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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 16:39:19 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352151718pkq1wi388e241fz.htm/, Retrieved Fri, 03 Feb 2023 09:39:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186314, Retrieved Fri, 03 Feb 2023 09:39:33 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws7] [2012-11-05 18:47:09] [5b6f1874b4b9118bec0ed9a6d3e17ca8]
- R PD    [Multiple Regression] [] [2012-11-05 21:39:19] [da7c95f16c64cb676a9e51b1b6b79fc0] [Current]
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Dataseries X:
2000	501	134	368	6.7	8.5	8.7
2000	485	124	361	6.8	8.4	8.6
2000	464	113	351	6.7	8.4	8.6
2000	460	109	351	6.6	8.3	8.5
2001	467	109	358	6.4	8.2	8.5
2001	460	106	354	6.3	8.2	8.5
2001	448	101	347	6.3	8.1	8.5
2001	443	98	345	6.5	8.1	8.5
2001	436	93	343	6.5	8.1	8.5
2001	431	91	340	6.4	8.1	8.5
2001	484	122	362	6.2	8.1	8.5
2001	510	139	370	6.2	8.1	8.6
2001	513	140	373	6.5	8.1	8.6
2001	503	132	371	7	8.2	8.6
2001	471	117	354	7.2	8.2	8.7
2001	471	114	357	7.3	8.3	8.7
2002	476	113	363	7.4	8.2	8.7
2002	475	110	364	7.4	8.3	8.8
2002	470	107	363	7.4	8.3	8.8
2002	461	103	358	7.3	8.4	8.9
2002	455	98	357	7.4	8.5	8.9
2002	456	98	357	7.4	8.5	8.9
2002	517	137	380	7.6	8.6	9
2002	525	148	378	7.6	8.6	9
2002	523	147	376	7.7	8.7	9
2002	519	139	380	7.7	8.7	9
2002	509	130	379	7.8	8.8	9
2002	512	128	384	7.8	8.8	9
2003	519	127	392	8	8.9	9.1
2003	517	123	394	8.1	9	9.1
2003	510	118	392	8.1	9	9.1
2003	509	114	396	8.2	9	9.1
2003	501	108	392	8.1	9	9.1
2003	507	111	396	8.1	9.1	9.1
2003	569	151	419	8.1	9.1	9.1
2003	580	159	421	8.1	9	9.1
2003	578	158	420	8.2	9.1	9.1
2003	565	148	418	8.2	9	9.1
2003	547	138	410	8.3	9.1	9.1
2003	555	137	418	8.4	9.1	9.2
2004	562	136	426	8.6	9.2	9.3
2004	561	133	428	8.6	9.2	9.3
2004	555	126	430	8.4	9.2	9.3
2004	544	120	424	8	9.2	9.2
2004	537	114	423	7.9	9.2	9.2
2004	543	116	427	8.1	9.3	9.2
2004	594	153	441	8.5	9.3	9.2
2004	611	162	449	8.8	9.3	9.2
2004	613	161	452	8.8	9.3	9.2
2004	611	149	462	8.5	9.3	9.2
2004	594	139	455	8.3	9.4	9.2
2004	595	135	461	8.3	9.4	9.2
2005	591	130	461	8.3	9.3	9.2
2005	589	127	463	8.4	9.3	9.2
2005	584	122	462	8.5	9.3	9.2
2005	573	117	456	8.5	9.3	9.2
2005	567	112	455	8.6	9.2	9.1
2005	569	113	456	8.5	9.2	9.1
2005	621	149	472	8.6	9.2	9
2005	629	157	472	8.6	9.1	8.9
2005	628	157	471	8.6	9.1	8.9
2005	612	147	465	8.5	9.1	9
2005	595	137	459	8.4	9.1	8.9
2005	597	132	465	8.4	9	8.8
2006	593	125	468	8.5	8.9	8.7
2006	590	123	467	8.5	8.8	8.6
2006	580	117	463	8.5	8.7	8.5
2006	574	114	460	8.6	8.6	8.5
2006	573	111	462	8.6	8.6	8.4
2006	573	112	461	8.4	8.5	8.3
2006	620	144	476	8.2	8.4	8.2
2006	626	150	476	8	8.4	8.2
2006	620	149	471	8	8.3	8.1
2006	588	134	453	8	8.2	8
2006	566	123	443	8	8.2	7.9
2006	557	116	442	7.9	8	7.8
2007	561	117	444	7.9	7.9	7.6
2007	549	111	438	7.9	7.8	7.5
2007	532	105	427	7.9	7.7	7.4
2007	526	102	424	8	7.6	7.3
2007	511	95	416	7.9	7.6	7.3
2007	499	93	406	7.4	7.6	7.2
2007	555	124	431	7.2	7.6	7.2
2007	565	130	434	7	7.6	7.2
2007	542	124	418	6.9	7.5	7.1
2007	527	115	412	7.1	7.5	7
2007	510	106	404	7.2	7.4	7
2007	514	105	409	7.2	7.4	6.9
2008	517	105	412	7.1	7.4	6.9
2008	508	101	406	6.9	7.3	6.8
2008	493	95	398	6.8	7.3	6.8
2008	490	93	397	6.8	7.4	6.8
2008	469	84	385	6.8	7.5	6.9
2008	478	87	390	6.9	7.6	7
2008	528	116	413	7.1	7.6	7
2008	534	120	413	7.2	7.7	7.1
2008	518	117	401	7.2	7.7	7.2
2008	506	109	397	7.1	7.9	7.3
2008	502	105	397	7.1	8.1	7.5
2008	516	107	409	7.2	8.4	7.7
2009	528	109	419	7.5	8.7	8.1
2009	533	109	424	7.7	9	8.4
2009	536	108	428	7.8	9.3	8.6
2009	537	107	430	7.7	9.4	8.8
2009	524	99	424	7.7	9.5	8.9
2009	536	103	433	7.8	9.6	9.1
2009	587	131	456	8	9.8	9.2
2009	597	137	459	8.1	9.8	9.3
2009	581	135	446	8.1	9.9	9.4
2009	564	124	441	8	10	9.4
2009	558	118	439	8.1	10	9.5
2010	575	121	454	8.2	10.1	9.5
2010	580	121	460	8.4	10.1	9.7
2010	575	118	457	8.5	10.1	9.7
2010	563	113	451	8.5	10.1	9.7
2010	552	107	444	8.5	10.2	9.7
2010	537	100	437	8.5	10.2	9.7
2010	545	102	443	8.5	10.1	9.6
2010	601	130	471	8.4	10.1	9.6
2010	604	136	469	8.3	10.1	9.6
2010	586	133	454	8.2	10.1	9.6
2010	564	120	444	8.1	10.1	9.6
2010	549	112	436	7.9	10.1	9.6
2010	551	109	442	7.6	10.1	9.6
2011	556	110	446	7.3	10	9.5
2011	548	106	442	7.1	9.9	9.5
2011	540	102	438	7	9.9	9.4
2011	531	98	433	7.1	9.9	9.4
2011	521	92	428	7.1	9.9	9.5
2011	519	92	426	7.1	10	9.5
2011	572	120	452	7.3	10.1	9.6
2011	581	127	455	7.3	10.2	9.7
2011	563	124	439	7.3	10.3	9.8
2011	548	114	434	7.2	10.5	9.9
2011	539	108	431	7.2	10.6	10
2011	541	106	435	7.1	10.7	10
2012	562	111	450	7.1	10.8	10.1
2012	559	110	449	7.1	10.9	10.2
2012	546	104	442	7.2	11	10.3
2012	536	100	437	7.3	11.2	10.3
2012	528	96	431	7.4	11.3	10.4
2012	530	98	433	7.4	11.4	10.5
2012	582	122	460	7.5	11.5	10.5
2012	599	134	465	7.4	11.5	10.6
2012	584	133	451	7.4	11.6	10.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Totaal_werklozen[t] = -110.139290815839 + 0.0557688214312652Jaartal[t] + 0.995998265315216Jonger_dan_25_jaar[t] + 1.00020013681646Vanaf_25_jaar[t] -0.0773667171300518Belgie[t] -0.45287714325954Eurogebied[t] + 0.376606852518923EU_27[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal_werklozen[t] =  -110.139290815839 +  0.0557688214312652Jaartal[t] +  0.995998265315216Jonger_dan_25_jaar[t] +  1.00020013681646Vanaf_25_jaar[t] -0.0773667171300518Belgie[t] -0.45287714325954Eurogebied[t] +  0.376606852518923EU_27[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal_werklozen[t] =  -110.139290815839 +  0.0557688214312652Jaartal[t] +  0.995998265315216Jonger_dan_25_jaar[t] +  1.00020013681646Vanaf_25_jaar[t] -0.0773667171300518Belgie[t] -0.45287714325954Eurogebied[t] +  0.376606852518923EU_27[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Totaal_werklozen[t] = -110.139290815839 + 0.0557688214312652Jaartal[t] + 0.995998265315216Jonger_dan_25_jaar[t] + 1.00020013681646Vanaf_25_jaar[t] -0.0773667171300518Belgie[t] -0.45287714325954Eurogebied[t] + 0.376606852518923EU_27[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-110.13929081583988.54661-1.24390.2156610.107831
Jaartal0.05576882143126520.0443311.2580.2105090.105255
Jonger_dan_25_jaar0.9959982653152160.003604276.390300
Vanaf_25_jaar1.000200136816460.003015331.71100
Belgie-0.07736671713005180.11269-0.68650.4935210.24676
Eurogebied-0.452877143259540.352526-1.28470.2010620.100531
EU_270.3766068525189230.3310261.13770.2572190.128609

\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) & -110.139290815839 & 88.54661 & -1.2439 & 0.215661 & 0.107831 \tabularnewline
Jaartal & 0.0557688214312652 & 0.044331 & 1.258 & 0.210509 & 0.105255 \tabularnewline
Jonger_dan_25_jaar & 0.995998265315216 & 0.003604 & 276.3903 & 0 & 0 \tabularnewline
Vanaf_25_jaar & 1.00020013681646 & 0.003015 & 331.711 & 0 & 0 \tabularnewline
Belgie & -0.0773667171300518 & 0.11269 & -0.6865 & 0.493521 & 0.24676 \tabularnewline
Eurogebied & -0.45287714325954 & 0.352526 & -1.2847 & 0.201062 & 0.100531 \tabularnewline
EU_27 & 0.376606852518923 & 0.331026 & 1.1377 & 0.257219 & 0.128609 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&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]-110.139290815839[/C][C]88.54661[/C][C]-1.2439[/C][C]0.215661[/C][C]0.107831[/C][/ROW]
[ROW][C]Jaartal[/C][C]0.0557688214312652[/C][C]0.044331[/C][C]1.258[/C][C]0.210509[/C][C]0.105255[/C][/ROW]
[ROW][C]Jonger_dan_25_jaar[/C][C]0.995998265315216[/C][C]0.003604[/C][C]276.3903[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vanaf_25_jaar[/C][C]1.00020013681646[/C][C]0.003015[/C][C]331.711[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belgie[/C][C]-0.0773667171300518[/C][C]0.11269[/C][C]-0.6865[/C][C]0.493521[/C][C]0.24676[/C][/ROW]
[ROW][C]Eurogebied[/C][C]-0.45287714325954[/C][C]0.352526[/C][C]-1.2847[/C][C]0.201062[/C][C]0.100531[/C][/ROW]
[ROW][C]EU_27[/C][C]0.376606852518923[/C][C]0.331026[/C][C]1.1377[/C][C]0.257219[/C][C]0.128609[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-110.13929081583988.54661-1.24390.2156610.107831
Jaartal0.05576882143126520.0443311.2580.2105090.105255
Jonger_dan_25_jaar0.9959982653152160.003604276.390300
Vanaf_25_jaar1.000200136816460.003015331.71100
Belgie-0.07736671713005180.11269-0.68650.4935210.24676
Eurogebied-0.452877143259540.352526-1.28470.2010620.100531
EU_270.3766068525189230.3310261.13770.2572190.128609







Multiple Linear Regression - Regression Statistics
Multiple R0.99994109880369
R-squared0.999882201076731
Adjusted R-squared0.999877079384415
F-TEST (value)195224.96459656
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.502362510695439
Sum Squared Residuals34.8267967170071

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99994109880369 \tabularnewline
R-squared & 0.999882201076731 \tabularnewline
Adjusted R-squared & 0.999877079384415 \tabularnewline
F-TEST (value) & 195224.96459656 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.502362510695439 \tabularnewline
Sum Squared Residuals & 34.8267967170071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99994109880369[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999882201076731[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999877079384415[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]195224.96459656[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.502362510695439[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.8267967170071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.99994109880369
R-squared0.999882201076731
Adjusted R-squared0.999877079384415
F-TEST (value)195224.96459656
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.502362510695439
Sum Squared Residuals34.8267967170071







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.844436841823-0.844436841823466
