.1652476780186[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1261.04024767802[/C][C]-122.040247678018[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1313.85274767802[/C][C]113.147252321981[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1453.22774767802[/C][C]33.7722523219814[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1652.79024767802[/C][C]-169.790247678018[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1769.41524767802[/C][C]-256.415247678019[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1326.86455108359[/C][C]30.1354489164072[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1151.60274767802[/C][C]13.3972523219817[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1202.16524767802[/C][C]79.8347523219817[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1088.97774767802[/C][C]21.0222523219809[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1226.29024767802[/C][C]70.7097523219816[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1170.54024767802[/C][C]14.4597523219815[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1246.16524767802[/C][C]-24.1652476780187[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1261.04024767802[/C][C]22.9597523219819[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1313.85274767802[/C][C]130.147252321981[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1453.22774767802[/C][C]121.772252321981[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1652.79024767802[/C][C]84.2097523219815[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1769.41524767802[/C][C]-6.41524767801867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871722.67569659441-35.6756965944053
215081547.41389318886-39.413893188857
315071597.97639318886-90.9763931888592
413851484.78889318885-99.7888931888466
516321622.101393188869.8986068111442
615111566.35139318886-55.3513931888552
715591641.97639318885-82.9763931888532
816301656.85139318886-26.8513931888614
915791709.66389318885-130.663893188851
1016531849.03889318885-196.038893188855
1121522048.60139318886103.398606811145
1221482165.22639318885-17.226393188854
1317521722.6756965944329.3243034055713
1417651547.41389318885217.586106811146
1517171597.97639318885119.023606811146
1615581484.7888931888573.211106811145
1715751622.10139318885-47.1013931888544
1815201566.35139318885-46.3513931888545
1918051641.97639318885163.023606811145
2018001656.85139318885143.148606811146
2117191709.663893188859.33610681114532
2220081849.03889318885158.961106811145
2322422048.60139318885193.398606811146
2424782165.22639318885312.773606811145
2520301722.67569659443307.324303405571
2616551547.41389318885107.586106811146
2716931597.9763931888595.0236068111458
2816231484.78889318885138.211106811145
2918051622.10139318885182.898606811146
3017461566.35139318885179.648606811145
3117951641.97639318885153.023606811145
3219261656.85139318885269.148606811146
3316191709.66389318885-90.6638931888547
3419921849.03889318885142.961106811145
3522332048.60139318885184.398606811146
3621922165.2263931888526.7736068111454
3720801722.67569659443357.324303405571
3817681547.41389318885220.586106811146
3918351597.97639318885237.023606811146
4015691484.7888931888584.211106811145
4119761622.10139318885353.898606811146
4218531566.35139318885286.648606811145
4319651641.97639318885323.023606811145
4416891656.8513931888532.148606811146
4517781709.6638931888568.3361068111453
4619761849.03889318885126.961106811145
4723972048.60139318885348.398606811146
4826542165.22639318885488.773606811145
4920971722.67569659443374.324303405571
5019631547.41389318885415.586106811145
5116771597.9763931888579.0236068111458
5219411484.78889318886456.211106811145
5320031622.10139318885380.898606811146
5418131566.35139318885246.648606811145
5520121641.97639318885370.023606811145
5619121656.85139318885255.148606811146
5720841709.66389318885374.336106811145
5820801849.03889318885230.961106811145
5921182048.6013931888569.3986068111455
6021502165.22639318885-15.2263931888546
6116081722.67569659443-114.675696594429
6215031547.41389318885-44.4138931888543
6315481597.97639318885-49.9763931888542
6413821484.78889318885-102.788893188855
6517311622.10139318885108.898606811146
6617981566.35139318885231.648606811145
6717791641.97639318885137.023606811145
6818871656.85139318885230.148606811146
6920041709.66389318885294.336106811145
7020771849.03889318885227.961106811145
7120922048.6013931888543.3986068111455
7220512165.22639318885-114.226393188855
7315771722.67569659443-145.675696594429
7413561547.41389318885-191.413893188854
7516521597.9763931888554.0236068111458
7613821484.78889318885-102.788893188855
7715191622.10139318885-103.101393188854
7814211566.35139318885-145.351393188855
7914421641.97639318885-199.976393188855
8015431656.85139318885-113.851393188854
8116561709.66389318885-53.6638931888547
8215611849.03889318885-288.038893188855
8319052048.60139318885-143.601393188855
8421992165.2263931888533.7736068111454
8514731722.67569659443-249.675696594429
8616551547.41389318885107.586106811146
8714071597.97639318885-190.976393188854
8813951484.78889318885-89.788893188855
8915301622.10139318885-92.1013931888544
9013091566.35139318885-257.351393188855
9115261641.97639318885-115.976393188855
9213271656.85139318885-329.851393188854
9316271709.66389318885-82.6638931888547
9417481849.03889318885-101.038893188855
9519582048.60139318885-90.6013931888545
9622742165.22639318885108.773606811145
9716481722.67569659443-74.6756965944287
9814011547.41389318885-146.413893188854
9914111597.97639318885-186.976393188854
10014031484.78889318885-81.788893188855
10113941622.10139318885-228.101393188854
10215201566.35139318885-46.3513931888545
10315281641.97639318885-113.976393188855
10416431656.85139318885-13.851393188854
10515151709.66389318885-194.663893188855
10616851849.03889318885-164.038893188855
10720002048.60139318885-48.6013931888545
10822152165.2263931888549.7736068111454
10919561722.67569659443233.324303405571
11014621547.41389318885-85.4138931888543
11115631597.97639318885-34.9763931888542
11214591484.78889318885-25.7888931888550
11314461622.10139318885-176.101393188854
11416221566.3513931888555.6486068111455
11516571641.9763931888515.0236068111454
11616381656.85139318885-18.851393188854
11716431709.66389318885-66.6638931888547
11816831849.03889318885-166.038893188855
11920502048.601393188851.39860681114553
12022622165.2263931888596.7736068111454
12118131722.6756965944390.3243034055712
12214451547.41389318885-102.413893188854
12317621597.97639318885164.023606811146
12414611484.78889318885-23.7888931888550
12515561622.10139318885-66.1013931888544
12614311566.35139318885-135.351393188855
12714271641.97639318885-214.976393188855
12815541656.85139318885-102.851393188854
12916451709.66389318885-64.6638931888547
13016531849.03889318885-196.038893188855
13120162048.60139318885-32.6013931888545
13222072165.2263931888541.7736068111454
13316651722.67569659443-57.6756965944287
13413611547.41389318885-186.413893188854
13515061597.97639318885-91.9763931888542
13613601484.78889318885-124.788893188855
13714531622.10139318885-169.101393188854
13815221566.35139318885-44.3513931888545
13914601641.97639318885-181.976393188855
14015521656.85139318885-104.851393188854
14115481709.66389318885-161.663893188855
14218271849.03889318885-22.0388931888545
14317372048.60139318885-311.601393188855
14419412165.22639318885-224.226393188855
