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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 computationTue, 23 Nov 2010 15:46:33 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290527268rxhu84096lhrgkd.htm/, Retrieved Fri, 19 Apr 2024 15:20:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99323, Retrieved Fri, 19 Apr 2024 15:20:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact193

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7, run s...] [2010-11-23 00:01:54] [3635fb7041b1998c5a1332cf9de22bce]
-           [Multiple Regression] [Workshop 7 Run se...] [2010-11-23 15:46:33] [9ac5e967b06232cfb69e0c18e3cc2b37] [Current]
-             [Multiple Regression] [WS 7 comp 3] [2010-11-23 19:55:17] [b659239b537e56f17142ee5c56ad6265]
-   PD          [Multiple Regression] [] [2010-11-26 10:44:36] [2db311435ed525bc1ed0ddec922afb8f]
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Dataset

Dataseries X:
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Histogram
Boxplots 
9	5,5	6	5,33	12
9	3,5	4	5,56	11
9	8,5	4	3,78	14
9	5	4	4,00	12
9	6	4,5	4,00	21
9	6	3,5	3,56	12
9	5,5	2	4,44	22
9	5,5	5,5	3,56	11
9	6	3,5	4,00	10
9	6,5	3,5	3,78	13
9	7	6	5,11	10
9	8	5	6,67	8
9	5,5	5	5,11	15
9	5	4	4,00	14
9	5,5	4	3,33	10
9	7,5	2	2,67	14
9	4,5	4,5	4,67	14
9	5,5	4	3,33	11
9	8,5	3,5	4,44	10
9	8,5	5,5	6,89	13
9	5,5	4,5	6,00	7
9	9	5,5	7,56	14
9	7	6,5	4,67	12
9	5	4	6,89	14
9	5,5	4	4,22	11
9	7,5	4,5	3,56	9
9	7,5	3	4,44	11
9	6,5	4,5	4,67	15
9	8	4,5	4,89	14
9	6,5	3	3,78	13
9	4,5	3	5,33	9
9	9	8	5,56	15
9	9	2,5	5,78	10
9	6	3,5	5,56	11
9	8,5	4,5	3,78	13
9	4,5	3	7,11	8
9	4,5	3	7,33	20
9	6	2,5	2,89	12
9	9	6	7,11	10
9	6	3,5	5,56	10
9	9	5	6,44	9
9	7	4,5	4,89	14
9	7,5	4	4,00	8
9	8	2,5	3,78	14
9	5	4	4,44	11
9	5,5	4	3,33	13
9	7	5	4,44	9
9	4,5	3	7,33	11
9	6	4	6,44	15
9	8,5	3,5	5,11	11
9	2,5	2	5,78	10
9	6	4	4,00	14
9	6	4	4,44	18
10	3	2	2,44	14
10	12	10	6,22	11
10	6	4	5,78	12
10	6	4	4,89	13
10	7	3	3,78	9
10	3,5	2	2,67	10
10	6,5	4	3,11	15
10	6	4,5	3,78	20
10	6,5	3	4,67	12
10	7	3,5	4,22	12
10	4	4,5	4,00	14
10	5,5	2,5	2,22	13
10	4,5	2,5	6,44	11
10	5,5	4	6,89	17
10	6,5	4	4,22	12
10	5	3	2,00	13
10	5,5	4	4,44	14
10	6	3,5	6,22	13
10	4,5	3,5	4,22	15
10	7,5	4,5	6,67	13
10	9	5,5	6,44	10
10	7,5	3	5,78	11
10	6	4	5,11	19
10	6,5	3	2,89	13
10	7	4,5	4,67	17
10	5	4	4,22	13
10	6,5	3	6,22	9
10	6,5	5	5,11	11
10	5,5	4	4,00	10
10	6,5	4	4,67	9
10	8	5	4,44	12
10	4	2,5	5,11	12
10	8	3,5	4,67	13
10	5,5	2,5	4,67	13
10	4,5	4	3,33	12
10	8	7	6,22	15
10	6	3,5	4,22	22
10	7	4	5,78	13
10	4	3	2,22	15
10	4,5	2,5	3,56	13
10	7,5	3	4,89	15
10	5,5	5	4,22	10
10	10,5	6	6,89	11
10	7	4,5	6,89	16
10	9	6	6,44	11
10	6	3,5	4,22	11
10	6,5	4	4,89	10
10	7,5	5	5,11	10
10	6	3	3,33	16
10	9,5	5	4,44	12
10	7,5	5	4,00	11
10	5,5	5	5,11	16
10	5,5	2,5	5,56	19
10	5	3,5	4,67	11
10	6,5	5	5,33	16
11	7,5	5,5	5,56	15
11	6	3	3,78	24
11	6	3,5	2,89	14
11	8	6	6,22	15
11	4,5	5,5	4,67	11
11	9	5,5	5,56	15
11	4	5,5	2,00	12
11	6,5	2,5	3,56	10
11	8,5	4	4,22	14
11	4,5	3	3,78	13
11	7,5	4,5	5,56	9
11	4	2	4,44	15
11	3,5	2	6,44	15
11	6	3,5	3,11	14
11	7	5,5	4,89	11
11	3	3	3,33	8
11	4	3,5	4,22	11
11	8,5	4	4,44	11
11	5	2	3,33	8
11	5,5	4	4,44	10
11	7	4,5	4,00	11
11	5,5	4	7,33	13
11	6,5	5,5	4,89	11
11	6	4	3,56	20
11	5,5	2,5	3,78	10
11	4,5	2	3,56	15
11	6	4	4,67	12
11	10	5	5,78	14
11	6	3	4,00	23
11	6,5	4,5	4,00	14
11	6	4,5	3,78	16
11	6	6,5	4,89	11
11	4,5	4,5	6,67	12
11	7,5	5	6,67	10
11	12	10	5,33	14
11	3,5	2,5	4,67	12
11	8,5	5,5	4,67	12
11	5,5	3	6,44	11
11	8,5	4,5	6,89	12
11	5,5	3,5	4,44	13
11	6	4,5	3,56	11
11	7	5	4,89	19
11	5,5	4,5	4,44	12
11	8	4	6,22	17
11	10,5	3,5	8,44	9
11	7	3	4,89	12
11	10	6,5	4,44	19
11	6,5	3	3,78	18
11	5,5	4	6,22	15
11	7,5	5	4,89	14
11	9,5	8	6,89	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Dep[t] = + 9.24355550579304 + 0.493286378792208M[t] -0.00260207016403216Expe[t] -0.0557478262810441Crit[t] -0.214965647664329Conc[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Dep[t] =  +  9.24355550579304 +  0.493286378792208M[t] -0.00260207016403216Expe[t] -0.0557478262810441Crit[t] -0.214965647664329Conc[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Dep[t] =  +  9.24355550579304 +  0.493286378792208M[t] -0.00260207016403216Expe[t] -0.0557478262810441Crit[t] -0.214965647664329Conc[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&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
Dep[t] = + 9.24355550579304 + 0.493286378792208M[t] -0.00260207016403216Expe[t] -0.0557478262810441Crit[t] -0.214965647664329Conc[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.243555505793043.3058742.79610.0058320.002916
M0.4932863787922080.309081.5960.1125420.056271
Expe-0.002602070164032160.183669-0.01420.9887150.494357
Crit-0.05574782628104410.232612-0.23970.8109120.405456
Conc-0.2149656476643290.210983-1.01890.309860.15493

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 9.24355550579304 & 3.305874 & 2.7961 & 0.005832 & 0.002916 \tabularnewline
M & 0.493286378792208 & 0.30908 & 1.596 & 0.112542 & 0.056271 \tabularnewline
Expe & -0.00260207016403216 & 0.183669 & -0.0142 & 0.988715 & 0.494357 \tabularnewline
Crit & -0.0557478262810441 & 0.232612 & -0.2397 & 0.810912 & 0.405456 \tabularnewline
Conc & -0.214965647664329 & 0.210983 & -1.0189 & 0.30986 & 0.15493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]9.24355550579304[/C][C]3.305874[/C][C]2.7961[/C][C]0.005832[/C][C]0.002916[/C][/ROW]
[ROW][C]M[/C][C]0.493286378792208[/C][C]0.30908[/C][C]1.596[/C][C]0.112542[/C][C]0.056271[/C][/ROW]
[ROW][C]Expe[/C][C]-0.00260207016403216[/C][C]0.183669[/C][C]-0.0142[/C][C]0.988715[/C][C]0.494357[/C][/ROW]
[ROW][C]Crit[/C][C]-0.0557478262810441[/C][C]0.232612[/C][C]-0.2397[/C][C]0.810912[/C][C]0.405456[/C][/ROW]
[ROW][C]Conc[/C][C]-0.214965647664329[/C][C]0.210983[/C][C]-1.0189[/C][C]0.30986[/C][C]0.15493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.243555505793043.3058742.79610.0058320.002916
M0.4932863787922080.309081.5960.1125420.056271
Expe-0.002602070164032160.183669-0.01420.9887150.494357
Crit-0.05574782628104410.232612-0.23970.8109120.405456
Conc-0.2149656476643290.210983-1.01890.309860.15493






Multiple Linear Regression - Regression Statistics
Multiple R0.161389265252687
R-squared0.0260464949388022
Adjusted R-squared0.000749001300848984
F-TEST (value)1.02960772761003
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value0.393886977846912
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1442247281482
Sum Squared Residuals1522.46696772919

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.161389265252687 \tabularnewline
R-squared & 0.0260464949388022 \tabularnewline
Adjusted R-squared & 0.000749001300848984 \tabularnewline
F-TEST (value) & 1.02960772761003 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0.393886977846912 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.1442247281482 \tabularnewline
Sum Squared Residuals & 1522.46696772919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.161389265252687[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0260464949388022[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.000749001300848984[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.02960772761003[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.393886977846912[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.1442247281482[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1522.46696772919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&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 R0.161389265252687
R-squared0.0260464949388022
Adjusted R-squared0.000749001300848984
F-TEST (value)1.02960772761003
F-TEST (DF numerator)4
F-TEST (DF denominator)154
p-value0.393886977846912
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1442247281482
Sum Squared Residuals1522.46696772919






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.1885676692836-0.18856766928365
21112.2558253632109-1.25582536321095
31412.62545386523331.37454613476670
41212.5872686683213-0.587268668321265
52112.55679268501678.44320731498328
61212.7071253962701-0.70712539627006
72212.60287840082909.39712159917097
81112.59693077879-1.59693077878999
91012.6125405112978-2.61254051129776
101312.65853191870190.341468081298109
111012.2319570065237-2.23195700652371
12811.9497563522844-3.94975635228437
131512.29160793805082.7083920619492
141412.58726866832131.41273133167873
151012.7299946171743-2.72999461717435
161412.97816345686681.02183654313317
171412.41666880632771.58333119367234
181112.7299946171744-1.72999461717435
191012.5114504509154-2.51145045091537
201311.87328896157571.12671103842433
21712.1281624247701-5.12816242477007
221411.72796094255862.27203905744144
231212.2986679783555-0.298667978355490
241411.96601794657142.03398205342865
251112.5386751907531-1.53867519075310
26912.6474744647430-3.64747446474297
271112.5419264342199-1.54192643421992
281512.41146466599962.58853533400041
291412.36026911826741.63973088173261
301312.68640583184240.313594168157587
31912.3584132182908-3.35841321829077
321512.01852267218462.98147732781539
331012.2778432742442-2.27784327424420
341112.2771941009414-1.27719410094140
351312.59757995209280.402420047907217
36811.9757743654483-3.97577436544826
372011.92848192296218.0715180770379
381212.9069002064862-0.906900206486205
391011.796821570867-1.79682157086698
401012.2771941009414-2.2771941009414
41911.9965963810831-2.99659638108313
421412.36287118843141.63712881156857
43812.5807634929112-4.58076349291119
441412.71037663973691.28962336026311
451112.4926837833490-1.49268378334896
461312.72999461717440.270005382825651