2485484.8829435883170.117056411682668
3464463.9326979733980.0673020266015946
4460459.9640686129250.0359313870753726
5467467.081999449823-0.0819994498230227
6460460.100940778325-0.100940778324548
7448448.164836208359-0.164836208359217
8443443.160967795355-0.160967795354641
9436436.180576195146-0.180576195145646
10431431.195715925779-0.195715925778848
11484484.091538503939-0.0915385039386273
12510509.0627707940810.937229205919149
13513513.036159454706-0.0361594547064236
14503502.9838019856610.0161980143392029
15471471.062613021879-0.0626130218786618
16471471.022194250343-0.0221942503434258
17476476.120716669971-0.120716669971165
18475474.1252949817680.87470501823209
19470470.137100049006-0.137100049005804
20461461.152215946302-0.152215946301601
21455455.11900009687-0.119000096870104
22456455.119000096870.880999903129895
23517516.9444352184420.0555647815580166
24525525.900015863276-0.900015863276438
25523522.8505929382890.149407061710655
26519518.8834073630330.116592636966544
27509508.8661984523410.133801547658903
28512511.8752026057930.124797394207046
29519518.9134738839410.0865261160594088
30517516.8768567102740.123143289726316
31510509.8964651100650.103534889935311
32509509.905535924357-0.905535924356648
33501499.9364824569131.06351754308748
34507506.8799900857980.120009914201947
35569569.724523845185-0.72452384518521
36580579.7381979556660.261802044334196
37578577.6889751674950.311024832504827
38565565.773879955036-0.773879955036055
39547547.759271821313-0.759271821313276
40555554.7947986640690.20520133593139
41562561.8330699422160.166930057783753
42561560.8454754199040.154524580096485
43555555.889361179756-0.889361179755928
44544543.9054567685660.0945432314339837
45537536.9370037115710.062996288428732
46543542.8690397317160.130960268284434
47594593.6928307769570.307169223043061
48611610.6352062441870.364793755813476
49613612.6398083893210.360191610679318
50611610.7130405888420.286959411158317
51594593.7218426070740.278157392925617
52595595.739050366712-0.739050366712259
53591590.8601155758930.139884424106596
54589589.864784381868-0.864784381867655
55584583.8768562467620.123143753237886
56573572.8956640992870.104335900712707
57567566.9153629932560.0846370067441861
58569568.91929806710.0807019328995088
59621620.7330404505470.266959549453318
60629628.7086536021420.291346397857532
61628627.7084534653260.291546534673988
62612611.792667348240.207332651759988
63595595.80155986065-0.801559860650222
64597596.8303963840470.169603615953054
65593592.9146681160820.0853318839178652
66590589.9300984777090.0699015222906932
67580579.9609353676260.0390646323737611
68574574.009891203844-0.00989120384416442
69573572.984635996280.0153640037204581
70573573.003534497278-0.00353449727837724
71620619.9015814121120.0984185878877877
72626625.893044347430.106955652570485
73620619.9036724271060.0963275728939233
74588586.9677230137561.03227698624434
75566565.9720800418720.0279199581281656
76557558.060543462962-1.06054346296188
77561561.082677167163-0.0826771671634487
78549549.113113783447-0.113113783447465
79532532.142549715649-0.142549715649194
80526526.153844866615-0.153844866615228
81511511.18799258659-0.187992586590061
82499499.195017361108-0.19501736110819
83555555.091440349717-0.0914403497173359
84565564.0835036954840.916496304515982
85542542.119675615317-0.119675615316462
86527527.101356377903-0.10135637790287
87510510.173321938147-0.173321938147215
88514514.140663671662-0.140663671662397
89517517.204769575256-0.204769575256036
90508507.2426760655960.757323934403502
91493493.272822050887-0.272822050886545
92490490.235337669114-0.235337669113703
93469469.261324610405-0.261324610405201
94478477.2349563896460.765043610353934
95528529.10803588714-1.10803588713985
96534533.0766652476140.923334752386349
97518518.123929495122-0.123929495122407
98506506.109964753648-0.109964753647833
99502502.110717634239-0.110717634238847
100516516.03683736248-0.0368373624796877
101528528.078173665597-0.0781736655966503
102533533.040819919031-0.0408199190307438
103536535.9773437567940.0226562432057249
104537537.019516093003-0.01951609300281
105524523.0427021205080.957297879491723
106536536.050793397582-0.0507933975820834
107587586.8749598863610.125040113639369
108597595.881473902241.11852609775981
109581580.8792485639220.12075143607825
110564564.884715918759-0.884715918759135
111558556.9382500667741.06174993322619
112575574.9319913503590.0680086496413746
113580580.993040198335-0.993040198335143
114575574.9967083202270.00329167977288161
115563564.015516172752-1.0155161727523
116552550.992837908821.00716209118016
117537537.019449093898-0.0194490938981244
118545545.020273474501-0.0202734745013668
119601600.9215654059010.0784345940987712
120604604.904891395873-0.904891395872614
121586586.921631219393-0.921631219393104
122564563.9793890738440.0206109261562677
123549548.0252752002160.974724799783648
124551551.061691240309-0.0616912403084659
125556556.145095918534-0.145095918533856
126548548.221063367759-0.221063367759126
127540540.206345745694-0.206345745693543
128531531.213615328637-0.213615328637379
129521520.2742857379160.725714262084305
130519518.2285977499570.771402250043174
131572572.098652363511-0.0986523635106971
132581582.063613602093-1.06361360209252
133563563.064789588009-0.0647895880094908
134548548.058628179088-0.0586281790880352
135539539.074411147673-0.0744111476733008
136541541.045664121696-0.0456641216957534
137562561.0767992928760.923200707124103
138559559.07297386167-0.0729738616701632
139546546.080219611277-0.080219611276598
140536536.996913765568-0.996913765568534
141528526.9963561826221.00364381737815
142530530.981125957811-0.981125957811138
143582581.8374636333820.162536366618282
144599598.8358408582110.16415914178851
145584583.791752963140.208247036860084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.844436841823 & -0.844436841823466 \tabularnewline
2 & 485 & 484.882943588317 & 0.117056411682668 \tabularnewline
3 & 464 & 463.932697973398 & 0.0673020266015946 \tabularnewline
4 & 460 & 459.964068612925 & 0.0359313870753726 \tabularnewline
5 & 467 & 467.081999449823 & -0.0819994498230227 \tabularnewline
6 & 460 & 460.100940778325 & -0.100940778324548 \tabularnewline
7 & 448 & 448.164836208359 & -0.164836208359217 \tabularnewline
8 & 443 & 443.160967795355 & -0.160967795354641 \tabularnewline
9 & 436 & 436.180576195146 & -0.180576195145646 \tabularnewline
10 & 431 & 431.195715925779 & -0.195715925778848 \tabularnewline
11 & 484 & 484.091538503939 & -0.0915385039386273 \tabularnewline
12 & 510 & 509.062770794081 & 0.937229205919149 \tabularnewline
13 & 513 & 513.036159454706 & -0.0361594547064236 \tabularnewline
14 & 503 & 502.983801985661 & 0.0161980143392029 \tabularnewline
15 & 471 & 471.062613021879 & -0.0626130218786618 \tabularnewline
16 & 471 & 471.022194250343 & -0.0221942503434258 \tabularnewline
17 & 476 & 476.120716669971 & -0.120716669971165 \tabularnewline
18 & 475 & 474.125294981768 & 0.87470501823209 \tabularnewline
19 & 470 & 470.137100049006 & -0.137100049005804 \tabularnewline
20 & 461 & 461.152215946302 & -0.152215946301601 \tabularnewline
21 & 455 & 455.11900009687 & -0.119000096870104 \tabularnewline
22 & 456 & 455.11900009687 & 0.880999903129895 \tabularnewline
23 & 517 & 516.944435218442 & 0.0555647815580166 \tabularnewline
24 & 525 & 525.900015863276 & -0.900015863276438 \tabularnewline
25 & 523 & 522.850592938289 & 0.149407061710655 \tabularnewline
26 & 519 & 518.883407363033 & 0.116592636966544 \tabularnewline
27 & 509 & 508.866198452341 & 0.133801547658903 \tabularnewline
28 & 512 & 511.875202605793 & 0.124797394207046 \tabularnewline
29 & 519 & 518.913473883941 & 0.0865261160594088 \tabularnewline
30 & 517 & 516.876856710274 & 0.123143289726316 \tabularnewline
31 & 510 & 509.896465110065 & 0.103534889935311 \tabularnewline
32 & 509 & 509.905535924357 & -0.905535924356648 \tabularnewline
33 & 501 & 499.936482456913 & 1.06351754308748 \tabularnewline
34 & 507 & 506.879990085798 & 0.120009914201947 \tabularnewline
35 & 569 & 569.724523845185 & -0.72452384518521 \tabularnewline
36 & 580 & 579.738197955666 & 0.261802044334196 \tabularnewline
37 & 578 & 577.688975167495 & 0.311024832504827 \tabularnewline
38 & 565 & 565.773879955036 & -0.773879955036055 \tabularnewline
39 & 547 & 547.759271821313 & -0.759271821313276 \tabularnewline
40 & 555 & 554.794798664069 & 0.20520133593139 \tabularnewline
41 & 562 & 561.833069942216 & 0.166930057783753 \tabularnewline
42 & 561 & 560.845475419904 & 0.154524580096485 \tabularnewline
43 & 555 & 555.889361179756 & -0.889361179755928 \tabularnewline
44 & 544 & 543.905456768566 & 0.0945432314339837 \tabularnewline
45 & 537 & 536.937003711571 & 0.062996288428732 \tabularnewline
46 & 543 & 542.869039731716 & 0.130960268284434 \tabularnewline
47 & 594 & 593.692830776957 & 0.307169223043061 \tabularnewline
48 & 611 & 610.635206244187 & 0.364793755813476 \tabularnewline
49 & 613 & 612.639808389321 & 0.360191610679318 \tabularnewline
50 & 611 & 610.713040588842 & 0.286959411158317 \tabularnewline
51 & 594 & 593.721842607074 & 0.278157392925617 \tabularnewline
52 & 595 & 595.739050366712 & -0.739050366712259 \tabularnewline
53 & 591 & 590.860115575893 & 0.139884424106596 \tabularnewline
54 & 589 & 589.864784381868 & -0.864784381867655 \tabularnewline
55 & 584 & 583.876856246762 & 0.123143753237886 \tabularnewline
56 & 573 & 572.895664099287 & 0.104335900712707 \tabularnewline
57 & 567 & 566.915362993256 & 0.0846370067441861 \tabularnewline
58 & 569 & 568.9192980671 & 0.0807019328995088 \tabularnewline
59 & 621 & 620.733040450547 & 0.266959549453318 \tabularnewline
60 & 629 & 628.708653602142 & 0.291346397857532 \tabularnewline
