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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, 12 Dec 2010 20:19:00 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/12/t1292185042tktfra56jvd9xs2.htm/, Retrieved Wed, 08 May 2024 01:33:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108663, Retrieved Wed, 08 May 2024 01:33:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS10 MR] [2010-12-12 20:02:08] [65eb19f81eab2b6e672eafaed2a27190]
-    D      [Multiple Regression] [WS10 MR] [2010-12-12 20:19:00] [8b27277f7b82c0354d659d066108e38e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5	2	1	3	73	62	66
12	1	1	1	58	54	54
11	1	1	3	68	41	82
6	1	1	3	62	49	61
12	1	2	3	65	49	65
11	1	1	3	81	72	77
12	1	1	1	73	78	66
7	2	4	3	64	58	66
8	1	1	3	68	58	66
13	1	1	1	51	23	48
12	1	1	1	68	39	57
13	1	1	3	61	63	80
12	1	1	1	69	46	60
12	1	3	3	73	58	70
11	2	1	3	61	39	85
12	2	1	1	62	44	59
12	1	1	1	63	49	72
12	1	6	1	69	57	70
11	2	1	3	47	76	74
13	2	1	1	66	63	70
9	1	1	3	58	18	51
11	2	1	3	63	40	70
11	1	1	1	69	59	71
11	2	1	3	59	62	72
9	1	1	1	59	70	50
11	2	1	4	63	65	69
12	2	1	3	65	56	73
12	1	1	3	65	45	66
10	2	1	3	71	57	73
12	1	4	3	60	50	58
12	2	1	1	81	40	78
12	1	1	3	67	58	83
9	2	1	3	66	49	76
9	1	1	3	62	49	77
12	1	1	3	63	27	79
14	2	1	1	73	51	71
12	2	1	3	55	75	79
11	1	1	1	59	65	60
9	1	1	2	64	47	73
11	2	1	3	63	49	70
7	1	1	1	64	65	42
15	1	1	1	73	61	74
11	1	1	3	54	46	68
12	1	1	3	76	69	83
12	2	2	1	74	55	62
9	2	1	3	63	78	79
12	2	1	3	73	58	61
11	2	1	3	67	34	86
11	2	2	3	68	67	64
8	1	4	3	66	45	75
7	2	1	1	62	68	59
12	2	4	3	71	49	82
8	1	1	2	63	19	61
10	1	1	1	75	72	69
12	1	2	2	77	59	60
15	2	3	3	62	46	59
12	1	1	3	74	56	81
12	2	2	1	67	45	65
12	2	1	3	56	53	60
12	2	1	1	60	67	60
8	2	1	3	58	73	45
10	1	1	3	65	46	75
14	2	1	3	49	70	84
10	1	1	3	61	38	77
12	2	1	3	66	54	64
14	2	1	3	64	46	54
6	2	1	1	65	46	72
11	1	1	3	46	45	56
10	2	1	3	65	47	67
14	2	1	3	81	25	81
12	1	1	1	72	63	73
13	2	1	1	65	46	67
11	2	1	3	74	69	72
11	1	1	3	59	43	69
12	1	1	1	69	49	71
13	2	2	3	58	39	77
12	1	1	1	71	65	63
8	2	1	3	79	54	49
12	2	1	3	68	50	74
11	1	1	3	66	42	76
10	2	1	3	62	45	65
12	1	1	3	69	50	65
11	2	2	7	63	55	69
12	1	1	1	62	38	71
12	1	1	3	61	40	68
10	2	1	1	65	51	49
12	1	1	3	64	49	86
12	2	1	1	56	39	63
11	2	1	3	56	57	77
10	1	1	3	48	30	52
12	1	1	1	74	51	73
11	1	1	1	69	48	63
12	1	4	3	62	56	54
12	1	1	2	73	66	56
10	1	1	1	64	72	54
11	1	1	1	57	28	61
10	1	1	2	57	52	70
11	2	1	2	60	53	68
11	2	1	1	61	70	63
12	1	1	2	72	63	76
11	1	1	3	57	46	69
11	1	2	3	51	45	71
7	1	1	2	63	68	39
12	1	1	3	54	54	54
8	1	1	1	72	60	64
10	1	1	3	62	50	70
12	1	1	2	68	66	76
11	1	1	3	62	56	71
13	2	1	2	63	54	73
9	1	1	3	77	72	81
11	1	1	1	57	34	50
13	1	1	1	57	39	42
8	1	1	3	61	66	66
12	1	1	3	65	27	77
11	1	1	3	63	63	62
11	2	1	1	66	65	66
12	1	1	3	68	63	69
13	1	1	3	72	49	72
11	1	1	1	68	42	67
10	1	1	1	59	51	59
10	1	4	3	56	50	66
10	1	1	1	62	64	68
12	2	1	3	72	68	72
12	2	1	3	68	66	73
13	1	1	3	67	59	69
11	1	2	1	54	32	57
11	2	1	1	69	62	55
12	1	2	3	61	52	72
9	1	1	3	55	34	68
11	2	1	3	75	63	83
12	1	1	3	55	48	74
12	1	1	3	49	53	72
13	2	1	3	54	39	66
6	1	1	3	66	51	61
11	1	1	3	73	60	86
10	2	1	2	63	70	81
12	2	4	3	61	40	79
11	1	1	3	74	61	73
12	2	5	3	81	35	59
12	1	1	1	62	39	64
7	1	1	2	64	31	75
12	1	1	3	62	36	68
12	1	1	1	85	51	84
9	1	1	1	74	55	68
12	1	1	3	51	67	68
12	1	1	3	66	40	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
FF[t] = + 9.18224164145946 + 0.298990332610133Geslacht[t] + 0.163161952286726Opvoeding[t] -0.257077272602828Huwelijksstatus[t] -0.0100266770906813TotNV[t] -0.0148141827227416TotAngst[t] + 0.0473935603538839TotGroep[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FF[t] =  +  9.18224164145946 +  0.298990332610133Geslacht[t] +  0.163161952286726Opvoeding[t] -0.257077272602828Huwelijksstatus[t] -0.0100266770906813TotNV[t] -0.0148141827227416TotAngst[t] +  0.0473935603538839TotGroep[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FF[t] =  +  9.18224164145946 +  0.298990332610133Geslacht[t] +  0.163161952286726Opvoeding[t] -0.257077272602828Huwelijksstatus[t] -0.0100266770906813TotNV[t] -0.0148141827227416TotAngst[t] +  0.0473935603538839TotGroep[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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
FF[t] = + 9.18224164145946 + 0.298990332610133Geslacht[t] + 0.163161952286726Opvoeding[t] -0.257077272602828Huwelijksstatus[t] -0.0100266770906813TotNV[t] -0.0148141827227416TotAngst[t] + 0.0473935603538839TotGroep[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.182241641459461.5578615.894100
Geslacht0.2989903326101330.3071360.97350.3320070.166004
Opvoeding0.1631619522867260.173090.94260.34750.17375
Huwelijksstatus-0.2570772726028280.161041-1.59640.1126810.056341
TotNV-0.01002667709068130.021414-0.46820.6403460.320173
TotAngst-0.01481418272274160.011896-1.24530.2151310.107566
TotGroep0.04739356035388390.0167192.83460.0052720.002636

\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) & 9.18224164145946 & 1.557861 & 5.8941 & 0 & 0 \tabularnewline
Geslacht & 0.298990332610133 & 0.307136 & 0.9735 & 0.332007 & 0.166004 \tabularnewline
Opvoeding & 0.163161952286726 & 0.17309 & 0.9426 & 0.3475 & 0.17375 \tabularnewline
Huwelijksstatus & -0.257077272602828 & 0.161041 & -1.5964 & 0.112681 & 0.056341 \tabularnewline
TotNV & -0.0100266770906813 & 0.021414 & -0.4682 & 0.640346 & 0.320173 \tabularnewline
TotAngst & -0.0148141827227416 & 0.011896 & -1.2453 & 0.215131 & 0.107566 \tabularnewline
TotGroep & 0.0473935603538839 & 0.016719 & 2.8346 & 0.005272 & 0.002636 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&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]9.18224164145946[/C][C]1.557861[/C][C]5.8941[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Geslacht[/C][C]0.298990332610133[/C][C]0.307136[/C][C]0.9735[/C][C]0.332007[/C][C]0.166004[/C][/ROW]
[ROW][C]Opvoeding[/C][C]0.163161952286726[/C][C]0.17309[/C][C]0.9426[/C][C]0.3475[/C][C]0.17375[/C][/ROW]
[ROW][C]Huwelijksstatus[/C][C]-0.257077272602828[/C][C]0.161041[/C][C]-1.5964[/C][C]0.112681[/C][C]0.056341[/C][/ROW]
[ROW][C]TotNV[/C][C]-0.0100266770906813[/C][C]0.021414[/C][C]-0.4682[/C][C]0.640346[/C][C]0.320173[/C][/ROW]
[ROW][C]TotAngst[/C][C]-0.0148141827227416[/C][C]0.011896[/C][C]-1.2453[/C][C]0.215131[/C][C]0.107566[/C][/ROW]
[ROW][C]TotGroep[/C][C]0.0473935603538839[/C][C]0.016719[/C][C]2.8346[/C][C]0.005272[/C][C]0.002636[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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)9.182241641459461.5578615.894100
Geslacht0.2989903326101330.3071360.97350.3320070.166004
Opvoeding0.1631619522867260.173090.94260.34750.17375
Huwelijksstatus-0.2570772726028280.161041-1.59640.1126810.056341
TotNV-0.01002667709068130.021414-0.46820.6403460.320173
TotAngst-0.01481418272274160.011896-1.24530.2151310.107566
TotGroep0.04739356035388390.0167192.83460.0052720.002636






Multiple Linear Regression - Regression Statistics
Multiple R0.274950342612271
R-squared0.075597690902605
Adjusted R-squared0.0356954329559548
F-TEST (value)1.89457175590615
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.0858767171679528
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76419276719941
Sum Squared Residuals432.620280657581

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.274950342612271 \tabularnewline
R-squared & 0.075597690902605 \tabularnewline
Adjusted R-squared & 0.0356954329559548 \tabularnewline
F-TEST (value) & 1.89457175590615 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0.0858767171679528 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.76419276719941 \tabularnewline
Sum Squared Residuals & 432.620280657581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.274950342612271[/C][/ROW]
[ROW][C]R-squared[/C][C]0.075597690902605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0356954329559548[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.89457175590615[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]0.0858767171679528[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.76419276719941[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]432.620280657581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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 R0.274950342612271
R-squared0.075597690902605
Adjusted R-squared0.0356954329559548
F-TEST (value)1.89457175590615
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.0858767171679528
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76419276719941
Sum Squared Residuals432.620280657581






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1510.6497006680846-5.64970066808459
21210.56505577457571.43494422542434
31111.4702385237676-0.470238523767581
4610.4166203570982-4.41662035709817
51210.73927651952841.26072348047161
61110.64368425541430.356315744585684
71210.62783795711621.37216204288375
8711.2886833496519-4.28868334965187
9810.4601004518188-2.46010045181883
101310.81012081649212.18987918350789
111210.82918242557161.17081757442838
121311.11972612279431.88027387720573
131210.85763715048341.1423628495166
141210.92586521235441.07413478764559
151112.0112246425196-1.01122464251962
161211.20904902781990.790950972180097
171211.44207738910590.557922610894129
181211.98442650550570.0155734944942877
191111.082144197155-0.0821441971549943
201311.40880201161781.59119798838219
21910.442031126327-1.44203112632705
221111.2654537003073-0.265453700307255
231111.1863819389805-0.186381938980484
241111.0744355094774-0.0744355094774334
25910.1284279325056-1.12842793250558
261110.5906282992820.409371700717996
271211.15055410362370.84944589637632
281210.68276485848651.31723514151348
291011.0755798583568-1.07557985835685
301210.76916470435531.23083529564468
311211.97827654071170.0217234592882804
321211.27581765492550.724182345074461
33911.3864073866538-2.38640738665384
34911.1749173227603-2.17491732276032
351211.58558978627770.414410213722283
361411.56377902500982.43622097499018
371211.25371276492170.746287235078295
381110.67643444965810.323565550341876
39911.2519953652117-2.25199536521173
401111.1321260558026-0.132126055802581
