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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 09 Dec 2012 10:02:16 -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/Dec/09/t1355065361s7o4nvlq1xe4ll2.htm/, Retrieved Thu, 18 Apr 2024 22:55:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197915, Retrieved Thu, 18 Apr 2024 22:55:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Ws 10: Pearson co...] [2011-12-10 19:14:19] [a9a952c1cbc7081c25fad93a34aab827]
- RMP       [Multiple Regression] [ws10 multiple reg...] [2012-12-09 15:02:16] [ed5689773f261aad7999fb516f38c1f9] [Current]
- R  D        [Multiple Regression] [ws10] [2012-12-12 17:16:20] [6f05baeb40d1ecbb8a22259f722de55b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	255202	64	92	34
0	135248	59	58	30
0	207223	64	62	42
1	189326	95	108	34
1	141365	46	55	25
0	65295	27	8	31
0	439387	103	134	29
0	33186	19	1	18
0	183696	51	64	30
0	186657	38	77	29
1	276696	99	86	42
1	194414	98	96	50
0	141409	59	44	33
1	306730	68	108	46
1	192691	74	63	38
1	333497	164	160	52
0	261835	59	109	32
1	263451	130	86	35
1	157448	49	93	25
1	232190	73	126	42
0	245725	64	110	40
0	388603	92	86	35
0	156540	34	50	25
0	156189	47	92	46
0	189726	106	123	39
0	192167	106	81	35
1	249893	122	93	38
1	236812	76	113	35
1	143160	47	52	28
0	259667	54	113	37
0	243020	68	113	40
0	176062	67	44	42
0	286683	79	123	44
1	87485	33	38	33
0	329737	88	111	38
1	247082	51	77	37
0	378463	108	102	41
1	191653	75	74	32
0	114673	31	33	17
0	301596	167	107	39
0	284195	73	108	33
1	155568	60	66	35
1	177306	67	69	32
1	144595	51	62	35
0	140319	73	50	45
1	405267	135	91	38
1	78800	42	20	26
1	201970	69	101	45
1	302705	101	129	44
1	164733	50	93	40
1	194221	68	89	33
0	24188	24	8	4
0	346142	288	80	41
0	65029	17	21	18
0	101097	64	30	14
1	253745	51	86	36
0	273513	77	116	49
1	282220	160	106	32
1	280928	120	132	37
1	214872	74	75	32
0	342048	127	139	43
0	273924	108	121	25
1	195726	92	57	42
1	231162	80	67	37
0	209798	61	45	33
1	201345	60	88	28
0	180231	118	79	31
1	204441	129	75	40
0	197813	67	114	32
1	136421	60	127	25
1	216092	59	86	42
1	73566	32	22	23
0	213998	70	67	42
1	181728	50	77	38
0	148758	51	105	34
0	308343	71	121	39
1	251437	78	88	32
0	202388	102	78	37
0	173286	56	122	34
0	155529	58	66	33
0	132672	41	58	25
1	390163	102	134	45
0	145905	66	30	26
0	228012	88	103	40
1	80953	25	49	8
0	130805	47	26	27
1	135163	49	67	32
1	333790	168	59	37
1	271806	95	95	50
1	164235	99	156	41
1	234092	80	74	37
0	207158	69	137	38
0	156583	57	37	28
0	242395	68	111	36
1	261601	70	58	32
1	178489	35	78	32
0	204221	44	88	33
1	268066	69	152	35
1	327622	133	130	58
1	361799	101	145	27
0	247131	107	108	45
1	265849	58	138	37
0	162336	162	62	32
1	43287	14	13	19
0	172244	68	89	22
0	189021	121	86	35
0	227681	43	116	36
0	269329	81	157	36
0	106503	56	28	23
1	117891	77	83	40
1	287201	59	72	40
0	266805	78	134	42
0	23623	11	12	1
1	174954	69	120	36
0	61857	25	23	11
1	144889	43	83	40
1	347988	103	126	34
0	21054	16	4	0
1	224051	46	71	27
1	31414	19	18	8
1	278660	107	98	35
0	209481	58	68	44
0	156870	75	44	40
1	112933	46	29	28
0	38214	34	16	8
0	166011	35	61	36
1	316044	73	117	47
1	181578	56	46	48
1	358903	72	129	45
1	275578	91	139	48
1	368796	106	136	49
1	172464	31	66	35
1	94381	35	42	32
1	250563	290	75	36
1	382499	154	97	42
1	118010	42	49	35
1	365575	122	127	42
1	147989	72	55	34
1	231681	46	101	41
0	193119	77	80	36
0	189020	108	29	32
0	341958	106	95	33
1	222060	79	120	35
0	173260	63	41	21
0	274787	91	128	42
1	130908	52	142	49
0	204009	75	88	33
0	262412	94	170	39
0	1	0	0	0
0	14688	10	4	0
0	98	1	0	0
0	455	2	0	0
1	0	0	0	0
0	0	0	0	0
1	195765	75	56	33
0	334258	129	121	47
0	0	0	0	0
0	203	4	0	0
0	7199	5	7	0
1	46660	20	12	5
1	17547	5	0	1
0	107465	38	37	38
1	969	2	0	0
1	179994	58	47	28

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&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]11 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=197915&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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 time11 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Geslacht[t] = + 0.251520009656094 -1.76024934139575e-07Time_in_RFC[t] -0.000189922670195479Logins[t] + 0.000269996695085935Blogged_computations[t] + 0.00864114427771298Reviewed_compendiums[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Geslacht[t] =  +  0.251520009656094 -1.76024934139575e-07Time_in_RFC[t] -0.000189922670195479Logins[t] +  0.000269996695085935Blogged_computations[t] +  0.00864114427771298Reviewed_compendiums[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Geslacht[t] =  +  0.251520009656094 -1.76024934139575e-07Time_in_RFC[t] -0.000189922670195479Logins[t] +  0.000269996695085935Blogged_computations[t] +  0.00864114427771298Reviewed_compendiums[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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
Geslacht[t] = + 0.251520009656094 -1.76024934139575e-07Time_in_RFC[t] -0.000189922670195479Logins[t] + 0.000269996695085935Blogged_computations[t] + 0.00864114427771298Reviewed_compendiums[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2515200096560940.1039662.41930.0166810.00834
Time_in_RFC-1.76024934139575e-071e-06-0.21850.8273510.413675
Logins-0.0001899226701954790.001267-0.14990.8810480.440524
Blogged_computations0.0002699966950859350.0016310.16550.8687560.434378
Reviewed_compendiums0.008641144277712980.004891.76730.0791040.039552

\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) & 0.251520009656094 & 0.103966 & 2.4193 & 0.016681 & 0.00834 \tabularnewline
Time_in_RFC & -1.76024934139575e-07 & 1e-06 & -0.2185 & 0.827351 & 0.413675 \tabularnewline
Logins & -0.000189922670195479 & 0.001267 & -0.1499 & 0.881048 & 0.440524 \tabularnewline
Blogged_computations & 0.000269996695085935 & 0.001631 & 0.1655 & 0.868756 & 0.434378 \tabularnewline
Reviewed_compendiums & 0.00864114427771298 & 0.00489 & 1.7673 & 0.079104 & 0.039552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&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]0.251520009656094[/C][C]0.103966[/C][C]2.4193[/C][C]0.016681[/C][C]0.00834[/C][/ROW]
[ROW][C]Time_in_RFC[/C][C]-1.76024934139575e-07[/C][C]1e-06[/C][C]-0.2185[/C][C]0.827351[/C][C]0.413675[/C][/ROW]
[ROW][C]Logins[/C][C]-0.000189922670195479[/C][C]0.001267[/C][C]-0.1499[/C][C]0.881048[/C][C]0.440524[/C][/ROW]
[ROW][C]Blogged_computations[/C][C]0.000269996695085935[/C][C]0.001631[/C][C]0.1655[/C][C]0.868756[/C][C]0.434378[/C][/ROW]
[ROW][C]Reviewed_compendiums[/C][C]0.00864114427771298[/C][C]0.00489[/C][C]1.7673[/C][C]0.079104[/C][C]0.039552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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)0.2515200096560940.1039662.41930.0166810.00834
Time_in_RFC-1.76024934139575e-071e-06-0.21850.8273510.413675
Logins-0.0001899226701954790.001267-0.14990.8810480.440524
Blogged_computations0.0002699966950859350.0016310.16550.8687560.434378
Reviewed_compendiums0.008641144277712980.004891.76730.0791040.039552






Multiple Linear Regression - Regression Statistics
Multiple R0.205187821213266
R-squared0.0421020419742471
Adjusted R-squared0.0180039801371213
F-TEST (value)1.74711320183369
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0.142259420960258
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.496996110478281
Sum Squared Residuals39.2738162790559

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.205187821213266 \tabularnewline
R-squared & 0.0421020419742471 \tabularnewline
Adjusted R-squared & 0.0180039801371213 \tabularnewline
F-TEST (value) & 1.74711320183369 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0.142259420960258 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.496996110478281 \tabularnewline
Sum Squared Residuals & 39.2738162790559 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.205187821213266[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0421020419742471[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0180039801371213[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.74711320183369[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.142259420960258[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.496996110478281[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]39.2738162790559[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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 R0.205187821213266
R-squared0.0421020419742471
Adjusted R-squared0.0180039801371213
F-TEST (value)1.74711320183369
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0.142259420960258
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.496996110478281
Sum Squared Residuals39.2738162790559






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.513081644911443-0.513081644911443
200.491401688468425-0.491401688468425
300.582556398595651-0.582556398595651
410.5231098078181370.476890192181863
510.4487782271850120.551221772814988
600.504933995655962-0.504933995655962
700.441387648084366-0.441387648084366
800.397880509151944-0.397880509151944
900.48601299399131-0.48601299399131
1000.482829591632269-0.482829591632269
1110.5701600455713930.429839954428607
1210.6566627730450250.343337226954975
1300.512460677951127-0.512460677951127
1410.6112654198782480.388734580121752
1510.5489205858208470.451079414179153
1610.6542078779381140.345792122061886
1700.500171340135307-0.500171340135307
1810.5061158831040210.493884116895979
1910.4556373245719240.544362675428076
2010.5937320685187290.406267931481271
2100.571456639390109-0.571456639390109
2200.491303072014013-0.491303072014013
2300.44703613737636-0.44703613737636
2400.637432818441284-0.637432818441284
2500.568205920287184-0.568205920287184
2600.521871805118489-0.521871805118489
2710.537835220221390.46216477977861
2810.5283507462824410.471649253717559
2910.4733857825059170.526614217494083
3000.545788283712408-0.545788283712408
3100.571983086241432-0.571983086241432
3200.582611803046241-0.582611803046241
3300.599472704231656-0.599472704231656
3410.5252706557542360.474729344245764
3500.535097996678143-0.535097996678143
3610.5388534444960470.461146555503953
3700.546216014910714-0.546216014910714
3810.5000364750119560.499963524988044
3900.381256443266403-0.381256443266403
4000.532608780901691-0.532608780901691
4100.501947652807837-0.501947652807837
4210.5330006340857660.466999365914234
4310.502731302628190.49726869737181
4410.5355614729394950.464438527060505
4500.615307339249674-0.615307339249674
4610.5074765340016750.492523465998325
4710.4597421778199420.540257822180058
4810.61898474816520.381015251834801
4910.5940941141630870.405905885836913
5010.5837822244222170.416217775577783
5110.5136049963764560.486395003623544
5200.279428725135974-0.279428725135974
5300.511779308879962-0.511779308879962
5400.398055126416247-0.398055126416247
5500.350645286737435-0.350645286737435
5610.5314694163379360.468530583662064
5700.643486542477629-0.643486542477629
5810.4765908920778710.523409107922129
5910.5346408585613980.465359141438602
6010.496409271431450.50359072856855
6100.576289598427298-0.576289598427298
6200.431489114263956-0.431489114263956
6310.5779123390225510.422087660977449
6410.5334480370610210.466551962938979
6500.500312660084951-0.500312660084951
6610.4703946580235580.529605341976442
6700.486589196189009-0.486589196189009