61 & 628 & 627.708453465326 & 0.291546534673988 \tabularnewline
62 & 612 & 611.79266734824 & 0.207332651759988 \tabularnewline
63 & 595 & 595.80155986065 & -0.801559860650222 \tabularnewline
64 & 597 & 596.830396384047 & 0.169603615953054 \tabularnewline
65 & 593 & 592.914668116082 & 0.0853318839178652 \tabularnewline
66 & 590 & 589.930098477709 & 0.0699015222906932 \tabularnewline
67 & 580 & 579.960935367626 & 0.0390646323737611 \tabularnewline
68 & 574 & 574.009891203844 & -0.00989120384416442 \tabularnewline
69 & 573 & 572.98463599628 & 0.0153640037204581 \tabularnewline
70 & 573 & 573.003534497278 & -0.00353449727837724 \tabularnewline
71 & 620 & 619.901581412112 & 0.0984185878877877 \tabularnewline
72 & 626 & 625.89304434743 & 0.106955652570485 \tabularnewline
73 & 620 & 619.903672427106 & 0.0963275728939233 \tabularnewline
74 & 588 & 586.967723013756 & 1.03227698624434 \tabularnewline
75 & 566 & 565.972080041872 & 0.0279199581281656 \tabularnewline
76 & 557 & 558.060543462962 & -1.06054346296188 \tabularnewline
77 & 561 & 561.082677167163 & -0.0826771671634487 \tabularnewline
78 & 549 & 549.113113783447 & -0.113113783447465 \tabularnewline
79 & 532 & 532.142549715649 & -0.142549715649194 \tabularnewline
80 & 526 & 526.153844866615 & -0.153844866615228 \tabularnewline
81 & 511 & 511.18799258659 & -0.187992586590061 \tabularnewline
82 & 499 & 499.195017361108 & -0.19501736110819 \tabularnewline
83 & 555 & 555.091440349717 & -0.0914403497173359 \tabularnewline
84 & 565 & 564.083503695484 & 0.916496304515982 \tabularnewline
85 & 542 & 542.119675615317 & -0.119675615316462 \tabularnewline
86 & 527 & 527.101356377903 & -0.10135637790287 \tabularnewline
87 & 510 & 510.173321938147 & -0.173321938147215 \tabularnewline
88 & 514 & 514.140663671662 & -0.140663671662397 \tabularnewline
89 & 517 & 517.204769575256 & -0.204769575256036 \tabularnewline
90 & 508 & 507.242676065596 & 0.757323934403502 \tabularnewline
91 & 493 & 493.272822050887 & -0.272822050886545 \tabularnewline
92 & 490 & 490.235337669114 & -0.235337669113703 \tabularnewline
93 & 469 & 469.261324610405 & -0.261324610405201 \tabularnewline
94 & 478 & 477.234956389646 & 0.765043610353934 \tabularnewline
95 & 528 & 529.10803588714 & -1.10803588713985 \tabularnewline
96 & 534 & 533.076665247614 & 0.923334752386349 \tabularnewline
97 & 518 & 518.123929495122 & -0.123929495122407 \tabularnewline
98 & 506 & 506.109964753648 & -0.109964753647833 \tabularnewline
99 & 502 & 502.110717634239 & -0.110717634238847 \tabularnewline
100 & 516 & 516.03683736248 & -0.0368373624796877 \tabularnewline
101 & 528 & 528.078173665597 & -0.0781736655966503 \tabularnewline
102 & 533 & 533.040819919031 & -0.0408199190307438 \tabularnewline
103 & 536 & 535.977343756794 & 0.0226562432057249 \tabularnewline
104 & 537 & 537.019516093003 & -0.01951609300281 \tabularnewline
105 & 524 & 523.042702120508 & 0.957297879491723 \tabularnewline
106 & 536 & 536.050793397582 & -0.0507933975820834 \tabularnewline
107 & 587 & 586.874959886361 & 0.125040113639369 \tabularnewline
108 & 597 & 595.88147390224 & 1.11852609775981 \tabularnewline
109 & 581 & 580.879248563922 & 0.12075143607825 \tabularnewline
110 & 564 & 564.884715918759 & -0.884715918759135 \tabularnewline
111 & 558 & 556.938250066774 & 1.06174993322619 \tabularnewline
112 & 575 & 574.931991350359 & 0.0680086496413746 \tabularnewline
113 & 580 & 580.993040198335 & -0.993040198335143 \tabularnewline
114 & 575 & 574.996708320227 & 0.00329167977288161 \tabularnewline
115 & 563 & 564.015516172752 & -1.0155161727523 \tabularnewline
116 & 552 & 550.99283790882 & 1.00716209118016 \tabularnewline
117 & 537 & 537.019449093898 & -0.0194490938981244 \tabularnewline
118 & 545 & 545.020273474501 & -0.0202734745013668 \tabularnewline
119 & 601 & 600.921565405901 & 0.0784345940987712 \tabularnewline
120 & 604 & 604.904891395873 & -0.904891395872614 \tabularnewline
121 & 586 & 586.921631219393 & -0.921631219393104 \tabularnewline
122 & 564 & 563.979389073844 & 0.0206109261562677 \tabularnewline
123 & 549 & 548.025275200216 & 0.974724799783648 \tabularnewline
124 & 551 & 551.061691240309 & -0.0616912403084659 \tabularnewline
125 & 556 & 556.145095918534 & -0.145095918533856 \tabularnewline
126 & 548 & 548.221063367759 & -0.221063367759126 \tabularnewline
127 & 540 & 540.206345745694 & -0.206345745693543 \tabularnewline
128 & 531 & 531.213615328637 & -0.213615328637379 \tabularnewline
129 & 521 & 520.274285737916 & 0.725714262084305 \tabularnewline
130 & 519 & 518.228597749957 & 0.771402250043174 \tabularnewline
131 & 572 & 572.098652363511 & -0.0986523635106971 \tabularnewline
132 & 581 & 582.063613602093 & -1.06361360209252 \tabularnewline
133 & 563 & 563.064789588009 & -0.0647895880094908 \tabularnewline
134 & 548 & 548.058628179088 & -0.0586281790880352 \tabularnewline
135 & 539 & 539.074411147673 & -0.0744111476733008 \tabularnewline
136 & 541 & 541.045664121696 & -0.0456641216957534 \tabularnewline
137 & 562 & 561.076799292876 & 0.923200707124103 \tabularnewline
138 & 559 & 559.07297386167 & -0.0729738616701632 \tabularnewline
139 & 546 & 546.080219611277 & -0.080219611276598 \tabularnewline
140 & 536 & 536.996913765568 & -0.996913765568534 \tabularnewline
141 & 528 & 526.996356182622 & 1.00364381737815 \tabularnewline
142 & 530 & 530.981125957811 & -0.981125957811138 \tabularnewline
143 & 582 & 581.837463633382 & 0.162536366618282 \tabularnewline
144 & 599 & 598.835840858211 & 0.16415914178851 \tabularnewline
145 & 584 & 583.79175296314 & 0.208247036860084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&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]501[/C][C]501.844436841823[/C][C]-0.844436841823466[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.882943588317[/C][C]0.117056411682668[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]463.932697973398[/C][C]0.0673020266015946[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.964068612925[/C][C]0.0359313870753726[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.081999449823[/C][C]-0.0819994498230227[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.100940778325[/C][C]-0.100940778324548[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]448.164836208359[/C][C]-0.164836208359217[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.160967795355[/C][C]-0.160967795354641[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.180576195146[/C][C]-0.180576195145646[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.195715925779[/C][C]-0.195715925778848[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.091538503939[/C][C]-0.0915385039386273[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.062770794081[/C][C]0.937229205919149[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]513.036159454706[/C][C]-0.0361594547064236[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.983801985661[/C][C]0.0161980143392029[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.062613021879[/C][C]-0.0626130218786618[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]471.022194250343[/C][C]-0.0221942503434258[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.120716669971[/C][C]-0.120716669971165[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.125294981768[/C][C]0.87470501823209[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]470.137100049006[/C][C]-0.137100049005804[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]461.152215946302[/C][C]-0.152215946301601[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.11900009687[/C][C]-0.119000096870104[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.11900009687[/C][C]0.880999903129895[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.944435218442[/C][C]0.0555647815580166[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]525.900015863276[/C][C]-0.900015863276438[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.850592938289[/C][C]0.149407061710655[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.883407363033[/C][C]0.116592636966544[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.866198452341[/C][C]0.133801547658903[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.875202605793[/C][C]0.124797394207046[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.913473883941[/C][C]0.0865261160594088[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.876856710274[/C][C]0.123143289726316[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.896465110065[/C][C]0.103534889935311[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.905535924357[/C][C]-0.905535924356648[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]499.936482456913[/C][C]1.06351754308748[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]506.879990085798[/C][C]0.120009914201947[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.724523845185[/C][C]-0.72452384518521[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.738197955666[/C][C]0.261802044334196[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.688975167495[/C][C]0.311024832504827[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.773879955036[/C][C]-0.773879955036055[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.759271821313[/C][C]-0.759271821313276[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.794798664069[/C][C]0.20520133593139[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.833069942216[/C][C]0.166930057783753[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.845475419904[/C][C]0.154524580096485[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.889361179756[/C][C]-0.889361179755928[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