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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 07:50:57 -0700
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/24/t12590743001wssx9mazhv643b.htm/, Retrieved Thu, 28 Mar 2024 16:03:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59100, Retrieved Thu, 28 Mar 2024 16:03:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
- R         [Multiple Regression] [] [2009-11-24 14:50:57] [7ed3c7cd7b86afd1930511b5492d29ea] [Current]
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Dataseries X:
1507	0	1508	1687
1385	0	1507	1508
1632	0	1385	1507
1511	0	1632	1385
1559	0	1511	1632
1630	0	1559	1511
1579	0	1630	1559
1653	0	1579	1630
2152	0	1653	1579
2148	0	2152	1653
1752	0	2148	2152
1765	0	1752	2148
1717	0	1765	1752
1558	0	1717	1765
1575	0	1558	1717
1520	0	1575	1558
1805	0	1520	1575
1800	0	1805	1520
1719	0	1800	1805
2008	0	1719	1800
2242	0	2008	1719
2478	0	2242	2008
2030	0	2478	2242
1655	0	2030	2478
1693	0	1655	2030
1623	0	1693	1655
1805	0	1623	1693
1746	0	1805	1623
1795	0	1746	1805
1926	0	1795	1746
1619	0	1926	1795
1992	0	1619	1926
2233	0	1992	1619
2192	0	2233	1992
2080	0	2192	2233
1768	0	2080	2192
1835	0	1768	2080
1569	0	1835	1768
1976	0	1569	1835
1853	0	1976	1569
1965	0	1853	1976
1689	0	1965	1853
1778	0	1689	1965
1976	0	1778	1689
2397	0	1976	1778
2654	0	2397	1976
2097	0	2654	2397
1963	0	2097	2654
1677	0	1963	2097
1941	0	1677	1963
2003	0	1941	1677
1813	0	2003	1941
2012	0	1813	2003
1912	0	2012	1813
2084	0	1912	2012
2080	0	2084	1912
2118	0	2080	2084
2150	0	2118	2080
1608	0	2150	2118
1503	0	1608	2150
1548	0	1503	1608
1382	0	1548	1503
1731	0	1382	1548
1798	0	1731	1382
1779	0	1798	1731
1887	0	1779	1798
2004	0	1887	1779
2077	0	2004	1887
2092	0	2077	2004
2051	0	2092	2077
1577	0	2051	2092
1356	0	1577	2051
1652	0	1356	1577
1382	0	1652	1356
1519	0	1382	1652
1421	0	1519	1382
1442	0	1421	1519
1543	0	1442	1421
1656	0	1543	1442
1561	0	1656	1543
1905	0	1561	1656
2199	0	1905	1561
1473	0	2199	1905
1655	0	1473	2199
1407	0	1655	1473
1395	0	1407	1655
1530	0	1395	1407
1309	0	1530	1395
1526	0	1309	1530
1327	0	1526	1309
1627	0	1327	1526
1748	0	1627	1327
1958	0	1748	1627
2274	0	1958	1748
1648	0	2274	1958
1401	0	1648	2274
1411	0	1401	1648
1403	0	1411	1401
1394	0	1403	1411
1520	0	1394	1403
1528	0	1520	1394
1643	0	1528	1520
1515	0	1643	1528
1685	0	1515	1643
2000	0	1685	1515
2215	0	2000	1685
1956	0	2215	2000
1462	0	1956	2215
1563	0	1462	1956
1459	0	1563	1462
1446	0	1459	1563
1622	0	1446	1459
1657	0	1622	1446
1638	0	1657	1622
1643	0	1638	1657
1683	0	1643	1638
2050	0	1683	1643
2262	0	2050	1683
1813	0	2262	2050
1445	0	1813	2262
1762	0	1445	1813
1461	0	1762	1445
1556	0	1461	1762
1431	0	1556	1461
1427	0	1431	1556
1554	0	1427	1431
1645	0	1554	1427
1653	0	1645	1554
2016	0	1653	1645
2207	0	2016	1653
1665	0	2207	2016
1361	0	1665	2207
1506	0	1361	1665
1360	0	1506	1361
1453	0	1360	1506
1522	0	1453	1360
1460	0	1522	1453
1552	0	1460	1522
1548	0	1552	1460
1827	0	1548	1552
1737	0	1827	1548
1941	0	1737	1827
1474	0	1941	1737
1458	0	1474	1941
1542	0	1458	1474
1404	0	1542	1458
1522	0	1404	1542
1385	0	1522	1404
1641	0	1385	1522
1510	0	1641	1385
1681	0	1510	1641
1938	0	1681	1510
1868	0	1938	1681
1726	0	1868	1938
1456	0	1726	1868
1445	0	1456	1726
1456	0	1445	1456
1365	0	1456	1445
1487	0	1365	1456
1558	0	1487	1365
1488	0	1558	1487
1684	0	1488	1558
1594	0	1684	1488
1850	0	1594	1684
1998	0	1850	1594
2079	0	1998	1850
1494	0	2079	1998
1057	1	1494	2079
1218	1	1057	1494
1168	1	1218	1057
1236	1	1168	1218
1076	1	1236	1168
1174	1	1076	1236
1139	1	1174	1076
1427	1	1139	1174
1487	1	1427	1139
1483	1	1487	1427
1513	1	1483	1487
1357	1	1513	1483
1165	1	1357	1513
1282	1	1165	1357
1110	1	1282	1165
1297	1	1110	1282
1185	1	1297	1110
1222	1	1185	1297
1284	1	1222	1185
1444	1	1284	1222
1575	1	1444	1284
1737	1	1575	1444
1763	1	1737	1575




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&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]6 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=59100&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 432.674018921448 -85.6151937996543X[t] + 0.412253807726456Y1[t] + 0.208748882344717Y2[t] + 221.708450693677M1[t] + 130.164095261294M2[t] + 304.321659011619M3[t] + 216.329773410414M4[t] + 287.012043536047M5[t] + 283.086050769294M6[t] + 314.717592403885M7[t] + 429.953249810296M8[t] + 561.771276261432M9[t] + 567.769451532068M10[t] + 38.9218710448567M11[t] -0.737951251841748t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  432.674018921448 -85.6151937996543X[t] +  0.412253807726456Y1[t] +  0.208748882344717Y2[t] +  221.708450693677M1[t] +  130.164095261294M2[t] +  304.321659011619M3[t] +  216.329773410414M4[t] +  287.012043536047M5[t] +  283.086050769294M6[t] +  314.717592403885M7[t] +  429.953249810296M8[t] +  561.771276261432M9[t] +  567.769451532068M10[t] +  38.9218710448567M11[t] -0.737951251841748t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  432.674018921448 -85.6151937996543X[t] +  0.412253807726456Y1[t] +  0.208748882344717Y2[t] +  221.708450693677M1[t] +  130.164095261294M2[t] +  304.321659011619M3[t] +  216.329773410414M4[t] +  287.012043536047M5[t] +  283.086050769294M6[t] +  314.717592403885M7[t] +  429.953249810296M8[t] +  561.771276261432M9[t] +  567.769451532068M10[t] +  38.9218710448567M11[t] -0.737951251841748t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 432.674018921448 -85.6151937996543X[t] + 0.412253807726456Y1[t] + 0.208748882344717Y2[t] + 221.708450693677M1[t] + 130.164095261294M2[t] + 304.321659011619M3[t] + 216.329773410414M4[t] + 287.012043536047M5[t] + 283.086050769294M6[t] + 314.717592403885M7[t] + 429.953249810296M8[t] + 561.771276261432M9[t] + 567.769451532068M10[t] + 38.9218710448567M11[t] -0.737951251841748t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)432.674018921448157.0255462.75540.0064850.003243
X-85.615193799654337.53598-2.28090.0237680.011884
Y10.4122538077264560.0742315.553700
Y20.2087488823447170.0733392.84640.0049540.002477
M1221.70845069367753.5106744.14335.3e-052.7e-05
M2130.16409526129462.3301832.08830.0382270.019113
M3304.32165901161958.7900685.17641e-060
M4216.32977341041465.8204223.28670.0012260.000613
M5287.01204353604758.2779374.92492e-061e-06
M6283.08605076929461.9853414.5679e-065e-06
M7314.71759240388558.6414595.366800
M8429.95324981029659.0118567.285900
M9561.77127626143260.3718079.305200
M10567.76945153206861.5890339.218700
M1138.921871044856759.896090.64980.5166630.258332
t-0.7379512518417480.239714-3.07850.0024180.001209

\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) & 432.674018921448 & 157.025546 & 2.7554 & 0.006485 & 0.003243 \tabularnewline
X & -85.6151937996543 & 37.53598 & -2.2809 & 0.023768 & 0.011884 \tabularnewline
Y1 & 0.412253807726456 & 0.074231 & 5.5537 & 0 & 0 \tabularnewline
Y2 & 0.208748882344717 & 0.073339 & 2.8464 & 0.004954 & 0.002477 \tabularnewline
M1 & 221.708450693677 & 53.510674 & 4.1433 & 5.3e-05 & 2.7e-05 \tabularnewline
M2 & 130.164095261294 & 62.330183 & 2.0883 & 0.038227 & 0.019113 \tabularnewline
M3 & 304.321659011619 & 58.790068 & 5.1764 & 1e-06 & 0 \tabularnewline
M4 & 216.329773410414 & 65.820422 & 3.2867 & 0.001226 & 0.000613 \tabularnewline
M5 & 287.012043536047 & 58.277937 & 4.9249 & 2e-06 & 1e-06 \tabularnewline
M6 & 283.086050769294 & 61.985341 & 4.567 & 9e-06 & 5e-06 \tabularnewline
M7 & 314.717592403885 & 58.641459 & 5.3668 & 0 & 0 \tabularnewline
M8 & 429.953249810296 & 59.011856 & 7.2859 & 0 & 0 \tabularnewline
M9 & 561.771276261432 & 60.371807 & 9.3052 & 0 & 0 \tabularnewline
M10 & 567.769451532068 & 61.589033 & 9.2187 & 0 & 0 \tabularnewline
M11 & 38.9218710448567 & 59.89609 & 0.6498 & 0.516663 & 0.258332 \tabularnewline
t & -0.737951251841748 & 0.239714 & -3.0785 & 0.002418 & 0.001209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&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]432.674018921448[/C][C]157.025546[/C][C]2.7554[/C][C]0.006485[/C][C]0.003243[/C][/ROW]
[ROW][C]X[/C][C]-85.6151937996543[/C][C]37.53598[/C][C]-2.2809[/C][C]0.023768[/C][C]0.011884[/C][/ROW]
[ROW][C]Y1[/C][C]0.412253807726456[/C][C]0.074231[/C][C]5.5537[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]0.208748882344717[/C][C]0.073339[/C][C]2.8464[/C][C]0.004954[/C][C]0.002477[/C][/ROW]
[ROW][C]M1[/C][C]221.708450693677[/C][C]53.510674[/C][C]4.1433[/C][C]5.3e-05[/C][C]2.7e-05[/C][/ROW]
[ROW][C]M2[/C][C]130.164095261294[/C][C]62.330183[/C][C]2.0883[/C][C]0.038227[/C][C]0.019113[/C][/ROW]
[ROW][C]M3[/C][C]304.321659011619[/C][C]58.790068[/C][C]5.1764[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]216.329773410414[/C][C]65.820422[/C][C]3.2867[/C][C]0.001226[/C][C]0.000613[/C][/ROW]
[ROW][C]M5[/C][C]287.012043536047[/C][C]58.277937[/C][C]4.9249[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M6[/C][C]283.086050769294[/C][C]61.985341[/C][C]4.567[/C][C]9e-06[/C][C]5e-06[/C][/ROW]
[ROW][C]M7[/C][C]314.717592403885[/C][C]58.641459[/C][C]5.3668[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]429.953249810296[/C][C]59.011856[/C][C]7.2859[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]561.771276261432[/C][C]60.371807[/C][C]9.3052[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]567.769451532068[/C][C]61.589033[/C][C]9.2187[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]38.9218710448567[/C][C]59.89609[/C][C]0.6498[/C][C]0.516663[/C][C]0.258332[/C][/ROW]
[ROW][C]t[/C][C]-0.737951251841748[/C][C]0.239714[/C][C]-3.0785[/C][C]0.002418[/C][C]0.001209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)432.674018921448157.0255462.75540.0064850.003243
X-85.615193799654337.53598-2.28090.0237680.011884
Y10.4122538077264560.0742315.553700
Y20.2087488823447170.0733392.84640.0049540.002477
M1221.70845069367753.5106744.14335.3e-052.7e-05
M2130.16409526129462.3301832.08830.0382270.019113
M3304.32165901161958.7900685.17641e-060
M4216.32977341041465.8204223.28670.0012260.000613
M5287.01204353604758.2779374.92492e-061e-06
M6283.08605076929461.9853414.5679e-065e-06
M7314.71759240388558.6414595.366800
M8429.95324981029659.0118567.285900
M9561.77127626143260.3718079.305200
M10567.76945153206861.5890339.218700
M1138.921871044856759.896090.64980.5166630.258332
t-0.7379512518417480.239714-3.07850.0024180.001209







Multiple Linear Regression - Regression Statistics
Multiple R0.908326322560294
R-squared0.825056708255908
Adjusted R-squared0.809975390002107
F-TEST (value)54.7072009469706
F-TEST (DF numerator)15
F-TEST (DF denominator)174
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation126.806940269663
Sum Squared Residuals2797920.01749639

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.908326322560294 \tabularnewline
R-squared & 0.825056708255908 \tabularnewline
Adjusted R-squared & 0.809975390002107 \tabularnewline
F-TEST (value) & 54.7072009469706 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 174 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 126.806940269663 \tabularnewline
Sum Squared Residuals & 2797920.01749639 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.908326322560294[/C][/ROW]
[ROW][C]R-squared[/C][C]0.825056708255908[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.809975390002107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.7072009469706[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]174[/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]126.806940269663[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2797920.01749639[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.908326322560294
R-squared0.825056708255908
Adjusted R-squared0.809975390002107
F-TEST (value)54.7072009469706
F-TEST (DF numerator)15
F-TEST (DF denominator)174
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation126.806940269663
Sum Squared Residuals2797920.01749639







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115071627.48262493032-120.482624930317
213851497.42201449866-112.422014498661
316321620.3379135721711.6620864278279
415111607.96740358150-96.967403581504
515591679.58998565954-120.589985659538
616301669.45560964810-39.4556096481024
715791739.63916673198-160.639166731977
816531847.93309933897-194.933099338972
921521998.87376331044153.126236689556
1021482225.29605467825-77.2960546782486
1117521798.22719999830-46.2271999983028
1217651594.47987431255170.520125687451
1317171738.14511584632-21.1451158463211
1415581628.78836186171-70.7883618617085
1515751726.63967257914-151.639672579138
1615201611.72707816463-91.727078164631
1718051662.54616861333142.453831386673