6810.5569287948804130.443071205119587
6900.511271410582657-0.511271410582657
7010.4664293391228490.533570660877151
7110.5884247674878070.411575232512193
7210.4371792795842160.562820720415784
7300.581574277121113-0.581574277121113
7410.5591884449897140.440811555010286
7500.537797394749654-0.537797394749654
7600.55343367074102-0.55343367074102
7710.4927231860699720.507276813930028
7800.537304643417598-0.537304643417598
7900.537120185630562-0.537120185630562
8000.516105055843162-0.516105055843162
8100.452068015373722-0.452068015373722
8210.5885005305560560.411499469443944
8300.446071847480673-0.446071847480673
8400.56812644809823-0.56812644809823
8510.3148811886887210.685118811311279
8600.459899512217264-0.459899512217264
8710.5130281361009810.486971863899019
8810.4965097815822550.503490218417745
8910.6433396226575950.356660377342405
9010.6002146100679670.399785389932033
9110.5348222608695940.465177739130406
9200.567303401885986-0.567303401885986
9300.465073822686718-0.465073822686718
9400.536988531324245-0.536988531324245
9510.4843535491483630.515646450851637
9610.5110305608331320.488969439166868
9700.516132894424665-0.516132894424665
9810.5347085927905630.465291407209437
9910.7048765920159440.295123407984056
10010.4411125911052980.558887408894702
10100.605708201512696-0.605708201512696
10210.5506903242659240.449309675734076
10300.485433765358088-0.485433765358088
10410.4089331992619210.591066800738079
10500.422420909299198-0.422420909299198
10600.520926722983789-0.520926722983789
10700.545676612435491-0.545676612435491
10800.542198329009542-0.542198329009542
10900.428443382414285-0.428443382414285
11010.5841997053410450.415800294658955
11110.5548455681594470.445154431840553
11200.588849325633198-0.588849325633198
11300.257153727883509-0.257153727883509
11410.551099876493130.44890012350687
11500.337146079591955-0.337146079591955
11610.5859047549557910.414095245044209
11710.4985218988676670.501478101132333
11800.245855204749935-0.245855204749935
11910.4558256651575480.544174334842452
12010.316370926374570.68362907362543
12110.511046901636220.48895309836378
12200.602200739041479-0.602200739041479
12300.567188403665258-0.567188403665258
12410.4726864868733730.527313513126627
12500.311785123379317-0.311785123379317
12600.543201633255717-0.543201633255717
12710.6197474248241810.380252575175819
12810.6361168579381270.363883142061873
12910.5983507666276930.401649233372307
13010.6380329133151580.361967086684842
13110.6266065351440580.373393464855942
13210.5355342742342130.464465725765787
13310.516115784970650.48388421502935
13410.4836680458557030.516331954144297
13510.5440602962498180.455939703750182
13610.5384404428092380.461559557190762
13710.5612167685340290.438783231465971
13810.5204445470956060.479555452904394
13910.5835585156506230.416441484349377
14000.535583134399484-0.535583134399484
14100.482082649268227-0.482082649268227
14200.482002519384565-0.482002519384565
14310.5322676749658840.467732325034116
14400.401590695675252-0.401590695675252
14500.583355319725839-0.583355319725839
14610.6803565590377250.319643440962276
14700.510282608934643-0.510282608934643
14800.5703802886357-0.5703802886357
14900.25151983363116-0.25151983363116
15000.248115315501841-0.248115315501841
15100.251312836542353-0.251312836542353
15200.25106007297067-0.25106007297067
15310.2515200096560940.748479990343906
15400.251520009656094-0.251520009656094
15510.503093864248940.49690613575106
15600.606985623923159-0.606985623923159
15700.251520009656094-0.251520009656094
15800.250724585913682-0.250724585913682
15900.251193169669847-0.251193169669847
16010.2859539145548280.714046085445172
16110.2561228310634830.743877168936517
16200.563739788912629-0.563739788912629
16310.2509695961545220.749030403845478
16410.463462947234240.53653705276576

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.513081644911443 & -0.513081644911443 \tabularnewline
2 & 0 & 0.491401688468425 & -0.491401688468425 \tabularnewline
3 & 0 & 0.582556398595651 & -0.582556398595651 \tabularnewline
4 & 1 & 0.523109807818137 & 0.476890192181863 \tabularnewline
5 & 1 & 0.448778227185012 & 0.551221772814988 \tabularnewline
6 & 0 & 0.504933995655962 & -0.504933995655962 \tabularnewline
7 & 0 & 0.441387648084366 & -0.441387648084366 \tabularnewline
8 & 0 & 0.397880509151944 & -0.397880509151944 \tabularnewline
9 & 0 & 0.48601299399131 & -0.48601299399131 \tabularnewline
10 & 0 & 0.482829591632269 & -0.482829591632269 \tabularnewline
11 & 1 & 0.570160045571393 & 0.429839954428607 \tabularnewline
12 & 1 & 0.656662773045025 & 0.343337226954975 \tabularnewline
13 & 0 & 0.512460677951127 & -0.512460677951127 \tabularnewline
14 & 1 & 0.611265419878248 & 0.388734580121752 \tabularnewline
15 & 1 & 0.548920585820847 & 0.451079414179153 \tabularnewline
16 & 1 & 0.654207877938114 & 0.345792122061886 \tabularnewline
17 & 0 & 0.500171340135307 & -0.500171340135307 \tabularnewline
18 & 1 & 0.506115883104021 & 0.493884116895979 \tabularnewline
19 & 1 & 0.455637324571924 & 0.544362675428076 \tabularnewline
20 & 1 & 0.593732068518729 & 0.406267931481271 \tabularnewline
21 & 0 & 0.571456639390109 & -0.571456639390109 \tabularnewline
22 & 0 & 0.491303072014013 & -0.491303072014013 \tabularnewline
23 & 0 & 0.44703613737636 & -0.44703613737636 \tabularnewline
24 & 0 & 0.637432818441284 & -0.637432818441284 \tabularnewline
25 & 0 & 0.568205920287184 & -0.568205920287184 \tabularnewline
26 & 0 & 0.521871805118489 & -0.521871805118489 \tabularnewline
27 & 1 & 0.53783522022139 & 0.46216477977861 \tabularnewline
28 & 1 & 0.528350746282441 & 0.471649253717559 \tabularnewline
29 & 1 & 0.473385782505917 & 0.526614217494083 \tabularnewline
30 & 0 & 0.545788283712408 & -0.545788283712408 \tabularnewline
31 & 0 & 0.571983086241432 & -0.571983086241432 \tabularnewline
32 & 0 & 0.582611803046241 & -0.582611803046241 \tabularnewline
33 & 0 & 0.599472704231656 & -0.599472704231656 \tabularnewline
34 & 1 & 0.525270655754236 & 0.474729344245764 \tabularnewline
35 & 0 & 0.535097996678143 & -0.535097996678143 \tabularnewline
36 & 1 & 0.538853444496047 & 0.461146555503953 \tabularnewline
37 & 0 & 0.546216014910714 & -0.546216014910714 \tabularnewline
38 & 1 & 0.500036475011956 & 0.499963524988044 \tabularnewline
39 & 0 & 0.381256443266403 & -0.381256443266403 \tabularnewline
40 & 0 & 0.532608780901691 & -0.532608780901691 \tabularnewline
41 & 0 & 0.501947652807837 & -0.501947652807837 \tabularnewline
42 & 1 & 0.533000634085766 & 0.466999365914234 \tabularnewline
43 & 1 & 0.50273130262819 & 0.49726869737181 \tabularnewline
44 & 1 & 0.535561472939495 & 0.464438527060505 \tabularnewline
45 & 0 & 0.615307339249674 & -0.615307339249674 \tabularnewline
46 & 1 & 0.507476534001675 & 0.492523465998325 \tabularnewline
47 & 1 & 0.459742177819942 & 0.540257822180058 \tabularnewline
48 & 1 & 0.6189847481652 & 0.381015251834801 \tabularnewline
49 & 1 & 0.594094114163087 & 0.405905885836913 \tabularnewline
50 & 1 & 0.583782224422217 & 0.416217775577783 \tabularnewline
51 & 1 & 0.513604996376456 & 0.486395003623544 \tabularnewline
52 & 0 & 0.279428725135974 & -0.279428725135974 \tabularnewline
53 & 0 & 0.511779308879962 & -0.511779308879962 \tabularnewline
54 & 0 & 0.398055126416247 & -0.398055126416247 \tabularnewline
55 & 0 & 0.350645286737435 & -0.350645286737435 \tabularnewline
56 & 1 & 0.531469416337936 & 0.468530583662064 \tabularnewline
57 & 0 & 0.643486542477629 & -0.643486542477629 \tabularnewline
58 & 1 & 0.476590892077871 & 0.523409107922129 \tabularnewline
59 & 1 & 0.534640858561398 & 0.465359141438602 \tabularnewline
60 & 1 & 0.49640927143145 & 0.50359072856855 \tabularnewline
61 & 0 & 0.576289598427298 & -0.576289598427298 \tabularnewline
62 & 0 & 0.431489114263956 & -0.431489114263956 \tabularnewline
63 & 1 & 0.577912339022551 & 0.422087660977449 \tabularnewline
64 & 1 & 0.533448037061021 & 0.466551962938979 \tabularnewline
65 & 0 & 0.500312660084951 & -0.500312660084951 \tabularnewline
66 & 1 & 0.470394658023558 & 0.529605341976442 \tabularnewline
67 & 0 & 0.486589196189009 & -0.486589196189009 \tabularnewline
68 & 1 & 0.556928794880413 & 0.443071205119587 \tabularnewline
69 & 0 & 0.511271410582657 & -0.511271410582657 \tabularnewline
70 & 1 & 0.466429339122849 & 0.533570660877151 \tabularnewline
71 & 1 & 0.588424767487807 & 0.411575232512193 \tabularnewline
72 & 1 & 0.437179279584216 & 0.562820720415784 \tabularnewline
73 & 0 & 0.581574277121113 & -0.581574277121113 \tabularnewline
74 & 1 & 0.559188444989714 & 0.440811555010286 \tabularnewline
75 & 0 & 0.537797394749654 & -0.537797394749654 \tabularnewline
76 & 0 & 0.55343367074102 & -0.55343367074102 \tabularnewline
77 & 1 & 0.492723186069972 & 0.507276813930028 \tabularnewline
78 & 0 & 0.537304643417598 & -0.537304643417598 \tabularnewline
79 & 0 & 0.537120185630562 & -0.537120185630562 \tabularnewline
80 & 0 & 0.516105055843162 & -0.516105055843162 \tabularnewline
81 & 0 & 0.452068015373722 & -0.452068015373722 \tabularnewline
82 & 1 & 0.588500530556056 & 0.411499469443944 \tabularnewline
83 & 0 & 0.446071847480673 & -0.446071847480673 \tabularnewline
84 & 0 & 0.56812644809823 & -0.56812644809823 \tabularnewline
85 & 1 & 0.314881188688721 & 0.685118811311279 \tabularnewline
86 & 0 & 0.459899512217264 & -0.459899512217264 \tabularnewline
87 & 1 & 0.513028136100981 & 0.486971863899019 \tabularnewline
88 & 1 & 0.496509781582255 & 0.503490218417745 \tabularnewline
89 & 1 & 0.643339622657595 & 0.356660377342405 \tabularnewline
90 & 1 & 0.600214610067967 & 0.399785389932033 \tabularnewline
91 & 1 & 0.534822260869594 & 0.465177739130406 \tabularnewline
92 & 0 & 0.567303401885986 & -0.567303401885986 \tabularnewline
93 & 0 & 0.465073822686718 & -0.465073822686718 \tabularnewline
94 & 0 & 0.536988531324245 & -0.536988531324245 \tabularnewline
95 & 1 & 0.484353549148363 & 0.515646450851637 \tabularnewline
96 & 1 & 0.511030560833132 & 0.488969439166868 \tabularnewline
97 & 0 & 0.516132894424665 & -0.516132894424665 \tabularnewline
98 & 1 & 0.534708592790563 & 0.465291407209437 \tabularnewline
99 & 1 & 0.704876592015944 & 0.295123407984056 \tabularnewline
100 & 1 & 0.441112591105298 & 0.558887408894702 \tabularnewline
101 & 0 & 0.605708201512696 & -0.605708201512696 \tabularnewline
102 & 1 & 0.550690324265924 & 0.449309675734076 \tabularnewline
103 & 0 & 0.485433765358088 & -0.485433765358088 \tabularnewline
104 & 1 & 0.408933199261921 & 0.591066800738079 \tabularnewline
105 & 0 & 0.422420909299198 & -0.422420909299198 \tabularnewline
106 & 0 & 0.520926722983789 & -0.520926722983789 \tabularnewline
107 & 0 & 0.545676612435491 & -0.545676612435491 \tabularnewline
108 & 0 & 0.542198329009542 & -0.542198329009542 \tabularnewline
109 & 0 & 0.428443382414285 & -0.428443382414285 \tabularnewline
110 & 1 & 0.584199705341045 & 0.415800294658955 \tabularnewline
111 & 1 & 0.554845568159447 & 0.445154431840553 \tabularnewline
112 & 0 & 0.588849325633198 & -0.588849325633198 \tabularnewline
113 & 0 & 0.257153727883509 & -0.257153727883509 \tabularnewline
114 & 1 & 0.55109987649313 & 0.44890012350687 \tabularnewline
115 & 0 & 0.337146079591955 & -0.337146079591955 \tabularnewline
116 & 1 & 0.585904754955791 & 0.414095245044209 \tabularnewline
117 & 1 & 0.498521898867667 & 0.501478101132333 \tabularnewline