4179.7732169778348-2.77321697783481
421511.25882754623393.74117245376607
431110.8730312444690.126968755530964
441211.02262155115930.97737844884075
451211.23111552612990.768884473870052
46911.129056800028-2.12905680002803
471210.47198959720611.52801040279386
481112.0725290539431-1.07252905394312
491110.69413797150320.305862028496751
50811.588766081441-3.58876608144097
51710.8535086424741-3.85350864247411
521212.1101212201839-0.110121220183915
53811.1080964342926-3.10809643429257
541010.838850380333-0.838850380332988
551210.49092403804621.50907596195379
561510.99159002174224.00840997825778
571211.14047216002850.859527839971514
581211.59162477405380.408375225946216
591210.66912046100751.33087953899246
601210.93576973973211.06423026026791
6189.6418800470631-1.64188004706309
621011.0944927189487-1.09449271894873
631411.62491154284892.37508845715108
641011.3479000098012-1.34790000980115
651210.74361374879351.25638625120647
661410.4082449612183.59175503878202
67611.7654569157029-5.76545691570287
681110.39933611967060.600663880329379
691010.9995203860051-0.99952038600505
701411.82851541740882.17148458259116
711211.19183229752520.808167702474758
721311.52848911393341.47151088606655
731110.8203360740580.179663925941977
741110.91473396753770.0852660324622618
751211.33452376620790.6654762337921
761311.82531814324731.17468185675268
771210.69829500563161.3017049943684
7889.9023635413064-1.90236354130641
791211.2567527290420.743247270958031
801111.1911163331029-0.191116333102898
811010.9644416620148-0.96444166201481
821210.52119367615621.4788063238438
831110.13070026098770.869299739012338
841211.56766651579280.432333484207174
851210.89172960117071.10827039882928
861010.6013341139498-0.60133411394983
871211.58140601176390.418593988236091
881211.53285424539320.467145754606766
891111.4155542561326-0.415554256132604
901010.4119212649148-0.411921264914847
911211.34954913601680.650450863983222
921110.97018946609960.0298105339004298
931210.4706520124221.52934798757803
941210.07459527364751.92540472635252
951010.2382404230222-0.238240423022224
961111.2920061249348-0.292006124934809
971011.1059305101711-1.10593051017114
981111.2652395080787-0.265239508078719
991111.0234811955348-0.023481195534839
1001211.07693570598410.923064294015935
1011110.89034477355090.109655226449124
1021111.2232680918122-0.223268091812199
10379.33954315309279-2.33954315309279
1041210.09100793773271.90899206226727
105810.8097328025085-2.80973280250851
1061010.8283482175604-0.828348217560387
1071211.07259986617860.927400133821434
1081110.78685668157780.213143318422178
1091311.45731309585341.54268690414665
110910.8733652051926-1.87336520519258
1111110.68179186470560.318208135294363
1121310.22857246826092.77142753173914
113810.4117737296717-2.41177372967167
1141211.47074931138860.529250688611413
1151110.2465886822430.753411317757005
1161111.1895994047568-0.189599404756792
1171210.52821021926681.47178978073322
1181310.83768275008412.16231724991592
1191111.2586754809422-0.258675480942236
1201010.8364394474226-0.836439447422622
1211011.1884198955491-1.18841989554912
1221011.0403170839399-1.04031708393989
1231210.85520361096211.14479638903787
1241210.97233224512421.02766775487578
1251310.59749362724842.40250637275158
1261111.2364171361871-0.236417136187077
1271110.68263275776030.317367242239749
1281211.06669560220010.933304397799921
129911.0407747600513-2.04077476005125
1301111.4205236571965-0.420523657196514
1311211.11773756405620.882262435943825
1321211.00903959227880.990960407721214
1331311.18093373543061.81906626456941
134610.34688528329-4.34688528328997
1351111.3282099079976-0.32820990799762
1361011.5994346551206-1.59943465512056
1371212.2015349545338-0.20153495453375
1381110.68725276358370.312747236416293
1391211.29036307154290.709636928457118
1401211.22109741059290.778902589407103
141711.5838094094834-4.58380940948336
1421210.9409596549711.059040345029
1431211.7605848519120.239415148087993
144911.0533246033564-2.05332460335639
1451210.59201343856351.40798656143649
1461210.88898977607121.11101022392881

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 10.6497006680846 & -5.64970066808459 \tabularnewline
2 & 12 & 10.5650557745757 & 1.43494422542434 \tabularnewline
3 & 11 & 11.4702385237676 & -0.470238523767581 \tabularnewline
4 & 6 & 10.4166203570982 & -4.41662035709817 \tabularnewline
5 & 12 & 10.7392765195284 & 1.26072348047161 \tabularnewline
6 & 11 & 10.6436842554143 & 0.356315744585684 \tabularnewline
7 & 12 & 10.6278379571162 & 1.37216204288375 \tabularnewline
8 & 7 & 11.2886833496519 & -4.28868334965187 \tabularnewline
9 & 8 & 10.4601004518188 & -2.46010045181883 \tabularnewline
10 & 13 & 10.8101208164921 & 2.18987918350789 \tabularnewline
11 & 12 & 10.8291824255716 & 1.17081757442838 \tabularnewline
12 & 13 & 11.1197261227943 & 1.88027387720573 \tabularnewline
13 & 12 & 10.8576371504834 & 1.1423628495166 \tabularnewline
14 & 12 & 10.9258652123544 & 1.07413478764559 \tabularnewline
15 & 11 & 12.0112246425196 & -1.01122464251962 \tabularnewline
16 & 12 & 11.2090490278199 & 0.790950972180097 \tabularnewline
17 & 12 & 11.4420773891059 & 0.557922610894129 \tabularnewline
18 & 12 & 11.9844265055057 & 0.0155734944942877 \tabularnewline
19 & 11 & 11.082144197155 & -0.0821441971549943 \tabularnewline
20 & 13 & 11.4088020116178 & 1.59119798838219 \tabularnewline
21 & 9 & 10.442031126327 & -1.44203112632705 \tabularnewline
22 & 11 & 11.2654537003073 & -0.265453700307255 \tabularnewline
23 & 11 & 11.1863819389805 & -0.186381938980484 \tabularnewline
24 & 11 & 11.0744355094774 & -0.0744355094774334 \tabularnewline
25 & 9 & 10.1284279325056 & -1.12842793250558 \tabularnewline
26 & 11 & 10.590628299282 & 0.409371700717996 \tabularnewline
27 & 12 & 11.1505541036237 & 0.84944589637632 \tabularnewline
28 & 12 & 10.6827648584865 & 1.31723514151348 \tabularnewline
29 & 10 & 11.0755798583568 & -1.07557985835685 \tabularnewline
30 & 12 & 10.7691647043553 & 1.23083529564468 \tabularnewline
31 & 12 & 11.9782765407117 & 0.0217234592882804 \tabularnewline
32 & 12 & 11.2758176549255 & 0.724182345074461 \tabularnewline
33 & 9 & 11.3864073866538 & -2.38640738665384 \tabularnewline
34 & 9 & 11.1749173227603 & -2.17491732276032 \tabularnewline
35 & 12 & 11.5855897862777 & 0.414410213722283 \tabularnewline
36 & 14 & 11.5637790250098 & 2.43622097499018 \tabularnewline
37 & 12 & 11.2537127649217 & 0.746287235078295 \tabularnewline
38 & 11 & 10.6764344496581 & 0.323565550341876 \tabularnewline
39 & 9 & 11.2519953652117 & -2.25199536521173 \tabularnewline
40 & 11 & 11.1321260558026 & -0.132126055802581 \tabularnewline
41 & 7 & 9.7732169778348 & -2.77321697783481 \tabularnewline
42 & 15 & 11.2588275462339 & 3.74117245376607 \tabularnewline
43 & 11 & 10.873031244469 & 0.126968755530964 \tabularnewline
44 & 12 & 11.0226215511593 & 0.97737844884075 \tabularnewline
45 & 12 & 11.2311155261299 & 0.768884473870052 \tabularnewline
46 & 9 & 11.129056800028 & -2.12905680002803 \tabularnewline
47 & 12 & 10.4719895972061 & 1.52801040279386 \tabularnewline
48 & 11 & 12.0725290539431 & -1.07252905394312 \tabularnewline
49 & 11 & 10.6941379715032 & 0.305862028496751 \tabularnewline
50 & 8 & 11.588766081441 & -3.58876608144097 \tabularnewline
51 & 7 & 10.8535086424741 & -3.85350864247411 \tabularnewline
52 & 12 & 12.1101212201839 & -0.110121220183915 \tabularnewline
53 & 8 & 11.1080964342926 & -3.10809643429257 \tabularnewline
54 & 10 & 10.838850380333 & -0.838850380332988 \tabularnewline
55 & 12 & 10.4909240380462 & 1.50907596195379 \tabularnewline
56 & 15 & 10.9915900217422 & 4.00840997825778 \tabularnewline
57 & 12 & 11.1404721600285 & 0.859527839971514 \tabularnewline
58 & 12 & 11.5916247740538 & 0.408375225946216 \tabularnewline
59 & 12 & 10.6691204610075 & 1.33087953899246 \tabularnewline
60 & 12 & 10.9357697397321 & 1.06423026026791 \tabularnewline
61 & 8 & 9.6418800470631 & -1.64188004706309 \tabularnewline
62 & 10 & 11.0944927189487 & -1.09449271894873 \tabularnewline
63 & 14 & 11.6249115428489 & 2.37508845715108 \tabularnewline
64 & 10 & 11.3479000098012 & -1.34790000980115 \tabularnewline
65 & 12 & 10.7436137487935 & 1.25638625120647 \tabularnewline
66 & 14 & 10.408244961218 & 3.59175503878202 \tabularnewline
67 & 6 & 11.7654569157029 & -5.76545691570287 \tabularnewline
68 & 11 & 10.3993361196706 & 0.600663880329379 \tabularnewline
69 & 10 & 10.9995203860051 & -0.99952038600505 \tabularnewline
70 & 14 & 11.8285154174088 & 2.17148458259116 \tabularnewline
71 & 12 & 11.1918322975252 & 0.808167702474758 \tabularnewline
72 & 13 & 11.5284891139334 & 1.47151088606655 \tabularnewline
73 & 11 & 10.820336074058 & 0.179663925941977 \tabularnewline
74 & 11 & 10.9147339675377 & 0.0852660324622618 \tabularnewline
75 & 12 & 11.3345237662079 & 0.6654762337921 \tabularnewline
76 & 13 & 11.8253181432473 & 1.17468185675268 \tabularnewline
77 & 12 & 10.6982950056316 & 1.3017049943684 \tabularnewline
78 & 8 & 9.9023635413064 & -1.90236354130641 \tabularnewline
79 & 12 & 11.256752729042 & 0.743247270958031 \tabularnewline
80 & 11 & 11.1911163331029 & -0.191116333102898 \tabularnewline
81 & 10 & 10.9644416620148 & -0.96444166201481 \tabularnewline
82 & 12 & 10.5211936761562 & 1.4788063238438 \tabularnewline
83 & 11 & 10.1307002609877 & 0.869299739012338 \tabularnewline
84 & 12 & 11.5676665157928 & 0.432333484207174 \tabularnewline
85 & 12 & 10.8917296011707 & 1.10827039882928 \tabularnewline
86 & 10 & 10.6013341139498 & -0.60133411394983 \tabularnewline
87 & 12 & 11.5814060117639 & 0.418593988236091 \tabularnewline
88 & 12 & 11.5328542453932 & 0.467145754606766 \tabularnewline
89 & 11 & 11.4155542561326 & -0.415554256132604 \tabularnewline
90 & 10 & 10.4119212649148 & -0.411921264914847 \tabularnewline
91 & 12 & 11.3495491360168 & 0.650450863983222 \tabularnewline
92 & 11 & 10.9701894660996 & 0.0298105339004298 \tabularnewline
93 & 12 & 10.470652012422 & 1.52934798757803 \tabularnewline
94 & 12 & 10.0745952736475 & 1.92540472635252 \tabularnewline