47912.4317318167399-3.43173181673985
481111.9284819229621-0.928481922962108
491512.06015041785632.93984958214373
501112.3674234669803-1.36742346698027
511012.3226306434509-2.32263064345093
521412.58466659815721.41533340184277
531812.49008171318495.50991828681507
541413.53260125036000.467398749640019
551112.2506298604642-1.25062986046417
561212.6953141241069-0.695314124106936
571312.88663355052820.113366449471811
58913.1783911755526-4.17839117555261
591013.4818581163152-3.48185811631517
601513.26797136828871.73202863171132
612013.09737150629516.90262849370493
621212.9883727842134-0.98837278421337
631213.0559323774398-1.05593237743978
641413.05528320413700.944716795863015
651313.5455146042955-0.545514604295531
661112.6409616413161-1.64096164131609
671712.45800329028154.54199670971845
681213.0293594993813-1.02935949938127
691313.5662341687232-0.566234168723177
701412.98466912705921.01533087294085
711312.62860315227520.371396847724847
721513.06243755284991.93756244715014
731312.47221767929910.527782320700887
741012.4620088467348-2.46200884673482
751112.7471588451419-1.74715884514193
761912.83934110804206.16065889195796
771313.3710116370559-0.371011637055876
781712.90345000970984.09654999029021
791313.0332626046273-0.0332626046273221
80912.6551760303337-3.65517603033366
811112.7822922466790-1.78229224667898
821013.0792540120315-3.07925401203146
83912.9326249579323-3.93262495793233
841212.9224161253680-0.922416125368029
851212.9281669877917-0.928166987791667
861312.95659576582680.0434042341732008
871313.0188487675179-0.0188487675179238
881213.2258830661306-1.22588306613059
891512.42828161996342.57171838003657
902213.05853444760388.94146555239619
911312.69271205394290.307287946057096
921513.52154379640111.47845620359894
931313.2600627065894-0.260062706589362
941512.93847827156322.06152172843682
951012.9762137432643-2.97621374326426
961112.3334972868993-1.33349728689930
971612.42622627189503.57377372810502
981112.4341349335943-1.43413493359429
991113.0585344476038-2.05853444760381
1001012.8853325154462-2.88533251544617
1011012.7796901765149-2.77969017651494
1021613.27772778716562.72227221283441
1031212.9185130201220-0.91851302012198
1041113.0183020454224-2.01830204542235
1051612.7848943168433.21510568315699
1061912.82752934109676.17247065890333
1071112.9644019763189-1.96440197631890
1081612.73499980419283.26500019580718
1091513.14836810071771.85163189928232
1102413.674279624508810.3257203754912
1111413.83772513778960.162274862210421
1121512.97731582503672.02268417496331
1131113.3474937376310-2.34749373763103
1141513.14446499547161.85553500452836
1151213.9227530519768-1.92275305197681
1161013.7481449450535-3.74814494505351
1171413.51744173784540.482558262154581
1181313.6781827297549-0.678182729754897
119913.2041159269987-4.20411592699873
1201513.59335426365951.4066457363405
1211513.16472400341291.83527599658714
1221413.79043269530340.209567304696573
1231113.2936961197348-2.2936961197348
124813.7788203764499-5.77882037644989
1251113.5570249667241-2.55702496672409
1261113.4701492953593-2.47014929535927
127813.8293640624029-5.82936406240287
1281013.4779555058514-3.47795550585136
1291113.5407633724371-2.5407633724371
1301312.85670478410150.143295215898549
1311113.2949971548168-2.29499715481682
1322013.66582424071406.33417575928604
1331013.7034545727314-3.70345457273139
1341513.78122299852211.21877700147791
1351213.4272123718066-1.42721237180655
1361413.12244439596200.877555604038027
1372313.62698718202279.3730128179773
1381413.54206440751910.457935592480886
1391613.59065788508732.40934211491272
1401113.2405503636178-2.24055036361779
1411212.9733102685834-0.973310268583418
1421012.9376301449508-2.9376301449508
1431412.93523566567761.06476433432236
1441213.5173392866382-1.51733928663820
1451213.3370854569749-1.33708545697490
1461113.1037720368037-2.10377203680375
1471212.9156095454411-0.915609545441137
1481313.5058294189919-0.505829418991885
1491113.6379503275734-2.63795032757343
1501913.32157003287535.67842996712468
1511213.4500815927108-1.45008159271084
1521713.08881147759883.91118852240122
153912.6329564775144-3.63295647751441
1541213.4330656854374-1.43306568543741
1551913.32687662441065.67312337558939
1561813.67297858942684.32702141057317
1571513.09531665300891.90468334699114
1581413.32026899779330.679731002206694
1591112.7178900832935-1.71789008329345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.1885676692836 & -0.18856766928365 \tabularnewline
2 & 11 & 12.2558253632109 & -1.25582536321095 \tabularnewline
3 & 14 & 12.6254538652333 & 1.37454613476670 \tabularnewline
4 & 12 & 12.5872686683213 & -0.587268668321265 \tabularnewline
5 & 21 & 12.5567926850167 & 8.44320731498328 \tabularnewline
6 & 12 & 12.7071253962701 & -0.70712539627006 \tabularnewline
7 & 22 & 12.6028784008290 & 9.39712159917097 \tabularnewline
8 & 11 & 12.59693077879 & -1.59693077878999 \tabularnewline
9 & 10 & 12.6125405112978 & -2.61254051129776 \tabularnewline
10 & 13 & 12.6585319187019 & 0.341468081298109 \tabularnewline
11 & 10 & 12.2319570065237 & -2.23195700652371 \tabularnewline
12 & 8 & 11.9497563522844 & -3.94975635228437 \tabularnewline
13 & 15 & 12.2916079380508 & 2.7083920619492 \tabularnewline
14 & 14 & 12.5872686683213 & 1.41273133167873 \tabularnewline
15 & 10 & 12.7299946171743 & -2.72999461717435 \tabularnewline
16 & 14 & 12.9781634568668 & 1.02183654313317 \tabularnewline
17 & 14 & 12.4166688063277 & 1.58333119367234 \tabularnewline
18 & 11 & 12.7299946171744 & -1.72999461717435 \tabularnewline
19 & 10 & 12.5114504509154 & -2.51145045091537 \tabularnewline
20 & 13 & 11.8732889615757 & 1.12671103842433 \tabularnewline
21 & 7 & 12.1281624247701 & -5.12816242477007 \tabularnewline
22 & 14 & 11.7279609425586 & 2.27203905744144 \tabularnewline
23 & 12 & 12.2986679783555 & -0.298667978355490 \tabularnewline
24 & 14 & 11.9660179465714 & 2.03398205342865 \tabularnewline
25 & 11 & 12.5386751907531 & -1.53867519075310 \tabularnewline
26 & 9 & 12.6474744647430 & -3.64747446474297 \tabularnewline
27 & 11 & 12.5419264342199 & -1.54192643421992 \tabularnewline
28 & 15 & 12.4114646659996 & 2.58853533400041 \tabularnewline
29 & 14 & 12.3602691182674 & 1.63973088173261 \tabularnewline
30 & 13 & 12.6864058318424 & 0.313594168157587 \tabularnewline
31 & 9 & 12.3584132182908 & -3.35841321829077 \tabularnewline
32 & 15 & 12.0185226721846 & 2.98147732781539 \tabularnewline
33 & 10 & 12.2778432742442 & -2.27784327424420 \tabularnewline
34 & 11 & 12.2771941009414 & -1.27719410094140 \tabularnewline
35 & 13 & 12.5975799520928 & 0.402420047907217 \tabularnewline
36 & 8 & 11.9757743654483 & -3.97577436544826 \tabularnewline
37 & 20 & 11.9284819229621 & 8.0715180770379 \tabularnewline
38 & 12 & 12.9069002064862 & -0.906900206486205 \tabularnewline
39 & 10 & 11.796821570867 & -1.79682157086698 \tabularnewline
40 & 10 & 12.2771941009414 & -2.2771941009414 \tabularnewline
41 & 9 & 11.9965963810831 & -2.99659638108313 \tabularnewline
42 & 14 & 12.3628711884314 & 1.63712881156857 \tabularnewline
43 & 8 & 12.5807634929112 & -4.58076349291119 \tabularnewline
44 & 14 & 12.7103766397369 & 1.28962336026311 \tabularnewline
45 & 11 & 12.4926837833490 & -1.49268378334896 \tabularnewline
46 & 13 & 12.7299946171744 & 0.270005382825651 \tabularnewline
47 & 9 & 12.4317318167399 & -3.43173181673985 \tabularnewline
48 & 11 & 11.9284819229621 & -0.928481922962108 \tabularnewline
49 & 15 & 12.0601504178563 & 2.93984958214373 \tabularnewline
50 & 11 & 12.3674234669803 & -1.36742346698027 \tabularnewline
51 & 10 & 12.3226306434509 & -2.32263064345093 \tabularnewline
52 & 14 & 12.5846665981572 & 1.41533340184277 \tabularnewline
53 & 18 & 12.4900817131849 & 5.50991828681507 \tabularnewline
54 & 14 & 13.5326012503600 & 0.467398749640019 \tabularnewline
55 & 11 & 12.2506298604642 & -1.25062986046417 \tabularnewline
56 & 12 & 12.6953141241069 & -0.695314124106936 \tabularnewline
57 & 13 & 12.8866335505282 & 0.113366449471811 \tabularnewline
58 & 9 & 13.1783911755526 & -4.17839117555261 \tabularnewline
59 & 10 & 13.4818581163152 & -3.48185811631517 \tabularnewline
60 & 15 & 13.2679713682887 & 1.73202863171132 \tabularnewline
61 & 20 & 13.0973715062951 & 6.90262849370493 \tabularnewline
62 & 12 & 12.9883727842134 & -0.98837278421337 \tabularnewline
63 & 12 & 13.0559323774398 & -1.05593237743978 \tabularnewline
64 & 14 & 13.0552832041370 & 0.944716795863015 \tabularnewline
65 & 13 & 13.5455146042955 & -0.545514604295531 \tabularnewline
66 & 11 & 12.6409616413161 & -1.64096164131609 \tabularnewline
67 & 17 & 12.4580032902815 & 4.54199670971845 \tabularnewline
68 & 12 & 13.0293594993813 & -1.02935949938127 \tabularnewline
69 & 13 & 13.5662341687232 & -0.566234168723177 \tabularnewline
70 & 14 & 12.9846691270592 & 1.01533087294085 \tabularnewline
71 & 13 & 12.6286031522752 & 0.371396847724847 \tabularnewline
72 & 15 & 13.0624375528499 & 1.93756244715014 \tabularnewline
73 & 13 & 12.4722176792991 & 0.527782320700887 \tabularnewline
74 & 10 & 12.4620088467348 & -2.46200884673482 \tabularnewline
75 & 11 & 12.7471588451419 & -1.74715884514193 \tabularnewline
76 & 19 & 12.8393411080420 & 6.16065889195796 \tabularnewline
77 & 13 & 13.3710116370559 & -0.371011637055876 \tabularnewline
78 & 17 & 12.9034500097098 & 4.09654999029021 \tabularnewline
79 & 13 & 13.0332626046273 & -0.0332626046273221 \tabularnewline
80 & 9 & 12.6551760303337 & -3.65517603033366 \tabularnewline
81 & 11 & 12.7822922466790 & -1.78229224667898 \tabularnewline
82 & 10 & 13.0792540120315 & -3.07925401203146 \tabularnewline
