FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 23 Nov 2008 05:04:48 -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/2008/Nov/23/t1227442567e24yus8vikel07o.htm/, Retrieved Mon, 03 Nov 2025 01:20:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25227, Retrieved Mon, 03 Nov 2025 01:20:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsVan Dooren Leen
Estimated Impact502

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F    D  [Multiple Regression] [The Seatbelt Law ...] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
F R P       [Multiple Regression] [Seatbelt Law Q1] [2008-11-23 12:04:48] [d175f84d503eb4f2a43145d5e67795b5] [Current]
Feedback Forum
2008-11-29 15:37:01 [Sofie Sergoynne] [reply] 
Goed antwoord, maar er zijn toch nog enkele aanvullingen vereist. Eerst en vooral geeft de student niets van informatie over bepaalde tabellen. Ze heeft deze wel gemaakt, maar niet vermeld in haar opdracht. We kunnen zien dat er gemiddeld 396 minder slachtoffers gevallen zijn na de invoering van de gordel.
Met een standaard deviatie van 57.79 (dus een significante daling t.o.v. voor het invoeren van de gordel – 2 maal de S.D. erbij optellen geeft nog steeds een negatief cijfer). De 0-hypothese (er is geen verschil na de invoering van de gordel) kan verworpen worden, aangezien de absolute waarde van de T-stat groter is dan 2. = er is dan minder dan 5% kans er een vergissing gebeurt bij het verwerpen. Vervolgens kijken we naar de tabel Multiple Linear Regression - Regression Statistics. Daaruit kunnen we aflezen dat de R-Squared gelijk is aan 19.82%, wat vrij klein is. De residuele standaard afwijking (die niet door het model worden verklaart) bedraagt 260, bij de voorspelling kan men er dus +- 260 eenheden (slachtoffers) naast zijn. De student kon ook nog de tabel Multiple Linear Regression - Actuals, Interpolation, and Residuals vermelden in haar oplossing verdere interpretaties geven. Deze zijn voor het grootste deel wel gegeven in de oplossing van haar Q1 vraag.
2008-11-29 20:31:29 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
Ik had nog twee tabellen toevoegen: Multiple Linear Regression - Estimated Regression Equation en Multiple Linear Regression - Ordinary Least Squares.
2008-12-01 14:32:13 [Stefan Temmerman] [reply] 
Q1: De student geeft een juiste verklaring hier, maar is onvolledig. Er wordt niets vermeld over de significantie (T-STAT en p-values), er worden geen precieze getallen gegeven in verband met ongevallen, er wordt ook maar 1 enkele grafiek gegeven. Ook is hier vergeten de seasonal dummies en de lineaire trend te gebruiken. Deze hebben beiden een gunstig effect op het model, waardoor het meer gedetailleerd en duidelijk wordt. Hieruit kan ook besloten worden dat het dragen van een gordel een significant en positief effect heeft op het aantal ongevallen.

Post a new message

Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&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]5 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=25227&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
S[t] = + 1717.75147928994 -396.055827116028D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
S[t] =  +  1717.75147928994 -396.055827116028D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]S[t] =  +  1717.75147928994 -396.055827116028D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
S[t] = + 1717.75147928994 -396.055827116028D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1717.7514792899420.00033485.886100
D-396.05582711602857.786173-6.853800

\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) & 1717.75147928994 & 20.000334 & 85.8861 & 0 & 0 \tabularnewline
D & -396.055827116028 & 57.786173 & -6.8538 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&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]1717.75147928994[/C][C]20.000334[/C][C]85.8861[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-396.055827116028[/C][C]57.786173[/C][C]-6.8538[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1717.7514792899420.00033485.886100
D-396.05582711602857.786173-6.853800






Multiple Linear Regression - Regression Statistics
Multiple R0.445226892939612
R-squared0.198226986196661
Adjusted R-squared0.194007128229275
F-TEST (value)46.9748005095663
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.762957109416e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004336317031
Sum Squared Residuals12844428.4316954

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.445226892939612 \tabularnewline
R-squared & 0.198226986196661 \tabularnewline
Adjusted R-squared & 0.194007128229275 \tabularnewline
F-TEST (value) & 46.9748005095663 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value & 9.762957109416e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 260.004336317031 \tabularnewline
Sum Squared Residuals & 12844428.4316954 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.445226892939612[/C][/ROW]
[ROW][C]R-squared[/C][C]0.198226986196661[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.194007128229275[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]46.9748005095663[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]190[/C][/ROW]
[ROW][C]p-value[/C][C]9.762957109416e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]260.004336317031[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12844428.4316954[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.445226892939612
R-squared0.198226986196661
Adjusted R-squared0.194007128229275
F-TEST (value)46.9748005095663
F-TEST (DF numerator)1
F-TEST (DF denominator)190
p-value9.762957109416e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation260.004336317031
Sum Squared Residuals12844428.4316954






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871717.75147928992-30.7514792899202
215081717.75147928994-209.751479289942
315071717.75147928994-210.751479289941
413851717.75147928994-332.751479289941
516321717.75147928994-85.751479289941
615111717.75147928994-206.751479289941
715591717.75147928994-158.751479289941
816301717.75147928994-87.751479289941
915791717.75147928994-138.751479289941
1016531717.75147928994-64.751479289941
1121521717.75147928994434.248520710059
1221481717.75147928994430.248520710059
1317521717.7514792899434.2485207100591
1417651717.7514792899447.2485207100591
1517171717.75147928994-0.751479289940944
1615581717.75147928994-159.751479289941
1715751717.75147928994-142.751479289941
1815201717.75147928994-197.751479289941
1918051717.7514792899487.248520710059
2018001717.7514792899482.248520710059
2117191717.751479289941.24852071005906
2220081717.75147928994290.248520710059
2322421717.75147928994524.248520710059
2424781717.75147928994760.248520710059
2520301717.75147928994312.248520710059
2616551717.75147928994-62.751479289941
2716931717.75147928994-24.7514792899409
2816231717.75147928994-94.751479289941
2918051717.7514792899487.248520710059
3017461717.7514792899428.2485207100591
3117951717.7514792899477.2485207100591
3219261717.75147928994208.248520710059
3316191717.75147928994-98.751479289941
3419921717.75147928994274.248520710059
3522331717.75147928994515.248520710059
3621921717.75147928994474.248520710059
3720801717.75147928994362.248520710059
3817681717.7514792899450.2485207100591
3918351717.75147928994117.248520710059
4015691717.75147928994-148.751479289941
4119761717.75147928994258.248520710059
4218531717.75147928994135.248520710059
4319651717.75147928994247.248520710059
4416891717.75147928994-28.7514792899409
4517781717.7514792899460.2485207100591
4619761717.75147928994258.248520710059
4723971717.75147928994679.248520710059
4826541717.75147928994936.248520710059
4920971717.75147928994379.248520710059
5019631717.75147928994245.248520710059
5116771717.75147928994-40.7514792899409
5219411717.75147928994223.248520710059
5320031717.75147928994285.248520710059
5418131717.7514792899495.248520710059
5520121717.75147928994294.248520710059
5619121717.75147928994194.248520710059
5720841717.75147928994366.248520710059
5820801717.75147928994362.248520710059
5921181717.75147928994400.248520710059
6021501717.75147928994432.248520710059
6116081717.75147928994-109.751479289941
6215031717.75147928994-214.751479289941
6315481717.75147928994-169.751479289941
6413821717.75147928994-335.751479289941
6517311717.7514792899413.2485207100591
6617981717.7514792899480.248520710059
6717791717.7514792899461.2485207100591
6818871717.75147928994169.248520710059
6920041717.75147928994286.248520710059
7020771717.75147928994359.248520710059
7120921717.75147928994374.248520710059
7220511717.75147928994333.248520710059
7315771717.75147928994-140.751479289941
7413561717.75147928994-361.751479289941
7516521717.75147928994-65.7514792899409
7613821717.75147928994-335.751479289941
7715191717.75147928994-198.751479289941
7814211717.75147928994-296.751479289941
7914421717.75147928994-275.751479289941
8015431717.75147928994-174.751479289941
8116561717.75147928994-61.7514792899409
8215611717.75147928994-156.751479289941
8319051717.75147928994187.248520710059
8421991717.75147928994481.248520710059
8514731717.75147928994-244.751479289941
8616551717.75147928994-62.751479289941
8714071717.75147928994-310.751479289941
8813951717.75147928994-322.751479289941
8915301717.75147928994-187.751479289941
9013091717.75147928994-408.751479289941
9115261717.75147928994-191.751479289941
9213271717.75147928994-390.751479289941
9316271717.75147928994-90.751479289941
9417481717.7514792899430.2485207100591
9519581717.75147928994240.248520710059
9622741717.75147928994556.248520710059
9716481717.75147928994-69.7514792899409
9814011717.75147928994-316.751479289941
9914111717.75147928994-306.751479289941
10014031717.75147928994-314.751479289941
10113941717.75147928994-323.751479289941
10215201717.75147928994-197.751479289941
10315281717.75147928994-189.751479289941
10416431717.75147928994-74.7514792899409
10515151717.75147928994-202.751479289941
10616851717.75147928994-32.7514792899409
10720001717.75147928994282.248520710059
10822151717.75147928994497.248520710059
10919561717.75147928994238.248520710059
11014621717.75147928994-255.751479289941
11115631717.75147928994-154.751479289941
11214591717.75147928994-258.751479289941
11314461717.75147928994-271.751479289941
11416221717.75147928994-95.751479289941
11516571717.75147928994-60.7514792899409
11616381717.75147928994-79.751479289941
11716431717.75147928994-74.7514792899409
11816831717.75147928994-34.7514792899409
11920501717.75147928994332.248520710059
12022621717.75147928994544.248520710059
12118131717.7514792899495.248520710059
12214451717.75147928994-272.751479289941
12317621717.7514792899444.2485207100591
12414611717.75147928994-256.751479289941
12515561717.75147928994-161.751479289941
12614311717.75147928994-286.751479289941
12714271717.75147928994-290.751479289941
12815541717.75147928994-163.751479289941
12916451717.75147928994-72.7514792899409
13016531717.75147928994-64.751479289941
13120161717.75147928994298.248520710059
13222071717.75147928994489.248520710059
13316651717.75147928994-52.7514792899409
13413611717.75147928994-356.751479289941
13515061717.75147928994-211.751479289941
13613601717.75147928994-357.751479289941
13714531717.75147928994-264.751479289941
13815221717.75147928994-195.751479289941
13914601717.75147928994-257.751479289941
14015521717.75147928994-165.751479289941
14115481717.75147928994-169.751479289941
14218271717.75147928994109.248520710059
14317371717.7514792899419.2485207100591
14419411717.75147928994223.248520710059
14514741717.75147928994-243.751479289941
14614581717.75147928994-259.751479289941
14715421717.75147928994-175.751479289941
14814041717.75147928994-313.751479289941
14915221717.75147928994-195.751479289941
15013851717.75147928994-332.751479289941
15116411717.75147928994-76.7514792899409
15215101717.75147928994-207.751479289941
15316811717.75147928994-36.7514792899409
15419381717.75147928994220.248520710059
15518681717.75147928994150.248520710059
15617261717.751479289948.24852071005906
15714561717.75147928994-261.751479289941
15814451717.75147928994-272.751479289941
15914561717.75147928994-261.751479289941
16013651717.75147928994-352.751479289941
16114871717.75147928994-230.751479289941
16215581717.75147928994-159.751479289941
16314881717.75147928994-229.751479289941
16416841717.75147928994-33.7514792899409
16515941717.75147928994-123.751479289941
16618501717.75147928994132.248520710059
16719981717.75147928994280.248520710059
16820791717.75147928994361.248520710059
16914941717.75147928994-223.751479289941
17010571321.69565217391-264.695652173913
17112181321.69565217391-103.695652173913
17211681321.69565217391-153.695652173913
17312361321.69565217391-85.695652173913
17410761321.69565217391-245.695652173913
17511741321.69565217391-147.695652173913
17611391321.69565217391-182.695652173913
17714271321.69565217391105.304347826087
17814871321.69565217391165.304347826087
17914831321.69565217391161.304347826087
18015131321.69565217391191.304347826087
18113571321.6956521739135.304347826087
18211651321.69565217391-156.695652173913
18312821321.69565217391-39.695652173913
18411101321.69565217391-211.695652173913