95 & 10 & 10.2382404230222 & -0.238240423022224 \tabularnewline
96 & 11 & 11.2920061249348 & -0.292006124934809 \tabularnewline
97 & 10 & 11.1059305101711 & -1.10593051017114 \tabularnewline
98 & 11 & 11.2652395080787 & -0.265239508078719 \tabularnewline
99 & 11 & 11.0234811955348 & -0.023481195534839 \tabularnewline
100 & 12 & 11.0769357059841 & 0.923064294015935 \tabularnewline
101 & 11 & 10.8903447735509 & 0.109655226449124 \tabularnewline
102 & 11 & 11.2232680918122 & -0.223268091812199 \tabularnewline
103 & 7 & 9.33954315309279 & -2.33954315309279 \tabularnewline
104 & 12 & 10.0910079377327 & 1.90899206226727 \tabularnewline
105 & 8 & 10.8097328025085 & -2.80973280250851 \tabularnewline
106 & 10 & 10.8283482175604 & -0.828348217560387 \tabularnewline
107 & 12 & 11.0725998661786 & 0.927400133821434 \tabularnewline
108 & 11 & 10.7868566815778 & 0.213143318422178 \tabularnewline
109 & 13 & 11.4573130958534 & 1.54268690414665 \tabularnewline
110 & 9 & 10.8733652051926 & -1.87336520519258 \tabularnewline
111 & 11 & 10.6817918647056 & 0.318208135294363 \tabularnewline
112 & 13 & 10.2285724682609 & 2.77142753173914 \tabularnewline
113 & 8 & 10.4117737296717 & -2.41177372967167 \tabularnewline
114 & 12 & 11.4707493113886 & 0.529250688611413 \tabularnewline
115 & 11 & 10.246588682243 & 0.753411317757005 \tabularnewline
116 & 11 & 11.1895994047568 & -0.189599404756792 \tabularnewline
117 & 12 & 10.5282102192668 & 1.47178978073322 \tabularnewline
118 & 13 & 10.8376827500841 & 2.16231724991592 \tabularnewline
119 & 11 & 11.2586754809422 & -0.258675480942236 \tabularnewline
120 & 10 & 10.8364394474226 & -0.836439447422622 \tabularnewline
121 & 10 & 11.1884198955491 & -1.18841989554912 \tabularnewline
122 & 10 & 11.0403170839399 & -1.04031708393989 \tabularnewline
123 & 12 & 10.8552036109621 & 1.14479638903787 \tabularnewline
124 & 12 & 10.9723322451242 & 1.02766775487578 \tabularnewline
125 & 13 & 10.5974936272484 & 2.40250637275158 \tabularnewline
126 & 11 & 11.2364171361871 & -0.236417136187077 \tabularnewline
127 & 11 & 10.6826327577603 & 0.317367242239749 \tabularnewline
128 & 12 & 11.0666956022001 & 0.933304397799921 \tabularnewline
129 & 9 & 11.0407747600513 & -2.04077476005125 \tabularnewline
130 & 11 & 11.4205236571965 & -0.420523657196514 \tabularnewline
131 & 12 & 11.1177375640562 & 0.882262435943825 \tabularnewline
132 & 12 & 11.0090395922788 & 0.990960407721214 \tabularnewline
133 & 13 & 11.1809337354306 & 1.81906626456941 \tabularnewline
134 & 6 & 10.34688528329 & -4.34688528328997 \tabularnewline
135 & 11 & 11.3282099079976 & -0.32820990799762 \tabularnewline
136 & 10 & 11.5994346551206 & -1.59943465512056 \tabularnewline
137 & 12 & 12.2015349545338 & -0.20153495453375 \tabularnewline
138 & 11 & 10.6872527635837 & 0.312747236416293 \tabularnewline
139 & 12 & 11.2903630715429 & 0.709636928457118 \tabularnewline
140 & 12 & 11.2210974105929 & 0.778902589407103 \tabularnewline
141 & 7 & 11.5838094094834 & -4.58380940948336 \tabularnewline
142 & 12 & 10.940959654971 & 1.059040345029 \tabularnewline
143 & 12 & 11.760584851912 & 0.239415148087993 \tabularnewline
144 & 9 & 11.0533246033564 & -2.05332460335639 \tabularnewline
145 & 12 & 10.5920134385635 & 1.40798656143649 \tabularnewline
146 & 12 & 10.8889897760712 & 1.11101022392881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&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]5[/C][C]10.6497006680846[/C][C]-5.64970066808459[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.5650557745757[/C][C]1.43494422542434[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]11.4702385237676[/C][C]-0.470238523767581[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]10.4166203570982[/C][C]-4.41662035709817[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]10.7392765195284[/C][C]1.26072348047161[/C][/ROW]
[ROW][C]6[/C][C]11[/C][C]10.6436842554143[/C][C]0.356315744585684[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]10.6278379571162[/C][C]1.37216204288375[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]11.2886833496519[/C][C]-4.28868334965187[/C][/ROW]
[ROW][C]9[/C][C]8[/C][C]10.4601004518188[/C][C]-2.46010045181883[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.8101208164921[/C][C]2.18987918350789[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]10.8291824255716[/C][C]1.17081757442838[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]11.1197261227943[/C][C]1.88027387720573[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]10.8576371504834[/C][C]1.1423628495166[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.9258652123544[/C][C]1.07413478764559[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]12.0112246425196[/C][C]-1.01122464251962[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.2090490278199[/C][C]0.790950972180097[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]11.4420773891059[/C][C]0.557922610894129[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]11.9844265055057[/C][C]0.0155734944942877[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]11.082144197155[/C][C]-0.0821441971549943[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.4088020116178[/C][C]1.59119798838219[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]10.442031126327[/C][C]-1.44203112632705[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.2654537003073[/C][C]-0.265453700307255[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.1863819389805[/C][C]-0.186381938980484[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.0744355094774[/C][C]-0.0744355094774334[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]10.1284279325056[/C][C]-1.12842793250558[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]10.590628299282[/C][C]0.409371700717996[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]11.1505541036237[/C][C]0.84944589637632[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]10.6827648584865[/C][C]1.31723514151348[/C][/ROW]
[ROW][C]29[/C][C]10[/C][C]11.0755798583568[/C][C]-1.07557985835685[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]10.7691647043553[/C][C]1.23083529564468[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.9782765407117[/C][C]0.0217234592882804[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.2758176549255[/C][C]0.724182345074461[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]11.3864073866538[/C][C]-2.38640738665384[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]11.1749173227603[/C][C]-2.17491732276032[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]11.5855897862777[/C][C]0.414410213722283[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]11.5637790250098[/C][C]2.43622097499018[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.2537127649217[/C][C]0.746287235078295[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]10.6764344496581[/C][C]0.323565550341876[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]11.2519953652117[/C][C]-2.25199536521173[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.1321260558026[/C][C]-0.132126055802581[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]9.7732169778348[/C][C]-2.77321697783481[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]11.2588275462339[/C][C]3.74117245376607[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.873031244469[/C][C]0.126968755530964[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]11.0226215511593[/C][C]0.97737844884075[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]11.2311155261299[/C][C]0.768884473870052[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]11.129056800028[/C][C]-2.12905680002803[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]10.4719895972061[/C][C]1.52801040279386[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.0725290539431[/C][C]-1.07252905394312[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]10.6941379715032[/C][C]0.305862028496751[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]11.588766081441[/C][C]-3.58876608144097[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]10.8535086424741[/C][C]-3.85350864247411[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.1101212201839[/C][C]-0.110121220183915[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]11.1080964342926[/C][C]-3.10809643429257[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.838850380333[/C][C]-0.838850380332988[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]10.4909240380462[/C][C]1.50907596195379[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]10.9915900217422[/C][C]4.00840997825778[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.1404721600285[/C][C]0.859527839971514[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.5916247740538[/C][C]0.408375225946216[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]10.6691204610075[/C][C]1.33087953899246[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]10.9357697397321[/C][C]1.06423026026791[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.6418800470631[/C][C]-1.64188004706309[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]11.0944927189487[/C][C]-1.09449271894873[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.6249115428489[/C][C]2.37508845715108[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]11.3479000098012[/C][C]-1.34790000980115[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]10.7436137487935[/C][C]1.25638625120647[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]10.408244961218[/C][C]3.59175503878202[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]11.7654569157029[/C][C]-5.76545691570287[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]10.3993361196706[/C][C]0.600663880329379[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]10.9995203860051[/C][C]-0.99952038600505[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]11.8285154174088[/C][C]2.17148458259116[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.1918322975252[/C][C]0.808167702474758[