1818001763.8933712678136.1066287321876
1917191852.21912408017-133.219124080174
2020081932.2805273971875.7194726028235
2122422165.5932935595076.406706440505
2224782327.64933558390150.350664416097
2320301944.2029409369685.7970590630432
2416551769.11814901216-114.118149012159
2516931741.97397126614-48.9739712661412
2616231587.0764783962535.9235216037480
2718051739.5707818829865.4292181170171
2817461711.2587162720234.7412837279785
2917951794.872357076690.127642923309917
3019261798.09266557835127.907334421647
3116191893.22020000816-274.220200008159
3219921908.5020907778683.497909222136
3322332129.2669293793103.733070620701
3421922311.74365417475-119.743654174749
3520801815.56419696399264.435803036013
3617681721.1732440257946.8267559742079
3718351790.1406806343744.8593193656343
3815691660.34972777626-91.3497277762608
3919761738.09600253660237.903997463397
4018531761.6262627245391.3737372754704
4119651865.8241583622799.1758416377336
4216891881.65652828063-192.656528280634
4317781822.14794255349-44.1479425534903
4419761915.7215460685760.2784539314281
4523972147.00652572639249.993474273615
4626542367.15788150227286.842118497729
4720972031.4048578160465.5951421839577
4819631815.7681273783147.2318726217
4916771865.22348911878-188.223489118784
5019411627.0642431906313.935756809400
5120031849.61668057828153.383319421722
5218131841.55628474328-28.5562847432778
5320121846.11481085441165.885189145585
5419121883.8270869278928.1729130721122
5520841915.03632412459168.963675875409
5620802079.566796973640.433203026361845
5721182244.90266470532-126.902664705319
5821502264.99353788834-114.993537888339
5916081756.53258552563-148.532585525632
6015031500.111163676222.88883632377516
6115481564.65311907595-16.6531190759465
6213821469.00360109322-87.003601093216
6317311583.38278121462147.617218785380
6417981603.87720878888194.122791211116
6517791774.295892718654.70410728134624
6618871775.78530147035111.214698529648
6720041847.23607432301156.763925676991
6820772032.5123552748044.4876447251971
6920922218.11057767246-126.110577672461
7020512244.79327721832-193.793277218316
7115771701.43657259765-124.436572597649
7213561457.80974126248-101.809741262477
7316521488.72517896537163.274821034630
7413821472.33649636999-90.3364963699931
7515191596.23724995637-77.2372499563697
7614211507.62398652877-86.6239865287743
7714421565.7660291266-123.766029126599
7815431549.30202460048-6.30202460047699
7916561626.2169760928429.7830239071621
8015611808.38299963731-247.382999637313
8119051923.88758680755-18.8875868075472
8221992051.13197686149147.868023138506
8314731714.55868012060-241.558680120601
8416551436.97476482384218.025235176158
8514071581.42376868963-174.423768689629
8613951424.89481427598-29.8948142759807
8715301541.59765826026-11.5976582602570
8813091506.01709886215-197.017098862146
8915261513.0344253449312.9655746550732
9013271551.69605460479-224.69605460479
9116271545.8496447187881.1503552812214
9217481742.482465604695.51753439531457
9319581986.06991624230-28.0699162422966
9422742103.16205464736170.837945352643
9516481747.68599144225-99.6859914422545
9614011515.91993232973-114.919932329725
9714111504.38694091533-93.3869409153337
9814031364.6661983692338.3338016307725
9913941536.87526922935-142.875269229347
10015201442.7651570480077.2348429519955
10115281562.77471575423-34.7747157542265
10216431587.7111613728855.2888386271229
10315151667.68393070293-152.683930702927
10416851753.41927093815-68.419270938152
10520001927.8626365108272.1373634891794
10622152098.47011996205116.529880037950
10719561723.27505482277232.724945177229
10814621621.72250602903-159.722506029034
10915631584.97366392672-21.9736639267193
11014591431.2070439445827.7929560554245
11114461582.83589755632-136.835897556324
11216221467.03687743898154.963122561017
11316571606.8241310021550.1758689978507
11416381653.32887354665-15.3288735466502
11516431683.69585246466-40.6958524646623
11616831796.28859889331-113.288598893314
11720501944.90257081339105.097429186609
11822622109.80989756158152.190102438417
11918131744.2330128810568.766987118951
12014451563.72599397225-118.725993972252
12117621539.25884399797222.741156002026
12214611500.84140566018-39.8414056601791
12315561616.34601773627-60.3460177362747
12414311503.94687903148-72.946879031482
12514271542.19061576221-115.190615762214
12615541509.7840462196244.2159537803766
12716451592.1988746542552.8011253457458
12816531770.72278536971-117.722785369709
12920161924.0970393241991.9029606758149
13022072080.67538660644126.324613393559
13116651705.60617643427-40.6061764342725
13213611482.37582687768-121.375826877676
13315061464.8792745398341.1207254601673
13413601368.91410974315-8.91410974314917
13514531512.41325425355-59.4132542535541
13615221431.5456846967490.4543153032604
13714601549.34916236171-89.3491623617147
13815521533.5291551458618.4708448541353
13915481589.40766513408-41.4076651340758
14018271721.46125323345105.538746766547
14117371966.72514525905-229.72514525905
14219411993.12346475664-52.1234647566392
14314741528.85031038276-54.8503103827582
14414581339.25273187613118.747268123873
14515421456.1414423393685.858557660643
14614041395.148473386648.85152661336181
14715221529.21196653583-7.211966535827
14813851460.32073323093-75.3207332309316
14916411498.41864856287142.581351437125
15015101570.69308244103-60.6930824410261
15116811601.0211378918679.9788621081426
15219381758.66814158049179.331858419508
15318682031.39350424643-163.393504246433
15417262061.44442448697-335.444424486968
15514561458.70643028663-2.70643028662715
15614451278.09573861084166.904261389164
15714561438.1692479346117.8307520653928
15813651348.1254954295816.8745045704194
15914871486.326249130750.673750869251373
16015581428.89522852696129.104771473039
16114881553.57693139539-65.5769313953855
16216841534.87639148241149.123608517587
16315941631.95930641542-37.9593064154182
16418501750.2689508141799.7310491858294
16519981968.0986013804129.9013986195864
16620792087.81210282297-8.81210282297075
16714941622.51396409678-128.513964096778
16810571272.97912995037-215.979129950371
16912181191.6766192440926.323380755914
17011681074.5439140191893.4560859808216
17112361260.95940618884-24.9594061888386
17210761189.82538414396-113.825384143956
17311741208.00401778095-34.0040177809543
17411391210.34112574440-71.341125744397
17514271247.26322332650179.736776673497
17614871473.1838152242313.8161847757738
17714831689.11879700239-206.118797002387
17815131705.25493873096-192.254938730958
17913571187.20202569432169.797974305680
18011651089.4930758626475.5069241373646
18112821198.7460185752283.253981424784
18211101114.6176219848-4.61762198480007
18312971241.5531987886655.4468012113349
18411851194.01001621717-9.01001621717475
18512221256.81794962406-34.8179496240648
18612841244.0275216687439.9724783312599
18714441308.20455677728135.795443222715
18815751501.6053028734673.3946971265411
18917371720.0904480600716.9095519399259
19017631819.48189251771-56.481892517712

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1507 & 1627.48262493032 & -120.482624930317 \tabularnewline
2 & 1385 & 1497.42201449866 & -112.422014498661 \tabularnewline
3 & 1632 & 1620.33791357217 & 11.6620864278279 \tabularnewline
4 & 1511 & 1607.96740358150 & -96.967403581504 \tabularnewline
5 & 1559 & 1679.58998565954 & -120.589985659538 \tabularnewline
6 & 1630 & 1669.45560964810 & -39.4556096481024 \tabularnewline
7 & 1579 & 1739.63916673198 & -160.639166731977 \tabularnewline
8 & 1653 & 1847.93309933897 & -194.933099338972 \tabularnewline
9 & 2152 & 1998.87376331044 & 153.126236689556 \tabularnewline
10 & 2148 & 2225.29605467825 & -77.2960546782486 \tabularnewline
11 & 1752 & 1798.22719999830 & -46.2271999983028 \tabularnewline
12 & 1765 & 1594.47987431255 & 170.520125687451 \tabularnewline
13 & 1717 & 1738.14511584632 & -21.1451158463211 \tabularnewline
14 & 1558 & 1628.78836186171 & -70.7883618617085 \tabularnewline
15 & 1575 & 1726.63967257914 & -151.639672579138 \tabularnewline
16 & 1520 & 1611.72707816463 & -91.727078164631 \tabularnewline
17 & 1805 & 1662.54616861333 & 142.453831386673 \tabularnewline
18 & 1800 & 1763.89337126781 & 36.1066287321876 \tabularnewline
19 & 1719 & 1852.21912408017 & -133.219124080174 \tabularnewline
20 & 2008 & 1932.28052739718 & 75.7194726028235 \tabularnewline
21 & 2242 & 2165.59329355950 & 76.406706440505 \tabularnewline
22 & 2478 & 2327.64933558390 & 150.350664416097 \tabularnewline
23 & 2030 & 1944.20294093696 & 85.7970590630432 \tabularnewline
24 & 1655 & 1769.11814901216 & -114.118149012159 \tabularnewline
25 & 1693 & 1741.97397126614 & -48.9739712661412 \tabularnewline
26 & 1623 & 1587.07647839625 & 35.9235216037480 \tabularnewline
27 & 1805 & 1739.57078188298 & 65.4292181170171 \tabularnewline
28 & 1746 & 1711.25871627202 & 34.7412837279785 \tabularnewline
29 & 1795 & 1794.87235707669 & 0.127642923309917 \tabularnewline
30 & 1926 & 1798.09266557835 & 127.907334421647 \tabularnewline
31 & 1619 & 1893.22020000816 & -274.220200008159 \tabularnewline
32 & 1992 & 1908.50209077786 & 83.497909222136 \tabularnewline
33 & 2233 & 2129.2669293793 & 103.733070620701 \tabularnewline
34 & 2192 & 2311.74365417475 & -119.743654174749 \tabularnewline
35 & 2080 & 1815.56419696399 & 264.435803036013 \tabularnewline
36 & 1768 & 1721.17324402579 & 46.8267559742079 \tabularnewline
37 & 1835 & 1790.14068063437 & 44.8593193656343 \tabularnewline
38 & 1569 & 1660.34972777626 & -91.3497277762608 \tabularnewline
39 & 1976 & 1738.09600253660 & 237.903997463397 \tabularnewline
40 & 1853 & 1761.62626272453 & 91.3737372754704 \tabularnewline
41 & 1965 & 1865.82415836227 & 99.1758416377336 \tabularnewline
42 & 1689 & 1881.65652828063 & -192.656528280634 \tabularnewline
43 & 1778 & 1822.14794255349 & -44.1479425534903 \tabularnewline
44 & 1976 & 1915.72154606857 & 60.2784539314281 \tabularnewline
45 & 2397 & 2147.00652572639 & 249.993474273615 \tabularnewline
46 & 2654 & 2367.15788150227 & 286.842118497729 \tabularnewline
47 & 2097 & 2031.40485781604 & 65.5951421839577 \tabularnewline
48 & 1963 & 1815.7681273783 & 147.2318726217 \tabularnewline
49 & 1677 & 1865.22348911878 & -188.223489118784 \tabularnewline
50 & 1941 & 1627.0642431906 & 313.935756809400 \tabularnewline
51 & 2003 & 1849.61668057828 & 153.383319421722 \tabularnewline
52 & 1813 & 1841.55628474328 & -28.5562847432778 \tabularnewline
53 & 2012 & 1846.11481085441 & 165.885189145585 \tabularnewline
54 & 1912 & 1883.82708692789 & 28.1729130721122 \tabularnewline
55 & 2084 & 1915.03632412459 & 168.963675875409 \tabularnewline
56 & 2080 & 2079.56679697364 & 0.433203026361845 \tabularnewline
57 & 2118 & 2244.90266470532 & -126.902664705319 \tabularnewline
58 & 2150 & 2264.99353788834 & -114.993537888339 \tabularnewline
59 & 1608 & 1756.53258552563 & -148.532585525632 \tabularnewline
60 & 1503 & 1500.11116367622 & 2.88883632377516 \tabularnewline
61 & 1548 & 1564.65311907595 & -16.6531190759465 \tabularnewline
62 & 1382 & 1469.00360109322 & -87.003601093216 \tabularnewline
63 & 1731 & 1583.38278121462 & 147.617218785380 \tabularnewline
64 & 1798 & 1603.87720878888 & 194.122791211116 \tabularnewline
65 & 1779 & 1774.29589271865 & 4.70410728134624 \tabularnewline
66 & 1887 & 1775.78530147035 & 111.214698529648 \tabularnewline
67 & 2004 & 1847.23607432301 & 156.763925676991 \tabularnewline
68 & 2077 & 2032.51235527480 & 44.4876447251971 \tabularnewline
69 & 2092 & 2218.11057767246 & -126.110577672461 \tabularnewline
70 & 2051 & 2244.79327721832 & -193.793277218316 \tabularnewline
71 & 1577 & 1701.43657259765 & -124.436572597649 \tabularnewline
72 & 1356 & 1457.80974126248 & -101.809741262477 \tabularnewline
73 & 1652 & 1488.72517896537 & 163.274821034630 \tabularnewline
74 & 1382 & 1472.33649636999 & -90.3364963699931 \tabularnewline
75 & 1519 & 1596.23724995637 & -77.2372499563697 \tabularnewline
76 & 1421 & 1507.62398652877 & -86.6239865287743 \tabularnewline
77 & 1442 & 1565.7660291266 & -123.766029126599 \tabularnewline
78 & 1543 & 1549.30202460048 & -6.30202460047699 \tabularnewline
79 & 1656 & 1626.21697609284 & 29.7830239071621 \tabularnewline
80 & 1561 & 1808.38299963731 & -247.382999637313 \tabularnewline
81 & 1905 & 1923.88758680755 & -18.8875868075472 \tabularnewline
82 & 2199 & 2051.13197686149 & 147.868023138506 \tabularnewline
83 & 1473 & 1714.55868012060 & -241.558680120601 \tabularnewline
84 & 1655 & 1436.97476482384 & 218.025235176158 \tabularnewline
85 & 1407 & 1581.42376868963 & -174.423768689629 \tabularnewline
86 & 1395 & 1424.89481427598 & -29.8948142759807 \tabularnewline