.905456768566[/C][C]0.0945432314339837[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]536.937003711571[/C][C]0.062996288428732[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]542.869039731716[/C][C]0.130960268284434[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.692830776957[/C][C]0.307169223043061[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.635206244187[/C][C]0.364793755813476[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.639808389321[/C][C]0.360191610679318[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.713040588842[/C][C]0.286959411158317[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.721842607074[/C][C]0.278157392925617[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.739050366712[/C][C]-0.739050366712259[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.860115575893[/C][C]0.139884424106596[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.864784381868[/C][C]-0.864784381867655[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.876856246762[/C][C]0.123143753237886[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.895664099287[/C][C]0.104335900712707[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]566.915362993256[/C][C]0.0846370067441861[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]568.9192980671[/C][C]0.0807019328995088[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.733040450547[/C][C]0.266959549453318[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.708653602142[/C][C]0.291346397857532[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.708453465326[/C][C]0.291546534673988[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.79266734824[/C][C]0.207332651759988[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.80155986065[/C][C]-0.801559860650222[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.830396384047[/C][C]0.169603615953054[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.914668116082[/C][C]0.0853318839178652[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]589.930098477709[/C][C]0.0699015222906932[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.960935367626[/C][C]0.0390646323737611[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]574.009891203844[/C][C]-0.00989120384416442[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]572.98463599628[/C][C]0.0153640037204581[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.003534497278[/C][C]-0.00353449727837724[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.901581412112[/C][C]0.0984185878877877[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]625.89304434743[/C][C]0.106955652570485[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.903672427106[/C][C]0.0963275728939233[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.967723013756[/C][C]1.03227698624434[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]565.972080041872[/C][C]0.0279199581281656[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]558.060543462962[/C][C]-1.06054346296188[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.082677167163[/C][C]-0.0826771671634487[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.113113783447[/C][C]-0.113113783447465[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]532.142549715649[/C][C]-0.142549715649194[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]526.153844866615[/C][C]-0.153844866615228[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.18799258659[/C][C]-0.187992586590061[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.195017361108[/C][C]-0.19501736110819[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]555.091440349717[/C][C]-0.0914403497173359[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.083503695484[/C][C]0.916496304515982[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.119675615317[/C][C]-0.119675615316462[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.101356377903[/C][C]-0.10135637790287[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.173321938147[/C][C]-0.173321938147215[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.140663671662[/C][C]-0.140663671662397[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.204769575256[/C][C]-0.204769575256036[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.242676065596[/C][C]0.757323934403502[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.272822050887[/C][C]-0.272822050886545[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.235337669114[/C][C]-0.235337669113703[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.261324610405[/C][C]-0.261324610405201[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.234956389646[/C][C]0.765043610353934[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.10803588714[/C][C]-1.10803588713985[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.076665247614[/C][C]0.923334752386349[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.123929495122[/C][C]-0.123929495122407[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.109964753648[/C][C]-0.109964753647833[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.110717634239[/C][C]-0.110717634238847[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]516.03683736248[/C][C]-0.0368373624796877[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.078173665597[/C][C]-0.0781736655966503[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.040819919031[/C][C]-0.0408199190307438[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.977343756794[/C][C]0.0226562432057249[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]537.019516093003[/C][C]-0.01951609300281[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.042702120508[/C][C]0.957297879491723[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.050793397582[/C][C]-0.0507933975820834[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.874959886361[/C][C]0.125040113639369[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]595.88147390224[/C][C]1.11852609775981[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.879248563922[/C][C]0.12075143607825[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.884715918759[/C][C]-0.884715918759135[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.938250066774[/C][C]1.06174993322619[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.931991350359[/C][C]0.0680086496413746[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.993040198335[/C][C]-0.993040198335143[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.996708320227[/C][C]0.00329167977288161[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]564.015516172752[/C][C]-1.0155161727523[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.99283790882[/C][C]1.00716209118016[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.019449093898[/C][C]-0.0194490938981244[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.020273474501[/C][C]-0.0202734745013668[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.921565405901[/C][C]0.0784345940987712[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]604.904891395873[/C][C]-0.904891395872614[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.921631219393[/C][C]-0.921631219393104[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.979389073844[/C][C]0.0206109261562677[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.025275200216[/C][C]0.974724799783648[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]551.061691240309[/C][C]-0.0616912403084659[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.145095918534[/C][C]-0.145095918533856[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.221063367759[/C][C]-0.221063367759126[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]540.206345745694[/C][C]-0.206345745693543[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.213615328637[/C][C]-0.213615328637379[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.274285737916[/C][C]0.725714262084305[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.228597749957[/C][C]0.771402250043174[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]572.098652363511[/C][C]-0.0986523635106971[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.063613602093[/C][C]-1.06361360209252[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]563.064789588009[/C][C]-0.0647895880094908[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.058628179088[/C][C]-0.0586281790880352[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.074411147673[/C][C]-0.0744111476733008[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]541.045664121696[/C][C]-0.0456641216957534[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.076799292876[/C][C]0.923200707124103[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.07297386167[/C][C]-0.0729738616701632[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]546.080219611277[/C][C]-0.080219611276598[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.996913765568[/C][C]-0.996913765568534[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]526.996356182622[/C][C]1.00364381737815[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]530.981125957811[/C][C]-0.981125957811138[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.837463633382[/C][C]0.162536366618282[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]598.835840858211[/C][C]0.16415914178851[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.79175296314[/C][C]0.208247036860084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.844436841823-0.844436841823466
2485484.8829435883170.117056411682668
3464463.9326979733980.0673020266015946
4460459.9640686129250.0359313870753726
