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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 computationThu, 24 Nov 2011 05:19:49 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t132213001279mims98osf2hf5.htm/, Retrieved Wed, 24 Apr 2024 04:57:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146609, Retrieved Wed, 24 Apr 2024 04:57:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2011-11-24 10:19:49] [694c30abd2a3b2ee5cb46fc74cb5bfb9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53
39	32	16	11	18	11	86
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	6	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	7	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	9	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	28	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	7	7	16	8	71
29	31	10	6	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	12	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	32	14	6	15	12	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	77
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
37	38	20	12	16	10	30
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	10	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	82
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	70
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
33	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	82
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	8	9	13	12	69
32	34	16	8	15	10	54
30	34	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
35	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	35	12	8	12	18	84
34	36	13	8	13	16	69

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 12.8461518525374 + 0.0143384575733923Connected[t] + 0.0718533302727543Separate[t] + 0.0695934569197246Learning[t] -0.0459177709224598Software[t] -0.355985031666435Depression[t] + 0.0326855604193598Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  12.8461518525374 +  0.0143384575733923Connected[t] +  0.0718533302727543Separate[t] +  0.0695934569197246Learning[t] -0.0459177709224598Software[t] -0.355985031666435Depression[t] +  0.0326855604193598Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  12.8461518525374 +  0.0143384575733923Connected[t] +  0.0718533302727543Separate[t] +  0.0695934569197246Learning[t] -0.0459177709224598Software[t] -0.355985031666435Depression[t] +  0.0326855604193598Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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
Happiness[t] = + 12.8461518525374 + 0.0143384575733923Connected[t] + 0.0718533302727543Separate[t] + 0.0695934569197246Learning[t] -0.0459177709224598Software[t] -0.355985031666435Depression[t] + 0.0326855604193598Belonging[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.84615185253742.5532925.03121e-061e-06
Connected0.01433845757339230.0501810.28570.7754640.387732
Separate0.07185333027275430.0466051.54180.1251730.062586
Learning0.06959345691972460.0842980.82560.4103170.205159
Software-0.04591777092245980.0857-0.53580.5928660.296433
Depression-0.3559850316664350.051724-6.882300
Belonging0.03268556041935980.0149132.19170.0298910.014945

\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) & 12.8461518525374 & 2.553292 & 5.0312 & 1e-06 & 1e-06 \tabularnewline
Connected & 0.0143384575733923 & 0.050181 & 0.2857 & 0.775464 & 0.387732 \tabularnewline
Separate & 0.0718533302727543 & 0.046605 & 1.5418 & 0.125173 & 0.062586 \tabularnewline
Learning & 0.0695934569197246 & 0.084298 & 0.8256 & 0.410317 & 0.205159 \tabularnewline
Software & -0.0459177709224598 & 0.0857 & -0.5358 & 0.592866 & 0.296433 \tabularnewline
Depression & -0.355985031666435 & 0.051724 & -6.8823 & 0 & 0 \tabularnewline
Belonging & 0.0326855604193598 & 0.014913 & 2.1917 & 0.029891 & 0.014945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&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]12.8461518525374[/C][C]2.553292[/C][C]5.0312[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Connected[/C][C]0.0143384575733923[/C][C]0.050181[/C][C]0.2857[/C][C]0.775464[/C][C]0.387732[/C][/ROW]
[ROW][C]Separate[/C][C]0.0718533302727543[/C][C]0.046605[/C][C]1.5418[/C][C]0.125173[/C][C]0.062586[/C][/ROW]
[ROW][C]Learning[/C][C]0.0695934569197246[/C][C]0.084298[/C][C]0.8256[/C][C]0.410317[/C][C]0.205159[/C][/ROW]
[ROW][C]Software[/C][C]-0.0459177709224598[/C][C]0.0857[/C][C]-0.5358[/C][C]0.592866[/C][C]0.296433[/C][/ROW]
[ROW][C]Depression[/C][C]-0.355985031666435[/C][C]0.051724[/C][C]-6.8823[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0326855604193598[/C][C]0.014913[/C][C]2.1917[/C][C]0.029891[/C][C]0.014945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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)12.84615185253742.5532925.03121e-061e-06
Connected0.01433845757339230.0501810.28570.7754640.387732
Separate0.07185333027275430.0466051.54180.1251730.062586
Learning0.06959345691972460.0842980.82560.4103170.205159
Software-0.04591777092245980.0857-0.53580.5928660.296433
Depression-0.3559850316664350.051724-6.882300
Belonging0.03268556041935980.0149132.19170.0298910.014945






Multiple Linear Regression - Regression Statistics
Multiple R0.578049037967349
R-squared0.334140690294978
Adjusted R-squared0.308365491209622
F-TEST (value)12.9636511899852
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value7.52042872420589e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.9440699307164
Sum Squared Residuals585.80822380493

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.578049037967349 \tabularnewline
R-squared & 0.334140690294978 \tabularnewline
Adjusted R-squared & 0.308365491209622 \tabularnewline
F-TEST (value) & 12.9636511899852 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 7.52042872420589e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.9440699307164 \tabularnewline
Sum Squared Residuals & 585.80822380493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.578049037967349[/C][/ROW]
[ROW][C]R-squared[/C][C]0.334140690294978[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.308365491209622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.9636511899852[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]7.52042872420589e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.9440699307164[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]585.80822380493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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 R0.578049037967349
R-squared0.334140690294978
Adjusted R-squared0.308365491209622
F-TEST (value)12.9636511899852
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value7.52042872420589e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.9440699307164
Sum Squared Residuals585.80822380493






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.97867117452690.0213288254730885
21815.20818094493052.79181905506949
31113.5981378012711-2.59813780127107
41214.3483113326744-2.34831133267443
51611.3780269318854.621973068115
61814.15493500896063.84506499103935
71410.30473137232483.69526862767519
81415.0204418459513-1.0204418459513
91515.1593791849008-0.159379184900801
101514.73179374158640.268206258413645
111715.29509303673771.70490696326226
121915.28497162341323.71502837658683
131013.3185189326193-3.31851893261934
141613.52540789814822.47459210185179
151815.75600981616962.2439901838304
161413.0810030576060.918996942394048
171413.71399362660080.286006373399196
181715.92278964658881.0772103534112
191415.415390542847-1.415390542847
201613.40204265552.5979573445
211815.94989399046812.05010600953194
221113.3194070006004-2.31940700060037
231414.3543227903953-0.354322790395339
241213.6339554341129-1.6339554341129
251715.36960994369521.63039005630485
26915.7304724759688-6.73047247596881
271615.09978772975750.900212270242545
281413.2944754273720.705524572628035
291513.81864618048631.18135381951373
301113.7820771331186-2.7820771331186
311615.20640543622690.793594563773123
321312.4256208790530.574379120946972
331714.880083181972.11991681802998
341515.3844664166661-0.384466416666138
351413.70348052339090.296519476609059
361615.31466673231070.68533326768926
37910.8493379773746-1.84933797737459
381514.20564577976430.794354220235656
391715.06799023731911.93200976268087
401315.0689287203695-2.06892872036947
411515.5451651390067-0.545165139006734
421613.59574104296212.40425895703787
431615.3105623929260.689437607074036
441212.9501471527352-0.950147152735165
451214.25750549622-2.25750549621999
461113.7008688141996-2.70086881419956
471515.4913666381083-0.491366638108339
481514.8367222385030.16327776149702
491713.78011870214883.2198812978512
501314.6491966379822-1.64919663798225
511615.29956559658130.700434403418695
521413.18185519548710.818144804512877
531111.6117103622778-0.611710362277839
541213.6233645517912-1.62336455179121
551214.1443962793682-2.14439627936817
561514.58706533469650.41293466530346
571614.20177304618841.79822695381163
581515.2535296741847-0.253529674184666
591215.3283966401698-3.32839664016977
601213.3935424527294-1.39354245272941
61810.4145299828531-2.41452998285311
621314.6414763015395-1.64147630153949
631114.5641402596738-3.56414025967376
641412.99058221206461.00941778793535
651513.26080955911591.73919044088405
661015.2917383185594-5.29173831855936
671112.5925377657149-1.59253776571486
681214.0781577721275-2.07815777212754
691513.9949780355331.00502196446696
701513.84056637294981.15943362705017
711413.86845266839650.131547331603542
721612.91552829205523.08447170794481
731514.76549770461520.234502295384773
741515.6422865654213-0.642286565421291
751314.6659331139386-1.66593311393859
761212.181909667302-0.181909667302006
771714.21199610925992.78800389074005
781312.37307060966310.626929390336934
791513.71776633256171.2822336674383
801314.4403517374108-1.44035173741085
811515.1348468267232-0.134846826723192
821614.3686737163591.63132628364103
831515.6858626521207-0.685862652120722
841614.35494881509621.64505118490382
851514.36834206800510.631657931994927
861414.0900745330328-0.0900745330327514
871514.5779531993260.422046800673962
881414.3082995303177-0.308299530317651
891312.54900053818890.450999461811128
90710.6396879868802-3.63968798688019
911714.05746597081592.94253402918414
921312.5812098853550.418790114645037
931514.74544920906220.254550790937841
941413.15020482464040.849795175359619
951314.8262632361197-1.82626323611972
961615.34539867442780.654601325572175
971212.9676487932958-0.967648793295776
981414.9093248428542-0.909324842854225
991715.113865117311.88613488269003
1001515.3310853475636-0.331085347563625
1011715.37263192315361.62736807684641
1021213.1902265768181-1.19022657681813
1031615.23046437547740.769535624522634
1041114.0970937555188-3.09709375551882
1051513.61639943013171.38360056986826
106911.5316009199423-2.53160091994231
1071615.1062225599790.893777440020956