87 & 1530 & 1541.59765826026 & -11.5976582602570 \tabularnewline
88 & 1309 & 1506.01709886215 & -197.017098862146 \tabularnewline
89 & 1526 & 1513.03442534493 & 12.9655746550732 \tabularnewline
90 & 1327 & 1551.69605460479 & -224.69605460479 \tabularnewline
91 & 1627 & 1545.84964471878 & 81.1503552812214 \tabularnewline
92 & 1748 & 1742.48246560469 & 5.51753439531457 \tabularnewline
93 & 1958 & 1986.06991624230 & -28.0699162422966 \tabularnewline
94 & 2274 & 2103.16205464736 & 170.837945352643 \tabularnewline
95 & 1648 & 1747.68599144225 & -99.6859914422545 \tabularnewline
96 & 1401 & 1515.91993232973 & -114.919932329725 \tabularnewline
97 & 1411 & 1504.38694091533 & -93.3869409153337 \tabularnewline
98 & 1403 & 1364.66619836923 & 38.3338016307725 \tabularnewline
99 & 1394 & 1536.87526922935 & -142.875269229347 \tabularnewline
100 & 1520 & 1442.76515704800 & 77.2348429519955 \tabularnewline
101 & 1528 & 1562.77471575423 & -34.7747157542265 \tabularnewline
102 & 1643 & 1587.71116137288 & 55.2888386271229 \tabularnewline
103 & 1515 & 1667.68393070293 & -152.683930702927 \tabularnewline
104 & 1685 & 1753.41927093815 & -68.419270938152 \tabularnewline
105 & 2000 & 1927.86263651082 & 72.1373634891794 \tabularnewline
106 & 2215 & 2098.47011996205 & 116.529880037950 \tabularnewline
107 & 1956 & 1723.27505482277 & 232.724945177229 \tabularnewline
108 & 1462 & 1621.72250602903 & -159.722506029034 \tabularnewline
109 & 1563 & 1584.97366392672 & -21.9736639267193 \tabularnewline
110 & 1459 & 1431.20704394458 & 27.7929560554245 \tabularnewline
111 & 1446 & 1582.83589755632 & -136.835897556324 \tabularnewline
112 & 1622 & 1467.03687743898 & 154.963122561017 \tabularnewline
113 & 1657 & 1606.82413100215 & 50.1758689978507 \tabularnewline
114 & 1638 & 1653.32887354665 & -15.3288735466502 \tabularnewline
115 & 1643 & 1683.69585246466 & -40.6958524646623 \tabularnewline
116 & 1683 & 1796.28859889331 & -113.288598893314 \tabularnewline
117 & 2050 & 1944.90257081339 & 105.097429186609 \tabularnewline
118 & 2262 & 2109.80989756158 & 152.190102438417 \tabularnewline
119 & 1813 & 1744.23301288105 & 68.766987118951 \tabularnewline
120 & 1445 & 1563.72599397225 & -118.725993972252 \tabularnewline
121 & 1762 & 1539.25884399797 & 222.741156002026 \tabularnewline
122 & 1461 & 1500.84140566018 & -39.8414056601791 \tabularnewline
123 & 1556 & 1616.34601773627 & -60.3460177362747 \tabularnewline
124 & 1431 & 1503.94687903148 & -72.946879031482 \tabularnewline
125 & 1427 & 1542.19061576221 & -115.190615762214 \tabularnewline
126 & 1554 & 1509.78404621962 & 44.2159537803766 \tabularnewline
127 & 1645 & 1592.19887465425 & 52.8011253457458 \tabularnewline
128 & 1653 & 1770.72278536971 & -117.722785369709 \tabularnewline
129 & 2016 & 1924.09703932419 & 91.9029606758149 \tabularnewline
130 & 2207 & 2080.67538660644 & 126.324613393559 \tabularnewline
131 & 1665 & 1705.60617643427 & -40.6061764342725 \tabularnewline
132 & 1361 & 1482.37582687768 & -121.375826877676 \tabularnewline
133 & 1506 & 1464.87927453983 & 41.1207254601673 \tabularnewline
134 & 1360 & 1368.91410974315 & -8.91410974314917 \tabularnewline
135 & 1453 & 1512.41325425355 & -59.4132542535541 \tabularnewline
136 & 1522 & 1431.54568469674 & 90.4543153032604 \tabularnewline
137 & 1460 & 1549.34916236171 & -89.3491623617147 \tabularnewline
138 & 1552 & 1533.52915514586 & 18.4708448541353 \tabularnewline
139 & 1548 & 1589.40766513408 & -41.4076651340758 \tabularnewline
140 & 1827 & 1721.46125323345 & 105.538746766547 \tabularnewline
141 & 1737 & 1966.72514525905 & -229.72514525905 \tabularnewline
142 & 1941 & 1993.12346475664 & -52.1234647566392 \tabularnewline
143 & 1474 & 1528.85031038276 & -54.8503103827582 \tabularnewline
144 & 1458 & 1339.25273187613 & 118.747268123873 \tabularnewline
145 & 1542 & 1456.14144233936 & 85.858557660643 \tabularnewline
146 & 1404 & 1395.14847338664 & 8.85152661336181 \tabularnewline
147 & 1522 & 1529.21196653583 & -7.211966535827 \tabularnewline
148 & 1385 & 1460.32073323093 & -75.3207332309316 \tabularnewline
149 & 1641 & 1498.41864856287 & 142.581351437125 \tabularnewline
150 & 1510 & 1570.69308244103 & -60.6930824410261 \tabularnewline
151 & 1681 & 1601.02113789186 & 79.9788621081426 \tabularnewline
152 & 1938 & 1758.66814158049 & 179.331858419508 \tabularnewline
153 & 1868 & 2031.39350424643 & -163.393504246433 \tabularnewline
154 & 1726 & 2061.44442448697 & -335.444424486968 \tabularnewline
155 & 1456 & 1458.70643028663 & -2.70643028662715 \tabularnewline
156 & 1445 & 1278.09573861084 & 166.904261389164 \tabularnewline
157 & 1456 & 1438.16924793461 & 17.8307520653928 \tabularnewline
158 & 1365 & 1348.12549542958 & 16.8745045704194 \tabularnewline
159 & 1487 & 1486.32624913075 & 0.673750869251373 \tabularnewline
160 & 1558 & 1428.89522852696 & 129.104771473039 \tabularnewline
161 & 1488 & 1553.57693139539 & -65.5769313953855 \tabularnewline
162 & 1684 & 1534.87639148241 & 149.123608517587 \tabularnewline
163 & 1594 & 1631.95930641542 & -37.9593064154182 \tabularnewline
164 & 1850 & 1750.26895081417 & 99.7310491858294 \tabularnewline
165 & 1998 & 1968.09860138041 & 29.9013986195864 \tabularnewline
166 & 2079 & 2087.81210282297 & -8.81210282297075 \tabularnewline
167 & 1494 & 1622.51396409678 & -128.513964096778 \tabularnewline
168 & 1057 & 1272.97912995037 & -215.979129950371 \tabularnewline
169 & 1218 & 1191.67661924409 & 26.323380755914 \tabularnewline
170 & 1168 & 1074.54391401918 & 93.4560859808216 \tabularnewline
171 & 1236 & 1260.95940618884 & -24.9594061888386 \tabularnewline
172 & 1076 & 1189.82538414396 & -113.825384143956 \tabularnewline
173 & 1174 & 1208.00401778095 & -34.0040177809543 \tabularnewline
174 & 1139 & 1210.34112574440 & -71.341125744397 \tabularnewline
175 & 1427 & 1247.26322332650 & 179.736776673497 \tabularnewline
176 & 1487 & 1473.18381522423 & 13.8161847757738 \tabularnewline
177 & 1483 & 1689.11879700239 & -206.118797002387 \tabularnewline
178 & 1513 & 1705.25493873096 & -192.254938730958 \tabularnewline
179 & 1357 & 1187.20202569432 & 169.797974305680 \tabularnewline
180 & 1165 & 1089.49307586264 & 75.5069241373646 \tabularnewline
181 & 1282 & 1198.74601857522 & 83.253981424784 \tabularnewline
182 & 1110 & 1114.6176219848 & -4.61762198480007 \tabularnewline
183 & 1297 & 1241.55319878866 & 55.4468012113349 \tabularnewline
184 & 1185 & 1194.01001621717 & -9.01001621717475 \tabularnewline
185 & 1222 & 1256.81794962406 & -34.8179496240648 \tabularnewline
186 & 1284 & 1244.02752166874 & 39.9724783312599 \tabularnewline
187 & 1444 & 1308.20455677728 & 135.795443222715 \tabularnewline
188 & 1575 & 1501.60530287346 & 73.3946971265411 \tabularnewline
189 & 1737 & 1720.09044806007 & 16.9095519399259 \tabularnewline
190 & 1763 & 1819.48189251771 & -56.481892517712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&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]1507[/C][C]1627.48262493032[/C][C]-120.482624930317[/C][/ROW]
[ROW][C]2[/C][C]1385[/C][C]1497.42201449866[/C][C]-112.422014498661[/C][/ROW]
[ROW][C]3[/C][C]1632[/C][C]1620.33791357217[/C][C]11.6620864278279[/C][/ROW]
[ROW][C]4[/C][C]1511[/C][C]1607.96740358150[/C][C]-96.967403581504[/C][/ROW]
[ROW][C]5[/C][C]1559[/C][C]1679.58998565954[/C][C]-120.589985659538[/C][/ROW]
[ROW][C]6[/C][C]1630[/C][C]1669.45560964810[/C][C]-39.4556096481024[/C][/ROW]
[ROW][C]7[/C][C]1579[/C][C]1739.63916673198[/C][C]-160.639166731977[/C][/ROW]
[ROW][C]8[/C][C]1653[/C][C]1847.93309933897[/C][C]-194.933099338972[/C][/ROW]
[ROW][C]9[/C][C]2152[/C][C]1998.87376331044[/C][C]153.126236689556[/C][/ROW]
[ROW][C]10[/C][C]2148[/C][C]2225.29605467825[/C][C]-77.2960546782486[/C][/ROW]
[ROW][C]11[/C][C]1752[/C][C]1798.22719999830[/C][C]-46.2271999983028[/C][/ROW]
[ROW][C]12[/C][C]1765[/C][C]1594.47987431255[/C][C]170.520125687451[/C][/ROW]
[ROW][C]13[/C][C]1717[/C][C]1738.14511584632[/C][C]-21.1451158463211[/C][/ROW]
[ROW][C]14[/C][C]1558[/C][C]1628.78836186171[/C][C]-70.7883618617085[/C][/ROW]
[ROW][C]15[/C][C]1575[/C][C]1726.63967257914[/C][C]-151.639672579138[/C][/ROW]
[ROW][C]16[/C][C]1520[/C][C]1611.72707816463[/C][C]-91.727078164631[/C][/ROW]
[ROW][C]17[/C][C]1805[/C][C]1662.54616861333[/C][C]142.453831386673[/C][/ROW]
[ROW][C]18[/C][C]1800[/C][C]1763.89337126781[/C][C]36.1066287321876[/C][/ROW]
[ROW][C]19[/C][C]1719[/C][C]1852.21912408017[/C][C]-133.219124080174[/C][/ROW]
[ROW][C]20[/C][C]2008[/C][C]1932.28052739718[/C][C]75.7194726028235[/C][/ROW]
[ROW][C]21[/C][C]2242[/C][C]2165.59329355950[/C][C]76.406706440505[/C][/ROW]
[ROW][C]22[/C][C]2478[/C][C]2327.64933558390[/C][C]150.350664416097[/C][/ROW]
[ROW][C]23[/C][C]2030[/C][C]1944.20294093696[/C][C]85.7970590630432[/C][/ROW]
[ROW][C]24[/C][C]1655[/C][C]1769.11814901216[/C][C]-114.118149012159[/C][/ROW]
[ROW][C]25[/C][C]1693[/C][C]1741.97397126614[/C][C]-48.9739712661412[/C][/ROW]
[ROW][C]26[/C][C]1623[/C][C]1587.07647839625[/C][C]35.9235216037480[/C][/ROW]
[ROW][C]27[/C][C]1805[/C][C]1739.57078188298[/C][C]65.4292181170171[/C][/ROW]
[ROW][C]28[/C][C]1746[/C][C]1711.25871627202[/C][C]34.7412837279785[/C][/ROW]
[ROW][C]29[/C][C]1795[/C][C]1794.87235707669[/C][C]0.127642923309917[/C][/ROW]
[ROW][C]30[/C][C]1926[/C][C]1798.09266557835[/C][C]127.907334421647[/C][/ROW]
[ROW][C]31[/C][C]1619[/C][C]1893.22020000816[/C][C]-274.220200008159[/C][/ROW]
[ROW][C]32[/C][C]1992[/C][C]1908.50209077786[/C][C]83.497909222136[/C][/ROW]
[ROW][C]33[/C][C]2233[/C][C]2129.2669293793[/C][C]103.733070620701[/C][/ROW]
[ROW][C]34[/C][C]2192[/C][C]2311.74365417475[/C][C]-119.743654174749[/C][/ROW]
[ROW][C]35[/C][C]2080[/C][C]1815.56419696399[/C][C]264.435803036013[/C][/ROW]
[ROW][C]36[/C][C]1768[/C][C]1721.17324402579[/C][C]46.8267559742079[/C][/ROW]
[ROW][C]37[/C][C]1835[/C][C]1790.14068063437[/C][C]44.8593193656343[/C][/ROW]
[ROW][C]38[/C][C]1569[/C][C]1660.34972777626[/C][C]-91.3497277762608[/C][/ROW]
[ROW][C]39[/C][C]1976[/C][C]1738.09600253660[/C][C]237.903997463397[/C][/ROW]
[ROW][C]40[/C][C]1853[/C][C]1761.62626272453[/C][C]91.3737372754704[/C][/ROW]
[ROW][C]41[/C][C]1965[/C][C]1865.82415836227[/C][C]99.1758416377336[/C][/ROW]
[ROW][C]42[/C][C]1689[/C][C]1881.65652828063[/C][C]-192.656528280634[/C][/ROW]
[ROW][C]43[/C][C]1778[/C][C]1822.14794255349[/C][C]-44.1479425534903[/C][/ROW]
[ROW][C]44[/C][C]1976[/C][C]1915.72154606857[/C][C]60.2784539314281[/C][/ROW]
[ROW][C]45[/C][C]2397[/C][C]2147.00652572639[/C][C]249.993474273615[/C][/ROW]
[ROW][C]46[/C][C]2654[/C][C]2367.15788150227[/C][C]286.842118497729[/C][/ROW]
[ROW][C]47[/C][C]2097[/C][C]2031.40485781604[/C][C]65.5951421839577[/C][/ROW]
[ROW][C]48[/C][C]1963[/C][C]1815.7681273783[/C][C]147.2318726217[/C][/ROW]
[ROW][C]49[/C][C]1677[/C][C]1865.22348911878[/C][C]-188.223489118784[/C][/ROW]
[ROW][C]50[/C][C]1941[/C][C]1627.0642431906[/C][C]313.935756809400[/C][/ROW]
[ROW][C]51[/C][C]2003[/C][C]1849.61668057828[/C][C]153.383319421722[/C][/ROW]
[ROW][C]52[/C][C]1813[/C][C]1841.55628474328[/C][C]-28.5562847432778[/C][/ROW]
[ROW][C]53[/C][C]2012[/C][C]1846.11481085441[/C][C]165.885189145585[/C][/ROW]
[ROW][C]54[/C][C]1912[/C][C]1883.82708692789[/C][C]28.1729130721122[/C][/ROW]
[ROW][C]55[/C][C]2084[/C][C]1915.03632412459[/C][C]168.963675875409[/C][/ROW]
[ROW][C]56[/C][C]2080[/C][C]2079.56679697364[/C][C]0.433203026361845[/C][/ROW]
[ROW][C]57[/C][C]2118[/C][C]2244.90266470532[/C][C]-126.902664705319[/C][/ROW]
[ROW][C]58[/C][C]2150[/C][C]2264.99353788834[/C][C]-114.993537888339[/C][/ROW]
[ROW][C]59[/C][C]1608[/C][C]1756.53258552563[/C][C]-148.532585525632[/C][/ROW]
[ROW][C]60[/C][C]1503[/C][C]1500.11116367622[/C][C]2.88883632377516[/C][/ROW]
[ROW][C]61[/C][C]1548[/C][C]1564.65311907595[/C][C]-16.6531190759465[/C][/ROW]
[ROW][C]62[/C][C]1382[/C][C]1469.00360109322[/C][C]-87.003601093216[/C][/ROW]
[ROW][C]63[/C][C]1731[/C][C]1583.38278121462[/C][C]147.617218785380[/C][/ROW]
[ROW][C]64[/C][C]1798[/C][C]1603.87720878888[/C][C]194.122791211116[/C][/ROW]
[ROW][C]65[/C][C]1779[/C][C]1774.29589271865[/C][C]4.70410728134624[/C][/ROW]
[ROW][C]66[/C][C]1887[/C][C]1775.78530147035[/C][C]111.214698529648[/C][/ROW]
[ROW][C]67[/C][C]2004[/C][C]1847.23607432301[/C][C]156.763925676991[/C][/ROW]
[ROW][C]68[/C][C]2077[/C][C]2032.51235527480[/C][C]44.4876447251971[/C][/ROW]
[ROW][C]69[/C][C]2092[/C][C]2218.11057767246[/C][C]-126.110577672461[/C][/ROW]
[ROW][C]70[/C][C]2051[/C][C]2244.79327721832[/C][C]-193.793277218316[/C][/ROW]
[ROW][C]71[/C][C]1577[/C][C]1701.43657259765[/C][C]-124.436572597649[/C][/ROW]