83 & 9 & 12.9326249579323 & -3.93262495793233 \tabularnewline
84 & 12 & 12.9224161253680 & -0.922416125368029 \tabularnewline
85 & 12 & 12.9281669877917 & -0.928166987791667 \tabularnewline
86 & 13 & 12.9565957658268 & 0.0434042341732008 \tabularnewline
87 & 13 & 13.0188487675179 & -0.0188487675179238 \tabularnewline
88 & 12 & 13.2258830661306 & -1.22588306613059 \tabularnewline
89 & 15 & 12.4282816199634 & 2.57171838003657 \tabularnewline
90 & 22 & 13.0585344476038 & 8.94146555239619 \tabularnewline
91 & 13 & 12.6927120539429 & 0.307287946057096 \tabularnewline
92 & 15 & 13.5215437964011 & 1.47845620359894 \tabularnewline
93 & 13 & 13.2600627065894 & -0.260062706589362 \tabularnewline
94 & 15 & 12.9384782715632 & 2.06152172843682 \tabularnewline
95 & 10 & 12.9762137432643 & -2.97621374326426 \tabularnewline
96 & 11 & 12.3334972868993 & -1.33349728689930 \tabularnewline
97 & 16 & 12.4262262718950 & 3.57377372810502 \tabularnewline
98 & 11 & 12.4341349335943 & -1.43413493359429 \tabularnewline
99 & 11 & 13.0585344476038 & -2.05853444760381 \tabularnewline
100 & 10 & 12.8853325154462 & -2.88533251544617 \tabularnewline
101 & 10 & 12.7796901765149 & -2.77969017651494 \tabularnewline
102 & 16 & 13.2777277871656 & 2.72227221283441 \tabularnewline
103 & 12 & 12.9185130201220 & -0.91851302012198 \tabularnewline
104 & 11 & 13.0183020454224 & -2.01830204542235 \tabularnewline
105 & 16 & 12.784894316843 & 3.21510568315699 \tabularnewline
106 & 19 & 12.8275293410967 & 6.17247065890333 \tabularnewline
107 & 11 & 12.9644019763189 & -1.96440197631890 \tabularnewline
108 & 16 & 12.7349998041928 & 3.26500019580718 \tabularnewline
109 & 15 & 13.1483681007177 & 1.85163189928232 \tabularnewline
110 & 24 & 13.6742796245088 & 10.3257203754912 \tabularnewline
111 & 14 & 13.8377251377896 & 0.162274862210421 \tabularnewline
112 & 15 & 12.9773158250367 & 2.02268417496331 \tabularnewline
113 & 11 & 13.3474937376310 & -2.34749373763103 \tabularnewline
114 & 15 & 13.1444649954716 & 1.85553500452836 \tabularnewline
115 & 12 & 13.9227530519768 & -1.92275305197681 \tabularnewline
116 & 10 & 13.7481449450535 & -3.74814494505351 \tabularnewline
117 & 14 & 13.5174417378454 & 0.482558262154581 \tabularnewline
118 & 13 & 13.6781827297549 & -0.678182729754897 \tabularnewline
119 & 9 & 13.2041159269987 & -4.20411592699873 \tabularnewline
120 & 15 & 13.5933542636595 & 1.4066457363405 \tabularnewline
121 & 15 & 13.1647240034129 & 1.83527599658714 \tabularnewline
122 & 14 & 13.7904326953034 & 0.209567304696573 \tabularnewline
123 & 11 & 13.2936961197348 & -2.2936961197348 \tabularnewline
124 & 8 & 13.7788203764499 & -5.77882037644989 \tabularnewline
125 & 11 & 13.5570249667241 & -2.55702496672409 \tabularnewline
126 & 11 & 13.4701492953593 & -2.47014929535927 \tabularnewline
127 & 8 & 13.8293640624029 & -5.82936406240287 \tabularnewline
128 & 10 & 13.4779555058514 & -3.47795550585136 \tabularnewline
129 & 11 & 13.5407633724371 & -2.5407633724371 \tabularnewline
130 & 13 & 12.8567047841015 & 0.143295215898549 \tabularnewline
131 & 11 & 13.2949971548168 & -2.29499715481682 \tabularnewline
132 & 20 & 13.6658242407140 & 6.33417575928604 \tabularnewline
133 & 10 & 13.7034545727314 & -3.70345457273139 \tabularnewline
134 & 15 & 13.7812229985221 & 1.21877700147791 \tabularnewline
135 & 12 & 13.4272123718066 & -1.42721237180655 \tabularnewline
136 & 14 & 13.1224443959620 & 0.877555604038027 \tabularnewline
137 & 23 & 13.6269871820227 & 9.3730128179773 \tabularnewline
138 & 14 & 13.5420644075191 & 0.457935592480886 \tabularnewline
139 & 16 & 13.5906578850873 & 2.40934211491272 \tabularnewline
140 & 11 & 13.2405503636178 & -2.24055036361779 \tabularnewline
141 & 12 & 12.9733102685834 & -0.973310268583418 \tabularnewline
142 & 10 & 12.9376301449508 & -2.9376301449508 \tabularnewline
143 & 14 & 12.9352356656776 & 1.06476433432236 \tabularnewline
144 & 12 & 13.5173392866382 & -1.51733928663820 \tabularnewline
145 & 12 & 13.3370854569749 & -1.33708545697490 \tabularnewline
146 & 11 & 13.1037720368037 & -2.10377203680375 \tabularnewline
147 & 12 & 12.9156095454411 & -0.915609545441137 \tabularnewline
148 & 13 & 13.5058294189919 & -0.505829418991885 \tabularnewline
149 & 11 & 13.6379503275734 & -2.63795032757343 \tabularnewline
150 & 19 & 13.3215700328753 & 5.67842996712468 \tabularnewline
151 & 12 & 13.4500815927108 & -1.45008159271084 \tabularnewline
152 & 17 & 13.0888114775988 & 3.91118852240122 \tabularnewline
153 & 9 & 12.6329564775144 & -3.63295647751441 \tabularnewline
154 & 12 & 13.4330656854374 & -1.43306568543741 \tabularnewline
155 & 19 & 13.3268766244106 & 5.67312337558939 \tabularnewline
156 & 18 & 13.6729785894268 & 4.32702141057317 \tabularnewline
157 & 15 & 13.0953166530089 & 1.90468334699114 \tabularnewline
158 & 14 & 13.3202689977933 & 0.679731002206694 \tabularnewline
159 & 11 & 12.7178900832935 & -1.71789008329345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&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]12[/C][C]12.1885676692836[/C][C]-0.18856766928365[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.2558253632109[/C][C]-1.25582536321095[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.6254538652333[/C][C]1.37454613476670[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.5872686683213[/C][C]-0.587268668321265[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.5567926850167[/C][C]8.44320731498328[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.7071253962701[/C][C]-0.70712539627006[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.6028784008290[/C][C]9.39712159917097[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.59693077879[/C][C]-1.59693077878999[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.6125405112978[/C][C]-2.61254051129776[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.6585319187019[/C][C]0.341468081298109[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.2319570065237[/C][C]-2.23195700652371[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]11.9497563522844[/C][C]-3.94975635228437[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2916079380508[/C][C]2.7083920619492[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.5872686683213[/C][C]1.41273133167873[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.7299946171743[/C][C]-2.72999461717435[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9781634568668[/C][C]1.02183654313317[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.4166688063277[/C][C]1.58333119367234[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.7299946171744[/C][C]-1.72999461717435[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.5114504509154[/C][C]-2.51145045091537[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.8732889615757[/C][C]1.12671103842433[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.1281624247701[/C][C]-5.12816242477007[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]11.7279609425586[/C][C]2.27203905744144[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.2986679783555[/C][C]-0.298667978355490[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]11.9660179465714[/C][C]2.03398205342865[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.5386751907531[/C][C]-1.53867519075310[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]12.6474744647430[/C][C]-3.64747446474297[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]12.5419264342199[/C][C]-1.54192643421992[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.4114646659996[/C][C]2.58853533400041[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.3602691182674[/C][C]1.63973088173261[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.6864058318424[/C][C]0.313594168157587[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.3584132182908[/C][C]-3.35841321829077[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.0185226721846[/C][C]2.98147732781539[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.2778432742442[/C][C]-2.27784327424420[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.2771941009414[/C][C]-1.27719410094140[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.5975799520928[/C][C]0.402420047907217[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]11.9757743654483[/C][C]-3.97577436544826[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]11.9284819229621[/C][C]8.0715180770379[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.9069002064862[/C][C]-0.906900206486205[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.796821570867[/C][C]-1.79682157086698[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.2771941009414[/C][C]-2.2771941009414[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.9965963810831[/C][C]-2.99659638108313[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.3628711884314[/C][C]1.63712881156857[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.5807634929112[/C][C]-4.58076349291119[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.7103766397369[/C][C]1.28962336026311[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.4926837833490[/C][C]-1.49268378334896[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.7299946171744[/C][C]0.270005382825651[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.4317318167399[/C][C]-3.43173181673985[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.9284819229621[/C][C]-0.928481922962108[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.0601504178563[/C][C]2.93984958214373[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.3674234669803[/C][C]-1.36742346698027[