1081513.42370483644151.57629516355846
1091012.9108475029433-2.91084750294334
110109.954137505524520.0458624944754825
1111513.97822637971631.02177362028372
1121113.2114064501507-2.21140645015069
1131315.88945629213-2.88945629212999
1141411.97803767463532.02196232536467
1151814.40791399771283.59208600228724
1161615.58284043344470.417159566555314
1171413.3520613575690.647938642430999
1181414.4749426681309-0.474942668130884
1191415.285245355753-1.285245355753
1201414.1421401525374-0.142140152537373
1211212.8027584200095-0.802758420009498
1221413.24756039194180.752439608058224
1231515.5401743388428-0.54017433884284
1241516.2139701894586-1.21397018945857
1251514.46395558500110.536044414998923
1261314.7528566079908-1.75285660799077
1271716.383202595610.616797404390015
1281715.81619830826061.18380169173941
1291915.26837780340963.73162219659043
1301513.77473870802691.22526129197311
1311314.7352994368216-1.73529943682158
132910.7753206767197-1.77532067671974
1331515.7813543545272-0.781354354527247
1341512.50370207035312.4962979296469
1351514.67730756995960.322692430040413
1361613.57804318657962.42195681342043
137118.897221466564662.10277853343534
1381413.46273220631750.537267793682496
1391112.1667822121604-1.16678221216039
1401514.4348309310450.565169068954988
1411313.9324816028532-0.932481602853203
1421514.69931881347660.30068118652343
1431614.11175735640271.88824264359734
1441414.8246321591517-0.82463215915169
1451513.79663823644791.20336176355209
1461614.96891532473831.03108467526174
1471614.11305582668751.88694417331251
1481113.6360861173835-2.63608611738349
1491214.6304994761086-2.63049947610859
150911.7610897469395-2.76108974693953
1511615.07425554935210.925744450647908
1521313.0843248573157-0.0843248573156846
1531615.88113083758750.118869162412537
1541215.069806403621-3.06980640362104
155911.8895653936498-2.88956539364978
1561312.01710973544650.98289026455349
1571312.5812098853550.418790114645037
1581413.86427725500520.135722744994763
1591915.26837780340963.73162219659043
1601315.7830875995577-2.78308759955769
1611212.6254848753197-0.625484875319742
1621313.0172952347015-0.0172952347014784

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.9786711745269 & 0.0213288254730885 \tabularnewline
2 & 18 & 15.2081809449305 & 2.79181905506949 \tabularnewline
3 & 11 & 13.5981378012711 & -2.59813780127107 \tabularnewline
4 & 12 & 14.3483113326744 & -2.34831133267443 \tabularnewline
5 & 16 & 11.378026931885 & 4.621973068115 \tabularnewline
6 & 18 & 14.1549350089606 & 3.84506499103935 \tabularnewline
7 & 14 & 10.3047313723248 & 3.69526862767519 \tabularnewline
8 & 14 & 15.0204418459513 & -1.0204418459513 \tabularnewline
9 & 15 & 15.1593791849008 & -0.159379184900801 \tabularnewline
10 & 15 & 14.7317937415864 & 0.268206258413645 \tabularnewline
11 & 17 & 15.2950930367377 & 1.70490696326226 \tabularnewline
12 & 19 & 15.2849716234132 & 3.71502837658683 \tabularnewline
13 & 10 & 13.3185189326193 & -3.31851893261934 \tabularnewline
14 & 16 & 13.5254078981482 & 2.47459210185179 \tabularnewline
15 & 18 & 15.7560098161696 & 2.2439901838304 \tabularnewline
16 & 14 & 13.081003057606 & 0.918996942394048 \tabularnewline
17 & 14 & 13.7139936266008 & 0.286006373399196 \tabularnewline
18 & 17 & 15.9227896465888 & 1.0772103534112 \tabularnewline
19 & 14 & 15.415390542847 & -1.415390542847 \tabularnewline
20 & 16 & 13.4020426555 & 2.5979573445 \tabularnewline
21 & 18 & 15.9498939904681 & 2.05010600953194 \tabularnewline
22 & 11 & 13.3194070006004 & -2.31940700060037 \tabularnewline
23 & 14 & 14.3543227903953 & -0.354322790395339 \tabularnewline
24 & 12 & 13.6339554341129 & -1.6339554341129 \tabularnewline
25 & 17 & 15.3696099436952 & 1.63039005630485 \tabularnewline
26 & 9 & 15.7304724759688 & -6.73047247596881 \tabularnewline
27 & 16 & 15.0997877297575 & 0.900212270242545 \tabularnewline
28 & 14 & 13.294475427372 & 0.705524572628035 \tabularnewline
29 & 15 & 13.8186461804863 & 1.18135381951373 \tabularnewline
30 & 11 & 13.7820771331186 & -2.7820771331186 \tabularnewline
31 & 16 & 15.2064054362269 & 0.793594563773123 \tabularnewline
32 & 13 & 12.425620879053 & 0.574379120946972 \tabularnewline
33 & 17 & 14.88008318197 & 2.11991681802998 \tabularnewline
34 & 15 & 15.3844664166661 & -0.384466416666138 \tabularnewline
35 & 14 & 13.7034805233909 & 0.296519476609059 \tabularnewline
36 & 16 & 15.3146667323107 & 0.68533326768926 \tabularnewline
37 & 9 & 10.8493379773746 & -1.84933797737459 \tabularnewline
38 & 15 & 14.2056457797643 & 0.794354220235656 \tabularnewline
39 & 17 & 15.0679902373191 & 1.93200976268087 \tabularnewline
40 & 13 & 15.0689287203695 & -2.06892872036947 \tabularnewline
41 & 15 & 15.5451651390067 & -0.545165139006734 \tabularnewline
42 & 16 & 13.5957410429621 & 2.40425895703787 \tabularnewline
43 & 16 & 15.310562392926 & 0.689437607074036 \tabularnewline
44 & 12 & 12.9501471527352 & -0.950147152735165 \tabularnewline
45 & 12 & 14.25750549622 & -2.25750549621999 \tabularnewline
46 & 11 & 13.7008688141996 & -2.70086881419956 \tabularnewline
47 & 15 & 15.4913666381083 & -0.491366638108339 \tabularnewline
48 & 15 & 14.836722238503 & 0.16327776149702 \tabularnewline
49 & 17 & 13.7801187021488 & 3.2198812978512 \tabularnewline
50 & 13 & 14.6491966379822 & -1.64919663798225 \tabularnewline
51 & 16 & 15.2995655965813 & 0.700434403418695 \tabularnewline
52 & 14 & 13.1818551954871 & 0.818144804512877 \tabularnewline
53 & 11 & 11.6117103622778 & -0.611710362277839 \tabularnewline
54 & 12 & 13.6233645517912 & -1.62336455179121 \tabularnewline
55 & 12 & 14.1443962793682 & -2.14439627936817 \tabularnewline
56 & 15 & 14.5870653346965 & 0.41293466530346 \tabularnewline
57 & 16 & 14.2017730461884 & 1.79822695381163 \tabularnewline
58 & 15 & 15.2535296741847 & -0.253529674184666 \tabularnewline
59 & 12 & 15.3283966401698 & -3.32839664016977 \tabularnewline
60 & 12 & 13.3935424527294 & -1.39354245272941 \tabularnewline
61 & 8 & 10.4145299828531 & -2.41452998285311 \tabularnewline
62 & 13 & 14.6414763015395 & -1.64147630153949 \tabularnewline
63 & 11 & 14.5641402596738 & -3.56414025967376 \tabularnewline
64 & 14 & 12.9905822120646 & 1.00941778793535 \tabularnewline
65 & 15 & 13.2608095591159 & 1.73919044088405 \tabularnewline
66 & 10 & 15.2917383185594 & -5.29173831855936 \tabularnewline
67 & 11 & 12.5925377657149 & -1.59253776571486 \tabularnewline
68 & 12 & 14.0781577721275 & -2.07815777212754 \tabularnewline
69 & 15 & 13.994978035533 & 1.00502196446696 \tabularnewline
70 & 15 & 13.8405663729498 & 1.15943362705017 \tabularnewline
71 & 14 & 13.8684526683965 & 0.131547331603542 \tabularnewline
72 & 16 & 12.9155282920552 & 3.08447170794481 \tabularnewline
73 & 15 & 14.7654977046152 & 0.234502295384773 \tabularnewline
74 & 15 & 15.6422865654213 & -0.642286565421291 \tabularnewline
75 & 13 & 14.6659331139386 & -1.66593311393859 \tabularnewline
76 & 12 & 12.181909667302 & -0.181909667302006 \tabularnewline
77 & 17 & 14.2119961092599 & 2.78800389074005 \tabularnewline
78 & 13 & 12.3730706096631 & 0.626929390336934 \tabularnewline
79 & 15 & 13.7177663325617 & 1.2822336674383 \tabularnewline
80 & 13 & 14.4403517374108 & -1.44035173741085 \tabularnewline
81 & 15 & 15.1348468267232 & -0.134846826723192 \tabularnewline
82 & 16 & 14.368673716359 & 1.63132628364103 \tabularnewline
83 & 15 & 15.6858626521207 & -0.685862652120722 \tabularnewline
84 & 16 & 14.3549488150962 & 1.64505118490382 \tabularnewline
85 & 15 & 14.3683420680051 & 0.631657931994927 \tabularnewline
86 & 14 & 14.0900745330328 & -0.0900745330327514 \tabularnewline
87 & 15 & 14.577953199326 & 0.422046800673962 \tabularnewline
88 & 14 & 14.3082995303177 & -0.308299530317651 \tabularnewline
89 & 13 & 12.5490005381889 & 0.450999461811128 \tabularnewline
90 & 7 & 10.6396879868802 & -3.63968798688019 \tabularnewline
91 & 17 & 14.0574659708159 & 2.94253402918414 \tabularnewline
92 & 13 & 12.581209885355 & 0.418790114645037 \tabularnewline
93 & 15 & 14.7454492090622 & 0.254550790937841 \tabularnewline
94 & 14 & 13.1502048246404 & 0.849795175359619 \tabularnewline
95 & 13 & 14.8262632361197 & -1.82626323611972 \tabularnewline
96 & 16 & 15.3453986744278 & 0.654601325572175 \tabularnewline
97 & 12 & 12.9676487932958 & -0.967648793295776 \tabularnewline
98 & 14 & 14.9093248428542 & -0.909324842854225 \tabularnewline
99 & 17 & 15.11386511731 & 1.88613488269003 \tabularnewline
100 & 15 & 15.3310853475636 & -0.331085347563625 \tabularnewline
101 & 17 & 15.3726319231536 & 1.62736807684641 \tabularnewline
102 & 12 & 13.1902265768181 & -1.19022657681813 \tabularnewline
103 & 16 & 15.2304643754774 & 0.769535624522634 \tabularnewline
104 & 11 & 14.0970937555188 & -3.09709375551882 \tabularnewline
105 & 15 & 13.6163994301317 & 1.38360056986826 \tabularnewline
106 & 9 & 11.5316009199423 & -2.53160091994231 \tabularnewline
107 & 16 & 15.106222559979 & 0.893777440020956 \tabularnewline
108 & 15 & 13.4237048364415 & 1.57629516355846 \tabularnewline
109 & 10 & 12.9108475029433 & -2.91084750294334 \tabularnewline
110 & 10 & 9.95413750552452 & 0.0458624944754825 \tabularnewline
111 & 15 & 13.9782263797163 & 1.02177362028372 \tabularnewline
112 & 11 & 13.2114064501507 & -2.21140645015069 \tabularnewline
113 & 13 & 15.88945629213 & -2.88945629212999 \tabularnewline
114 & 14 & 11.9780376746353 & 2.02196232536467 \tabularnewline
115 & 18 & 14.4079139977128 & 3.59208600228724 \tabularnewline
116 & 16 & 15.5828404334447 & 0.417159566555314 \tabularnewline
117 & 14 & 13.352061357569 & 0.647938642430999 \tabularnewline
118 & 14 & 14.4749426681309 & -0.474942668130884 \tabularnewline
119 & 14 & 15.285245355753 & -1.285245355753 \tabularnewline
120 & 14 & 14.1421401525374 & -0.142140152537373 \tabularnewline
121 & 12 & 12.8027584200095 & -0.802758420009498 \tabularnewline
122 & 14 & 13.2475603919418 & 0.752439608058224 \tabularnewline
123 & 15 & 15.5401743388428 & -0.54017433884284 \tabularnewline
124 & 15 & 16.2139701894586 & -1.21397018945857 \tabularnewline
125 & 15 & 14.4639555850011 & 0.536044414998923 \tabularnewline
126 & 13 & 14.7528566079908 & -1.75285660799077 \tabularnewline
127 & 17 & 16.38320259561 & 0.616797404390015 \tabularnewline
128 & 17 & 15.8161983082606 & 1.18380169173941 \tabularnewline
129 & 19 & 15.2683778034096 & 3.73162219659043 \tabularnewline
130 & 15 & 13.7747387080269 & 1.22526129197311 \tabularnewline
131 & 13 & 14.7352994368216 & -1.73529943682158 \tabularnewline
132 & 9 & 10.7753206767197 & -1.77532067671974 \tabularnewline
133 & 15 & 15.7813543545272 & -0.781354354527247 \tabularnewline
134 & 15 & 12.5037020703531 & 2.4962979296469 \tabularnewline
135 & 15 & 14.6773075699596 & 0.322692430040413 \tabularnewline
136 & 16 & 13.5780431865796 & 2.42195681342043 \tabularnewline
137 & 11 & 8.89722146656466 & 2.10277853343534 \tabularnewline