[ROW][C]72[/C][C]1356[/C][C]1457.80974126248[/C][C]-101.809741262477[/C][/ROW]
[ROW][C]73[/C][C]1652[/C][C]1488.72517896537[/C][C]163.274821034630[/C][/ROW]
[ROW][C]74[/C][C]1382[/C][C]1472.33649636999[/C][C]-90.3364963699931[/C][/ROW]
[ROW][C]75[/C][C]1519[/C][C]1596.23724995637[/C][C]-77.2372499563697[/C][/ROW]
[ROW][C]76[/C][C]1421[/C][C]1507.62398652877[/C][C]-86.6239865287743[/C][/ROW]
[ROW][C]77[/C][C]1442[/C][C]1565.7660291266[/C][C]-123.766029126599[/C][/ROW]
[ROW][C]78[/C][C]1543[/C][C]1549.30202460048[/C][C]-6.30202460047699[/C][/ROW]
[ROW][C]79[/C][C]1656[/C][C]1626.21697609284[/C][C]29.7830239071621[/C][/ROW]
[ROW][C]80[/C][C]1561[/C][C]1808.38299963731[/C][C]-247.382999637313[/C][/ROW]
[ROW][C]81[/C][C]1905[/C][C]1923.88758680755[/C][C]-18.8875868075472[/C][/ROW]
[ROW][C]82[/C][C]2199[/C][C]2051.13197686149[/C][C]147.868023138506[/C][/ROW]
[ROW][C]83[/C][C]1473[/C][C]1714.55868012060[/C][C]-241.558680120601[/C][/ROW]
[ROW][C]84[/C][C]1655[/C][C]1436.97476482384[/C][C]218.025235176158[/C][/ROW]
[ROW][C]85[/C][C]1407[/C][C]1581.42376868963[/C][C]-174.423768689629[/C][/ROW]
[ROW][C]86[/C][C]1395[/C][C]1424.89481427598[/C][C]-29.8948142759807[/C][/ROW]
[ROW][C]87[/C][C]1530[/C][C]1541.59765826026[/C][C]-11.5976582602570[/C][/ROW]
[ROW][C]88[/C][C]1309[/C][C]1506.01709886215[/C][C]-197.017098862146[/C][/ROW]
[ROW][C]89[/C][C]1526[/C][C]1513.03442534493[/C][C]12.9655746550732[/C][/ROW]
[ROW][C]90[/C][C]1327[/C][C]1551.69605460479[/C][C]-224.69605460479[/C][/ROW]
[ROW][C]91[/C][C]1627[/C][C]1545.84964471878[/C][C]81.1503552812214[/C][/ROW]
[ROW][C]92[/C][C]1748[/C][C]1742.48246560469[/C][C]5.51753439531457[/C][/ROW]
[ROW][C]93[/C][C]1958[/C][C]1986.06991624230[/C][C]-28.0699162422966[/C][/ROW]
[ROW][C]94[/C][C]2274[/C][C]2103.16205464736[/C][C]170.837945352643[/C][/ROW]
[ROW][C]95[/C][C]1648[/C][C]1747.68599144225[/C][C]-99.6859914422545[/C][/ROW]
[ROW][C]96[/C][C]1401[/C][C]1515.91993232973[/C][C]-114.919932329725[/C][/ROW]
[ROW][C]97[/C][C]1411[/C][C]1504.38694091533[/C][C]-93.3869409153337[/C][/ROW]
[ROW][C]98[/C][C]1403[/C][C]1364.66619836923[/C][C]38.3338016307725[/C][/ROW]
[ROW][C]99[/C][C]1394[/C][C]1536.87526922935[/C][C]-142.875269229347[/C][/ROW]
[ROW][C]100[/C][C]1520[/C][C]1442.76515704800[/C][C]77.2348429519955[/C][/ROW]
[ROW][C]101[/C][C]1528[/C][C]1562.77471575423[/C][C]-34.7747157542265[/C][/ROW]
[ROW][C]102[/C][C]1643[/C][C]1587.71116137288[/C][C]55.2888386271229[/C][/ROW]
[ROW][C]103[/C][C]1515[/C][C]1667.68393070293[/C][C]-152.683930702927[/C][/ROW]
[ROW][C]104[/C][C]1685[/C][C]1753.41927093815[/C][C]-68.419270938152[/C][/ROW]
[ROW][C]105[/C][C]2000[/C][C]1927.86263651082[/C][C]72.1373634891794[/C][/ROW]
[ROW][C]106[/C][C]2215[/C][C]2098.47011996205[/C][C]116.529880037950[/C][/ROW]
[ROW][C]107[/C][C]1956[/C][C]1723.27505482277[/C][C]232.724945177229[/C][/ROW]
[ROW][C]108[/C][C]1462[/C][C]1621.72250602903[/C][C]-159.722506029034[/C][/ROW]
[ROW][C]109[/C][C]1563[/C][C]1584.97366392672[/C][C]-21.9736639267193[/C][/ROW]
[ROW][C]110[/C][C]1459[/C][C]1431.20704394458[/C][C]27.7929560554245[/C][/ROW]
[ROW][C]111[/C][C]1446[/C][C]1582.83589755632[/C][C]-136.835897556324[/C][/ROW]
[ROW][C]112[/C][C]1622[/C][C]1467.03687743898[/C][C]154.963122561017[/C][/ROW]
[ROW][C]113[/C][C]1657[/C][C]1606.82413100215[/C][C]50.1758689978507[/C][/ROW]
[ROW][C]114[/C][C]1638[/C][C]1653.32887354665[/C][C]-15.3288735466502[/C][/ROW]
[ROW][C]115[/C][C]1643[/C][C]1683.69585246466[/C][C]-40.6958524646623[/C][/ROW]
[ROW][C]116[/C][C]1683[/C][C]1796.28859889331[/C][C]-113.288598893314[/C][/ROW]
[ROW][C]117[/C][C]2050[/C][C]1944.90257081339[/C][C]105.097429186609[/C][/ROW]
[ROW][C]118[/C][C]2262[/C][C]2109.80989756158[/C][C]152.190102438417[/C][/ROW]
[ROW][C]119[/C][C]1813[/C][C]1744.23301288105[/C][C]68.766987118951[/C][/ROW]
[ROW][C]120[/C][C]1445[/C][C]1563.72599397225[/C][C]-118.725993972252[/C][/ROW]
[ROW][C]121[/C][C]1762[/C][C]1539.25884399797[/C][C]222.741156002026[/C][/ROW]
[ROW][C]122[/C][C]1461[/C][C]1500.84140566018[/C][C]-39.8414056601791[/C][/ROW]
[ROW][C]123[/C][C]1556[/C][C]1616.34601773627[/C][C]-60.3460177362747[/C][/ROW]
[ROW][C]124[/C][C]1431[/C][C]1503.94687903148[/C][C]-72.946879031482[/C][/ROW]
[ROW][C]125[/C][C]1427[/C][C]1542.19061576221[/C][C]-115.190615762214[/C][/ROW]
[ROW][C]126[/C][C]1554[/C][C]1509.78404621962[/C][C]44.2159537803766[/C][/ROW]
[ROW][C]127[/C][C]1645[/C][C]1592.19887465425[/C][C]52.8011253457458[/C][/ROW]
[ROW][C]128[/C][C]1653[/C][C]1770.72278536971[/C][C]-117.722785369709[/C][/ROW]
[ROW][C]129[/C][C]2016[/C][C]1924.09703932419[/C][C]91.9029606758149[/C][/ROW]
[ROW][C]130[/C][C]2207[/C][C]2080.67538660644[/C][C]126.324613393559[/C][/ROW]
[ROW][C]131[/C][C]1665[/C][C]1705.60617643427[/C][C]-40.6061764342725[/C][/ROW]
[ROW][C]132[/C][C]1361[/C][C]1482.37582687768[/C][C]-121.375826877676[/C][/ROW]
[ROW][C]133[/C][C]1506[/C][C]1464.87927453983[/C][C]41.1207254601673[/C][/ROW]
[ROW][C]134[/C][C]1360[/C][C]1368.91410974315[/C][C]-8.91410974314917[/C][/ROW]
[ROW][C]135[/C][C]1453[/C][C]1512.41325425355[/C][C]-59.4132542535541[/C][/ROW]
[ROW][C]136[/C][C]1522[/C][C]1431.54568469674[/C][C]90.4543153032604[/C][/ROW]
[ROW][C]137[/C][C]1460[/C][C]1549.34916236171[/C][C]-89.3491623617147[/C][/ROW]
[ROW][C]138[/C][C]1552[/C][C]1533.52915514586[/C][C]18.4708448541353[/C][/ROW]
[ROW][C]139[/C][C]1548[/C][C]1589.40766513408[/C][C]-41.4076651340758[/C][/ROW]
[ROW][C]140[/C][C]1827[/C][C]1721.46125323345[/C][C]105.538746766547[/C][/ROW]
[ROW][C]141[/C][C]1737[/C][C]1966.72514525905[/C][C]-229.72514525905[/C][/ROW]
[ROW][C]142[/C][C]1941[/C][C]1993.12346475664[/C][C]-52.1234647566392[/C][/ROW]
[ROW][C]143[/C][C]1474[/C][C]1528.85031038276[/C][C]-54.8503103827582[/C][/ROW]
[ROW][C]144[/C][C]1458[/C][C]1339.25273187613[/C][C]118.747268123873[/C][/ROW]
[ROW][C]145[/C][C]1542[/C][C]1456.14144233936[/C][C]85.858557660643[/C][/ROW]
[ROW][C]146[/C][C]1404[/C][C]1395.14847338664[/C][C]8.85152661336181[/C][/ROW]
[ROW][C]147[/C][C]1522[/C][C]1529.21196653583[/C][C]-7.211966535827[/C][/ROW]
[ROW][C]148[/C][C]1385[/C][C]1460.32073323093[/C][C]-75.3207332309316[/C][/ROW]
[ROW][C]149[/C][C]1641[/C][C]1498.41864856287[/C][C]142.581351437125[/C][/ROW]
[ROW][C]150[/C][C]1510[/C][C]1570.69308244103[/C][C]-60.6930824410261[/C][/ROW]
[ROW][C]151[/C][C]1681[/C][C]1601.02113789186[/C][C]79.9788621081426[/C][/ROW]
[ROW][C]152[/C][C]1938[/C][C]1758.66814158049[/C][C]179.331858419508[/C][/ROW]
[ROW][C]153[/C][C]1868[/C][C]2031.39350424643[/C][C]-163.393504246433[/C][/ROW]
[ROW][C]154[/C][C]1726[/C][C]2061.44442448697[/C][C]-335.444424486968[/C][/ROW]
[ROW][C]155[/C][C]1456[/C][C]1458.70643028663[/C][C]-2.70643028662715[/C][/ROW]
[ROW][C]156[/C][C]1445[/C][C]1278.09573861084[/C][C]166.904261389164[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1438.16924793461[/C][C]17.8307520653928[/C][/ROW]
[ROW][C]158[/C][C]1365[/C][C]1348.12549542958[/C][C]16.8745045704194[/C][/ROW]
[ROW][C]159[/C][C]1487[/C][C]1486.32624913075[/C][C]0.673750869251373[/C][/ROW]
[ROW][C]160[/C][C]1558[/C][C]1428.89522852696[/C][C]129.104771473039[/C][/ROW]
[ROW][C]161[/C][C]1488[/C][C]1553.57693139539[/C][C]-65.5769313953855[/C][/ROW]
[ROW][C]162[/C][C]1684[/C][C]1534.87639148241[/C][C]149.123608517587[/C][/ROW]
[ROW][C]163[/C][C]1594[/C][C]1631.95930641542[/C][C]-37.9593064154182[/C][/ROW]
[ROW][C]164[/C][C]1850[/C][C]1750.26895081417[/C][C]99.7310491858294[/C][/ROW]
[ROW][C]165[/C][C]1998[/C][C]1968.09860138041[/C][C]29.9013986195864[/C][/ROW]
[ROW][C]166[/C][C]2079[/C][C]2087.81210282297[/C][C]-8.81210282297075[/C][/ROW]
[ROW][C]167[/C][C]1494[/C][C]1622.51396409678[/C][C]-128.513964096778[/C][/ROW]
[ROW][C]168[/C][C]1057[/C][C]1272.97912995037[/C][C]-215.979129950371[/C][/ROW]
[ROW][C]169[/C][C]1218[/C][C]1191.67661924409[/C][C]26.323380755914[/C][/ROW]
[ROW][C]170[/C][C]1168[/C][C]1074.54391401918[/C][C]93.4560859808216[/C][/ROW]
[ROW][C]171[/C][C]1236[/C][C]1260.95940618884[/C][C]-24.9594061888386[/C][/ROW]
[ROW][C]172[/C][C]1076[/C][C]1189.82538414396[/C][C]-113.825384143956[/C][/ROW]
[ROW][C]173[/C][C]1174[/C][C]1208.00401778095[/C][C]-34.0040177809543[/C][/ROW]
[ROW][C]174[/C][C]1139[/C][C]1210.34112574440[/C][C]-71.341125744397[/C][/ROW]
[ROW][C]175[/C][C]1427[/C][C]1247.26322332650[/C][C]179.736776673497[/C][/ROW]
[ROW][C]176[/C][C]1487[/C][C]1473.18381522423[/C][C]13.8161847757738[/C][/ROW]
[ROW][C]177[/C][C]1483[/C][C]1689.11879700239[/C][C]-206.118797002387[/C][/ROW]
[ROW][C]178[/C][C]1513[/C][C]1705.25493873096[/C][C]-192.254938730958[/C][/ROW]
[ROW][C]179[/C][C]1357[/C][C]1187.20202569432[/C][C]169.797974305680[/C][/ROW]
[ROW][C]180[/C][C]1165[/C][C]1089.49307586264[/C][C]75.5069241373646[/C][/ROW]
[ROW][C]181[/C][C]1282[/C][C]1198.74601857522[/C][C]83.253981424784[/C][/ROW]
[ROW][C]182[/C][C]1110[/C][C]1114.6176219848[/C][C]-4.61762198480007[/C][/ROW]
[ROW][C]183[/C][C]1297[/C][C]1241.55319878866[/C][C]55.4468012113349[/C][/ROW]
[ROW][C]184[/C][C]1185[/C][C]1194.01001621717[/C][C]-9.01001621717475[/C][/ROW]
[ROW][C]185[/C][C]1222[/C][C]1256.81794962406[/C][C]-34.8179496240648[/C][/ROW]
[ROW][C]186[/C][C]1284[/C][C]1244.02752166874[/C][C]39.9724783312599[/C][/ROW]
[ROW][C]187[/C][C]1444[/C][C]1308.20455677728[/C][C]135.795443222715[/C][/ROW]
[ROW][C]188[/C][C]1575[/C][C]1501.60530287346[/C][C]73.3946971265411[/C][/ROW]
[ROW][C]189[/C][C]1737[/C][C]1720.09044806007[/C][C]16.9095519399259[/C][/ROW]
[ROW][C]190[/C][C]1763[/C][C]1819.48189251771[/C][C]-56.481892517712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115071627.48262493032-120.482624930317
213851497.42201449866-112.422014498661
316321620.3379135721711.6620864278279
415111607.96740358150-96.967403581504
515591679.58998565954-120.589985659538
616301669.45560964810-39.4556096481024
715791739.63916673198-160.639166731977
816531847.93309933897-194.933099338972
921521998.87376331044153.126236689556
1021482225.29605467825-77.2960546782486
1117521798.22719999830-46.2271999983028
1217651594.47987431255170.520125687451
1317171738.14511584632-21.1451158463211
1415581628.78836186171-70.7883618617085
1515751726.63967257914-151.639672579138
1615201611.72707816463-91.727078164631
1718051662.54616861333142.453831386673
1818001763.8933712678136.1066287321876
1917191852.21912408017-133.219124080174
2020081932.2805273971875.7194726028235
2122422165.5932935595076.406706440505
2224782327.64933558390150.350664416097
2320301944.2029409369685.7970590630432
2416551769.11814901216-114.118149012159
2516931741.97397126614-48.9739712661412
2616231587.0764783962535.9235216037480
2718051739.5707818829865.4292181170171
2817461711.2587162720234.7412837279785
2917951794.872357076690.127642923309917
3019261798.09266557835127.907334421647
3116191893.22020000816-274.220200008159
3219921908.5020907778683.497909222136
3322332129.2669293793103.733070620701
3421922311.74365417475-119.743654174749
3520801815.56419696399264.435803036013
3617681721.1732440257946.8267559742079
3718351790.1406806343744.8593193656343
3815691660.34972777626-91.3497277762608
3919761738.09600253660237.903997463397
4018531761.6262627245391.3737372754704
4119651865.8241583622799.1758416377336
4216891881.65652828063-192.656528280634
4317781822.14794255349-44.1479425534903
4419761915.7215460685760.2784539314281
4523972147.00652572639249.993474273615
4626542367.15788150227286.842118497729
4720972031.4048578160465.5951421839577
4819631815.7681273783147.2318726217
4916771865.22348911878-188.223489118784
5019411627.0642431906313.935756809400
5120031849.61668057828153.383319421722
5218131841.55628474328-28.5562847432778
5320121846.11481085441165.885189145585
5419121883.8270869278928.1729130721122
5520841915.03632412459168.963675875409
5620802079.566796973640.433203026361845
5721182244.90266470532-126.902664705319
5821502264.99353788834-114.993537888339
5916081756.53258552563-148.532585525632
6015031500.111163676222.88883632377516
6115481564.65311907595-16.6531190759465
6213821469.00360109322-87.003601093216
6317311583.38278121462147.617218785380
6417981603.87720878888194.122791211116
6517791774.295892718654.70410728134624
6618871775.78530147035111.214698529648
6720041847.23607432301156.763925676991
6820772032.5123552748044.4876447251971
6920922218.11057767246-126.110577672461
7020512244.79327721832-193.793277218316
7115771701.43657259765-124.436572597649
7213561457.80974126248-101.809741262477
7316521488.72517896537163.274821034630
7413821472.33649636999-90.3364963699931
7515191596.23724995637-77.2372499563697
7614211507.62398652877-86.6239865287743