118 & 0 & 0.245855204749935 & -0.245855204749935 \tabularnewline
119 & 1 & 0.455825665157548 & 0.544174334842452 \tabularnewline
120 & 1 & 0.31637092637457 & 0.68362907362543 \tabularnewline
121 & 1 & 0.51104690163622 & 0.48895309836378 \tabularnewline
122 & 0 & 0.602200739041479 & -0.602200739041479 \tabularnewline
123 & 0 & 0.567188403665258 & -0.567188403665258 \tabularnewline
124 & 1 & 0.472686486873373 & 0.527313513126627 \tabularnewline
125 & 0 & 0.311785123379317 & -0.311785123379317 \tabularnewline
126 & 0 & 0.543201633255717 & -0.543201633255717 \tabularnewline
127 & 1 & 0.619747424824181 & 0.380252575175819 \tabularnewline
128 & 1 & 0.636116857938127 & 0.363883142061873 \tabularnewline
129 & 1 & 0.598350766627693 & 0.401649233372307 \tabularnewline
130 & 1 & 0.638032913315158 & 0.361967086684842 \tabularnewline
131 & 1 & 0.626606535144058 & 0.373393464855942 \tabularnewline
132 & 1 & 0.535534274234213 & 0.464465725765787 \tabularnewline
133 & 1 & 0.51611578497065 & 0.48388421502935 \tabularnewline
134 & 1 & 0.483668045855703 & 0.516331954144297 \tabularnewline
135 & 1 & 0.544060296249818 & 0.455939703750182 \tabularnewline
136 & 1 & 0.538440442809238 & 0.461559557190762 \tabularnewline
137 & 1 & 0.561216768534029 & 0.438783231465971 \tabularnewline
138 & 1 & 0.520444547095606 & 0.479555452904394 \tabularnewline
139 & 1 & 0.583558515650623 & 0.416441484349377 \tabularnewline
140 & 0 & 0.535583134399484 & -0.535583134399484 \tabularnewline
141 & 0 & 0.482082649268227 & -0.482082649268227 \tabularnewline
142 & 0 & 0.482002519384565 & -0.482002519384565 \tabularnewline
143 & 1 & 0.532267674965884 & 0.467732325034116 \tabularnewline
144 & 0 & 0.401590695675252 & -0.401590695675252 \tabularnewline
145 & 0 & 0.583355319725839 & -0.583355319725839 \tabularnewline
146 & 1 & 0.680356559037725 & 0.319643440962276 \tabularnewline
147 & 0 & 0.510282608934643 & -0.510282608934643 \tabularnewline
148 & 0 & 0.5703802886357 & -0.5703802886357 \tabularnewline
149 & 0 & 0.25151983363116 & -0.25151983363116 \tabularnewline
150 & 0 & 0.248115315501841 & -0.248115315501841 \tabularnewline
151 & 0 & 0.251312836542353 & -0.251312836542353 \tabularnewline
152 & 0 & 0.25106007297067 & -0.25106007297067 \tabularnewline
153 & 1 & 0.251520009656094 & 0.748479990343906 \tabularnewline
154 & 0 & 0.251520009656094 & -0.251520009656094 \tabularnewline
155 & 1 & 0.50309386424894 & 0.49690613575106 \tabularnewline
156 & 0 & 0.606985623923159 & -0.606985623923159 \tabularnewline
157 & 0 & 0.251520009656094 & -0.251520009656094 \tabularnewline
158 & 0 & 0.250724585913682 & -0.250724585913682 \tabularnewline
159 & 0 & 0.251193169669847 & -0.251193169669847 \tabularnewline
160 & 1 & 0.285953914554828 & 0.714046085445172 \tabularnewline
161 & 1 & 0.256122831063483 & 0.743877168936517 \tabularnewline
162 & 0 & 0.563739788912629 & -0.563739788912629 \tabularnewline
163 & 1 & 0.250969596154522 & 0.749030403845478 \tabularnewline
164 & 1 & 0.46346294723424 & 0.53653705276576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&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]0[/C][C]0.513081644911443[/C][C]-0.513081644911443[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.491401688468425[/C][C]-0.491401688468425[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.582556398595651[/C][C]-0.582556398595651[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]0.523109807818137[/C][C]0.476890192181863[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.448778227185012[/C][C]0.551221772814988[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.504933995655962[/C][C]-0.504933995655962[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.441387648084366[/C][C]-0.441387648084366[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.397880509151944[/C][C]-0.397880509151944[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.48601299399131[/C][C]-0.48601299399131[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.482829591632269[/C][C]-0.482829591632269[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.570160045571393[/C][C]0.429839954428607[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.656662773045025[/C][C]0.343337226954975[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.512460677951127[/C][C]-0.512460677951127[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.611265419878248[/C][C]0.388734580121752[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.548920585820847[/C][C]0.451079414179153[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.654207877938114[/C][C]0.345792122061886[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.500171340135307[/C][C]-0.500171340135307[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.506115883104021[/C][C]0.493884116895979[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.455637324571924[/C][C]0.544362675428076[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.593732068518729[/C][C]0.406267931481271[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.571456639390109[/C][C]-0.571456639390109[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.491303072014013[/C][C]-0.491303072014013[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.44703613737636[/C][C]-0.44703613737636[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.637432818441284[/C][C]-0.637432818441284[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.568205920287184[/C][C]-0.568205920287184[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.521871805118489[/C][C]-0.521871805118489[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.53783522022139[/C][C]0.46216477977861[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.528350746282441[/C][C]0.471649253717559[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.473385782505917[/C][C]0.526614217494083[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.545788283712408[/C][C]-0.545788283712408[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.571983086241432[/C][C]-0.571983086241432[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.582611803046241[/C][C]-0.582611803046241[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.599472704231656[/C][C]-0.599472704231656[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.525270655754236[/C][C]0.474729344245764[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.535097996678143[/C][C]-0.535097996678143[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]0.538853444496047[/C][C]0.461146555503953[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.546216014910714[/C][C]-0.546216014910714[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.500036475011956[/C][C]0.499963524988044[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.381256443266403[/C][C]-0.381256443266403[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.532608780901691[/C][C]-0.532608780901691[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.501947652807837[/C][C]-0.501947652807837[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.533000634085766[/C][C]0.466999365914234[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.50273130262819[/C][C]0.49726869737181[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.535561472939495[/C][C]0.464438527060505[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.615307339249674[/C][C]-0.615307339249674[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.507476534001675[/C][C]0.492523465998325[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.459742177819942[/C][C]0.540257822180058[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.6189847481652[/C][C]0.381015251834801[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.594094114163087[/C][C]0.405905885836913[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]0.583782224422217[/C][C]0.416217775577783[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.513604996376456[/C][C]0.486395003623544[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.279428725135974[/C][C]-0.279428725135974[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.511779308879962[/C][C]-0.511779308879962[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.398055126416247[/C][C]-0.398055126416247[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.350645286737435[/C][C]-0.350645286737435[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.531469416337936[/C][C]0.468530583662064[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.643486542477629[/C][C]-0.643486542477629[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.476590892077871[/C][C]0.523409107922129[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.534640858561398[/C][C]0.465359141438602[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.49640927143145[/C][C]0.50359072856855[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.576289598427298[/C][C]-0.576289598427298[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.431489114263956[/C][C]-0.431489114263956[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.577912339022551[/C][C]0.422087660977449[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.533448037061021[/C][C]0.466551962938979[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.500312660084951[/C][C]-0.500312660084951[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.470394658023558[/C][C]0.529605341976442[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.486589196189009[/C][C]-0.486589196189009[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]0.556928794880413[/C][C]0.443071205119587[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.511271410582657[/C][C]-0.511271410582657[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.466429339122849[/C][C]0.533570660877151[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.588424767487807[/C][C]0.411575232512193[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]0.437179279584216[/C][C]0.562820720415784[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.581574277121113[/C][C]-0.581574277121113[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.559188444989714[/C][C]0.440811555010286[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.537797394749654[/C][C]-0.537797394749654[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.55343367074102[/C][C]-0.55343367074102[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.492723186069972[/C][C]0.507276813930028[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.537304643417598[/C][C]-0.537304643417598[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.537120185630562[/C][C]-0.537120185630562[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.516105055843162[/C][C]-0.516105055843162[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.452068015373722[/C][C]-0.452068015373722[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.588500530556056[/C][C]0.411499469443944[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.446071847480673[/C][C]-0.446071847480673[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.56812644809823[/C][C]-0.56812644809823[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0.314881188688721[/C][C]0.685118811311279[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.459899512217264[/C][C]-0.459899512217264[