138 & 14 & 13.4627322063175 & 0.537267793682496 \tabularnewline
139 & 11 & 12.1667822121604 & -1.16678221216039 \tabularnewline
140 & 15 & 14.434830931045 & 0.565169068954988 \tabularnewline
141 & 13 & 13.9324816028532 & -0.932481602853203 \tabularnewline
142 & 15 & 14.6993188134766 & 0.30068118652343 \tabularnewline
143 & 16 & 14.1117573564027 & 1.88824264359734 \tabularnewline
144 & 14 & 14.8246321591517 & -0.82463215915169 \tabularnewline
145 & 15 & 13.7966382364479 & 1.20336176355209 \tabularnewline
146 & 16 & 14.9689153247383 & 1.03108467526174 \tabularnewline
147 & 16 & 14.1130558266875 & 1.88694417331251 \tabularnewline
148 & 11 & 13.6360861173835 & -2.63608611738349 \tabularnewline
149 & 12 & 14.6304994761086 & -2.63049947610859 \tabularnewline
150 & 9 & 11.7610897469395 & -2.76108974693953 \tabularnewline
151 & 16 & 15.0742555493521 & 0.925744450647908 \tabularnewline
152 & 13 & 13.0843248573157 & -0.0843248573156846 \tabularnewline
153 & 16 & 15.8811308375875 & 0.118869162412537 \tabularnewline
154 & 12 & 15.069806403621 & -3.06980640362104 \tabularnewline
155 & 9 & 11.8895653936498 & -2.88956539364978 \tabularnewline
156 & 13 & 12.0171097354465 & 0.98289026455349 \tabularnewline
157 & 13 & 12.581209885355 & 0.418790114645037 \tabularnewline
158 & 14 & 13.8642772550052 & 0.135722744994763 \tabularnewline
159 & 19 & 15.2683778034096 & 3.73162219659043 \tabularnewline
160 & 13 & 15.7830875995577 & -2.78308759955769 \tabularnewline
161 & 12 & 12.6254848753197 & -0.625484875319742 \tabularnewline
162 & 13 & 13.0172952347015 & -0.0172952347014784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&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]14[/C][C]13.9786711745269[/C][C]0.0213288254730885[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2081809449305[/C][C]2.79181905506949[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.5981378012711[/C][C]-2.59813780127107[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.3483113326744[/C][C]-2.34831133267443[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.378026931885[/C][C]4.621973068115[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.1549350089606[/C][C]3.84506499103935[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.3047313723248[/C][C]3.69526862767519[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.0204418459513[/C][C]-1.0204418459513[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1593791849008[/C][C]-0.159379184900801[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.7317937415864[/C][C]0.268206258413645[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2950930367377[/C][C]1.70490696326226[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.2849716234132[/C][C]3.71502837658683[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3185189326193[/C][C]-3.31851893261934[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.5254078981482[/C][C]2.47459210185179[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.7560098161696[/C][C]2.2439901838304[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.081003057606[/C][C]0.918996942394048[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.7139936266008[/C][C]0.286006373399196[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.9227896465888[/C][C]1.0772103534112[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.415390542847[/C][C]-1.415390542847[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.4020426555[/C][C]2.5979573445[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.9498939904681[/C][C]2.05010600953194[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.3194070006004[/C][C]-2.31940700060037[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.3543227903953[/C][C]-0.354322790395339[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.6339554341129[/C][C]-1.6339554341129[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.3696099436952[/C][C]1.63039005630485[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.7304724759688[/C][C]-6.73047247596881[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0997877297575[/C][C]0.900212270242545[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.294475427372[/C][C]0.705524572628035[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.8186461804863[/C][C]1.18135381951373[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.7820771331186[/C][C]-2.7820771331186[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.2064054362269[/C][C]0.793594563773123[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.425620879053[/C][C]0.574379120946972[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.88008318197[/C][C]2.11991681802998[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.3844664166661[/C][C]-0.384466416666138[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.7034805233909[/C][C]0.296519476609059[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3146667323107[/C][C]0.68533326768926[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.8493379773746[/C][C]-1.84933797737459[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2056457797643[/C][C]0.794354220235656[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.0679902373191[/C][C]1.93200976268087[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.0689287203695[/C][C]-2.06892872036947[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.5451651390067[/C][C]-0.545165139006734[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.5957410429621[/C][C]2.40425895703787[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.310562392926[/C][C]0.689437607074036[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.9501471527352[/C][C]-0.950147152735165[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.25750549622[/C][C]-2.25750549621999[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.7008688141996[/C][C]-2.70086881419956[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.4913666381083[/C][C]-0.491366638108339[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.836722238503[/C][C]0.16327776149702[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.7801187021488[/C][C]3.2198812978512[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6491966379822[/C][C]-1.64919663798225[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2995655965813[/C][C]0.700434403418695[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.1818551954871[/C][C]0.818144804512877[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.6117103622778[/C][C]-0.611710362277839[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.6233645517912[/C][C]-1.62336455179121[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.1443962793682[/C][C]-2.14439627936817[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.5870653346965[/C][C]0.41293466530346[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.2017730461884[/C][C]1.79822695381163[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.2535296741847[/C][C]-0.253529674184666[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.3283966401698[/C][C]-3.32839664016977[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3935424527294[/C][C]-1.39354245272941[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.4145299828531[/C][C]-2.41452998285311[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.6414763015395[/C][C]-1.64147630153949[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.5641402596738[/C][C]-3.56414025967376[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.9905822120646[/C][C]1.00941778793535[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.2608095591159[/C][C]1.73919044088405[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.2917383185594[/C][C]-5.29173831855936[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.5925377657149[/C][C]-1.59253776571486[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0781577721275[/C][C]-2.07815777212754[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.994978035533[/C][C]1.00502196446696[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.8405663729498[/C][C]1.15943362705017[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.8684526683965[/C][C]0.131547331603542[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.9155282920552[/C][C]3.08447170794481[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.7654977046152[/C][C]0.234502295384773[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.6422865654213[/C][C]-0.642286565421291[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.6659331139386[/C][C]-1.66593311393859[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.181909667302[/C][C]-0.181909667302006[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.2119961092599[/C][C]2.78800389074005[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.3730706096631[/C][C]0.626929390336934[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.7177663325617[/C][C]1.2822336674383[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.4403517374108[/C][C]-1.44035173741085[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.1348468267232[/C][C]-0.134846826723192[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.368673716359[/C][C]1.63132628364103[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6858626521207[/C][C]-0.685862652120722[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.3549488150962[/C][C]1.64505118490382[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.3683420680051[/C][C]0.631657931994927[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0900745330328[/C][C]-0.0900745330327514[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.577953199326[/C][C]0.422046800673962[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.3082995303177[/C][C]-0.308299530317651[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.5490005381889[/C][C]0.450999461811128[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.6396879868802[/C][C]-3.63968798688019[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.0574659708159[/C][C]2.94253402918414[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.581209885355[/C][C]0.418790114645037[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.7454492090622[/C][C]0.254550790937841[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.1502048246404[/C][C]0.849795175359619[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8262632361197[/C][C]-1.82626323611972[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.3453986744278[/C][C]0.654601325572175[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.9676487932958[/C][C]-0.967648793295776[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.9093248428542[/C][C]-0.909324842854225[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.11386511731[/C][C]1.88613488269003[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.3310853475636[/C][C]-0.331085347563625[