7714421565.7660291266-123.766029126599
7815431549.30202460048-6.30202460047699
7916561626.2169760928429.7830239071621
8015611808.38299963731-247.382999637313
8119051923.88758680755-18.8875868075472
8221992051.13197686149147.868023138506
8314731714.55868012060-241.558680120601
8416551436.97476482384218.025235176158
8514071581.42376868963-174.423768689629
8613951424.89481427598-29.8948142759807
8715301541.59765826026-11.5976582602570
8813091506.01709886215-197.017098862146
8915261513.0344253449312.9655746550732
9013271551.69605460479-224.69605460479
9116271545.8496447187881.1503552812214
9217481742.482465604695.51753439531457
9319581986.06991624230-28.0699162422966
9422742103.16205464736170.837945352643
9516481747.68599144225-99.6859914422545
9614011515.91993232973-114.919932329725
9714111504.38694091533-93.3869409153337
9814031364.6661983692338.3338016307725
9913941536.87526922935-142.875269229347
10015201442.7651570480077.2348429519955
10115281562.77471575423-34.7747157542265
10216431587.7111613728855.2888386271229
10315151667.68393070293-152.683930702927
10416851753.41927093815-68.419270938152
10520001927.8626365108272.1373634891794
10622152098.47011996205116.529880037950
10719561723.27505482277232.724945177229
10814621621.72250602903-159.722506029034
10915631584.97366392672-21.9736639267193
11014591431.2070439445827.7929560554245
11114461582.83589755632-136.835897556324
11216221467.03687743898154.963122561017
11316571606.8241310021550.1758689978507
11416381653.32887354665-15.3288735466502
11516431683.69585246466-40.6958524646623
11616831796.28859889331-113.288598893314
11720501944.90257081339105.097429186609
11822622109.80989756158152.190102438417
11918131744.2330128810568.766987118951
12014451563.72599397225-118.725993972252
12117621539.25884399797222.741156002026
12214611500.84140566018-39.8414056601791
12315561616.34601773627-60.3460177362747
12414311503.94687903148-72.946879031482
12514271542.19061576221-115.190615762214
12615541509.7840462196244.2159537803766
12716451592.1988746542552.8011253457458
12816531770.72278536971-117.722785369709
12920161924.0970393241991.9029606758149
13022072080.67538660644126.324613393559
13116651705.60617643427-40.6061764342725
13213611482.37582687768-121.375826877676
13315061464.8792745398341.1207254601673
13413601368.91410974315-8.91410974314917
13514531512.41325425355-59.4132542535541
13615221431.5456846967490.4543153032604
13714601549.34916236171-89.3491623617147
13815521533.5291551458618.4708448541353
13915481589.40766513408-41.4076651340758
14018271721.46125323345105.538746766547
14117371966.72514525905-229.72514525905
14219411993.12346475664-52.1234647566392
14314741528.85031038276-54.8503103827582
14414581339.25273187613118.747268123873
14515421456.1414423393685.858557660643
14614041395.148473386648.85152661336181
14715221529.21196653583-7.211966535827
14813851460.32073323093-75.3207332309316
14916411498.41864856287142.581351437125
15015101570.69308244103-60.6930824410261
15116811601.0211378918679.9788621081426
15219381758.66814158049179.331858419508
15318682031.39350424643-163.393504246433
15417262061.44442448697-335.444424486968
15514561458.70643028663-2.70643028662715
15614451278.09573861084166.904261389164
15714561438.1692479346117.8307520653928
15813651348.1254954295816.8745045704194
15914871486.326249130750.673750869251373
16015581428.89522852696129.104771473039
16114881553.57693139539-65.5769313953855
16216841534.87639148241149.123608517587
16315941631.95930641542-37.9593064154182
16418501750.2689508141799.7310491858294
16519981968.0986013804129.9013986195864
16620792087.81210282297-8.81210282297075
16714941622.51396409678-128.513964096778
16810571272.97912995037-215.979129950371
16912181191.6766192440926.323380755914
17011681074.5439140191893.4560859808216
17112361260.95940618884-24.9594061888386
17210761189.82538414396-113.825384143956
17311741208.00401778095-34.0040177809543
17411391210.34112574440-71.341125744397
17514271247.26322332650179.736776673497
17614871473.1838152242313.8161847757738
17714831689.11879700239-206.118797002387
17815131705.25493873096-192.254938730958
17913571187.20202569432169.797974305680
18011651089.4930758626475.5069241373646
18112821198.7460185752283.253981424784
18211101114.6176219848-4.61762198480007
18312971241.5531987886655.4468012113349
18411851194.01001621717-9.01001621717475
18512221256.81794962406-34.8179496240648
18612841244.0275216687439.9724783312599
18714441308.20455677728135.795443222715
18815751501.6053028734673.3946971265411
18917371720.0904480600716.9095519399259
19017631819.48189251771-56.481892517712







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.2837473952543320.5674947905086640.716252604745668
200.4438422983520260.8876845967040520.556157701647974
210.3413175178282880.6826350356565760.658682482171712
220.4119830925982180.8239661851964350.588016907401782
230.3366149237342740.6732298474685470.663385076265727
240.4308048344640920.8616096689281850.569195165535908
250.3837439189769570.7674878379539130.616256081023044
260.3247391602375910.6494783204751820.675260839762409
270.2435778973691980.4871557947383960.756422102630802
280.1775834120678390.3551668241356790.82241658793216
290.140632751517090.281265503034180.85936724848291
300.1018587039881490.2037174079762980.89814129601185
310.1936480374846510.3872960749693030.806351962515349
320.1434814180343510.2869628360687020.856518581965649
330.1420717396350960.2841434792701920.857928260364904
340.2416082397968380.4832164795936750.758391760203162
350.2284588062794050.4569176125588110.771541193720595
360.1922848703132110.3845697406264220.807715129686789
370.1481663410741560.2963326821483130.851833658925844
380.1441326647701040.2882653295402080.855867335229896
390.1573497986348270.3146995972696540.842650201365173
400.1325853416893290.2651706833786580.867414658310671
410.1056906869279280.2113813738558560.894309313072072
420.2339885162236210.4679770324472420.766011483776379
430.1935894111513910.3871788223027820.80641058884861
440.1615963806149640.3231927612299270.838403619385036
450.1543147319924290.3086294639848580.845685268007571
460.2404135556942640.4808271113885270.759586444305736
470.2077959408171300.4155918816342610.79220405918287
480.1900846477966140.3801692955932270.809915352203386
490.2754419336671110.5508838673342210.72455806633289
500.3792643021138170.7585286042276340.620735697886183
510.3706346839544050.741269367908810.629365316045595
520.3323259827305290.6646519654610570.667674017269472
530.3292920668448830.6585841336897660.670707933155117
540.2985664261239520.5971328522479040.701433573876048
550.3663700371610370.7327400743220750.633629962838963
560.3280344005329930.6560688010659850.671965599467007
570.5673338341121420.8653323317757150.432666165887858
580.8063170459707720.3873659080584560.193682954029228
590.9309328803290050.1381342393419890.0690671196709945
600.9230418065262080.1539163869475840.076958193473792
610.9043442190138760.1913115619722480.0956557809861242
620.8987146023163820.2025707953672370.101285397683618
630.8966097934056770.2067804131886460.103390206594323
640.92101445670640.1579710865871990.0789855432935993
650.9162242687429620.1675514625140760.083775731257038
660.9205827101324050.1588345797351900.0794172898675948
670.9469445301283450.1061109397433110.0530554698716554
680.951210961038870.09757807792225750.0487890389611287
690.969117568767210.06176486246558190.0308824312327910
700.9775431966102620.04491360677947590.0224568033897379
710.9782845446295530.04343091074089320.0217154553704466
720.9771670129483820.04566597410323630.0228329870516182
730.981947643311720.03610471337655910.0180523566882795
740.979513837748180.04097232450364060.0204861622518203
750.9783364458117370.04332710837652580.0216635541882629
760.9741632448772690.05167351024546290.0258367551227314
770.9730730517330220.05385389653395570.0269269482669779
780.965186259700240.06962748059952150.0348137402997607
790.9580767130312080.08384657393758370.0419232869687919
800.9767144834349360.04657103313012830.0232855165650641
810.969972038476120.06005592304776050.0300279615238802
820.973415887595830.05316822480833890.0265841124041695
830.9840883994737980.03182320105240450.0159116005262023
840.9915562205386140.01688755892277170.00844377946138584
850.9932283214080060.01354335718398710.00677167859199355
860.9907740230194980.01845195396100420.00922597698050211
870.9877727777960190.02445444440796240.0122272222039812
880.9914321813512420.01713563729751620.0085678186487581
890.9885424061469880.02291518770602480.0114575938530124
900.9950061943810890.009987611237822470.00499380561891123
910.9943311386653380.01133772266932420.0056688613346621
920.9932675031655440.01346499366891230.00673249683445614
930.990986500194550.01802699961089860.0090134998054493
940.9936319594371740.01273608112565220.00636804056282608
950.9922933682675680.01541326346486470.00770663173243235
960.9913248511536020.01735029769279640.00867514884639819
970.991851855931350.01629628813729870.00814814406864935
980.989461551871260.02107689625747950.0105384481287398
990.9914413341262510.01711733174749750.00855866587374875
1000.9895170615289670.02096587694206650.0104829384710332
1010.9865728678165260.02685426436694740.0134271321834737
1020.9831473261596220.03370534768075510.0168526738403776
1030.9874659560969450.02506808780611070.0125340439030553
1040.9861258512562630.02774829748747390.0138741487437369
1050.982129693980250.0357406120395010.0178703060197505
1060.9808912513357780.03821749732844340.0191087486642217
1070.9937193642488510.01256127150229760.00628063575114878
1080.9932421017838160.01351579643236730.00675789821618364
1090.9906919576895540.01861608462089290.00930804231044647
1100.9875688843724470.02486223125510570.0124311156275529
1110.988062257449820.02387548510035810.0119377425501791
1120.989973468810750.02005306237849860.0100265311892493
1130.987652112925870.02469577414826190.0123478870741309
1140.983577279040160.03284544191968230.0164227209598411
1150.9785503356311010.04289932873779770.0214496643688989
1160.979972806071650.04005438785670050.0200271939283502
1170.9805462677802430.03890746443951420.0194537322197571
1180.9890929483100180.0218141033799630.0109070516899815
1190.992814858641440.01437028271712110.00718514135856056
1200.9908858266575740.01822834668485310.00911417334242654
1210.9974867717672990.005026456465402430.00251322823270121
1220.9965443281305120.006911343738976050.00345567186948803
1230.995836118038990.008327763922021520.00416388196101076
1240.9941916940058260.01161661198834900.00580830599417448
1250.9927393569465160.01452128610696860.0072606430534843
1260.9901772047256670.01964559054866540.00982279527433268
1270.987006017170570.02598796565885860.0129939828294293
1280.9897325105769070.02053497884618690.0102674894230935
1290.9925596676029780.01488066479404500.00744033239702252
1300.9988989526896340.002202094620731060.00110104731036553
1310.9996272867027750.0007454265944500210.000372713297225010
1320.9994382175478850.001123564904229470.000561782452114733
1330.9991740604552350.001651879089529840.000825939544764918
1340.9986950709514480.002609858097104340.00130492904855217
1350.9979860218515950.004027956296810270.00201397814840513
1360.9983339077110840.003332184577831480.00166609228891574
1370.9974874934615230.005025013076953720.00251250653847686
1380.9967998148870220.006400370225956510.00320018511297826
1390.9953651281129120.009269743774176720.00463487188708836
1400.9938824954295580.01223500914088490.00611750457044246
1410.9947800110532650.01043997789346950.00521998894673473
1420.9941261594376030.01174768112479320.00587384056239662
1430.9910617965177520.01787640696449690.00893820348224847
1440.9893243374483970.02135132510320550.0106756625516027
1450.9864674677868350.02706506442633090.0135325322131655
1460.9810203054259980.03795938914800450.0189796945740022
1470.9733473880670790.05330522386584230.0266526119329211
1480.9627475858547040.07450482829059150.0372524141452958
1490.9815224997918420.03695500041631670.0184775002081583
1500.9727003440980970.05459931180380570.0272996559019029
1510.966674434264810.06665113147037770.0333255657351889
1520.9842506674697880.03149866506042310.0157493325302115
1530.9797351009386070.04052979812278670.0202648990613934
1540.9888917820433360.02221643591332710.0111082179566636
1550.9843894896405070.03122102071898530.0156105103594927
1560.976678216218640.04664356756271840.0233217837813592
1570.9707601722934720.05847965541305610.0292398277065280
1580.965543048199720.06891390360056170.0344569518002809