14514741722.67569659443-248.675696594429
14614581547.41389318885-89.4138931888543
14715421597.97639318885-55.9763931888542
14814041484.78889318885-80.788893188855
14915221622.10139318885-100.101393188854
15013851566.35139318885-181.351393188855
15116411641.97639318885-0.9763931888546
15215101656.85139318885-146.851393188854
15316811709.66389318885-28.6638931888547
15419381849.0388931888588.9611068111455
15518682048.60139318885-180.601393188855
15617262165.22639318885-439.226393188855
15714561722.67569659443-266.675696594429
15814451547.41389318885-102.413893188854
15914561597.97639318885-141.976393188854
16013651484.78889318885-119.788893188855
16114871622.10139318885-135.101393188854
16215581566.35139318885-8.3513931888545
16314881641.97639318885-153.976393188855
16416841656.8513931888527.148606811146
16515941709.66389318885-115.663893188855
16618501849.038893188850.9611068111455
16719982048.60139318885-50.6013931888545
16820792165.22639318885-86.2263931888546
16914941722.67569659443-228.675696594429
17010571151.60274767802-94.6027476780183
17112181202.1652476780215.8347523219817
17211681088.9777476780279.022252321981
17312361226.290247678029.70975232198158
17410761170.54024767802-94.5402476780185
17511741246.16524767802-72.1652476780186
17611391261.04024767802-122.040247678018
17714271313.85274767802113.147252321981
17814871453.2277476780233.7722523219814
17914831652.79024767802-169.790247678018
18015131769.41524767802-256.415247678019
18113571326.8645510835930.1354489164072
18211651151.6027476780213.3972523219817
18312821202.1652476780279.8347523219817
18411101088.9777476780221.0222523219809
18512971226.2902476780270.7097523219816
18611851170.5402476780214.4597523219815
18712221246.16524767802-24.1652476780187
18812841261.0402476780222.9597523219819
18914441313.85274767802130.147252321981
19015751453.22774767802121.772252321981
19117371652.7902476780284.2097523219815
19217631769.41524767802-6.41524767801867






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4774123925937360.9548247851874730.522587607406264
170.3177830613741790.6355661227483580.682216938625821
180.1918850330176080.3837700660352150.808114966982392
190.219496442452130.438992884904260.78050355754787
200.1828713287849740.3657426575699470.817128671215026
210.1388506413979020.2777012827958030.861149358602098
220.2470668210919270.4941336421838540.752933178908073
230.1865730770935170.3731461541870330.813426922906483
240.2681743220394330.5363486440788670.731825677960567
250.3753173479730120.7506346959460240.624682652026988
260.2984712257880200.5969424515760410.70152877421198
270.2399252673916360.4798505347832720.760074732608364
280.2130450390493630.4260900780987270.786954960950637
290.2137688746654890.4275377493309780.786231125334511
300.2297752313907650.4595504627815310.770224768609235
310.1947715355403670.3895430710807330.805228464459633
320.2077651117228950.4155302234457910.792234888277105
330.1621565160742570.3243130321485150.837843483925743
340.1481164301044300.2962328602088590.85188356989557
350.1178312959736230.2356625919472460.882168704026377
360.09738211722199170.1947642344439830.902617882778008
370.1414009615478070.2828019230956140.858599038452193
380.1297822915516910.2595645831033810.87021770844831
390.1397728276513450.2795456553026900.860227172348655
400.1105854094843160.2211708189686310.889414590515685
410.1841247186689660.3682494373379320.815875281331034
420.2348993285677120.4697986571354230.765100671432288
430.2916501478873060.5833002957746130.708349852112694
440.2552543762765750.5105087525531490.744745623723425
450.2319339781760510.4638679563521020.768066021823949
460.2039096313316780.4078192626633560.796090368668322
470.2431477080700400.4862954161400810.756852291929960
480.473887762165810.947775524331620.52611223783419
490.5691985323093710.8616029353812580.430801467690629
500.7264188828149550.547162234370090.273581117185045
510.6883967611186170.6232064777627660.311603238881383
520.8882423937637220.2235152124725560.111757606236278
530.9448774632618280.1102450734763440.0551225367381722
540.9546545717721840.09069085645563120.0453454282278156
550.981115207137360.03776958572528140.0188847928626407
560.9865139644489280.02697207110214390.0134860355510720
570.9978479988944410.004304002211117370.00215200110555868
580.9985628936079760.002874212784048820.00143710639202441
590.9985002905584210.002999418883158080.00149970944157904
600.9985432203954620.002913559209076210.00145677960453811
610.9990717312162770.001856537567446680.000928268783723342
620.9991151039029790.001769792194042620.000884896097021311
630.9988719053312270.002256189337546440.00112809466877322
640.9989103460697690.002179307860462730.00108965393023137
650.9989306229093290.002138754181342430.00106937709067122
660.9993975059603050.001204988079390.000602494039695
670.9995047596873250.0009904806253494490.000495240312674724
680.9997607135953660.0004785728092682090.000239286404634104
690.9999484555580240.0001030888839515535.15444419757764e-05
700.9999799755985484.00488029040693e-052.00244014520346e-05
710.9999803059758693.93880482627085e-051.96940241313543e-05
720.9999839540974833.20918050348279e-051.60459025174140e-05
730.9999887792284692.24415430626064e-051.12207715313032e-05
740.999994095788111.18084237801413e-055.90421189007064e-06
750.9999921392217381.57215565242678e-057.8607782621339e-06
760.9999907574149911.84851700178040e-059.24258500890202e-06
770.9999917059601141.65880797721136e-058.29403988605678e-06
780.9999931962960761.36074078485785e-056.80370392428923e-06
790.9999967089921266.58201574797166e-063.29100787398583e-06
800.999996782748756.43450250216893e-063.21725125108447e-06
810.9999953790095749.24198085107973e-064.62099042553986e-06
820.9999989147619072.17047618646408e-061.08523809323204e-06
830.999999055368061.88926387964174e-069.4463193982087e-07
840.9999988296198942.34076021144639e-061.17038010572320e-06
850.9999995137226139.7255477329988e-074.8627738664994e-07
860.9999996539047166.92190568997731e-073.46095284498866e-07
870.999999722268035.5546393885066e-072.7773196942533e-07
880.9999995861537358.27692529861566e-074.13846264930783e-07
890.9999994841506181.03169876404525e-065.15849382022623e-07
900.9999997829307224.34138555732368e-072.17069277866184e-07
910.9999997341194975.31761004889164e-072.65880502444582e-07
920.9999999593390588.13218832371426e-084.06609416185713e-08
930.9999999348798061.30240387583734e-076.51201937918671e-08
940.9999999010283021.97943395314047e-079.89716976570233e-08
950.9999998612210932.77557813291379e-071.38778906645690e-07
960.9999999140799731.71840054584607e-078.59200272923037e-08
970.999999860121822.79756359989457e-071.39878179994729e-07
980.9999998250211583.49957683513652e-071.74978841756826e-07
990.9999998469509363.06098128076187e-071.53049064038094e-07
1000.9999997471545825.05690835816319e-072.52845417908160e-07
1010.999999812583293.7483341941049e-071.87416709705245e-07
1020.9999996851519436.29696114916211e-073.14848057458106e-07
1030.999999548876739.02246538632805e-074.51123269316402e-07
1040.999999292225961.41554807902718e-067.0777403951359e-07
1050.9999993645267471.27094650632298e-066.3547325316149e-07
1060.9999992972496571.40550068602918e-067.02750343014592e-07
1070.999998958728012.08254397902858e-061.04127198951429e-06
1080.9999991521463351.69570732985605e-068.47853664928023e-07
1090.9999999198513551.60297289800619e-078.01486449003093e-08