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.5284891139334[/C][C]1.47151088606655[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]10.820336074058[/C][C]0.179663925941977[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]10.9147339675377[/C][C]0.0852660324622618[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.3345237662079[/C][C]0.6654762337921[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.8253181432473[/C][C]1.17468185675268[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.6982950056316[/C][C]1.3017049943684[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]9.9023635413064[/C][C]-1.90236354130641[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.256752729042[/C][C]0.743247270958031[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.1911163331029[/C][C]-0.191116333102898[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]10.9644416620148[/C][C]-0.96444166201481[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]10.5211936761562[/C][C]1.4788063238438[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]10.1307002609877[/C][C]0.869299739012338[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.5676665157928[/C][C]0.432333484207174[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.8917296011707[/C][C]1.10827039882928[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]10.6013341139498[/C][C]-0.60133411394983[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.5814060117639[/C][C]0.418593988236091[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.5328542453932[/C][C]0.467145754606766[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]11.4155542561326[/C][C]-0.415554256132604[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4119212649148[/C][C]-0.411921264914847[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.3495491360168[/C][C]0.650450863983222[/C][/ROW]
[ROW][C]92[/C][C]11[/C][C]10.9701894660996[/C][C]0.0298105339004298[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.470652012422[/C][C]1.52934798757803[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.0745952736475[/C][C]1.92540472635252[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]10.2382404230222[/C][C]-0.238240423022224[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.2920061249348[/C][C]-0.292006124934809[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]11.1059305101711[/C][C]-1.10593051017114[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]11.2652395080787[/C][C]-0.265239508078719[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]11.0234811955348[/C][C]-0.023481195534839[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]11.0769357059841[/C][C]0.923064294015935[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.8903447735509[/C][C]0.109655226449124[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.2232680918122[/C][C]-0.223268091812199[/C][/ROW]
[ROW][C]103[/C][C]7[/C][C]9.33954315309279[/C][C]-2.33954315309279[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]10.0910079377327[/C][C]1.90899206226727[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]10.8097328025085[/C][C]-2.80973280250851[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]10.8283482175604[/C][C]-0.828348217560387[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.0725998661786[/C][C]0.927400133821434[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]10.7868566815778[/C][C]0.213143318422178[/C][/ROW]
[ROW][C]109[/C][C]13[/C][C]11.4573130958534[/C][C]1.54268690414665[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]10.8733652051926[/C][C]-1.87336520519258[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]10.6817918647056[/C][C]0.318208135294363[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]10.2285724682609[/C][C]2.77142753173914[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.4117737296717[/C][C]-2.41177372967167[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]11.4707493113886[/C][C]0.529250688611413[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]10.246588682243[/C][C]0.753411317757005[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]11.1895994047568[/C][C]-0.189599404756792[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]10.5282102192668[/C][C]1.47178978073322[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]10.8376827500841[/C][C]2.16231724991592[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]11.2586754809422[/C][C]-0.258675480942236[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]10.8364394474226[/C][C]-0.836439447422622[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]11.1884198955491[/C][C]-1.18841989554912[/C][/ROW]
[ROW][C]122[/C][C]10[/C][C]11.0403170839399[/C][C]-1.04031708393989[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.8552036109621[/C][C]1.14479638903787[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]10.9723322451242[/C][C]1.02766775487578[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]10.5974936272484[/C][C]2.40250637275158[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]11.2364171361871[/C][C]-0.236417136187077[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.6826327577603[/C][C]0.317367242239749[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.0666956022001[/C][C]0.933304397799921[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.0407747600513[/C][C]-2.04077476005125[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]11.4205236571965[/C][C]-0.420523657196514[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]11.1177375640562[/C][C]0.882262435943825[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.0090395922788[/C][C]0.990960407721214[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]11.1809337354306[/C][C]1.81906626456941[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]10.34688528329[/C][C]-4.34688528328997[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]11.3282099079976[/C][C]-0.32820990799762[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]11.5994346551206[/C][C]-1.59943465512056[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]12.2015349545338[/C][C]-0.20153495453375[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.6872527635837[/C][C]0.312747236416293[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]11.2903630715429[/C][C]0.709636928457118[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.2210974105929[/C][C]0.778902589407103[/C][/ROW]
[ROW][C]141[/C][C]7[/C][C]11.5838094094834[/C][C]-4.58380940948336[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]10.940959654971[/C][C]1.059040345029[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.760584851912[/C][C]0.239415148087993[/C][/ROW]
[ROW][C]144[/C][C]9[/C][C]11.0533246033564[/C][C]-2.05332460335639[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.5920134385635[/C][C]1.40798656143649[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]10.8889897760712[/C][C]1.11101022392881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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
1510.6497006680846-5.64970066808459
21210.56505577457571.43494422542434
31111.4702385237676-0.470238523767581
4610.4166203570982-4.41662035709817
51210.73927651952841.26072348047161
61110.64368425541430.356315744585684
71210.62783795711621.37216204288375
8711.2886833496519-4.28868334965187
9810.4601004518188-2.46010045181883
101310.81012081649212.18987918350789
111210.82918242557161.17081757442838
121311.11972612279431.88027387720573
131210.85763715048341.1423628495166
141210.92586521235441.07413478764559
151112.0112246425196-1.01122464251962
161211.20904902781990.790950972180097
171211.44207738910590.557922610894129
181211.98442650550570.0155734944942877
191111.082144197155-0.0821441971549943
201311.40880201161781.59119798838219
21910.442031126327-1.44203112632705
221111.2654537003073-0.265453700307255
231111.1863819389805-0.186381938980484
241111.0744355094774-0.0744355094774334
25910.1284279325056-1.12842793250558
261110.5906282992820.409371700717996
271211.15055410362370.84944589637632
281210.68276485848651.31723514151348
291011.0755798583568-1.07557985835685
301210.76916470435531.23083529564468
311211.97827654071170.0217234592882804
321211.27581765492550.724182345074461
33911.3864073866538-2.38640738665384
34911.1749173227603-2.17491732276032
351211.58558978627770.414410213722283
361411.56377902500982.43622097499018
371211.25371276492170.746287235078295
381110.67643444965810.323565550341876
39911.2519953652117-2.25199536521173
401111.1321260558026-0.132126055802581
4179.7732169778348-2.77321697783481
421511.25882754623393.74117245376607
431110.8730312444690.126968755530964
441211.02262155115930.97737844884075
451211.23111552612990.768884473870052
46911.129056800028-2.12905680002803
471210.47198959720611.52801040279386
481112.0725290539431-1.07252905394312
491110.69413797150320.305862028496751
50811.588766081441-3.58876608144097
51710.8535086424741-3.85350864247411
521212.1101212201839-0.110121220183915
53811.1080964342926-3.10809643429257
541010.838850380333-0.838850380332988
551210.49092403804621.50907596195379
561510.99159002174224.00840997825778
571211.14047216002850.859527839971514
581211.59162477405380.408375225946216
591210.66912046100751.33087953899246
601210.93576973973211.06423026026791
6189.6418800470631-1.64188004706309
621011.0944927189487-1.09449271894873
631411.62491154284892.37508845715108
641011.3479000098012-1.34790000980115
651210.74361374879351.25638625120647
661410.4082449612183.59175503878202
67611.7654569157029-5.76545691570287
681110.39933611967060.600663880329379
691010.9995203860051-0.99952038600505
701411.82851541740882.17148458259116
711211.19183229752520.808167702474758
721311.52848911393341.47151088606655
731110.8203360740580.179663925941977
741110.91473396753770.0852660324622618
751211.33452376620790.6654762337921
761311.82531814324731.17468185675268
771210.69829500563161.3017049943684
7889.9023635413064-1.90236354130641
791211.2567527290420.743247270958031