18512971321.69565217391-24.6956521739130
18611851321.69565217391-136.695652173913
18712221321.69565217391-99.695652173913
18812841321.69565217391-37.695652173913
18914441321.69565217391122.304347826087
19015751321.69565217391253.304347826087
19117371321.69565217391415.304347826087
19217631321.69565217391441.304347826087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1717.75147928992 & -30.7514792899202 \tabularnewline
2 & 1508 & 1717.75147928994 & -209.751479289942 \tabularnewline
3 & 1507 & 1717.75147928994 & -210.751479289941 \tabularnewline
4 & 1385 & 1717.75147928994 & -332.751479289941 \tabularnewline
5 & 1632 & 1717.75147928994 & -85.751479289941 \tabularnewline
6 & 1511 & 1717.75147928994 & -206.751479289941 \tabularnewline
7 & 1559 & 1717.75147928994 & -158.751479289941 \tabularnewline
8 & 1630 & 1717.75147928994 & -87.751479289941 \tabularnewline
9 & 1579 & 1717.75147928994 & -138.751479289941 \tabularnewline
10 & 1653 & 1717.75147928994 & -64.751479289941 \tabularnewline
11 & 2152 & 1717.75147928994 & 434.248520710059 \tabularnewline
12 & 2148 & 1717.75147928994 & 430.248520710059 \tabularnewline
13 & 1752 & 1717.75147928994 & 34.2485207100591 \tabularnewline
14 & 1765 & 1717.75147928994 & 47.2485207100591 \tabularnewline
15 & 1717 & 1717.75147928994 & -0.751479289940944 \tabularnewline
16 & 1558 & 1717.75147928994 & -159.751479289941 \tabularnewline
17 & 1575 & 1717.75147928994 & -142.751479289941 \tabularnewline
18 & 1520 & 1717.75147928994 & -197.751479289941 \tabularnewline
19 & 1805 & 1717.75147928994 & 87.248520710059 \tabularnewline
20 & 1800 & 1717.75147928994 & 82.248520710059 \tabularnewline
21 & 1719 & 1717.75147928994 & 1.24852071005906 \tabularnewline
22 & 2008 & 1717.75147928994 & 290.248520710059 \tabularnewline
23 & 2242 & 1717.75147928994 & 524.248520710059 \tabularnewline
24 & 2478 & 1717.75147928994 & 760.248520710059 \tabularnewline
25 & 2030 & 1717.75147928994 & 312.248520710059 \tabularnewline
26 & 1655 & 1717.75147928994 & -62.751479289941 \tabularnewline
27 & 1693 & 1717.75147928994 & -24.7514792899409 \tabularnewline
28 & 1623 & 1717.75147928994 & -94.751479289941 \tabularnewline
29 & 1805 & 1717.75147928994 & 87.248520710059 \tabularnewline
30 & 1746 & 1717.75147928994 & 28.2485207100591 \tabularnewline
31 & 1795 & 1717.75147928994 & 77.2485207100591 \tabularnewline
32 & 1926 & 1717.75147928994 & 208.248520710059 \tabularnewline
33 & 1619 & 1717.75147928994 & -98.751479289941 \tabularnewline
34 & 1992 & 1717.75147928994 & 274.248520710059 \tabularnewline
35 & 2233 & 1717.75147928994 & 515.248520710059 \tabularnewline
36 & 2192 & 1717.75147928994 & 474.248520710059 \tabularnewline
37 & 2080 & 1717.75147928994 & 362.248520710059 \tabularnewline
38 & 1768 & 1717.75147928994 & 50.2485207100591 \tabularnewline
39 & 1835 & 1717.75147928994 & 117.248520710059 \tabularnewline
40 & 1569 & 1717.75147928994 & -148.751479289941 \tabularnewline
41 & 1976 & 1717.75147928994 & 258.248520710059 \tabularnewline
42 & 1853 & 1717.75147928994 & 135.248520710059 \tabularnewline
43 & 1965 & 1717.75147928994 & 247.248520710059 \tabularnewline
44 & 1689 & 1717.75147928994 & -28.7514792899409 \tabularnewline
45 & 1778 & 1717.75147928994 & 60.2485207100591 \tabularnewline
46 & 1976 & 1717.75147928994 & 258.248520710059 \tabularnewline
47 & 2397 & 1717.75147928994 & 679.248520710059 \tabularnewline
48 & 2654 & 1717.75147928994 & 936.248520710059 \tabularnewline
49 & 2097 & 1717.75147928994 & 379.248520710059 \tabularnewline
50 & 1963 & 1717.75147928994 & 245.248520710059 \tabularnewline
51 & 1677 & 1717.75147928994 & -40.7514792899409 \tabularnewline
52 & 1941 & 1717.75147928994 & 223.248520710059 \tabularnewline
53 & 2003 & 1717.75147928994 & 285.248520710059 \tabularnewline
54 & 1813 & 1717.75147928994 & 95.248520710059 \tabularnewline
55 & 2012 & 1717.75147928994 & 294.248520710059 \tabularnewline
56 & 1912 & 1717.75147928994 & 194.248520710059 \tabularnewline
57 & 2084 & 1717.75147928994 & 366.248520710059 \tabularnewline
58 & 2080 & 1717.75147928994 & 362.248520710059 \tabularnewline
59 & 2118 & 1717.75147928994 & 400.248520710059 \tabularnewline
60 & 2150 & 1717.75147928994 & 432.248520710059 \tabularnewline
61 & 1608 & 1717.75147928994 & -109.751479289941 \tabularnewline
62 & 1503 & 1717.75147928994 & -214.751479289941 \tabularnewline
63 & 1548 & 1717.75147928994 & -169.751479289941 \tabularnewline
64 & 1382 & 1717.75147928994 & -335.751479289941 \tabularnewline
65 & 1731 & 1717.75147928994 & 13.2485207100591 \tabularnewline
66 & 1798 & 1717.75147928994 & 80.248520710059 \tabularnewline
67 & 1779 & 1717.75147928994 & 61.2485207100591 \tabularnewline
68 & 1887 & 1717.75147928994 & 169.248520710059 \tabularnewline
69 & 2004 & 1717.75147928994 & 286.248520710059 \tabularnewline
70 & 2077 & 1717.75147928994 & 359.248520710059 \tabularnewline
71 & 2092 & 1717.75147928994 & 374.248520710059 \tabularnewline
72 & 2051 & 1717.75147928994 & 333.248520710059 \tabularnewline
73 & 1577 & 1717.75147928994 & -140.751479289941 \tabularnewline
74 & 1356 & 1717.75147928994 & -361.751479289941 \tabularnewline
75 & 1652 & 1717.75147928994 & -65.7514792899409 \tabularnewline
76 & 1382 & 1717.75147928994 & -335.751479289941 \tabularnewline
77 & 1519 & 1717.75147928994 & -198.751479289941 \tabularnewline
78 & 1421 & 1717.75147928994 & -296.751479289941 \tabularnewline
79 & 1442 & 1717.75147928994 & -275.751479289941 \tabularnewline
80 & 1543 & 1717.75147928994 & -174.751479289941 \tabularnewline
81 & 1656 & 1717.75147928994 & -61.7514792899409 \tabularnewline
82 & 1561 & 1717.75147928994 & -156.751479289941 \tabularnewline
83 & 1905 & 1717.75147928994 & 187.248520710059 \tabularnewline
84 & 2199 & 1717.75147928994 & 481.248520710059 \tabularnewline
85 & 1473 & 1717.75147928994 & -244.751479289941 \tabularnewline
86 & 1655 & 1717.75147928994 & -62.751479289941 \tabularnewline
87 & 1407 & 1717.75147928994 & -310.751479289941 \tabularnewline
88 & 1395 & 1717.75147928994 & -322.751479289941 \tabularnewline
89 & 1530 & 1717.75147928994 & -187.751479289941 \tabularnewline
90 & 1309 & 1717.75147928994 & -408.751479289941 \tabularnewline
91 & 1526 & 1717.75147928994 & -191.751479289941 \tabularnewline
92 & 1327 & 1717.75147928994 & -390.751479289941 \tabularnewline
93 & 1627 & 1717.75147928994 & -90.751479289941 \tabularnewline
94 & 1748 & 1717.75147928994 & 30.2485207100591 \tabularnewline
95 & 1958 & 1717.75147928994 & 240.248520710059 \tabularnewline
96 & 2274 & 1717.75147928994 & 556.248520710059 \tabularnewline
97 & 1648 & 1717.75147928994 & -69.7514792899409 \tabularnewline
98 & 1401 & 1717.75147928994 & -316.751479289941 \tabularnewline
99 & 1411 & 1717.75147928994 & -306.751479289941 \tabularnewline
100 & 1403 & 1717.75147928994 & -314.751479289941 \tabularnewline
101 & 1394 & 1717.75147928994 & -323.751479289941 \tabularnewline
102 & 1520 & 1717.75147928994 & -197.751479289941 \tabularnewline
103 & 1528 & 1717.75147928994 & -189.751479289941 \tabularnewline
104 & 1643 & 1717.75147928994 & -74.7514792899409 \tabularnewline
105 & 1515 & 1717.75147928994 & -202.751479289941 \tabularnewline
106 & 1685 & 1717.75147928994 & -32.7514792899409 \tabularnewline
107 & 2000 & 1717.75147928994 & 282.248520710059 \tabularnewline
108 & 2215 & 1717.75147928994 & 497.248520710059 \tabularnewline
109 & 1956 & 1717.75147928994 & 238.248520710059 \tabularnewline
110 & 1462 & 1717.75147928994 & -255.751479289941 \tabularnewline
111 & 1563 & 1717.75147928994 & -154.751479289941 \tabularnewline
112 & 1459 & 1717.75147928994 & -258.751479289941 \tabularnewline
113 & 1446 & 1717.75147928994 & -271.751479289941 \tabularnewline
114 & 1622 & 1717.75147928994 & -95.751479289941 \tabularnewline
115 & 1657 & 1717.75147928994 & -60.7514792899409 \tabularnewline
116 & 1638 & 1717.75147928994 & -79.751479289941 \tabularnewline
117 & 1643 & 1717.75147928994 & -74.7514792899409 \tabularnewline
118 & 1683 & 1717.75147928994 & -34.7514792899409 \tabularnewline
119 & 2050 & 1717.75147928994 & 332.248520710059 \tabularnewline
120 & 2262 & 1717.75147928994 & 544.248520710059 \tabularnewline
121 & 1813 & 1717.75147928994 & 95.248520710059 \tabularnewline
122 & 1445 & 1717.75147928994 & -272.751479289941 \tabularnewline
123 & 1762 & 1717.75147928994 & 44.2485207100591 \tabularnewline
124 & 1461 & 1717.75147928994 & -256.751479289941 \tabularnewline
125 & 1556 & 1717.75147928994 & -161.751479289941 \tabularnewline
126 & 1431 & 1717.75147928994 & -286.751479289941 \tabularnewline
127 & 1427 & 1717.75147928994 & -290.751479289941 \tabularnewline
128 & 1554 & 1717.75147928994 & -163.751479289941 \tabularnewline
129 & 1645 & 1717.75147928994 & -72.7514792899409 \tabularnewline
130 & 1653 & 1717.75147928994 & -64.751479289941 \tabularnewline
131 & 2016 & 1717.75147928994 & 298.248520710059 \tabularnewline
132 & 2207 & 1717.75147928994 & 489.248520710059 \tabularnewline
133 & 1665 & 1717.75147928994 & -52.7514792899409 \tabularnewline
134 & 1361 & 1717.75147928994 & -356.751479289941 \tabularnewline
135 & 1506 & 1717.75147928994 & -211.751479289941 \tabularnewline
136 & 1360 & 1717.75147928994 & -357.751479289941 \tabularnewline
137 & 1453 & 1717.75147928994 & -264.751479289941 \tabularnewline
138 & 1522 & 1717.75147928994 & -195.751479289941 \tabularnewline
139 & 1460 & 1717.75147928994 & -257.751479289941 \tabularnewline
140 & 1552 & 1717.75147928994 & -165.751479289941 \tabularnewline
141 & 1548 & 1717.75147928994 & -169.751479289941 \tabularnewline
142 & 1827 & 1717.75147928994 & 109.248520710059 \tabularnewline
143 & 1737 & 1717.75147928994 & 19.2485207100591 \tabularnewline
144 & 1941 & 1717.75147928994 & 223.248520710059 \tabularnewline
145 & 1474 & 1717.75147928994 & -243.751479289941 \tabularnewline
146 & 1458 & 1717.75147928994 & -259.751479289941 \tabularnewline
147 & 1542 & 1717.75147928994 & -175.751479289941 \tabularnewline
148 & 1404 & 1717.75147928994 & -313.751479289941 \tabularnewline
149 & 1522 & 1717.75147928994 & -195.751479289941 \tabularnewline
150 & 1385 & 1717.75147928994 & -332.751479289941 \tabularnewline
151 & 1641 & 1717.75147928994 & -76.7514792899409 \tabularnewline
152 & 1510 & 1717.75147928994 & -207.751479289941 \tabularnewline
153 & 1681 & 1717.75147928994 & -36.7514792899409 \tabularnewline
154 & 1938 & 1717.75147928994 & 220.248520710059 \tabularnewline
155 & 1868 & 1717.75147928994 & 150.248520710059 \tabularnewline
156 & 1726 & 1717.75147928994 & 8.24852071005906 \tabularnewline
157 & 1456 & 1717.75147928994 & -261.751479289941 \tabularnewline
158 & 1445 & 1717.75147928994 & -272.751479289941 \tabularnewline
159 & 1456 & 1717.75147928994 & -261.751479289941 \tabularnewline
160 & 1365 & 1717.75147928994 & -352.751479289941 \tabularnewline
161 & 1487 & 1717.75147928994 & -230.751479289941 \tabularnewline
162 & 1558 & 1717.75147928994 & -159.751479289941 \tabularnewline
163 & 1488 & 1717.75147928994 & -229.751479289941 \tabularnewline
164 & 1684 & 1717.75147928994 & -33.7514792899409 \tabularnewline
165 & 1594 & 1717.75147928994 & -123.751479289941 \tabularnewline
166 & 1850 & 1717.75147928994 & 132.248520710059 \tabularnewline
167 & 1998 & 1717.75147928994 & 280.248520710059 \tabularnewline
168 & 2079 & 1717.75147928994 & 361.248520710059 \tabularnewline
169 & 1494 & 1717.75147928994 & -223.751479289941 \tabularnewline
170 & 1057 & 1321.69565217391 & -264.695652173913 \tabularnewline
171 & 1218 & 1321.69565217391 & -103.695652173913 \tabularnewline
172 & 1168 & 1321.69565217391 & -153.695652173913 \tabularnewline
173 & 1236 & 1321.69565217391 & -85.695652173913 \tabularnewline
174 & 1076 & 1321.69565217391 & -245.695652173913 \tabularnewline
175 & 1174 & 1321.69565217391 & -147.695652173913 \tabularnewline
176 & 1139 & 1321.69565217391 & -182.695652173913 \tabularnewline
177 & 1427 & 1321.69565217391 & 105.304347826087 \tabularnewline
178 & 1487 & 1321.69565217391 & 165.304347826087 \tabularnewline
179 & 1483 & 1321.69565217391 & 161.304347826087 \tabularnewline
180 & 1513 & 1321.69565217391 & 191.304347826087 \tabularnewline
181 & 1357 & 1321.69565217391 & 35.304347826087 \tabularnewline
182 & 1165 & 1321.69565217391 & -156.695652173913 \tabularnewline
183 & 1282 & 1321.69565217391 & -39.695652173913 \tabularnewline
184 & 1110 & 1321.69565217391 & -211.695652173913 \tabularnewline
185 & 1297 & 1321.69565217391 & -24.6956521739130 \tabularnewline
186 & 1185 & 1321.69565217391 & -136.695652173913 \tabularnewline
187 & 1222 & 1321.69565217391 & -99.695652173913 \tabularnewline
188 & 1284 & 1321.69565217391 & -37.695652173913 \tabularnewline
189 & 1444 & 1321.69565217391 & 122.304347826087 \tabularnewline
190 & 1575 & 1321.69565217391 & 253.304347826087 \tabularnewline
191 & 1737 & 1321.69565217391 & 415.304347826087 \tabularnewline