1590.9657918666798070.06841626664038660.0342081333201933
1600.9523013485512540.0953973028974920.047698651448746
1610.926149765694760.1477004686104790.0738502343052394
1620.9397456782047280.1205086435905430.0602543217952716
1630.9468972755317710.1062054489364580.0531027244682289
1640.9149151775377740.1701696449244520.085084822462226
1650.8746592871703010.2506814256593970.125340712829699
1660.9700296112690990.05994077746180270.0299703887309014
1670.941207039985490.1175859200290220.0587929600145108
1680.8941076502659350.2117846994681290.105892349734065
1690.8344947942909440.3310104114181120.165505205709056
1700.8478285302299650.3043429395400710.152171469770036
1710.7095157454468680.5809685091062650.290484254553132

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.283747395254332 & 0.567494790508664 & 0.716252604745668 \tabularnewline
20 & 0.443842298352026 & 0.887684596704052 & 0.556157701647974 \tabularnewline
21 & 0.341317517828288 & 0.682635035656576 & 0.658682482171712 \tabularnewline
22 & 0.411983092598218 & 0.823966185196435 & 0.588016907401782 \tabularnewline
23 & 0.336614923734274 & 0.673229847468547 & 0.663385076265727 \tabularnewline
24 & 0.430804834464092 & 0.861609668928185 & 0.569195165535908 \tabularnewline
25 & 0.383743918976957 & 0.767487837953913 & 0.616256081023044 \tabularnewline
26 & 0.324739160237591 & 0.649478320475182 & 0.675260839762409 \tabularnewline
27 & 0.243577897369198 & 0.487155794738396 & 0.756422102630802 \tabularnewline
28 & 0.177583412067839 & 0.355166824135679 & 0.82241658793216 \tabularnewline
29 & 0.14063275151709 & 0.28126550303418 & 0.85936724848291 \tabularnewline
30 & 0.101858703988149 & 0.203717407976298 & 0.89814129601185 \tabularnewline
31 & 0.193648037484651 & 0.387296074969303 & 0.806351962515349 \tabularnewline
32 & 0.143481418034351 & 0.286962836068702 & 0.856518581965649 \tabularnewline
33 & 0.142071739635096 & 0.284143479270192 & 0.857928260364904 \tabularnewline
34 & 0.241608239796838 & 0.483216479593675 & 0.758391760203162 \tabularnewline
35 & 0.228458806279405 & 0.456917612558811 & 0.771541193720595 \tabularnewline
36 & 0.192284870313211 & 0.384569740626422 & 0.807715129686789 \tabularnewline
37 & 0.148166341074156 & 0.296332682148313 & 0.851833658925844 \tabularnewline
38 & 0.144132664770104 & 0.288265329540208 & 0.855867335229896 \tabularnewline
39 & 0.157349798634827 & 0.314699597269654 & 0.842650201365173 \tabularnewline
40 & 0.132585341689329 & 0.265170683378658 & 0.867414658310671 \tabularnewline
41 & 0.105690686927928 & 0.211381373855856 & 0.894309313072072 \tabularnewline
42 & 0.233988516223621 & 0.467977032447242 & 0.766011483776379 \tabularnewline
43 & 0.193589411151391 & 0.387178822302782 & 0.80641058884861 \tabularnewline
44 & 0.161596380614964 & 0.323192761229927 & 0.838403619385036 \tabularnewline
45 & 0.154314731992429 & 0.308629463984858 & 0.845685268007571 \tabularnewline
46 & 0.240413555694264 & 0.480827111388527 & 0.759586444305736 \tabularnewline
47 & 0.207795940817130 & 0.415591881634261 & 0.79220405918287 \tabularnewline
48 & 0.190084647796614 & 0.380169295593227 & 0.809915352203386 \tabularnewline
49 & 0.275441933667111 & 0.550883867334221 & 0.72455806633289 \tabularnewline
50 & 0.379264302113817 & 0.758528604227634 & 0.620735697886183 \tabularnewline
51 & 0.370634683954405 & 0.74126936790881 & 0.629365316045595 \tabularnewline
52 & 0.332325982730529 & 0.664651965461057 & 0.667674017269472 \tabularnewline
53 & 0.329292066844883 & 0.658584133689766 & 0.670707933155117 \tabularnewline
54 & 0.298566426123952 & 0.597132852247904 & 0.701433573876048 \tabularnewline
55 & 0.366370037161037 & 0.732740074322075 & 0.633629962838963 \tabularnewline
56 & 0.328034400532993 & 0.656068801065985 & 0.671965599467007 \tabularnewline
57 & 0.567333834112142 & 0.865332331775715 & 0.432666165887858 \tabularnewline
58 & 0.806317045970772 & 0.387365908058456 & 0.193682954029228 \tabularnewline
59 & 0.930932880329005 & 0.138134239341989 & 0.0690671196709945 \tabularnewline
60 & 0.923041806526208 & 0.153916386947584 & 0.076958193473792 \tabularnewline
61 & 0.904344219013876 & 0.191311561972248 & 0.0956557809861242 \tabularnewline
62 & 0.898714602316382 & 0.202570795367237 & 0.101285397683618 \tabularnewline
63 & 0.896609793405677 & 0.206780413188646 & 0.103390206594323 \tabularnewline
64 & 0.9210144567064 & 0.157971086587199 & 0.0789855432935993 \tabularnewline
65 & 0.916224268742962 & 0.167551462514076 & 0.083775731257038 \tabularnewline
66 & 0.920582710132405 & 0.158834579735190 & 0.0794172898675948 \tabularnewline
67 & 0.946944530128345 & 0.106110939743311 & 0.0530554698716554 \tabularnewline
68 & 0.95121096103887 & 0.0975780779222575 & 0.0487890389611287 \tabularnewline
69 & 0.96911756876721 & 0.0617648624655819 & 0.0308824312327910 \tabularnewline
70 & 0.977543196610262 & 0.0449136067794759 & 0.0224568033897379 \tabularnewline
71 & 0.978284544629553 & 0.0434309107408932 & 0.0217154553704466 \tabularnewline
72 & 0.977167012948382 & 0.0456659741032363 & 0.0228329870516182 \tabularnewline
73 & 0.98194764331172 & 0.0361047133765591 & 0.0180523566882795 \tabularnewline
74 & 0.97951383774818 & 0.0409723245036406 & 0.0204861622518203 \tabularnewline
75 & 0.978336445811737 & 0.0433271083765258 & 0.0216635541882629 \tabularnewline
76 & 0.974163244877269 & 0.0516735102454629 & 0.0258367551227314 \tabularnewline
77 & 0.973073051733022 & 0.0538538965339557 & 0.0269269482669779 \tabularnewline
78 & 0.96518625970024 & 0.0696274805995215 & 0.0348137402997607 \tabularnewline
79 & 0.958076713031208 & 0.0838465739375837 & 0.0419232869687919 \tabularnewline
80 & 0.976714483434936 & 0.0465710331301283 & 0.0232855165650641 \tabularnewline
81 & 0.96997203847612 & 0.0600559230477605 & 0.0300279615238802 \tabularnewline
82 & 0.97341588759583 & 0.0531682248083389 & 0.0265841124041695 \tabularnewline
83 & 0.984088399473798 & 0.0318232010524045 & 0.0159116005262023 \tabularnewline
84 & 0.991556220538614 & 0.0168875589227717 & 0.00844377946138584 \tabularnewline
85 & 0.993228321408006 & 0.0135433571839871 & 0.00677167859199355 \tabularnewline
86 & 0.990774023019498 & 0.0184519539610042 & 0.00922597698050211 \tabularnewline
87 & 0.987772777796019 & 0.0244544444079624 & 0.0122272222039812 \tabularnewline
88 & 0.991432181351242 & 0.0171356372975162 & 0.0085678186487581 \tabularnewline
89 & 0.988542406146988 & 0.0229151877060248 & 0.0114575938530124 \tabularnewline
90 & 0.995006194381089 & 0.00998761123782247 & 0.00499380561891123 \tabularnewline
91 & 0.994331138665338 & 0.0113377226693242 & 0.0056688613346621 \tabularnewline
92 & 0.993267503165544 & 0.0134649936689123 & 0.00673249683445614 \tabularnewline
93 & 0.99098650019455 & 0.0180269996108986 & 0.0090134998054493 \tabularnewline
94 & 0.993631959437174 & 0.0127360811256522 & 0.00636804056282608 \tabularnewline
95 & 0.992293368267568 & 0.0154132634648647 & 0.00770663173243235 \tabularnewline
96 & 0.991324851153602 & 0.0173502976927964 & 0.00867514884639819 \tabularnewline
97 & 0.99185185593135 & 0.0162962881372987 & 0.00814814406864935 \tabularnewline
98 & 0.98946155187126 & 0.0210768962574795 & 0.0105384481287398 \tabularnewline
99 & 0.991441334126251 & 0.0171173317474975 & 0.00855866587374875 \tabularnewline
100 & 0.989517061528967 & 0.0209658769420665 & 0.0104829384710332 \tabularnewline
101 & 0.986572867816526 & 0.0268542643669474 & 0.0134271321834737 \tabularnewline
102 & 0.983147326159622 & 0.0337053476807551 & 0.0168526738403776 \tabularnewline
103 & 0.987465956096945 & 0.0250680878061107 & 0.0125340439030553 \tabularnewline
104 & 0.986125851256263 & 0.0277482974874739 & 0.0138741487437369 \tabularnewline
105 & 0.98212969398025 & 0.035740612039501 & 0.0178703060197505 \tabularnewline
106 & 0.980891251335778 & 0.0382174973284434 & 0.0191087486642217 \tabularnewline
107 & 0.993719364248851 & 0.0125612715022976 & 0.00628063575114878 \tabularnewline
108 & 0.993242101783816 & 0.0135157964323673 & 0.00675789821618364 \tabularnewline
109 & 0.990691957689554 & 0.0186160846208929 & 0.00930804231044647 \tabularnewline
110 & 0.987568884372447 & 0.0248622312551057 & 0.0124311156275529 \tabularnewline
111 & 0.98806225744982 & 0.0238754851003581 & 0.0119377425501791 \tabularnewline
112 & 0.98997346881075 & 0.0200530623784986 & 0.0100265311892493 \tabularnewline
113 & 0.98765211292587 & 0.0246957741482619 & 0.0123478870741309 \tabularnewline
114 & 0.98357727904016 & 0.0328454419196823 & 0.0164227209598411 \tabularnewline
115 & 0.978550335631101 & 0.0428993287377977 & 0.0214496643688989 \tabularnewline
116 & 0.97997280607165 & 0.0400543878567005 & 0.0200271939283502 \tabularnewline
117 & 0.980546267780243 & 0.0389074644395142 & 0.0194537322197571 \tabularnewline
118 & 0.989092948310018 & 0.021814103379963 & 0.0109070516899815 \tabularnewline
119 & 0.99281485864144 & 0.0143702827171211 & 0.00718514135856056 \tabularnewline
120 & 0.990885826657574 & 0.0182283466848531 & 0.00911417334242654 \tabularnewline
121 & 0.997486771767299 & 0.00502645646540243 & 0.00251322823270121 \tabularnewline
122 & 0.996544328130512 & 0.00691134373897605 & 0.00345567186948803 \tabularnewline
123 & 0.99583611803899 & 0.00832776392202152 & 0.00416388196101076 \tabularnewline
124 & 0.994191694005826 & 0.0116166119883490 & 0.00580830599417448 \tabularnewline
125 & 0.992739356946516 & 0.0145212861069686 & 0.0072606430534843 \tabularnewline
126 & 0.990177204725667 & 0.0196455905486654 & 0.00982279527433268 \tabularnewline
127 & 0.98700601717057 & 0.0259879656588586 & 0.0129939828294293 \tabularnewline
128 & 0.989732510576907 & 0.0205349788461869 & 0.0102674894230935 \tabularnewline
129 & 0.992559667602978 & 0.0148806647940450 & 0.00744033239702252 \tabularnewline
130 & 0.998898952689634 & 0.00220209462073106 & 0.00110104731036553 \tabularnewline
131 & 0.999627286702775 & 0.000745426594450021 & 0.000372713297225010 \tabularnewline
132 & 0.999438217547885 & 0.00112356490422947 & 0.000561782452114733 \tabularnewline
133 & 0.999174060455235 & 0.00165187908952984 & 0.000825939544764918 \tabularnewline
134 & 0.998695070951448 & 0.00260985809710434 & 0.00130492904855217 \tabularnewline
135 & 0.997986021851595 & 0.00402795629681027 & 0.00201397814840513 \tabularnewline
136 & 0.998333907711084 & 0.00333218457783148 & 0.00166609228891574 \tabularnewline
137 & 0.997487493461523 & 0.00502501307695372 & 0.00251250653847686 \tabularnewline
138 & 0.996799814887022 & 0.00640037022595651 & 0.00320018511297826 \tabularnewline
139 & 0.995365128112912 & 0.00926974377417672 & 0.00463487188708836 \tabularnewline
140 & 0.993882495429558 & 0.0122350091408849 & 0.00611750457044246 \tabularnewline
141 & 0.994780011053265 & 0.0104399778934695 & 0.00521998894673473 \tabularnewline
142 & 0.994126159437603 & 0.0117476811247932 & 0.00587384056239662 \tabularnewline
143 & 0.991061796517752 & 0.0178764069644969 & 0.00893820348224847 \tabularnewline
144 & 0.989324337448397 & 0.0213513251032055 & 0.0106756625516027 \tabularnewline
145 & 0.986467467786835 & 0.0270650644263309 & 0.0135325322131655 \tabularnewline
146 & 0.981020305425998 & 0.0379593891480045 & 0.0189796945740022 \tabularnewline
147 & 0.973347388067079 & 0.0533052238658423 & 0.0266526119329211 \tabularnewline
148 & 0.962747585854704 & 0.0745048282905915 & 0.0372524141452958 \tabularnewline
149 & 0.981522499791842 & 0.0369550004163167 & 0.0184775002081583 \tabularnewline
150 & 0.972700344098097 & 0.0545993118038057 & 0.0272996559019029 \tabularnewline
151 & 0.96667443426481 & 0.0666511314703777 & 0.0333255657351889 \tabularnewline
152 & 0.984250667469788 & 0.0314986650604231 & 0.0157493325302115 \tabularnewline
153 & 0.979735100938607 & 0.0405297981227867 & 0.0202648990613934 \tabularnewline
154 & 0.988891782043336 & 0.0222164359133271 & 0.0111082179566636 \tabularnewline
155 & 0.984389489640507 & 0.0312210207189853 & 0.0156105103594927 \tabularnewline
156 & 0.97667821621864 & 0.0466435675627184 & 0.0233217837813592 \tabularnewline
157 & 0.970760172293472 & 0.0584796554130561 & 0.0292398277065280 \tabularnewline
158 & 0.96554304819972 & 0.0689139036005617 & 0.0344569518002809 \tabularnewline
159 & 0.965791866679807 & 0.0684162666403866 & 0.0342081333201933 \tabularnewline
160 & 0.952301348551254 & 0.095397302897492 & 0.047698651448746 \tabularnewline
161 & 0.92614976569476 & 0.147700468610479 & 0.0738502343052394 \tabularnewline
162 & 0.939745678204728 & 0.120508643590543 & 0.0602543217952716 \tabularnewline