801111.1911163331029-0.191116333102898
811010.9644416620148-0.96444166201481
821210.52119367615621.4788063238438
831110.13070026098770.869299739012338
841211.56766651579280.432333484207174
851210.89172960117071.10827039882928
861010.6013341139498-0.60133411394983
871211.58140601176390.418593988236091
881211.53285424539320.467145754606766
891111.4155542561326-0.415554256132604
901010.4119212649148-0.411921264914847
911211.34954913601680.650450863983222
921110.97018946609960.0298105339004298
931210.4706520124221.52934798757803
941210.07459527364751.92540472635252
951010.2382404230222-0.238240423022224
961111.2920061249348-0.292006124934809
971011.1059305101711-1.10593051017114
981111.2652395080787-0.265239508078719
991111.0234811955348-0.023481195534839
1001211.07693570598410.923064294015935
1011110.89034477355090.109655226449124
1021111.2232680918122-0.223268091812199
10379.33954315309279-2.33954315309279
1041210.09100793773271.90899206226727
105810.8097328025085-2.80973280250851
1061010.8283482175604-0.828348217560387
1071211.07259986617860.927400133821434
1081110.78685668157780.213143318422178
1091311.45731309585341.54268690414665
110910.8733652051926-1.87336520519258
1111110.68179186470560.318208135294363
1121310.22857246826092.77142753173914
113810.4117737296717-2.41177372967167
1141211.47074931138860.529250688611413
1151110.2465886822430.753411317757005
1161111.1895994047568-0.189599404756792
1171210.52821021926681.47178978073322
1181310.83768275008412.16231724991592
1191111.2586754809422-0.258675480942236
1201010.8364394474226-0.836439447422622
1211011.1884198955491-1.18841989554912
1221011.0403170839399-1.04031708393989
1231210.85520361096211.14479638903787
1241210.97233224512421.02766775487578
1251310.59749362724842.40250637275158
1261111.2364171361871-0.236417136187077
1271110.68263275776030.317367242239749
1281211.06669560220010.933304397799921
129911.0407747600513-2.04077476005125
1301111.4205236571965-0.420523657196514
1311211.11773756405620.882262435943825
1321211.00903959227880.990960407721214
1331311.18093373543061.81906626456941
134610.34688528329-4.34688528328997
1351111.3282099079976-0.32820990799762
1361011.5994346551206-1.59943465512056
1371212.2015349545338-0.20153495453375
1381110.68725276358370.312747236416293
1391211.29036307154290.709636928457118
1401211.22109741059290.778902589407103
141711.5838094094834-4.58380940948336
1421210.9409596549711.059040345029
1431211.7605848519120.239415148087993
144911.0533246033564-2.05332460335639
1451210.59201343856351.40798656143649
1461210.88898977607121.11101022392881






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5097637846740130.9804724306519750.490236215325988
110.7380818571967820.5238362856064360.261918142803218
120.7590336550013240.4819326899973530.240966344998676
130.6639768744463440.6720462511073130.336023125553656
140.5721671438217810.8556657123564370.427832856178219
150.515475940355650.96904811928870.48452405964435
160.5784399571975780.8431200856048450.421560042802422
170.7618299525536210.4763400948927570.238170047446379
180.8073832911267140.3852334177465720.192616708873286
190.767519308420090.464961383159820.23248069157991
200.7431325552163580.5137348895672840.256867444783642
210.6880890587919970.6238218824160060.311910941208003
220.6956926876494950.608614624701010.304307312350505
230.7142396998787590.5715206002424830.285760300121241
240.680733108042260.638533783915480.31926689195774
250.6647035858885670.6705928282228650.335296414111433
260.7627394446039770.4745211107920450.237260555396023
270.7593147119109850.481370576178030.240685288089015
280.7535185483120150.4929629033759690.246481451687985
290.7053647088637550.589270582272490.294635291136245
300.7294825055074250.541034988985150.270517494492575
310.6743620182742690.6512759634514620.325637981725731
320.6174446637881370.7651106724237260.382555336211863
330.6221736372161030.7556527255677940.377826362783897
340.6832020171322980.6335959657354040.316797982867702
350.6280547052400880.7438905895198240.371945294759912
360.6700494453679780.6599011092640440.329950554632022
370.6260049666409250.747990066718150.373995033359075
380.5820752879034690.8358494241930620.417924712096531
390.658965940409370.6820681191812590.341034059590629
400.6135997615561460.7728004768877090.386400238443854
410.6612824285495290.6774351429009420.338717571450471
420.7625433021807350.4749133956385310.237456697819265
430.7180559680535830.5638880638928340.281944031946417
440.6849341380069170.6301317239861660.315065861993083
450.6507261609618480.6985476780763050.349273839038152
460.6613921807108770.6772156385782450.338607819289123
470.7294829505262680.5410340989474650.270517049473733
480.7035218952725350.592956209454930.296478104727465
490.6801265263922130.6397469472155750.319873473607787
500.787961650890110.4240766982197810.212038349109891
510.9067107728266860.1865784543466280.093289227173314
520.8886143050101570.2227713899796850.111385694989843
530.9271437849513650.1457124300972710.0728562150486355
540.9187162632694710.1625674734610580.0812837367305291
550.9168751883004690.1662496233990630.0831248116995314
560.9796428438039240.04071431239215150.0203571561960757
570.9744309397184720.05113812056305550.0255690602815278
580.9662941369067280.06741172618654320.0337058630932716
590.9647096111159860.07058077776802760.0352903888840138
600.9575345358030350.08493092839392920.0424654641969646
610.9552883534406980.08942329311860380.0447116465593019
620.947015198706260.1059696025874790.0529848012937395
630.9552525883294530.08949482334109430.0447474116705471
640.9500486416818650.09990271663626970.0499513583181349
650.9454295166539710.1091409666920570.0545704833460286
660.9779539076691260.04409218466174780.0220460923308739
670.999410129318950.001179741362099370.000589870681049683
680.9991411639225530.001717672154893230.000858836077446615
690.9989174018989320.002165196202136570.00108259810106829
700.9990811317351560.001837736529688340.000918868264844172
710.998743093362390.00251381327522150.00125690663761075
720.998569614134160.002860771731679610.0014303858658398
730.9978702438018950.004259512396210160.00212975619810508
740.9968798399344040.006240320131192360.00312016006559618
750.9957796164367470.008440767126505410.00422038356325271
760.9947507857280690.0104984285438620.00524921427193098
770.9940956928399980.01180861432000360.0059043071600018
780.9951305132363370.009738973527325960.00486948676366298
790.9933342135960150.01333157280797040.0066657864039852
800.9906326282431450.01873474351370930.00936737175685467
810.9891221763976870.02175564720462580.0108778236023129
820.9877353871992870.02452922560142690.0122646128007134
830.985288529039180.02942294192163850.0147114709608192
840.9810362096261990.03792758074760290.0189637903738015
850.9765760182267780.04684796354644470.0234239817732223
860.9706863952470580.05862720950588390.029313604752942
870.962290778942010.0754184421159790.0377092210579895
880.9511306093295670.0977387813408670.0488693906704335
890.9387332750985350.1225334498029310.0612667249014653
900.9262968036197570.1474063927604870.0737031963802434
910.9158929158658650.1682141682682710.0841070841341353
920.8952419251551230.2095161496897540.104758074844877
930.889580887784420.2208382244311610.11041911221558
940.8969612412167310.2060775175665370.103038758783269
950.8727894023180180.2544211953639640.127210597681982
960.8430869005154210.3138261989691580.156913099484579
970.8189308566210510.3621382867578970.181069143378948
980.7873800904368980.4252398191262030.212619909563102
990.7457201043822550.5085597912354910.254279895617745
1000.7312333398559630.5375333202880740.268766660144037
1010.6842801287281050.631439742543790.315719871271895
1020.6345884621287540.7308230757424910.365411537871246
1030.7054995425124150.5890009149751710.294500457487585
1040.684336022740220.6313279545195590.31566397725978
1050.7314602419732860.5370795160534280.268539758026714
1060.6967907000494920.6064185999010150.303209299950508
1070.6799106362306260.6401787275387490.320089363769374
1080.6259893450337520.7480213099324970.374010654966248
1090.6037482969691690.7925034060616620.396251703030831
1100.5936971651095180.8126056697809630.406302834890482
1110.534371042929180.931257914141640.46562895707082
1120.6044371976687660.7911256046624680.395562802331234
1130.6925446449100650.614910710179870.307455355089935
1140.6462947958968510.7074104082062980.353705204103149
1150.5873284266121750.825343146775650.412671573387825
1160.522760262509640.954479474980720.47723973749036
1170.478535476565820.957070953131640.52146452343418
1180.5046994357728590.9906011284542820.495300564227141
1190.4479670737510680.8959341475021360.552032926248932
1200.383844317141240.7676886342824810.61615568285876
1210.3648796524424380.7297593048848770.635120347557562
1220.314068502502950.6281370050059010.68593149749705
1230.2596336346047440.5192672692094870.740366365395256
1240.2091076830161870.4182153660323740.790892316983813
1250.2357230771205640.4714461542411270.764276922879436
1260.178995689691050.35799137938210.82100431030895
1270.1354547451915640.2709094903831280.864545254808436
1280.1002834096893150.200566819378630.899716590310685
1290.09769764240667330.1953952848133470.902302357593327
1300.06438892699388090.1287778539877620.935611073006119
1310.0425406183378110.0850812366756220.95745938166219
1320.02722053123414730.05444106246829460.972779468765853
1330.03741510674772740.07483021349545490.962584893252273
1340.2226354369543880.4452708739087760.777364563045612
1350.1378534817502010.2757069635004020.862146518249799
1360.08344325621964760.1668865124392950.916556743780352

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.509763784674013 & 0.980472430651975 & 0.490236215325988 \tabularnewline
11 & 0.738081857196782 & 0.523836285606436 & 0.261918142803218 \tabularnewline
12 & 0.759033655001324 & 0.481932689997353 & 0.240966344998676 \tabularnewline
13 & 0.663976874446344 & 0.672046251107313 & 0.336023125553656 \tabularnewline