1100.9999998710871182.57825763589014e-071.28912881794507e-07
1110.9999997606059254.78788149181416e-072.39394074590708e-07
1120.9999995839660988.32067803672855e-074.16033901836428e-07
1130.9999994948233761.01035324743262e-065.0517662371631e-07
1140.9999994802675611.03946487774175e-065.19732438870876e-07
1150.9999994524008361.09519832770823e-065.47599163854117e-07
1160.9999991628744191.67425116273504e-068.37125581367521e-07
1170.9999985308737672.93825246625430e-061.46912623312715e-06
1180.9999984927537093.01449258219802e-061.50724629109901e-06
1190.9999983229212913.35415741711385e-061.67707870855692e-06
1200.9999996178671167.64265768332042e-073.82132884166021e-07
1210.9999998933733432.13253314532940e-071.06626657266470e-07
1220.999999818611413.62777181927527e-071.81388590963763e-07
1230.9999999493565521.01286896749012e-075.0643448374506e-08
1240.9999999120661831.75867634100664e-078.7933817050332e-08
1250.9999998422740743.15451851057457e-071.57725925528729e-07
1260.9999997331275695.33744862044504e-072.66872431022252e-07
1270.9999996892012636.21597472934647e-073.10798736467324e-07
1280.99999943877581.12244840081945e-065.61224200409725e-07
1290.9999989356780242.12864395257080e-061.06432197628540e-06
1300.999999427726231.14454753850936e-065.72273769254682e-07
1310.9999993909970021.21800599684692e-066.09002998423458e-07
1320.9999999357444851.28511029699837e-076.42555148499187e-08
1330.9999999456898831.08620234260407e-075.43101171302033e-08
1340.9999999155595621.68880875030271e-078.44404375151355e-08
1350.999999827304643.45390721431426e-071.72695360715713e-07
1360.999999681112796.37774418162373e-073.18887209081187e-07
1370.9999995163213779.67357246883755e-074.83678623441878e-07
1380.9999992181697841.56366043213988e-067.81830216069941e-07
1390.9999987851041472.42979170547042e-061.21489585273521e-06
1400.999997645868014.70826397889355e-062.35413198944678e-06
1410.9999975123879174.97522416654345e-062.48761208327172e-06
1420.9999950610824989.87783500307573e-064.93891750153786e-06
1430.9999977378293514.52434129751273e-062.26217064875636e-06
1440.9999964692147257.06157054924965e-063.53078527462482e-06
1450.9999948623953031.02752093948037e-055.13760469740187e-06
1460.9999902717740881.94564518248318e-059.7282259124159e-06
1470.999980945438123.81091237617349e-051.90545618808674e-05
1480.99996251312627.49737476012236e-053.74868738006118e-05
1490.9999284328706930.0001431342586141197.15671293070596e-05
1500.9998940027453560.0002119945092869740.000105997254643487
1510.9998940207980520.0002119584038961480.000105979201948074
1520.999817241528260.0003655169434797710.000182758471739886
1530.9996499702514620.000700059497075910.000350029748537955
1540.9995311834273330.0009376331453337170.000468816572666858
1550.9992521017508740.001495796498252030.000747898249126013
1560.9998484865948990.0003030268102022800.000151513405101140
1570.9998217739455420.0003564521089157640.000178226054457882
1580.9996466639037490.0007066721925024810.000353336096251241
1590.999472246590460.001055506819079270.000527753409539633
1600.9991619418563270.001676116287346330.000838058143673166
1610.998794430703550.002411138592899180.00120556929644959
1620.9981324186713650.00373516265726970.00186758132863485
1630.9965413176793270.006917364641346630.00345868232067332
1640.996430642396850.007138715206302260.00356935760315113
1650.996302913049990.007394173900021560.00369708695001078
1660.9926248205431520.01475035891369490.00737517945684745
1670.9869490046325480.02610199073490310.0130509953674515
1680.9909147209212230.01817055815755360.0090852790787768
1690.9822710463111070.03545790737778660.0177289536888933
1700.9718771603734020.05624567925319610.0281228396265981
1710.9497551587281750.1004896825436500.0502448412718248
1720.9122010401935920.1755979196128150.0877989598064076
1730.8524618585293440.2950762829413130.147538141470656
1740.7823573198125330.4352853603749330.217642680187467
1750.6569428186407690.6861143627184620.343057181359231
1760.5500728500896410.8998542998207170.449927149910359

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.477412392593736 & 0.954824785187473 & 0.522587607406264 \tabularnewline
17 & 0.317783061374179 & 0.635566122748358 & 0.682216938625821 \tabularnewline
18 & 0.191885033017608 & 0.383770066035215 & 0.808114966982392 \tabularnewline
19 & 0.21949644245213 & 0.43899288490426 & 0.78050355754787 \tabularnewline
20 & 0.182871328784974 & 0.365742657569947 & 0.817128671215026 \tabularnewline
21 & 0.138850641397902 & 0.277701282795803 & 0.861149358602098 \tabularnewline
22 & 0.247066821091927 & 0.494133642183854 & 0.752933178908073 \tabularnewline
23 & 0.186573077093517 & 0.373146154187033 & 0.813426922906483 \tabularnewline
24 & 0.268174322039433 & 0.536348644078867 & 0.731825677960567 \tabularnewline
25 & 0.375317347973012 & 0.750634695946024 & 0.624682652026988 \tabularnewline
26 & 0.298471225788020 & 0.596942451576041 & 0.70152877421198 \tabularnewline
27 & 0.239925267391636 & 0.479850534783272 & 0.760074732608364 \tabularnewline
28 & 0.213045039049363 & 0.426090078098727 & 0.786954960950637 \tabularnewline
29 & 0.213768874665489 & 0.427537749330978 & 0.786231125334511 \tabularnewline
30 & 0.229775231390765 & 0.459550462781531 & 0.770224768609235 \tabularnewline
31 & 0.194771535540367 & 0.389543071080733 & 0.805228464459633 \tabularnewline
32 & 0.207765111722895 & 0.415530223445791 & 0.792234888277105 \tabularnewline
33 & 0.162156516074257 & 0.324313032148515 & 0.837843483925743 \tabularnewline
34 & 0.148116430104430 & 0.296232860208859 & 0.85188356989557 \tabularnewline
35 & 0.117831295973623 & 0.235662591947246 & 0.882168704026377 \tabularnewline
36 & 0.0973821172219917 & 0.194764234443983 & 0.902617882778008 \tabularnewline
37 & 0.141400961547807 & 0.282801923095614 & 0.858599038452193 \tabularnewline
38 & 0.129782291551691 & 0.259564583103381 & 0.87021770844831 \tabularnewline
39 & 0.139772827651345 & 0.279545655302690 & 0.860227172348655 \tabularnewline
40 & 0.110585409484316 & 0.221170818968631 & 0.889414590515685 \tabularnewline
41 & 0.184124718668966 & 0.368249437337932 & 0.815875281331034 \tabularnewline
42 & 0.234899328567712 & 0.469798657135423 & 0.765100671432288 \tabularnewline
43 & 0.291650147887306 & 0.583300295774613 & 0.708349852112694 \tabularnewline
44 & 0.255254376276575 & 0.510508752553149 & 0.744745623723425 \tabularnewline
45 & 0.231933978176051 & 0.463867956352102 & 0.768066021823949 \tabularnewline
46 & 0.203909631331678 & 0.407819262663356 & 0.796090368668322 \tabularnewline
47 & 0.243147708070040 & 0.486295416140081 & 0.756852291929960 \tabularnewline
48 & 0.47388776216581 & 0.94777552433162 & 0.52611223783419 \tabularnewline
49 & 0.569198532309371 & 0.861602935381258 & 0.430801467690629 \tabularnewline
50 & 0.726418882814955 & 0.54716223437009 & 0.273581117185045 \tabularnewline
51 & 0.688396761118617 & 0.623206477762766 & 0.311603238881383 \tabularnewline
52 & 0.888242393763722 & 0.223515212472556 & 0.111757606236278 \tabularnewline
53 & 0.944877463261828 & 0.110245073476344 & 0.0551225367381722 \tabularnewline
54 & 0.954654571772184 & 0.0906908564556312 & 0.0453454282278156 \tabularnewline
55 & 0.98111520713736 & 0.0377695857252814 & 0.0188847928626407 \tabularnewline
56 & 0.986513964448928 & 0.0269720711021439 & 0.0134860355510720 \tabularnewline
57 & 0.997847998894441 & 0.00430400221111737 & 0.00215200110555868 \tabularnewline