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.3226306434509[/C][C]-2.32263064345093[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.5846665981572[/C][C]1.41533340184277[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.4900817131849[/C][C]5.50991828681507[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.5326012503600[/C][C]0.467398749640019[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.2506298604642[/C][C]-1.25062986046417[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.6953141241069[/C][C]-0.695314124106936[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.8866335505282[/C][C]0.113366449471811[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.1783911755526[/C][C]-4.17839117555261[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.4818581163152[/C][C]-3.48185811631517[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.2679713682887[/C][C]1.73202863171132[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]13.0973715062951[/C][C]6.90262849370493[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9883727842134[/C][C]-0.98837278421337[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]13.0559323774398[/C][C]-1.05593237743978[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.0552832041370[/C][C]0.944716795863015[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.5455146042955[/C][C]-0.545514604295531[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.6409616413161[/C][C]-1.64096164131609[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.4580032902815[/C][C]4.54199670971845[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.0293594993813[/C][C]-1.02935949938127[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.5662341687232[/C][C]-0.566234168723177[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.9846691270592[/C][C]1.01533087294085[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.6286031522752[/C][C]0.371396847724847[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0624375528499[/C][C]1.93756244715014[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.4722176792991[/C][C]0.527782320700887[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.4620088467348[/C][C]-2.46200884673482[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.7471588451419[/C][C]-1.74715884514193[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.8393411080420[/C][C]6.16065889195796[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.3710116370559[/C][C]-0.371011637055876[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.9034500097098[/C][C]4.09654999029021[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.0332626046273[/C][C]-0.0332626046273221[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.6551760303337[/C][C]-3.65517603033366[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.7822922466790[/C][C]-1.78229224667898[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.0792540120315[/C][C]-3.07925401203146[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.9326249579323[/C][C]-3.93262495793233[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9224161253680[/C][C]-0.922416125368029[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.9281669877917[/C][C]-0.928166987791667[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.9565957658268[/C][C]0.0434042341732008[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.0188487675179[/C][C]-0.0188487675179238[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.2258830661306[/C][C]-1.22588306613059[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.4282816199634[/C][C]2.57171838003657[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0585344476038[/C][C]8.94146555239619[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6927120539429[/C][C]0.307287946057096[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5215437964011[/C][C]1.47845620359894[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.2600627065894[/C][C]-0.260062706589362[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9384782715632[/C][C]2.06152172843682[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]12.9762137432643[/C][C]-2.97621374326426[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.3334972868993[/C][C]-1.33349728689930[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.4262262718950[/C][C]3.57377372810502[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.4341349335943[/C][C]-1.43413493359429[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.0585344476038[/C][C]-2.05853444760381[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.8853325154462[/C][C]-2.88533251544617[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.7796901765149[/C][C]-2.77969017651494[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.2777277871656[/C][C]2.72227221283441[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.9185130201220[/C][C]-0.91851302012198[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.0183020454224[/C][C]-2.01830204542235[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.784894316843[/C][C]3.21510568315699[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.8275293410967[/C][C]6.17247065890333[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]12.9644019763189[/C][C]-1.96440197631890[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.7349998041928[/C][C]3.26500019580718[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.1483681007177[/C][C]1.85163189928232[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]13.6742796245088[/C][C]10.3257203754912[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.8377251377896[/C][C]0.162274862210421[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.9773158250367[/C][C]2.02268417496331[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.3474937376310[/C][C]-2.34749373763103[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.1444649954716[/C][C]1.85553500452836[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.9227530519768[/C][C]-1.92275305197681[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.7481449450535[/C][C]-3.74814494505351[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.5174417378454[/C][C]0.482558262154581[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.6781827297549[/C][C]-0.678182729754897[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.2041159269987[/C][C]-4.20411592699873[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.5933542636595[/C][C]1.4066457363405[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.1647240034129[/C][C]1.83527599658714[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.7904326953034[/C][C]0.209567304696573[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]13.2936961197348[/C][C]-2.2936961197348[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.7788203764499[/C][C]-5.77882037644989[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.5570249667241[/C][C]-2.55702496672409[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.4701492953593[/C][C]-2.47014929535927[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.8293640624029[/C][C]-5.82936406240287[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]13.4779555058514[/C][C]-3.47795550585136[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]13.5407633724371[/C][C]-2.5407633724371[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8567047841015[/C][C]0.143295215898549[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.2949971548168[/C][C]-2.29499715481682[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.6658242407140[/C][C]6.33417575928604[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.7034545727314[/C][C]-3.70345457273139[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.7812229985221[/C][C]1.21877700147791[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]13.4272123718066[/C][C]-1.42721237180655[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]13.1224443959620[/C][C]0.877555604038027[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]13.6269871820227[/C][C]9.3730128179773[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.5420644075191[/C][C]0.457935592480886[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.5906578850873[/C][C]2.40934211491272[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2405503636178[/C][C]-2.24055036361779[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.9733102685834[/C][C]-0.973310268583418[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.9376301449508[/C][C]-2.9376301449508[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.9352356656776[/C][C]1.06476433432236[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.5173392866382[/C][C]-1.51733928663820[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]13.3370854569749[/C][C]-1.33708545697490[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]13.1037720368037[/C][C]-2.10377203680375[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.9156095454411[/C][C]-0.915609545441137[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.5058294189919[/C][C]-0.505829418991885[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.6379503275734[/C][C]-2.63795032757343[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.3215700328753[/C][C]5.67842996712468[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.4500815927108[/C][C]-1.45008159271084[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.0888114775988[/C][C]3.91118852240122[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.6329564775144[/C][C]-3.63295647751441[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.4330656854374[/C][C]-1.43306568543741[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.3268766244106[/C][C]5.67312337558939[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.6729785894268[/C][C]4.32702141057317[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.0953166530089[/C][C]1.90468334699114[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.3202689977933[/C][C]0.679731002206694[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.7178900832935[/C][C]-1.71789008329345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&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