5467467.081999449823-0.0819994498230227
6460460.100940778325-0.100940778324548
7448448.164836208359-0.164836208359217
8443443.160967795355-0.160967795354641
9436436.180576195146-0.180576195145646
10431431.195715925779-0.195715925778848
11484484.091538503939-0.0915385039386273
12510509.0627707940810.937229205919149
13513513.036159454706-0.0361594547064236
14503502.9838019856610.0161980143392029
15471471.062613021879-0.0626130218786618
16471471.022194250343-0.0221942503434258
17476476.120716669971-0.120716669971165
18475474.1252949817680.87470501823209
19470470.137100049006-0.137100049005804
20461461.152215946302-0.152215946301601
21455455.11900009687-0.119000096870104
22456455.119000096870.880999903129895
23517516.9444352184420.0555647815580166
24525525.900015863276-0.900015863276438
25523522.8505929382890.149407061710655
26519518.8834073630330.116592636966544
27509508.8661984523410.133801547658903
28512511.8752026057930.124797394207046
29519518.9134738839410.0865261160594088
30517516.8768567102740.123143289726316
31510509.8964651100650.103534889935311
32509509.905535924357-0.905535924356648
33501499.9364824569131.06351754308748
34507506.8799900857980.120009914201947
35569569.724523845185-0.72452384518521
36580579.7381979556660.261802044334196
37578577.6889751674950.311024832504827
38565565.773879955036-0.773879955036055
39547547.759271821313-0.759271821313276
40555554.7947986640690.20520133593139
41562561.8330699422160.166930057783753
42561560.8454754199040.154524580096485
43555555.889361179756-0.889361179755928
44544543.9054567685660.0945432314339837
45537536.9370037115710.062996288428732
46543542.8690397317160.130960268284434
47594593.6928307769570.307169223043061
48611610.6352062441870.364793755813476
49613612.6398083893210.360191610679318
50611610.7130405888420.286959411158317
51594593.7218426070740.278157392925617
52595595.739050366712-0.739050366712259
53591590.8601155758930.139884424106596
54589589.864784381868-0.864784381867655
55584583.8768562467620.123143753237886
56573572.8956640992870.104335900712707
57567566.9153629932560.0846370067441861
58569568.91929806710.0807019328995088
59621620.7330404505470.266959549453318
60629628.7086536021420.291346397857532
61628627.7084534653260.291546534673988
62612611.792667348240.207332651759988
63595595.80155986065-0.801559860650222
64597596.8303963840470.169603615953054
65593592.9146681160820.0853318839178652
66590589.9300984777090.0699015222906932
67580579.9609353676260.0390646323737611
68574574.009891203844-0.00989120384416442
69573572.984635996280.0153640037204581
70573573.003534497278-0.00353449727837724
71620619.9015814121120.0984185878877877
72626625.893044347430.106955652570485
73620619.9036724271060.0963275728939233
74588586.9677230137561.03227698624434
75566565.9720800418720.0279199581281656
76557558.060543462962-1.06054346296188
77561561.082677167163-0.0826771671634487
78549549.113113783447-0.113113783447465
79532532.142549715649-0.142549715649194
80526526.153844866615-0.153844866615228
81511511.18799258659-0.187992586590061
82499499.195017361108-0.19501736110819
83555555.091440349717-0.0914403497173359
84565564.0835036954840.916496304515982
85542542.119675615317-0.119675615316462
86527527.101356377903-0.10135637790287
87510510.173321938147-0.173321938147215
88514514.140663671662-0.140663671662397
89517517.204769575256-0.204769575256036
90508507.2426760655960.757323934403502
91493493.272822050887-0.272822050886545
92490490.235337669114-0.235337669113703
93469469.261324610405-0.261324610405201
94478477.2349563896460.765043610353934
95528529.10803588714-1.10803588713985
96534533.0766652476140.923334752386349
97518518.123929495122-0.123929495122407
98506506.109964753648-0.109964753647833
99502502.110717634239-0.110717634238847
100516516.03683736248-0.0368373624796877
101528528.078173665597-0.0781736655966503
102533533.040819919031-0.0408199190307438
103536535.9773437567940.0226562432057249
104537537.019516093003-0.01951609300281
105524523.0427021205080.957297879491723
106536536.050793397582-0.0507933975820834
107587586.8749598863610.125040113639369
108597595.881473902241.11852609775981
109581580.8792485639220.12075143607825
110564564.884715918759-0.884715918759135
111558556.9382500667741.06174993322619
112575574.9319913503590.0680086496413746
113580580.993040198335-0.993040198335143
114575574.9967083202270.00329167977288161
115563564.015516172752-1.0155161727523
116552550.992837908821.00716209118016
117537537.019449093898-0.0194490938981244
118545545.020273474501-0.0202734745013668
119601600.9215654059010.0784345940987712
120604604.904891395873-0.904891395872614
121586586.921631219393-0.921631219393104
122564563.9793890738440.0206109261562677
123549548.0252752002160.974724799783648
124551551.061691240309-0.0616912403084659
125556556.145095918534-0.145095918533856
126548548.221063367759-0.221063367759126
127540540.206345745694-0.206345745693543
128531531.213615328637-0.213615328637379
129521520.2742857379160.725714262084305
130519518.2285977499570.771402250043174
131572572.098652363511-0.0986523635106971
132581582.063613602093-1.06361360209252
133563563.064789588009-0.0647895880094908
134548548.058628179088-0.0586281790880352
135539539.074411147673-0.0744111476733008
136541541.045664121696-0.0456641216957534
137562561.0767992928760.923200707124103
138559559.07297386167-0.0729738616701632
139546546.080219611277-0.080219611276598
140536536.996913765568-0.996913765568534
141528526.9963561826221.00364381737815
142530530.981125957811-0.981125957811138
143582581.8374636333820.162536366618282
144599598.8358408582110.16415914178851
145584583.791752963140.208247036860084







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.04241168505114850.08482337010229690.957588314948852
110.01204180850424380.02408361700848770.987958191495756
120.2301180282865160.4602360565730320.769881971713484
130.2058510033593240.4117020067186480.794148996640676
140.1250557011729130.2501114023458260.874944298827087
150.07183069843197170.1436613968639430.928169301568028
160.07063788977614190.1412757795522840.929362110223858
170.04187079226224720.08374158452449450.958129207737753
180.2231356176982310.4462712353964630.776864382301769
190.2076072802017020.4152145604034050.792392719798298
200.1608993918434380.3217987836868750.839100608156562
210.1129288284456750.225857656891350.887071171554325
220.2277414633952360.4554829267904720.772258536604764
230.1861359320678420.3722718641356840.813864067932158
240.1923424812078490.3846849624156980.807657518792151
250.2704950228560460.5409900457120920.729504977143954
260.2194350652038370.4388701304076750.780564934796163
270.1721475932104720.3442951864209440.827852406789528
280.1311596081837390.2623192163674780.868840391816261
290.101023619116940.202047238233880.89897638088306
300.07418221920329690.1483644384065940.925817780796703
310.05354960946590770.1070992189318150.946450390534092
320.1423282242303870.2846564484607740.857671775769613
330.2929108308856050.585821661771210.707089169114395
340.2421804886598130.4843609773196260.757819511340187
350.2753261682542460.5506523365084920.724673831745754
360.2523923117607050.504784623521410.747607688239295
370.2398989691901420.4797979383802850.760101030809858
380.3081998948715510.6163997897431010.691800105128449
390.3442351844032180.6884703688064370.655764815596782
400.2975802122251490.5951604244502980.702419787774851
410.2501497152057810.5002994304115620.749850284794219
420.2072718901441740.4145437802883480.792728109855826
430.3425039922017150.6850079844034290.657496007798285
440.2964276566157080.5928553132314170.703572343384292
450.2513069769895070.5026139539790140.748693023010493
460.2138142231869120.4276284463738240.786185776813088
470.2017038612393610.4034077224787220.798296138760639
480.1904299296900680.3808598593801360.809570070309932
490.170318172922440.3406363458448790.82968182707756
500.1447587703804470.2895175407608950.855241229619553
510.1211960678326550.242392135665310.878803932167345
520.1621619042887630.3243238085775260.837838095711237
530.1318376694745820.2636753389491630.868162330525418
540.2077165167307730.4154330334615470.792283483269227
550.175651895236080.3513037904721610.82434810476392
560.1454778878835380.2909557757670750.854522112116462
570.1182020660472920.2364041320945840.881797933952708
580.09501230762440340.1900246152488070.904987692375597
590.07598912057036840.1519782411407370.924010879429632
600.05986071095387310.1197214219077460.940139289046127
610.04676522151713980.09353044303427960.95323477848286
620.03574601028097480.07149202056194960.964253989719025
630.07681658047091180.1536331609418240.923183419529088
640.061003303101730.122006606203460.93899669689827
650.04764702829110670.09529405658221340.952352971708893
660.03675970745400650.07351941490801290.963240292545994
670.02810114305609030.05620228611218060.97189885694391
680.02149906500792750.04299813001585490.978500934992073
690.01592699844579670.03185399689159330.984073001554203
700.01172049691796740.02344099383593470.988279503082033
710.008392693262502150.01678538652500430.991607306737498
720.005922558706164490.0118451174123290.994077441293835
730.004125670290038120.008251340580076240.995874329709962
740.00879790097917190.01759580195834380.991202099020828
750.006718103470191320.01343620694038260.993281896529809
760.029847573753430.059695147506860.97015242624657
770.02266908096029330.04533816192058660.977330919039707
780.01701895823772440.03403791647544890.982981041762276
790.01267694358219740.02535388716439470.987323056417803
800.00927086954214480.01854173908428960.990729130457855
810.006919199117050630.01383839823410130.993080800882949
820.005549509955059660.01109901991011930.99445049004494