192 & 1763 & 1321.69565217391 & 441.304347826087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1687[/C][C]1717.75147928992[/C][C]-30.7514792899202[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1717.75147928994[/C][C]-209.751479289942[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1717.75147928994[/C][C]-210.751479289941[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1717.75147928994[/C][C]-332.751479289941[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1717.75147928994[/C][C]-85.751479289941[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1717.75147928994[/C][C]-206.751479289941[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1717.75147928994[/C][C]-158.751479289941[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1717.75147928994[/C][C]-87.751479289941[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1717.75147928994[/C][C]-138.751479289941[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1717.75147928994[/C][C]-64.751479289941[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]1717.75147928994[/C][C]434.248520710059[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]1717.75147928994[/C][C]430.248520710059[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1717.75147928994[/C][C]34.2485207100591[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1717.75147928994[/C][C]47.2485207100591[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1717.75147928994[/C][C]-0.751479289940944[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1717.75147928994[/C][C]-159.751479289941[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1717.75147928994[/C][C]-142.751479289941[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1717.75147928994[/C][C]-197.751479289941[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1717.75147928994[/C][C]87.248520710059[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1717.75147928994[/C][C]82.248520710059[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1717.75147928994[/C][C]1.24852071005906[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1717.75147928994[/C][C]290.248520710059[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]1717.75147928994[/C][C]524.248520710059[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]1717.75147928994[/C][C]760.248520710059[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1717.75147928994[/C][C]312.248520710059[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1717.75147928994[/C][C]-62.751479289941[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1717.75147928994[/C][C]-24.7514792899409[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1717.75147928994[/C][C]-94.751479289941[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.75147928994[/C][C]87.248520710059[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1717.75147928994[/C][C]28.2485207100591[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1717.75147928994[/C][C]77.2485207100591[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1717.75147928994[/C][C]208.248520710059[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1717.75147928994[/C][C]-98.751479289941[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1717.75147928994[/C][C]274.248520710059[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]1717.75147928994[/C][C]515.248520710059[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]1717.75147928994[/C][C]474.248520710059[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1717.75147928994[/C][C]362.248520710059[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1717.75147928994[/C][C]50.2485207100591[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1717.75147928994[/C][C]117.248520710059[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1717.75147928994[/C][C]-148.751479289941[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1717.75147928994[/C][C]258.248520710059[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1717.75147928994[/C][C]135.248520710059[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1717.75147928994[/C][C]247.248520710059[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1717.75147928994[/C][C]-28.7514792899409[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1717.75147928994[/C][C]60.2485207100591[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1717.75147928994[/C][C]258.248520710059[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]1717.75147928994[/C][C]679.248520710059[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]1717.75147928994[/C][C]936.248520710059[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1717.75147928994[/C][C]379.248520710059[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1717.75147928994[/C][C]245.248520710059[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1717.75147928994[/C][C]-40.7514792899409[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1717.75147928994[/C][C]223.248520710059[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1717.75147928994[/C][C]285.248520710059[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1717.75147928994[/C][C]95.248520710059[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1717.75147928994[/C][C]294.248520710059[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1717.75147928994[/C][C]194.248520710059[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1717.75147928994[/C][C]366.248520710059[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1717.75147928994[/C][C]362.248520710059[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]1717.75147928994[/C][C]400.248520710059[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]1717.75147928994[/C][C]432.248520710059[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1717.75147928994[/C][C]-109.751479289941[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1717.75147928994[/C][C]-214.751479289941[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1717.75147928994[/C][C]-169.751479289941[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1717.75147928994[/C][C]-335.751479289941[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1717.75147928994[/C][C]13.2485207100591[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1717.75147928994[/C][C]80.248520710059[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1717.75147928994[/C][C]61.2485207100591[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1717.75147928994[/C][C]169.248520710059[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1717.75147928994[/C][C]286.248520710059[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1717.75147928994[/C][C]359.248520710059[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]1717.75147928994[/C][C]374.248520710059[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]1717.75147928994[/C][C]333.248520710059[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1717.75147928994[/C][C]-140.751479289941[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1717.75147928994[/C][C]-361.751479289941[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1717.75147928994[/C][C]-65.7514792899409[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1717.75147928994[/C][C]-335.751479289941[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1717.75147928994[/C][C]-198.751479289941[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1717.75147928994[/C][C]-296.751479289941[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1717.75147928994[/C][C]-275.751479289941[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1717.75147928994[/C][C]-174.751479289941[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1717.75147928994[/C][C]-61.7514792899409[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1717.75147928994[/C][C]-156.751479289941[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]1717.75147928994[/C][C]187.248520710059[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]1717.75147928994[/C][C]481.248520710059[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1717.75147928994[/C][C]-244.751479289941[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1717.75147928994[/C][C]-62.751479289941[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1717.75147928994[/C][C]-310.751479289941[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1717.75147928994[/C][C]-322.751479289941[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1717.75147928994[/C][C]-187.751479289941[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1717.75147928994[/C][C]-408.751479289941[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1717.75147928994[/C][C]-191.751479289941[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1717.75147928994[/C][C]-390.751479289941[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1717.75147928994[/C][C]-90.751479289941[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1717.75147928994[/C][C]30.2485207100591[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]1717.75147928994[/C][C]240.248520710059[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]1717.75147928994[/C][C]556.248520710059[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1717.75147928994[/C][C]-69.7514792899409[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1717.75147928994[/C][C]-316.751479289941[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1717.75147928994[/C][C]-306.751479289941[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1717.75147928994[/C][C]-314.751479289941[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1717.75147928994[/C][C]-323.751479289941[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1717.75147928994[/C][C]-197.751479289941[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1717.75147928994[/C][C]-189.751479289941[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1717.75147928994[/C][C]-74.7514792899409[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1717.75147928994[/C][C]-202.751479289941[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1717.75147928994[/C][C]-32.7514792899409[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]1717.75147928994[/C][C]282.248520710059[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]1717.75147928994[/C][C]497.248520710059[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1717.75147928994[/C][C]238.248520710059[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1717.75147928994[/C][C]-255.751479289941[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1717.75147928994[/C][C]-154.751479289941[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1717.75147928994[/C][C]-258.751479289941[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1717.75147928994[/C][C]-271.751479289941[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1717.75147928994[/C][C]-95.751479289941[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1717.75147928994[/C][C]-60.7514792899409[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1717.75147928994[/C][C]-79.751479289941[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1717.75147928994[/C][C]-74.7514792899409[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1717.75147928994[/C][C]-34.7514792899409[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]1717.75147928994[/C][C]332.248520710059[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]1717.75147928994[/C][C]544.248520710059[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1717.75147928994[/C][C]95.248520710059[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1717.75147928994[/C][C]-272.751479289941[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1717.75147928994[/C][C]44.2485207100591[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1717.75147928994[/C][C]-256.751479289941[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1717.75147928994[/C][C]-161.751479289941[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1717.75147928994[/C][C]-286.751479289941[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1717.75147928994[/C][C]-290.751479289941[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1717.75147928994[/C][C]-163.751479289941[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1717.75147928994[/C][C]-72.7514792899409[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1717.75147928994[/C][C]-64.751479289941[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1717.75147928994[/C][C]298.248520710059[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]1717.75147928994[/C][C]489.248520710059[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1717.75147928994[/C][C]-52.7514792899409[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1717.75147928994[/C][C]-356.751479289941[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1717.75147928994[/C][C]-211.751479289941[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1717.75147928994[/C][C]-357.751479289941[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1717.75147928994[/C][C]-264.751479289941[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1717.75147928994[/C][C]-195.751479289941[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1717.75147928994[/C][C]-257.751479289941[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1717.75147928994[/C][C]-165.751479289941[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1717.75147928994[/C][C]-169.751479289941[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1717.75147928994[/C][C]109.248520710059[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]1717.75147928994[/C][C]19.2485207100591[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]1717.75147928994[/C][C]223.248520710059[