11212.1885676692836-0.18856766928365
21112.2558253632109-1.25582536321095
31412.62545386523331.37454613476670
41212.5872686683213-0.587268668321265
52112.55679268501678.44320731498328
61212.7071253962701-0.70712539627006
72212.60287840082909.39712159917097
81112.59693077879-1.59693077878999
91012.6125405112978-2.61254051129776
101312.65853191870190.341468081298109
111012.2319570065237-2.23195700652371
12811.9497563522844-3.94975635228437
131512.29160793805082.7083920619492
141412.58726866832131.41273133167873
151012.7299946171743-2.72999461717435
161412.97816345686681.02183654313317
171412.41666880632771.58333119367234
181112.7299946171744-1.72999461717435
191012.5114504509154-2.51145045091537
201311.87328896157571.12671103842433
21712.1281624247701-5.12816242477007
221411.72796094255862.27203905744144
231212.2986679783555-0.298667978355490
241411.96601794657142.03398205342865
251112.5386751907531-1.53867519075310
26912.6474744647430-3.64747446474297
271112.5419264342199-1.54192643421992
281512.41146466599962.58853533400041
291412.36026911826741.63973088173261
301312.68640583184240.313594168157587
31912.3584132182908-3.35841321829077
321512.01852267218462.98147732781539
331012.2778432742442-2.27784327424420
341112.2771941009414-1.27719410094140
351312.59757995209280.402420047907217
36811.9757743654483-3.97577436544826
372011.92848192296218.0715180770379
381212.9069002064862-0.906900206486205
391011.796821570867-1.79682157086698
401012.2771941009414-2.2771941009414
41911.9965963810831-2.99659638108313
421412.36287118843141.63712881156857
43812.5807634929112-4.58076349291119
441412.71037663973691.28962336026311
451112.4926837833490-1.49268378334896
461312.72999461717440.270005382825651
47912.4317318167399-3.43173181673985
481111.9284819229621-0.928481922962108
491512.06015041785632.93984958214373
501112.3674234669803-1.36742346698027
511012.3226306434509-2.32263064345093
521412.58466659815721.41533340184277
531812.49008171318495.50991828681507
541413.53260125036000.467398749640019
551112.2506298604642-1.25062986046417
561212.6953141241069-0.695314124106936
571312.88663355052820.113366449471811
58913.1783911755526-4.17839117555261
591013.4818581163152-3.48185811631517
601513.26797136828871.73202863171132
612013.09737150629516.90262849370493
621212.9883727842134-0.98837278421337
631213.0559323774398-1.05593237743978
641413.05528320413700.944716795863015
651313.5455146042955-0.545514604295531
661112.6409616413161-1.64096164131609
671712.45800329028154.54199670971845
681213.0293594993813-1.02935949938127
691313.5662341687232-0.566234168723177
701412.98466912705921.01533087294085
711312.62860315227520.371396847724847
721513.06243755284991.93756244715014
731312.47221767929910.527782320700887
741012.4620088467348-2.46200884673482
751112.7471588451419-1.74715884514193
761912.83934110804206.16065889195796
771313.3710116370559-0.371011637055876
781712.90345000970984.09654999029021
791313.0332626046273-0.0332626046273221
80912.6551760303337-3.65517603033366
811112.7822922466790-1.78229224667898
821013.0792540120315-3.07925401203146
83912.9326249579323-3.93262495793233
841212.9224161253680-0.922416125368029
851212.9281669877917-0.928166987791667
861312.95659576582680.0434042341732008
871313.0188487675179-0.0188487675179238
881213.2258830661306-1.22588306613059
891512.42828161996342.57171838003657
902213.05853444760388.94146555239619
911312.69271205394290.307287946057096
921513.52154379640111.47845620359894
931313.2600627065894-0.260062706589362
941512.93847827156322.06152172843682
951012.9762137432643-2.97621374326426
961112.3334972868993-1.33349728689930
971612.42622627189503.57377372810502
981112.4341349335943-1.43413493359429
991113.0585344476038-2.05853444760381
1001012.8853325154462-2.88533251544617
1011012.7796901765149-2.77969017651494
1021613.27772778716562.72227221283441
1031212.9185130201220-0.91851302012198
1041113.0183020454224-2.01830204542235
1051612.7848943168433.21510568315699
1061912.82752934109676.17247065890333
1071112.9644019763189-1.96440197631890
1081612.73499980419283.26500019580718
1091513.14836810071771.85163189928232
1102413.674279624508810.3257203754912
1111413.83772513778960.162274862210421
1121512.97731582503672.02268417496331
1131113.3474937376310-2.34749373763103
1141513.14446499547161.85553500452836
1151213.9227530519768-1.92275305197681
1161013.7481449450535-3.74814494505351
1171413.51744173784540.482558262154581
1181313.6781827297549-0.678182729754897
119913.2041159269987-4.20411592699873
1201513.59335426365951.4066457363405
1211513.16472400341291.83527599658714
1221413.79043269530340.209567304696573
1231113.2936961197348-2.2936961197348
124813.7788203764499-5.77882037644989
1251113.5570249667241-2.55702496672409
1261113.4701492953593-2.47014929535927
127813.8293640624029-5.82936406240287
1281013.4779555058514-3.47795550585136
1291113.5407633724371-2.5407633724371
1301312.85670478410150.143295215898549
1311113.2949971548168-2.29499715481682
1322013.66582424071406.33417575928604
1331013.7034545727314-3.70345457273139
1341513.78122299852211.21877700147791
1351213.4272123718066-1.42721237180655
1361413.12244439596200.877555604038027
1372313.62698718202279.3730128179773
1381413.54206440751910.457935592480886
1391613.59065788508732.40934211491272
1401113.2405503636178-2.24055036361779
1411212.9733102685834-0.973310268583418
1421012.9376301449508-2.9376301449508
1431412.93523566567761.06476433432236
1441213.5173392866382-1.51733928663820
1451213.3370854569749-1.33708545697490
1461113.1037720368037-2.10377203680375
1471212.9156095454411-0.915609545441137
1481313.5058294189919-0.505829418991885
1491113.6379503275734-2.63795032757343
1501913.32157003287535.67842996712468
1511213.4500815927108-1.45008159271084
1521713.08881147759883.91118852240122
153912.6329564775144-3.63295647751441
1541213.4330656854374-1.43306568543741
1551913.32687662441065.67312337558939
1561813.67297858942684.32702141057317
1571513.09531665300891.90468334699114
1581413.32026899779330.679731002206694
1591112.7178900832935-1.71789008329345






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9807716383459180.0384567233081650.0192283616540825
90.9876951778288520.02460964434229550.0123048221711478
100.9778016105866470.04439677882670530.0221983894133526
110.9635699942173620.07286001156527530.0364300057826377
120.9599843397747890.08003132045042230.0400156602252111
130.9545700212468070.09085995750638690.0454299787531934
140.9282524517589520.1434950964820950.0717475482410476
150.9356621344480910.1286757311038180.0643378655519088
160.9151309118196630.1697381763606730.0848690881803367
170.881399655300350.2372006893993000.118600344699650
180.8582284338356110.2835431323287780.141771566164389
190.8390598112450350.3218803775099290.160940188754965
200.8141401528493970.3717196943012070.185859847150603
210.8853711198333430.2292577603333130.114628880166657
220.8790513587899280.2418972824201450.120948641210072
230.8475878881764070.3048242236471860.152412111823593
240.8087671076349880.3824657847300240.191232892365012
250.7758530448392560.4482939103214870.224146955160744
260.7710377619932270.4579244760135460.228962238006773
270.7441055469952030.5117889060095940.255894453004797
280.7272512801673630.5454974396652740.272748719832637
290.6902397295644820.6195205408710360.309760270435518
300.6343534358742140.7312931282515720.365646564125786
310.679448959670990.641102080658020.32055104032901
320.6930707669845230.6138584660309530.306929233015477
330.6730043621164310.6539912757671380.326995637883569
340.6265872120021590.7468255759956820.373412787997841
350.5708137319116150.858372536176770.429186268088385
360.5800830531465920.8398338937068160.419916946853408
370.8267861086969570.3464277826060870.173213891303043
380.7914599186394520.4170801627210950.208540081360548
390.7631646649401060.4736706701197880.236835335059894
400.7433170491990770.5133659016018450.256682950800923
410.7318401769883170.5363196460233660.268159823011683
420.6988588651010520.6022822697978960.301141134898948
430.7371771127665460.5256457744669080.262822887233454
440.7023696000463570.5952607999072850.297630399953643
450.6676597189870260.6646805620259480.332340281012974
460.619604889662290.760790220675420.38039511033771
470.6241591265217690.7516817469564620.375840873478231
480.5831529493392760.8336941013214480.416847050660724
490.5703328558749940.8593342882500120.429667144125006
500.530025772100260.939948455799480.46997422789974
510.5184820201821860.9630359596356270.481517979817814
520.4777777140880260.9555554281760510.522222285911974
530.5687725113746050.862454977250790.431227488625395
540.5198159218780090.9603681562439820.480184078121991
550.4762570491930730.9525140983861470.523742950806926
560.4291544808916060.8583089617832120.570845519108394
570.3819696050850210.7639392101700420.618030394914979
580.4020609659036140.8041219318072280.597939034096386