58 & 0.998562893607976 & 0.00287421278404882 & 0.00143710639202441 \tabularnewline
59 & 0.998500290558421 & 0.00299941888315808 & 0.00149970944157904 \tabularnewline
60 & 0.998543220395462 & 0.00291355920907621 & 0.00145677960453811 \tabularnewline
61 & 0.999071731216277 & 0.00185653756744668 & 0.000928268783723342 \tabularnewline
62 & 0.999115103902979 & 0.00176979219404262 & 0.000884896097021311 \tabularnewline
63 & 0.998871905331227 & 0.00225618933754644 & 0.00112809466877322 \tabularnewline
64 & 0.998910346069769 & 0.00217930786046273 & 0.00108965393023137 \tabularnewline
65 & 0.998930622909329 & 0.00213875418134243 & 0.00106937709067122 \tabularnewline
66 & 0.999397505960305 & 0.00120498807939 & 0.000602494039695 \tabularnewline
67 & 0.999504759687325 & 0.000990480625349449 & 0.000495240312674724 \tabularnewline
68 & 0.999760713595366 & 0.000478572809268209 & 0.000239286404634104 \tabularnewline
69 & 0.999948455558024 & 0.000103088883951553 & 5.15444419757764e-05 \tabularnewline
70 & 0.999979975598548 & 4.00488029040693e-05 & 2.00244014520346e-05 \tabularnewline
71 & 0.999980305975869 & 3.93880482627085e-05 & 1.96940241313543e-05 \tabularnewline
72 & 0.999983954097483 & 3.20918050348279e-05 & 1.60459025174140e-05 \tabularnewline
73 & 0.999988779228469 & 2.24415430626064e-05 & 1.12207715313032e-05 \tabularnewline
74 & 0.99999409578811 & 1.18084237801413e-05 & 5.90421189007064e-06 \tabularnewline
75 & 0.999992139221738 & 1.57215565242678e-05 & 7.8607782621339e-06 \tabularnewline
76 & 0.999990757414991 & 1.84851700178040e-05 & 9.24258500890202e-06 \tabularnewline
77 & 0.999991705960114 & 1.65880797721136e-05 & 8.29403988605678e-06 \tabularnewline
78 & 0.999993196296076 & 1.36074078485785e-05 & 6.80370392428923e-06 \tabularnewline
79 & 0.999996708992126 & 6.58201574797166e-06 & 3.29100787398583e-06 \tabularnewline
80 & 0.99999678274875 & 6.43450250216893e-06 & 3.21725125108447e-06 \tabularnewline
81 & 0.999995379009574 & 9.24198085107973e-06 & 4.62099042553986e-06 \tabularnewline
82 & 0.999998914761907 & 2.17047618646408e-06 & 1.08523809323204e-06 \tabularnewline
83 & 0.99999905536806 & 1.88926387964174e-06 & 9.4463193982087e-07 \tabularnewline
84 & 0.999998829619894 & 2.34076021144639e-06 & 1.17038010572320e-06 \tabularnewline
85 & 0.999999513722613 & 9.7255477329988e-07 & 4.8627738664994e-07 \tabularnewline
86 & 0.999999653904716 & 6.92190568997731e-07 & 3.46095284498866e-07 \tabularnewline
87 & 0.99999972226803 & 5.5546393885066e-07 & 2.7773196942533e-07 \tabularnewline
88 & 0.999999586153735 & 8.27692529861566e-07 & 4.13846264930783e-07 \tabularnewline
89 & 0.999999484150618 & 1.03169876404525e-06 & 5.15849382022623e-07 \tabularnewline
90 & 0.999999782930722 & 4.34138555732368e-07 & 2.17069277866184e-07 \tabularnewline
91 & 0.999999734119497 & 5.31761004889164e-07 & 2.65880502444582e-07 \tabularnewline
92 & 0.999999959339058 & 8.13218832371426e-08 & 4.06609416185713e-08 \tabularnewline
93 & 0.999999934879806 & 1.30240387583734e-07 & 6.51201937918671e-08 \tabularnewline
94 & 0.999999901028302 & 1.97943395314047e-07 & 9.89716976570233e-08 \tabularnewline
95 & 0.999999861221093 & 2.77557813291379e-07 & 1.38778906645690e-07 \tabularnewline
96 & 0.999999914079973 & 1.71840054584607e-07 & 8.59200272923037e-08 \tabularnewline
97 & 0.99999986012182 & 2.79756359989457e-07 & 1.39878179994729e-07 \tabularnewline
98 & 0.999999825021158 & 3.49957683513652e-07 & 1.74978841756826e-07 \tabularnewline
99 & 0.999999846950936 & 3.06098128076187e-07 & 1.53049064038094e-07 \tabularnewline
100 & 0.999999747154582 & 5.05690835816319e-07 & 2.52845417908160e-07 \tabularnewline
101 & 0.99999981258329 & 3.7483341941049e-07 & 1.87416709705245e-07 \tabularnewline
102 & 0.999999685151943 & 6.29696114916211e-07 & 3.14848057458106e-07 \tabularnewline
103 & 0.99999954887673 & 9.02246538632805e-07 & 4.51123269316402e-07 \tabularnewline
104 & 0.99999929222596 & 1.41554807902718e-06 & 7.0777403951359e-07 \tabularnewline
105 & 0.999999364526747 & 1.27094650632298e-06 & 6.3547325316149e-07 \tabularnewline
106 & 0.999999297249657 & 1.40550068602918e-06 & 7.02750343014592e-07 \tabularnewline
107 & 0.99999895872801 & 2.08254397902858e-06 & 1.04127198951429e-06 \tabularnewline
108 & 0.999999152146335 & 1.69570732985605e-06 & 8.47853664928023e-07 \tabularnewline
109 & 0.999999919851355 & 1.60297289800619e-07 & 8.01486449003093e-08 \tabularnewline
110 & 0.999999871087118 & 2.57825763589014e-07 & 1.28912881794507e-07 \tabularnewline
111 & 0.999999760605925 & 4.78788149181416e-07 & 2.39394074590708e-07 \tabularnewline
112 & 0.999999583966098 & 8.32067803672855e-07 & 4.16033901836428e-07 \tabularnewline
113 & 0.999999494823376 & 1.01035324743262e-06 & 5.0517662371631e-07 \tabularnewline
114 & 0.999999480267561 & 1.03946487774175e-06 & 5.19732438870876e-07 \tabularnewline
115 & 0.999999452400836 & 1.09519832770823e-06 & 5.47599163854117e-07 \tabularnewline
116 & 0.999999162874419 & 1.67425116273504e-06 & 8.37125581367521e-07 \tabularnewline
117 & 0.999998530873767 & 2.93825246625430e-06 & 1.46912623312715e-06 \tabularnewline
118 & 0.999998492753709 & 3.01449258219802e-06 & 1.50724629109901e-06 \tabularnewline
119 & 0.999998322921291 & 3.35415741711385e-06 & 1.67707870855692e-06 \tabularnewline
120 & 0.999999617867116 & 7.64265768332042e-07 & 3.82132884166021e-07 \tabularnewline
121 & 0.999999893373343 & 2.13253314532940e-07 & 1.06626657266470e-07 \tabularnewline
122 & 0.99999981861141 & 3.62777181927527e-07 & 1.81388590963763e-07 \tabularnewline
123 & 0.999999949356552 & 1.01286896749012e-07 & 5.0643448374506e-08 \tabularnewline
124 & 0.999999912066183 & 1.75867634100664e-07 & 8.7933817050332e-08 \tabularnewline
125 & 0.999999842274074 & 3.15451851057457e-07 & 1.57725925528729e-07 \tabularnewline
126 & 0.999999733127569 & 5.33744862044504e-07 & 2.66872431022252e-07 \tabularnewline
127 & 0.999999689201263 & 6.21597472934647e-07 & 3.10798736467324e-07 \tabularnewline
128 & 0.9999994387758 & 1.12244840081945e-06 & 5.61224200409725e-07 \tabularnewline
129 & 0.999998935678024 & 2.12864395257080e-06 & 1.06432197628540e-06 \tabularnewline
130 & 0.99999942772623 & 1.14454753850936e-06 & 5.72273769254682e-07 \tabularnewline
131 & 0.999999390997002 & 1.21800599684692e-06 & 6.09002998423458e-07 \tabularnewline
132 & 0.999999935744485 & 1.28511029699837e-07 & 6.42555148499187e-08 \tabularnewline
133 & 0.999999945689883 & 1.08620234260407e-07 & 5.43101171302033e-08 \tabularnewline
134 & 0.999999915559562 & 1.68880875030271e-07 & 8.44404375151355e-08 \tabularnewline
135 & 0.99999982730464 & 3.45390721431426e-07 & 1.72695360715713e-07 \tabularnewline
136 & 0.99999968111279 & 6.37774418162373e-07 & 3.18887209081187e-07 \tabularnewline
137 & 0.999999516321377 & 9.67357246883755e-07 & 4.83678623441878e-07 \tabularnewline
138 & 0.999999218169784 & 1.56366043213988e-06 & 7.81830216069941e-07 \tabularnewline
139 & 0.999998785104147 & 2.42979170547042e-06 & 1.21489585273521e-06 \tabularnewline
140 & 0.99999764586801 & 4.70826397889355e-06 & 2.35413198944678e-06 \tabularnewline
141 & 0.999997512387917 & 4.97522416654345e-06 & 2.48761208327172e-06 \tabularnewline
142 & 0.999995061082498 & 9.87783500307573e-06 & 4.93891750153786e-06 \tabularnewline
143 & 0.999997737829351 & 4.52434129751273e-06 & 2.26217064875636e-06 \tabularnewline