14 & 0.572167143821781 & 0.855665712356437 & 0.427832856178219 \tabularnewline
15 & 0.51547594035565 & 0.9690481192887 & 0.48452405964435 \tabularnewline
16 & 0.578439957197578 & 0.843120085604845 & 0.421560042802422 \tabularnewline
17 & 0.761829952553621 & 0.476340094892757 & 0.238170047446379 \tabularnewline
18 & 0.807383291126714 & 0.385233417746572 & 0.192616708873286 \tabularnewline
19 & 0.76751930842009 & 0.46496138315982 & 0.23248069157991 \tabularnewline
20 & 0.743132555216358 & 0.513734889567284 & 0.256867444783642 \tabularnewline
21 & 0.688089058791997 & 0.623821882416006 & 0.311910941208003 \tabularnewline
22 & 0.695692687649495 & 0.60861462470101 & 0.304307312350505 \tabularnewline
23 & 0.714239699878759 & 0.571520600242483 & 0.285760300121241 \tabularnewline
24 & 0.68073310804226 & 0.63853378391548 & 0.31926689195774 \tabularnewline
25 & 0.664703585888567 & 0.670592828222865 & 0.335296414111433 \tabularnewline
26 & 0.762739444603977 & 0.474521110792045 & 0.237260555396023 \tabularnewline
27 & 0.759314711910985 & 0.48137057617803 & 0.240685288089015 \tabularnewline
28 & 0.753518548312015 & 0.492962903375969 & 0.246481451687985 \tabularnewline
29 & 0.705364708863755 & 0.58927058227249 & 0.294635291136245 \tabularnewline
30 & 0.729482505507425 & 0.54103498898515 & 0.270517494492575 \tabularnewline
31 & 0.674362018274269 & 0.651275963451462 & 0.325637981725731 \tabularnewline
32 & 0.617444663788137 & 0.765110672423726 & 0.382555336211863 \tabularnewline
33 & 0.622173637216103 & 0.755652725567794 & 0.377826362783897 \tabularnewline
34 & 0.683202017132298 & 0.633595965735404 & 0.316797982867702 \tabularnewline
35 & 0.628054705240088 & 0.743890589519824 & 0.371945294759912 \tabularnewline
36 & 0.670049445367978 & 0.659901109264044 & 0.329950554632022 \tabularnewline
37 & 0.626004966640925 & 0.74799006671815 & 0.373995033359075 \tabularnewline
38 & 0.582075287903469 & 0.835849424193062 & 0.417924712096531 \tabularnewline
39 & 0.65896594040937 & 0.682068119181259 & 0.341034059590629 \tabularnewline
40 & 0.613599761556146 & 0.772800476887709 & 0.386400238443854 \tabularnewline
41 & 0.661282428549529 & 0.677435142900942 & 0.338717571450471 \tabularnewline
42 & 0.762543302180735 & 0.474913395638531 & 0.237456697819265 \tabularnewline
43 & 0.718055968053583 & 0.563888063892834 & 0.281944031946417 \tabularnewline
44 & 0.684934138006917 & 0.630131723986166 & 0.315065861993083 \tabularnewline
45 & 0.650726160961848 & 0.698547678076305 & 0.349273839038152 \tabularnewline
46 & 0.661392180710877 & 0.677215638578245 & 0.338607819289123 \tabularnewline
47 & 0.729482950526268 & 0.541034098947465 & 0.270517049473733 \tabularnewline
48 & 0.703521895272535 & 0.59295620945493 & 0.296478104727465 \tabularnewline
49 & 0.680126526392213 & 0.639746947215575 & 0.319873473607787 \tabularnewline
50 & 0.78796165089011 & 0.424076698219781 & 0.212038349109891 \tabularnewline
51 & 0.906710772826686 & 0.186578454346628 & 0.093289227173314 \tabularnewline
52 & 0.888614305010157 & 0.222771389979685 & 0.111385694989843 \tabularnewline
53 & 0.927143784951365 & 0.145712430097271 & 0.0728562150486355 \tabularnewline
54 & 0.918716263269471 & 0.162567473461058 & 0.0812837367305291 \tabularnewline
55 & 0.916875188300469 & 0.166249623399063 & 0.0831248116995314 \tabularnewline
56 & 0.979642843803924 & 0.0407143123921515 & 0.0203571561960757 \tabularnewline
57 & 0.974430939718472 & 0.0511381205630555 & 0.0255690602815278 \tabularnewline
58 & 0.966294136906728 & 0.0674117261865432 & 0.0337058630932716 \tabularnewline
59 & 0.964709611115986 & 0.0705807777680276 & 0.0352903888840138 \tabularnewline
60 & 0.957534535803035 & 0.0849309283939292 & 0.0424654641969646 \tabularnewline
61 & 0.955288353440698 & 0.0894232931186038 & 0.0447116465593019 \tabularnewline
62 & 0.94701519870626 & 0.105969602587479 & 0.0529848012937395 \tabularnewline
63 & 0.955252588329453 & 0.0894948233410943 & 0.0447474116705471 \tabularnewline
64 & 0.950048641681865 & 0.0999027166362697 & 0.0499513583181349 \tabularnewline
65 & 0.945429516653971 & 0.109140966692057 & 0.0545704833460286 \tabularnewline
66 & 0.977953907669126 & 0.0440921846617478 & 0.0220460923308739 \tabularnewline
67 & 0.99941012931895 & 0.00117974136209937 & 0.000589870681049683 \tabularnewline
68 & 0.999141163922553 & 0.00171767215489323 & 0.000858836077446615 \tabularnewline
69 & 0.998917401898932 & 0.00216519620213657 & 0.00108259810106829 \tabularnewline
70 & 0.999081131735156 & 0.00183773652968834 & 0.000918868264844172 \tabularnewline
71 & 0.99874309336239 & 0.0025138132752215 & 0.00125690663761075 \tabularnewline
72 & 0.99856961413416 & 0.00286077173167961 & 0.0014303858658398 \tabularnewline
73 & 0.997870243801895 & 0.00425951239621016 & 0.00212975619810508 \tabularnewline
74 & 0.996879839934404 & 0.00624032013119236 & 0.00312016006559618 \tabularnewline
75 & 0.995779616436747 & 0.00844076712650541 & 0.00422038356325271 \tabularnewline
76 & 0.994750785728069 & 0.010498428543862 & 0.00524921427193098 \tabularnewline
77 & 0.994095692839998 & 0.0118086143200036 & 0.0059043071600018 \tabularnewline
78 & 0.995130513236337 & 0.00973897352732596 & 0.00486948676366298 \tabularnewline
79 & 0.993334213596015 & 0.0133315728079704 & 0.0066657864039852 \tabularnewline
80 & 0.990632628243145 & 0.0187347435137093 & 0.00936737175685467 \tabularnewline
81 & 0.989122176397687 & 0.0217556472046258 & 0.0108778236023129 \tabularnewline
82 & 0.987735387199287 & 0.0245292256014269 & 0.0122646128007134 \tabularnewline
83 & 0.98528852903918 & 0.0294229419216385 & 0.0147114709608192 \tabularnewline
84 & 0.981036209626199 & 0.0379275807476029 & 0.0189637903738015 \tabularnewline
85 & 0.976576018226778 & 0.0468479635464447 & 0.0234239817732223 \tabularnewline
86 & 0.970686395247058 & 0.0586272095058839 & 0.029313604752942 \tabularnewline
87 & 0.96229077894201 & 0.075418442115979 & 0.0377092210579895 \tabularnewline
88 & 0.951130609329567 & 0.097738781340867 & 0.0488693906704335 \tabularnewline
89 & 0.938733275098535 & 0.122533449802931 & 0.0612667249014653 \tabularnewline
90 & 0.926296803619757 & 0.147406392760487 & 0.0737031963802434 \tabularnewline
91 & 0.915892915865865 & 0.168214168268271 & 0.0841070841341353 \tabularnewline
92 & 0.895241925155123 & 0.209516149689754 & 0.104758074844877 \tabularnewline
93 & 0.88958088778442 & 0.220838224431161 & 0.11041911221558 \tabularnewline
94 & 0.896961241216731 & 0.206077517566537 & 0.103038758783269 \tabularnewline
95 & 0.872789402318018 & 0.254421195363964 & 0.127210597681982 \tabularnewline
96 & 0.843086900515421 & 0.313826198969158 & 0.156913099484579 \tabularnewline
97 & 0.818930856621051 & 0.362138286757897 & 0.181069143378948 \tabularnewline
98 & 0.787380090436898 & 0.425239819126203 & 0.212619909563102 \tabularnewline
99 & 0.745720104382255 & 0.508559791235491 & 0.254279895617745 \tabularnewline
100 & 0.731233339855963 & 0.537533320288074 & 0.268766660144037 \tabularnewline
101 & 0.684280128728105 & 0.63143974254379 & 0.315719871271895 \tabularnewline
102 & 0.634588462128754 & 0.730823075742491 & 0.365411537871246 \tabularnewline
103 & 0.705499542512415 & 0.589000914975171 & 0.294500457487585 \tabularnewline
104 & 0.68433602274022 & 0.631327954519559 & 0.31566397725978 \tabularnewline
105 & 0.731460241973286 & 0.537079516053428 & 0.268539758026714 \tabularnewline
106 & 0.696790700049492 & 0.606418599901015 & 0.303209299950508 \tabularnewline
107 & 0.679910636230626 & 0.640178727538749 & 0.320089363769374 \tabularnewline
108 & 0.625989345033752 & 0.748021309932497 & 0.374010654966248 \tabularnewline
109 & 0.603748296969169 & 0.792503406061662 & 0.396251703030831 \tabularnewline
110 & 0.593697165109518 & 0.812605669780963 & 0.406302834890482 \tabularnewline
111 & 0.53437104292918 & 0.93125791414164 & 0.46562895707082 \tabularnewline
112 & 0.604437197668766 & 0.791125604662468 & 0.395562802331234 \tabularnewline
113 & 0.692544644910065 & 0.61491071017987 & 0.307455355089935 \tabularnewline
114 & 0.646294795896851 & 0.707410408206298 & 0.353705204103149 \tabularnewline
115 & 0.587328426612175 & 0.82534314677565 & 0.412671573387825 \tabularnewline
116 & 0.52276026250964 & 0.95447947498072 & 0.47723973749036 \tabularnewline
117 & 0.47853547656582 & 0.95707095313164 & 0.52146452343418 \tabularnewline
118 & 0.504699435772859 & 0.990601128454282 & 0.495300564227141 \tabularnewline
119 & 0.447967073751068 & 0.895934147502136 & 0.552032926248932 \tabularnewline
120 & 0.38384431714124 & 0.767688634282481 & 0.61615568285876 \tabularnewline
121 & 0.364879652442438 & 0.729759304884877 & 0.635120347557562 \tabularnewline
122 & 0.31406850250295 & 0.628137005005901 & 0.68593149749705 \tabularnewline
123 & 0.259633634604744 & 0.519267269209487 & 0.740366365395256 \tabularnewline
124 & 0.209107683016187 & 0.418215366032374 & 0.790892316983813 \tabularnewline
125 & 0.235723077120564 & 0.471446154241127 & 0.764276922879436 \tabularnewline
126 & 0.17899568969105 & 0.3579913793821 & 0.82100431030895 \tabularnewline
127 & 0.135454745191564 & 0.270909490383128 & 0.864545254808436 \tabularnewline
128 & 0.100283409689315 & 0.20056681937863 & 0.899716590310685 \tabularnewline
129 & 0.0976976424066733 & 0.195395284813347 & 0.902302357593327 \tabularnewline
130 & 0.0643889269938809 & 0.128777853987762 & 0.935611073006119 \tabularnewline
131 & 0.042540618337811 & 0.085081236675622 & 0.95745938166219 \tabularnewline
132 & 0.0272205312341473 & 0.0544410624682946 & 0.972779468765853 \tabularnewline
133 & 0.0374151067477274 & 0.0748302134954549 & 0.962584893252273 \tabularnewline
134 & 0.222635436954388 & 0.445270873908776 & 0.777364563045612 \tabularnewline
135 & 0.137853481750201 & 0.275706963500402 & 0.862146518249799 \tabularnewline
136 & 0.0834432562196476 & 0.166886512439295 & 0.916556743780352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.509763784674013[/C][C]0.980472430651975[/C][C]0.490236215325988[/C][/ROW]
[ROW][C]11[/C][C]0.738081857196782[/C][C]0.523836285606436[/C][C]0.261918142803218[/C][/ROW]