590.3947948139025360.7895896278050710.605205186097464
600.3792359081131090.7584718162262180.620764091886891
610.5672474077670140.8655051844659720.432752592232986
620.5241341308126820.9517317383746360.475865869187318
630.4815029330962190.9630058661924370.518497066903781
640.4374482691155260.8748965382310520.562551730884474
650.3930252376919850.786050475383970.606974762308015
660.3582296756746720.7164593513493440.641770324325328
670.4063858407895080.8127716815790150.593614159210492
680.3668276224861740.7336552449723490.633172377513826
690.3259031839900470.6518063679800930.674096816009953
700.2878654901090790.5757309802181590.71213450989092
710.2494724519537200.4989449039074410.75052754804628
720.2246873857564750.4493747715129500.775312614243525
730.1917934314762960.3835868629525930.808206568523704
740.1797489423967540.3594978847935090.820251057603246
750.1597421070433920.3194842140867840.840257892956608
760.2465074287461440.4930148574922880.753492571253856
770.2130075854384190.4260151708768380.786992414561581
780.2312021944911930.4624043889823860.768797805508807
790.1981860953099000.3963721906198010.8018139046901
800.2110563808012440.4221127616024880.788943619198756
810.1905084304535570.3810168609071140.809491569546443
820.1920851887905080.3841703775810170.807914811209492
830.2137014145578440.4274028291156880.786298585442156
840.1861521737129020.3723043474258030.813847826287098
850.1598148332348880.3196296664697760.840185166765112
860.1356540477582870.2713080955165740.864345952241713
870.1127559338048640.2255118676097270.887244066195136
880.09649273969805970.1929854793961190.90350726030194
890.08864493804555450.1772898760911090.911355061954445
900.275190743214180.550381486428360.72480925678582
910.2375852288597860.4751704577195720.762414771140214
920.2082641119203290.4165282238406580.791735888079671
930.1766616229648730.3533232459297450.823338377035127
940.1572889635776920.3145779271553830.842711036422308
950.1544766001748860.3089532003497730.845523399825114
960.1349953016279340.2699906032558690.865004698372066
970.1395561212244310.2791122424488620.860443878775569
980.1204140156380860.2408280312761720.879585984361914
990.1096297052819640.2192594105639280.890370294718036
1000.111158658138070.222317316276140.88884134186193
1010.1157947648702860.2315895297405720.884205235129714
1020.1020180433639880.2040360867279750.897981956636013
1030.09556489326963850.1911297865392770.904435106730362
1040.1074628010119490.2149256020238980.892537198988051
1050.09413347971980850.1882669594396170.905866520280192
1060.1281709350623210.2563418701246410.87182906493768
1070.1274085407518610.2548170815037210.87259145924814
1080.1108365602335720.2216731204671440.889163439766428
1090.0957010341922650.191402068384530.904298965807735
1100.4169827505309480.8339655010618960.583017249469052
1110.3694246301781370.7388492603562750.630575369821863
1120.3407676554119760.6815353108239530.659232344588024
1130.3184310281573040.6368620563146070.681568971842696
1140.2834845215059190.5669690430118390.71651547849408
1150.2558027075852520.5116054151705050.744197292414748
1160.2844719433736890.5689438867473790.71552805662631
1170.2414561597015190.4829123194030380.758543840298481
1180.2041497160248030.4082994320496060.795850283975197
1190.2321146986831290.4642293973662580.767885301316871
1200.2052185562413900.4104371124827810.79478144375861
1210.2103392721451490.4206785442902980.78966072785485
1220.1727325644310270.3454651288620540.827267435568973
1230.1545213003184960.3090426006369920.845478699681504
1240.2148148670895280.4296297341790560.785185132910472
1250.192699444913650.38539888982730.80730055508635
1260.1923518751613530.3847037503227070.807648124838647
1270.3500408142555190.7000816285110380.649959185744481
1280.3705989715922700.7411979431845410.62940102840773
1290.3968293662848670.7936587325697330.603170633715133
1300.3783597175768740.7567194351537470.621640282423126
1310.3515852092433460.7031704184866930.648414790756654
1320.4391215502277450.878243100455490.560878449772255
1330.563410023230290.8731799535394210.436589976769711
1340.5034837380485210.9930325239029590.496516261951479
1350.4684680292223880.9369360584447760.531531970777612
1360.4026585868725260.8053171737450520.597341413127474
1370.768458119523340.463083760953320.23154188047666
1380.7097308434636060.5805383130727870.290269156536394
1390.6531012998362260.6937974003275470.346898700163773
1400.6100718837923120.7798562324153760.389928116207688
1410.5463084654745310.9073830690509380.453691534525469
1420.4920270839530540.9840541679061080.507972916046946
1430.4181325241619460.8362650483238910.581867475838054
1440.3372767735418770.6745535470837530.662723226458123
1450.3303527112221660.6607054224443320.669647288777834
1460.2483972161023470.4967944322046930.751602783897653
1470.1750156400064780.3500312800129550.824984359993522
1480.1207472820106720.2414945640213450.879252717989327
1490.2253276506243100.4506553012486190.77467234937569
1500.2508217649231870.5016435298463740.749178235076813
1510.2570209650744540.5140419301489070.742979034925546

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.980771638345918 & 0.038456723308165 & 0.0192283616540825 \tabularnewline
9 & 0.987695177828852 & 0.0246096443422955 & 0.0123048221711478 \tabularnewline
10 & 0.977801610586647 & 0.0443967788267053 & 0.0221983894133526 \tabularnewline
11 & 0.963569994217362 & 0.0728600115652753 & 0.0364300057826377 \tabularnewline
12 & 0.959984339774789 & 0.0800313204504223 & 0.0400156602252111 \tabularnewline
13 & 0.954570021246807 & 0.0908599575063869 & 0.0454299787531934 \tabularnewline
14 & 0.928252451758952 & 0.143495096482095 & 0.0717475482410476 \tabularnewline
15 & 0.935662134448091 & 0.128675731103818 & 0.0643378655519088 \tabularnewline
16 & 0.915130911819663 & 0.169738176360673 & 0.0848690881803367 \tabularnewline
17 & 0.88139965530035 & 0.237200689399300 & 0.118600344699650 \tabularnewline
18 & 0.858228433835611 & 0.283543132328778 & 0.141771566164389 \tabularnewline
19 & 0.839059811245035 & 0.321880377509929 & 0.160940188754965 \tabularnewline
20 & 0.814140152849397 & 0.371719694301207 & 0.185859847150603 \tabularnewline
21 & 0.885371119833343 & 0.229257760333313 & 0.114628880166657 \tabularnewline
22 & 0.879051358789928 & 0.241897282420145 & 0.120948641210072 \tabularnewline
23 & 0.847587888176407 & 0.304824223647186 & 0.152412111823593 \tabularnewline
24 & 0.808767107634988 & 0.382465784730024 & 0.191232892365012 \tabularnewline
25 & 0.775853044839256 & 0.448293910321487 & 0.224146955160744 \tabularnewline
26 & 0.771037761993227 & 0.457924476013546 & 0.228962238006773 \tabularnewline
27 & 0.744105546995203 & 0.511788906009594 & 0.255894453004797 \tabularnewline
28 & 0.727251280167363 & 0.545497439665274 & 0.272748719832637 \tabularnewline
29 & 0.690239729564482 & 0.619520540871036 & 0.309760270435518 \tabularnewline
30 & 0.634353435874214 & 0.731293128251572 & 0.365646564125786 \tabularnewline
31 & 0.67944895967099 & 0.64110208065802 & 0.32055104032901 \tabularnewline
32 & 0.693070766984523 & 0.613858466030953 & 0.306929233015477 \tabularnewline
33 & 0.673004362116431 & 0.653991275767138 & 0.326995637883569 \tabularnewline
34 & 0.626587212002159 & 0.746825575995682 & 0.373412787997841 \tabularnewline
35 & 0.570813731911615 & 0.85837253617677 & 0.429186268088385 \tabularnewline
36 & 0.580083053146592 & 0.839833893706816 & 0.419916946853408 \tabularnewline
37 & 0.826786108696957 & 0.346427782606087 & 0.173213891303043 \tabularnewline
38 & 0.791459918639452 & 0.417080162721095 & 0.208540081360548 \tabularnewline
39 & 0.763164664940106 & 0.473670670119788 & 0.236835335059894 \tabularnewline
40 & 0.743317049199077 & 0.513365901601845 & 0.256682950800923 \tabularnewline
41 & 0.731840176988317 & 0.536319646023366 & 0.268159823011683 \tabularnewline
42 & 0.698858865101052 & 0.602282269797896 & 0.301141134898948 \tabularnewline
43 & 0.737177112766546 & 0.525645774466908 & 0.262822887233454 \tabularnewline
44 & 0.702369600046357 & 0.595260799907285 & 0.297630399953643 \tabularnewline
45 & 0.667659718987026 & 0.664680562025948 & 0.332340281012974 \tabularnewline
46 & 0.61960488966229 & 0.76079022067542 & 0.38039511033771 \tabularnewline
47 & 0.624159126521769 & 0.751681746956462 & 0.375840873478231 \tabularnewline
48 & 0.583152949339276 & 0.833694101321448 & 0.416847050660724 \tabularnewline
49 & 0.570332855874994 & 0.859334288250012 & 0.429667144125006 \tabularnewline
50 & 0.53002577210026 & 0.93994845579948 & 0.46997422789974 \tabularnewline
51 & 0.518482020182186 & 0.963035959635627 & 0.481517979817814 \tabularnewline
52 & 0.477777714088026 & 0.955555428176051 & 0.522222285911974 \tabularnewline
53 & 0.568772511374605 & 0.86245497725079 & 0.431227488625395 \tabularnewline
54 & 0.519815921878009 & 0.960368156243982 & 0.480184078121991 \tabularnewline
55 & 0.476257049193073 & 0.952514098386147 & 0.523742950806926 \tabularnewline
56 & 0.429154480891606 & 0.858308961783212 & 0.570845519108394 \tabularnewline
57 & 0.381969605085021 & 0.763939210170042 & 0.618030394914979 \tabularnewline
58 & 0.402060965903614 & 0.804121931807228 & 0.597939034096386 \tabularnewline
59 & 0.394794813902536 & 0.789589627805071 & 0.605205186097464 \tabularnewline
60 & 0.379235908113109 & 0.758471816226218 & 0.620764091886891 \tabularnewline
61 & 0.567247407767014 & 0.865505184465972 & 0.432752592232986 \tabularnewline
62 & 0.524134130812682 & 0.951731738374636 & 0.475865869187318 \tabularnewline
63 & 0.481502933096219 & 0.963005866192437 & 0.518497066903781 \tabularnewline