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1717.75147928994[/C][C]-243.751479289941[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1717.75147928994[/C][C]-259.751479289941[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1717.75147928994[/C][C]-175.751479289941[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1717.75147928994[/C][C]-313.751479289941[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1717.75147928994[/C][C]-195.751479289941[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1717.75147928994[/C][C]-332.751479289941[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1717.75147928994[/C][C]-76.7514792899409[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1717.75147928994[/C][C]-207.751479289941[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1717.75147928994[/C][C]-36.7514792899409[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1717.75147928994[/C][C]220.248520710059[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]1717.75147928994[/C][C]150.248520710059[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]1717.75147928994[/C][C]8.24852071005906[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1717.75147928994[/C][C]-261.751479289941[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1717.75147928994[/C][C]-272.751479289941[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1717.75147928994[/C][C]-261.751479289941[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1717.75147928994[/C][C]-352.751479289941[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1717.75147928994[/C][C]-230.751479289941[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1717.75147928994[/C][C]-159.751479289941[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1717.75147928994[/C][C]-229.751479289941[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1717.75147928994[/C][C]-33.7514792899409[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1717.75147928994[/C][C]-123.751479289941[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1717.75147928994[/C][C]132.248520710059[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]1717.75147928994[/C][C]280.248520710059[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]1717.75147928994[/C][C]361.248520710059[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1717.75147928994[/C][C]-223.751479289941[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1321.69565217391[/C][C]-264.695652173913[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1321.69565217391[/C][C]-103.695652173913[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1321.69565217391[/C][C]-153.695652173913[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1321.69565217391[/C][C]-85.695652173913[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1321.69565217391[/C][C]-245.695652173913[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1321.69565217391[/C][C]-147.695652173913[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1321.69565217391[/C][C]-182.695652173913[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1321.69565217391[/C][C]105.304347826087[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1321.69565217391[/C][C]165.304347826087[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1321.69565217391[/C][C]161.304347826087[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1321.69565217391[/C][C]191.304347826087[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1321.69565217391[/C][C]35.304347826087[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1321.69565217391[/C][C]-156.695652173913[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1321.69565217391[/C][C]-39.695652173913[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1321.69565217391[/C][C]-211.695652173913[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1321.69565217391[/C][C]-24.6956521739130[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1321.69565217391[/C][C]-136.695652173913[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1321.69565217391[/C][C]-99.695652173913[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1321.69565217391[/C][C]-37.695652173913[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1321.69565217391[/C][C]122.304347826087[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1321.69565217391[/C][C]253.304347826087[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1321.69565217391[/C][C]415.304347826087[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1321.69565217391[/C][C]441.304347826087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871717.75147928992-30.7514792899202
215081717.75147928994-209.751479289942
315071717.75147928994-210.751479289941
413851717.75147928994-332.751479289941
516321717.75147928994-85.751479289941
615111717.75147928994-206.751479289941
715591717.75147928994-158.751479289941
816301717.75147928994-87.751479289941
915791717.75147928994-138.751479289941
1016531717.75147928994-64.751479289941
1121521717.75147928994434.248520710059
1221481717.75147928994430.248520710059
1317521717.7514792899434.2485207100591
1417651717.7514792899447.2485207100591
1517171717.75147928994-0.751479289940944
1615581717.75147928994-159.751479289941
1715751717.75147928994-142.751479289941
1815201717.75147928994-197.751479289941
1918051717.7514792899487.248520710059
2018001717.7514792899482.248520710059
2117191717.751479289941.24852071005906
2220081717.75147928994290.248520710059
2322421717.75147928994524.248520710059
2424781717.75147928994760.248520710059
2520301717.75147928994312.248520710059
2616551717.75147928994-62.751479289941
2716931717.75147928994-24.7514792899409
2816231717.75147928994-94.751479289941
2918051717.7514792899487.248520710059
3017461717.7514792899428.2485207100591
3117951717.7514792899477.2485207100591
3219261717.75147928994208.248520710059
3316191717.75147928994-98.751479289941
3419921717.75147928994274.248520710059
3522331717.75147928994515.248520710059
3621921717.75147928994474.248520710059
3720801717.75147928994362.248520710059
3817681717.7514792899450.2485207100591
3918351717.75147928994117.248520710059
4015691717.75147928994-148.751479289941
4119761717.75147928994258.248520710059
4218531717.75147928994135.248520710059
4319651717.75147928994247.248520710059
4416891717.75147928994-28.7514792899409
4517781717.7514792899460.2485207100591
4619761717.75147928994258.248520710059
4723971717.75147928994679.248520710059
4826541717.75147928994936.248520710059
4920971717.75147928994379.248520710059
5019631717.75147928994245.248520710059
5116771717.75147928994-40.7514792899409
5219411717.75147928994223.248520710059
5320031717.75147928994285.248520710059
5418131717.7514792899495.248520710059
5520121717.75147928994294.248520710059
5619121717.75147928994194.248520710059
5720841717.75147928994366.248520710059
5820801717.75147928994362.248520710059
5921181717.75147928994400.248520710059
6021501717.75147928994432.248520710059
6116081717.75147928994-109.751479289941
6215031717.75147928994-214.751479289941
6315481717.75147928994-169.751479289941
6413821717.75147928994-335.751479289941
6517311717.7514792899413.2485207100591
6617981717.7514792899480.248520710059
6717791717.7514792899461.2485207100591
6818871717.75147928994169.248520710059
6920041717.75147928994286.248520710059
7020771717.75147928994359.248520710059
7120921717.75147928994374.248520710059
7220511717.75147928994333.248520710059
7315771717.75147928994-140.751479289941
7413561717.75147928994-361.751479289941
7516521717.75147928994-65.7514792899409
7613821717.75147928994-335.751479289941
7715191717.75147928994-198.751479289941
7814211717.75147928994-296.751479289941
7914421717.75147928994-275.751479289941
8015431717.75147928994-174.751479289941
8116561717.75147928994-61.7514792899409
8215611717.75147928994-156.751479289941
8319051717.75147928994187.248520710059
8421991717.75147928994481.248520710059
8514731717.75147928994-244.751479289941
8616551717.75147928994-62.751479289941
8714071717.75147928994-310.751479289941
8813951717.75147928994-322.751479289941
8915301717.75147928994-187.751479289941
9013091717.75147928994-408.751479289941
9115261717.75147928994-191.751479289941
9213271717.75147928994-390.751479289941
9316271717.75147928994-90.751479289941
9417481717.7514792899430.2485207100591
9519581717.75147928994240.248520710059
9622741717.75147928994556.248520710059
9716481717.75147928994-69.7514792899409
9814011717.75147928994-316.751479289941
9914111717.75147928994-306.751479289941
10014031717.75147928994-314.751479289941
10113941717.75147928994-323.751479289941
10215201717.75147928994-197.751479289941
10315281717.75147928994-189.751479289941
10416431717.75147928994-74.7514792899409
10515151717.75147928994-202.751479289941
10616851717.75147928994-32.7514792899409
10720001717.75147928994282.248520710059
10822151717.75147928994497.248520710059
10919561717.75147928994238.248520710059
11014621717.75147928994-255.751479289941
11115631717.75147928994-154.751479289941
11214591717.75147928994-258.751479289941
11314461717.75147928994-271.751479289941
11416221717.75147928994-95.751479289941
11516571717.75147928994-60.7514792899409
11616381717.75147928994-79.751479289941
11716431717.75147928994-74.7514792899409
11816831717.75147928994-34.7514792899409
11920501717.75147928994332.248520710059
12022621717.75147928994544.248520710059
12118131717.7514792899495.248520710059
12214451717.75147928994-272.751479289941
12317621717.7514792899444.2485207100591
12414611717.75147928994-256.751479289941
12515561717.75147928994-161.751479289941
12614311717.75147928994-286.751479289941
12714271717.75147928994-290.751479289941
12815541717.75147928994-163.751479289941
12916451717.75147928994-72.7514792899409
13016531717.75147928994-64.751479289941
13120161717.75147928994298.248520710059
13222071717.75147928994489.248520710059
13316651717.75147928994-52.7514792899409
13413611717.75147928994-356.751479289941
13515061717.75147928994-211.751479289941
13613601717.75147928994-357.751479289941
13714531717.75147928994-264.751479289941
13815221717.75147928994-195.751479289941
13914601717.75147928994-257.751479289941
14015521717.75147928994-165.751479289941
14115481717.75147928994-169.751479289941
14218271717.75147928994109.248520710059
14317371717.7514792899419.2485207100591
14419411717.75147928994223.248520710059
14514741717.75147928994-243.751479289941
14614581717.75147928994-259.751479289941
14715421717.75147928994-175.751479289941
14814041717.75147928994-313.751479289941
14915221717.75147928994-195.751479289941
15013851717.75147928994-332.751479289941
15116411717.75147928994-76.7514792899409
15215101717.75147928994-207.751479289941
15316811717.75147928994-36.7514792899409
15419381717.75147928994220.248520710059
15518681717.75147928994150.248520710059
15617261717.751479289948.24852071005906
15714561717.75147928994-261.751479289941
15814451717.75147928994-272.751479289941
15914561717.75147928994-261.751479289941
16013651717.75147928994-352.751479289941
16114871717.75147928994-230.751479289941
16215581717.75147928994-159.751479289941
16314881717.75147928994-229.751479289941
16416841717.75147928994-33.7514792899409
16515941717.75147928994-123.751479289941
16618501717.75147928994132.248520710059
16719981717.75147928994280.248520710059
16820791717.75147928994361.248520710059
16914941717.75147928994-223.751479289941
17010571321.69565217391-264.695652173913
17112181321.69565217391-103.695652173913
17211681321.69565217391-153.695652173913
17312361321.69565217391-85.695652173913
17410761321.69565217391-245.695652173913
17511741321.69565217391-147.695652173913
17611391321.69565217391-182.695652173913
17714271321.69565217391105.304347826087
17814871321.69565217391165.304347826087
17914831321.69565217391161.304347826087
18015131321.69565217391191.304347826087
18113571321.6956521739135.304347826087
18211651321.69565217391-156.695652173913
18312821321.69565217391-39.695652173913
18411101321.69565217391-211.695652173913
18512971321.69565217391-24.6956521739130
18611851321.69565217391-136.695652173913
18712221321.69565217391-99.695652173913
18812841321.69565217391-37.695652173913
18914441321.69565217391122.304347826087
19015751321.69565217391253.304347826087
19117371321.69565217391415.304347826087
19217631321.69565217391441.304347826087






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1559308029189450.3118616058378900.844069197081055
60.06649352737734470.1329870547546890.933506472622655
70.02567383314294040.05134766628588070.97432616685706
80.01221739680733030.02443479361466060.98778260319267
90.004379978104602910.008759956209205820.995620021895397
100.002240159817864130.004480319635728260.997759840182136
110.2235801583455500.4471603166911010.77641984165445
120.5039813540008240.992037291998350.496018645999176
130.4208412053123040.8416824106246090.579158794687696
140.3453656525640860.6907313051281720.654634347435914
150.2700182093786310.5400364187572620.729981790621369
160.2190644244158520.4381288488317040.780935575584148
170.1710750448748180.3421500897496370.828924955125181
180.1407115265465930.2814230530931850.859288473453407
190.1140922425588580.2281844851177170.885907757441142
200.09007928733986680.1801585746797340.909920712660133
210.06388476436807580.1277695287361520.936115235631924