144 & 0.999996469214725 & 7.06157054924965e-06 & 3.53078527462482e-06 \tabularnewline
145 & 0.999994862395303 & 1.02752093948037e-05 & 5.13760469740187e-06 \tabularnewline
146 & 0.999990271774088 & 1.94564518248318e-05 & 9.7282259124159e-06 \tabularnewline
147 & 0.99998094543812 & 3.81091237617349e-05 & 1.90545618808674e-05 \tabularnewline
148 & 0.9999625131262 & 7.49737476012236e-05 & 3.74868738006118e-05 \tabularnewline
149 & 0.999928432870693 & 0.000143134258614119 & 7.15671293070596e-05 \tabularnewline
150 & 0.999894002745356 & 0.000211994509286974 & 0.000105997254643487 \tabularnewline
151 & 0.999894020798052 & 0.000211958403896148 & 0.000105979201948074 \tabularnewline
152 & 0.99981724152826 & 0.000365516943479771 & 0.000182758471739886 \tabularnewline
153 & 0.999649970251462 & 0.00070005949707591 & 0.000350029748537955 \tabularnewline
154 & 0.999531183427333 & 0.000937633145333717 & 0.000468816572666858 \tabularnewline
155 & 0.999252101750874 & 0.00149579649825203 & 0.000747898249126013 \tabularnewline
156 & 0.999848486594899 & 0.000303026810202280 & 0.000151513405101140 \tabularnewline
157 & 0.999821773945542 & 0.000356452108915764 & 0.000178226054457882 \tabularnewline
158 & 0.999646663903749 & 0.000706672192502481 & 0.000353336096251241 \tabularnewline
159 & 0.99947224659046 & 0.00105550681907927 & 0.000527753409539633 \tabularnewline
160 & 0.999161941856327 & 0.00167611628734633 & 0.000838058143673166 \tabularnewline
161 & 0.99879443070355 & 0.00241113859289918 & 0.00120556929644959 \tabularnewline
162 & 0.998132418671365 & 0.0037351626572697 & 0.00186758132863485 \tabularnewline
163 & 0.996541317679327 & 0.00691736464134663 & 0.00345868232067332 \tabularnewline
164 & 0.99643064239685 & 0.00713871520630226 & 0.00356935760315113 \tabularnewline
165 & 0.99630291304999 & 0.00739417390002156 & 0.00369708695001078 \tabularnewline
166 & 0.992624820543152 & 0.0147503589136949 & 0.00737517945684745 \tabularnewline
167 & 0.986949004632548 & 0.0261019907349031 & 0.0130509953674515 \tabularnewline
168 & 0.990914720921223 & 0.0181705581575536 & 0.0090852790787768 \tabularnewline
169 & 0.982271046311107 & 0.0354579073777866 & 0.0177289536888933 \tabularnewline
170 & 0.971877160373402 & 0.0562456792531961 & 0.0281228396265981 \tabularnewline
171 & 0.949755158728175 & 0.100489682543650 & 0.0502448412718248 \tabularnewline
172 & 0.912201040193592 & 0.175597919612815 & 0.0877989598064076 \tabularnewline
173 & 0.852461858529344 & 0.295076282941313 & 0.147538141470656 \tabularnewline
174 & 0.782357319812533 & 0.435285360374933 & 0.217642680187467 \tabularnewline
175 & 0.656942818640769 & 0.686114362718462 & 0.343057181359231 \tabularnewline
176 & 0.550072850089641 & 0.899854299820717 & 0.449927149910359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&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]16[/C][C]0.477412392593736[/C][C]0.954824785187473[/C][C]0.522587607406264[/C][/ROW]
[ROW][C]17[/C][C]0.317783061374179[/C][C]0.635566122748358[/C][C]0.682216938625821[/C][/ROW]
[ROW][C]18[/C][C]0.191885033017608[/C][C]0.383770066035215[/C][C]0.808114966982392[/C][/ROW]
[ROW][C]19[/C][C]0.21949644245213[/C][C]0.43899288490426[/C][C]0.78050355754787[/C][/ROW]
[ROW][C]20[/C][C]0.182871328784974[/C][C]0.365742657569947[/C][C]0.817128671215026[/C][/ROW]
[ROW][C]21[/C][C]0.138850641397902[/C][C]0.277701282795803[/C][C]0.861149358602098[/C][/ROW]
[ROW][C]22[/C][C]0.247066821091927[/C][C]0.494133642183854[/C][C]0.752933178908073[/C][/ROW]
[ROW][C]23[/C][C]0.186573077093517[/C][C]0.373146154187033[/C][C]0.813426922906483[/C][/ROW]
[ROW][C]24[/C][C]0.268174322039433[/C][C]0.536348644078867[/C][C]0.731825677960567[/C][/ROW]
[ROW][C]25[/C][C]0.375317347973012[/C][C]0.750634695946024[/C][C]0.624682652026988[/C][/ROW]
[ROW][C]26[/C][C]0.298471225788020[/C][C]0.596942451576041[/C][C]0.70152877421198[/C][/ROW]
[ROW][C]27[/C][C]0.239925267391636[/C][C]0.479850534783272[/C][C]0.760074732608364[/C][/ROW]
[ROW][C]28[/C][C]0.213045039049363[/C][C]0.426090078098727[/C][C]0.786954960950637[/C][/ROW]
[ROW][C]29[/C][C]0.213768874665489[/C][C]0.427537749330978[/C][C]0.786231125334511[/C][/ROW]
[ROW][C]30[/C][C]0.229775231390765[/C][C]0.459550462781531[/C][C]0.770224768609235[/C][/ROW]
[ROW][C]31[/C][C]0.194771535540367[/C][C]0.389543071080733[/C][C]0.805228464459633[/C][/ROW]
[ROW][C]32[/C][C]0.207765111722895[/C][C]0.415530223445791[/C][C]0.792234888277105[/C][/ROW]
[ROW][C]33[/C][C]0.162156516074257[/C][C]0.324313032148515[/C][C]0.837843483925743[/C][/ROW]
[ROW][C]34[/C][C]0.148116430104430[/C][C]0.296232860208859[/C][C]0.85188356989557[/C][/ROW]
[ROW][C]35[/C][C]0.117831295973623[/C][C]0.235662591947246[/C][C]0.882168704026377[/C][/ROW]
[ROW][C]36[/C][C]0.0973821172219917[/C][C]0.194764234443983[/C][C]0.902617882778008[/C][/ROW]
[ROW][C]37[/C][C]0.141400961547807[/C][C]0.282801923095614[/C][C]0.858599038452193[/C][/ROW]
[ROW][C]38[/C][C]0.129782291551691[/C][C]0.259564583103381[/C][C]0.87021770844831[/C][/ROW]
[ROW][C]39[/C][C]0.139772827651345[/C][C]0.279545655302690[/C][C]0.860227172348655[/C][/ROW]
[ROW][C]40[/C][C]0.110585409484316[/C][C]0.221170818968631[/C][C]0.889414590515685[/C][/ROW]
[ROW][C]41[/C][C]0.184124718668966[/C][C]0.368249437337932[/C][C]0.815875281331034[/C][/ROW]
[ROW][C]42[/C][C]0.234899328567712[/C][C]0.469798657135423[/C][C]0.765100671432288[/C][/ROW]
[ROW][C]43[/C][C]0.291650147887306[/C][C]0.583300295774613[/C][C]0.708349852112694[/C][/ROW]
[ROW][C]44[/C][C]0.255254376276575[/C][C]0.510508752553149[/C][C]0.744745623723425[/C][/ROW]
[ROW][C]45[/C][C]0.231933978176051[/C][C]0.463867956352102[/C][C]0.768066021823949[/C][/ROW]
[ROW][C]46[/C][C]0.203909631331678[/C][C]0.407819262663356[/C][C]0.796090368668322[/C][/ROW]
[ROW][C]47[/C][C]0.243147708070040[/C][C]0.486295416140081[/C][C]0.756852291929960[/C][/ROW]
[ROW][C]48[/C][C]0.47388776216581[/C][C]0.94777552433162[/C][C]0.52611223783419[/C][/ROW]
[ROW][C]49[/C][C]0.569198532309371[/C][C]0.861602935381258[/C][C]0.430801467690629[/C][/ROW]
[ROW][C]50[/C][C]0.726418882814955[/C][C]0.54716223437009[/C][C]0.273581117185045[/C][/ROW]
[ROW][C]51[/C][C]0.688396761118617[/C][C]0.623206477762766[/C][C]0.311603238881383[/C][/ROW]
[ROW][C]52[/C][C]0.888242393763722[/C][C]0.223515212472556[/C][C]0.111757606236278[/C][/ROW]
[ROW][C]53[/C][C]0.944877463261828[/C][C]0.110245073476344[/C][C]0.0551225367381722[/C][/ROW]
[ROW][C]54[/C][C]0.954654571772184[/C][C]0.0906908564556312[/C][C]0.0453454282278156[/C][/ROW]
[ROW][C]55[/C][C]0.98111520713736[/C][C]0.0377695857252814[/C][C]0.0188847928626407[/C][/ROW]
[ROW][C]56[/C][C]0.986513964448928[/C][C]0.0269720711021439[/C][C]0.0134860355510720[/C][/ROW]
[ROW][C]57[/C][C]0.997847998894441[/C][C]0.00430400221111737[/C][C]0.00215200110555868[/C][/ROW]
[ROW][C]58[/C][C]0.998562893607976[/C][C]0.00287421278404882[/C][C]0.00143710639202441[/C][/ROW]
[ROW][C]59[/C][C]0.998500290558421[/C][C]0.00299941888315808[/C][C]0.00149970944157904[/C][/ROW]
[ROW][C]60[/C][C]0.998543220395462[/C][C]0.00291355920907621[/C][C]0.00145677960453811[/C][/ROW]
[ROW][C]61[/C][C]0.999071731216277[/C][C]0.00185653756744668[/C][C]0.000928268783723342[/C][/ROW]
[ROW][C]62[/C][C]0.999115103902979[/C][C]0.00176979219404262[/C][C]0.000884896097021311[/C][/ROW]
[ROW][C]63[/C][C]0.998871905331227[/C][C]0.00225618933754644[/C][C]0.00112809466877322[/C][/ROW]