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.513028136100981[/C][C]0.486971863899019[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.496509781582255[/C][C]0.503490218417745[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0.643339622657595[/C][C]0.356660377342405[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.600214610067967[/C][C]0.399785389932033[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.534822260869594[/C][C]0.465177739130406[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.567303401885986[/C][C]-0.567303401885986[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.465073822686718[/C][C]-0.465073822686718[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.536988531324245[/C][C]-0.536988531324245[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.484353549148363[/C][C]0.515646450851637[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.511030560833132[/C][C]0.488969439166868[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.516132894424665[/C][C]-0.516132894424665[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0.534708592790563[/C][C]0.465291407209437[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0.704876592015944[/C][C]0.295123407984056[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0.441112591105298[/C][C]0.558887408894702[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.605708201512696[/C][C]-0.605708201512696[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0.550690324265924[/C][C]0.449309675734076[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.485433765358088[/C][C]-0.485433765358088[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0.408933199261921[/C][C]0.591066800738079[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.422420909299198[/C][C]-0.422420909299198[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.520926722983789[/C][C]-0.520926722983789[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.545676612435491[/C][C]-0.545676612435491[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.542198329009542[/C][C]-0.542198329009542[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.428443382414285[/C][C]-0.428443382414285[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0.584199705341045[/C][C]0.415800294658955[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0.554845568159447[/C][C]0.445154431840553[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.588849325633198[/C][C]-0.588849325633198[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.257153727883509[/C][C]-0.257153727883509[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.55109987649313[/C][C]0.44890012350687[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.337146079591955[/C][C]-0.337146079591955[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0.585904754955791[/C][C]0.414095245044209[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.498521898867667[/C][C]0.501478101132333[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.245855204749935[/C][C]-0.245855204749935[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0.455825665157548[/C][C]0.544174334842452[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0.31637092637457[/C][C]0.68362907362543[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0.51104690163622[/C][C]0.48895309836378[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.602200739041479[/C][C]-0.602200739041479[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.567188403665258[/C][C]-0.567188403665258[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0.472686486873373[/C][C]0.527313513126627[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.311785123379317[/C][C]-0.311785123379317[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.543201633255717[/C][C]-0.543201633255717[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0.619747424824181[/C][C]0.380252575175819[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0.636116857938127[/C][C]0.363883142061873[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0.598350766627693[/C][C]0.401649233372307[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0.638032913315158[/C][C]0.361967086684842[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0.626606535144058[/C][C]0.373393464855942[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0.535534274234213[/C][C]0.464465725765787[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0.51611578497065[/C][C]0.48388421502935[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0.483668045855703[/C][C]0.516331954144297[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0.544060296249818[/C][C]0.455939703750182[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0.538440442809238[/C][C]0.461559557190762[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.561216768534029[/C][C]0.438783231465971[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0.520444547095606[/C][C]0.479555452904394[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0.583558515650623[/C][C]0.416441484349377[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.535583134399484[/C][C]-0.535583134399484[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]0.482082649268227[/C][C]-0.482082649268227[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.482002519384565[/C][C]-0.482002519384565[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0.532267674965884[/C][C]0.467732325034116[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.401590695675252[/C][C]-0.401590695675252[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.583355319725839[/C][C]-0.583355319725839[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0.680356559037725[/C][C]0.319643440962276[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.510282608934643[/C][C]-0.510282608934643[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.5703802886357[/C][C]-0.5703802886357[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.25151983363116[/C][C]-0.25151983363116[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.248115315501841[/C][C]-0.248115315501841[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.251312836542353[/C][C]-0.251312836542353[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.25106007297067[/C][C]-0.25106007297067[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.251520009656094[/C][C]0.748479990343906[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.251520009656094[/C][C]-0.251520009656094[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]0.50309386424894[/C][C]0.49690613575106[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]0.606985623923159[/C][C]-0.606985623923159[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]0.251520009656094[/C][C]-0.251520009656094[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]0.250724585913682[/C][C]-0.250724585913682[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]0.251193169669847[/C][C]-0.251193169669847[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]0.285953914554828[/C][C]0.714046085445172[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]0.256122831063483[/C][C]0.743877168936517[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]0.563739788912629[/C][C]-0.563739788912629[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]0.250969596154522[/C][C]0.749030403845478[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]0.46346294723424[/C][C]0.53653705276576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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
100.513081644911443-0.513081644911443
200.491401688468425-0.491401688468425
300.582556398595651-0.582556398595651
410.5231098078181370.476890192181863
510.4487782271850120.551221772814988
600.504933995655962-0.504933995655962
700.441387648084366-0.441387648084366
800.397880509151944-0.397880509151944
900.48601299399131-0.48601299399131
1000.482829591632269-0.482829591632269
1110.5701600455713930.429839954428607
1210.6566627730450250.343337226954975
1300.512460677951127-0.512460677951127
1410.6112654198782480.388734580121752
1510.5489205858208470.451079414179153
1610.6542078779381140.345792122061886
1700.500171340135307-0.500171340135307
1810.5061158831040210.493884116895979
1910.4556373245719240.544362675428076
2010.5937320685187290.406267931481271
2100.571456639390109-0.571456639390109
2200.491303072014013-0.491303072014013
2300.44703613737636-0.44703613737636
2400.637432818441284-0.637432818441284
2500.568205920287184-0.568205920287184
2600.521871805118489-0.521871805118489
2710.537835220221390.46216477977861
2810.5283507462824410.471649253717559
2910.4733857825059170.526614217494083
3000.545788283712408-0.545788283712408
3100.571983086241432-0.571983086241432
3200.582611803046241-0.582611803046241
3300.599472704231656-0.599472704231656
3410.5252706557542360.474729344245764
3500.535097996678143-0.535097996678143
3610.5388534444960470.461146555503953
3700.546216014910714-0.546216014910714
3810.5000364750119560.499963524988044
3900.381256443266403-0.381256443266403
4000.532608780901691-0.532608780901691
4100.501947652807837-0.501947652807837
4210.5330006340857660.466999365914234
4310.502731302628190.49726869737181
4410.5355614729394950.464438527060505
4500.615307339249674-0.615307339249674
4610.5074765340016750.492523465998325
4710.4597421778199420.540257822180058
4810.61898474816520.381015251834801
4910.5940941141630870.405905885836913
5010.5837822244222170.416217775577783
5110.5136049963764560.486395003623544
5200.279428725135974-0.279428725135974
5300.511779308879962-0.511779308879962
5400.398055126416247-0.398055126416247
5500.350645286737435-0.350645286737435
5610.5314694163379360.468530583662064
5700.643486542477629-0.643486542477629
5810.4765908920778710.523409107922129
5910.5346408585613980.465359141438602
6010.496409271431450.50359072856855
6100.576289598427298-0.576289598427298
6200.431489114263956-0.431489114263956
6310.5779123390225510.422087660977449
6410.5334480370610210.466551962938979
6500.500312660084951-0.500312660084951
6610.4703946580235580.529605341976442
6700.486589196189009-0.486589196189009
6810.5569287948804130.443071205119587
6900.511271410582657-0.511271410582657
7010.4664293391228490.533570660877151
7110.5884247674878070.411575232512193
7210.4371792795842160.562820720415784
7300.581574277121113-0.581574277121113
7410.5591884449897140.440811555010286
7500.537797394749654-0.537797394749654
7600.55343367074102-0.55343367074102
7710.4927231860699720.507276813930028
7800.537304643417598-0.537304643417598
7900.537120185630562-0.537120185630562
8000.516105055843162-0.516105055843162
8100.452068015373722-0.452068015373722
8210.5885005305560560.411499469443944
8300.446071847480673-0.446071847480673
8400.56812644809823-0.56812644809823
8510.3148811886887210.685118811311279
8600.459899512217264-0.459899512217264
8710.5130281361009810.486971863899019
8810.4965097815822550.503490218417745
8910.6433396226575950.356660377342405
9010.6002146100679670.399785389932033
9110.5348222608695940.465177739130406
9200.567303401885986-0.567303401885986
9300.465073822686718-0.465073822686718
9400.536988531324245-0.536988531324245
9510.4843535491483630.515646450851637
9610.5110305608331320.488969439166868
9700.516132894424665-0.516132894424665
9810.5347085927905630.465291407209437