64 & 0.437448269115526 & 0.874896538231052 & 0.562551730884474 \tabularnewline
65 & 0.393025237691985 & 0.78605047538397 & 0.606974762308015 \tabularnewline
66 & 0.358229675674672 & 0.716459351349344 & 0.641770324325328 \tabularnewline
67 & 0.406385840789508 & 0.812771681579015 & 0.593614159210492 \tabularnewline
68 & 0.366827622486174 & 0.733655244972349 & 0.633172377513826 \tabularnewline
69 & 0.325903183990047 & 0.651806367980093 & 0.674096816009953 \tabularnewline
70 & 0.287865490109079 & 0.575730980218159 & 0.71213450989092 \tabularnewline
71 & 0.249472451953720 & 0.498944903907441 & 0.75052754804628 \tabularnewline
72 & 0.224687385756475 & 0.449374771512950 & 0.775312614243525 \tabularnewline
73 & 0.191793431476296 & 0.383586862952593 & 0.808206568523704 \tabularnewline
74 & 0.179748942396754 & 0.359497884793509 & 0.820251057603246 \tabularnewline
75 & 0.159742107043392 & 0.319484214086784 & 0.840257892956608 \tabularnewline
76 & 0.246507428746144 & 0.493014857492288 & 0.753492571253856 \tabularnewline
77 & 0.213007585438419 & 0.426015170876838 & 0.786992414561581 \tabularnewline
78 & 0.231202194491193 & 0.462404388982386 & 0.768797805508807 \tabularnewline
79 & 0.198186095309900 & 0.396372190619801 & 0.8018139046901 \tabularnewline
80 & 0.211056380801244 & 0.422112761602488 & 0.788943619198756 \tabularnewline
81 & 0.190508430453557 & 0.381016860907114 & 0.809491569546443 \tabularnewline
82 & 0.192085188790508 & 0.384170377581017 & 0.807914811209492 \tabularnewline
83 & 0.213701414557844 & 0.427402829115688 & 0.786298585442156 \tabularnewline
84 & 0.186152173712902 & 0.372304347425803 & 0.813847826287098 \tabularnewline
85 & 0.159814833234888 & 0.319629666469776 & 0.840185166765112 \tabularnewline
86 & 0.135654047758287 & 0.271308095516574 & 0.864345952241713 \tabularnewline
87 & 0.112755933804864 & 0.225511867609727 & 0.887244066195136 \tabularnewline
88 & 0.0964927396980597 & 0.192985479396119 & 0.90350726030194 \tabularnewline
89 & 0.0886449380455545 & 0.177289876091109 & 0.911355061954445 \tabularnewline
90 & 0.27519074321418 & 0.55038148642836 & 0.72480925678582 \tabularnewline
91 & 0.237585228859786 & 0.475170457719572 & 0.762414771140214 \tabularnewline
92 & 0.208264111920329 & 0.416528223840658 & 0.791735888079671 \tabularnewline
93 & 0.176661622964873 & 0.353323245929745 & 0.823338377035127 \tabularnewline
94 & 0.157288963577692 & 0.314577927155383 & 0.842711036422308 \tabularnewline
95 & 0.154476600174886 & 0.308953200349773 & 0.845523399825114 \tabularnewline
96 & 0.134995301627934 & 0.269990603255869 & 0.865004698372066 \tabularnewline
97 & 0.139556121224431 & 0.279112242448862 & 0.860443878775569 \tabularnewline
98 & 0.120414015638086 & 0.240828031276172 & 0.879585984361914 \tabularnewline
99 & 0.109629705281964 & 0.219259410563928 & 0.890370294718036 \tabularnewline
100 & 0.11115865813807 & 0.22231731627614 & 0.88884134186193 \tabularnewline
101 & 0.115794764870286 & 0.231589529740572 & 0.884205235129714 \tabularnewline
102 & 0.102018043363988 & 0.204036086727975 & 0.897981956636013 \tabularnewline
103 & 0.0955648932696385 & 0.191129786539277 & 0.904435106730362 \tabularnewline
104 & 0.107462801011949 & 0.214925602023898 & 0.892537198988051 \tabularnewline
105 & 0.0941334797198085 & 0.188266959439617 & 0.905866520280192 \tabularnewline
106 & 0.128170935062321 & 0.256341870124641 & 0.87182906493768 \tabularnewline
107 & 0.127408540751861 & 0.254817081503721 & 0.87259145924814 \tabularnewline
108 & 0.110836560233572 & 0.221673120467144 & 0.889163439766428 \tabularnewline
109 & 0.095701034192265 & 0.19140206838453 & 0.904298965807735 \tabularnewline
110 & 0.416982750530948 & 0.833965501061896 & 0.583017249469052 \tabularnewline
111 & 0.369424630178137 & 0.738849260356275 & 0.630575369821863 \tabularnewline
112 & 0.340767655411976 & 0.681535310823953 & 0.659232344588024 \tabularnewline
113 & 0.318431028157304 & 0.636862056314607 & 0.681568971842696 \tabularnewline
114 & 0.283484521505919 & 0.566969043011839 & 0.71651547849408 \tabularnewline
115 & 0.255802707585252 & 0.511605415170505 & 0.744197292414748 \tabularnewline
116 & 0.284471943373689 & 0.568943886747379 & 0.71552805662631 \tabularnewline
117 & 0.241456159701519 & 0.482912319403038 & 0.758543840298481 \tabularnewline
118 & 0.204149716024803 & 0.408299432049606 & 0.795850283975197 \tabularnewline
119 & 0.232114698683129 & 0.464229397366258 & 0.767885301316871 \tabularnewline
120 & 0.205218556241390 & 0.410437112482781 & 0.79478144375861 \tabularnewline
121 & 0.210339272145149 & 0.420678544290298 & 0.78966072785485 \tabularnewline
122 & 0.172732564431027 & 0.345465128862054 & 0.827267435568973 \tabularnewline
123 & 0.154521300318496 & 0.309042600636992 & 0.845478699681504 \tabularnewline
124 & 0.214814867089528 & 0.429629734179056 & 0.785185132910472 \tabularnewline
125 & 0.19269944491365 & 0.3853988898273 & 0.80730055508635 \tabularnewline
126 & 0.192351875161353 & 0.384703750322707 & 0.807648124838647 \tabularnewline
127 & 0.350040814255519 & 0.700081628511038 & 0.649959185744481 \tabularnewline
128 & 0.370598971592270 & 0.741197943184541 & 0.62940102840773 \tabularnewline
129 & 0.396829366284867 & 0.793658732569733 & 0.603170633715133 \tabularnewline
130 & 0.378359717576874 & 0.756719435153747 & 0.621640282423126 \tabularnewline
131 & 0.351585209243346 & 0.703170418486693 & 0.648414790756654 \tabularnewline
132 & 0.439121550227745 & 0.87824310045549 & 0.560878449772255 \tabularnewline
133 & 0.56341002323029 & 0.873179953539421 & 0.436589976769711 \tabularnewline
134 & 0.503483738048521 & 0.993032523902959 & 0.496516261951479 \tabularnewline
135 & 0.468468029222388 & 0.936936058444776 & 0.531531970777612 \tabularnewline
136 & 0.402658586872526 & 0.805317173745052 & 0.597341413127474 \tabularnewline
137 & 0.76845811952334 & 0.46308376095332 & 0.23154188047666 \tabularnewline
138 & 0.709730843463606 & 0.580538313072787 & 0.290269156536394 \tabularnewline
139 & 0.653101299836226 & 0.693797400327547 & 0.346898700163773 \tabularnewline
140 & 0.610071883792312 & 0.779856232415376 & 0.389928116207688 \tabularnewline
141 & 0.546308465474531 & 0.907383069050938 & 0.453691534525469 \tabularnewline
142 & 0.492027083953054 & 0.984054167906108 & 0.507972916046946 \tabularnewline
143 & 0.418132524161946 & 0.836265048323891 & 0.581867475838054 \tabularnewline
144 & 0.337276773541877 & 0.674553547083753 & 0.662723226458123 \tabularnewline
145 & 0.330352711222166 & 0.660705422444332 & 0.669647288777834 \tabularnewline
146 & 0.248397216102347 & 0.496794432204693 & 0.751602783897653 \tabularnewline
147 & 0.175015640006478 & 0.350031280012955 & 0.824984359993522 \tabularnewline
148 & 0.120747282010672 & 0.241494564021345 & 0.879252717989327 \tabularnewline
149 & 0.225327650624310 & 0.450655301248619 & 0.77467234937569 \tabularnewline
150 & 0.250821764923187 & 0.501643529846374 & 0.749178235076813 \tabularnewline
151 & 0.257020965074454 & 0.514041930148907 & 0.742979034925546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.980771638345918[/C][C]0.038456723308165[/C][C]0.0192283616540825[/C][/ROW]
[ROW][C]9[/C][C]0.987695177828852[/C][C]0.0246096443422955[/C][C]0.0123048221711478[/C][/ROW]
[ROW][C]10[/C][C]0.977801610586647[/C][C]0.0443967788267053[/C][C]0.0221983894133526[/C][/ROW]
[ROW][C]11[/C][C]0.963569994217362[/C][C]0.0728600115652753[/C][C]0.0364300057826377[/C][/ROW]
[ROW][C]12[/C][C]0.959984339774789[/C][C]0.0800313204504223[/C][C]0.0400156602252111[/C][/ROW]
[ROW][C]13[/C][C]0.954570021246807[/C][C]0.0908599575063869[/C][C]0.0454299787531934[/C][/ROW]
[ROW][C]14[/C][C]0.928252451758952[/C][C]0.143495096482095[/C][C]0.0717475482410476[/C][/ROW]
[ROW][C]15[/C][C]0.935662134448091[/C][C]0.128675731103818[/C][C]0.0643378655519088[/C][/ROW]
[ROW][C]16[/C][C]0.915130911819663[/C][C]0.169738176360673[/C][C]0.0848690881803367[/C][/ROW]
[ROW][C]17[/C][C]0.88139965530035[/C][C]0.237200689399300[/C][C]0.118600344699650[/C][/ROW]
[ROW][C]18[/C][C]0.858228433835611[/C][C]0.283543132328778[/C][C]0.141771566164389[/C][/ROW]
[ROW][C]19[/C][C]0.839059811245035[/C][C]0.321880377509929[/C][C]0.160940188754965[/C][/ROW]
[ROW][C]20[/C][C]0.814140152849397[/C][C]0.371719694301207[/C][C]0.185859847150603[/C][/ROW]
[ROW][C]21[/C][C]0.885371119833343[/C][C]0.229257760333313[/C][C]0.114628880166657[/C][/ROW]
[ROW][C]22[/C][C]0.879051358789928[/C][C]0.241897282420145[/C][C]0.120948641210072[/C][/ROW]
[ROW][C]23[/C][C]0.847587888176407[/C][C]0.304824223647186[/C][C]0.152412111823593[/C][/ROW]
[ROW][C]24[/C][C]0.808767107634988[/C][C]0.382465784730024[/C][C]0.191232892365012[/C][/ROW]
[ROW][C]25[/C][C]0.775853044839256[/C][C]0.448293910321487[/C][C]0.224146955160744[/C][/ROW]
[ROW][C]26[/C][C]0.771037761993227[/C][C]0.457924476013546[/C][C]0.228962238006773[/C][/ROW]
[ROW][C]27[/C][C]0.744105546995203[/C][C]0.511788906009594[/C][C]0.255894453004797[/C][/ROW]
[ROW][C]28[/C][C]0.727251280167363[/C][C]0.545497439665274[/C][C]0.272748719832637[/C][/ROW]
[ROW][C]29[/C][C]0.690239729564482[/C][C]0.619520540871036[/C][C]0.309760270435518[/C][/ROW]
[ROW][C]30[/C][C]0.634353435874214[/C][C]0.731293128251572[/C][C]0.365646564125786[/C][/ROW]
[ROW][C]31[/C][C]0.67944895967099[/C][C]0.64110208065802[/C][C]0.32055104032901[/C][/ROW]
[ROW][C]32[/C][C]0.693070766984523[/C][C]0.613858466030953[/C][C]0.306929233015477[/C][/ROW]
[ROW][C]33[/C][C]0.673004362116431[/C][C]0.653991275767138[/C][C]0.326995637883569[/C][/ROW]
[ROW][C]34[/C][C]0.626587212002159[/C][C]0.746825575995682[/C][C]0.373412787997841[/C][/ROW]