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.3726319231536[/C][C]1.62736807684641[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.1902265768181[/C][C]-1.19022657681813[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.2304643754774[/C][C]0.769535624522634[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0970937555188[/C][C]-3.09709375551882[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.6163994301317[/C][C]1.38360056986826[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.5316009199423[/C][C]-2.53160091994231[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.106222559979[/C][C]0.893777440020956[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.4237048364415[/C][C]1.57629516355846[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.9108475029433[/C][C]-2.91084750294334[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.95413750552452[/C][C]0.0458624944754825[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.9782263797163[/C][C]1.02177362028372[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.2114064501507[/C][C]-2.21140645015069[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.88945629213[/C][C]-2.88945629212999[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.9780376746353[/C][C]2.02196232536467[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.4079139977128[/C][C]3.59208600228724[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.5828404334447[/C][C]0.417159566555314[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.352061357569[/C][C]0.647938642430999[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.4749426681309[/C][C]-0.474942668130884[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.285245355753[/C][C]-1.285245355753[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.1421401525374[/C][C]-0.142140152537373[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.8027584200095[/C][C]-0.802758420009498[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.2475603919418[/C][C]0.752439608058224[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.5401743388428[/C][C]-0.54017433884284[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.2139701894586[/C][C]-1.21397018945857[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.4639555850011[/C][C]0.536044414998923[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7528566079908[/C][C]-1.75285660799077[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]16.38320259561[/C][C]0.616797404390015[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.8161983082606[/C][C]1.18380169173941[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.2683778034096[/C][C]3.73162219659043[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.7747387080269[/C][C]1.22526129197311[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.7352994368216[/C][C]-1.73529943682158[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.7753206767197[/C][C]-1.77532067671974[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.7813543545272[/C][C]-0.781354354527247[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.5037020703531[/C][C]2.4962979296469[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.6773075699596[/C][C]0.322692430040413[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.5780431865796[/C][C]2.42195681342043[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.89722146656466[/C][C]2.10277853343534[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.4627322063175[/C][C]0.537267793682496[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.1667822121604[/C][C]-1.16678221216039[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.434830931045[/C][C]0.565169068954988[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.9324816028532[/C][C]-0.932481602853203[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.6993188134766[/C][C]0.30068118652343[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.1117573564027[/C][C]1.88824264359734[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.8246321591517[/C][C]-0.82463215915169[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.7966382364479[/C][C]1.20336176355209[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.9689153247383[/C][C]1.03108467526174[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.1130558266875[/C][C]1.88694417331251[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.6360861173835[/C][C]-2.63608611738349[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.6304994761086[/C][C]-2.63049947610859[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.7610897469395[/C][C]-2.76108974693953[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.0742555493521[/C][C]0.925744450647908[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.0843248573157[/C][C]-0.0843248573156846[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.8811308375875[/C][C]0.118869162412537[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.069806403621[/C][C]-3.06980640362104[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.8895653936498[/C][C]-2.88956539364978[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.0171097354465[/C][C]0.98289026455349[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.581209885355[/C][C]0.418790114645037[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8642772550052[/C][C]0.135722744994763[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.2683778034096[/C][C]3.73162219659043[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.7830875995577[/C][C]-2.78308759955769[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6254848753197[/C][C]-0.625484875319742[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.0172952347015[/C][C]-0.0172952347014784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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
11413.97867117452690.0213288254730885
21815.20818094493052.79181905506949
31113.5981378012711-2.59813780127107
41214.3483113326744-2.34831133267443
51611.3780269318854.621973068115
61814.15493500896063.84506499103935
71410.30473137232483.69526862767519
81415.0204418459513-1.0204418459513
91515.1593791849008-0.159379184900801
101514.73179374158640.268206258413645
111715.29509303673771.70490696326226
121915.28497162341323.71502837658683
131013.3185189326193-3.31851893261934
141613.52540789814822.47459210185179
151815.75600981616962.2439901838304
161413.0810030576060.918996942394048
171413.71399362660080.286006373399196
181715.92278964658881.0772103534112
191415.415390542847-1.415390542847
201613.40204265552.5979573445
211815.94989399046812.05010600953194
221113.3194070006004-2.31940700060037
231414.3543227903953-0.354322790395339
241213.6339554341129-1.6339554341129
251715.36960994369521.63039005630485
26915.7304724759688-6.73047247596881
271615.09978772975750.900212270242545
281413.2944754273720.705524572628035
291513.81864618048631.18135381951373
301113.7820771331186-2.7820771331186
311615.20640543622690.793594563773123
321312.4256208790530.574379120946972
331714.880083181972.11991681802998
341515.3844664166661-0.384466416666138
351413.70348052339090.296519476609059
361615.31466673231070.68533326768926
37910.8493379773746-1.84933797737459
381514.20564577976430.794354220235656
391715.06799023731911.93200976268087
401315.0689287203695-2.06892872036947
411515.5451651390067-0.545165139006734
421613.59574104296212.40425895703787
431615.3105623929260.689437607074036
441212.9501471527352-0.950147152735165
451214.25750549622-2.25750549621999
461113.7008688141996-2.70086881419956
471515.4913666381083-0.491366638108339
481514.8367222385030.16327776149702
491713.78011870214883.2198812978512
501314.6491966379822-1.64919663798225
511615.29956559658130.700434403418695
521413.18185519548710.818144804512877
531111.6117103622778-0.611710362277839
541213.6233645517912-1.62336455179121
551214.1443962793682-2.14439627936817
561514.58706533469650.41293466530346
571614.20177304618841.79822695381163
581515.2535296741847-0.253529674184666
591215.3283966401698-3.32839664016977
601213.3935424527294-1.39354245272941
61810.4145299828531-2.41452998285311
621314.6414763015395-1.64147630153949
631114.5641402596738-3.56414025967376
641412.99058221206461.00941778793535
651513.26080955911591.73919044088405
661015.2917383185594-5.29173831855936
671112.5925377657149-1.59253776571486
681214.0781577721275-2.07815777212754
691513.9949780355331.00502196446696
701513.84056637294981.15943362705017
711413.86845266839650.131547331603542
721612.91552829205523.08447170794481
731514.76549770461520.234502295384773
741515.6422865654213-0.642286565421291
751314.6659331139386-1.66593311393859
761212.181909667302-0.181909667302006
771714.21199610925992.78800389074005
781312.37307060966310.626929390336934
791513.71776633256171.2822336674383
801314.4403517374108-1.44035173741085
811515.1348468267232-0.134846826723192
821614.3686737163591.63132628364103
831515.6858626521207-0.685862652120722
841614.35494881509621.64505118490382
851514.36834206800510.631657931994927
861414.0900745330328-0.0900745330327514
871514.5779531993260.422046800673962
881414.3082995303177-0.308299530317651
891312.54900053818890.450999461811128
90710.6396879868802-3.63968798688019
911714.05746597081592.94253402918414
921312.5812098853550.418790114645037
931514.74544920906220.254550790937841
941413.15020482464040.849795175359619
951314.8262632361197-1.82626323611972
961615.34539867442780.654601325572175
971212.9676487932958-0.967648793295776
981414.9093248428542-0.909324842854225
991715.113865117311.88613488269003
1001515.3310853475636-0.331085347563625
1011715.37263192315361.62736807684641
1021213.1902265768181-1.19022657681813
1031615.23046437547740.769535624522634
1041114.0970937555188-3.09709375551882
1051513.61639943013171.38360056986826
106911.5316009199423-2.53160091994231
1071615.1062225599790.893777440020956
1081513.42370483644151.57629516355846
1091012.9108475029433-2.91084750294334
110109.954137505524520.0458624944754825
1111513.97822637971631.02177362028372
1121113.2114064501507-2.21140645015069
1131315.88945629213-2.88945629212999
1141411.97803767463532.02196232536467
1151814.40791399771283.59208600228724
1161615.58284043344470.417159566555314
1171413.3520613575690.647938642430999
1181414.4749426681309-0.474942668130884
1191415.285245355753-1.285245355753
1201414.1421401525374-0.142140152537373
1211212.8027584200095-0.802758420009498
1221413.24756039194180.752439608058224
1231515.5401743388428-0.54017433884284
1241516.2139701894586-1.21397018945857
1251514.46395558500110.536044414998923
1261314.7528566079908-1.75285660799077
1271716.383202595610.616797404390015
1281715.81619830826061.18380169173941
1291915.26837780340963.73162219659043
1301513.77473870802691.22526129197311
1311314.7352994368216-1.73529943682158
132910.7753206767197-1.77532067671974
1331515.7813543545272-0.781354354527247