830.003994139578895450.007988279157790890.996005860421105
840.007952423938484060.01590484787696810.992047576061516
850.005916219491977280.01183243898395460.994083780508023
860.004215487052270170.008430974104540340.99578451294773
870.003141119698788880.006282239397577770.996858880301211
880.002288643953555090.004577287907110180.997711356046445
890.00158738391202430.003174767824048590.998412616087976
900.002834583827984090.005669167655968180.997165416172016
910.002156417249887310.004312834499774610.997843582750113
920.001556090308250720.003112180616501440.998443909691749
930.001261921002423660.002523842004847330.998738078997576
940.001786009769145010.003572019538290030.998213990230855
950.006324301076658230.01264860215331650.993675698923342
960.01613861287541290.03227722575082570.983861387124587
970.0122213016887650.02444260337753010.987778698311235
980.008772884351601580.01754576870320320.991227115648398
990.006344127615595090.01268825523119020.993655872384405
1000.004678657324457240.009357314648914480.995321342675543
1010.003236591851021160.006473183702042320.996763408148979
1020.00219373204943830.00438746409887660.997806267950562
1030.001519612290923350.00303922458184670.998480387709077
1040.001226627007051270.002453254014102540.998773372992949
1050.001413117829175990.002826235658351980.998586882170824
1060.001188472256856830.002376944513713650.998811527743143
1070.0007628564118164890.001525712823632980.999237143588183
1080.002761898694509560.005523797389019120.99723810130549
1090.001995260449237360.003990520898474710.998004739550763
1100.008909391052555970.01781878210511190.991090608947444
1110.01400166591937660.02800333183875320.985998334080623
1120.01000313076368430.02000626152736860.989996869236316
1130.01992791868387270.03985583736774540.980072081316127
1140.01445514352299880.02891028704599750.985544856477001
1150.03607550675542910.07215101351085820.963924493244571
1160.06676001766176550.1335200353235310.933239982338234
1170.04897022680537780.09794045361075550.951029773194622
1180.03483931232894750.06967862465789510.965160687671052
1190.03123620049908660.06247240099817330.968763799500913
1200.03265013389850870.06530026779701730.967349866101491
1210.05497993596291780.1099598719258360.945020064037082
1220.04313629946460970.08627259892921950.95686370053539
1230.0703966023671230.1407932047342460.929603397632877
1240.06049471981950740.1209894396390150.939505280180493
1250.0412074424922920.0824148849845840.958792557507708
1260.02816314371226440.05632628742452890.971836856287736
1270.02172652607006110.04345305214012220.978273473929939
1280.01992857014900220.03985714029800440.980071429850998
1290.0217037827097050.043407565419410.978296217290295
1300.02749395881425610.05498791762851220.972506041185744
1310.01941068026495080.03882136052990160.980589319735049
1320.03680303837218380.07360607674436760.963196961627816
1330.02648605797458620.05297211594917250.973513942025414
1340.02167768897703030.04335537795406070.97832231102297
1350.2956662147164710.5913324294329420.704333785283529

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0424116850511485 & 0.0848233701022969 & 0.957588314948852 \tabularnewline
11 & 0.0120418085042438 & 0.0240836170084877 & 0.987958191495756 \tabularnewline
12 & 0.230118028286516 & 0.460236056573032 & 0.769881971713484 \tabularnewline
13 & 0.205851003359324 & 0.411702006718648 & 0.794148996640676 \tabularnewline
14 & 0.125055701172913 & 0.250111402345826 & 0.874944298827087 \tabularnewline
15 & 0.0718306984319717 & 0.143661396863943 & 0.928169301568028 \tabularnewline
16 & 0.0706378897761419 & 0.141275779552284 & 0.929362110223858 \tabularnewline
17 & 0.0418707922622472 & 0.0837415845244945 & 0.958129207737753 \tabularnewline
18 & 0.223135617698231 & 0.446271235396463 & 0.776864382301769 \tabularnewline
19 & 0.207607280201702 & 0.415214560403405 & 0.792392719798298 \tabularnewline
20 & 0.160899391843438 & 0.321798783686875 & 0.839100608156562 \tabularnewline
21 & 0.112928828445675 & 0.22585765689135 & 0.887071171554325 \tabularnewline
22 & 0.227741463395236 & 0.455482926790472 & 0.772258536604764 \tabularnewline
23 & 0.186135932067842 & 0.372271864135684 & 0.813864067932158 \tabularnewline
24 & 0.192342481207849 & 0.384684962415698 & 0.807657518792151 \tabularnewline
25 & 0.270495022856046 & 0.540990045712092 & 0.729504977143954 \tabularnewline
26 & 0.219435065203837 & 0.438870130407675 & 0.780564934796163 \tabularnewline
27 & 0.172147593210472 & 0.344295186420944 & 0.827852406789528 \tabularnewline
28 & 0.131159608183739 & 0.262319216367478 & 0.868840391816261 \tabularnewline
29 & 0.10102361911694 & 0.20204723823388 & 0.89897638088306 \tabularnewline
30 & 0.0741822192032969 & 0.148364438406594 & 0.925817780796703 \tabularnewline
31 & 0.0535496094659077 & 0.107099218931815 & 0.946450390534092 \tabularnewline
32 & 0.142328224230387 & 0.284656448460774 & 0.857671775769613 \tabularnewline
33 & 0.292910830885605 & 0.58582166177121 & 0.707089169114395 \tabularnewline
34 & 0.242180488659813 & 0.484360977319626 & 0.757819511340187 \tabularnewline
35 & 0.275326168254246 & 0.550652336508492 & 0.724673831745754 \tabularnewline
36 & 0.252392311760705 & 0.50478462352141 & 0.747607688239295 \tabularnewline
37 & 0.239898969190142 & 0.479797938380285 & 0.760101030809858 \tabularnewline
38 & 0.308199894871551 & 0.616399789743101 & 0.691800105128449 \tabularnewline
39 & 0.344235184403218 & 0.688470368806437 & 0.655764815596782 \tabularnewline
40 & 0.297580212225149 & 0.595160424450298 & 0.702419787774851 \tabularnewline
41 & 0.250149715205781 & 0.500299430411562 & 0.749850284794219 \tabularnewline
42 & 0.207271890144174 & 0.414543780288348 & 0.792728109855826 \tabularnewline
43 & 0.342503992201715 & 0.685007984403429 & 0.657496007798285 \tabularnewline
44 & 0.296427656615708 & 0.592855313231417 & 0.703572343384292 \tabularnewline
45 & 0.251306976989507 & 0.502613953979014 & 0.748693023010493 \tabularnewline
46 & 0.213814223186912 & 0.427628446373824 & 0.786185776813088 \tabularnewline
47 & 0.201703861239361 & 0.403407722478722 & 0.798296138760639 \tabularnewline
48 & 0.190429929690068 & 0.380859859380136 & 0.809570070309932 \tabularnewline
49 & 0.17031817292244 & 0.340636345844879 & 0.82968182707756 \tabularnewline
50 & 0.144758770380447 & 0.289517540760895 & 0.855241229619553 \tabularnewline
51 & 0.121196067832655 & 0.24239213566531 & 0.878803932167345 \tabularnewline
52 & 0.162161904288763 & 0.324323808577526 & 0.837838095711237 \tabularnewline
53 & 0.131837669474582 & 0.263675338949163 & 0.868162330525418 \tabularnewline
54 & 0.207716516730773 & 0.415433033461547 & 0.792283483269227 \tabularnewline
55 & 0.17565189523608 & 0.351303790472161 & 0.82434810476392 \tabularnewline
56 & 0.145477887883538 & 0.290955775767075 & 0.854522112116462 \tabularnewline
57 & 0.118202066047292 & 0.236404132094584 & 0.881797933952708 \tabularnewline
58 & 0.0950123076244034 & 0.190024615248807 & 0.904987692375597 \tabularnewline
59 & 0.0759891205703684 & 0.151978241140737 & 0.924010879429632 \tabularnewline
60 & 0.0598607109538731 & 0.119721421907746 & 0.940139289046127 \tabularnewline
61 & 0.0467652215171398 & 0.0935304430342796 & 0.95323477848286 \tabularnewline
62 & 0.0357460102809748 & 0.0714920205619496 & 0.964253989719025 \tabularnewline
63 & 0.0768165804709118 & 0.153633160941824 & 0.923183419529088 \tabularnewline
64 & 0.06100330310173 & 0.12200660620346 & 0.93899669689827 \tabularnewline
65 & 0.0476470282911067 & 0.0952940565822134 & 0.952352971708893 \tabularnewline
66 & 0.0367597074540065 & 0.0735194149080129 & 0.963240292545994 \tabularnewline
67 & 0.0281011430560903 & 0.0562022861121806 & 0.97189885694391 \tabularnewline
68 & 0.0214990650079275 & 0.0429981300158549 & 0.978500934992073 \tabularnewline
69 & 0.0159269984457967 & 0.0318539968915933 & 0.984073001554203 \tabularnewline
70 & 0.0117204969179674 & 0.0234409938359347 & 0.988279503082033 \tabularnewline
71 & 0.00839269326250215 & 0.0167853865250043 & 0.991607306737498 \tabularnewline
72 & 0.00592255870616449 & 0.011845117412329 & 0.994077441293835 \tabularnewline
73 & 0.00412567029003812 & 0.00825134058007624 & 0.995874329709962 \tabularnewline
74 & 0.0087979009791719 & 0.0175958019583438 & 0.991202099020828 \tabularnewline
75 & 0.00671810347019132 & 0.0134362069403826 & 0.993281896529809 \tabularnewline
76 & 0.02984757375343 & 0.05969514750686 & 0.97015242624657 \tabularnewline
77 & 0.0226690809602933 & 0.0453381619205866 & 0.977330919039707 \tabularnewline
78 & 0.0170189582377244 & 0.0340379164754489 & 0.982981041762276 \tabularnewline
79 & 0.0126769435821974 & 0.0253538871643947 & 0.987323056417803 \tabularnewline
80 & 0.0092708695421448 & 0.0185417390842896 & 0.990729130457855 \tabularnewline
81 & 0.00691919911705063 & 0.0138383982341013 & 0.993080800882949 \tabularnewline
82 & 0.00554950995505966 & 0.0110990199101193 & 0.99445049004494 \tabularnewline
83 & 0.00399413957889545 & 0.00798827915779089 & 0.996005860421105 \tabularnewline
84 & 0.00795242393848406 & 0.0159048478769681 & 0.992047576061516 \tabularnewline
85 & 0.00591621949197728 & 0.0118324389839546 & 0.994083780508023 \tabularnewline
86 & 0.00421548705227017 & 0.00843097410454034 & 0.99578451294773 \tabularnewline
87 & 0.00314111969878888 & 0.00628223939757777 & 0.996858880301211 \tabularnewline
88 & 0.00228864395355509 & 0.00457728790711018 & 0.997711356046445 \tabularnewline
89 & 0.0015873839120243 & 0.00317476782404859 & 0.998412616087976 \tabularnewline
90 & 0.00283458382798409 & 0.00566916765596818 & 0.997165416172016 \tabularnewline
91 & 0.00215641724988731 & 0.00431283449977461 & 0.997843582750113 \tabularnewline