9910.7048765920159440.295123407984056
10010.4411125911052980.558887408894702
10100.605708201512696-0.605708201512696
10210.5506903242659240.449309675734076
10300.485433765358088-0.485433765358088
10410.4089331992619210.591066800738079
10500.422420909299198-0.422420909299198
10600.520926722983789-0.520926722983789
10700.545676612435491-0.545676612435491
10800.542198329009542-0.542198329009542
10900.428443382414285-0.428443382414285
11010.5841997053410450.415800294658955
11110.5548455681594470.445154431840553
11200.588849325633198-0.588849325633198
11300.257153727883509-0.257153727883509
11410.551099876493130.44890012350687
11500.337146079591955-0.337146079591955
11610.5859047549557910.414095245044209
11710.4985218988676670.501478101132333
11800.245855204749935-0.245855204749935
11910.4558256651575480.544174334842452
12010.316370926374570.68362907362543
12110.511046901636220.48895309836378
12200.602200739041479-0.602200739041479
12300.567188403665258-0.567188403665258
12410.4726864868733730.527313513126627
12500.311785123379317-0.311785123379317
12600.543201633255717-0.543201633255717
12710.6197474248241810.380252575175819
12810.6361168579381270.363883142061873
12910.5983507666276930.401649233372307
13010.6380329133151580.361967086684842
13110.6266065351440580.373393464855942
13210.5355342742342130.464465725765787
13310.516115784970650.48388421502935
13410.4836680458557030.516331954144297
13510.5440602962498180.455939703750182
13610.5384404428092380.461559557190762
13710.5612167685340290.438783231465971
13810.5204445470956060.479555452904394
13910.5835585156506230.416441484349377
14000.535583134399484-0.535583134399484
14100.482082649268227-0.482082649268227
14200.482002519384565-0.482002519384565
14310.5322676749658840.467732325034116
14400.401590695675252-0.401590695675252
14500.583355319725839-0.583355319725839
14610.6803565590377250.319643440962276
14700.510282608934643-0.510282608934643
14800.5703802886357-0.5703802886357
14900.25151983363116-0.25151983363116
15000.248115315501841-0.248115315501841
15100.251312836542353-0.251312836542353
15200.25106007297067-0.25106007297067
15310.2515200096560940.748479990343906
15400.251520009656094-0.251520009656094
15510.503093864248940.49690613575106
15600.606985623923159-0.606985623923159
15700.251520009656094-0.251520009656094
15800.250724585913682-0.250724585913682
15900.251193169669847-0.251193169669847
16010.2859539145548280.714046085445172
16110.2561228310634830.743877168936517
16200.563739788912629-0.563739788912629
16310.2509695961545220.749030403845478
16410.463462947234240.53653705276576






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5464956551617460.9070086896765080.453504344838254
90.4168326629364250.833665325872850.583167337063575
100.3062774028166820.6125548056333640.693722597183318
110.3767012862762760.7534025725525510.623298713723724
120.2680331653387260.5360663306774520.731966834661274
130.2411646740565780.4823293481131560.758835325943422
140.3819711516953480.7639423033906960.618028848304652
150.3968483489495640.7936966978991280.603151651050436
160.3801994756482440.7603989512964870.619800524351756
170.3330564937932890.6661129875865780.666943506206711
180.2780566857861240.5561133715722470.721943314213876
190.3340443859462510.6680887718925020.665955614053749
200.2773613212008410.5547226424016820.722638678799159
210.2971583821631630.5943167643263260.702841617836837
220.241887551308220.4837751026164390.75811244869178
230.1932612919999810.3865225839999620.806738708000019
240.2111470513196220.4222941026392450.788852948680378
250.3831194308188410.7662388616376810.61688056918116
260.4323000735608890.8646001471217780.567699926439111
270.398415777297060.796831554594120.60158422270294
280.407861795023210.815723590046420.59213820497679
290.477174554170060.954349108340120.52282544582994
300.4485755871386990.8971511742773970.551424412861301
310.4379691452851010.8759382905702030.562030854714899
320.4196648051734190.8393296103468380.580335194826581
330.40976914476060.8195382895212010.5902308552394
340.466999099837870.9339981996757390.53300090016213
350.4433739895431460.8867479790862920.556626010456854
360.5257825183465910.9484349633068170.474217481653409
370.5094211348042160.9811577303915670.490578865195784
380.5144783690870330.9710432618259330.485521630912967
390.4799781563974460.9599563127948910.520021843602554
400.5425815497578560.9148369004842880.457418450242144
410.522308619569410.955382760861180.47769138043059
420.5292575183989090.9414849632021820.470742481601091
430.5406891230475570.9186217539048860.459310876952443
440.5432661543740580.9134676912518850.456733845625942
450.5637363419187450.8725273161625090.436263658081255
460.5965463195804160.8069073608391670.403453680419584
470.6053808977934960.7892382044130080.394619102206504
480.5962948769263340.8074102461473320.403705123073666
490.5857653489558270.8284693020883460.414234651044173
500.5766236414751740.8467527170496520.423376358524826
510.5727156619985540.8545686760028930.427284338001446
520.5421110375633420.9157779248733160.457888962436658
530.5656464941748440.8687070116503120.434353505825156
540.5403947194234370.9192105611531260.459605280576563
550.5081294682150090.9837410635699810.491870531784991
560.5144810989682970.9710378020634050.485518901031703
570.5491685494163540.9016629011672920.450831450583646
580.5524584777824050.895083044435190.447541522217595
590.5369865848673730.9260268302652530.463013415132627
600.5439555911382840.9120888177234330.456044408861716
610.5640735173300480.8718529653399040.435926482669952
620.5564865338847270.8870269322305460.443513466115273
630.5473385456694660.9053229086610690.452661454330534
640.5480104112652680.9039791774694640.451989588734732
650.541050602047350.9178987959053010.45894939795265
660.547964502128440.9040709957431190.45203549787156
670.5486353874869490.9027292250261030.451364612513051
680.5371639558214410.9256720883571180.462836044178559
690.5425937781420850.914812443715830.457406221857915
700.5435582910285770.9128834179428460.456441708971423
710.5288421172379910.9423157655240180.471157882762009
720.5450624205691820.9098751588616370.454937579430818
730.5591482724807880.8817034550384250.440851727519212
740.5487441214726970.9025117570546070.451255878527303
750.562365837566950.87526832486610.43763416243305
760.57436461403640.85127077192720.4256353859636
770.5777065969302490.8445868061395030.422293403069751
780.5852985925439470.8294028149121050.414701407456053
790.5942358949310370.8115282101379260.405764105068963
800.596796991624460.806406016751080.40320300837554
810.5880184090346740.8239631819306520.411981590965326
820.5741914555596040.8516170888807910.425808544440396
830.5644899880679850.8710200238640310.435510011932015
840.5804548313037460.8390903373925090.419545168696254
850.6216457689661990.7567084620676020.378354231033801
860.6179497387475680.7641005225048650.382050261252432
870.6145065143651850.770986971269630.385493485634815
880.6119986775730930.7760026448538140.388001322426907
890.5879121384513590.8241757230972820.412087861548641
900.5813756976906010.8372486046187990.418624302309399
910.5717427955251350.856514408949730.428257204474865
920.5805096084081630.8389807831836730.419490391591836
930.5795952240107950.840809551978410.420404775989205
940.5901826246978820.8196347506042360.409817375302118
950.5857542311105910.8284915377788170.414245768889409
960.5805996435910540.8388007128178930.419400356408946
970.5893980588432340.8212038823135320.410601941156766
980.5827283200181460.8345433599637080.417271679981854
990.549261816165280.9014763676694410.45073818383472
1000.5574375200538780.8851249598922430.442562479946122
1010.5815579942714440.8368840114571120.418442005728556
1020.5730626849682760.8538746300634480.426937315031724
1030.5703924042817190.8592151914365610.429607595718281
1040.5822872239182410.8354255521635170.417712776081759
1050.5665600897418250.8668798205163490.433439910258175
1060.5753873286293370.8492253427413260.424612671370663
1070.5866693456264840.8266613087470310.413330654373516
1080.6012239368410660.7975521263178680.398776063158934
1090.5951764640789220.8096470718421560.404823535921078
1100.574780419349320.8504391613013610.42521958065068
1110.5542924311238750.891415137752250.445707568876125
1120.5910987402180690.8178025195638630.408901259781931
1130.5571878349835870.8856243300328260.442812165016413
1140.5371157148116860.9257685703766290.462884285188314
1150.5139054997539120.9721890004921750.486094500246088
1160.4921576034394290.9843152068788580.507842396560571
1170.4760364008904060.9520728017808130.523963599109593
1180.441159380150140.8823187603002790.55884061984986
1190.4392056815346810.8784113630693620.560794318465319
1200.4754895151847790.9509790303695570.524510484815221
1210.4627747371233970.9255494742467940.537225262876603
1220.501230999884380.997538000231240.49876900011562
1230.5502923406033630.8994153187932740.449707659396637
1240.5356781460735760.9286437078528480.464321853926424
1250.506466740306260.9870665193874810.49353325969374
1260.5466422211654830.9067155576690340.453357778834517
1270.5106891386884010.9786217226231990.489310861311599
1280.4627660723956640.9255321447913280.537233927604336
1290.4405039892955980.8810079785911950.559496010704402
1300.409337705408470.818675410816940.59066229459153
1310.3912534583101940.7825069166203870.608746541689806
1320.3755349512210350.7510699024420710.624465048778964
1330.3538370928705190.7076741857410380.646162907129481
1340.369789885397670.7395797707953410.63021011460233
1350.4309036207890630.8618072415781250.569096379210937
1360.3979989284637120.7959978569274240.602001071536288
1370.4544220075189790.9088440150379580.545577992481021
1380.4951715234563010.9903430469126010.504828476543699
1390.4441137275402860.8882274550805720.555886272459714
1400.4127184515571890.8254369031143780.587281548442811
1410.355475182697170.710950365394340.64452481730283
1420.3050647644085030.6101295288170050.694935235591497
1430.3524753898086350.704950779617270.647524610191365
1440.3060441190898040.6120882381796080.693955880910196
1450.2869136990906040.5738273981812080.713086300909396
1460.5847735386249190.8304529227501630.415226461375081
1470.5374685394536610.9250629210926770.462531460546339
1480.7140733071186750.5718533857626510.285926692881325
1490.6583108138183820.6833783723632350.341689186181618
1500.647951045421370.7040979091572590.352048954578629
1510.6024191216092320.7951617567815370.397580878390768
1520.5791089624386880.8417820751226250.420891037561312
1530.6909760984120740.6180478031758520.309023901587926
1540.6295063751754910.7409872496490170.370493624824509
1550.5148601954052480.9702796091895040.485139804594752
1560.3694248433072260.7388496866144520.630575156692774

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.546495655161746 & 0.907008689676508 & 0.453504344838254 \tabularnewline
9 & 0.416832662936425 & 0.83366532587285 & 0.583167337063575 \tabularnewline
10 & 0.306277402816682 & 0.612554805633364 & 0.693722597183318 \tabularnewline
11 & 0.376701286276276 & 0.753402572552551 & 0.623298713723724 \tabularnewline
12 & 0.268033165338726 & 0.536066330677452 & 0.731966834661274 \tabularnewline
13 & 0.241164674056578 & 0.482329348113156 & 0.758835325943422 \tabularnewline