1341512.50370207035312.4962979296469
1351514.67730756995960.322692430040413
1361613.57804318657962.42195681342043
137118.897221466564662.10277853343534
1381413.46273220631750.537267793682496
1391112.1667822121604-1.16678221216039
1401514.4348309310450.565169068954988
1411313.9324816028532-0.932481602853203
1421514.69931881347660.30068118652343
1431614.11175735640271.88824264359734
1441414.8246321591517-0.82463215915169
1451513.79663823644791.20336176355209
1461614.96891532473831.03108467526174
1471614.11305582668751.88694417331251
1481113.6360861173835-2.63608611738349
1491214.6304994761086-2.63049947610859
150911.7610897469395-2.76108974693953
1511615.07425554935210.925744450647908
1521313.0843248573157-0.0843248573156846
1531615.88113083758750.118869162412537
1541215.069806403621-3.06980640362104
155911.8895653936498-2.88956539364978
1561312.01710973544650.98289026455349
1571312.5812098853550.418790114645037
1581413.86427725500520.135722744994763
1591915.26837780340963.73162219659043
1601315.7830875995577-2.78308759955769
1611212.6254848753197-0.625484875319742
1621313.0172952347015-0.0172952347014784






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.059174710541920.118349421083840.94082528945808
110.01894938827606330.03789877655212660.981050611723937
120.8856141072428390.2287717855143220.114385892757161
130.9833655506249910.03326889875001870.0166344493750093
140.9838403366867690.0323193266264620.016159663313231
150.9890581244337040.02188375113259150.0109418755662957
160.9810255623357090.03794887532858230.0189744376642911
170.9741885111777040.05162297764459190.025811488822296
180.9621184917988730.07576301640225470.0378815082011274
190.9495578940982010.1008842118035990.0504421059017993
200.9496679889226530.1006640221546930.0503320110773467
210.9489761516595230.1020476966809540.051023848340477
220.9701322387017480.05973552259650350.0298677612982517
230.9568421095618790.08631578087624210.043157890438121
240.9468439498067850.106312100386430.0531560501932152
250.9292123629862130.1415752740275740.0707876370137872
260.9995447784489020.0009104431021963970.000455221551098198
270.9992603771648950.001479245670210590.000739622835105293
280.9987860439299110.002427912140177990.001213956070089
290.9981221118910530.003755776217894430.00187788810894722
300.9991055814018240.001788837196352750.000894418598176377
310.9985796068746570.002840786250685780.00142039312534289
320.9978038333207310.004392333358538270.00219616667926914
330.99743275371860.00513449256279930.00256724628139965
340.9961076826523110.007784634695377450.00389231734768872
350.994466732000090.01106653599982080.00553326799991039
360.9919388732933420.01612225341331620.00806112670665812
370.9940746920768940.01185061584621190.00592530792310593
380.9915681902608770.01686361947824610.00843180973912306
390.9908833863966340.01823322720673220.00911661360336611
400.9910506814421690.01789863711566290.00894931855783146
410.9877081379642550.02458372407149060.0122918620357453
420.9873508590128610.02529828197427880.0126491409871394
430.9829431088777650.03411378224447040.0170568911222352
440.9788169227333890.04236615453322220.0211830772666111
450.982336443489370.03532711302126070.0176635565106303
460.9870290267862880.02594194642742320.0129709732137116
470.982303665966240.0353926680675210.0176963340337605
480.9760208699782390.04795826004352120.0239791300217606
490.9832872961642220.0334254076715560.016712703835778
500.9817985223538470.03640295529230520.0182014776461526
510.9760408728815510.04791825423689770.0239591271184488
520.9690260104814270.06194797903714650.0309739895185732
530.9618229961721280.07635400765574460.0381770038278723
540.9592494643002810.0815010713994380.040750535699719
550.9592505338771780.08149893224564370.0407494661228219
560.9477922992400810.1044154015198370.0522077007599186
570.9436581415767960.1126837168464090.0563418584232043
580.9289006800872010.1421986398255990.0710993199127994
590.9531379316877650.09372413662447070.0468620683122354
600.948840360062840.102319279874320.05115963993716
610.9561101258402420.0877797483195150.0438898741597575
620.9537698205286230.09246035894275460.0462301794713773
630.9756253878355180.0487492243289630.0243746121644815
640.9704285728359690.05914285432806270.0295714271640313
650.96804186943070.06391626113860040.0319581305693002
660.9948992848033960.01020143039320840.0051007151966042
670.994361017403630.01127796519273910.00563898259636954
680.9948213284891210.01035734302175820.00517867151087911
690.9934050600128010.01318987997439760.0065949399871988
700.9918623167243520.01627536655129550.00813768327564775
710.9889048204780370.02219035904392510.0110951795219626
720.9930185359326710.01396292813465810.00698146406732903
730.9904909078966890.01901818420662140.00950909210331072
740.9874175750655090.02516484986898130.0125824249344906
750.9865770173082710.02684596538345720.0134229826917286
760.9820666293118050.03586674137638910.0179333706881945
770.9869035993467350.02619280130653040.0130964006532652
780.9829837936536270.03403241269274530.0170162063463727
790.979970497843020.04005900431396060.0200295021569803
800.9788450185047220.04230996299055560.0211549814952778
810.9721547705570490.05569045888590190.0278452294429509
820.9700983798461380.05980324030772330.0299016201538616
830.9623592688057810.07528146238843860.0376407311942193
840.9590491742682720.08190165146345580.0409508257317279
850.9489706461674530.1020587076650940.0510293538325468
860.9354817808232950.1290364383534110.0645182191767053
870.9200246053173070.1599507893653860.0799753946826932
880.9013798536152880.1972402927694230.0986201463847116
890.8823920411564480.2352159176871040.117607958843552
900.9270712052264310.1458575895471390.0729287947735693
910.9467340744366360.1065318511267280.0532659255633639
920.9339458356413390.1321083287173230.0660541643586614
930.9189188151623480.1621623696753040.0810811848376521
940.9029456512357190.1941086975285620.0970543487642808
950.9025043356073620.1949913287852770.0974956643926384
960.8823374990458290.2353250019083420.117662500954171
970.8619616224390170.2760767551219650.138038377560983
980.8432329980343670.3135340039312660.156767001965633
990.83985117489470.32029765021060.1601488251053
1000.8092960179498250.381407964100350.190703982050175
1010.802206134081970.3955877318360590.19779386591803
1020.7786297319481470.4427405361037070.221370268051853
1030.7530935399673880.4938129200652240.246906460032612
1040.8239128943188760.3521742113622490.176087105681124
1050.8257231933870320.3485536132259370.174276806612968
1060.8564698862264430.2870602275471150.143530113773557
1070.8329203275316310.3341593449367380.167079672468369
1080.8378651130472220.3242697739055550.162134886952778
1090.8708446176191640.2583107647616730.129155382380836
1100.8474393538996960.3051212922006080.152560646100304
1110.8349505141435280.3300989717129440.165049485856472
1120.8324119390449630.3351761219100740.167588060955037
1130.8442374537069420.3115250925861160.155762546293058
1140.8525931506704490.2948136986591020.147406849329551
1150.9127027635043050.1745944729913890.0872972364956947
1160.8896731116688770.2206537766622460.110326888331123
1170.8893247080626770.2213505838746460.110675291937323
1180.8625494350754410.2749011298491170.137450564924559
1190.8440995498845050.3118009002309910.155900450115495
1200.8082484192895460.3835031614209070.191751580710454
1210.7704372963707920.4591254072584170.229562703629208
1220.7280446608907860.5439106782184280.271955339109214
1230.6797299355717070.6405401288565870.320270064428293
1240.636948297475260.726103405049480.36305170252474
1250.5826456371943930.8347087256112150.417354362805607
1260.5794870193536350.841025961292730.420512980646365
1270.5257795458521370.9484409082957260.474220454147863
1280.519855272548710.9602894549025790.480144727451289
1290.6744442166612050.6511115666775910.325555783338795
1300.637360731017750.7252785379644990.36263926898225
1310.6320366347786680.7359267304426650.367963365221332
1320.6294252779823850.7411494440352310.370574722017615
1330.5681484344283870.8637031311432250.431851565571613
1340.5665521830829960.8668956338340080.433447816917004
1350.5192049058811190.9615901882377630.480795094118881
1360.5167799691342570.9664400617314850.483220030865743
1370.5103944453854670.9792111092290660.489605554614533
1380.4689461721594210.9378923443188420.531053827840579
1390.3991597493817190.7983194987634390.600840250618281
1400.3388780327545610.6777560655091230.661121967245438
1410.290581233316710.581162466633420.70941876668329
1420.2417691982683970.4835383965367940.758230801731603
1430.2333998169894090.4667996339788180.766600183010591
1440.1910828401035330.3821656802070660.808917159896467
1450.1520688680992720.3041377361985440.847931131900728
1460.121000006107350.24200001221470.87899999389265
1470.2076689713699170.4153379427398350.792331028630083
1480.5070385299660850.9859229400678290.492961470033915
1490.5510341325996580.8979317348006830.448965867400342
1500.4344636491139750.868927298227950.565536350886025
1510.3533643422127480.7067286844254970.646635657787252
1520.2191343760142560.4382687520285110.780865623985744

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.05917471054192 & 0.11834942108384 & 0.94082528945808 \tabularnewline
11 & 0.0189493882760633 & 0.0378987765521266 & 0.981050611723937 \tabularnewline
12 & 0.885614107242839 & 0.228771785514322 & 0.114385892757161 \tabularnewline
13 & 0.983365550624991 & 0.0332688987500187 & 0.0166344493750093 \tabularnewline
14 & 0.983840336686769 & 0.032319326626462 & 0.016159663313231 \tabularnewline
15 & 0.989058124433704 & 0.0218837511325915 & 0.0109418755662957 \tabularnewline
16 & 0.981025562335709 & 0.0379488753285823 & 0.0189744376642911 \tabularnewline
17 & 0.974188511177704 & 0.0516229776445919 & 0.025811488822296 \tabularnewline
18 & 0.962118491798873 & 0.0757630164022547 & 0.0378815082011274 \tabularnewline
19 & 0.949557894098201 & 0.100884211803599 & 0.0504421059017993 \tabularnewline
20 & 0.949667988922653 & 0.100664022154693 & 0.0503320110773467 \tabularnewline
21 & 0.948976151659523 & 0.102047696680954 & 0.051023848340477 \tabularnewline
22 & 0.970132238701748 & 0.0597355225965035 & 0.0298677612982517 \tabularnewline
23 & 0.956842109561879 & 0.0863157808762421 & 0.043157890438121 \tabularnewline
24 & 0.946843949806785 & 0.10631210038643 & 0.0531560501932152 \tabularnewline
25 & 0.929212362986213 & 0.141575274027574 & 0.0707876370137872 \tabularnewline
26 & 0.999544778448902 & 0.000910443102196397 & 0.000455221551098198 \tabularnewline
27 & 0.999260377164895 & 0.00147924567021059 & 0.000739622835105293 \tabularnewline
28 & 0.998786043929911 & 0.00242791214017799 & 0.001213956070089 \tabularnewline
29 & 0.998122111891053 & 0.00375577621789443 & 0.00187788810894722 \tabularnewline
30 & 0.999105581401824 & 0.00178883719635275 & 0.000894418598176377 \tabularnewline