220.08828604303362040.1765720860672410.91171395696638
230.2540366087170370.5080732174340740.745963391282963
240.7041345159405080.5917309681189840.295865484059492
250.7099494063259890.5801011873480230.290050593674011
260.6617327503331530.6765344993336940.338267249666847
270.60652227047790.78695545904420.3934777295221
280.5597189438136960.8805621123726080.440281056186304
290.5037375415697180.9925249168605630.496262458430282
300.4449608348989570.8899216697979150.555039165101043
310.3901828560403290.7803657120806580.609817143959671
320.3621281127465610.7242562254931220.637871887253439
330.3227248265256100.6454496530512190.67727517347439
340.3184699397357430.6369398794714860.681530060264257
350.4475027759995330.8950055519990660.552497224000467
360.5401905183494390.9196189633011230.459809481650561
370.5622945098448040.8754109803103930.437705490155196
380.5106860002932690.9786279994134620.489313999706731
390.4622777891552320.9245555783104640.537722210844768
400.4432414467682050.886482893536410.556758553231795
410.4257386735252520.8514773470505040.574261326474748
420.3820739067215540.7641478134431070.617926093278446
430.3620115673263830.7240231346527670.637988432673617
440.3219927317477250.643985463495450.678007268252275
450.2794282905145570.5588565810291140.720571709485443
460.2656486170042540.5312972340085080.734351382995746
470.4937822328476520.9875644656953050.506217767152348
480.889937540791620.2201249184167600.110062459208380
490.9011136623684070.1977726752631860.0988863376315932
500.8919312802842860.2161374394314280.108068719715714
510.8749587704768280.2500824590463430.125041229523172
520.8619242688046240.2761514623907530.138075731195376
530.857741867660570.284516264678860.14225813233943
540.833787220623450.3324255587531000.166212779376550
550.831683517129930.3366329657401390.168316482870069
560.813452152132250.3730956957355000.186547847867750
570.8299900464796020.3400199070407960.170009953520398
580.8452939376681130.3094121246637740.154706062331887
590.8698865567999510.2602268864000980.130113443200049
600.8999731806219020.2000536387561960.100026819378098
610.8929115381189760.2141769237620470.107088461881024
620.899813657648210.2003726847035820.100186342351791
630.8987191232890920.2025617534218170.101280876710908
640.9242215772354540.1515568455290930.0757784227645463
650.9102741546203680.1794516907592640.089725845379632
660.8947925365983940.2104149268032130.105207463401606
670.8771697946178040.2456604107643910.122830205382196
680.8640972653708260.2718054692583480.135902734629174
690.8678736410287260.2642527179425470.132126358971274
700.8871764413456120.2256471173087760.112823558654388
710.9084496324115170.1831007351769670.0915503675884835
720.9208695043335420.1582609913329160.0791304956664582
730.9160102994141060.1679794011717880.0839897005858942
740.9404025394138450.1191949211723100.0595974605861551
750.9312922476935140.1374155046129720.0687077523064859
760.9469560558531370.1060878882937270.0530439441468633
770.9458816420170680.1082367159658630.0541183579829316
780.9534363149400040.09312737011999190.0465636850599960
790.9577669083245750.08446618335085050.0422330916754253
800.954411751576560.09117649684687990.0455882484234399
810.9457911981509690.1084176036980630.0542088018490313
820.9401750357771450.1196499284457110.0598249642228553
830.9361310619796960.1277378760406090.0638689380203045
840.9663385663714640.06732286725707290.0336614336285365
850.966916074785160.0661678504296810.0330839252148405
860.9601641193441550.07967176131169080.0398358806558454
870.9652770693352230.06944586132955330.0347229306647767
880.9703794550258670.05924108994826570.0296205449741328
890.9674640511590650.06507189768186920.0325359488409346
900.977737303238430.0445253935231380.022262696761569
910.97532264804520.04935470390960060.0246773519548003
920.9821276036826250.03574479263475090.0178723963173755
930.9779039316311570.04419213673768560.0220960683688428
940.9725800354559540.05483992908809150.0274199645440457
950.9736521992866830.05269560142663340.0263478007133167
960.9921478667486580.01570426650268480.00785213325134238
970.9899441772202630.02011164555947350.0100558227797368
980.991080474977210.01783905004557930.00891952502278966
990.991852053029110.01629589394178160.00814794697089078
1000.9926891558394360.01462168832112710.00731084416056357
1010.9935879656446240.01282406871075100.00641203435537551
1020.9925874706751830.01482505864963360.0074125293248168
1030.9913343111926220.01733137761475530.00866568880737765
1040.9887866234665020.02242675306699630.0112133765334982
1050.9871974269032140.02560514619357130.0128025730967857
1060.9834887348306130.03302253033877300.0165112651693865
1070.9862100740678750.02757985186424990.0137899259321250
1080.9954812256947070.009037548610585450.00451877430529273
1090.9960743394012850.007851321197429140.00392566059871457
1100.9958082135513870.008383572897225740.00419178644861287
1110.9947125552945240.01057488941095250.00528744470547626
1120.994383716676210.01123256664757900.00561628332378951
1130.9942027655540470.01159446889190660.0057972344459533
1140.9923718888698070.01525622226038550.00762811113019273
1150.989937178797550.02012564240489920.0100628212024496
1160.9868899382317050.02622012353658970.0131100617682949
1170.983038753707430.03392249258514220.0169612462925711
1180.9782010296121180.0435979407757630.0217989703878815
1190.9854990285980250.029001942803950.014500971401975
1200.9972737379465190.00545252410696250.00272626205348125
1210.9968405001714030.006318999657194810.00315949982859741
1220.9966043287120030.006791342575994760.00339567128799738
1230.9957319034927090.00853619301458290.00426809650729145
1240.9952319354744660.00953612905106840.0047680645255342
1250.993835719564880.01232856087024070.00616428043512033
1260.9935791126770050.01284177464598940.00642088732299468
1270.9933935035118350.01321299297633020.0066064964881651
1280.9915246851074790.01695062978504220.0084753148925211
1290.9886554883036150.02268902339277020.0113445116963851
1300.984959359786210.03008128042757920.0150406402137896
1310.990221626959950.01955674608010080.00977837304005038
1320.9982799910709060.003440017858188490.00172000892909425
1330.9975916247830560.004816750433887600.00240837521694380
1340.997892812725710.004214374548578250.00210718727428912
1350.997293898802090.005412202395820720.00270610119791036
1360.9976776922153970.004644615569205920.00232230778460296
1370.997354591339330.005290817321341420.00264540866067071
1380.9965465538433560.00690689231328840.0034534461566442
1390.99604528987740.007909420245200840.00395471012260042
1400.9946869708300080.01062605833998430.00531302916999215
1410.9929574252000930.01408514959981320.0070425747999066
1420.9920330096054330.01593398078913350.00796699039456675
1430.9895837712358810.0208324575282370.0104162287641185
1440.9920816591491310.01583668170173780.00791834085086891
1450.9904494844043830.01910103119123490.00955051559561747
1460.9889497073390560.02210058532188870.0110502926609443
1470.9854560608942660.02908787821146910.0145439391057345
1480.9855711345140450.02885773097190930.0144288654859547
1490.9817801746925750.03643965061485040.0182198253074252
1500.9835227096634280.03295458067314440.0164772903365722
1510.9772488977752520.04550220444949560.0227511022247478
1520.9726400953125070.05471980937498520.0273599046874926
1530.9629255490837120.07414890183257680.0370744509162884
1540.9673025224909830.06539495501803440.0326974775090172
1550.9666855442480890.06662891150382230.0333144557519111
1560.9571917212437640.08561655751247290.0428082787562364
1570.9511038397950780.09779232040984470.0488961602049223
1580.9465733352094960.1068533295810080.0534266647905039
1590.941989759761460.1160204804770810.0580102402385405
1600.9539463886338830.09210722273223420.0460536113661171
1610.9517820151660470.09643596966790650.0482179848339532
1620.9437816756742470.1124366486515060.0562183243257532
1630.9488528298300860.1022943403398280.0511471701699138
1640.933869588053220.1322608238935590.0661304119467793
1650.932017991595130.135964016809740.06798200840487
1660.9084084086103110.1831831827793770.0915915913896886
1670.8966955716601020.2066088566797950.103304428339898
1680.9463249453451660.1073501093096680.0536750546548338
1690.925761978392230.1484760432155410.0742380216077707
1700.9320741472713060.1358517054573880.0679258527286942
1710.9121986283845660.1756027432308670.0878013716154337
1720.8971397501106840.2057204997786310.102860249889316
1730.8679723755156910.2640552489686180.132027624484309
1740.8840554441978720.2318891116042550.115944555802128
1750.8717410280954920.2565179438090150.128258971904508
1760.8759804634073520.2480390731852970.124019536592648
1770.8294723493283920.3410553013432160.170527650671608
1780.7812488548959410.4375022902081180.218751145104059
1790.7230854296552360.5538291406895280.276914570344764
1800.668097907094830.6638041858103410.331902092905171
1810.5736344001114210.8527311997771580.426365599888579
1820.5400640857924250.919871828415150.459935914207575
1830.4498561837194880.8997123674389760.550143816280512
1840.4910294892336790.9820589784673570.508970510766321
1850.4034626872200280.8069253744400570.596537312779972
1860.4288955432867320.8577910865734640.571104456713268
1870.4881409628525440.9762819257050890.511859037147456

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.155930802918945 & 0.311861605837890 & 0.844069197081055 \tabularnewline
6 & 0.0664935273773447 & 0.132987054754689 & 0.933506472622655 \tabularnewline
7 & 0.0256738331429404 & 0.0513476662858807 & 0.97432616685706 \tabularnewline
8 & 0.0122173968073303 & 0.0244347936146606 & 0.98778260319267 \tabularnewline
9 & 0.00437997810460291 & 0.00875995620920582 & 0.995620021895397 \tabularnewline
10 & 0.00224015981786413 & 0.00448031963572826 & 0.997759840182136 \tabularnewline
11 & 0.223580158345550 & 0.447160316691101 & 0.77641984165445 \tabularnewline
12 & 0.503981354000824 & 0.99203729199835 & 0.496018645999176 \tabularnewline
13 & 0.420841205312304 & 0.841682410624609 & 0.579158794687696 \tabularnewline
14 & 0.345365652564086 & 0.690731305128172 & 0.654634347435914 \tabularnewline
15 & 0.270018209378631 & 0.540036418757262 & 0.729981790621369 \tabularnewline
16 & 0.219064424415852 & 0.438128848831704 & 0.780935575584148 \tabularnewline
17 & 0.171075044874818 & 0.342150089749637 & 0.828924955125181 \tabularnewline
18 & 0.140711526546593 & 0.281423053093185 & 0.859288473453407 \tabularnewline
19 & 0.114092242558858 & 0.228184485117717 & 0.885907757441142 \tabularnewline
20 & 0.0900792873398668 & 0.180158574679734 & 0.909920712660133 \tabularnewline
21 & 0.0638847643680758 & 0.127769528736152 & 0.936115235631924 \tabularnewline
22 & 0.0882860430336204 & 0.176572086067241 & 0.91171395696638 \tabularnewline
23 & 0.254036608717037 & 0.508073217434074 & 0.745963391282963 \tabularnewline
24 & 0.704134515940508 & 0.591730968118984 & 0.295865484059492 \tabularnewline
25 & 0.709949406325989 & 0.580101187348023 & 0.290050593674011 \tabularnewline
26 & 0.661732750333153 & 0.676534499333694 & 0.338267249666847 \tabularnewline
27 & 0.6065222704779 & 0.7869554590442 & 0.3934777295221 \tabularnewline
28 & 0.559718943813696 & 0.880562112372608 & 0.440281056186304 \tabularnewline
29 & 0.503737541569718 & 0.992524916860563 & 0.496262458430282 \tabularnewline
30 & 0.444960834898957 & 0.889921669797915 & 0.555039165101043 \tabularnewline
31 & 0.390182856040329 & 0.780365712080658 & 0.609817143959671 \tabularnewline
32 & 0.362128112746561 & 0.724256225493122 & 0.637871887253439 \tabularnewline
33 & 0.322724826525610 & 0.645449653051219 & 0.67727517347439 \tabularnewline
34 & 0.318469939735743 & 0.636939879471486 & 0.681530060264257 \tabularnewline
35 & 0.447502775999533 & 0.895005551999066 & 0.552497224000467 \tabularnewline
36 & 0.540190518349439 & 0.919618963301123 & 0.459809481650561 \tabularnewline
37 & 0.562294509844804 & 0.875410980310393 & 0.437705490155196 \tabularnewline
38 & 0.510686000293269 & 0.978627999413462 & 0.489313999706731 \tabularnewline
39 & 0.462277789155232 & 0.924555578310464 & 0.537722210844768 \tabularnewline
40 & 0.443241446768205 & 0.88648289353641 & 0.556758553231795 \tabularnewline
41 & 0.425738673525252 & 0.851477347050504 & 0.574261326474748 \tabularnewline
42 & 0.382073906721554 & 0.764147813443107 & 0.617926093278446 \tabularnewline
43 & 0.362011567326383 & 0.724023134652767 & 0.637988432673617 \tabularnewline
44 & 0.321992731747725 & 0.64398546349545 & 0.678007268252275 \tabularnewline
45 & 0.279428290514557 & 0.558856581029114 & 0.720571709485443 \tabularnewline
46 & 0.265648617004254 & 0.531297234008508 & 0.734351382995746 \tabularnewline
47 & 0.493782232847652 & 0.987564465695305 & 0.506217767152348 \tabularnewline
48 & 0.88993754079162 & 0.220124918416760 & 0.110062459208380 \tabularnewline
49 & 0.901113662368407 & 0.197772675263186 & 0.0988863376315932 \tabularnewline
50 & 0.891931280284286 & 0.216137439431428 & 0.108068719715714 \tabularnewline
51 & 0.874958770476828 & 0.250082459046343 & 0.125041229523172 \tabularnewline
52 & 0.861924268804624 & 0.276151462390753 & 0.138075731195376 \tabularnewline
53 & 0.85774186766057 & 0.28451626467886 & 0.14225813233943 \tabularnewline