[ROW][C]12[/C][C]0.759033655001324[/C][C]0.481932689997353[/C][C]0.240966344998676[/C][/ROW]
[ROW][C]13[/C][C]0.663976874446344[/C][C]0.672046251107313[/C][C]0.336023125553656[/C][/ROW]
[ROW][C]14[/C][C]0.572167143821781[/C][C]0.855665712356437[/C][C]0.427832856178219[/C][/ROW]
[ROW][C]15[/C][C]0.51547594035565[/C][C]0.9690481192887[/C][C]0.48452405964435[/C][/ROW]
[ROW][C]16[/C][C]0.578439957197578[/C][C]0.843120085604845[/C][C]0.421560042802422[/C][/ROW]
[ROW][C]17[/C][C]0.761829952553621[/C][C]0.476340094892757[/C][C]0.238170047446379[/C][/ROW]
[ROW][C]18[/C][C]0.807383291126714[/C][C]0.385233417746572[/C][C]0.192616708873286[/C][/ROW]
[ROW][C]19[/C][C]0.76751930842009[/C][C]0.46496138315982[/C][C]0.23248069157991[/C][/ROW]
[ROW][C]20[/C][C]0.743132555216358[/C][C]0.513734889567284[/C][C]0.256867444783642[/C][/ROW]
[ROW][C]21[/C][C]0.688089058791997[/C][C]0.623821882416006[/C][C]0.311910941208003[/C][/ROW]
[ROW][C]22[/C][C]0.695692687649495[/C][C]0.60861462470101[/C][C]0.304307312350505[/C][/ROW]
[ROW][C]23[/C][C]0.714239699878759[/C][C]0.571520600242483[/C][C]0.285760300121241[/C][/ROW]
[ROW][C]24[/C][C]0.68073310804226[/C][C]0.63853378391548[/C][C]0.31926689195774[/C][/ROW]
[ROW][C]25[/C][C]0.664703585888567[/C][C]0.670592828222865[/C][C]0.335296414111433[/C][/ROW]
[ROW][C]26[/C][C]0.762739444603977[/C][C]0.474521110792045[/C][C]0.237260555396023[/C][/ROW]
[ROW][C]27[/C][C]0.759314711910985[/C][C]0.48137057617803[/C][C]0.240685288089015[/C][/ROW]
[ROW][C]28[/C][C]0.753518548312015[/C][C]0.492962903375969[/C][C]0.246481451687985[/C][/ROW]
[ROW][C]29[/C][C]0.705364708863755[/C][C]0.58927058227249[/C][C]0.294635291136245[/C][/ROW]
[ROW][C]30[/C][C]0.729482505507425[/C][C]0.54103498898515[/C][C]0.270517494492575[/C][/ROW]
[ROW][C]31[/C][C]0.674362018274269[/C][C]0.651275963451462[/C][C]0.325637981725731[/C][/ROW]
[ROW][C]32[/C][C]0.617444663788137[/C][C]0.765110672423726[/C][C]0.382555336211863[/C][/ROW]
[ROW][C]33[/C][C]0.622173637216103[/C][C]0.755652725567794[/C][C]0.377826362783897[/C][/ROW]
[ROW][C]34[/C][C]0.683202017132298[/C][C]0.633595965735404[/C][C]0.316797982867702[/C][/ROW]
[ROW][C]35[/C][C]0.628054705240088[/C][C]0.743890589519824[/C][C]0.371945294759912[/C][/ROW]
[ROW][C]36[/C][C]0.670049445367978[/C][C]0.659901109264044[/C][C]0.329950554632022[/C][/ROW]
[ROW][C]37[/C][C]0.626004966640925[/C][C]0.74799006671815[/C][C]0.373995033359075[/C][/ROW]
[ROW][C]38[/C][C]0.582075287903469[/C][C]0.835849424193062[/C][C]0.417924712096531[/C][/ROW]
[ROW][C]39[/C][C]0.65896594040937[/C][C]0.682068119181259[/C][C]0.341034059590629[/C][/ROW]
[ROW][C]40[/C][C]0.613599761556146[/C][C]0.772800476887709[/C][C]0.386400238443854[/C][/ROW]
[ROW][C]41[/C][C]0.661282428549529[/C][C]0.677435142900942[/C][C]0.338717571450471[/C][/ROW]
[ROW][C]42[/C][C]0.762543302180735[/C][C]0.474913395638531[/C][C]0.237456697819265[/C][/ROW]
[ROW][C]43[/C][C]0.718055968053583[/C][C]0.563888063892834[/C][C]0.281944031946417[/C][/ROW]
[ROW][C]44[/C][C]0.684934138006917[/C][C]0.630131723986166[/C][C]0.315065861993083[/C][/ROW]
[ROW][C]45[/C][C]0.650726160961848[/C][C]0.698547678076305[/C][C]0.349273839038152[/C][/ROW]
[ROW][C]46[/C][C]0.661392180710877[/C][C]0.677215638578245[/C][C]0.338607819289123[/C][/ROW]
[ROW][C]47[/C][C]0.729482950526268[/C][C]0.541034098947465[/C][C]0.270517049473733[/C][/ROW]
[ROW][C]48[/C][C]0.703521895272535[/C][C]0.59295620945493[/C][C]0.296478104727465[/C][/ROW]
[ROW][C]49[/C][C]0.680126526392213[/C][C]0.639746947215575[/C][C]0.319873473607787[/C][/ROW]
[ROW][C]50[/C][C]0.78796165089011[/C][C]0.424076698219781[/C][C]0.212038349109891[/C][/ROW]
[ROW][C]51[/C][C]0.906710772826686[/C][C]0.186578454346628[/C][C]0.093289227173314[/C][/ROW]
[ROW][C]52[/C][C]0.888614305010157[/C][C]0.222771389979685[/C][C]0.111385694989843[/C][/ROW]
[ROW][C]53[/C][C]0.927143784951365[/C][C]0.145712430097271[/C][C]0.0728562150486355[/C][/ROW]
[ROW][C]54[/C][C]0.918716263269471[/C][C]0.162567473461058[/C][C]0.0812837367305291[/C][/ROW]
[ROW][C]55[/C][C]0.916875188300469[/C][C]0.166249623399063[/C][C]0.0831248116995314[/C][/ROW]
[ROW][C]56[/C][C]0.979642843803924[/C][C]0.0407143123921515[/C][C]0.0203571561960757[/C][/ROW]
[ROW][C]57[/C][C]0.974430939718472[/C][C]0.0511381205630555[/C][C]0.0255690602815278[/C][/ROW]
[ROW][C]58[/C][C]0.966294136906728[/C][C]0.0674117261865432[/C][C]0.0337058630932716[/C][/ROW]
[ROW][C]59[/C][C]0.964709611115986[/C][C]0.0705807777680276[/C][C]0.0352903888840138[/C][/ROW]
[ROW][C]60[/C][C]0.957534535803035[/C][C]0.0849309283939292[/C][C]0.0424654641969646[/C][/ROW]
[ROW][C]61[/C][C]0.955288353440698[/C][C]0.0894232931186038[/C][C]0.0447116465593019[/C][/ROW]
[ROW][C]62[/C][C]0.94701519870626[/C][C]0.105969602587479[/C][C]0.0529848012937395[/C][/ROW]
[ROW][C]63[/C][C]0.955252588329453[/C][C]0.0894948233410943[/C][C]0.0447474116705471[/C][/ROW]
[ROW][C]64[/C][C]0.950048641681865[/C][C]0.0999027166362697[/C][C]0.0499513583181349[/C][/ROW]
[ROW][C]65[/C][C]0.945429516653971[/C][C]0.109140966692057[/C][C]0.0545704833460286[/C][/ROW]
[ROW][C]66[/C][C]0.977953907669126[/C][C]0.0440921846617478[/C][C]0.0220460923308739[/C][/ROW]
[ROW][C]67[/C][C]0.99941012931895[/C][C]0.00117974136209937[/C][C]0.000589870681049683[/C][/ROW]
[ROW][C]68[/C][C]0.999141163922553[/C][C]0.00171767215489323[/C][C]0.000858836077446615[/C][/ROW]
[ROW][C]69[/C][C]0.998917401898932[/C][C]0.00216519620213657[/C][C]0.00108259810106829[/C][/ROW]
[ROW][C]70[/C][C]0.999081131735156[/C][C]0.00183773652968834[/C][C]0.000918868264844172[/C][/ROW]
[ROW][C]71[/C][C]0.99874309336239[/C][C]0.0025138132752215[/C][C]0.00125690663761075[/C][/ROW]
[ROW][C]72[/C][C]0.99856961413416[/C][C]0.00286077173167961[/C][C]0.0014303858658398[/C][/ROW]
[ROW][C]73[/C][C]0.997870243801895[/C][C]0.00425951239621016[/C][C]0.00212975619810508[/C][/ROW]
[ROW][C]74[/C][C]0.996879839934404[/C][C]0.00624032013119236[/C][C]0.00312016006559618[/C][/ROW]
[ROW][C]75[/C][C]0.995779616436747[/C][C]0.00844076712650541[/C][C]0.00422038356325271[/C][/ROW]
[ROW][C]76[/C][C]0.994750785728069[/C][C]0.010498428543862[/C][C]0.00524921427193098[/C][/ROW]
[ROW][C]77[/C][C]0.994095692839998[/C][C]0.0118086143200036[/C][C]0.0059043071600018[/C][/ROW]
[ROW][C]78[/C][C]0.995130513236337[/C][C]0.00973897352732596[/C][C]0.00486948676366298[/C][/ROW]
[ROW][C]79[/C][C]0.993334213596015[/C][C]0.0133315728079704[/C][C]0.0066657864039852[/C][/ROW]
[ROW][C]80[/C][C]0.990632628243145[/C][C]0.0187347435137093[/C][C]0.00936737175685467[/C][/ROW]
[ROW][C]81[/C][C]0.989122176397687[/C][C]0.0217556472046258[/C][C]0.0108778236023129[/C][/ROW]
[ROW][C]82[/C][C]0.987735387199287[/C][C]0.0245292256014269[/C][C]0.0122646128007134[/C][/ROW]
[ROW][C]83[/C][C]0.98528852903918[/C][C]0.0294229419216385[/C][C]0.0147114709608192[/C][/ROW]
[ROW][C]84[/C][C]0.981036209626199[/C][C]0.0379275807476029[/C][C]0.0189637903738015[/C][/ROW]
[ROW][C]85[/C][C]0.976576018226778[/C][C]0.0468479635464447[/C][C]0.0234239817732223[/C][/ROW]
[ROW][C]86[/C][C]0.970686395247058[/C][C]0.0586272095058839[/C][C]0.029313604752942[/C][/ROW]
[ROW][C]87[/C][C]0.96229077894201[/C][C]0.075418442115979[/C][C]0.0377092210579895[/C][/ROW]
[ROW][C]88[/C][C]0.951130609329567[/C][C]0.097738781340867[/C][C]0.0488693906704335[/C][/ROW]
[ROW][C]89[/C][C]0.938733275098535[/C][C]0.122533449802931[/C][C]0.0612667249014653[/C][/ROW]
[ROW][C]90[/C][C]0.926296803619757[/C][C]0.147406392760487[/C][C]0.0737031963802434[/C][/ROW]
[ROW][C]91[/C][C]0.915892915865865[/C][C]0.168214168268271[/C][C]0.0841070841341353[/C][/ROW]
[ROW][C]92[/C][C]0.895241925155123[/C][C]0.209516149689754[/C][C]0.104758074844877[/C][/ROW]
[ROW][C]93[/C][C]0.88958088778442[/C][C]0.220838224431161[/C][C]0.11041911221558[/C][/ROW]
[ROW][C]94[/C][C]0.896961241216731[/C][C]0.206077517566537[/C][C]0.103038758783269[/C][/ROW]
[ROW][C]95[/C][C]0.872789402318018[/C][C]0.254421195363964[/C][C]0.127210597681982[/C][/ROW]
[ROW][C]96[/C][C]0.843086900515421[/C][C]0.313826198969158[/C][C]0.156913099484579[/C][/ROW]
[ROW][C]97[/C][C]0.818930856621051[/C][C]0.362138286757897[/C][C]0.181069143378948[/C][/ROW]
[ROW][C]98[/C][C]0.787380090436898[/C][C]0.425239819126203[/C][C]0.212619909563102[/C][/ROW]
[ROW][C]99[/C][C]0.745720104382255[/C][C]0.508559791235491[/C][C]0.254279895617745[/C][/ROW]
[ROW][C]100[/C][C]0.731233339855963[/C][C]0.537533320288074[/C][C]0.268766660144037[/C][/ROW]
[ROW][C]101[/C][C]0.684280128728105[/C][C]0.63143974254379[/C][C]0.315719871271895[/C][/ROW]
[ROW][C]102[/C][C]0.634588462128754[/C][C]0.730823075742491[/C][C]0.365411537871246[/C][/ROW]
[ROW][C]103[/C][C]0.705499542512415[/C][C]0.589000914975171[/C][C]0.294500457487585[/C][/ROW]
[ROW][C]104[/C][C]0.68433602274022[/C][C]0.631327954519559[/C][C]0.31566397725978[/C][/ROW]
[ROW][C]105[/C][C]0.731460241973286[/C][C]0.537079516053428[/C][C]0.268539758026714[/C][/ROW]
[ROW][C]106[/C][C]0.696790700049492[/C][C]0.606418599901015[/C][C]0.303209299950508[/C][/ROW]
[ROW][C]107[/C][C]0.679910636230626[/C][C]0.640178727538749[/C][C]0.320089363769374[/C][/ROW]
[ROW][C]108[/C][C]0.625989345033752[/C][C]0.748021309932497[/C][C]0.374010654966248[/C][/ROW]
[ROW][C]109[/C][C]0.603748296969169[/C][C]0.792503406061662[/C][C]0.396251703030831[/C][/ROW]
[ROW][C]110[/C][C]0.593697165109518[/C][C]0.812605669780963[/C][C]0.406302834890482[/C][/ROW]
[ROW][C]111[/C][C]0.53437104292918[/C][C]0.93125791414164[/C][C]0.46562895707082[/C][/ROW]
[ROW][C]112[/C][C]0.604437197668766[/C][C]0.791125604662468[/C][C]0.395562802331234[/C][/ROW]
[ROW][C]113[/C][C]0.692544644910065[/C][C]0.61491071017987[/C][C]0.307455355089935[/C][/ROW]
[ROW][C]114[/C][C]0.646294795896851[/C][C]0.707410408206298[/C][C]0.353705204103149[/C][/ROW]
[ROW][C]115[/C][C]0.587328426612175[/C][C]0.82534314677565[/C][C]0.412671573387825[/C][/ROW]
[ROW][C]116[/C][C]0.52276026250964[/C][C]0.95447947498072[/C][C]0.47723973749036[/C][/ROW]
[ROW][C]117[/C][C]0.47853547656582[/C][C]0.95707095313164[/C][C]0.52146452343418[/C][/ROW]