31 & 0.998579606874657 & 0.00284078625068578 & 0.00142039312534289 \tabularnewline
32 & 0.997803833320731 & 0.00439233335853827 & 0.00219616667926914 \tabularnewline
33 & 0.9974327537186 & 0.0051344925627993 & 0.00256724628139965 \tabularnewline
34 & 0.996107682652311 & 0.00778463469537745 & 0.00389231734768872 \tabularnewline
35 & 0.99446673200009 & 0.0110665359998208 & 0.00553326799991039 \tabularnewline
36 & 0.991938873293342 & 0.0161222534133162 & 0.00806112670665812 \tabularnewline
37 & 0.994074692076894 & 0.0118506158462119 & 0.00592530792310593 \tabularnewline
38 & 0.991568190260877 & 0.0168636194782461 & 0.00843180973912306 \tabularnewline
39 & 0.990883386396634 & 0.0182332272067322 & 0.00911661360336611 \tabularnewline
40 & 0.991050681442169 & 0.0178986371156629 & 0.00894931855783146 \tabularnewline
41 & 0.987708137964255 & 0.0245837240714906 & 0.0122918620357453 \tabularnewline
42 & 0.987350859012861 & 0.0252982819742788 & 0.0126491409871394 \tabularnewline
43 & 0.982943108877765 & 0.0341137822444704 & 0.0170568911222352 \tabularnewline
44 & 0.978816922733389 & 0.0423661545332222 & 0.0211830772666111 \tabularnewline
45 & 0.98233644348937 & 0.0353271130212607 & 0.0176635565106303 \tabularnewline
46 & 0.987029026786288 & 0.0259419464274232 & 0.0129709732137116 \tabularnewline
47 & 0.98230366596624 & 0.035392668067521 & 0.0176963340337605 \tabularnewline
48 & 0.976020869978239 & 0.0479582600435212 & 0.0239791300217606 \tabularnewline
49 & 0.983287296164222 & 0.033425407671556 & 0.016712703835778 \tabularnewline
50 & 0.981798522353847 & 0.0364029552923052 & 0.0182014776461526 \tabularnewline
51 & 0.976040872881551 & 0.0479182542368977 & 0.0239591271184488 \tabularnewline
52 & 0.969026010481427 & 0.0619479790371465 & 0.0309739895185732 \tabularnewline
53 & 0.961822996172128 & 0.0763540076557446 & 0.0381770038278723 \tabularnewline
54 & 0.959249464300281 & 0.081501071399438 & 0.040750535699719 \tabularnewline
55 & 0.959250533877178 & 0.0814989322456437 & 0.0407494661228219 \tabularnewline
56 & 0.947792299240081 & 0.104415401519837 & 0.0522077007599186 \tabularnewline
57 & 0.943658141576796 & 0.112683716846409 & 0.0563418584232043 \tabularnewline
58 & 0.928900680087201 & 0.142198639825599 & 0.0710993199127994 \tabularnewline
59 & 0.953137931687765 & 0.0937241366244707 & 0.0468620683122354 \tabularnewline
60 & 0.94884036006284 & 0.10231927987432 & 0.05115963993716 \tabularnewline
61 & 0.956110125840242 & 0.087779748319515 & 0.0438898741597575 \tabularnewline
62 & 0.953769820528623 & 0.0924603589427546 & 0.0462301794713773 \tabularnewline
63 & 0.975625387835518 & 0.048749224328963 & 0.0243746121644815 \tabularnewline
64 & 0.970428572835969 & 0.0591428543280627 & 0.0295714271640313 \tabularnewline
65 & 0.9680418694307 & 0.0639162611386004 & 0.0319581305693002 \tabularnewline
66 & 0.994899284803396 & 0.0102014303932084 & 0.0051007151966042 \tabularnewline
67 & 0.99436101740363 & 0.0112779651927391 & 0.00563898259636954 \tabularnewline
68 & 0.994821328489121 & 0.0103573430217582 & 0.00517867151087911 \tabularnewline
69 & 0.993405060012801 & 0.0131898799743976 & 0.0065949399871988 \tabularnewline
70 & 0.991862316724352 & 0.0162753665512955 & 0.00813768327564775 \tabularnewline
71 & 0.988904820478037 & 0.0221903590439251 & 0.0110951795219626 \tabularnewline
72 & 0.993018535932671 & 0.0139629281346581 & 0.00698146406732903 \tabularnewline
73 & 0.990490907896689 & 0.0190181842066214 & 0.00950909210331072 \tabularnewline
74 & 0.987417575065509 & 0.0251648498689813 & 0.0125824249344906 \tabularnewline
75 & 0.986577017308271 & 0.0268459653834572 & 0.0134229826917286 \tabularnewline
76 & 0.982066629311805 & 0.0358667413763891 & 0.0179333706881945 \tabularnewline
77 & 0.986903599346735 & 0.0261928013065304 & 0.0130964006532652 \tabularnewline
78 & 0.982983793653627 & 0.0340324126927453 & 0.0170162063463727 \tabularnewline
79 & 0.97997049784302 & 0.0400590043139606 & 0.0200295021569803 \tabularnewline
80 & 0.978845018504722 & 0.0423099629905556 & 0.0211549814952778 \tabularnewline
81 & 0.972154770557049 & 0.0556904588859019 & 0.0278452294429509 \tabularnewline
82 & 0.970098379846138 & 0.0598032403077233 & 0.0299016201538616 \tabularnewline
83 & 0.962359268805781 & 0.0752814623884386 & 0.0376407311942193 \tabularnewline
84 & 0.959049174268272 & 0.0819016514634558 & 0.0409508257317279 \tabularnewline
85 & 0.948970646167453 & 0.102058707665094 & 0.0510293538325468 \tabularnewline
86 & 0.935481780823295 & 0.129036438353411 & 0.0645182191767053 \tabularnewline
87 & 0.920024605317307 & 0.159950789365386 & 0.0799753946826932 \tabularnewline
88 & 0.901379853615288 & 0.197240292769423 & 0.0986201463847116 \tabularnewline
89 & 0.882392041156448 & 0.235215917687104 & 0.117607958843552 \tabularnewline
90 & 0.927071205226431 & 0.145857589547139 & 0.0729287947735693 \tabularnewline
91 & 0.946734074436636 & 0.106531851126728 & 0.0532659255633639 \tabularnewline
92 & 0.933945835641339 & 0.132108328717323 & 0.0660541643586614 \tabularnewline
93 & 0.918918815162348 & 0.162162369675304 & 0.0810811848376521 \tabularnewline
94 & 0.902945651235719 & 0.194108697528562 & 0.0970543487642808 \tabularnewline
95 & 0.902504335607362 & 0.194991328785277 & 0.0974956643926384 \tabularnewline
96 & 0.882337499045829 & 0.235325001908342 & 0.117662500954171 \tabularnewline
97 & 0.861961622439017 & 0.276076755121965 & 0.138038377560983 \tabularnewline
98 & 0.843232998034367 & 0.313534003931266 & 0.156767001965633 \tabularnewline
99 & 0.8398511748947 & 0.3202976502106 & 0.1601488251053 \tabularnewline
100 & 0.809296017949825 & 0.38140796410035 & 0.190703982050175 \tabularnewline
101 & 0.80220613408197 & 0.395587731836059 & 0.19779386591803 \tabularnewline
102 & 0.778629731948147 & 0.442740536103707 & 0.221370268051853 \tabularnewline
103 & 0.753093539967388 & 0.493812920065224 & 0.246906460032612 \tabularnewline
104 & 0.823912894318876 & 0.352174211362249 & 0.176087105681124 \tabularnewline
105 & 0.825723193387032 & 0.348553613225937 & 0.174276806612968 \tabularnewline
106 & 0.856469886226443 & 0.287060227547115 & 0.143530113773557 \tabularnewline
107 & 0.832920327531631 & 0.334159344936738 & 0.167079672468369 \tabularnewline
108 & 0.837865113047222 & 0.324269773905555 & 0.162134886952778 \tabularnewline
109 & 0.870844617619164 & 0.258310764761673 & 0.129155382380836 \tabularnewline
110 & 0.847439353899696 & 0.305121292200608 & 0.152560646100304 \tabularnewline
111 & 0.834950514143528 & 0.330098971712944 & 0.165049485856472 \tabularnewline
112 & 0.832411939044963 & 0.335176121910074 & 0.167588060955037 \tabularnewline
113 & 0.844237453706942 & 0.311525092586116 & 0.155762546293058 \tabularnewline
114 & 0.852593150670449 & 0.294813698659102 & 0.147406849329551 \tabularnewline
115 & 0.912702763504305 & 0.174594472991389 & 0.0872972364956947 \tabularnewline
116 & 0.889673111668877 & 0.220653776662246 & 0.110326888331123 \tabularnewline
117 & 0.889324708062677 & 0.221350583874646 & 0.110675291937323 \tabularnewline
118 & 0.862549435075441 & 0.274901129849117 & 0.137450564924559 \tabularnewline
119 & 0.844099549884505 & 0.311800900230991 & 0.155900450115495 \tabularnewline
120 & 0.808248419289546 & 0.383503161420907 & 0.191751580710454 \tabularnewline
121 & 0.770437296370792 & 0.459125407258417 & 0.229562703629208 \tabularnewline
122 & 0.728044660890786 & 0.543910678218428 & 0.271955339109214 \tabularnewline
123 & 0.679729935571707 & 0.640540128856587 & 0.320270064428293 \tabularnewline
124 & 0.63694829747526 & 0.72610340504948 & 0.36305170252474 \tabularnewline
125 & 0.582645637194393 & 0.834708725611215 & 0.417354362805607 \tabularnewline
126 & 0.579487019353635 & 0.84102596129273 & 0.420512980646365 \tabularnewline
127 & 0.525779545852137 & 0.948440908295726 & 0.474220454147863 \tabularnewline
128 & 0.51985527254871 & 0.960289454902579 & 0.480144727451289 \tabularnewline
129 & 0.674444216661205 & 0.651111566677591 & 0.325555783338795 \tabularnewline
130 & 0.63736073101775 & 0.725278537964499 & 0.36263926898225 \tabularnewline
131 & 0.632036634778668 & 0.735926730442665 & 0.367963365221332 \tabularnewline
132 & 0.629425277982385 & 0.741149444035231 & 0.370574722017615 \tabularnewline
133 & 0.568148434428387 & 0.863703131143225 & 0.431851565571613 \tabularnewline
134 & 0.566552183082996 & 0.866895633834008 & 0.433447816917004 \tabularnewline
135 & 0.519204905881119 & 0.961590188237763 & 0.480795094118881 \tabularnewline
136 & 0.516779969134257 & 0.966440061731485 & 0.483220030865743 \tabularnewline
137 & 0.510394445385467 & 0.979211109229066 & 0.489605554614533 \tabularnewline
138 & 0.468946172159421 & 0.937892344318842 & 0.531053827840579 \tabularnewline
139 & 0.399159749381719 & 0.798319498763439 & 0.600840250618281 \tabularnewline
140 & 0.338878032754561 & 0.677756065509123 & 0.661121967245438 \tabularnewline
141 & 0.29058123331671 & 0.58116246663342 & 0.70941876668329 \tabularnewline
142 & 0.241769198268397 & 0.483538396536794 & 0.758230801731603 \tabularnewline
143 & 0.233399816989409 & 0.466799633978818 & 0.766600183010591 \tabularnewline
144 & 0.191082840103533 & 0.382165680207066 & 0.808917159896467 \tabularnewline
145 & 0.152068868099272 & 0.304137736198544 & 0.847931131900728 \tabularnewline
146 & 0.12100000610735 & 0.2420000122147 & 0.87899999389265 \tabularnewline
147 & 0.207668971369917 & 0.415337942739835 & 0.792331028630083 \tabularnewline
148 & 0.507038529966085 & 0.985922940067829 & 0.492961470033915 \tabularnewline
149 & 0.551034132599658 & 0.897931734800683 & 0.448965867400342 \tabularnewline
150 & 0.434463649113975 & 0.86892729822795 & 0.565536350886025 \tabularnewline
151 & 0.353364342212748 & 0.706728684425497 & 0.646635657787252 \tabularnewline
152 & 0.219134376014256 & 0.438268752028511 & 0.780865623985744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&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]0.05917471054192[/C][C]0.11834942108384[/C][C]0.94082528945808[/C][/ROW]
[ROW][C]11[/C][C]0.0189493882760633[/C][C]0.0378987765521266[/C][C]0.981050611723937[/C][/ROW]
[ROW][C]12[/C][C]0.885614107242839[/C][C]0.228771785514322[/C][C]0.114385892757161[/C][/ROW]
[ROW][C]13[/C][C]0.983365550624991[/C][C]0.0332688987500187[/C][C]0.0166344493750093[/C][/ROW]
[ROW][C]14[/C][C]0.983840336686769[/C][C]0.032319326626462[/C][C]0.016159663313231[/C][/ROW]
[ROW][C]15[/C][C]0.989058124433704[/C][C]0.0218837511325915[/C][C]0.0109418755662957[/C][/ROW]
[ROW][C]16[/C][C]0.981025562335709[/C][C]0.0379488753285823[/C][C]0.0189744376642911[/C][/ROW]
[ROW][C]17[/C][C]0.974188511177704[/C][C]0.0516229776445919[/C][C]0.025811488822296[/C][/ROW]
[ROW][C]18[/C][C]0.962118491798873[/C][C]0.0757630164022547[/C][C]0.0378815082011274[/C][/ROW]
[ROW][C]19[/C][C]0.949557894098201[/C][C]0.100884211803599[/C][C]0.0504421059017993[/C][/ROW]