92 & 0.00155609030825072 & 0.00311218061650144 & 0.998443909691749 \tabularnewline
93 & 0.00126192100242366 & 0.00252384200484733 & 0.998738078997576 \tabularnewline
94 & 0.00178600976914501 & 0.00357201953829003 & 0.998213990230855 \tabularnewline
95 & 0.00632430107665823 & 0.0126486021533165 & 0.993675698923342 \tabularnewline
96 & 0.0161386128754129 & 0.0322772257508257 & 0.983861387124587 \tabularnewline
97 & 0.012221301688765 & 0.0244426033775301 & 0.987778698311235 \tabularnewline
98 & 0.00877288435160158 & 0.0175457687032032 & 0.991227115648398 \tabularnewline
99 & 0.00634412761559509 & 0.0126882552311902 & 0.993655872384405 \tabularnewline
100 & 0.00467865732445724 & 0.00935731464891448 & 0.995321342675543 \tabularnewline
101 & 0.00323659185102116 & 0.00647318370204232 & 0.996763408148979 \tabularnewline
102 & 0.0021937320494383 & 0.0043874640988766 & 0.997806267950562 \tabularnewline
103 & 0.00151961229092335 & 0.0030392245818467 & 0.998480387709077 \tabularnewline
104 & 0.00122662700705127 & 0.00245325401410254 & 0.998773372992949 \tabularnewline
105 & 0.00141311782917599 & 0.00282623565835198 & 0.998586882170824 \tabularnewline
106 & 0.00118847225685683 & 0.00237694451371365 & 0.998811527743143 \tabularnewline
107 & 0.000762856411816489 & 0.00152571282363298 & 0.999237143588183 \tabularnewline
108 & 0.00276189869450956 & 0.00552379738901912 & 0.99723810130549 \tabularnewline
109 & 0.00199526044923736 & 0.00399052089847471 & 0.998004739550763 \tabularnewline
110 & 0.00890939105255597 & 0.0178187821051119 & 0.991090608947444 \tabularnewline
111 & 0.0140016659193766 & 0.0280033318387532 & 0.985998334080623 \tabularnewline
112 & 0.0100031307636843 & 0.0200062615273686 & 0.989996869236316 \tabularnewline
113 & 0.0199279186838727 & 0.0398558373677454 & 0.980072081316127 \tabularnewline
114 & 0.0144551435229988 & 0.0289102870459975 & 0.985544856477001 \tabularnewline
115 & 0.0360755067554291 & 0.0721510135108582 & 0.963924493244571 \tabularnewline
116 & 0.0667600176617655 & 0.133520035323531 & 0.933239982338234 \tabularnewline
117 & 0.0489702268053778 & 0.0979404536107555 & 0.951029773194622 \tabularnewline
118 & 0.0348393123289475 & 0.0696786246578951 & 0.965160687671052 \tabularnewline
119 & 0.0312362004990866 & 0.0624724009981733 & 0.968763799500913 \tabularnewline
120 & 0.0326501338985087 & 0.0653002677970173 & 0.967349866101491 \tabularnewline
121 & 0.0549799359629178 & 0.109959871925836 & 0.945020064037082 \tabularnewline
122 & 0.0431362994646097 & 0.0862725989292195 & 0.95686370053539 \tabularnewline
123 & 0.070396602367123 & 0.140793204734246 & 0.929603397632877 \tabularnewline
124 & 0.0604947198195074 & 0.120989439639015 & 0.939505280180493 \tabularnewline
125 & 0.041207442492292 & 0.082414884984584 & 0.958792557507708 \tabularnewline
126 & 0.0281631437122644 & 0.0563262874245289 & 0.971836856287736 \tabularnewline
127 & 0.0217265260700611 & 0.0434530521401222 & 0.978273473929939 \tabularnewline
128 & 0.0199285701490022 & 0.0398571402980044 & 0.980071429850998 \tabularnewline
129 & 0.021703782709705 & 0.04340756541941 & 0.978296217290295 \tabularnewline
130 & 0.0274939588142561 & 0.0549879176285122 & 0.972506041185744 \tabularnewline
131 & 0.0194106802649508 & 0.0388213605299016 & 0.980589319735049 \tabularnewline
132 & 0.0368030383721838 & 0.0736060767443676 & 0.963196961627816 \tabularnewline
133 & 0.0264860579745862 & 0.0529721159491725 & 0.973513942025414 \tabularnewline
134 & 0.0216776889770303 & 0.0433553779540607 & 0.97832231102297 \tabularnewline
135 & 0.295666214716471 & 0.591332429432942 & 0.704333785283529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&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]10[/C][C]0.0424116850511485[/C][C]0.0848233701022969[/C][C]0.957588314948852[/C][/ROW]
[ROW][C]11[/C][C]0.0120418085042438[/C][C]0.0240836170084877[/C][C]0.987958191495756[/C][/ROW]
[ROW][C]12[/C][C]0.230118028286516[/C][C]0.460236056573032[/C][C]0.769881971713484[/C][/ROW]
[ROW][C]13[/C][C]0.205851003359324[/C][C]0.411702006718648[/C][C]0.794148996640676[/C][/ROW]
[ROW][C]14[/C][C]0.125055701172913[/C][C]0.250111402345826[/C][C]0.874944298827087[/C][/ROW]
[ROW][C]15[/C][C]0.0718306984319717[/C][C]0.143661396863943[/C][C]0.928169301568028[/C][/ROW]
[ROW][C]16[/C][C]0.0706378897761419[/C][C]0.141275779552284[/C][C]0.929362110223858[/C][/ROW]
[ROW][C]17[/C][C]0.0418707922622472[/C][C]0.0837415845244945[/C][C]0.958129207737753[/C][/ROW]
[ROW][C]18[/C][C]0.223135617698231[/C][C]0.446271235396463[/C][C]0.776864382301769[/C][/ROW]
[ROW][C]19[/C][C]0.207607280201702[/C][C]0.415214560403405[/C][C]0.792392719798298[/C][/ROW]
[ROW][C]20[/C][C]0.160899391843438[/C][C]0.321798783686875[/C][C]0.839100608156562[/C][/ROW]
[ROW][C]21[/C][C]0.112928828445675[/C][C]0.22585765689135[/C][C]0.887071171554325[/C][/ROW]
[ROW][C]22[/C][C]0.227741463395236[/C][C]0.455482926790472[/C][C]0.772258536604764[/C][/ROW]
[ROW][C]23[/C][C]0.186135932067842[/C][C]0.372271864135684[/C][C]0.813864067932158[/C][/ROW]
[ROW][C]24[/C][C]0.192342481207849[/C][C]0.384684962415698[/C][C]0.807657518792151[/C][/ROW]
[ROW][C]25[/C][C]0.270495022856046[/C][C]0.540990045712092[/C][C]0.729504977143954[/C][/ROW]
[ROW][C]26[/C][C]0.219435065203837[/C][C]0.438870130407675[/C][C]0.780564934796163[/C][/ROW]
[ROW][C]27[/C][C]0.172147593210472[/C][C]0.344295186420944[/C][C]0.827852406789528[/C][/ROW]
[ROW][C]28[/C][C]0.131159608183739[/C][C]0.262319216367478[/C][C]0.868840391816261[/C][/ROW]
[ROW][C]29[/C][C]0.10102361911694[/C][C]0.20204723823388[/C][C]0.89897638088306[/C][/ROW]
[ROW][C]30[/C][C]0.0741822192032969[/C][C]0.148364438406594[/C][C]0.925817780796703[/C][/ROW]
[ROW][C]31[/C][C]0.0535496094659077[/C][C]0.107099218931815[/C][C]0.946450390534092[/C][/ROW]
[ROW][C]32[/C][C]0.142328224230387[/C][C]0.284656448460774[/C][C]0.857671775769613[/C][/ROW]
[ROW][C]33[/C][C]0.292910830885605[/C][C]0.58582166177121[/C][C]0.707089169114395[/C][/ROW]
[ROW][C]34[/C][C]0.242180488659813[/C][C]0.484360977319626[/C][C]0.757819511340187[/C][/ROW]
[ROW][C]35[/C][C]0.275326168254246[/C][C]0.550652336508492[/C][C]0.724673831745754[/C][/ROW]
[ROW][C]36[/C][C]0.252392311760705[/C][C]0.50478462352141[/C][C]0.747607688239295[/C][/ROW]
[ROW][C]37[/C][C]0.239898969190142[/C][C]0.479797938380285[/C][C]0.760101030809858[/C][/ROW]
[ROW][C]38[/C][C]0.308199894871551[/C][C]0.616399789743101[/C][C]0.691800105128449[/C][/ROW]
[ROW][C]39[/C][C]0.344235184403218[/C][C]0.688470368806437[/C][C]0.655764815596782[/C][/ROW]
[ROW][C]40[/C][C]0.297580212225149[/C][C]0.595160424450298[/C][C]0.702419787774851[/C][/ROW]
[ROW][C]41[/C][C]0.250149715205781[/C][C]0.500299430411562[/C][C]0.749850284794219[/C][/ROW]
[ROW][C]42[/C][C]0.207271890144174[/C][C]0.414543780288348[/C][C]0.792728109855826[/C][/ROW]
[ROW][C]43[/C][C]0.342503992201715[/C][C]0.685007984403429[/C][C]0.657496007798285[/C][/ROW]
[ROW][C]44[/C][C]0.296427656615708[/C][C]0.592855313231417[/C][C]0.703572343384292[/C][/ROW]
[ROW][C]45[/C][C]0.251306976989507[/C][C]0.502613953979014[/C][C]0.748693023010493[/C][/ROW]
[ROW][C]46[/C][C]0.213814223186912[/C][C]0.427628446373824[/C][C]0.786185776813088[/C][/ROW]
[ROW][C]47[/C][C]0.201703861239361[/C][C]0.403407722478722[/C][C]0.798296138760639[/C][/ROW]
[ROW][C]48[/C][C]0.190429929690068[/C][C]0.380859859380136[/C][C]0.809570070309932[/C][/ROW]
[ROW][C]49[/C][C]0.17031817292244[/C][C]0.340636345844879[/C][C]0.82968182707756[/C][/ROW]
[ROW][C]50[/C][C]0.144758770380447[/C][C]0.289517540760895[/C][C]0.855241229619553[/C][/ROW]
[ROW][C]51[/C][C]0.121196067832655[/C][C]0.24239213566531[/C][C]0.878803932167345[/C][/ROW]
[ROW][C]52[/C][C]0.162161904288763[/C][C]0.324323808577526[/C][C]0.837838095711237[/C][/ROW]
[ROW][C]53[/C][C]0.131837669474582[/C][C]0.263675338949163[/C][C]0.868162330525418[/C][/ROW]
[ROW][C]54[/C][C]0.207716516730773[/C][C]0.415433033461547[/C][C]0.792283483269227[/C][/ROW]
[ROW][C]55[/C][C]0.17565189523608[/C][C]0.351303790472161[/C][C]0.82434810476392[/C][/ROW]
[ROW][C]56[/C][C]0.145477887883538[/C][C]0.290955775767075[/C][C]0.854522112116462[/C][/ROW]
[ROW][C]57[/C][C]0.118202066047292[/C][C]0.236404132094584[/C][C]0.881797933952708[/C][/ROW]
[ROW][C]58[/C][C]0.0950123076244034[/C][C]0.190024615248807[/C][C]0.904987692375597[/C][/ROW]
[ROW][C]59[/C][C]0.0759891205703684[/C][C]0.151978241140737[/C][C]0.924010879429632[/C][/ROW]
[ROW][C]60[/C][C]0.0598607109538731[/C][C]0.119721421907746[/C][C]0.940139289046127[/C][/ROW]
[ROW][C]61[/C][C]0.0467652215171398[/C][C]0.0935304430342796[/C][C]0.95323477848286[/C][/ROW]
[ROW][C]62[/C][C]0.0357460102809748[/C][C]0.0714920205619496[/C][C]0.964253989719025[/C][/ROW]
[ROW][C]63[/C][C]0.0768165804709118[/C][C]0.153633160941824[/C][C]0.923183419529088[/C][/ROW]
[ROW][C]64[/C][C]0.06100330310173[/C][C]0.12200660620346[/C][C]0.93899669689827[/C][/ROW]
[ROW][C]65[/C][C]0.0476470282911067[/C][C]0.0952940565822134[/C][C]0.952352971708893[/C][/ROW]
[ROW][C]66[/C][C]0.0367597074540065[/C][C]0.0735194149080129[/C][C]0.963240292545994[/C][/ROW]
[ROW][C]67[/C][C]0.0281011430560903[/C][C]0.0562022861121806[/C][C]0.97189885694391[/C][/ROW]
[ROW][C]68[/C][C]0.0214990650079275[/C][C]0.0429981300158549[/C][C]0.978500934992073[/C][/ROW]
[ROW][C]69[/C][C]0.0159269984457967[/C][C]0.0318539968915933[/C][C]0.984073001554203[/C][/ROW]
[ROW][C]70[/C][C]0.0117204969179674[/C][C]0.0234409938359347[/C][C]0.988279503082033[/C][/ROW]
[ROW][C]71[/C][C]0.00839269326250215[/C][C]0.0167853865250043[/C][C]0.991607306737498[/C][/ROW]
[ROW][C]72[/C][C]0.00592255870616449[/C][C]0.011845117412329[/C][C]0.994077441293835[/C][/ROW]