14 & 0.381971151695348 & 0.763942303390696 & 0.618028848304652 \tabularnewline
15 & 0.396848348949564 & 0.793696697899128 & 0.603151651050436 \tabularnewline
16 & 0.380199475648244 & 0.760398951296487 & 0.619800524351756 \tabularnewline
17 & 0.333056493793289 & 0.666112987586578 & 0.666943506206711 \tabularnewline
18 & 0.278056685786124 & 0.556113371572247 & 0.721943314213876 \tabularnewline
19 & 0.334044385946251 & 0.668088771892502 & 0.665955614053749 \tabularnewline
20 & 0.277361321200841 & 0.554722642401682 & 0.722638678799159 \tabularnewline
21 & 0.297158382163163 & 0.594316764326326 & 0.702841617836837 \tabularnewline
22 & 0.24188755130822 & 0.483775102616439 & 0.75811244869178 \tabularnewline
23 & 0.193261291999981 & 0.386522583999962 & 0.806738708000019 \tabularnewline
24 & 0.211147051319622 & 0.422294102639245 & 0.788852948680378 \tabularnewline
25 & 0.383119430818841 & 0.766238861637681 & 0.61688056918116 \tabularnewline
26 & 0.432300073560889 & 0.864600147121778 & 0.567699926439111 \tabularnewline
27 & 0.39841577729706 & 0.79683155459412 & 0.60158422270294 \tabularnewline
28 & 0.40786179502321 & 0.81572359004642 & 0.59213820497679 \tabularnewline
29 & 0.47717455417006 & 0.95434910834012 & 0.52282544582994 \tabularnewline
30 & 0.448575587138699 & 0.897151174277397 & 0.551424412861301 \tabularnewline
31 & 0.437969145285101 & 0.875938290570203 & 0.562030854714899 \tabularnewline
32 & 0.419664805173419 & 0.839329610346838 & 0.580335194826581 \tabularnewline
33 & 0.4097691447606 & 0.819538289521201 & 0.5902308552394 \tabularnewline
34 & 0.46699909983787 & 0.933998199675739 & 0.53300090016213 \tabularnewline
35 & 0.443373989543146 & 0.886747979086292 & 0.556626010456854 \tabularnewline
36 & 0.525782518346591 & 0.948434963306817 & 0.474217481653409 \tabularnewline
37 & 0.509421134804216 & 0.981157730391567 & 0.490578865195784 \tabularnewline
38 & 0.514478369087033 & 0.971043261825933 & 0.485521630912967 \tabularnewline
39 & 0.479978156397446 & 0.959956312794891 & 0.520021843602554 \tabularnewline
40 & 0.542581549757856 & 0.914836900484288 & 0.457418450242144 \tabularnewline
41 & 0.52230861956941 & 0.95538276086118 & 0.47769138043059 \tabularnewline
42 & 0.529257518398909 & 0.941484963202182 & 0.470742481601091 \tabularnewline
43 & 0.540689123047557 & 0.918621753904886 & 0.459310876952443 \tabularnewline
44 & 0.543266154374058 & 0.913467691251885 & 0.456733845625942 \tabularnewline
45 & 0.563736341918745 & 0.872527316162509 & 0.436263658081255 \tabularnewline
46 & 0.596546319580416 & 0.806907360839167 & 0.403453680419584 \tabularnewline
47 & 0.605380897793496 & 0.789238204413008 & 0.394619102206504 \tabularnewline
48 & 0.596294876926334 & 0.807410246147332 & 0.403705123073666 \tabularnewline
49 & 0.585765348955827 & 0.828469302088346 & 0.414234651044173 \tabularnewline
50 & 0.576623641475174 & 0.846752717049652 & 0.423376358524826 \tabularnewline
51 & 0.572715661998554 & 0.854568676002893 & 0.427284338001446 \tabularnewline
52 & 0.542111037563342 & 0.915777924873316 & 0.457888962436658 \tabularnewline
53 & 0.565646494174844 & 0.868707011650312 & 0.434353505825156 \tabularnewline
54 & 0.540394719423437 & 0.919210561153126 & 0.459605280576563 \tabularnewline
55 & 0.508129468215009 & 0.983741063569981 & 0.491870531784991 \tabularnewline
56 & 0.514481098968297 & 0.971037802063405 & 0.485518901031703 \tabularnewline
57 & 0.549168549416354 & 0.901662901167292 & 0.450831450583646 \tabularnewline
58 & 0.552458477782405 & 0.89508304443519 & 0.447541522217595 \tabularnewline
59 & 0.536986584867373 & 0.926026830265253 & 0.463013415132627 \tabularnewline
60 & 0.543955591138284 & 0.912088817723433 & 0.456044408861716 \tabularnewline
61 & 0.564073517330048 & 0.871852965339904 & 0.435926482669952 \tabularnewline
62 & 0.556486533884727 & 0.887026932230546 & 0.443513466115273 \tabularnewline
63 & 0.547338545669466 & 0.905322908661069 & 0.452661454330534 \tabularnewline
64 & 0.548010411265268 & 0.903979177469464 & 0.451989588734732 \tabularnewline
65 & 0.54105060204735 & 0.917898795905301 & 0.45894939795265 \tabularnewline
66 & 0.54796450212844 & 0.904070995743119 & 0.45203549787156 \tabularnewline
67 & 0.548635387486949 & 0.902729225026103 & 0.451364612513051 \tabularnewline
68 & 0.537163955821441 & 0.925672088357118 & 0.462836044178559 \tabularnewline
69 & 0.542593778142085 & 0.91481244371583 & 0.457406221857915 \tabularnewline
70 & 0.543558291028577 & 0.912883417942846 & 0.456441708971423 \tabularnewline
71 & 0.528842117237991 & 0.942315765524018 & 0.471157882762009 \tabularnewline
72 & 0.545062420569182 & 0.909875158861637 & 0.454937579430818 \tabularnewline
73 & 0.559148272480788 & 0.881703455038425 & 0.440851727519212 \tabularnewline
74 & 0.548744121472697 & 0.902511757054607 & 0.451255878527303 \tabularnewline
75 & 0.56236583756695 & 0.8752683248661 & 0.43763416243305 \tabularnewline
76 & 0.5743646140364 & 0.8512707719272 & 0.4256353859636 \tabularnewline
77 & 0.577706596930249 & 0.844586806139503 & 0.422293403069751 \tabularnewline
78 & 0.585298592543947 & 0.829402814912105 & 0.414701407456053 \tabularnewline
79 & 0.594235894931037 & 0.811528210137926 & 0.405764105068963 \tabularnewline
80 & 0.59679699162446 & 0.80640601675108 & 0.40320300837554 \tabularnewline
81 & 0.588018409034674 & 0.823963181930652 & 0.411981590965326 \tabularnewline
82 & 0.574191455559604 & 0.851617088880791 & 0.425808544440396 \tabularnewline
83 & 0.564489988067985 & 0.871020023864031 & 0.435510011932015 \tabularnewline
84 & 0.580454831303746 & 0.839090337392509 & 0.419545168696254 \tabularnewline
85 & 0.621645768966199 & 0.756708462067602 & 0.378354231033801 \tabularnewline
86 & 0.617949738747568 & 0.764100522504865 & 0.382050261252432 \tabularnewline
87 & 0.614506514365185 & 0.77098697126963 & 0.385493485634815 \tabularnewline
88 & 0.611998677573093 & 0.776002644853814 & 0.388001322426907 \tabularnewline
89 & 0.587912138451359 & 0.824175723097282 & 0.412087861548641 \tabularnewline
90 & 0.581375697690601 & 0.837248604618799 & 0.418624302309399 \tabularnewline
91 & 0.571742795525135 & 0.85651440894973 & 0.428257204474865 \tabularnewline
92 & 0.580509608408163 & 0.838980783183673 & 0.419490391591836 \tabularnewline
93 & 0.579595224010795 & 0.84080955197841 & 0.420404775989205 \tabularnewline
94 & 0.590182624697882 & 0.819634750604236 & 0.409817375302118 \tabularnewline
95 & 0.585754231110591 & 0.828491537778817 & 0.414245768889409 \tabularnewline
96 & 0.580599643591054 & 0.838800712817893 & 0.419400356408946 \tabularnewline
97 & 0.589398058843234 & 0.821203882313532 & 0.410601941156766 \tabularnewline
98 & 0.582728320018146 & 0.834543359963708 & 0.417271679981854 \tabularnewline
99 & 0.54926181616528 & 0.901476367669441 & 0.45073818383472 \tabularnewline
100 & 0.557437520053878 & 0.885124959892243 & 0.442562479946122 \tabularnewline
101 & 0.581557994271444 & 0.836884011457112 & 0.418442005728556 \tabularnewline
102 & 0.573062684968276 & 0.853874630063448 & 0.426937315031724 \tabularnewline
103 & 0.570392404281719 & 0.859215191436561 & 0.429607595718281 \tabularnewline
104 & 0.582287223918241 & 0.835425552163517 & 0.417712776081759 \tabularnewline
105 & 0.566560089741825 & 0.866879820516349 & 0.433439910258175 \tabularnewline
106 & 0.575387328629337 & 0.849225342741326 & 0.424612671370663 \tabularnewline
107 & 0.586669345626484 & 0.826661308747031 & 0.413330654373516 \tabularnewline
108 & 0.601223936841066 & 0.797552126317868 & 0.398776063158934 \tabularnewline
109 & 0.595176464078922 & 0.809647071842156 & 0.404823535921078 \tabularnewline
110 & 0.57478041934932 & 0.850439161301361 & 0.42521958065068 \tabularnewline
111 & 0.554292431123875 & 0.89141513775225 & 0.445707568876125 \tabularnewline
112 & 0.591098740218069 & 0.817802519563863 & 0.408901259781931 \tabularnewline
113 & 0.557187834983587 & 0.885624330032826 & 0.442812165016413 \tabularnewline
114 & 0.537115714811686 & 0.925768570376629 & 0.462884285188314 \tabularnewline
115 & 0.513905499753912 & 0.972189000492175 & 0.486094500246088 \tabularnewline
116 & 0.492157603439429 & 0.984315206878858 & 0.507842396560571 \tabularnewline
117 & 0.476036400890406 & 0.952072801780813 & 0.523963599109593 \tabularnewline
118 & 0.44115938015014 & 0.882318760300279 & 0.55884061984986 \tabularnewline
119 & 0.439205681534681 & 0.878411363069362 & 0.560794318465319 \tabularnewline
120 & 0.475489515184779 & 0.950979030369557 & 0.524510484815221 \tabularnewline
121 & 0.462774737123397 & 0.925549474246794 & 0.537225262876603 \tabularnewline
122 & 0.50123099988438 & 0.99753800023124 & 0.49876900011562 \tabularnewline
123 & 0.550292340603363 & 0.899415318793274 & 0.449707659396637 \tabularnewline
124 & 0.535678146073576 & 0.928643707852848 & 0.464321853926424 \tabularnewline
125 & 0.50646674030626 & 0.987066519387481 & 0.49353325969374 \tabularnewline
126 & 0.546642221165483 & 0.906715557669034 & 0.453357778834517 \tabularnewline
127 & 0.510689138688401 & 0.978621722623199 & 0.489310861311599 \tabularnewline
128 & 0.462766072395664 & 0.925532144791328 & 0.537233927604336 \tabularnewline
129 & 0.440503989295598 & 0.881007978591195 & 0.559496010704402 \tabularnewline
130 & 0.40933770540847 & 0.81867541081694 & 0.59066229459153 \tabularnewline
131 & 0.391253458310194 & 0.782506916620387 & 0.608746541689806 \tabularnewline
132 & 0.375534951221035 & 0.751069902442071 & 0.624465048778964 \tabularnewline
133 & 0.353837092870519 & 0.707674185741038 & 0.646162907129481 \tabularnewline
134 & 0.36978988539767 & 0.739579770795341 & 0.63021011460233 \tabularnewline
135 & 0.430903620789063 & 0.861807241578125 & 0.569096379210937 \tabularnewline
136 & 0.397998928463712 & 0.795997856927424 & 0.602001071536288 \tabularnewline
137 & 0.454422007518979 & 0.908844015037958 & 0.545577992481021 \tabularnewline
138 & 0.495171523456301 & 0.990343046912601 & 0.504828476543699 \tabularnewline
139 & 0.444113727540286 & 0.888227455080572 & 0.555886272459714 \tabularnewline
140 & 0.412718451557189 & 0.825436903114378 & 0.587281548442811 \tabularnewline
141 & 0.35547518269717 & 0.71095036539434 & 0.64452481730283 \tabularnewline
142 & 0.305064764408503 & 0.610129528817005 & 0.694935235591497 \tabularnewline
143 & 0.352475389808635 & 0.70495077961727 & 0.647524610191365 \tabularnewline
144 & 0.306044119089804 & 0.612088238179608 & 0.693955880910196 \tabularnewline
145 & 0.286913699090604 & 0.573827398181208 & 0.713086300909396 \tabularnewline
146 & 0.584773538624919 & 0.830452922750163 & 0.415226461375081 \tabularnewline
147 & 0.537468539453661 & 0.925062921092677 & 0.462531460546339 \tabularnewline
148 & 0.714073307118675 & 0.571853385762651 & 0.285926692881325 \tabularnewline
149 & 0.658310813818382 & 0.683378372363235 & 0.341689186181618 \tabularnewline
150 & 0.64795104542137 & 0.704097909157259 & 0.352048954578629 \tabularnewline
151 & 0.602419121609232 & 0.795161756781537 & 0.397580878390768 \tabularnewline
152 & 0.579108962438688 & 0.841782075122625 & 0.420891037561312 \tabularnewline
153 & 0.690976098412074 & 0.618047803175852 & 0.309023901587926 \tabularnewline
154 & 0.629506375175491 & 0.740987249649017 & 0.370493624824509 \tabularnewline
155 & 0.514860195405248 & 0.970279609189504 & 0.485139804594752 \tabularnewline