[ROW][C]20[/C][C]0.949667988922653[/C][C]0.100664022154693[/C][C]0.0503320110773467[/C][/ROW]
[ROW][C]21[/C][C]0.948976151659523[/C][C]0.102047696680954[/C][C]0.051023848340477[/C][/ROW]
[ROW][C]22[/C][C]0.970132238701748[/C][C]0.0597355225965035[/C][C]0.0298677612982517[/C][/ROW]
[ROW][C]23[/C][C]0.956842109561879[/C][C]0.0863157808762421[/C][C]0.043157890438121[/C][/ROW]
[ROW][C]24[/C][C]0.946843949806785[/C][C]0.10631210038643[/C][C]0.0531560501932152[/C][/ROW]
[ROW][C]25[/C][C]0.929212362986213[/C][C]0.141575274027574[/C][C]0.0707876370137872[/C][/ROW]
[ROW][C]26[/C][C]0.999544778448902[/C][C]0.000910443102196397[/C][C]0.000455221551098198[/C][/ROW]
[ROW][C]27[/C][C]0.999260377164895[/C][C]0.00147924567021059[/C][C]0.000739622835105293[/C][/ROW]
[ROW][C]28[/C][C]0.998786043929911[/C][C]0.00242791214017799[/C][C]0.001213956070089[/C][/ROW]
[ROW][C]29[/C][C]0.998122111891053[/C][C]0.00375577621789443[/C][C]0.00187788810894722[/C][/ROW]
[ROW][C]30[/C][C]0.999105581401824[/C][C]0.00178883719635275[/C][C]0.000894418598176377[/C][/ROW]
[ROW][C]31[/C][C]0.998579606874657[/C][C]0.00284078625068578[/C][C]0.00142039312534289[/C][/ROW]
[ROW][C]32[/C][C]0.997803833320731[/C][C]0.00439233335853827[/C][C]0.00219616667926914[/C][/ROW]
[ROW][C]33[/C][C]0.9974327537186[/C][C]0.0051344925627993[/C][C]0.00256724628139965[/C][/ROW]
[ROW][C]34[/C][C]0.996107682652311[/C][C]0.00778463469537745[/C][C]0.00389231734768872[/C][/ROW]
[ROW][C]35[/C][C]0.99446673200009[/C][C]0.0110665359998208[/C][C]0.00553326799991039[/C][/ROW]
[ROW][C]36[/C][C]0.991938873293342[/C][C]0.0161222534133162[/C][C]0.00806112670665812[/C][/ROW]
[ROW][C]37[/C][C]0.994074692076894[/C][C]0.0118506158462119[/C][C]0.00592530792310593[/C][/ROW]
[ROW][C]38[/C][C]0.991568190260877[/C][C]0.0168636194782461[/C][C]0.00843180973912306[/C][/ROW]
[ROW][C]39[/C][C]0.990883386396634[/C][C]0.0182332272067322[/C][C]0.00911661360336611[/C][/ROW]
[ROW][C]40[/C][C]0.991050681442169[/C][C]0.0178986371156629[/C][C]0.00894931855783146[/C][/ROW]
[ROW][C]41[/C][C]0.987708137964255[/C][C]0.0245837240714906[/C][C]0.0122918620357453[/C][/ROW]
[ROW][C]42[/C][C]0.987350859012861[/C][C]0.0252982819742788[/C][C]0.0126491409871394[/C][/ROW]
[ROW][C]43[/C][C]0.982943108877765[/C][C]0.0341137822444704[/C][C]0.0170568911222352[/C][/ROW]
[ROW][C]44[/C][C]0.978816922733389[/C][C]0.0423661545332222[/C][C]0.0211830772666111[/C][/ROW]
[ROW][C]45[/C][C]0.98233644348937[/C][C]0.0353271130212607[/C][C]0.0176635565106303[/C][/ROW]
[ROW][C]46[/C][C]0.987029026786288[/C][C]0.0259419464274232[/C][C]0.0129709732137116[/C][/ROW]
[ROW][C]47[/C][C]0.98230366596624[/C][C]0.035392668067521[/C][C]0.0176963340337605[/C][/ROW]
[ROW][C]48[/C][C]0.976020869978239[/C][C]0.0479582600435212[/C][C]0.0239791300217606[/C][/ROW]
[ROW][C]49[/C][C]0.983287296164222[/C][C]0.033425407671556[/C][C]0.016712703835778[/C][/ROW]
[ROW][C]50[/C][C]0.981798522353847[/C][C]0.0364029552923052[/C][C]0.0182014776461526[/C][/ROW]
[ROW][C]51[/C][C]0.976040872881551[/C][C]0.0479182542368977[/C][C]0.0239591271184488[/C][/ROW]
[ROW][C]52[/C][C]0.969026010481427[/C][C]0.0619479790371465[/C][C]0.0309739895185732[/C][/ROW]
[ROW][C]53[/C][C]0.961822996172128[/C][C]0.0763540076557446[/C][C]0.0381770038278723[/C][/ROW]
[ROW][C]54[/C][C]0.959249464300281[/C][C]0.081501071399438[/C][C]0.040750535699719[/C][/ROW]
[ROW][C]55[/C][C]0.959250533877178[/C][C]0.0814989322456437[/C][C]0.0407494661228219[/C][/ROW]
[ROW][C]56[/C][C]0.947792299240081[/C][C]0.104415401519837[/C][C]0.0522077007599186[/C][/ROW]
[ROW][C]57[/C][C]0.943658141576796[/C][C]0.112683716846409[/C][C]0.0563418584232043[/C][/ROW]
[ROW][C]58[/C][C]0.928900680087201[/C][C]0.142198639825599[/C][C]0.0710993199127994[/C][/ROW]
[ROW][C]59[/C][C]0.953137931687765[/C][C]0.0937241366244707[/C][C]0.0468620683122354[/C][/ROW]
[ROW][C]60[/C][C]0.94884036006284[/C][C]0.10231927987432[/C][C]0.05115963993716[/C][/ROW]
[ROW][C]61[/C][C]0.956110125840242[/C][C]0.087779748319515[/C][C]0.0438898741597575[/C][/ROW]
[ROW][C]62[/C][C]0.953769820528623[/C][C]0.0924603589427546[/C][C]0.0462301794713773[/C][/ROW]
[ROW][C]63[/C][C]0.975625387835518[/C][C]0.048749224328963[/C][C]0.0243746121644815[/C][/ROW]
[ROW][C]64[/C][C]0.970428572835969[/C][C]0.0591428543280627[/C][C]0.0295714271640313[/C][/ROW]
[ROW][C]65[/C][C]0.9680418694307[/C][C]0.0639162611386004[/C][C]0.0319581305693002[/C][/ROW]
[ROW][C]66[/C][C]0.994899284803396[/C][C]0.0102014303932084[/C][C]0.0051007151966042[/C][/ROW]
[ROW][C]67[/C][C]0.99436101740363[/C][C]0.0112779651927391[/C][C]0.00563898259636954[/C][/ROW]
[ROW][C]68[/C][C]0.994821328489121[/C][C]0.0103573430217582[/C][C]0.00517867151087911[/C][/ROW]
[ROW][C]69[/C][C]0.993405060012801[/C][C]0.0131898799743976[/C][C]0.0065949399871988[/C][/ROW]
[ROW][C]70[/C][C]0.991862316724352[/C][C]0.0162753665512955[/C][C]0.00813768327564775[/C][/ROW]
[ROW][C]71[/C][C]0.988904820478037[/C][C]0.0221903590439251[/C][C]0.0110951795219626[/C][/ROW]
[ROW][C]72[/C][C]0.993018535932671[/C][C]0.0139629281346581[/C][C]0.00698146406732903[/C][/ROW]
[ROW][C]73[/C][C]0.990490907896689[/C][C]0.0190181842066214[/C][C]0.00950909210331072[/C][/ROW]
[ROW][C]74[/C][C]0.987417575065509[/C][C]0.0251648498689813[/C][C]0.0125824249344906[/C][/ROW]
[ROW][C]75[/C][C]0.986577017308271[/C][C]0.0268459653834572[/C][C]0.0134229826917286[/C][/ROW]
[ROW][C]76[/C][C]0.982066629311805[/C][C]0.0358667413763891[/C][C]0.0179333706881945[/C][/ROW]
[ROW][C]77[/C][C]0.986903599346735[/C][C]0.0261928013065304[/C][C]0.0130964006532652[/C][/ROW]
[ROW][C]78[/C][C]0.982983793653627[/C][C]0.0340324126927453[/C][C]0.0170162063463727[/C][/ROW]
[ROW][C]79[/C][C]0.97997049784302[/C][C]0.0400590043139606[/C][C]0.0200295021569803[/C][/ROW]
[ROW][C]80[/C][C]0.978845018504722[/C][C]0.0423099629905556[/C][C]0.0211549814952778[/C][/ROW]
[ROW][C]81[/C][C]0.972154770557049[/C][C]0.0556904588859019[/C][C]0.0278452294429509[/C][/ROW]
[ROW][C]82[/C][C]0.970098379846138[/C][C]0.0598032403077233[/C][C]0.0299016201538616[/C][/ROW]
[ROW][C]83[/C][C]0.962359268805781[/C][C]0.0752814623884386[/C][C]0.0376407311942193[/C][/ROW]
[ROW][C]84[/C][C]0.959049174268272[/C][C]0.0819016514634558[/C][C]0.0409508257317279[/C][/ROW]
[ROW][C]85[/C][C]0.948970646167453[/C][C]0.102058707665094[/C][C]0.0510293538325468[/C][/ROW]
[ROW][C]86[/C][C]0.935481780823295[/C][C]0.129036438353411[/C][C]0.0645182191767053[/C][/ROW]
[ROW][C]87[/C][C]0.920024605317307[/C][C]0.159950789365386[/C][C]0.0799753946826932[/C][/ROW]
[ROW][C]88[/C][C]0.901379853615288[/C][C]0.197240292769423[/C][C]0.0986201463847116[/C][/ROW]
[ROW][C]89[/C][C]0.882392041156448[/C][C]0.235215917687104[/C][C]0.117607958843552[/C][/ROW]
[ROW][C]90[/C][C]0.927071205226431[/C][C]0.145857589547139[/C][C]0.0729287947735693[/C][/ROW]
[ROW][C]91[/C][C]0.946734074436636[/C][C]0.106531851126728[/C][C]0.0532659255633639[/C][/ROW]
[ROW][C]92[/C][C]0.933945835641339[/C][C]0.132108328717323[/C][C]0.0660541643586614[/C][/ROW]
[ROW][C]93[/C][C]0.918918815162348[/C][C]0.162162369675304[/C][C]0.0810811848376521[/C][/ROW]
[ROW][C]94[/C][C]0.902945651235719[/C][C]0.194108697528562[/C][C]0.0970543487642808[/C][/ROW]
[ROW][C]95[/C][C]0.902504335607362[/C][C]0.194991328785277[/C][C]0.0974956643926384[/C][/ROW]
[ROW][C]96[/C][C]0.882337499045829[/C][C]0.235325001908342[/C][C]0.117662500954171[/C][/ROW]
[ROW][C]97[/C][C]0.861961622439017[/C][C]0.276076755121965[/C][C]0.138038377560983[/C][/ROW]
[ROW][C]98[/C][C]0.843232998034367[/C][C]0.313534003931266[/C][C]0.156767001965633[/C][/ROW]
[ROW][C]99[/C][C]0.8398511748947[/C][C]0.3202976502106[/C][C]0.1601488251053[/C][/ROW]
[ROW][C]100[/C][C]0.809296017949825[/C][C]0.38140796410035[/C][C]0.190703982050175[/C][/ROW]
[ROW][C]101[/C][C]0.80220613408197[/C][C]0.395587731836059[/C][C]0.19779386591803[/C][/ROW]
[ROW][C]102[/C][C]0.778629731948147[/C][C]0.442740536103707[/C][C]0.221370268051853[/C][/ROW]
[ROW][C]103[/C][C]0.753093539967388[/C][C]0.493812920065224[/C][C]0.246906460032612[/C][/ROW]
[ROW][C]104[/C][C]0.823912894318876[/C][C]0.352174211362249[/C][C]0.176087105681124[/C][/ROW]
[ROW][C]105[/C][C]0.825723193387032[/C][C]0.348553613225937[/C][C]0.174276806612968[/C][/ROW]
[ROW][C]106[/C][C]0.856469886226443[/C][C]0.287060227547115[/C][C]0.143530113773557[/C][/ROW]
[ROW][C]107[/C][C]0.832920327531631[/C][C]0.334159344936738[/C][C]0.167079672468369[/C][/ROW]
[ROW][C]108[/C][C]0.837865113047222[/C][C]0.324269773905555[/C][C]0.162134886952778[/C][/ROW]
[ROW][C]109[/C][C]0.870844617619164[/C][C]0.258310764761673[/C][C]0.129155382380836[/C][/ROW]
[ROW][C]110[/C][C]0.847439353899696[/C][C]0.305121292200608[/C][C]0.152560646100304[/C][/ROW]
[ROW][C]111[/C][C]0.834950514143528[/C][C]0.330098971712944[/C][C]0.165049485856472[/C][/ROW]
[ROW][C]112[/C][C]0.832411939044963[/C][C]0.335176121910074[/C][C]0.167588060955037[/C][/ROW]
[ROW][C]113[/C][C]0.844237453706942[/C][C]0.311525092586116[/C][C]0.155762546293058[/C][/ROW]
[ROW][C]114[/C][C]0.852593150670449[/C][C]0.294813698659102[/C][C]0.147406849329551[/C][/ROW]
[ROW][C]115[/C][C]0.912702763504305[/C][C]0.174594472991389[/C][C]0.0872972364956947[/C][/ROW]
[ROW][C]116[/C][C]0.889673111668877[/C][C]0.220653776662246[/C][C]0.110326888331123[/C][/ROW]
[ROW][C]117[/C][C]0.889324708062677[/C][C]0.221350583874646[/C][C]0.110675291937323[/C][/ROW]
[ROW][C]118[/C][C]0.862549435075441[/C][C]0.274901129849117[/C][C]0.137450564924559[/C][/ROW]
[ROW][C]119[/C][C]0.844099549884505[/C][C]0.311800900230991[/C][C]0.155900450115495[/C][/ROW]
[ROW][C]120[/C][C]0.808248419289546[/C][C]0.383503161420907[/C][C]0.191751580710454[/C][/ROW]
[ROW][C]121[/C][C]0.770437296370792[/C][C]0.459125407258417[/C][C]0.229562703629208[/C][/ROW]
[ROW][C]122[/C][C]0.728044660890786[/C][C]0.543910678218428[/C][C]0.271955339109214[/C][/ROW]
[ROW][C]123[/C][C]0.679729935571707[/C][C]0.640540128856587[/C][C]0.320270064428293[/C][/ROW]
[ROW][C]124[/C][C]0.63694829747526[/C][C]0.72610340504948[/C][C]0.36305170252474[/C][/ROW]
[ROW][C]125[/C][C]0.582645637194393[/C][C]0.834708725611215[/C][C]0.417354362805607[/C][/ROW]
[ROW][C]126[/C][C]0.579487019353635[/C][C]0.84102596129273[/C][C]0.420512980646365[/C][/ROW]
[ROW][C]127[/C][C]0.525779545852137[/C][C]0.948440908295726[/C][C]0.474220454147863[/C][/ROW]
[ROW][C]128[/C][C]0.51985527254871[/C][C]0.960289454902579[/C][C]0.480144727451289[/C][/ROW]
[ROW][C]129[/C][C]0.674444216661205[/C][C]0.651111566677591[/C][C]0.325555783338795[/C][/ROW]
[ROW][C]130[/C][C]0.63736073101775[/C][C]0.725278537964499[/C][C]0.36263926898225[/C][/ROW]
[ROW][C]131[/C][C]0.632036634778668[/C][C]0.735926730442665[/C][C]0.367963365221332[/C][/ROW]
[ROW][C]132[/C][C]0.629425277982385[/C][C]0.741149444035231[/C][C]0.370574722017615[/C][/ROW]
[ROW][C]133[/C][C]0.568148434428387[/C][C]0.863703131143225[/C][C]0.431851565571613[/C][/ROW]