54 & 0.83378722062345 & 0.332425558753100 & 0.166212779376550 \tabularnewline
55 & 0.83168351712993 & 0.336632965740139 & 0.168316482870069 \tabularnewline
56 & 0.81345215213225 & 0.373095695735500 & 0.186547847867750 \tabularnewline
57 & 0.829990046479602 & 0.340019907040796 & 0.170009953520398 \tabularnewline
58 & 0.845293937668113 & 0.309412124663774 & 0.154706062331887 \tabularnewline
59 & 0.869886556799951 & 0.260226886400098 & 0.130113443200049 \tabularnewline
60 & 0.899973180621902 & 0.200053638756196 & 0.100026819378098 \tabularnewline
61 & 0.892911538118976 & 0.214176923762047 & 0.107088461881024 \tabularnewline
62 & 0.89981365764821 & 0.200372684703582 & 0.100186342351791 \tabularnewline
63 & 0.898719123289092 & 0.202561753421817 & 0.101280876710908 \tabularnewline
64 & 0.924221577235454 & 0.151556845529093 & 0.0757784227645463 \tabularnewline
65 & 0.910274154620368 & 0.179451690759264 & 0.089725845379632 \tabularnewline
66 & 0.894792536598394 & 0.210414926803213 & 0.105207463401606 \tabularnewline
67 & 0.877169794617804 & 0.245660410764391 & 0.122830205382196 \tabularnewline
68 & 0.864097265370826 & 0.271805469258348 & 0.135902734629174 \tabularnewline
69 & 0.867873641028726 & 0.264252717942547 & 0.132126358971274 \tabularnewline
70 & 0.887176441345612 & 0.225647117308776 & 0.112823558654388 \tabularnewline
71 & 0.908449632411517 & 0.183100735176967 & 0.0915503675884835 \tabularnewline
72 & 0.920869504333542 & 0.158260991332916 & 0.0791304956664582 \tabularnewline
73 & 0.916010299414106 & 0.167979401171788 & 0.0839897005858942 \tabularnewline
74 & 0.940402539413845 & 0.119194921172310 & 0.0595974605861551 \tabularnewline
75 & 0.931292247693514 & 0.137415504612972 & 0.0687077523064859 \tabularnewline
76 & 0.946956055853137 & 0.106087888293727 & 0.0530439441468633 \tabularnewline
77 & 0.945881642017068 & 0.108236715965863 & 0.0541183579829316 \tabularnewline
78 & 0.953436314940004 & 0.0931273701199919 & 0.0465636850599960 \tabularnewline
79 & 0.957766908324575 & 0.0844661833508505 & 0.0422330916754253 \tabularnewline
80 & 0.95441175157656 & 0.0911764968468799 & 0.0455882484234399 \tabularnewline
81 & 0.945791198150969 & 0.108417603698063 & 0.0542088018490313 \tabularnewline
82 & 0.940175035777145 & 0.119649928445711 & 0.0598249642228553 \tabularnewline
83 & 0.936131061979696 & 0.127737876040609 & 0.0638689380203045 \tabularnewline
84 & 0.966338566371464 & 0.0673228672570729 & 0.0336614336285365 \tabularnewline
85 & 0.96691607478516 & 0.066167850429681 & 0.0330839252148405 \tabularnewline
86 & 0.960164119344155 & 0.0796717613116908 & 0.0398358806558454 \tabularnewline
87 & 0.965277069335223 & 0.0694458613295533 & 0.0347229306647767 \tabularnewline
88 & 0.970379455025867 & 0.0592410899482657 & 0.0296205449741328 \tabularnewline
89 & 0.967464051159065 & 0.0650718976818692 & 0.0325359488409346 \tabularnewline
90 & 0.97773730323843 & 0.044525393523138 & 0.022262696761569 \tabularnewline
91 & 0.9753226480452 & 0.0493547039096006 & 0.0246773519548003 \tabularnewline
92 & 0.982127603682625 & 0.0357447926347509 & 0.0178723963173755 \tabularnewline
93 & 0.977903931631157 & 0.0441921367376856 & 0.0220960683688428 \tabularnewline
94 & 0.972580035455954 & 0.0548399290880915 & 0.0274199645440457 \tabularnewline
95 & 0.973652199286683 & 0.0526956014266334 & 0.0263478007133167 \tabularnewline
96 & 0.992147866748658 & 0.0157042665026848 & 0.00785213325134238 \tabularnewline
97 & 0.989944177220263 & 0.0201116455594735 & 0.0100558227797368 \tabularnewline
98 & 0.99108047497721 & 0.0178390500455793 & 0.00891952502278966 \tabularnewline
99 & 0.99185205302911 & 0.0162958939417816 & 0.00814794697089078 \tabularnewline
100 & 0.992689155839436 & 0.0146216883211271 & 0.00731084416056357 \tabularnewline
101 & 0.993587965644624 & 0.0128240687107510 & 0.00641203435537551 \tabularnewline
102 & 0.992587470675183 & 0.0148250586496336 & 0.0074125293248168 \tabularnewline
103 & 0.991334311192622 & 0.0173313776147553 & 0.00866568880737765 \tabularnewline
104 & 0.988786623466502 & 0.0224267530669963 & 0.0112133765334982 \tabularnewline
105 & 0.987197426903214 & 0.0256051461935713 & 0.0128025730967857 \tabularnewline
106 & 0.983488734830613 & 0.0330225303387730 & 0.0165112651693865 \tabularnewline
107 & 0.986210074067875 & 0.0275798518642499 & 0.0137899259321250 \tabularnewline
108 & 0.995481225694707 & 0.00903754861058545 & 0.00451877430529273 \tabularnewline
109 & 0.996074339401285 & 0.00785132119742914 & 0.00392566059871457 \tabularnewline
110 & 0.995808213551387 & 0.00838357289722574 & 0.00419178644861287 \tabularnewline
111 & 0.994712555294524 & 0.0105748894109525 & 0.00528744470547626 \tabularnewline
112 & 0.99438371667621 & 0.0112325666475790 & 0.00561628332378951 \tabularnewline
113 & 0.994202765554047 & 0.0115944688919066 & 0.0057972344459533 \tabularnewline
114 & 0.992371888869807 & 0.0152562222603855 & 0.00762811113019273 \tabularnewline
115 & 0.98993717879755 & 0.0201256424048992 & 0.0100628212024496 \tabularnewline
116 & 0.986889938231705 & 0.0262201235365897 & 0.0131100617682949 \tabularnewline
117 & 0.98303875370743 & 0.0339224925851422 & 0.0169612462925711 \tabularnewline
118 & 0.978201029612118 & 0.043597940775763 & 0.0217989703878815 \tabularnewline
119 & 0.985499028598025 & 0.02900194280395 & 0.014500971401975 \tabularnewline
120 & 0.997273737946519 & 0.0054525241069625 & 0.00272626205348125 \tabularnewline
121 & 0.996840500171403 & 0.00631899965719481 & 0.00315949982859741 \tabularnewline
122 & 0.996604328712003 & 0.00679134257599476 & 0.00339567128799738 \tabularnewline
123 & 0.995731903492709 & 0.0085361930145829 & 0.00426809650729145 \tabularnewline
124 & 0.995231935474466 & 0.0095361290510684 & 0.0047680645255342 \tabularnewline
125 & 0.99383571956488 & 0.0123285608702407 & 0.00616428043512033 \tabularnewline
126 & 0.993579112677005 & 0.0128417746459894 & 0.00642088732299468 \tabularnewline
127 & 0.993393503511835 & 0.0132129929763302 & 0.0066064964881651 \tabularnewline
128 & 0.991524685107479 & 0.0169506297850422 & 0.0084753148925211 \tabularnewline
129 & 0.988655488303615 & 0.0226890233927702 & 0.0113445116963851 \tabularnewline
130 & 0.98495935978621 & 0.0300812804275792 & 0.0150406402137896 \tabularnewline
131 & 0.99022162695995 & 0.0195567460801008 & 0.00977837304005038 \tabularnewline
132 & 0.998279991070906 & 0.00344001785818849 & 0.00172000892909425 \tabularnewline
133 & 0.997591624783056 & 0.00481675043388760 & 0.00240837521694380 \tabularnewline
134 & 0.99789281272571 & 0.00421437454857825 & 0.00210718727428912 \tabularnewline
135 & 0.99729389880209 & 0.00541220239582072 & 0.00270610119791036 \tabularnewline
136 & 0.997677692215397 & 0.00464461556920592 & 0.00232230778460296 \tabularnewline
137 & 0.99735459133933 & 0.00529081732134142 & 0.00264540866067071 \tabularnewline
138 & 0.996546553843356 & 0.0069068923132884 & 0.0034534461566442 \tabularnewline
139 & 0.9960452898774 & 0.00790942024520084 & 0.00395471012260042 \tabularnewline
140 & 0.994686970830008 & 0.0106260583399843 & 0.00531302916999215 \tabularnewline
141 & 0.992957425200093 & 0.0140851495998132 & 0.0070425747999066 \tabularnewline
142 & 0.992033009605433 & 0.0159339807891335 & 0.00796699039456675 \tabularnewline
143 & 0.989583771235881 & 0.020832457528237 & 0.0104162287641185 \tabularnewline
144 & 0.992081659149131 & 0.0158366817017378 & 0.00791834085086891 \tabularnewline
145 & 0.990449484404383 & 0.0191010311912349 & 0.00955051559561747 \tabularnewline
146 & 0.988949707339056 & 0.0221005853218887 & 0.0110502926609443 \tabularnewline
147 & 0.985456060894266 & 0.0290878782114691 & 0.0145439391057345 \tabularnewline
148 & 0.985571134514045 & 0.0288577309719093 & 0.0144288654859547 \tabularnewline
149 & 0.981780174692575 & 0.0364396506148504 & 0.0182198253074252 \tabularnewline
150 & 0.983522709663428 & 0.0329545806731444 & 0.0164772903365722 \tabularnewline
151 & 0.977248897775252 & 0.0455022044494956 & 0.0227511022247478 \tabularnewline
152 & 0.972640095312507 & 0.0547198093749852 & 0.0273599046874926 \tabularnewline
153 & 0.962925549083712 & 0.0741489018325768 & 0.0370744509162884 \tabularnewline
154 & 0.967302522490983 & 0.0653949550180344 & 0.0326974775090172 \tabularnewline
155 & 0.966685544248089 & 0.0666289115038223 & 0.0333144557519111 \tabularnewline
156 & 0.957191721243764 & 0.0856165575124729 & 0.0428082787562364 \tabularnewline
157 & 0.951103839795078 & 0.0977923204098447 & 0.0488961602049223 \tabularnewline
158 & 0.946573335209496 & 0.106853329581008 & 0.0534266647905039 \tabularnewline
159 & 0.94198975976146 & 0.116020480477081 & 0.0580102402385405 \tabularnewline
160 & 0.953946388633883 & 0.0921072227322342 & 0.0460536113661171 \tabularnewline
161 & 0.951782015166047 & 0.0964359696679065 & 0.0482179848339532 \tabularnewline
162 & 0.943781675674247 & 0.112436648651506 & 0.0562183243257532 \tabularnewline
163 & 0.948852829830086 & 0.102294340339828 & 0.0511471701699138 \tabularnewline
164 & 0.93386958805322 & 0.132260823893559 & 0.0661304119467793 \tabularnewline
165 & 0.93201799159513 & 0.13596401680974 & 0.06798200840487 \tabularnewline
166 & 0.908408408610311 & 0.183183182779377 & 0.0915915913896886 \tabularnewline
167 & 0.896695571660102 & 0.206608856679795 & 0.103304428339898 \tabularnewline
168 & 0.946324945345166 & 0.107350109309668 & 0.0536750546548338 \tabularnewline
169 & 0.92576197839223 & 0.148476043215541 & 0.0742380216077707 \tabularnewline
170 & 0.932074147271306 & 0.135851705457388 & 0.0679258527286942 \tabularnewline
171 & 0.912198628384566 & 0.175602743230867 & 0.0878013716154337 \tabularnewline
172 & 0.897139750110684 & 0.205720499778631 & 0.102860249889316 \tabularnewline
173 & 0.867972375515691 & 0.264055248968618 & 0.132027624484309 \tabularnewline
174 & 0.884055444197872 & 0.231889111604255 & 0.115944555802128 \tabularnewline
175 & 0.871741028095492 & 0.256517943809015 & 0.128258971904508 \tabularnewline
176 & 0.875980463407352 & 0.248039073185297 & 0.124019536592648 \tabularnewline
177 & 0.829472349328392 & 0.341055301343216 & 0.170527650671608 \tabularnewline
178 & 0.781248854895941 & 0.437502290208118 & 0.218751145104059 \tabularnewline
179 & 0.723085429655236 & 0.553829140689528 & 0.276914570344764 \tabularnewline
180 & 0.66809790709483 & 0.663804185810341 & 0.331902092905171 \tabularnewline
181 & 0.573634400111421 & 0.852731199777158 & 0.426365599888579 \tabularnewline
182 & 0.540064085792425 & 0.91987182841515 & 0.459935914207575 \tabularnewline
183 & 0.449856183719488 & 0.899712367438976 & 0.550143816280512 \tabularnewline
184 & 0.491029489233679 & 0.982058978467357 & 0.508970510766321 \tabularnewline
185 & 0.403462687220028 & 0.806925374440057 & 0.596537312779972 \tabularnewline
186 & 0.428895543286732 & 0.857791086573464 & 0.571104456713268 \tabularnewline
187 & 0.488140962852544 & 0.976281925705089 & 0.511859037147456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&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]5[/C][C]0.155930802918945[/C][C]0.311861605837890[/C][C]0.844069197081055[/C][/ROW]
[ROW][C]6[/C][C]0.0664935273773447[/C][C]0.132987054754689[/C][C]0.933506472622655[/C][/ROW]
[ROW][C]7[/C][C]0.0256738331429404[/C][C]0.0513476662858807[/C][C]0.97432616685706[/C][/ROW]
[ROW][C]8[/C][C]0.0122173968073303[/C][C]0.0244347936146606[/C][C]0.98778260319267[/C][/ROW]
[ROW][C]9[/C][C]0.00437997810460291[/C][C]0.00875995620920582[/C][C]0.995620021895397[/C][/ROW]
[ROW][C]10[/C][C]0.00224015981786413[/C][C]0.00448031963572826[/C][C]0.997759840182136[/C][/ROW]
[ROW][C]11[/C][C]0.223580158345550[/C][C]0.447160316691101[/C][C]0.77641984165445[/C][/ROW]
[ROW][C]12[/C][C]0.503981354000824[/C][C]0.99203729199835[/C][C]0.496018645999176[/C][/ROW]
[ROW][C]13[/C][C]0.420841205312304[/C][C]0.841682410624609[/C][C]0.579158794687696[/C][/ROW]
[ROW][C]14[/C][C]0.345365652564086[/C][C]0.690731305128172[/C][C]0.654634347435914[/C][/ROW]
[ROW][C]15[/C][C]0.270018209378631[/C][C]0.540036418757262[/C][C]0.729981790621369[/C][/ROW]
[ROW][C]16[/C][C]0.219064424415852[/C][C]0.438128848831704[/C][C]0.780935575584148[/C][/ROW]
[ROW][C]17[/C][C]0.171075044874818[/C][C]0.342150089749637[/C][C]0.828924955125181[/C][/ROW]
[ROW][C]18[/C][C]0.140711526546593[/C][C]0.281423053093185[/C][C]0.859288473453407[/C][/ROW]
[ROW][C]19[/C][C]0.114092242558858[/C][C]0.228184485117717[/C][C]0.885907757441142[/C][/ROW]
[ROW][C]20[/C][C]0.0900792873398668[/C][C]0.180158574679734[/C][C]0.909920712660133[/C][/ROW]
[ROW][C]21[/C][C]0.0638847643680758[/C][C]0.127769528736152[/C][C]0.936115235631924[/C][/ROW]
[ROW][C]22[/C][C]0.0882860430336204[/C][C]0.176572086067241[/C][C]0.91171395696638[/C][/ROW]
[ROW][C]23[/C][C]0.254036608717037[/C][C]0.508073217434074[/C][C]0.745963391282963[/C][/ROW]
[ROW][C]24[/C][C]0.704134515940508[/C][C]0.591730968118984[/C][C]0.295865484059492[/C][/ROW]
[ROW][C]25[/C][C]0.709949406325989[/C][C]0.580101187348023[/C][C]0.290050593674011[/C][/ROW]
[ROW][C]26[/C][C]0.661732750333153[/C][C]0.676534499333694[/C][C]0.338267249666847[/C][/ROW]
[ROW][C]27[/C][C]0.6065222704779[/C][C]0.7869554590442[/C][C]0.3934777295221[/C][/ROW]
[ROW][C]28[/C][C]0.559718943813696[/C][C]0.880562112372608[/C][C]0.440281056186304[/C][/ROW]