[ROW][C]118[/C][C]0.504699435772859[/C][C]0.990601128454282[/C][C]0.495300564227141[/C][/ROW]
[ROW][C]119[/C][C]0.447967073751068[/C][C]0.895934147502136[/C][C]0.552032926248932[/C][/ROW]
[ROW][C]120[/C][C]0.38384431714124[/C][C]0.767688634282481[/C][C]0.61615568285876[/C][/ROW]
[ROW][C]121[/C][C]0.364879652442438[/C][C]0.729759304884877[/C][C]0.635120347557562[/C][/ROW]
[ROW][C]122[/C][C]0.31406850250295[/C][C]0.628137005005901[/C][C]0.68593149749705[/C][/ROW]
[ROW][C]123[/C][C]0.259633634604744[/C][C]0.519267269209487[/C][C]0.740366365395256[/C][/ROW]
[ROW][C]124[/C][C]0.209107683016187[/C][C]0.418215366032374[/C][C]0.790892316983813[/C][/ROW]
[ROW][C]125[/C][C]0.235723077120564[/C][C]0.471446154241127[/C][C]0.764276922879436[/C][/ROW]
[ROW][C]126[/C][C]0.17899568969105[/C][C]0.3579913793821[/C][C]0.82100431030895[/C][/ROW]
[ROW][C]127[/C][C]0.135454745191564[/C][C]0.270909490383128[/C][C]0.864545254808436[/C][/ROW]
[ROW][C]128[/C][C]0.100283409689315[/C][C]0.20056681937863[/C][C]0.899716590310685[/C][/ROW]
[ROW][C]129[/C][C]0.0976976424066733[/C][C]0.195395284813347[/C][C]0.902302357593327[/C][/ROW]
[ROW][C]130[/C][C]0.0643889269938809[/C][C]0.128777853987762[/C][C]0.935611073006119[/C][/ROW]
[ROW][C]131[/C][C]0.042540618337811[/C][C]0.085081236675622[/C][C]0.95745938166219[/C][/ROW]
[ROW][C]132[/C][C]0.0272205312341473[/C][C]0.0544410624682946[/C][C]0.972779468765853[/C][/ROW]
[ROW][C]133[/C][C]0.0374151067477274[/C][C]0.0748302134954549[/C][C]0.962584893252273[/C][/ROW]
[ROW][C]134[/C][C]0.222635436954388[/C][C]0.445270873908776[/C][C]0.777364563045612[/C][/ROW]
[ROW][C]135[/C][C]0.137853481750201[/C][C]0.275706963500402[/C][C]0.862146518249799[/C][/ROW]
[ROW][C]136[/C][C]0.0834432562196476[/C][C]0.166886512439295[/C][C]0.916556743780352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&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
100.5097637846740130.9804724306519750.490236215325988
110.7380818571967820.5238362856064360.261918142803218
120.7590336550013240.4819326899973530.240966344998676
130.6639768744463440.6720462511073130.336023125553656
140.5721671438217810.8556657123564370.427832856178219
150.515475940355650.96904811928870.48452405964435
160.5784399571975780.8431200856048450.421560042802422
170.7618299525536210.4763400948927570.238170047446379
180.8073832911267140.3852334177465720.192616708873286
190.767519308420090.464961383159820.23248069157991
200.7431325552163580.5137348895672840.256867444783642
210.6880890587919970.6238218824160060.311910941208003
220.6956926876494950.608614624701010.304307312350505
230.7142396998787590.5715206002424830.285760300121241
240.680733108042260.638533783915480.31926689195774
250.6647035858885670.6705928282228650.335296414111433
260.7627394446039770.4745211107920450.237260555396023
270.7593147119109850.481370576178030.240685288089015
280.7535185483120150.4929629033759690.246481451687985
290.7053647088637550.589270582272490.294635291136245
300.7294825055074250.541034988985150.270517494492575
310.6743620182742690.6512759634514620.325637981725731
320.6174446637881370.7651106724237260.382555336211863
330.6221736372161030.7556527255677940.377826362783897
340.6832020171322980.6335959657354040.316797982867702
350.6280547052400880.7438905895198240.371945294759912
360.6700494453679780.6599011092640440.329950554632022
370.6260049666409250.747990066718150.373995033359075
380.5820752879034690.8358494241930620.417924712096531
390.658965940409370.6820681191812590.341034059590629
400.6135997615561460.7728004768877090.386400238443854
410.6612824285495290.6774351429009420.338717571450471
420.7625433021807350.4749133956385310.237456697819265
430.7180559680535830.5638880638928340.281944031946417
440.6849341380069170.6301317239861660.315065861993083
450.6507261609618480.6985476780763050.349273839038152
460.6613921807108770.6772156385782450.338607819289123
470.7294829505262680.5410340989474650.270517049473733
480.7035218952725350.592956209454930.296478104727465
490.6801265263922130.6397469472155750.319873473607787
500.787961650890110.4240766982197810.212038349109891
510.9067107728266860.1865784543466280.093289227173314
520.8886143050101570.2227713899796850.111385694989843
530.9271437849513650.1457124300972710.0728562150486355
540.9187162632694710.1625674734610580.0812837367305291
550.9168751883004690.1662496233990630.0831248116995314
560.9796428438039240.04071431239215150.0203571561960757
570.9744309397184720.05113812056305550.0255690602815278
580.9662941369067280.06741172618654320.0337058630932716
590.9647096111159860.07058077776802760.0352903888840138
600.9575345358030350.08493092839392920.0424654641969646
610.9552883534406980.08942329311860380.0447116465593019
620.947015198706260.1059696025874790.0529848012937395
630.9552525883294530.08949482334109430.0447474116705471
640.9500486416818650.09990271663626970.0499513583181349
650.9454295166539710.1091409666920570.0545704833460286
660.9779539076691260.04409218466174780.0220460923308739
670.999410129318950.001179741362099370.000589870681049683
680.9991411639225530.001717672154893230.000858836077446615
690.9989174018989320.002165196202136570.00108259810106829
700.9990811317351560.001837736529688340.000918868264844172
710.998743093362390.00251381327522150.00125690663761075
720.998569614134160.002860771731679610.0014303858658398
730.9978702438018950.004259512396210160.00212975619810508
740.9968798399344040.006240320131192360.00312016006559618
750.9957796164367470.008440767126505410.00422038356325271
760.9947507857280690.0104984285438620.00524921427193098
770.9940956928399980.01180861432000360.0059043071600018
780.9951305132363370.009738973527325960.00486948676366298
790.9933342135960150.01333157280797040.0066657864039852
800.9906326282431450.01873474351370930.00936737175685467
810.9891221763976870.02175564720462580.0108778236023129
820.9877353871992870.02452922560142690.0122646128007134
830.985288529039180.02942294192163850.0147114709608192
840.9810362096261990.03792758074760290.0189637903738015
850.9765760182267780.04684796354644470.0234239817732223
860.9706863952470580.05862720950588390.029313604752942
870.962290778942010.0754184421159790.0377092210579895
880.9511306093295670.0977387813408670.0488693906704335
890.9387332750985350.1225334498029310.0612667249014653
900.9262968036197570.1474063927604870.0737031963802434
910.9158929158658650.1682141682682710.0841070841341353
920.8952419251551230.2095161496897540.104758074844877
930.889580887784420.2208382244311610.11041911221558
940.8969612412167310.2060775175665370.103038758783269
950.8727894023180180.2544211953639640.127210597681982
960.8430869005154210.3138261989691580.156913099484579
970.8189308566210510.3621382867578970.181069143378948
980.7873800904368980.4252398191262030.212619909563102
990.7457201043822550.5085597912354910.254279895617745
1000.7312333398559630.5375333202880740.268766660144037
1010.6842801287281050.631439742543790.315719871271895
1020.6345884621287540.7308230757424910.365411537871246
1030.7054995425124150.5890009149751710.294500457487585
1040.684336022740220.6313279545195590.31566397725978
1050.7314602419732860.5370795160534280.268539758026714
1060.6967907000494920.6064185999010150.303209299950508
1070.6799106362306260.6401787275387490.320089363769374
1080.6259893450337520.7480213099324970.374010654966248
1090.6037482969691690.7925034060616620.396251703030831
1100.5936971651095180.8126056697809630.406302834890482
1110.534371042929180.931257914141640.46562895707082
1120.6044371976687660.7911256046624680.395562802331234
1130.6925446449100650.614910710179870.307455355089935
1140.6462947958968510.7074104082062980.353705204103149
1150.5873284266121750.825343146775650.412671573387825
1160.522760262509640.954479474980720.47723973749036
1170.478535476565820.957070953131640.52146452343418
1180.5046994357728590.9906011284542820.495300564227141
1190.4479670737510680.8959341475021360.552032926248932
1200.383844317141240.7676886342824810.61615568285876
1210.3648796524424380.7297593048848770.635120347557562
1220.314068502502950.6281370050059010.68593149749705
1230.2596336346047440.5192672692094870.740366365395256
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1250.2357230771205640.4714461542411270.764276922879436
1260.178995689691050.35799137938210.82100431030895
1270.1354547451915640.2709094903831280.864545254808436
1280.1002834096893150.200566819378630.899716590310685
1290.09769764240667330.1953952848133470.902302357593327
1300.06438892699388090.1287778539877620.935611073006119
1310.0425406183378110.0850812366756220.95745938166219
1320.02722053123414730.05444106246829460.972779468765853
1330.03741510674772740.07483021349545490.962584893252273
1340.2226354369543880.4452708739087760.777364563045612
1350.1378534817502010.2757069635004020.862146518249799
1360.08344325621964760.1668865124392950.916556743780352






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.078740157480315NOK
5% type I error level210.165354330708661NOK
10% type I error level340.267716535433071NOK

\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 & 10 & 0.078740157480315 & NOK \tabularnewline
5% type I error level & 21 & 0.165354330708661 & NOK \tabularnewline
10% type I error level & 34 & 0.267716535433071 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108663&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]10[/C][C]0.078740157480315[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.165354330708661[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.267716535433071[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108663&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108663&T=6

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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 level100.078740157480315NOK
5% type I error level210.165354330708661NOK
10% type I error level340.267716535433071NOK


Figures (Output of Computation)

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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')
}