[ROW][C]134[/C][C]0.566552183082996[/C][C]0.866895633834008[/C][C]0.433447816917004[/C][/ROW]
[ROW][C]135[/C][C]0.519204905881119[/C][C]0.961590188237763[/C][C]0.480795094118881[/C][/ROW]
[ROW][C]136[/C][C]0.516779969134257[/C][C]0.966440061731485[/C][C]0.483220030865743[/C][/ROW]
[ROW][C]137[/C][C]0.510394445385467[/C][C]0.979211109229066[/C][C]0.489605554614533[/C][/ROW]
[ROW][C]138[/C][C]0.468946172159421[/C][C]0.937892344318842[/C][C]0.531053827840579[/C][/ROW]
[ROW][C]139[/C][C]0.399159749381719[/C][C]0.798319498763439[/C][C]0.600840250618281[/C][/ROW]
[ROW][C]140[/C][C]0.338878032754561[/C][C]0.677756065509123[/C][C]0.661121967245438[/C][/ROW]
[ROW][C]141[/C][C]0.29058123331671[/C][C]0.58116246663342[/C][C]0.70941876668329[/C][/ROW]
[ROW][C]142[/C][C]0.241769198268397[/C][C]0.483538396536794[/C][C]0.758230801731603[/C][/ROW]
[ROW][C]143[/C][C]0.233399816989409[/C][C]0.466799633978818[/C][C]0.766600183010591[/C][/ROW]
[ROW][C]144[/C][C]0.191082840103533[/C][C]0.382165680207066[/C][C]0.808917159896467[/C][/ROW]
[ROW][C]145[/C][C]0.152068868099272[/C][C]0.304137736198544[/C][C]0.847931131900728[/C][/ROW]
[ROW][C]146[/C][C]0.12100000610735[/C][C]0.2420000122147[/C][C]0.87899999389265[/C][/ROW]
[ROW][C]147[/C][C]0.207668971369917[/C][C]0.415337942739835[/C][C]0.792331028630083[/C][/ROW]
[ROW][C]148[/C][C]0.507038529966085[/C][C]0.985922940067829[/C][C]0.492961470033915[/C][/ROW]
[ROW][C]149[/C][C]0.551034132599658[/C][C]0.897931734800683[/C][C]0.448965867400342[/C][/ROW]
[ROW][C]150[/C][C]0.434463649113975[/C][C]0.86892729822795[/C][C]0.565536350886025[/C][/ROW]
[ROW][C]151[/C][C]0.353364342212748[/C][C]0.706728684425497[/C][C]0.646635657787252[/C][/ROW]
[ROW][C]152[/C][C]0.219134376014256[/C][C]0.438268752028511[/C][C]0.780865623985744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&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
100.059174710541920.118349421083840.94082528945808
110.01894938827606330.03789877655212660.981050611723937
120.8856141072428390.2287717855143220.114385892757161
130.9833655506249910.03326889875001870.0166344493750093
140.9838403366867690.0323193266264620.016159663313231
150.9890581244337040.02188375113259150.0109418755662957
160.9810255623357090.03794887532858230.0189744376642911
170.9741885111777040.05162297764459190.025811488822296
180.9621184917988730.07576301640225470.0378815082011274
190.9495578940982010.1008842118035990.0504421059017993
200.9496679889226530.1006640221546930.0503320110773467
210.9489761516595230.1020476966809540.051023848340477
220.9701322387017480.05973552259650350.0298677612982517
230.9568421095618790.08631578087624210.043157890438121
240.9468439498067850.106312100386430.0531560501932152
250.9292123629862130.1415752740275740.0707876370137872
260.9995447784489020.0009104431021963970.000455221551098198
270.9992603771648950.001479245670210590.000739622835105293
280.9987860439299110.002427912140177990.001213956070089
290.9981221118910530.003755776217894430.00187788810894722
300.9991055814018240.001788837196352750.000894418598176377
310.9985796068746570.002840786250685780.00142039312534289
320.9978038333207310.004392333358538270.00219616667926914
330.99743275371860.00513449256279930.00256724628139965
340.9961076826523110.007784634695377450.00389231734768872
350.994466732000090.01106653599982080.00553326799991039
360.9919388732933420.01612225341331620.00806112670665812
370.9940746920768940.01185061584621190.00592530792310593
380.9915681902608770.01686361947824610.00843180973912306
390.9908833863966340.01823322720673220.00911661360336611
400.9910506814421690.01789863711566290.00894931855783146
410.9877081379642550.02458372407149060.0122918620357453
420.9873508590128610.02529828197427880.0126491409871394
430.9829431088777650.03411378224447040.0170568911222352
440.9788169227333890.04236615453322220.0211830772666111
450.982336443489370.03532711302126070.0176635565106303
460.9870290267862880.02594194642742320.0129709732137116
470.982303665966240.0353926680675210.0176963340337605
480.9760208699782390.04795826004352120.0239791300217606
490.9832872961642220.0334254076715560.016712703835778
500.9817985223538470.03640295529230520.0182014776461526
510.9760408728815510.04791825423689770.0239591271184488
520.9690260104814270.06194797903714650.0309739895185732
530.9618229961721280.07635400765574460.0381770038278723
540.9592494643002810.0815010713994380.040750535699719
550.9592505338771780.08149893224564370.0407494661228219
560.9477922992400810.1044154015198370.0522077007599186
570.9436581415767960.1126837168464090.0563418584232043
580.9289006800872010.1421986398255990.0710993199127994
590.9531379316877650.09372413662447070.0468620683122354
600.948840360062840.102319279874320.05115963993716
610.9561101258402420.0877797483195150.0438898741597575
620.9537698205286230.09246035894275460.0462301794713773
630.9756253878355180.0487492243289630.0243746121644815
640.9704285728359690.05914285432806270.0295714271640313
650.96804186943070.06391626113860040.0319581305693002
660.9948992848033960.01020143039320840.0051007151966042
670.994361017403630.01127796519273910.00563898259636954
680.9948213284891210.01035734302175820.00517867151087911
690.9934050600128010.01318987997439760.0065949399871988
700.9918623167243520.01627536655129550.00813768327564775
710.9889048204780370.02219035904392510.0110951795219626
720.9930185359326710.01396292813465810.00698146406732903
730.9904909078966890.01901818420662140.00950909210331072
740.9874175750655090.02516484986898130.0125824249344906
750.9865770173082710.02684596538345720.0134229826917286
760.9820666293118050.03586674137638910.0179333706881945
770.9869035993467350.02619280130653040.0130964006532652
780.9829837936536270.03403241269274530.0170162063463727
790.979970497843020.04005900431396060.0200295021569803
800.9788450185047220.04230996299055560.0211549814952778
810.9721547705570490.05569045888590190.0278452294429509
820.9700983798461380.05980324030772330.0299016201538616
830.9623592688057810.07528146238843860.0376407311942193
840.9590491742682720.08190165146345580.0409508257317279
850.9489706461674530.1020587076650940.0510293538325468
860.9354817808232950.1290364383534110.0645182191767053
870.9200246053173070.1599507893653860.0799753946826932
880.9013798536152880.1972402927694230.0986201463847116
890.8823920411564480.2352159176871040.117607958843552
900.9270712052264310.1458575895471390.0729287947735693
910.9467340744366360.1065318511267280.0532659255633639
920.9339458356413390.1321083287173230.0660541643586614
930.9189188151623480.1621623696753040.0810811848376521
940.9029456512357190.1941086975285620.0970543487642808
950.9025043356073620.1949913287852770.0974956643926384
960.8823374990458290.2353250019083420.117662500954171
970.8619616224390170.2760767551219650.138038377560983
980.8432329980343670.3135340039312660.156767001965633
990.83985117489470.32029765021060.1601488251053
1000.8092960179498250.381407964100350.190703982050175
1010.802206134081970.3955877318360590.19779386591803
1020.7786297319481470.4427405361037070.221370268051853
1030.7530935399673880.4938129200652240.246906460032612
1040.8239128943188760.3521742113622490.176087105681124
1050.8257231933870320.3485536132259370.174276806612968
1060.8564698862264430.2870602275471150.143530113773557
1070.8329203275316310.3341593449367380.167079672468369
1080.8378651130472220.3242697739055550.162134886952778
1090.8708446176191640.2583107647616730.129155382380836
1100.8474393538996960.3051212922006080.152560646100304
1110.8349505141435280.3300989717129440.165049485856472
1120.8324119390449630.3351761219100740.167588060955037
1130.8442374537069420.3115250925861160.155762546293058
1140.8525931506704490.2948136986591020.147406849329551
1150.9127027635043050.1745944729913890.0872972364956947
1160.8896731116688770.2206537766622460.110326888331123
1170.8893247080626770.2213505838746460.110675291937323
1180.8625494350754410.2749011298491170.137450564924559
1190.8440995498845050.3118009002309910.155900450115495
1200.8082484192895460.3835031614209070.191751580710454
1210.7704372963707920.4591254072584170.229562703629208
1220.7280446608907860.5439106782184280.271955339109214
1230.6797299355717070.6405401288565870.320270064428293
1240.636948297475260.726103405049480.36305170252474
1250.5826456371943930.8347087256112150.417354362805607
1260.5794870193536350.841025961292730.420512980646365
1270.5257795458521370.9484409082957260.474220454147863
1280.519855272548710.9602894549025790.480144727451289
1290.6744442166612050.6511115666775910.325555783338795
1300.637360731017750.7252785379644990.36263926898225
1310.6320366347786680.7359267304426650.367963365221332
1320.6294252779823850.7411494440352310.370574722017615
1330.5681484344283870.8637031311432250.431851565571613
1340.5665521830829960.8668956338340080.433447816917004
1350.5192049058811190.9615901882377630.480795094118881
1360.5167799691342570.9664400617314850.483220030865743
1370.5103944453854670.9792111092290660.489605554614533
1380.4689461721594210.9378923443188420.531053827840579
1390.3991597493817190.7983194987634390.600840250618281
1400.3388780327545610.6777560655091230.661121967245438
1410.290581233316710.581162466633420.70941876668329
1420.2417691982683970.4835383965367940.758230801731603
1430.2333998169894090.4667996339788180.766600183010591
1440.1910828401035330.3821656802070660.808917159896467
1450.1520688680992720.3041377361985440.847931131900728
1460.121000006107350.24200001221470.87899999389265
1470.2076689713699170.4153379427398350.792331028630083
1480.5070385299660850.9859229400678290.492961470033915
1490.5510341325996580.8979317348006830.448965867400342
1500.4344636491139750.868927298227950.565536350886025
1510.3533643422127480.7067286844254970.646635657787252
1520.2191343760142560.4382687520285110.780865623985744






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0629370629370629NOK
5% type I error level470.328671328671329NOK
10% type I error level640.447552447552448NOK

\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 & 9 & 0.0629370629370629 & NOK \tabularnewline
5% type I error level & 47 & 0.328671328671329 & NOK \tabularnewline
10% type I error level & 64 & 0.447552447552448 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146609&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]9[/C][C]0.0629370629370629[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.328671328671329[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]64[/C][C]0.447552447552448[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146609&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146609&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0629370629370629NOK
5% type I error level470.328671328671329NOK
10% type I error level640.447552447552448NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}