[ROW][C]64[/C][C]0.998910346069769[/C][C]0.00217930786046273[/C][C]0.00108965393023137[/C][/ROW]
[ROW][C]65[/C][C]0.998930622909329[/C][C]0.00213875418134243[/C][C]0.00106937709067122[/C][/ROW]
[ROW][C]66[/C][C]0.999397505960305[/C][C]0.00120498807939[/C][C]0.000602494039695[/C][/ROW]
[ROW][C]67[/C][C]0.999504759687325[/C][C]0.000990480625349449[/C][C]0.000495240312674724[/C][/ROW]
[ROW][C]68[/C][C]0.999760713595366[/C][C]0.000478572809268209[/C][C]0.000239286404634104[/C][/ROW]
[ROW][C]69[/C][C]0.999948455558024[/C][C]0.000103088883951553[/C][C]5.15444419757764e-05[/C][/ROW]
[ROW][C]70[/C][C]0.999979975598548[/C][C]4.00488029040693e-05[/C][C]2.00244014520346e-05[/C][/ROW]
[ROW][C]71[/C][C]0.999980305975869[/C][C]3.93880482627085e-05[/C][C]1.96940241313543e-05[/C][/ROW]
[ROW][C]72[/C][C]0.999983954097483[/C][C]3.20918050348279e-05[/C][C]1.60459025174140e-05[/C][/ROW]
[ROW][C]73[/C][C]0.999988779228469[/C][C]2.24415430626064e-05[/C][C]1.12207715313032e-05[/C][/ROW]
[ROW][C]74[/C][C]0.99999409578811[/C][C]1.18084237801413e-05[/C][C]5.90421189007064e-06[/C][/ROW]
[ROW][C]75[/C][C]0.999992139221738[/C][C]1.57215565242678e-05[/C][C]7.8607782621339e-06[/C][/ROW]
[ROW][C]76[/C][C]0.999990757414991[/C][C]1.84851700178040e-05[/C][C]9.24258500890202e-06[/C][/ROW]
[ROW][C]77[/C][C]0.999991705960114[/C][C]1.65880797721136e-05[/C][C]8.29403988605678e-06[/C][/ROW]
[ROW][C]78[/C][C]0.999993196296076[/C][C]1.36074078485785e-05[/C][C]6.80370392428923e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999996708992126[/C][C]6.58201574797166e-06[/C][C]3.29100787398583e-06[/C][/ROW]
[ROW][C]80[/C][C]0.99999678274875[/C][C]6.43450250216893e-06[/C][C]3.21725125108447e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999995379009574[/C][C]9.24198085107973e-06[/C][C]4.62099042553986e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999998914761907[/C][C]2.17047618646408e-06[/C][C]1.08523809323204e-06[/C][/ROW]
[ROW][C]83[/C][C]0.99999905536806[/C][C]1.88926387964174e-06[/C][C]9.4463193982087e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999998829619894[/C][C]2.34076021144639e-06[/C][C]1.17038010572320e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999999513722613[/C][C]9.7255477329988e-07[/C][C]4.8627738664994e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999653904716[/C][C]6.92190568997731e-07[/C][C]3.46095284498866e-07[/C][/ROW]
[ROW][C]87[/C][C]0.99999972226803[/C][C]5.5546393885066e-07[/C][C]2.7773196942533e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999586153735[/C][C]8.27692529861566e-07[/C][C]4.13846264930783e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999999484150618[/C][C]1.03169876404525e-06[/C][C]5.15849382022623e-07[/C][/ROW]
[ROW][C]90[/C][C]0.999999782930722[/C][C]4.34138555732368e-07[/C][C]2.17069277866184e-07[/C][/ROW]
[ROW][C]91[/C][C]0.999999734119497[/C][C]5.31761004889164e-07[/C][C]2.65880502444582e-07[/C][/ROW]
[ROW][C]92[/C][C]0.999999959339058[/C][C]8.13218832371426e-08[/C][C]4.06609416185713e-08[/C][/ROW]
[ROW][C]93[/C][C]0.999999934879806[/C][C]1.30240387583734e-07[/C][C]6.51201937918671e-08[/C][/ROW]
[ROW][C]94[/C][C]0.999999901028302[/C][C]1.97943395314047e-07[/C][C]9.89716976570233e-08[/C][/ROW]
[ROW][C]95[/C][C]0.999999861221093[/C][C]2.77557813291379e-07[/C][C]1.38778906645690e-07[/C][/ROW]
[ROW][C]96[/C][C]0.999999914079973[/C][C]1.71840054584607e-07[/C][C]8.59200272923037e-08[/C][/ROW]
[ROW][C]97[/C][C]0.99999986012182[/C][C]2.79756359989457e-07[/C][C]1.39878179994729e-07[/C][/ROW]
[ROW][C]98[/C][C]0.999999825021158[/C][C]3.49957683513652e-07[/C][C]1.74978841756826e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999846950936[/C][C]3.06098128076187e-07[/C][C]1.53049064038094e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999999747154582[/C][C]5.05690835816319e-07[/C][C]2.52845417908160e-07[/C][/ROW]
[ROW][C]101[/C][C]0.99999981258329[/C][C]3.7483341941049e-07[/C][C]1.87416709705245e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999999685151943[/C][C]6.29696114916211e-07[/C][C]3.14848057458106e-07[/C][/ROW]
[ROW][C]103[/C][C]0.99999954887673[/C][C]9.02246538632805e-07[/C][C]4.51123269316402e-07[/C][/ROW]
[ROW][C]104[/C][C]0.99999929222596[/C][C]1.41554807902718e-06[/C][C]7.0777403951359e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999999364526747[/C][C]1.27094650632298e-06[/C][C]6.3547325316149e-07[/C][/ROW]
[ROW][C]106[/C][C]0.999999297249657[/C][C]1.40550068602918e-06[/C][C]7.02750343014592e-07[/C][/ROW]
[ROW][C]107[/C][C]0.99999895872801[/C][C]2.08254397902858e-06[/C][C]1.04127198951429e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999152146335[/C][C]1.69570732985605e-06[/C][C]8.47853664928023e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999999919851355[/C][C]1.60297289800619e-07[/C][C]8.01486449003093e-08[/C][/ROW]
[ROW][C]110[/C][C]0.999999871087118[/C][C]2.57825763589014e-07[/C][C]1.28912881794507e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999999760605925[/C][C]4.78788149181416e-07[/C][C]2.39394074590708e-07[/C][/ROW]
[ROW][C]112[/C][C]0.999999583966098[/C][C]8.32067803672855e-07[/C][C]4.16033901836428e-07[/C][/ROW]
[ROW][C]113[/C][C]0.999999494823376[/C][C]1.01035324743262e-06[/C][C]5.0517662371631e-07[/C][/ROW]
[ROW][C]114[/C][C]0.999999480267561[/C][C]1.03946487774175e-06[/C][C]5.19732438870876e-07[/C][/ROW]
[ROW][C]115[/C][C]0.999999452400836[/C][C]1.09519832770823e-06[/C][C]5.47599163854117e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999162874419[/C][C]1.67425116273504e-06[/C][C]8.37125581367521e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999998530873767[/C][C]2.93825246625430e-06[/C][C]1.46912623312715e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999998492753709[/C][C]3.01449258219802e-06[/C][C]1.50724629109901e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999998322921291[/C][C]3.35415741711385e-06[/C][C]1.67707870855692e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999999617867116[/C][C]7.64265768332042e-07[/C][C]3.82132884166021e-07[/C][/ROW]
[ROW][C]121[/C][C]0.999999893373343[/C][C]2.13253314532940e-07[/C][C]1.06626657266470e-07[/C][/ROW]
[ROW][C]122[/C][C]0.99999981861141[/C][C]3.62777181927527e-07[/C][C]1.81388590963763e-07[/C][/ROW]
[ROW][C]123[/C][C]0.999999949356552[/C][C]1.01286896749012e-07[/C][C]5.0643448374506e-08[/C][/ROW]
[ROW][C]124[/C][C]0.999999912066183[/C][C]1.75867634100664e-07[/C][C]8.7933817050332e-08[/C][/ROW]
[ROW][C]125[/C][C]0.999999842274074[/C][C]3.15451851057457e-07[/C][C]1.57725925528729e-07[/C][/ROW]
[ROW][C]126[/C][C]0.999999733127569[/C][C]5.33744862044504e-07[/C][C]2.66872431022252e-07[/C][/ROW]
[ROW][C]127[/C][C]0.999999689201263[/C][C]6.21597472934647e-07[/C][C]3.10798736467324e-07[/C][/ROW]
[ROW][C]128[/C][C]0.9999994387758[/C][C]1.12244840081945e-06[/C][C]5.61224200409725e-07[/C][/ROW]
[ROW][C]129[/C][C]0.999998935678024[/C][C]2.12864395257080e-06[/C][C]1.06432197628540e-06[/C][/ROW]
[ROW][C]130[/C][C]0.99999942772623[/C][C]1.14454753850936e-06[/C][C]5.72273769254682e-07[/C][/ROW]
[ROW][C]131[/C][C]0.999999390997002[/C][C]1.21800599684692e-06[/C][C]6.09002998423458e-07[/C][/ROW]
[ROW][C]132[/C][C]0.999999935744485[/C][C]1.28511029699837e-07[/C][C]6.42555148499187e-08[/C][/ROW]
[ROW][C]133[/C][C]0.999999945689883[/C][C]1.08620234260407e-07[/C][C]5.43101171302033e-08[/C][/ROW]
[ROW][C]134[/C][C]0.999999915559562[/C][C]1.68880875030271e-07[/C][C]8.44404375151355e-08[/C][/ROW]