[ROW][C]29[/C][C]0.503737541569718[/C][C]0.992524916860563[/C][C]0.496262458430282[/C][/ROW]
[ROW][C]30[/C][C]0.444960834898957[/C][C]0.889921669797915[/C][C]0.555039165101043[/C][/ROW]
[ROW][C]31[/C][C]0.390182856040329[/C][C]0.780365712080658[/C][C]0.609817143959671[/C][/ROW]
[ROW][C]32[/C][C]0.362128112746561[/C][C]0.724256225493122[/C][C]0.637871887253439[/C][/ROW]
[ROW][C]33[/C][C]0.322724826525610[/C][C]0.645449653051219[/C][C]0.67727517347439[/C][/ROW]
[ROW][C]34[/C][C]0.318469939735743[/C][C]0.636939879471486[/C][C]0.681530060264257[/C][/ROW]
[ROW][C]35[/C][C]0.447502775999533[/C][C]0.895005551999066[/C][C]0.552497224000467[/C][/ROW]
[ROW][C]36[/C][C]0.540190518349439[/C][C]0.919618963301123[/C][C]0.459809481650561[/C][/ROW]
[ROW][C]37[/C][C]0.562294509844804[/C][C]0.875410980310393[/C][C]0.437705490155196[/C][/ROW]
[ROW][C]38[/C][C]0.510686000293269[/C][C]0.978627999413462[/C][C]0.489313999706731[/C][/ROW]
[ROW][C]39[/C][C]0.462277789155232[/C][C]0.924555578310464[/C][C]0.537722210844768[/C][/ROW]
[ROW][C]40[/C][C]0.443241446768205[/C][C]0.88648289353641[/C][C]0.556758553231795[/C][/ROW]
[ROW][C]41[/C][C]0.425738673525252[/C][C]0.851477347050504[/C][C]0.574261326474748[/C][/ROW]
[ROW][C]42[/C][C]0.382073906721554[/C][C]0.764147813443107[/C][C]0.617926093278446[/C][/ROW]
[ROW][C]43[/C][C]0.362011567326383[/C][C]0.724023134652767[/C][C]0.637988432673617[/C][/ROW]
[ROW][C]44[/C][C]0.321992731747725[/C][C]0.64398546349545[/C][C]0.678007268252275[/C][/ROW]
[ROW][C]45[/C][C]0.279428290514557[/C][C]0.558856581029114[/C][C]0.720571709485443[/C][/ROW]
[ROW][C]46[/C][C]0.265648617004254[/C][C]0.531297234008508[/C][C]0.734351382995746[/C][/ROW]
[ROW][C]47[/C][C]0.493782232847652[/C][C]0.987564465695305[/C][C]0.506217767152348[/C][/ROW]
[ROW][C]48[/C][C]0.88993754079162[/C][C]0.220124918416760[/C][C]0.110062459208380[/C][/ROW]
[ROW][C]49[/C][C]0.901113662368407[/C][C]0.197772675263186[/C][C]0.0988863376315932[/C][/ROW]
[ROW][C]50[/C][C]0.891931280284286[/C][C]0.216137439431428[/C][C]0.108068719715714[/C][/ROW]
[ROW][C]51[/C][C]0.874958770476828[/C][C]0.250082459046343[/C][C]0.125041229523172[/C][/ROW]
[ROW][C]52[/C][C]0.861924268804624[/C][C]0.276151462390753[/C][C]0.138075731195376[/C][/ROW]
[ROW][C]53[/C][C]0.85774186766057[/C][C]0.28451626467886[/C][C]0.14225813233943[/C][/ROW]
[ROW][C]54[/C][C]0.83378722062345[/C][C]0.332425558753100[/C][C]0.166212779376550[/C][/ROW]
[ROW][C]55[/C][C]0.83168351712993[/C][C]0.336632965740139[/C][C]0.168316482870069[/C][/ROW]
[ROW][C]56[/C][C]0.81345215213225[/C][C]0.373095695735500[/C][C]0.186547847867750[/C][/ROW]
[ROW][C]57[/C][C]0.829990046479602[/C][C]0.340019907040796[/C][C]0.170009953520398[/C][/ROW]
[ROW][C]58[/C][C]0.845293937668113[/C][C]0.309412124663774[/C][C]0.154706062331887[/C][/ROW]
[ROW][C]59[/C][C]0.869886556799951[/C][C]0.260226886400098[/C][C]0.130113443200049[/C][/ROW]
[ROW][C]60[/C][C]0.899973180621902[/C][C]0.200053638756196[/C][C]0.100026819378098[/C][/ROW]
[ROW][C]61[/C][C]0.892911538118976[/C][C]0.214176923762047[/C][C]0.107088461881024[/C][/ROW]
[ROW][C]62[/C][C]0.89981365764821[/C][C]0.200372684703582[/C][C]0.100186342351791[/C][/ROW]
[ROW][C]63[/C][C]0.898719123289092[/C][C]0.202561753421817[/C][C]0.101280876710908[/C][/ROW]
[ROW][C]64[/C][C]0.924221577235454[/C][C]0.151556845529093[/C][C]0.0757784227645463[/C][/ROW]
[ROW][C]65[/C][C]0.910274154620368[/C][C]0.179451690759264[/C][C]0.089725845379632[/C][/ROW]
[ROW][C]66[/C][C]0.894792536598394[/C][C]0.210414926803213[/C][C]0.105207463401606[/C][/ROW]
[ROW][C]67[/C][C]0.877169794617804[/C][C]0.245660410764391[/C][C]0.122830205382196[/C][/ROW]
[ROW][C]68[/C][C]0.864097265370826[/C][C]0.271805469258348[/C][C]0.135902734629174[/C][/ROW]
[ROW][C]69[/C][C]0.867873641028726[/C][C]0.264252717942547[/C][C]0.132126358971274[/C][/ROW]
[ROW][C]70[/C][C]0.887176441345612[/C][C]0.225647117308776[/C][C]0.112823558654388[/C][/ROW]
[ROW][C]71[/C][C]0.908449632411517[/C][C]0.183100735176967[/C][C]0.0915503675884835[/C][/ROW]
[ROW][C]72[/C][C]0.920869504333542[/C][C]0.158260991332916[/C][C]0.0791304956664582[/C][/ROW]
[ROW][C]73[/C][C]0.916010299414106[/C][C]0.167979401171788[/C][C]0.0839897005858942[/C][/ROW]
[ROW][C]74[/C][C]0.940402539413845[/C][C]0.119194921172310[/C][C]0.0595974605861551[/C][/ROW]
[ROW][C]75[/C][C]0.931292247693514[/C][C]0.137415504612972[/C][C]0.0687077523064859[/C][/ROW]
[ROW][C]76[/C][C]0.946956055853137[/C][C]0.106087888293727[/C][C]0.0530439441468633[/C][/ROW]
[ROW][C]77[/C][C]0.945881642017068[/C][C]0.108236715965863[/C][C]0.0541183579829316[/C][/ROW]
[ROW][C]78[/C][C]0.953436314940004[/C][C]0.0931273701199919[/C][C]0.0465636850599960[/C][/ROW]
[ROW][C]79[/C][C]0.957766908324575[/C][C]0.0844661833508505[/C][C]0.0422330916754253[/C][/ROW]
[ROW][C]80[/C][C]0.95441175157656[/C][C]0.0911764968468799[/C][C]0.0455882484234399[/C][/ROW]
[ROW][C]81[/C][C]0.945791198150969[/C][C]0.108417603698063[/C][C]0.0542088018490313[/C][/ROW]
[ROW][C]82[/C][C]0.940175035777145[/C][C]0.119649928445711[/C][C]0.0598249642228553[/C][/ROW]
[ROW][C]83[/C][C]0.936131061979696[/C][C]0.127737876040609[/C][C]0.0638689380203045[/C][/ROW]
[ROW][C]84[/C][C]0.966338566371464[/C][C]0.0673228672570729[/C][C]0.0336614336285365[/C][/ROW]
[ROW][C]85[/C][C]0.96691607478516[/C][C]0.066167850429681[/C][C]0.0330839252148405[/C][/ROW]
[ROW][C]86[/C][C]0.960164119344155[/C][C]0.0796717613116908[/C][C]0.0398358806558454[/C][/ROW]
[ROW][C]87[/C][C]0.965277069335223[/C][C]0.0694458613295533[/C][C]0.0347229306647767[/C][/ROW]
[ROW][C]88[/C][C]0.970379455025867[/C][C]0.0592410899482657[/C][C]0.0296205449741328[/C][/ROW]
[ROW][C]89[/C][C]0.967464051159065[/C][C]0.0650718976818692[/C][C]0.0325359488409346[/C][/ROW]
[ROW][C]90[/C][C]0.97773730323843[/C][C]0.044525393523138[/C][C]0.022262696761569[/C][/ROW]
[ROW][C]91[/C][C]0.9753226480452[/C][C]0.0493547039096006[/C][C]0.0246773519548003[/C][/ROW]
[ROW][C]92[/C][C]0.982127603682625[/C][C]0.0357447926347509[/C][C]0.0178723963173755[/C][/ROW]
[ROW][C]93[/C][C]0.977903931631157[/C][C]0.0441921367376856[/C][C]0.0220960683688428[/C][/ROW]
[ROW][C]94[/C][C]0.972580035455954[/C][C]0.0548399290880915[/C][C]0.0274199645440457[/C][/ROW]
[ROW][C]95[/C][C]0.973652199286683[/C][C]0.0526956014266334[/C][C]0.0263478007133167[/C][/ROW]
[ROW][C]96[/C][C]0.992147866748658[/C][C]0.0157042665026848[/C][C]0.00785213325134238[/C][/ROW]
[ROW][C]97[/C][C]0.989944177220263[/C][C]0.0201116455594735[/C][C]0.0100558227797368[/C][/ROW]
[ROW][C]98[/C][C]0.99108047497721[/C][C]0.0178390500455793[/C][C]0.00891952502278966[/C][/ROW]
[ROW][C]99[/C][C]0.99185205302911[/C][C]0.0162958939417816[/C][C]0.00814794697089078[/C][/ROW]
[ROW][C]100[/C][C]0.992689155839436[/C][C]0.0146216883211271[/C][C]0.00731084416056357[/C][/ROW]
[ROW][C]101[/C][C]0.993587965644624[/C][C]0.0128240687107510[/C][C]0.00641203435537551[/C][/ROW]
[ROW][C]102[/C][C]0.992587470675183[/C][C]0.0148250586496336[/C][C]0.0074125293248168[/C][/ROW]
[ROW][C]103[/C][C]0.991334311192622[/C][C]0.0173313776147553[/C][C]0.00866568880737765[/C][/ROW]
[ROW][C]104[/C][C]0.988786623466502[/C][C]0.0224267530669963[/C][C]0.0112133765334982[/C][/ROW]
[ROW][C]105[/C][C]0.987197426903214[/C][C]0.0256051461935713[/C][C]0.0128025730967857[/C][/ROW]
[ROW][C]106[/C][C]0.983488734830613[/C][C]0.0330225303387730[/C][C]0.0165112651693865[/C][/ROW]
[ROW][C]107[/C][C]0.986210074067875[/C][C]0.0275798518642499[/C][C]0.0137899259321250[/C][/ROW]
[ROW][C]108[/C][C]0.995481225694707[/C][C]0.00903754861058545[/C][C]0.00451877430529273[/C][/ROW]
[ROW][C]109[/C][C]0.996074339401285[/C][C]0.00785132119742914[/C][C]0.00392566059871457[/C][/ROW]
[ROW][C]110[/C][C]0.995808213551387[/C][C]0.00838357289722574[/C][C]0.00419178644861287[/C][/ROW]
[ROW][C]111[/C][C]0.994712555294524[/C][C]0.0105748894109525[/C][C]0.00528744470547626[/C][/ROW]
[ROW][C]112[/C][C]0.99438371667621[/C][C]0.0112325666475790[/C][C]0.00561628332378951[/C][/ROW]
[ROW][C]113[/C][C]0.994202765554047[/C][C]0.0115944688919066[/C][C]0.0057972344459533[/C][/ROW]
[ROW][C]114[/C][C]0.992371888869807[/C][C]0.0152562222603855[/C][C]0.00762811113019273[/C][/ROW]
[ROW][C]115[/C][C]0.98993717879755[/C][C]0.0201256424048992[/C][C]0.0100628212024496[/C][/ROW]
[ROW][C]116[/C][C]0.986889938231705[/C][C]0.0262201235365897[/C][C]0.0131100617682949[/C][/ROW]
[ROW][C]117[/C][C]0.98303875370743[/C][C]0.0339224925851422[/C][C]0.0169612462925711[/C][/ROW]
[ROW][C]118[/C][C]0.978201029612118[/C][C]0.043597940775763[/C][C]0.0217989703878815[/C][/ROW]
[ROW][C]119[/C][C]0.985499028598025[/C][C]0.02900194280395[/C][C]0.014500971401975[/C][/ROW]
[ROW][C]120[/C][C]0.997273737946519[/C][C]0.0054525241069625[/C][C]0.00272626205348125[/C][/ROW]
[ROW][C]121[/C][C]0.996840500171403[/C][C]0.00631899965719481[/C][C]0.00315949982859741[/C][/ROW]
[ROW][C]122[/C][C]0.996604328712003[/C][C]0.00679134257599476[/C][C]0.00339567128799738[/C][/ROW]
[ROW][C]123[/C][C]0.995731903492709[/C][C]0.0085361930145829[/C][C]0.00426809650729145[/C][/ROW]
[ROW][C]124[/C][C]0.995231935474466[/C][C]0.0095361290510684[/C][C]0.0047680645255342[/C][/ROW]
[ROW][C]125[/C][C]0.99383571956488[/C][C]0.0123285608702407[/C][C]0.00616428043512033[/C][/ROW]
[ROW][C]126[/C][C]0.993579112677005[/C][C]0.0128417746459894[/C][C]0.00642088732299468[/C][/ROW]
[ROW][C]127[/C][C]0.993393503511835[/C][C]0.0132129929763302[/C][C]0.0066064964881651[/C][/ROW]
[ROW][C]128[/C][C]0.991524685107479[/C][C]0.0169506297850422[/C][C]0.0084753148925211[/C][/ROW]
[ROW][C]129[/C][C]0.988655488303615[/C][C]0.0226890233927702[/C][C]0.0113445116963851[/C][/ROW]
[ROW][C]130[/C][C]0.98495935978621[/C][C]0.0300812804275792[/C][C]0.0150406402137896[/C][/ROW]
[ROW][C]131[/C][C]0.99022162695995[/C][C]0.0195567460801008[/C][C]0.00977837304005038[/C][/ROW]
[ROW][C]132[/C][C]0.998279991070906[/C][C]0.00344001785818849[/C][C]0.00172000892909425[/C][/ROW]
[ROW][C]133[/C][C]0.997591624783056[/C][C]0.00481675043388760[/C][C]0.00240837521694380[/C][/ROW]
[ROW][C]134[/C][C]0.99789281272571[/C][C]0.00421437454857825[/C][C]0.00210718727428912[/C][/ROW]
[ROW][C]135[/C][C]0.99729389880209[/C][C]0.00541220239582072[/C][C]0.00270610119791036[/C][/ROW]
[ROW][C]136[/C][C]0.997677692215397[/C][C]0.00464461556920592[/C][C]0.00232230778460296[/C][/ROW]
[ROW][C]137[/C][C]0.99735459133933[/C][C]0.00529081732134142[/C][C]0.00264540866067071[/C][/ROW]
[ROW][C]138[/C][C]0.996546553843356[/C][C]0.0069068923132884[/C][C]0.0034534461566442[/C][/ROW]
[ROW][C]139[/C][C]0.9960452898774[/C][C]0.00790942024520084[/C][C]0.00395471012260042[/C][/ROW]
[ROW][C]140[/C][C]0.994686970830008[/C][C]0.0106260583399843[/C][C]0.00531302916999215[/C][/ROW]
[ROW][C]141[/C][C]0.992957425200093[/C][C]0.0140851495998132[/C][C]0.0070425747999066[/C][/ROW]
[ROW][C]142[/C][C]0.992033009605433[/C][C]0.0159339807891335[/C][C]0.00796699039456675[/C][/ROW]
[ROW][C]143[/C][C]0.989583771235881[/C][C]0.020832457528237[/C][C]0.0104162287641185[/C][/ROW]
[ROW][C]144[/C][C]0.992081659149131[/C][C]0.0158366817017378[/C][C]0.00791834085086891[/C][/ROW]
[ROW][C]145[/C][C]0.990449484404383[/C][C]0.0191010311912349[/C][C]0.00955051559561747[/C][/ROW]
[ROW][C]146[/C][C]0.988949707339056[/C][C]0.0221005853218887[/C][C]0.0110502926609443[/C][/ROW]
[ROW][C]147[/C][C]0.985456060894266[/C][C]0.0290878782114691[/C][C]0.0145439391057345[/C][/ROW]
[ROW][C]148[/C][C]0.985571134514045[/C][C]0.0288577309719093[/C][C]0.0144288654859547[/C][/ROW]
[ROW][C]149[/C][C]0.981780174692575[/C][C]0.0364396506148504[/C][C]0.0182198253074252[/C][/ROW]
[ROW][C]150[/C][C]0.983522709663428[/C][C]0.0329545806731444[/C][C]0.0164772903365722[/C][/ROW]
[ROW][C]151[/C][C]0.977248897775252[/C][C]0.0455022044494956[/C][C]0.0227511022247478[/C][/ROW]
[ROW][C]152[/C][C]0.972640095312507[/C][C]0.0547198093749852[/C][C]0.0273599046874926[/C][/ROW]
[ROW][C]153[/C][C]0.962925549083712[/C][C]0.0741489018325768[/C][C]0.0370744509162884[/C][/ROW]
[ROW][C]154[/C][C]0.967302522490983[/C][C]0.0653949550180344[/C][C]0.0326974775090172[/C][/ROW]
[ROW][C]155[/C][C]0.966685544248089[/C][C]0.0666289115038223[/C][C]0.0333144557519111[/C][/ROW]
[ROW][C]156[/C][C]0.957191721243764[/C][C]0.0856165575124729[/C][C]0.0428082787562364[/C][/ROW]
[ROW][C]157[/C][C]0.951103839795078[/C][C]0.0977923204098447[/C][C]0.0488961602049223[/C][/ROW]
[ROW][C]158[/C][C]0.946573335209496[/C][C]0.106853329581008[/C][C]0.0534266647905039[/C][/ROW]
[ROW][C]159[/C][C]0.94198975976146[/C][C]0.116020480477081[/C][C]0.0580102402385405[/C][/ROW]
[ROW][C]160[/C][C]0.953946388633883[/C][C]0.0921072227322342[/C][C]0.0460536113661171[/C][/ROW]
[ROW][C]161[/C][C]0.951782015166047[/C][C]0.0964359696679065[/C][C]0.0482179848339532[/C][/ROW]
[ROW][C]162[/C][C]0.943781675674247[/C][C]0.112436648651506[/C][C]0.0562183243257532[/C][/ROW]
[ROW][C]163[/C][C]0.948852829830086[/C][C]0.102294340339828[/C][C]0.0511471701699138[/C][/ROW]
[ROW][C]164[/C][C]0.93386958805322[/C][C]0.132260823893559[/C][C]0.0661304119467793[/C][/ROW]
[ROW][C]165[/C][C]0.93201799159513[/C][C]0.13596401680974[/C][C]0.06798200840487[/C][/ROW]
[ROW][C]166[/C][C]0.908408408610311[/C][C]0.183183182779377[/C][C]0.0915915913896886[/C][/ROW]