163 & 0.946897275531771 & 0.106205448936458 & 0.0531027244682289 \tabularnewline
164 & 0.914915177537774 & 0.170169644924452 & 0.085084822462226 \tabularnewline
165 & 0.874659287170301 & 0.250681425659397 & 0.125340712829699 \tabularnewline
166 & 0.970029611269099 & 0.0599407774618027 & 0.0299703887309014 \tabularnewline
167 & 0.94120703998549 & 0.117585920029022 & 0.0587929600145108 \tabularnewline
168 & 0.894107650265935 & 0.211784699468129 & 0.105892349734065 \tabularnewline
169 & 0.834494794290944 & 0.331010411418112 & 0.165505205709056 \tabularnewline
170 & 0.847828530229965 & 0.304342939540071 & 0.152171469770036 \tabularnewline
171 & 0.709515745446868 & 0.580968509106265 & 0.290484254553132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&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]19[/C][C]0.283747395254332[/C][C]0.567494790508664[/C][C]0.716252604745668[/C][/ROW]
[ROW][C]20[/C][C]0.443842298352026[/C][C]0.887684596704052[/C][C]0.556157701647974[/C][/ROW]
[ROW][C]21[/C][C]0.341317517828288[/C][C]0.682635035656576[/C][C]0.658682482171712[/C][/ROW]
[ROW][C]22[/C][C]0.411983092598218[/C][C]0.823966185196435[/C][C]0.588016907401782[/C][/ROW]
[ROW][C]23[/C][C]0.336614923734274[/C][C]0.673229847468547[/C][C]0.663385076265727[/C][/ROW]
[ROW][C]24[/C][C]0.430804834464092[/C][C]0.861609668928185[/C][C]0.569195165535908[/C][/ROW]
[ROW][C]25[/C][C]0.383743918976957[/C][C]0.767487837953913[/C][C]0.616256081023044[/C][/ROW]
[ROW][C]26[/C][C]0.324739160237591[/C][C]0.649478320475182[/C][C]0.675260839762409[/C][/ROW]
[ROW][C]27[/C][C]0.243577897369198[/C][C]0.487155794738396[/C][C]0.756422102630802[/C][/ROW]
[ROW][C]28[/C][C]0.177583412067839[/C][C]0.355166824135679[/C][C]0.82241658793216[/C][/ROW]
[ROW][C]29[/C][C]0.14063275151709[/C][C]0.28126550303418[/C][C]0.85936724848291[/C][/ROW]
[ROW][C]30[/C][C]0.101858703988149[/C][C]0.203717407976298[/C][C]0.89814129601185[/C][/ROW]
[ROW][C]31[/C][C]0.193648037484651[/C][C]0.387296074969303[/C][C]0.806351962515349[/C][/ROW]
[ROW][C]32[/C][C]0.143481418034351[/C][C]0.286962836068702[/C][C]0.856518581965649[/C][/ROW]
[ROW][C]33[/C][C]0.142071739635096[/C][C]0.284143479270192[/C][C]0.857928260364904[/C][/ROW]
[ROW][C]34[/C][C]0.241608239796838[/C][C]0.483216479593675[/C][C]0.758391760203162[/C][/ROW]
[ROW][C]35[/C][C]0.228458806279405[/C][C]0.456917612558811[/C][C]0.771541193720595[/C][/ROW]
[ROW][C]36[/C][C]0.192284870313211[/C][C]0.384569740626422[/C][C]0.807715129686789[/C][/ROW]
[ROW][C]37[/C][C]0.148166341074156[/C][C]0.296332682148313[/C][C]0.851833658925844[/C][/ROW]
[ROW][C]38[/C][C]0.144132664770104[/C][C]0.288265329540208[/C][C]0.855867335229896[/C][/ROW]
[ROW][C]39[/C][C]0.157349798634827[/C][C]0.314699597269654[/C][C]0.842650201365173[/C][/ROW]
[ROW][C]40[/C][C]0.132585341689329[/C][C]0.265170683378658[/C][C]0.867414658310671[/C][/ROW]
[ROW][C]41[/C][C]0.105690686927928[/C][C]0.211381373855856[/C][C]0.894309313072072[/C][/ROW]
[ROW][C]42[/C][C]0.233988516223621[/C][C]0.467977032447242[/C][C]0.766011483776379[/C][/ROW]
[ROW][C]43[/C][C]0.193589411151391[/C][C]0.387178822302782[/C][C]0.80641058884861[/C][/ROW]
[ROW][C]44[/C][C]0.161596380614964[/C][C]0.323192761229927[/C][C]0.838403619385036[/C][/ROW]
[ROW][C]45[/C][C]0.154314731992429[/C][C]0.308629463984858[/C][C]0.845685268007571[/C][/ROW]
[ROW][C]46[/C][C]0.240413555694264[/C][C]0.480827111388527[/C][C]0.759586444305736[/C][/ROW]
[ROW][C]47[/C][C]0.207795940817130[/C][C]0.415591881634261[/C][C]0.79220405918287[/C][/ROW]
[ROW][C]48[/C][C]0.190084647796614[/C][C]0.380169295593227[/C][C]0.809915352203386[/C][/ROW]
[ROW][C]49[/C][C]0.275441933667111[/C][C]0.550883867334221[/C][C]0.72455806633289[/C][/ROW]
[ROW][C]50[/C][C]0.379264302113817[/C][C]0.758528604227634[/C][C]0.620735697886183[/C][/ROW]
[ROW][C]51[/C][C]0.370634683954405[/C][C]0.74126936790881[/C][C]0.629365316045595[/C][/ROW]
[ROW][C]52[/C][C]0.332325982730529[/C][C]0.664651965461057[/C][C]0.667674017269472[/C][/ROW]
[ROW][C]53[/C][C]0.329292066844883[/C][C]0.658584133689766[/C][C]0.670707933155117[/C][/ROW]
[ROW][C]54[/C][C]0.298566426123952[/C][C]0.597132852247904[/C][C]0.701433573876048[/C][/ROW]
[ROW][C]55[/C][C]0.366370037161037[/C][C]0.732740074322075[/C][C]0.633629962838963[/C][/ROW]
[ROW][C]56[/C][C]0.328034400532993[/C][C]0.656068801065985[/C][C]0.671965599467007[/C][/ROW]
[ROW][C]57[/C][C]0.567333834112142[/C][C]0.865332331775715[/C][C]0.432666165887858[/C][/ROW]
[ROW][C]58[/C][C]0.806317045970772[/C][C]0.387365908058456[/C][C]0.193682954029228[/C][/ROW]
[ROW][C]59[/C][C]0.930932880329005[/C][C]0.138134239341989[/C][C]0.0690671196709945[/C][/ROW]
[ROW][C]60[/C][C]0.923041806526208[/C][C]0.153916386947584[/C][C]0.076958193473792[/C][/ROW]
[ROW][C]61[/C][C]0.904344219013876[/C][C]0.191311561972248[/C][C]0.0956557809861242[/C][/ROW]
[ROW][C]62[/C][C]0.898714602316382[/C][C]0.202570795367237[/C][C]0.101285397683618[/C][/ROW]
[ROW][C]63[/C][C]0.896609793405677[/C][C]0.206780413188646[/C][C]0.103390206594323[/C][/ROW]
[ROW][C]64[/C][C]0.9210144567064[/C][C]0.157971086587199[/C][C]0.0789855432935993[/C][/ROW]
[ROW][C]65[/C][C]0.916224268742962[/C][C]0.167551462514076[/C][C]0.083775731257038[/C][/ROW]
[ROW][C]66[/C][C]0.920582710132405[/C][C]0.158834579735190[/C][C]0.0794172898675948[/C][/ROW]
[ROW][C]67[/C][C]0.946944530128345[/C][C]0.106110939743311[/C][C]0.0530554698716554[/C][/ROW]
[ROW][C]68[/C][C]0.95121096103887[/C][C]0.0975780779222575[/C][C]0.0487890389611287[/C][/ROW]
[ROW][C]69[/C][C]0.96911756876721[/C][C]0.0617648624655819[/C][C]0.0308824312327910[/C][/ROW]
[ROW][C]70[/C][C]0.977543196610262[/C][C]0.0449136067794759[/C][C]0.0224568033897379[/C][/ROW]
[ROW][C]71[/C][C]0.978284544629553[/C][C]0.0434309107408932[/C][C]0.0217154553704466[/C][/ROW]
[ROW][C]72[/C][C]0.977167012948382[/C][C]0.0456659741032363[/C][C]0.0228329870516182[/C][/ROW]
[ROW][C]73[/C][C]0.98194764331172[/C][C]0.0361047133765591[/C][C]0.0180523566882795[/C][/ROW]
[ROW][C]74[/C][C]0.97951383774818[/C][C]0.0409723245036406[/C][C]0.0204861622518203[/C][/ROW]
[ROW][C]75[/C][C]0.978336445811737[/C][C]0.0433271083765258[/C][C]0.0216635541882629[/C][/ROW]
[ROW][C]76[/C][C]0.974163244877269[/C][C]0.0516735102454629[/C][C]0.0258367551227314[/C][/ROW]
[ROW][C]77[/C][C]0.973073051733022[/C][C]0.0538538965339557[/C][C]0.0269269482669779[/C][/ROW]
[ROW][C]78[/C][C]0.96518625970024[/C][C]0.0696274805995215[/C][C]0.0348137402997607[/C][/ROW]
[ROW][C]79[/C][C]0.958076713031208[/C][C]0.0838465739375837[/C][C]0.0419232869687919[/C][/ROW]
[ROW][C]80[/C][C]0.976714483434936[/C][C]0.0465710331301283[/C][C]0.0232855165650641[/C][/ROW]
[ROW][C]81[/C][C]0.96997203847612[/C][C]0.0600559230477605[/C][C]0.0300279615238802[/C][/ROW]
[ROW][C]82[/C][C]0.97341588759583[/C][C]0.0531682248083389[/C][C]0.0265841124041695[/C][/ROW]
[ROW][C]83[/C][C]0.984088399473798[/C][C]0.0318232010524045[/C][C]0.0159116005262023[/C][/ROW]
[ROW][C]84[/C][C]0.991556220538614[/C][C]0.0168875589227717[/C][C]0.00844377946138584[/C][/ROW]
[ROW][C]85[/C][C]0.993228321408006[/C][C]0.0135433571839871[/C][C]0.00677167859199355[/C][/ROW]
[ROW][C]86[/C][C]0.990774023019498[/C][C]0.0184519539610042[/C][C]0.00922597698050211[/C][/ROW]
[ROW][C]87[/C][C]0.987772777796019[/C][C]0.0244544444079624[/C][C]0.0122272222039812[/C][/ROW]
[ROW][C]88[/C][C]0.991432181351242[/C][C]0.0171356372975162[/C][C]0.0085678186487581[/C][/ROW]
[ROW][C]89[/C][C]0.988542406146988[/C][C]0.0229151877060248[/C][C]0.0114575938530124[/C][/ROW]
[ROW][C]90[/C][C]0.995006194381089[/C][C]0.00998761123782247[/C][C]0.00499380561891123[/C][/ROW]
[ROW][C]91[/C][C]0.994331138665338[/C][C]0.0113377226693242[/C][C]0.0056688613346621[/C][/ROW]
[ROW][C]92[/C][C]0.993267503165544[/C][C]0.0134649936689123[/C][C]0.00673249683445614[/C][/ROW]
[ROW][C]93[/C][C]0.99098650019455[/C][C]0.0180269996108986[/C][C]0.0090134998054493[/C][/ROW]
[ROW][C]94[/C][C]0.993631959437174[/C][C]0.0127360811256522[/C][C]0.00636804056282608[/C][/ROW]
[ROW][C]95[/C][C]0.992293368267568[/C][C]0.0154132634648647[/C][C]0.00770663173243235[/C][/ROW]
[ROW][C]96[/C][C]0.991324851153602[/C][C]0.0173502976927964[/C][C]0.00867514884639819[/C][/ROW]
[ROW][C]97[/C][C]0.99185185593135[/C][C]0.0162962881372987[/C][C]0.00814814406864935[/C][/ROW]
[ROW][C]98[/C][C]0.98946155187126[/C][C]0.0210768962574795[/C][C]0.0105384481287398[/C][/ROW]
[ROW][C]99[/C][C]0.991441334126251[/C][C]0.0171173317474975[/C][C]0.00855866587374875[/C][/ROW]
[ROW][C]100[/C][C]0.989517061528967[/C][C]0.0209658769420665[/C][C]0.0104829384710332[/C][/ROW]
[ROW][C]101[/C][C]0.986572867816526[/C][C]0.0268542643669474[/C][C]0.0134271321834737[/C][/ROW]
[ROW][C]102[/C][C]0.983147326159622[/C][C]0.0337053476807551[/C][C]0.0168526738403776[/C][/ROW]
[ROW][C]103[/C][C]0.987465956096945[/C][C]0.0250680878061107[/C][C]0.0125340439030553[/C][/ROW]
[ROW][C]104[/C][C]0.986125851256263[/C][C]0.0277482974874739[/C][C]0.0138741487437369[/C][/ROW]
[ROW][C]105[/C][C]0.98212969398025[/C][C]0.035740612039501[/C][C]0.0178703060197505[/C][/ROW]
[ROW][C]106[/C][C]0.980891251335778[/C][C]0.0382174973284434[/C][C]0.0191087486642217[/C][/ROW]
[ROW][C]107[/C][C]0.993719364248851[/C][C]0.0125612715022976[/C][C]0.00628063575114878[/C][/ROW]
[ROW][C]108[/C][C]0.993242101783816[/C][C]0.0135157964323673[/C][C]0.00675789821618364[/C][/ROW]
[ROW][C]109[/C][C]0.990691957689554[/C][C]0.0186160846208929[/C][C]0.00930804231044647[/C][/ROW]
[ROW][C]110[/C][C]0.987568884372447[/C][C]0.0248622312551057[/C][C]0.0124311156275529[/C][/ROW]
[ROW][C]111[/C][C]0.98806225744982[/C][C]0.0238754851003581[/C][C]0.0119377425501791[/C][/ROW]
[ROW][C]112[/C][C]0.98997346881075[/C][C]0.0200530623784986[/C][C]0.0100265311892493[/C][/ROW]
[ROW][C]113[/C][C]0.98765211292587[/C][C]0.0246957741482619[/C][C]0.0123478870741309[/C][/ROW]
[ROW][C]114[/C][C]0.98357727904016[/C][C]0.0328454419196823[/C][C]0.0164227209598411[/C][/ROW]
[ROW][C]115[/C][C]0.978550335631101[/C][C]0.0428993287377977[/C][C]0.0214496643688989[/C][/ROW]
[ROW][C]116[/C][C]0.97997280607165[/C][C]0.0400543878567005[/C][C]0.0200271939283502[/C][/ROW]
[ROW][C]117[/C][C]0.980546267780243[/C][C]0.0389074644395142[/C][C]0.0194537322197571[/C][/ROW]
[ROW][C]118[/C][C]0.989092948310018[/C][C]0.021814103379963[/C][C]0.0109070516899815[/C][/ROW]
[ROW][C]119[/C][C]0.99281485864144[/C][C]0.0143702827171211[/C][C]0.00718514135856056[/C][/ROW]
[ROW][C]120[/C][C]0.990885826657574[/C][C]0.0182283466848531[/C][C]0.00911417334242654[/C][/ROW]
[ROW][C]121[/C][C]0.997486771767299[/C][C]0.00502645646540243[/C][C]0.00251322823270121[/C][/ROW]
[ROW][C]122[/C][C]0.996544328130512[/C][C]0.00691134373897605[/C][C]0.00345567186948803[/C][/ROW]
[ROW][C]123[/C][C]0.99583611803899[/C][C]0.00832776392202152[/C][C]0.00416388196101076[/C][/ROW]
[ROW][C]124[/C][C]0.994191694005826[/C][C]0.0116166119883490[/C][C]0.00580830599417448[/C][/ROW]
[ROW][C]125[/C][C]0.992739356946516[/C][C]0.0145212861069686[/C][C]0.0072606430534843[/C][/ROW]
[ROW][C]126[/C][C]0.990177204725667[/C][C]0.0196455905486654[/C][C]0.00982279527433268[/C][/ROW]
[ROW][C]127[/C][C]0.98700601717057[/C][C]0.0259879656588586[/C][C]0.0129939828294293[/C][/ROW]
[ROW][C]128[/C][C]0.989732510576907[/C][C]0.0205349788461869[/C][C]0.0102674894230935[/C][/ROW]
[ROW][C]129[/C][C]0.992559667602978[/C][C]0.0148806647940450[/C][C]0.00744033239702252[/C][/ROW]
[ROW][C]130[/C][C]0.998898952689634[/C][C]0.00220209462073106[/C][C]0.00110104731036553[/C][/ROW]
[ROW][C]131[/C][C]0.999627286702775[/C][C]0.000745426594450021[/C][C]0.000372713297225010[/C][/ROW]
[ROW][C]132[/C][C]0.999438217547885[/C][C]0.00112356490422947[/C][C]0.000561782452114733[/C][/ROW]
[ROW][C]133[/C][C]0.999174060455235[/C][C]0.00165187908952984[/C][C]0.000825939544764918[/C][/ROW]
[ROW][C]134[/C][C]0.998695070951448[/C][C]0.00260985809710434[/C][C]0.00130492904855217[/C][/ROW]
[ROW][C]135[/C][C]0.997986021851595[/C][C]0.00402795629681027[/C][C]0.00201397814840513[/C][/ROW]
[ROW][C]136[/C][C]0.998333907711084[/C][C]0.00333218457783148[/C][C]0.00166609228891574[/C][/ROW]
[ROW][C]137[/C][C]0.997487493461523[/C][C]0.00502501307695372[/C][C]0.00251250653847686[/C][/ROW]
[ROW][C]138[/C][C]0.996799814887022[/C][C]0.00640037022595651[/C][C]0.00320018511297826[/C][/ROW]
[ROW][C]139[/C][C]0.995365128112912[/C][C]0.00926974377417672[/C][C]0.00463487188708836[/C][/ROW]
[ROW][C]140[/C][C]0.993882495429558[/C][C]0.0122350091408849[/C][C]0.00611750457044246[/C][/ROW]
[ROW][C]141[/C][C]0.994780011053265[/C][C]0.0104399778934695[/C][C]0.00521998894673473[/C][/ROW]