156 & 0.369424843307226 & 0.738849686614452 & 0.630575156692774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&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]8[/C][C]0.546495655161746[/C][C]0.907008689676508[/C][C]0.453504344838254[/C][/ROW]
[ROW][C]9[/C][C]0.416832662936425[/C][C]0.83366532587285[/C][C]0.583167337063575[/C][/ROW]
[ROW][C]10[/C][C]0.306277402816682[/C][C]0.612554805633364[/C][C]0.693722597183318[/C][/ROW]
[ROW][C]11[/C][C]0.376701286276276[/C][C]0.753402572552551[/C][C]0.623298713723724[/C][/ROW]
[ROW][C]12[/C][C]0.268033165338726[/C][C]0.536066330677452[/C][C]0.731966834661274[/C][/ROW]
[ROW][C]13[/C][C]0.241164674056578[/C][C]0.482329348113156[/C][C]0.758835325943422[/C][/ROW]
[ROW][C]14[/C][C]0.381971151695348[/C][C]0.763942303390696[/C][C]0.618028848304652[/C][/ROW]
[ROW][C]15[/C][C]0.396848348949564[/C][C]0.793696697899128[/C][C]0.603151651050436[/C][/ROW]
[ROW][C]16[/C][C]0.380199475648244[/C][C]0.760398951296487[/C][C]0.619800524351756[/C][/ROW]
[ROW][C]17[/C][C]0.333056493793289[/C][C]0.666112987586578[/C][C]0.666943506206711[/C][/ROW]
[ROW][C]18[/C][C]0.278056685786124[/C][C]0.556113371572247[/C][C]0.721943314213876[/C][/ROW]
[ROW][C]19[/C][C]0.334044385946251[/C][C]0.668088771892502[/C][C]0.665955614053749[/C][/ROW]
[ROW][C]20[/C][C]0.277361321200841[/C][C]0.554722642401682[/C][C]0.722638678799159[/C][/ROW]
[ROW][C]21[/C][C]0.297158382163163[/C][C]0.594316764326326[/C][C]0.702841617836837[/C][/ROW]
[ROW][C]22[/C][C]0.24188755130822[/C][C]0.483775102616439[/C][C]0.75811244869178[/C][/ROW]
[ROW][C]23[/C][C]0.193261291999981[/C][C]0.386522583999962[/C][C]0.806738708000019[/C][/ROW]
[ROW][C]24[/C][C]0.211147051319622[/C][C]0.422294102639245[/C][C]0.788852948680378[/C][/ROW]
[ROW][C]25[/C][C]0.383119430818841[/C][C]0.766238861637681[/C][C]0.61688056918116[/C][/ROW]
[ROW][C]26[/C][C]0.432300073560889[/C][C]0.864600147121778[/C][C]0.567699926439111[/C][/ROW]
[ROW][C]27[/C][C]0.39841577729706[/C][C]0.79683155459412[/C][C]0.60158422270294[/C][/ROW]
[ROW][C]28[/C][C]0.40786179502321[/C][C]0.81572359004642[/C][C]0.59213820497679[/C][/ROW]
[ROW][C]29[/C][C]0.47717455417006[/C][C]0.95434910834012[/C][C]0.52282544582994[/C][/ROW]
[ROW][C]30[/C][C]0.448575587138699[/C][C]0.897151174277397[/C][C]0.551424412861301[/C][/ROW]
[ROW][C]31[/C][C]0.437969145285101[/C][C]0.875938290570203[/C][C]0.562030854714899[/C][/ROW]
[ROW][C]32[/C][C]0.419664805173419[/C][C]0.839329610346838[/C][C]0.580335194826581[/C][/ROW]
[ROW][C]33[/C][C]0.4097691447606[/C][C]0.819538289521201[/C][C]0.5902308552394[/C][/ROW]
[ROW][C]34[/C][C]0.46699909983787[/C][C]0.933998199675739[/C][C]0.53300090016213[/C][/ROW]
[ROW][C]35[/C][C]0.443373989543146[/C][C]0.886747979086292[/C][C]0.556626010456854[/C][/ROW]
[ROW][C]36[/C][C]0.525782518346591[/C][C]0.948434963306817[/C][C]0.474217481653409[/C][/ROW]
[ROW][C]37[/C][C]0.509421134804216[/C][C]0.981157730391567[/C][C]0.490578865195784[/C][/ROW]
[ROW][C]38[/C][C]0.514478369087033[/C][C]0.971043261825933[/C][C]0.485521630912967[/C][/ROW]
[ROW][C]39[/C][C]0.479978156397446[/C][C]0.959956312794891[/C][C]0.520021843602554[/C][/ROW]
[ROW][C]40[/C][C]0.542581549757856[/C][C]0.914836900484288[/C][C]0.457418450242144[/C][/ROW]
[ROW][C]41[/C][C]0.52230861956941[/C][C]0.95538276086118[/C][C]0.47769138043059[/C][/ROW]
[ROW][C]42[/C][C]0.529257518398909[/C][C]0.941484963202182[/C][C]0.470742481601091[/C][/ROW]
[ROW][C]43[/C][C]0.540689123047557[/C][C]0.918621753904886[/C][C]0.459310876952443[/C][/ROW]
[ROW][C]44[/C][C]0.543266154374058[/C][C]0.913467691251885[/C][C]0.456733845625942[/C][/ROW]
[ROW][C]45[/C][C]0.563736341918745[/C][C]0.872527316162509[/C][C]0.436263658081255[/C][/ROW]
[ROW][C]46[/C][C]0.596546319580416[/C][C]0.806907360839167[/C][C]0.403453680419584[/C][/ROW]
[ROW][C]47[/C][C]0.605380897793496[/C][C]0.789238204413008[/C][C]0.394619102206504[/C][/ROW]
[ROW][C]48[/C][C]0.596294876926334[/C][C]0.807410246147332[/C][C]0.403705123073666[/C][/ROW]
[ROW][C]49[/C][C]0.585765348955827[/C][C]0.828469302088346[/C][C]0.414234651044173[/C][/ROW]
[ROW][C]50[/C][C]0.576623641475174[/C][C]0.846752717049652[/C][C]0.423376358524826[/C][/ROW]
[ROW][C]51[/C][C]0.572715661998554[/C][C]0.854568676002893[/C][C]0.427284338001446[/C][/ROW]
[ROW][C]52[/C][C]0.542111037563342[/C][C]0.915777924873316[/C][C]0.457888962436658[/C][/ROW]
[ROW][C]53[/C][C]0.565646494174844[/C][C]0.868707011650312[/C][C]0.434353505825156[/C][/ROW]
[ROW][C]54[/C][C]0.540394719423437[/C][C]0.919210561153126[/C][C]0.459605280576563[/C][/ROW]
[ROW][C]55[/C][C]0.508129468215009[/C][C]0.983741063569981[/C][C]0.491870531784991[/C][/ROW]
[ROW][C]56[/C][C]0.514481098968297[/C][C]0.971037802063405[/C][C]0.485518901031703[/C][/ROW]
[ROW][C]57[/C][C]0.549168549416354[/C][C]0.901662901167292[/C][C]0.450831450583646[/C][/ROW]
[ROW][C]58[/C][C]0.552458477782405[/C][C]0.89508304443519[/C][C]0.447541522217595[/C][/ROW]
[ROW][C]59[/C][C]0.536986584867373[/C][C]0.926026830265253[/C][C]0.463013415132627[/C][/ROW]
[ROW][C]60[/C][C]0.543955591138284[/C][C]0.912088817723433[/C][C]0.456044408861716[/C][/ROW]
[ROW][C]61[/C][C]0.564073517330048[/C][C]0.871852965339904[/C][C]0.435926482669952[/C][/ROW]
[ROW][C]62[/C][C]0.556486533884727[/C][C]0.887026932230546[/C][C]0.443513466115273[/C][/ROW]
[ROW][C]63[/C][C]0.547338545669466[/C][C]0.905322908661069[/C][C]0.452661454330534[/C][/ROW]
[ROW][C]64[/C][C]0.548010411265268[/C][C]0.903979177469464[/C][C]0.451989588734732[/C][/ROW]
[ROW][C]65[/C][C]0.54105060204735[/C][C]0.917898795905301[/C][C]0.45894939795265[/C][/ROW]
[ROW][C]66[/C][C]0.54796450212844[/C][C]0.904070995743119[/C][C]0.45203549787156[/C][/ROW]
[ROW][C]67[/C][C]0.548635387486949[/C][C]0.902729225026103[/C][C]0.451364612513051[/C][/ROW]
[ROW][C]68[/C][C]0.537163955821441[/C][C]0.925672088357118[/C][C]0.462836044178559[/C][/ROW]
[ROW][C]69[/C][C]0.542593778142085[/C][C]0.91481244371583[/C][C]0.457406221857915[/C][/ROW]
[ROW][C]70[/C][C]0.543558291028577[/C][C]0.912883417942846[/C][C]0.456441708971423[/C][/ROW]
[ROW][C]71[/C][C]0.528842117237991[/C][C]0.942315765524018[/C][C]0.471157882762009[/C][/ROW]
[ROW][C]72[/C][C]0.545062420569182[/C][C]0.909875158861637[/C][C]0.454937579430818[/C][/ROW]
[ROW][C]73[/C][C]0.559148272480788[/C][C]0.881703455038425[/C][C]0.440851727519212[/C][/ROW]
[ROW][C]74[/C][C]0.548744121472697[/C][C]0.902511757054607[/C][C]0.451255878527303[/C][/ROW]
[ROW][C]75[/C][C]0.56236583756695[/C][C]0.8752683248661[/C][C]0.43763416243305[/C][/ROW]
[ROW][C]76[/C][C]0.5743646140364[/C][C]0.8512707719272[/C][C]0.4256353859636[/C][/ROW]
[ROW][C]77[/C][C]0.577706596930249[/C][C]0.844586806139503[/C][C]0.422293403069751[/C][/ROW]
[ROW][C]78[/C][C]0.585298592543947[/C][C]0.829402814912105[/C][C]0.414701407456053[/C][/ROW]
[ROW][C]79[/C][C]0.594235894931037[/C][C]0.811528210137926[/C][C]0.405764105068963[/C][/ROW]
[ROW][C]80[/C][C]0.59679699162446[/C][C]0.80640601675108[/C][C]0.40320300837554[/C][/ROW]
[ROW][C]81[/C][C]0.588018409034674[/C][C]0.823963181930652[/C][C]0.411981590965326[/C][/ROW]
[ROW][C]82[/C][C]0.574191455559604[/C][C]0.851617088880791[/C][C]0.425808544440396[/C][/ROW]
[ROW][C]83[/C][C]0.564489988067985[/C][C]0.871020023864031[/C][C]0.435510011932015[/C][/ROW]
[ROW][C]84[/C][C]0.580454831303746[/C][C]0.839090337392509[/C][C]0.419545168696254[/C][/ROW]
[ROW][C]85[/C][C]0.621645768966199[/C][C]0.756708462067602[/C][C]0.378354231033801[/C][/ROW]
[ROW][C]86[/C][C]0.617949738747568[/C][C]0.764100522504865[/C][C]0.382050261252432[/C][/ROW]
[ROW][C]87[/C][C]0.614506514365185[/C][C]0.77098697126963[/C][C]0.385493485634815[/C][/ROW]
[ROW][C]88[/C][C]0.611998677573093[/C][C]0.776002644853814[/C][C]0.388001322426907[/C][/ROW]
[ROW][C]89[/C][C]0.587912138451359[/C][C]0.824175723097282[/C][C]0.412087861548641[/C][/ROW]
[ROW][C]90[/C][C]0.581375697690601[/C][C]0.837248604618799[/C][C]0.418624302309399[/C][/ROW]
[ROW][C]91[/C][C]0.571742795525135[/C][C]0.85651440894973[/C][C]0.428257204474865[/C][/ROW]
[ROW][C]92[/C][C]0.580509608408163[/C][C]0.838980783183673[/C][C]0.419490391591836[/C][/ROW]
[ROW][C]93[/C][C]0.579595224010795[/C][C]0.84080955197841[/C][C]0.420404775989205[/C][/ROW]
[ROW][C]94[/C][C]0.590182624697882[/C][C]0.819634750604236[/C][C]0.409817375302118[/C][/ROW]
[ROW][C]95[/C][C]0.585754231110591[/C][C]0.828491537778817[/C][C]0.414245768889409[/C][/ROW]
[ROW][C]96[/C][C]0.580599643591054[/C][C]0.838800712817893[/C][C]0.419400356408946[/C][/ROW]
[ROW][C]97[/C][C]0.589398058843234[/C][C]0.821203882313532[/C][C]0.410601941156766[/C][/ROW]
[ROW][C]98[/C][C]0.582728320018146[/C][C]0.834543359963708[/C][C]0.417271679981854[/C][/ROW]
[ROW][C]99[/C][C]0.54926181616528[/C][C]0.901476367669441[/C][C]0.45073818383472[/C][/ROW]
[ROW][C]100[/C][C]0.557437520053878[/C][C]0.885124959892243[/C][C]0.442562479946122[/C][/ROW]
[ROW][C]101[/C][C]0.581557994271444[/C][C]0.836884011457112[/C][C]0.418442005728556[/C][/ROW]
[ROW][C]102[/C][C]0.573062684968276[/C][C]0.853874630063448[/C][C]0.426937315031724[/C][/ROW]
[ROW][C]103[/C][C]0.570392404281719[/C][C]0.859215191436561[/C][C]0.429607595718281[/C][/ROW]
[ROW][C]104[/C][C]0.582287223918241[/C][C]0.835425552163517[/C][C]0.417712776081759[/C][/ROW]
[ROW][C]105[/C][C]0.566560089741825[/C][C]0.866879820516349[/C][C]0.433439910258175[/C][/ROW]
[ROW][C]106[/C][C]0.575387328629337[/C][C]0.849225342741326[/C][C]0.424612671370663[/C][/ROW]
[ROW][C]107[/C][C]0.586669345626484[/C][C]0.826661308747031[/C][C]0.413330654373516[/C][/ROW]
[ROW][C]108[/C][C]0.601223936841066[/C][C]0.797552126317868[/C][C]0.398776063158934[/C][/ROW]
[ROW][C]109[/C][C]0.595176464078922[/C][C]0.809647071842156[/C][C]0.404823535921078[/C][/ROW]
[ROW][C]110[/C][C]0.57478041934932[/C][C]0.850439161301361[/C][C]0.42521958065068[/C][/ROW]
[ROW][C]111[/C][C]0.554292431123875[/C][C]0.89141513775225[/C][C]0.445707568876125[/C][/ROW]
[ROW][C]112[/C][C]0.591098740218069[/C][C]0.817802519563863[/C][C]0.408901259781931[/C][/ROW]
[ROW][C]113[/C][C]0.557187834983587[/C][C]0.885624330032826[/C][C]0.442812165016413[/C][/ROW]
[ROW][C]114[/C][C]0.537115714811686[/C][C]0.925768570376629[/C][C]0.462884285188314[/C][/ROW]
[ROW][C]115[/C][C]0.513905499753912[/C][C]0.972189000492175[/C][C]0.486094500246088[/C][/ROW]
[ROW][C]116[/C][C]0.492157603439429[/C][C]0.984315206878858[/C][C]0.507842396560571[/C][/ROW]
[ROW][C]117[/C][C]0.476036400890406[/C][C]0.952072801780813[/C][C]0.523963599109593[/C][/ROW]
[ROW][C]118[/C][C]0.44115938015014[/C][C]0.882318760300279[/C][C]0.55884061984986[/C][/ROW]
[ROW][C]119[/C][C]0.439205681534681[/C][C]0.878411363069362[/C][C]0.560794318465319[/C][/ROW]
[ROW][C]120[/C][C]0.475489515184779[/C][C]0.950979030369557[/C][C]0.524510484815221[/C][/ROW]
[ROW][C]121[/C][C]0.462774737123397[/C][C]0.925549474246794[/C][C]0.537225262876603[/C][/ROW]
[ROW][C]122[/C][C]0.50123099988438[/C][C]0.99753800023124[/C][C]0.49876900011562[/C][/ROW]
[ROW][C]123[/C][C]0.550292340603363[/C][C]0.899415318793274[/C][C]0.449707659396637[/C][/ROW]
[ROW][C]124[/C][C]0.535678146073576[/C][C]0.928643707852848[/C][C]0.464321853926424[/C][/ROW]
[ROW][C]125[/C][C]0.50646674030626[/C][C]0.987066519387481[/C][C]0.49353325969374[/C][/ROW]
[ROW][C]126[/C][C]0.546642221165483[/C][C]0.906715557669034[/C][C]0.453357778834517[/C][/ROW]
[ROW][C]127[/C][C]0.510689138688401[/C][C]0.978621722623199[/C][C]0.489310861311599[/C][/ROW]