[ROW][C]73[/C][C]0.00412567029003812[/C][C]0.00825134058007624[/C][C]0.995874329709962[/C][/ROW]
[ROW][C]74[/C][C]0.0087979009791719[/C][C]0.0175958019583438[/C][C]0.991202099020828[/C][/ROW]
[ROW][C]75[/C][C]0.00671810347019132[/C][C]0.0134362069403826[/C][C]0.993281896529809[/C][/ROW]
[ROW][C]76[/C][C]0.02984757375343[/C][C]0.05969514750686[/C][C]0.97015242624657[/C][/ROW]
[ROW][C]77[/C][C]0.0226690809602933[/C][C]0.0453381619205866[/C][C]0.977330919039707[/C][/ROW]
[ROW][C]78[/C][C]0.0170189582377244[/C][C]0.0340379164754489[/C][C]0.982981041762276[/C][/ROW]
[ROW][C]79[/C][C]0.0126769435821974[/C][C]0.0253538871643947[/C][C]0.987323056417803[/C][/ROW]
[ROW][C]80[/C][C]0.0092708695421448[/C][C]0.0185417390842896[/C][C]0.990729130457855[/C][/ROW]
[ROW][C]81[/C][C]0.00691919911705063[/C][C]0.0138383982341013[/C][C]0.993080800882949[/C][/ROW]
[ROW][C]82[/C][C]0.00554950995505966[/C][C]0.0110990199101193[/C][C]0.99445049004494[/C][/ROW]
[ROW][C]83[/C][C]0.00399413957889545[/C][C]0.00798827915779089[/C][C]0.996005860421105[/C][/ROW]
[ROW][C]84[/C][C]0.00795242393848406[/C][C]0.0159048478769681[/C][C]0.992047576061516[/C][/ROW]
[ROW][C]85[/C][C]0.00591621949197728[/C][C]0.0118324389839546[/C][C]0.994083780508023[/C][/ROW]
[ROW][C]86[/C][C]0.00421548705227017[/C][C]0.00843097410454034[/C][C]0.99578451294773[/C][/ROW]
[ROW][C]87[/C][C]0.00314111969878888[/C][C]0.00628223939757777[/C][C]0.996858880301211[/C][/ROW]
[ROW][C]88[/C][C]0.00228864395355509[/C][C]0.00457728790711018[/C][C]0.997711356046445[/C][/ROW]
[ROW][C]89[/C][C]0.0015873839120243[/C][C]0.00317476782404859[/C][C]0.998412616087976[/C][/ROW]
[ROW][C]90[/C][C]0.00283458382798409[/C][C]0.00566916765596818[/C][C]0.997165416172016[/C][/ROW]
[ROW][C]91[/C][C]0.00215641724988731[/C][C]0.00431283449977461[/C][C]0.997843582750113[/C][/ROW]
[ROW][C]92[/C][C]0.00155609030825072[/C][C]0.00311218061650144[/C][C]0.998443909691749[/C][/ROW]
[ROW][C]93[/C][C]0.00126192100242366[/C][C]0.00252384200484733[/C][C]0.998738078997576[/C][/ROW]
[ROW][C]94[/C][C]0.00178600976914501[/C][C]0.00357201953829003[/C][C]0.998213990230855[/C][/ROW]
[ROW][C]95[/C][C]0.00632430107665823[/C][C]0.0126486021533165[/C][C]0.993675698923342[/C][/ROW]
[ROW][C]96[/C][C]0.0161386128754129[/C][C]0.0322772257508257[/C][C]0.983861387124587[/C][/ROW]
[ROW][C]97[/C][C]0.012221301688765[/C][C]0.0244426033775301[/C][C]0.987778698311235[/C][/ROW]
[ROW][C]98[/C][C]0.00877288435160158[/C][C]0.0175457687032032[/C][C]0.991227115648398[/C][/ROW]
[ROW][C]99[/C][C]0.00634412761559509[/C][C]0.0126882552311902[/C][C]0.993655872384405[/C][/ROW]
[ROW][C]100[/C][C]0.00467865732445724[/C][C]0.00935731464891448[/C][C]0.995321342675543[/C][/ROW]
[ROW][C]101[/C][C]0.00323659185102116[/C][C]0.00647318370204232[/C][C]0.996763408148979[/C][/ROW]
[ROW][C]102[/C][C]0.0021937320494383[/C][C]0.0043874640988766[/C][C]0.997806267950562[/C][/ROW]
[ROW][C]103[/C][C]0.00151961229092335[/C][C]0.0030392245818467[/C][C]0.998480387709077[/C][/ROW]
[ROW][C]104[/C][C]0.00122662700705127[/C][C]0.00245325401410254[/C][C]0.998773372992949[/C][/ROW]
[ROW][C]105[/C][C]0.00141311782917599[/C][C]0.00282623565835198[/C][C]0.998586882170824[/C][/ROW]
[ROW][C]106[/C][C]0.00118847225685683[/C][C]0.00237694451371365[/C][C]0.998811527743143[/C][/ROW]
[ROW][C]107[/C][C]0.000762856411816489[/C][C]0.00152571282363298[/C][C]0.999237143588183[/C][/ROW]
[ROW][C]108[/C][C]0.00276189869450956[/C][C]0.00552379738901912[/C][C]0.99723810130549[/C][/ROW]
[ROW][C]109[/C][C]0.00199526044923736[/C][C]0.00399052089847471[/C][C]0.998004739550763[/C][/ROW]
[ROW][C]110[/C][C]0.00890939105255597[/C][C]0.0178187821051119[/C][C]0.991090608947444[/C][/ROW]
[ROW][C]111[/C][C]0.0140016659193766[/C][C]0.0280033318387532[/C][C]0.985998334080623[/C][/ROW]
[ROW][C]112[/C][C]0.0100031307636843[/C][C]0.0200062615273686[/C][C]0.989996869236316[/C][/ROW]
[ROW][C]113[/C][C]0.0199279186838727[/C][C]0.0398558373677454[/C][C]0.980072081316127[/C][/ROW]
[ROW][C]114[/C][C]0.0144551435229988[/C][C]0.0289102870459975[/C][C]0.985544856477001[/C][/ROW]
[ROW][C]115[/C][C]0.0360755067554291[/C][C]0.0721510135108582[/C][C]0.963924493244571[/C][/ROW]
[ROW][C]116[/C][C]0.0667600176617655[/C][C]0.133520035323531[/C][C]0.933239982338234[/C][/ROW]
[ROW][C]117[/C][C]0.0489702268053778[/C][C]0.0979404536107555[/C][C]0.951029773194622[/C][/ROW]
[ROW][C]118[/C][C]0.0348393123289475[/C][C]0.0696786246578951[/C][C]0.965160687671052[/C][/ROW]
[ROW][C]119[/C][C]0.0312362004990866[/C][C]0.0624724009981733[/C][C]0.968763799500913[/C][/ROW]
[ROW][C]120[/C][C]0.0326501338985087[/C][C]0.0653002677970173[/C][C]0.967349866101491[/C][/ROW]
[ROW][C]121[/C][C]0.0549799359629178[/C][C]0.109959871925836[/C][C]0.945020064037082[/C][/ROW]
[ROW][C]122[/C][C]0.0431362994646097[/C][C]0.0862725989292195[/C][C]0.95686370053539[/C][/ROW]
[ROW][C]123[/C][C]0.070396602367123[/C][C]0.140793204734246[/C][C]0.929603397632877[/C][/ROW]
[ROW][C]124[/C][C]0.0604947198195074[/C][C]0.120989439639015[/C][C]0.939505280180493[/C][/ROW]
[ROW][C]125[/C][C]0.041207442492292[/C][C]0.082414884984584[/C][C]0.958792557507708[/C][/ROW]
[ROW][C]126[/C][C]0.0281631437122644[/C][C]0.0563262874245289[/C][C]0.971836856287736[/C][/ROW]
[ROW][C]127[/C][C]0.0217265260700611[/C][C]0.0434530521401222[/C][C]0.978273473929939[/C][/ROW]
[ROW][C]128[/C][C]0.0199285701490022[/C][C]0.0398571402980044[/C][C]0.980071429850998[/C][/ROW]
[ROW][C]129[/C][C]0.021703782709705[/C][C]0.04340756541941[/C][C]0.978296217290295[/C][/ROW]
[ROW][C]130[/C][C]0.0274939588142561[/C][C]0.0549879176285122[/C][C]0.972506041185744[/C][/ROW]
[ROW][C]131[/C][C]0.0194106802649508[/C][C]0.0388213605299016[/C][C]0.980589319735049[/C][/ROW]
[ROW][C]132[/C][C]0.0368030383721838[/C][C]0.0736060767443676[/C][C]0.963196961627816[/C][/ROW]
[ROW][C]133[/C][C]0.0264860579745862[/C][C]0.0529721159491725[/C][C]0.973513942025414[/C][/ROW]
[ROW][C]134[/C][C]0.0216776889770303[/C][C]0.0433553779540607[/C][C]0.97832231102297[/C][/ROW]
[ROW][C]135[/C][C]0.295666214716471[/C][C]0.591332429432942[/C][C]0.704333785283529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.04241168505114850.08482337010229690.957588314948852
110.01204180850424380.02408361700848770.987958191495756
120.2301180282865160.4602360565730320.769881971713484
130.2058510033593240.4117020067186480.794148996640676
140.1250557011729130.2501114023458260.874944298827087
150.07183069843197170.1436613968639430.928169301568028
160.07063788977614190.1412757795522840.929362110223858
170.04187079226224720.08374158452449450.958129207737753
180.2231356176982310.4462712353964630.776864382301769
190.2076072802017020.4152145604034050.792392719798298
200.1608993918434380.3217987836868750.839100608156562
210.1129288284456750.225857656891350.887071171554325
220.2277414633952360.4554829267904720.772258536604764
230.1861359320678420.3722718641356840.813864067932158
240.1923424812078490.3846849624156980.807657518792151
250.2704950228560460.5409900457120920.729504977143954
260.2194350652038370.4388701304076750.780564934796163
270.1721475932104720.3442951864209440.827852406789528
280.1311596081837390.2623192163674780.868840391816261
290.101023619116940.202047238233880.89897638088306
300.07418221920329690.1483644384065940.925817780796703
310.05354960946590770.1070992189318150.946450390534092
320.1423282242303870.2846564484607740.857671775769613
330.2929108308856050.585821661771210.707089169114395
340.2421804886598130.4843609773196260.757819511340187
350.2753261682542460.5506523365084920.724673831745754
360.2523923117607050.504784623521410.747607688239295
370.2398989691901420.4797979383802850.760101030809858
380.3081998948715510.6163997897431010.691800105128449
390.3442351844032180.6884703688064370.655764815596782
400.2975802122251490.5951604244502980.702419787774851
410.2501497152057810.5002994304115620.749850284794219
420.2072718901441740.4145437802883480.792728109855826
430.3425039922017150.6850079844034290.657496007798285
440.2964276566157080.5928553132314170.703572343384292
450.2513069769895070.5026139539790140.748693023010493
460.2138142231869120.4276284463738240.786185776813088
470.2017038612393610.4034077224787220.798296138760639
480.1904299296900680.3808598593801360.809570070309932
490.170318172922440.3406363458448790.82968182707756
500.1447587703804470.2895175407608950.855241229619553
510.1211960678326550.242392135665310.878803932167345
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800.00927086954214480.01854173908428960.990729130457855
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1340.02167768897703030.04335537795406070.97832231102297
1350.2956662147164710.5913324294329420.704333785283529







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level520.412698412698413NOK
10% type I error level710.563492063492063NOK

\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 & 21 & 0.166666666666667 & NOK \tabularnewline
5% type I error level & 52 & 0.412698412698413 & NOK \tabularnewline
10% type I error level & 71 & 0.563492063492063 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186314&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]21[/C][C]0.166666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]52[/C][C]0.412698412698413[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.563492063492063[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186314&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186314&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.166666666666667NOK
5% type I error level520.412698412698413NOK
10% type I error level710.563492063492063NOK



Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 2 ; 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')
}