[ROW][C]135[/C][C]0.99999982730464[/C][C]3.45390721431426e-07[/C][C]1.72695360715713e-07[/C][/ROW]
[ROW][C]136[/C][C]0.99999968111279[/C][C]6.37774418162373e-07[/C][C]3.18887209081187e-07[/C][/ROW]
[ROW][C]137[/C][C]0.999999516321377[/C][C]9.67357246883755e-07[/C][C]4.83678623441878e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999218169784[/C][C]1.56366043213988e-06[/C][C]7.81830216069941e-07[/C][/ROW]
[ROW][C]139[/C][C]0.999998785104147[/C][C]2.42979170547042e-06[/C][C]1.21489585273521e-06[/C][/ROW]
[ROW][C]140[/C][C]0.99999764586801[/C][C]4.70826397889355e-06[/C][C]2.35413198944678e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999997512387917[/C][C]4.97522416654345e-06[/C][C]2.48761208327172e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999995061082498[/C][C]9.87783500307573e-06[/C][C]4.93891750153786e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999997737829351[/C][C]4.52434129751273e-06[/C][C]2.26217064875636e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999996469214725[/C][C]7.06157054924965e-06[/C][C]3.53078527462482e-06[/C][/ROW]
[ROW][C]145[/C][C]0.999994862395303[/C][C]1.02752093948037e-05[/C][C]5.13760469740187e-06[/C][/ROW]
[ROW][C]146[/C][C]0.999990271774088[/C][C]1.94564518248318e-05[/C][C]9.7282259124159e-06[/C][/ROW]
[ROW][C]147[/C][C]0.99998094543812[/C][C]3.81091237617349e-05[/C][C]1.90545618808674e-05[/C][/ROW]
[ROW][C]148[/C][C]0.9999625131262[/C][C]7.49737476012236e-05[/C][C]3.74868738006118e-05[/C][/ROW]
[ROW][C]149[/C][C]0.999928432870693[/C][C]0.000143134258614119[/C][C]7.15671293070596e-05[/C][/ROW]
[ROW][C]150[/C][C]0.999894002745356[/C][C]0.000211994509286974[/C][C]0.000105997254643487[/C][/ROW]
[ROW][C]151[/C][C]0.999894020798052[/C][C]0.000211958403896148[/C][C]0.000105979201948074[/C][/ROW]
[ROW][C]152[/C][C]0.99981724152826[/C][C]0.000365516943479771[/C][C]0.000182758471739886[/C][/ROW]
[ROW][C]153[/C][C]0.999649970251462[/C][C]0.00070005949707591[/C][C]0.000350029748537955[/C][/ROW]
[ROW][C]154[/C][C]0.999531183427333[/C][C]0.000937633145333717[/C][C]0.000468816572666858[/C][/ROW]
[ROW][C]155[/C][C]0.999252101750874[/C][C]0.00149579649825203[/C][C]0.000747898249126013[/C][/ROW]
[ROW][C]156[/C][C]0.999848486594899[/C][C]0.000303026810202280[/C][C]0.000151513405101140[/C][/ROW]
[ROW][C]157[/C][C]0.999821773945542[/C][C]0.000356452108915764[/C][C]0.000178226054457882[/C][/ROW]
[ROW][C]158[/C][C]0.999646663903749[/C][C]0.000706672192502481[/C][C]0.000353336096251241[/C][/ROW]
[ROW][C]159[/C][C]0.99947224659046[/C][C]0.00105550681907927[/C][C]0.000527753409539633[/C][/ROW]
[ROW][C]160[/C][C]0.999161941856327[/C][C]0.00167611628734633[/C][C]0.000838058143673166[/C][/ROW]
[ROW][C]161[/C][C]0.99879443070355[/C][C]0.00241113859289918[/C][C]0.00120556929644959[/C][/ROW]
[ROW][C]162[/C][C]0.998132418671365[/C][C]0.0037351626572697[/C][C]0.00186758132863485[/C][/ROW]
[ROW][C]163[/C][C]0.996541317679327[/C][C]0.00691736464134663[/C][C]0.00345868232067332[/C][/ROW]
[ROW][C]164[/C][C]0.99643064239685[/C][C]0.00713871520630226[/C][C]0.00356935760315113[/C][/ROW]
[ROW][C]165[/C][C]0.99630291304999[/C][C]0.00739417390002156[/C][C]0.00369708695001078[/C][/ROW]
[ROW][C]166[/C][C]0.992624820543152[/C][C]0.0147503589136949[/C][C]0.00737517945684745[/C][/ROW]
[ROW][C]167[/C][C]0.986949004632548[/C][C]0.0261019907349031[/C][C]0.0130509953674515[/C][/ROW]
[ROW][C]168[/C][C]0.990914720921223[/C][C]0.0181705581575536[/C][C]0.0090852790787768[/C][/ROW]
[ROW][C]169[/C][C]0.982271046311107[/C][C]0.0354579073777866[/C][C]0.0177289536888933[/C][/ROW]
[ROW][C]170[/C][C]0.971877160373402[/C][C]0.0562456792531961[/C][C]0.0281228396265981[/C][/ROW]
[ROW][C]171[/C][C]0.949755158728175[/C][C]0.100489682543650[/C][C]0.0502448412718248[/C][/ROW]
[ROW][C]172[/C][C]0.912201040193592[/C][C]0.175597919612815[/C][C]0.0877989598064076[/C][/ROW]
[ROW][C]173[/C][C]0.852461858529344[/C][C]0.295076282941313[/C][C]0.147538141470656[/C][/ROW]
[ROW][C]174[/C][C]0.782357319812533[/C][C]0.435285360374933[/C][C]0.217642680187467[/C][/ROW]
[ROW][C]175[/C][C]0.656942818640769[/C][C]0.686114362718462[/C][C]0.343057181359231[/C][/ROW]
[ROW][C]176[/C][C]0.550072850089641[/C][C]0.899854299820717[/C][C]0.449927149910359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4774123925937360.9548247851874730.522587607406264
170.3177830613741790.6355661227483580.682216938625821
180.1918850330176080.3837700660352150.808114966982392
190.219496442452130.438992884904260.78050355754787
200.1828713287849740.3657426575699470.817128671215026
210.1388506413979020.2777012827958030.861149358602098
220.2470668210919270.4941336421838540.752933178908073
230.1865730770935170.3731461541870330.813426922906483
240.2681743220394330.5363486440788670.731825677960567
250.3753173479730120.7506346959460240.624682652026988
260.2984712257880200.5969424515760410.70152877421198
270.2399252673916360.4798505347832720.760074732608364
280.2130450390493630.4260900780987270.786954960950637
290.2137688746654890.4275377493309780.786231125334511
300.2297752313907650.4595504627815310.770224768609235
310.1947715355403670.3895430710807330.805228464459633
320.2077651117228950.4155302234457910.792234888277105
330.1621565160742570.3243130321485150.837843483925743
340.1481164301044300.2962328602088590.85188356989557
350.1178312959736230.2356625919472460.882168704026377
360.09738211722199170.1947642344439830.902617882778008
370.1414009615478070.2828019230956140.858599038452193
380.1297822915516910.2595645831033810.87021770844831
390.1397728276513450.2795456553026900.860227172348655
400.1105854094843160.2211708189686310.889414590515685
410.1841247186689660.3682494373379320.815875281331034
420.2348993285677120.4697986571354230.765100671432288
430.2916501478873060.5833002957746130.708349852112694
440.2552543762765750.5105087525531490.744745623723425
450.2319339781760510.4638679563521020.768066021823949
460.2039096313316780.4078192626633560.796090368668322
470.2431477080700400.4862954161400810.756852291929960
480.473887762165810.947775524331620.52611223783419
490.5691985323093710.8616029353812580.430801467690629
500.7264188828149550.547162234370090.273581117185045
510.6883967611186170.6232064777627660.311603238881383
520.8882423937637220.2235152124725560.111757606236278
530.9448774632618280.1102450734763440.0551225367381722
540.9546545717721840.09069085645563120.0453454282278156
550.981115207137360.03776958572528140.0188847928626407
560.9865139644489280.02697207110214390.0134860355510720
570.9978479988944410.004304002211117370.00215200110555868
580.9985628936079760.002874212784048820.00143710639202441
590.9985002905584210.002999418883158080.00149970944157904
600.9985432203954620.002913559209076210.00145677960453811
610.9990717312162770.001856537567446680.000928268783723342
620.9991151039029790.001769792194042620.000884896097021311
630.9988719053312270.002256189337546440.00112809466877322
640.9989103460697690.002179307860462730.00108965393023137
650.9989306229093290.002138754181342430.00106937709067122
660.9993975059603050.001204988079390.000602494039695
670.9995047596873250.0009904806253494490.000495240312674724
680.9997607135953660.0004785728092682090.000239286404634104
690.9999484555580240.0001030888839515535.15444419757764e-05
700.9999799755985484.00488029040693e-052.00244014520346e-05
710.9999803059758693.93880482627085e-051.96940241313543e-05