[ROW][C]128[/C][C]0.462766072395664[/C][C]0.925532144791328[/C][C]0.537233927604336[/C][/ROW]
[ROW][C]129[/C][C]0.440503989295598[/C][C]0.881007978591195[/C][C]0.559496010704402[/C][/ROW]
[ROW][C]130[/C][C]0.40933770540847[/C][C]0.81867541081694[/C][C]0.59066229459153[/C][/ROW]
[ROW][C]131[/C][C]0.391253458310194[/C][C]0.782506916620387[/C][C]0.608746541689806[/C][/ROW]
[ROW][C]132[/C][C]0.375534951221035[/C][C]0.751069902442071[/C][C]0.624465048778964[/C][/ROW]
[ROW][C]133[/C][C]0.353837092870519[/C][C]0.707674185741038[/C][C]0.646162907129481[/C][/ROW]
[ROW][C]134[/C][C]0.36978988539767[/C][C]0.739579770795341[/C][C]0.63021011460233[/C][/ROW]
[ROW][C]135[/C][C]0.430903620789063[/C][C]0.861807241578125[/C][C]0.569096379210937[/C][/ROW]
[ROW][C]136[/C][C]0.397998928463712[/C][C]0.795997856927424[/C][C]0.602001071536288[/C][/ROW]
[ROW][C]137[/C][C]0.454422007518979[/C][C]0.908844015037958[/C][C]0.545577992481021[/C][/ROW]
[ROW][C]138[/C][C]0.495171523456301[/C][C]0.990343046912601[/C][C]0.504828476543699[/C][/ROW]
[ROW][C]139[/C][C]0.444113727540286[/C][C]0.888227455080572[/C][C]0.555886272459714[/C][/ROW]
[ROW][C]140[/C][C]0.412718451557189[/C][C]0.825436903114378[/C][C]0.587281548442811[/C][/ROW]
[ROW][C]141[/C][C]0.35547518269717[/C][C]0.71095036539434[/C][C]0.64452481730283[/C][/ROW]
[ROW][C]142[/C][C]0.305064764408503[/C][C]0.610129528817005[/C][C]0.694935235591497[/C][/ROW]
[ROW][C]143[/C][C]0.352475389808635[/C][C]0.70495077961727[/C][C]0.647524610191365[/C][/ROW]
[ROW][C]144[/C][C]0.306044119089804[/C][C]0.612088238179608[/C][C]0.693955880910196[/C][/ROW]
[ROW][C]145[/C][C]0.286913699090604[/C][C]0.573827398181208[/C][C]0.713086300909396[/C][/ROW]
[ROW][C]146[/C][C]0.584773538624919[/C][C]0.830452922750163[/C][C]0.415226461375081[/C][/ROW]
[ROW][C]147[/C][C]0.537468539453661[/C][C]0.925062921092677[/C][C]0.462531460546339[/C][/ROW]
[ROW][C]148[/C][C]0.714073307118675[/C][C]0.571853385762651[/C][C]0.285926692881325[/C][/ROW]
[ROW][C]149[/C][C]0.658310813818382[/C][C]0.683378372363235[/C][C]0.341689186181618[/C][/ROW]
[ROW][C]150[/C][C]0.64795104542137[/C][C]0.704097909157259[/C][C]0.352048954578629[/C][/ROW]
[ROW][C]151[/C][C]0.602419121609232[/C][C]0.795161756781537[/C][C]0.397580878390768[/C][/ROW]
[ROW][C]152[/C][C]0.579108962438688[/C][C]0.841782075122625[/C][C]0.420891037561312[/C][/ROW]
[ROW][C]153[/C][C]0.690976098412074[/C][C]0.618047803175852[/C][C]0.309023901587926[/C][/ROW]
[ROW][C]154[/C][C]0.629506375175491[/C][C]0.740987249649017[/C][C]0.370493624824509[/C][/ROW]
[ROW][C]155[/C][C]0.514860195405248[/C][C]0.970279609189504[/C][C]0.485139804594752[/C][/ROW]
[ROW][C]156[/C][C]0.369424843307226[/C][C]0.738849686614452[/C][C]0.630575156692774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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
80.5464956551617460.9070086896765080.453504344838254
90.4168326629364250.833665325872850.583167337063575
100.3062774028166820.6125548056333640.693722597183318
110.3767012862762760.7534025725525510.623298713723724
120.2680331653387260.5360663306774520.731966834661274
130.2411646740565780.4823293481131560.758835325943422
140.3819711516953480.7639423033906960.618028848304652
150.3968483489495640.7936966978991280.603151651050436
160.3801994756482440.7603989512964870.619800524351756
170.3330564937932890.6661129875865780.666943506206711
180.2780566857861240.5561133715722470.721943314213876
190.3340443859462510.6680887718925020.665955614053749
200.2773613212008410.5547226424016820.722638678799159
210.2971583821631630.5943167643263260.702841617836837
220.241887551308220.4837751026164390.75811244869178
230.1932612919999810.3865225839999620.806738708000019
240.2111470513196220.4222941026392450.788852948680378
250.3831194308188410.7662388616376810.61688056918116
260.4323000735608890.8646001471217780.567699926439111
270.398415777297060.796831554594120.60158422270294
280.407861795023210.815723590046420.59213820497679
290.477174554170060.954349108340120.52282544582994
300.4485755871386990.8971511742773970.551424412861301
310.4379691452851010.8759382905702030.562030854714899
320.4196648051734190.8393296103468380.580335194826581
330.40976914476060.8195382895212010.5902308552394
340.466999099837870.9339981996757390.53300090016213
350.4433739895431460.8867479790862920.556626010456854
360.5257825183465910.9484349633068170.474217481653409
370.5094211348042160.9811577303915670.490578865195784
380.5144783690870330.9710432618259330.485521630912967
390.4799781563974460.9599563127948910.520021843602554
400.5425815497578560.9148369004842880.457418450242144
410.522308619569410.955382760861180.47769138043059
420.5292575183989090.9414849632021820.470742481601091
430.5406891230475570.9186217539048860.459310876952443
440.5432661543740580.9134676912518850.456733845625942
450.5637363419187450.8725273161625090.436263658081255
460.5965463195804160.8069073608391670.403453680419584
470.6053808977934960.7892382044130080.394619102206504
480.5962948769263340.8074102461473320.403705123073666
490.5857653489558270.8284693020883460.414234651044173
500.5766236414751740.8467527170496520.423376358524826
510.5727156619985540.8545686760028930.427284338001446
520.5421110375633420.9157779248733160.457888962436658
530.5656464941748440.8687070116503120.434353505825156
540.5403947194234370.9192105611531260.459605280576563
550.5081294682150090.9837410635699810.491870531784991
560.5144810989682970.9710378020634050.485518901031703
570.5491685494163540.9016629011672920.450831450583646
580.5524584777824050.895083044435190.447541522217595
590.5369865848673730.9260268302652530.463013415132627
600.5439555911382840.9120888177234330.456044408861716
610.5640735173300480.8718529653399040.435926482669952
620.5564865338847270.8870269322305460.443513466115273
630.5473385456694660.9053229086610690.452661454330534
640.5480104112652680.9039791774694640.451989588734732
650.541050602047350.9178987959053010.45894939795265
660.547964502128440.9040709957431190.45203549787156
670.5486353874869490.9027292250261030.451364612513051
680.5371639558214410.9256720883571180.462836044178559
690.5425937781420850.914812443715830.457406221857915
700.5435582910285770.9128834179428460.456441708971423
710.5288421172379910.9423157655240180.471157882762009
720.5450624205691820.9098751588616370.454937579430818
730.5591482724807880.8817034550384250.440851727519212
740.5487441214726970.9025117570546070.451255878527303
750.562365837566950.87526832486610.43763416243305
760.57436461403640.85127077192720.4256353859636
770.5777065969302490.8445868061395030.422293403069751
780.5852985925439470.8294028149121050.414701407456053
790.5942358949310370.8115282101379260.405764105068963
800.596796991624460.806406016751080.40320300837554
810.5880184090346740.8239631819306520.411981590965326
820.5741914555596040.8516170888807910.425808544440396
830.5644899880679850.8710200238640310.435510011932015
840.5804548313037460.8390903373925090.419545168696254
850.6216457689661990.7567084620676020.378354231033801
860.6179497387475680.7641005225048650.382050261252432
870.6145065143651850.770986971269630.385493485634815
880.6119986775730930.7760026448538140.388001322426907
890.5879121384513590.8241757230972820.412087861548641
900.5813756976906010.8372486046187990.418624302309399
910.5717427955251350.856514408949730.428257204474865
920.5805096084081630.8389807831836730.419490391591836
930.5795952240107950.840809551978410.420404775989205
940.5901826246978820.8196347506042360.409817375302118
950.5857542311105910.8284915377788170.414245768889409
960.5805996435910540.8388007128178930.419400356408946
970.5893980588432340.8212038823135320.410601941156766
980.5827283200181460.8345433599637080.417271679981854
990.549261816165280.9014763676694410.45073818383472
1000.5574375200538780.8851249598922430.442562479946122
1010.5815579942714440.8368840114571120.418442005728556
1020.5730626849682760.8538746300634480.426937315031724
1030.5703924042817190.8592151914365610.429607595718281
1040.5822872239182410.8354255521635170.417712776081759
1050.5665600897418250.8668798205163490.433439910258175
1060.5753873286293370.8492253427413260.424612671370663
1070.5866693456264840.8266613087470310.413330654373516
1080.6012239368410660.7975521263178680.398776063158934
1090.5951764640789220.8096470718421560.404823535921078
1100.574780419349320.8504391613013610.42521958065068
1110.5542924311238750.891415137752250.445707568876125
1120.5910987402180690.8178025195638630.408901259781931
1130.5571878349835870.8856243300328260.442812165016413
1140.5371157148116860.9257685703766290.462884285188314
1150.5139054997539120.9721890004921750.486094500246088
1160.4921576034394290.9843152068788580.507842396560571
1170.4760364008904060.9520728017808130.523963599109593
1180.441159380150140.8823187603002790.55884061984986
1190.4392056815346810.8784113630693620.560794318465319
1200.4754895151847790.9509790303695570.524510484815221
1210.4627747371233970.9255494742467940.537225262876603
1220.501230999884380.997538000231240.49876900011562
1230.5502923406033630.8994153187932740.449707659396637
1240.5356781460735760.9286437078528480.464321853926424
1250.506466740306260.9870665193874810.49353325969374
1260.5466422211654830.9067155576690340.453357778834517
1270.5106891386884010.9786217226231990.489310861311599
1280.4627660723956640.9255321447913280.537233927604336
1290.4405039892955980.8810079785911950.559496010704402
1300.409337705408470.818675410816940.59066229459153
1310.3912534583101940.7825069166203870.608746541689806
1320.3755349512210350.7510699024420710.624465048778964
1330.3538370928705190.7076741857410380.646162907129481
1340.369789885397670.7395797707953410.63021011460233
1350.4309036207890630.8618072415781250.569096379210937
1360.3979989284637120.7959978569274240.602001071536288
1370.4544220075189790.9088440150379580.545577992481021
1380.4951715234563010.9903430469126010.504828476543699
1390.4441137275402860.8882274550805720.555886272459714
1400.4127184515571890.8254369031143780.587281548442811
1410.355475182697170.710950365394340.64452481730283
1420.3050647644085030.6101295288170050.694935235591497
1430.3524753898086350.704950779617270.647524610191365
1440.3060441190898040.6120882381796080.693955880910196
1450.2869136990906040.5738273981812080.713086300909396
1460.5847735386249190.8304529227501630.415226461375081
1470.5374685394536610.9250629210926770.462531460546339
1480.7140733071186750.5718533857626510.285926692881325
1490.6583108138183820.6833783723632350.341689186181618
1500.647951045421370.7040979091572590.352048954578629
1510.6024191216092320.7951617567815370.397580878390768
1520.5791089624386880.8417820751226250.420891037561312
1530.6909760984120740.6180478031758520.309023901587926
1540.6295063751754910.7409872496490170.370493624824509
1550.5148601954052480.9702796091895040.485139804594752
1560.3694248433072260.7388496866144520.630575156692774






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197915&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197915&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197915&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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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')
}