[ROW][C]167[/C][C]0.896695571660102[/C][C]0.206608856679795[/C][C]0.103304428339898[/C][/ROW]
[ROW][C]168[/C][C]0.946324945345166[/C][C]0.107350109309668[/C][C]0.0536750546548338[/C][/ROW]
[ROW][C]169[/C][C]0.92576197839223[/C][C]0.148476043215541[/C][C]0.0742380216077707[/C][/ROW]
[ROW][C]170[/C][C]0.932074147271306[/C][C]0.135851705457388[/C][C]0.0679258527286942[/C][/ROW]
[ROW][C]171[/C][C]0.912198628384566[/C][C]0.175602743230867[/C][C]0.0878013716154337[/C][/ROW]
[ROW][C]172[/C][C]0.897139750110684[/C][C]0.205720499778631[/C][C]0.102860249889316[/C][/ROW]
[ROW][C]173[/C][C]0.867972375515691[/C][C]0.264055248968618[/C][C]0.132027624484309[/C][/ROW]
[ROW][C]174[/C][C]0.884055444197872[/C][C]0.231889111604255[/C][C]0.115944555802128[/C][/ROW]
[ROW][C]175[/C][C]0.871741028095492[/C][C]0.256517943809015[/C][C]0.128258971904508[/C][/ROW]
[ROW][C]176[/C][C]0.875980463407352[/C][C]0.248039073185297[/C][C]0.124019536592648[/C][/ROW]
[ROW][C]177[/C][C]0.829472349328392[/C][C]0.341055301343216[/C][C]0.170527650671608[/C][/ROW]
[ROW][C]178[/C][C]0.781248854895941[/C][C]0.437502290208118[/C][C]0.218751145104059[/C][/ROW]
[ROW][C]179[/C][C]0.723085429655236[/C][C]0.553829140689528[/C][C]0.276914570344764[/C][/ROW]
[ROW][C]180[/C][C]0.66809790709483[/C][C]0.663804185810341[/C][C]0.331902092905171[/C][/ROW]
[ROW][C]181[/C][C]0.573634400111421[/C][C]0.852731199777158[/C][C]0.426365599888579[/C][/ROW]
[ROW][C]182[/C][C]0.540064085792425[/C][C]0.91987182841515[/C][C]0.459935914207575[/C][/ROW]
[ROW][C]183[/C][C]0.449856183719488[/C][C]0.899712367438976[/C][C]0.550143816280512[/C][/ROW]
[ROW][C]184[/C][C]0.491029489233679[/C][C]0.982058978467357[/C][C]0.508970510766321[/C][/ROW]
[ROW][C]185[/C][C]0.403462687220028[/C][C]0.806925374440057[/C][C]0.596537312779972[/C][/ROW]
[ROW][C]186[/C][C]0.428895543286732[/C][C]0.857791086573464[/C][C]0.571104456713268[/C][/ROW]
[ROW][C]187[/C][C]0.488140962852544[/C][C]0.976281925705089[/C][C]0.511859037147456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1559308029189450.3118616058378900.844069197081055
60.06649352737734470.1329870547546890.933506472622655
70.02567383314294040.05134766628588070.97432616685706
80.01221739680733030.02443479361466060.98778260319267
90.004379978104602910.008759956209205820.995620021895397
100.002240159817864130.004480319635728260.997759840182136
110.2235801583455500.4471603166911010.77641984165445
120.5039813540008240.992037291998350.496018645999176
130.4208412053123040.8416824106246090.579158794687696
140.3453656525640860.6907313051281720.654634347435914
150.2700182093786310.5400364187572620.729981790621369
160.2190644244158520.4381288488317040.780935575584148
170.1710750448748180.3421500897496370.828924955125181
180.1407115265465930.2814230530931850.859288473453407
190.1140922425588580.2281844851177170.885907757441142
200.09007928733986680.1801585746797340.909920712660133
210.06388476436807580.1277695287361520.936115235631924
220.08828604303362040.1765720860672410.91171395696638
230.2540366087170370.5080732174340740.745963391282963
240.7041345159405080.5917309681189840.295865484059492
250.7099494063259890.5801011873480230.290050593674011
260.6617327503331530.6765344993336940.338267249666847
270.60652227047790.78695545904420.3934777295221
280.5597189438136960.8805621123726080.440281056186304
290.5037375415697180.9925249168605630.496262458430282
300.4449608348989570.8899216697979150.555039165101043
310.3901828560403290.7803657120806580.609817143959671
320.3621281127465610.7242562254931220.637871887253439
330.3227248265256100.6454496530512190.67727517347439
340.3184699397357430.6369398794714860.681530060264257
350.4475027759995330.8950055519990660.552497224000467
360.5401905183494390.9196189633011230.459809481650561
370.5622945098448040.8754109803103930.437705490155196
380.5106860002932690.9786279994134620.489313999706731
390.4622777891552320.9245555783104640.537722210844768
400.4432414467682050.886482893536410.556758553231795
410.4257386735252520.8514773470505040.574261326474748
420.3820739067215540.7641478134431070.617926093278446
430.3620115673263830.7240231346527670.637988432673617
440.3219927317477250.643985463495450.678007268252275
450.2794282905145570.5588565810291140.720571709485443
460.2656486170042540.5312972340085080.734351382995746
470.4937822328476520.9875644656953050.506217767152348
480.889937540791620.2201249184167600.110062459208380
490.9011136623684070.1977726752631860.0988863376315932
500.8919312802842860.2161374394314280.108068719715714
510.8749587704768280.2500824590463430.125041229523172
520.8619242688046240.2761514623907530.138075731195376
530.857741867660570.284516264678860.14225813233943
540.833787220623450.3324255587531000.166212779376550
550.831683517129930.3366329657401390.168316482870069
560.813452152132250.3730956957355000.186547847867750
570.8299900464796020.3400199070407960.170009953520398
580.8452939376681130.3094121246637740.154706062331887
590.8698865567999510.2602268864000980.130113443200049
600.8999731806219020.2000536387561960.100026819378098
610.8929115381189760.2141769237620470.107088461881024
620.899813657648210.2003726847035820.100186342351791
630.8987191232890920.2025617534218170.101280876710908
640.9242215772354540.1515568455290930.0757784227645463
650.9102741546203680.1794516907592640.089725845379632
660.8947925365983940.2104149268032130.105207463401606
670.8771697946178040.2456604107643910.122830205382196
680.8640972653708260.2718054692583480.135902734629174
690.8678736410287260.2642527179425470.132126358971274
700.8871764413456120.2256471173087760.112823558654388
710.9084496324115170.1831007351769670.0915503675884835
720.9208695043335420.1582609913329160.0791304956664582
730.9160102994141060.1679794011717880.0839897005858942
740.9404025394138450.1191949211723100.0595974605861551
750.9312922476935140.1374155046129720.0687077523064859
760.9469560558531370.1060878882937270.0530439441468633
770.9458816420170680.1082367159658630.0541183579829316
780.9534363149400040.09312737011999190.0465636850599960
790.9577669083245750.08446618335085050.0422330916754253
800.954411751576560.09117649684687990.0455882484234399
810.9457911981509690.1084176036980630.0542088018490313
820.9401750357771450.1196499284457110.0598249642228553
830.9361310619796960.1277378760406090.0638689380203045
840.9663385663714640.06732286725707290.0336614336285365
850.966916074785160.0661678504296810.0330839252148405
860.9601641193441550.07967176131169080.0398358806558454
870.9652770693352230.06944586132955330.0347229306647767
880.9703794550258670.05924108994826570.0296205449741328
890.9674640511590650.06507189768186920.0325359488409346
900.977737303238430.0445253935231380.022262696761569
910.97532264804520.04935470390960060.0246773519548003
920.9821276036826250.03574479263475090.0178723963173755
930.9779039316311570.04419213673768560.0220960683688428
940.9725800354559540.05483992908809150.0274199645440457
950.9736521992866830.05269560142663340.0263478007133167
960.9921478667486580.01570426650268480.00785213325134238
970.9899441772202630.02011164555947350.0100558227797368
980.991080474977210.01783905004557930.00891952502278966
990.991852053029110.01629589394178160.00814794697089078
1000.9926891558394360.01462168832112710.00731084416056357
1010.9935879656446240.01282406871075100.00641203435537551
1020.9925874706751830.01482505864963360.0074125293248168
1030.9913343111926220.01733137761475530.00866568880737765
1040.9887866234665020.02242675306699630.0112133765334982
1050.9871974269032140.02560514619357130.0128025730967857
1060.9834887348306130.03302253033877300.0165112651693865
1070.9862100740678750.02757985186424990.0137899259321250
1080.9954812256947070.009037548610585450.00451877430529273
1090.9960743394012850.007851321197429140.00392566059871457
1100.9958082135513870.008383572897225740.00419178644861287
1110.9947125552945240.01057488941095250.00528744470547626
1120.994383716676210.01123256664757900.00561628332378951
1130.9942027655540470.01159446889190660.0057972344459533
1140.9923718888698070.01525622226038550.00762811113019273
1150.989937178797550.02012564240489920.0100628212024496
1160.9868899382317050.02622012353658970.0131100617682949
1170.983038753707430.03392249258514220.0169612462925711
1180.9782010296121180.0435979407757630.0217989703878815
1190.9854990285980250.029001942803950.014500971401975
1200.9972737379465190.00545252410696250.00272626205348125
1210.9968405001714030.006318999657194810.00315949982859741
1220.9966043287120030.006791342575994760.00339567128799738
1230.9957319034927090.00853619301458290.00426809650729145
1240.9952319354744660.00953612905106840.0047680645255342
1250.993835719564880.01232856087024070.00616428043512033
1260.9935791126770050.01284177464598940.00642088732299468
1270.9933935035118350.01321299297633020.0066064964881651
1280.9915246851074790.01695062978504220.0084753148925211
1290.9886554883036150.02268902339277020.0113445116963851
1300.984959359786210.03008128042757920.0150406402137896
1310.990221626959950.01955674608010080.00977837304005038
1320.9982799910709060.003440017858188490.00172000892909425
1330.9975916247830560.004816750433887600.00240837521694380
1340.997892812725710.004214374548578250.00210718727428912
1350.997293898802090.005412202395820720.00270610119791036
1360.9976776922153970.004644615569205920.00232230778460296
1370.997354591339330.005290817321341420.00264540866067071
1380.9965465538433560.00690689231328840.0034534461566442
1390.99604528987740.007909420245200840.00395471012260042
1400.9946869708300080.01062605833998430.00531302916999215
1410.9929574252000930.01408514959981320.0070425747999066
1420.9920330096054330.01593398078913350.00796699039456675
1430.9895837712358810.0208324575282370.0104162287641185
1440.9920816591491310.01583668170173780.00791834085086891
1450.9904494844043830.01910103119123490.00955051559561747
1460.9889497073390560.02210058532188870.0110502926609443
1470.9854560608942660.02908787821146910.0145439391057345
1480.9855711345140450.02885773097190930.0144288654859547
1490.9817801746925750.03643965061485040.0182198253074252
1500.9835227096634280.03295458067314440.0164772903365722
1510.9772488977752520.04550220444949560.0227511022247478
1520.9726400953125070.05471980937498520.0273599046874926
1530.9629255490837120.07414890183257680.0370744509162884
1540.9673025224909830.06539495501803440.0326974775090172
1550.9666855442480890.06662891150382230.0333144557519111
1560.9571917212437640.08561655751247290.0428082787562364
1570.9511038397950780.09779232040984470.0488961602049223
1580.9465733352094960.1068533295810080.0534266647905039
1590.941989759761460.1160204804770810.0580102402385405
1600.9539463886338830.09210722273223420.0460536113661171
1610.9517820151660470.09643596966790650.0482179848339532
1620.9437816756742470.1124366486515060.0562183243257532
1630.9488528298300860.1022943403398280.0511471701699138
1640.933869588053220.1322608238935590.0661304119467793
1650.932017991595130.135964016809740.06798200840487
1660.9084084086103110.1831831827793770.0915915913896886
1670.8966955716601020.2066088566797950.103304428339898
1680.9463249453451660.1073501093096680.0536750546548338
1690.925761978392230.1484760432155410.0742380216077707
1700.9320741472713060.1358517054573880.0679258527286942
1710.9121986283845660.1756027432308670.0878013716154337
1720.8971397501106840.2057204997786310.102860249889316
1730.8679723755156910.2640552489686180.132027624484309
1740.8840554441978720.2318891116042550.115944555802128
1750.8717410280954920.2565179438090150.128258971904508
1760.8759804634073520.2480390731852970.124019536592648
1770.8294723493283920.3410553013432160.170527650671608
1780.7812488548959410.4375022902081180.218751145104059
1790.7230854296552360.5538291406895280.276914570344764
1800.668097907094830.6638041858103410.331902092905171
1810.5736344001114210.8527311997771580.426365599888579
1820.5400640857924250.919871828415150.459935914207575
1830.4498561837194880.8997123674389760.550143816280512
1840.4910294892336790.9820589784673570.508970510766321
1850.4034626872200280.8069253744400570.596537312779972
1860.4288955432867320.8577910865734640.571104456713268
1870.4881409628525440.9762819257050890.511859037147456






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.098360655737705NOK
5% type I error level630.344262295081967NOK
10% type I error level830.453551912568306NOK

\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 & 18 & 0.098360655737705 & NOK \tabularnewline
5% type I error level & 63 & 0.344262295081967 & NOK \tabularnewline
10% type I error level & 83 & 0.453551912568306 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25227&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]18[/C][C]0.098360655737705[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]63[/C][C]0.344262295081967[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]83[/C][C]0.453551912568306[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25227&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25227&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.098360655737705NOK
5% type I error level630.344262295081967NOK
10% type I error level830.453551912568306NOK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}