720.9999839540974833.20918050348279e-051.60459025174140e-05
730.9999887792284692.24415430626064e-051.12207715313032e-05
740.999994095788111.18084237801413e-055.90421189007064e-06
750.9999921392217381.57215565242678e-057.8607782621339e-06
760.9999907574149911.84851700178040e-059.24258500890202e-06
770.9999917059601141.65880797721136e-058.29403988605678e-06
780.9999931962960761.36074078485785e-056.80370392428923e-06
790.9999967089921266.58201574797166e-063.29100787398583e-06
800.999996782748756.43450250216893e-063.21725125108447e-06
810.9999953790095749.24198085107973e-064.62099042553986e-06
820.9999989147619072.17047618646408e-061.08523809323204e-06
830.999999055368061.88926387964174e-069.4463193982087e-07
840.9999988296198942.34076021144639e-061.17038010572320e-06
850.9999995137226139.7255477329988e-074.8627738664994e-07
860.9999996539047166.92190568997731e-073.46095284498866e-07
870.999999722268035.5546393885066e-072.7773196942533e-07
880.9999995861537358.27692529861566e-074.13846264930783e-07
890.9999994841506181.03169876404525e-065.15849382022623e-07
900.9999997829307224.34138555732368e-072.17069277866184e-07
910.9999997341194975.31761004889164e-072.65880502444582e-07
920.9999999593390588.13218832371426e-084.06609416185713e-08
930.9999999348798061.30240387583734e-076.51201937918671e-08
940.9999999010283021.97943395314047e-079.89716976570233e-08
950.9999998612210932.77557813291379e-071.38778906645690e-07
960.9999999140799731.71840054584607e-078.59200272923037e-08
970.999999860121822.79756359989457e-071.39878179994729e-07
980.9999998250211583.49957683513652e-071.74978841756826e-07
990.9999998469509363.06098128076187e-071.53049064038094e-07
1000.9999997471545825.05690835816319e-072.52845417908160e-07
1010.999999812583293.7483341941049e-071.87416709705245e-07
1020.9999996851519436.29696114916211e-073.14848057458106e-07
1030.999999548876739.02246538632805e-074.51123269316402e-07
1040.999999292225961.41554807902718e-067.0777403951359e-07
1050.9999993645267471.27094650632298e-066.3547325316149e-07
1060.9999992972496571.40550068602918e-067.02750343014592e-07
1070.999998958728012.08254397902858e-061.04127198951429e-06
1080.9999991521463351.69570732985605e-068.47853664928023e-07
1090.9999999198513551.60297289800619e-078.01486449003093e-08
1100.9999998710871182.57825763589014e-071.28912881794507e-07
1110.9999997606059254.78788149181416e-072.39394074590708e-07
1120.9999995839660988.32067803672855e-074.16033901836428e-07
1130.9999994948233761.01035324743262e-065.0517662371631e-07
1140.9999994802675611.03946487774175e-065.19732438870876e-07
1150.9999994524008361.09519832770823e-065.47599163854117e-07
1160.9999991628744191.67425116273504e-068.37125581367521e-07
1170.9999985308737672.93825246625430e-061.46912623312715e-06
1180.9999984927537093.01449258219802e-061.50724629109901e-06
1190.9999983229212913.35415741711385e-061.67707870855692e-06
1200.9999996178671167.64265768332042e-073.82132884166021e-07
1210.9999998933733432.13253314532940e-071.06626657266470e-07
1220.999999818611413.62777181927527e-071.81388590963763e-07
1230.9999999493565521.01286896749012e-075.0643448374506e-08
1240.9999999120661831.75867634100664e-078.7933817050332e-08
1250.9999998422740743.15451851057457e-071.57725925528729e-07
1260.9999997331275695.33744862044504e-072.66872431022252e-07
1270.9999996892012636.21597472934647e-073.10798736467324e-07
1280.99999943877581.12244840081945e-065.61224200409725e-07
1290.9999989356780242.12864395257080e-061.06432197628540e-06
1300.999999427726231.14454753850936e-065.72273769254682e-07
1310.9999993909970021.21800599684692e-066.09002998423458e-07
1320.9999999357444851.28511029699837e-076.42555148499187e-08
1330.9999999456898831.08620234260407e-075.43101171302033e-08
1340.9999999155595621.68880875030271e-078.44404375151355e-08
1350.999999827304643.45390721431426e-071.72695360715713e-07
1360.999999681112796.37774418162373e-073.18887209081187e-07
1370.9999995163213779.67357246883755e-074.83678623441878e-07
1380.9999992181697841.56366043213988e-067.81830216069941e-07
1390.9999987851041472.42979170547042e-061.21489585273521e-06
1400.999997645868014.70826397889355e-062.35413198944678e-06
1410.9999975123879174.97522416654345e-062.48761208327172e-06
1420.9999950610824989.87783500307573e-064.93891750153786e-06
1430.9999977378293514.52434129751273e-062.26217064875636e-06
1440.9999964692147257.06157054924965e-063.53078527462482e-06
1450.9999948623953031.02752093948037e-055.13760469740187e-06
1460.9999902717740881.94564518248318e-059.7282259124159e-06
1470.999980945438123.81091237617349e-051.90545618808674e-05
1480.99996251312627.49737476012236e-053.74868738006118e-05
1490.9999284328706930.0001431342586141197.15671293070596e-05
1500.9998940027453560.0002119945092869740.000105997254643487
1510.9998940207980520.0002119584038961480.000105979201948074
1520.999817241528260.0003655169434797710.000182758471739886
1530.9996499702514620.000700059497075910.000350029748537955
1540.9995311834273330.0009376331453337170.000468816572666858
1550.9992521017508740.001495796498252030.000747898249126013
1560.9998484865948990.0003030268102022800.000151513405101140
1570.9998217739455420.0003564521089157640.000178226054457882
1580.9996466639037490.0007066721925024810.000353336096251241
1590.999472246590460.001055506819079270.000527753409539633
1600.9991619418563270.001676116287346330.000838058143673166
1610.998794430703550.002411138592899180.00120556929644959
1620.9981324186713650.00373516265726970.00186758132863485
1630.9965413176793270.006917364641346630.00345868232067332
1640.996430642396850.007138715206302260.00356935760315113
1650.996302913049990.007394173900021560.00369708695001078
1660.9926248205431520.01475035891369490.00737517945684745
1670.9869490046325480.02610199073490310.0130509953674515
1680.9909147209212230.01817055815755360.0090852790787768
1690.9822710463111070.03545790737778660.0177289536888933
1700.9718771603734020.05624567925319610.0281228396265981
1710.9497551587281750.1004896825436500.0502448412718248
1720.9122010401935920.1755979196128150.0877989598064076
1730.8524618585293440.2950762829413130.147538141470656
1740.7823573198125330.4352853603749330.217642680187467
1750.6569428186407690.6861143627184620.343057181359231
1760.5500728500896410.8998542998207170.449927149910359






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1090.677018633540373NOK
5% type I error level1150.714285714285714NOK
10% type I error level1170.726708074534162NOK

\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 & 109 & 0.677018633540373 & NOK \tabularnewline
5% type I error level & 115 & 0.714285714285714 & NOK \tabularnewline
10% type I error level & 117 & 0.726708074534162 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55996&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]109[/C][C]0.677018633540373[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]115[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]117[/C][C]0.726708074534162[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55996&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55996&T=6

As an alternative you can also use a QR Code:  
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 level1090.677018633540373NOK
5% type I error level1150.714285714285714NOK
10% type I error level1170.726708074534162NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')
}