[ROW][C]142[/C][C]0.994126159437603[/C][C]0.0117476811247932[/C][C]0.00587384056239662[/C][/ROW]
[ROW][C]143[/C][C]0.991061796517752[/C][C]0.0178764069644969[/C][C]0.00893820348224847[/C][/ROW]
[ROW][C]144[/C][C]0.989324337448397[/C][C]0.0213513251032055[/C][C]0.0106756625516027[/C][/ROW]
[ROW][C]145[/C][C]0.986467467786835[/C][C]0.0270650644263309[/C][C]0.0135325322131655[/C][/ROW]
[ROW][C]146[/C][C]0.981020305425998[/C][C]0.0379593891480045[/C][C]0.0189796945740022[/C][/ROW]
[ROW][C]147[/C][C]0.973347388067079[/C][C]0.0533052238658423[/C][C]0.0266526119329211[/C][/ROW]
[ROW][C]148[/C][C]0.962747585854704[/C][C]0.0745048282905915[/C][C]0.0372524141452958[/C][/ROW]
[ROW][C]149[/C][C]0.981522499791842[/C][C]0.0369550004163167[/C][C]0.0184775002081583[/C][/ROW]
[ROW][C]150[/C][C]0.972700344098097[/C][C]0.0545993118038057[/C][C]0.0272996559019029[/C][/ROW]
[ROW][C]151[/C][C]0.96667443426481[/C][C]0.0666511314703777[/C][C]0.0333255657351889[/C][/ROW]
[ROW][C]152[/C][C]0.984250667469788[/C][C]0.0314986650604231[/C][C]0.0157493325302115[/C][/ROW]
[ROW][C]153[/C][C]0.979735100938607[/C][C]0.0405297981227867[/C][C]0.0202648990613934[/C][/ROW]
[ROW][C]154[/C][C]0.988891782043336[/C][C]0.0222164359133271[/C][C]0.0111082179566636[/C][/ROW]
[ROW][C]155[/C][C]0.984389489640507[/C][C]0.0312210207189853[/C][C]0.0156105103594927[/C][/ROW]
[ROW][C]156[/C][C]0.97667821621864[/C][C]0.0466435675627184[/C][C]0.0233217837813592[/C][/ROW]
[ROW][C]157[/C][C]0.970760172293472[/C][C]0.0584796554130561[/C][C]0.0292398277065280[/C][/ROW]
[ROW][C]158[/C][C]0.96554304819972[/C][C]0.0689139036005617[/C][C]0.0344569518002809[/C][/ROW]
[ROW][C]159[/C][C]0.965791866679807[/C][C]0.0684162666403866[/C][C]0.0342081333201933[/C][/ROW]
[ROW][C]160[/C][C]0.952301348551254[/C][C]0.095397302897492[/C][C]0.047698651448746[/C][/ROW]
[ROW][C]161[/C][C]0.92614976569476[/C][C]0.147700468610479[/C][C]0.0738502343052394[/C][/ROW]
[ROW][C]162[/C][C]0.939745678204728[/C][C]0.120508643590543[/C][C]0.0602543217952716[/C][/ROW]
[ROW][C]163[/C][C]0.946897275531771[/C][C]0.106205448936458[/C][C]0.0531027244682289[/C][/ROW]
[ROW][C]164[/C][C]0.914915177537774[/C][C]0.170169644924452[/C][C]0.085084822462226[/C][/ROW]
[ROW][C]165[/C][C]0.874659287170301[/C][C]0.250681425659397[/C][C]0.125340712829699[/C][/ROW]
[ROW][C]166[/C][C]0.970029611269099[/C][C]0.0599407774618027[/C][C]0.0299703887309014[/C][/ROW]
[ROW][C]167[/C][C]0.94120703998549[/C][C]0.117585920029022[/C][C]0.0587929600145108[/C][/ROW]
[ROW][C]168[/C][C]0.894107650265935[/C][C]0.211784699468129[/C][C]0.105892349734065[/C][/ROW]
[ROW][C]169[/C][C]0.834494794290944[/C][C]0.331010411418112[/C][C]0.165505205709056[/C][/ROW]
[ROW][C]170[/C][C]0.847828530229965[/C][C]0.304342939540071[/C][C]0.152171469770036[/C][/ROW]
[ROW][C]171[/C][C]0.709515745446868[/C][C]0.580968509106265[/C][C]0.290484254553132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.2837473952543320.5674947905086640.716252604745668
200.4438422983520260.8876845967040520.556157701647974
210.3413175178282880.6826350356565760.658682482171712
220.4119830925982180.8239661851964350.588016907401782
230.3366149237342740.6732298474685470.663385076265727
240.4308048344640920.8616096689281850.569195165535908
250.3837439189769570.7674878379539130.616256081023044
260.3247391602375910.6494783204751820.675260839762409
270.2435778973691980.4871557947383960.756422102630802
280.1775834120678390.3551668241356790.82241658793216
290.140632751517090.281265503034180.85936724848291
300.1018587039881490.2037174079762980.89814129601185
310.1936480374846510.3872960749693030.806351962515349
320.1434814180343510.2869628360687020.856518581965649
330.1420717396350960.2841434792701920.857928260364904
340.2416082397968380.4832164795936750.758391760203162
350.2284588062794050.4569176125588110.771541193720595
360.1922848703132110.3845697406264220.807715129686789
370.1481663410741560.2963326821483130.851833658925844
380.1441326647701040.2882653295402080.855867335229896
390.1573497986348270.3146995972696540.842650201365173
400.1325853416893290.2651706833786580.867414658310671
410.1056906869279280.2113813738558560.894309313072072
420.2339885162236210.4679770324472420.766011483776379
430.1935894111513910.3871788223027820.80641058884861
440.1615963806149640.3231927612299270.838403619385036
450.1543147319924290.3086294639848580.845685268007571
460.2404135556942640.4808271113885270.759586444305736
470.2077959408171300.4155918816342610.79220405918287
480.1900846477966140.3801692955932270.809915352203386
490.2754419336671110.5508838673342210.72455806633289
500.3792643021138170.7585286042276340.620735697886183
510.3706346839544050.741269367908810.629365316045595
520.3323259827305290.6646519654610570.667674017269472
530.3292920668448830.6585841336897660.670707933155117
540.2985664261239520.5971328522479040.701433573876048
550.3663700371610370.7327400743220750.633629962838963
560.3280344005329930.6560688010659850.671965599467007
570.5673338341121420.8653323317757150.432666165887858
580.8063170459707720.3873659080584560.193682954029228
590.9309328803290050.1381342393419890.0690671196709945
600.9230418065262080.1539163869475840.076958193473792
610.9043442190138760.1913115619722480.0956557809861242
620.8987146023163820.2025707953672370.101285397683618
630.8966097934056770.2067804131886460.103390206594323
640.92101445670640.1579710865871990.0789855432935993
650.9162242687429620.1675514625140760.083775731257038
660.9205827101324050.1588345797351900.0794172898675948
670.9469445301283450.1061109397433110.0530554698716554
680.951210961038870.09757807792225750.0487890389611287
690.969117568767210.06176486246558190.0308824312327910
700.9775431966102620.04491360677947590.0224568033897379
710.9782845446295530.04343091074089320.0217154553704466
720.9771670129483820.04566597410323630.0228329870516182
730.981947643311720.03610471337655910.0180523566882795
740.979513837748180.04097232450364060.0204861622518203
750.9783364458117370.04332710837652580.0216635541882629
760.9741632448772690.05167351024546290.0258367551227314
770.9730730517330220.05385389653395570.0269269482669779
780.965186259700240.06962748059952150.0348137402997607
790.9580767130312080.08384657393758370.0419232869687919
800.9767144834349360.04657103313012830.0232855165650641
810.969972038476120.06005592304776050.0300279615238802
820.973415887595830.05316822480833890.0265841124041695
830.9840883994737980.03182320105240450.0159116005262023
840.9915562205386140.01688755892277170.00844377946138584
850.9932283214080060.01354335718398710.00677167859199355
860.9907740230194980.01845195396100420.00922597698050211
870.9877727777960190.02445444440796240.0122272222039812
880.9914321813512420.01713563729751620.0085678186487581
890.9885424061469880.02291518770602480.0114575938530124
900.9950061943810890.009987611237822470.00499380561891123
910.9943311386653380.01133772266932420.0056688613346621
920.9932675031655440.01346499366891230.00673249683445614
930.990986500194550.01802699961089860.0090134998054493
940.9936319594371740.01273608112565220.00636804056282608
950.9922933682675680.01541326346486470.00770663173243235
960.9913248511536020.01735029769279640.00867514884639819
970.991851855931350.01629628813729870.00814814406864935
980.989461551871260.02107689625747950.0105384481287398
990.9914413341262510.01711733174749750.00855866587374875
1000.9895170615289670.02096587694206650.0104829384710332
1010.9865728678165260.02685426436694740.0134271321834737
1020.9831473261596220.03370534768075510.0168526738403776
1030.9874659560969450.02506808780611070.0125340439030553
1040.9861258512562630.02774829748747390.0138741487437369
1050.982129693980250.0357406120395010.0178703060197505
1060.9808912513357780.03821749732844340.0191087486642217
1070.9937193642488510.01256127150229760.00628063575114878
1080.9932421017838160.01351579643236730.00675789821618364
1090.9906919576895540.01861608462089290.00930804231044647
1100.9875688843724470.02486223125510570.0124311156275529
1110.988062257449820.02387548510035810.0119377425501791
1120.989973468810750.02005306237849860.0100265311892493
1130.987652112925870.02469577414826190.0123478870741309
1140.983577279040160.03284544191968230.0164227209598411
1150.9785503356311010.04289932873779770.0214496643688989
1160.979972806071650.04005438785670050.0200271939283502
1170.9805462677802430.03890746443951420.0194537322197571
1180.9890929483100180.0218141033799630.0109070516899815
1190.992814858641440.01437028271712110.00718514135856056
1200.9908858266575740.01822834668485310.00911417334242654
1210.9974867717672990.005026456465402430.00251322823270121
1220.9965443281305120.006911343738976050.00345567186948803
1230.995836118038990.008327763922021520.00416388196101076
1240.9941916940058260.01161661198834900.00580830599417448
1250.9927393569465160.01452128610696860.0072606430534843
1260.9901772047256670.01964559054866540.00982279527433268
1270.987006017170570.02598796565885860.0129939828294293
1280.9897325105769070.02053497884618690.0102674894230935
1290.9925596676029780.01488066479404500.00744033239702252
1300.9988989526896340.002202094620731060.00110104731036553
1310.9996272867027750.0007454265944500210.000372713297225010
1320.9994382175478850.001123564904229470.000561782452114733
1330.9991740604552350.001651879089529840.000825939544764918
1340.9986950709514480.002609858097104340.00130492904855217
1350.9979860218515950.004027956296810270.00201397814840513
1360.9983339077110840.003332184577831480.00166609228891574
1370.9974874934615230.005025013076953720.00251250653847686
1380.9967998148870220.006400370225956510.00320018511297826
1390.9953651281129120.009269743774176720.00463487188708836
1400.9938824954295580.01223500914088490.00611750457044246
1410.9947800110532650.01043997789346950.00521998894673473
1420.9941261594376030.01174768112479320.00587384056239662
1430.9910617965177520.01787640696449690.00893820348224847
1440.9893243374483970.02135132510320550.0106756625516027
1450.9864674677868350.02706506442633090.0135325322131655
1460.9810203054259980.03795938914800450.0189796945740022
1470.9733473880670790.05330522386584230.0266526119329211
1480.9627475858547040.07450482829059150.0372524141452958
1490.9815224997918420.03695500041631670.0184775002081583
1500.9727003440980970.05459931180380570.0272996559019029
1510.966674434264810.06665113147037770.0333255657351889
1520.9842506674697880.03149866506042310.0157493325302115
1530.9797351009386070.04052979812278670.0202648990613934
1540.9888917820433360.02221643591332710.0111082179566636
1550.9843894896405070.03122102071898530.0156105103594927
1560.976678216218640.04664356756271840.0233217837813592
1570.9707601722934720.05847965541305610.0292398277065280
1580.965543048199720.06891390360056170.0344569518002809
1590.9657918666798070.06841626664038660.0342081333201933
1600.9523013485512540.0953973028974920.047698651448746
1610.926149765694760.1477004686104790.0738502343052394
1620.9397456782047280.1205086435905430.0602543217952716
1630.9468972755317710.1062054489364580.0531027244682289
1640.9149151775377740.1701696449244520.085084822462226
1650.8746592871703010.2506814256593970.125340712829699
1660.9700296112690990.05994077746180270.0299703887309014
1670.941207039985490.1175859200290220.0587929600145108
1680.8941076502659350.2117846994681290.105892349734065
1690.8344947942909440.3310104114181120.165505205709056
1700.8478285302299650.3043429395400710.152171469770036
1710.7095157454468680.5809685091062650.290484254553132







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0915032679738562NOK
5% type I error level770.503267973856209NOK
10% type I error level940.61437908496732NOK

\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 & 14 & 0.0915032679738562 & NOK \tabularnewline
5% type I error level & 77 & 0.503267973856209 & NOK \tabularnewline
10% type I error level & 94 & 0.61437908496732 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59100&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]14[/C][C]0.0915032679738562[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]77[/C][C]0.503267973856209[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]94[/C][C]0.61437908496732[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59100&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59100&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.0915032679738562NOK
5% type I error level770.503267973856209NOK
10% type I error level940.61437908496732NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}