[ROW][C]35[/C][C]0.570813731911615[/C][C]0.85837253617677[/C][C]0.429186268088385[/C][/ROW]
[ROW][C]36[/C][C]0.580083053146592[/C][C]0.839833893706816[/C][C]0.419916946853408[/C][/ROW]
[ROW][C]37[/C][C]0.826786108696957[/C][C]0.346427782606087[/C][C]0.173213891303043[/C][/ROW]
[ROW][C]38[/C][C]0.791459918639452[/C][C]0.417080162721095[/C][C]0.208540081360548[/C][/ROW]
[ROW][C]39[/C][C]0.763164664940106[/C][C]0.473670670119788[/C][C]0.236835335059894[/C][/ROW]
[ROW][C]40[/C][C]0.743317049199077[/C][C]0.513365901601845[/C][C]0.256682950800923[/C][/ROW]
[ROW][C]41[/C][C]0.731840176988317[/C][C]0.536319646023366[/C][C]0.268159823011683[/C][/ROW]
[ROW][C]42[/C][C]0.698858865101052[/C][C]0.602282269797896[/C][C]0.301141134898948[/C][/ROW]
[ROW][C]43[/C][C]0.737177112766546[/C][C]0.525645774466908[/C][C]0.262822887233454[/C][/ROW]
[ROW][C]44[/C][C]0.702369600046357[/C][C]0.595260799907285[/C][C]0.297630399953643[/C][/ROW]
[ROW][C]45[/C][C]0.667659718987026[/C][C]0.664680562025948[/C][C]0.332340281012974[/C][/ROW]
[ROW][C]46[/C][C]0.61960488966229[/C][C]0.76079022067542[/C][C]0.38039511033771[/C][/ROW]
[ROW][C]47[/C][C]0.624159126521769[/C][C]0.751681746956462[/C][C]0.375840873478231[/C][/ROW]
[ROW][C]48[/C][C]0.583152949339276[/C][C]0.833694101321448[/C][C]0.416847050660724[/C][/ROW]
[ROW][C]49[/C][C]0.570332855874994[/C][C]0.859334288250012[/C][C]0.429667144125006[/C][/ROW]
[ROW][C]50[/C][C]0.53002577210026[/C][C]0.93994845579948[/C][C]0.46997422789974[/C][/ROW]
[ROW][C]51[/C][C]0.518482020182186[/C][C]0.963035959635627[/C][C]0.481517979817814[/C][/ROW]
[ROW][C]52[/C][C]0.477777714088026[/C][C]0.955555428176051[/C][C]0.522222285911974[/C][/ROW]
[ROW][C]53[/C][C]0.568772511374605[/C][C]0.86245497725079[/C][C]0.431227488625395[/C][/ROW]
[ROW][C]54[/C][C]0.519815921878009[/C][C]0.960368156243982[/C][C]0.480184078121991[/C][/ROW]
[ROW][C]55[/C][C]0.476257049193073[/C][C]0.952514098386147[/C][C]0.523742950806926[/C][/ROW]
[ROW][C]56[/C][C]0.429154480891606[/C][C]0.858308961783212[/C][C]0.570845519108394[/C][/ROW]
[ROW][C]57[/C][C]0.381969605085021[/C][C]0.763939210170042[/C][C]0.618030394914979[/C][/ROW]
[ROW][C]58[/C][C]0.402060965903614[/C][C]0.804121931807228[/C][C]0.597939034096386[/C][/ROW]
[ROW][C]59[/C][C]0.394794813902536[/C][C]0.789589627805071[/C][C]0.605205186097464[/C][/ROW]
[ROW][C]60[/C][C]0.379235908113109[/C][C]0.758471816226218[/C][C]0.620764091886891[/C][/ROW]
[ROW][C]61[/C][C]0.567247407767014[/C][C]0.865505184465972[/C][C]0.432752592232986[/C][/ROW]
[ROW][C]62[/C][C]0.524134130812682[/C][C]0.951731738374636[/C][C]0.475865869187318[/C][/ROW]
[ROW][C]63[/C][C]0.481502933096219[/C][C]0.963005866192437[/C][C]0.518497066903781[/C][/ROW]
[ROW][C]64[/C][C]0.437448269115526[/C][C]0.874896538231052[/C][C]0.562551730884474[/C][/ROW]
[ROW][C]65[/C][C]0.393025237691985[/C][C]0.78605047538397[/C][C]0.606974762308015[/C][/ROW]
[ROW][C]66[/C][C]0.358229675674672[/C][C]0.716459351349344[/C][C]0.641770324325328[/C][/ROW]
[ROW][C]67[/C][C]0.406385840789508[/C][C]0.812771681579015[/C][C]0.593614159210492[/C][/ROW]
[ROW][C]68[/C][C]0.366827622486174[/C][C]0.733655244972349[/C][C]0.633172377513826[/C][/ROW]
[ROW][C]69[/C][C]0.325903183990047[/C][C]0.651806367980093[/C][C]0.674096816009953[/C][/ROW]
[ROW][C]70[/C][C]0.287865490109079[/C][C]0.575730980218159[/C][C]0.71213450989092[/C][/ROW]
[ROW][C]71[/C][C]0.249472451953720[/C][C]0.498944903907441[/C][C]0.75052754804628[/C][/ROW]
[ROW][C]72[/C][C]0.224687385756475[/C][C]0.449374771512950[/C][C]0.775312614243525[/C][/ROW]
[ROW][C]73[/C][C]0.191793431476296[/C][C]0.383586862952593[/C][C]0.808206568523704[/C][/ROW]
[ROW][C]74[/C][C]0.179748942396754[/C][C]0.359497884793509[/C][C]0.820251057603246[/C][/ROW]
[ROW][C]75[/C][C]0.159742107043392[/C][C]0.319484214086784[/C][C]0.840257892956608[/C][/ROW]
[ROW][C]76[/C][C]0.246507428746144[/C][C]0.493014857492288[/C][C]0.753492571253856[/C][/ROW]
[ROW][C]77[/C][C]0.213007585438419[/C][C]0.426015170876838[/C][C]0.786992414561581[/C][/ROW]
[ROW][C]78[/C][C]0.231202194491193[/C][C]0.462404388982386[/C][C]0.768797805508807[/C][/ROW]
[ROW][C]79[/C][C]0.198186095309900[/C][C]0.396372190619801[/C][C]0.8018139046901[/C][/ROW]
[ROW][C]80[/C][C]0.211056380801244[/C][C]0.422112761602488[/C][C]0.788943619198756[/C][/ROW]
[ROW][C]81[/C][C]0.190508430453557[/C][C]0.381016860907114[/C][C]0.809491569546443[/C][/ROW]
[ROW][C]82[/C][C]0.192085188790508[/C][C]0.384170377581017[/C][C]0.807914811209492[/C][/ROW]
[ROW][C]83[/C][C]0.213701414557844[/C][C]0.427402829115688[/C][C]0.786298585442156[/C][/ROW]
[ROW][C]84[/C][C]0.186152173712902[/C][C]0.372304347425803[/C][C]0.813847826287098[/C][/ROW]
[ROW][C]85[/C][C]0.159814833234888[/C][C]0.319629666469776[/C][C]0.840185166765112[/C][/ROW]
[ROW][C]86[/C][C]0.135654047758287[/C][C]0.271308095516574[/C][C]0.864345952241713[/C][/ROW]
[ROW][C]87[/C][C]0.112755933804864[/C][C]0.225511867609727[/C][C]0.887244066195136[/C][/ROW]
[ROW][C]88[/C][C]0.0964927396980597[/C][C]0.192985479396119[/C][C]0.90350726030194[/C][/ROW]
[ROW][C]89[/C][C]0.0886449380455545[/C][C]0.177289876091109[/C][C]0.911355061954445[/C][/ROW]
[ROW][C]90[/C][C]0.27519074321418[/C][C]0.55038148642836[/C][C]0.72480925678582[/C][/ROW]
[ROW][C]91[/C][C]0.237585228859786[/C][C]0.475170457719572[/C][C]0.762414771140214[/C][/ROW]
[ROW][C]92[/C][C]0.208264111920329[/C][C]0.416528223840658[/C][C]0.791735888079671[/C][/ROW]
[ROW][C]93[/C][C]0.176661622964873[/C][C]0.353323245929745[/C][C]0.823338377035127[/C][/ROW]
[ROW][C]94[/C][C]0.157288963577692[/C][C]0.314577927155383[/C][C]0.842711036422308[/C][/ROW]
[ROW][C]95[/C][C]0.154476600174886[/C][C]0.308953200349773[/C][C]0.845523399825114[/C][/ROW]
[ROW][C]96[/C][C]0.134995301627934[/C][C]0.269990603255869[/C][C]0.865004698372066[/C][/ROW]
[ROW][C]97[/C][C]0.139556121224431[/C][C]0.279112242448862[/C][C]0.860443878775569[/C][/ROW]
[ROW][C]98[/C][C]0.120414015638086[/C][C]0.240828031276172[/C][C]0.879585984361914[/C][/ROW]
[ROW][C]99[/C][C]0.109629705281964[/C][C]0.219259410563928[/C][C]0.890370294718036[/C][/ROW]
[ROW][C]100[/C][C]0.11115865813807[/C][C]0.22231731627614[/C][C]0.88884134186193[/C][/ROW]
[ROW][C]101[/C][C]0.115794764870286[/C][C]0.231589529740572[/C][C]0.884205235129714[/C][/ROW]
[ROW][C]102[/C][C]0.102018043363988[/C][C]0.204036086727975[/C][C]0.897981956636013[/C][/ROW]
[ROW][C]103[/C][C]0.0955648932696385[/C][C]0.191129786539277[/C][C]0.904435106730362[/C][/ROW]
[ROW][C]104[/C][C]0.107462801011949[/C][C]0.214925602023898[/C][C]0.892537198988051[/C][/ROW]
[ROW][C]105[/C][C]0.0941334797198085[/C][C]0.188266959439617[/C][C]0.905866520280192[/C][/ROW]
[ROW][C]106[/C][C]0.128170935062321[/C][C]0.256341870124641[/C][C]0.87182906493768[/C][/ROW]
[ROW][C]107[/C][C]0.127408540751861[/C][C]0.254817081503721[/C][C]0.87259145924814[/C][/ROW]
[ROW][C]108[/C][C]0.110836560233572[/C][C]0.221673120467144[/C][C]0.889163439766428[/C][/ROW]
[ROW][C]109[/C][C]0.095701034192265[/C][C]0.19140206838453[/C][C]0.904298965807735[/C][/ROW]
[ROW][C]110[/C][C]0.416982750530948[/C][C]0.833965501061896[/C][C]0.583017249469052[/C][/ROW]
[ROW][C]111[/C][C]0.369424630178137[/C][C]0.738849260356275[/C][C]0.630575369821863[/C][/ROW]
[ROW][C]112[/C][C]0.340767655411976[/C][C]0.681535310823953[/C][C]0.659232344588024[/C][/ROW]
[ROW][C]113[/C][C]0.318431028157304[/C][C]0.636862056314607[/C][C]0.681568971842696[/C][/ROW]
[ROW][C]114[/C][C]0.283484521505919[/C][C]0.566969043011839[/C][C]0.71651547849408[/C][/ROW]
[ROW][C]115[/C][C]0.255802707585252[/C][C]0.511605415170505[/C][C]0.744197292414748[/C][/ROW]
[ROW][C]116[/C][C]0.284471943373689[/C][C]0.568943886747379[/C][C]0.71552805662631[/C][/ROW]
[ROW][C]117[/C][C]0.241456159701519[/C][C]0.482912319403038[/C][C]0.758543840298481[/C][/ROW]
[ROW][C]118[/C][C]0.204149716024803[/C][C]0.408299432049606[/C][C]0.795850283975197[/C][/ROW]
[ROW][C]119[/C][C]0.232114698683129[/C][C]0.464229397366258[/C][C]0.767885301316871[/C][/ROW]
[ROW][C]120[/C][C]0.205218556241390[/C][C]0.410437112482781[/C][C]0.79478144375861[/C][/ROW]
[ROW][C]121[/C][C]0.210339272145149[/C][C]0.420678544290298[/C][C]0.78966072785485[/C][/ROW]
[ROW][C]122[/C][C]0.172732564431027[/C][C]0.345465128862054[/C][C]0.827267435568973[/C][/ROW]
[ROW][C]123[/C][C]0.154521300318496[/C][C]0.309042600636992[/C][C]0.845478699681504[/C][/ROW]
[ROW][C]124[/C][C]0.214814867089528[/C][C]0.429629734179056[/C][C]0.785185132910472[/C][/ROW]
[ROW][C]125[/C][C]0.19269944491365[/C][C]0.3853988898273[/C][C]0.80730055508635[/C][/ROW]
[ROW][C]126[/C][C]0.192351875161353[/C][C]0.384703750322707[/C][C]0.807648124838647[/C][/ROW]
[ROW][C]127[/C][C]0.350040814255519[/C][C]0.700081628511038[/C][C]0.649959185744481[/C][/ROW]
[ROW][C]128[/C][C]0.370598971592270[/C][C]0.741197943184541[/C][C]0.62940102840773[/C][/ROW]
[ROW][C]129[/C][C]0.396829366284867[/C][C]0.793658732569733[/C][C]0.603170633715133[/C][/ROW]
[ROW][C]130[/C][C]0.378359717576874[/C][C]0.756719435153747[/C][C]0.621640282423126[/C][/ROW]
[ROW][C]131[/C][C]0.351585209243346[/C][C]0.703170418486693[/C][C]0.648414790756654[/C][/ROW]
[ROW][C]132[/C][C]0.439121550227745[/C][C]0.87824310045549[/C][C]0.560878449772255[/C][/ROW]
[ROW][C]133[/C][C]0.56341002323029[/C][C]0.873179953539421[/C][C]0.436589976769711[/C][/ROW]
[ROW][C]134[/C][C]0.503483738048521[/C][C]0.993032523902959[/C][C]0.496516261951479[/C][/ROW]
[ROW][C]135[/C][C]0.468468029222388[/C][C]0.936936058444776[/C][C]0.531531970777612[/C][/ROW]
[ROW][C]136[/C][C]0.402658586872526[/C][C]0.805317173745052[/C][C]0.597341413127474[/C][/ROW]
[ROW][C]137[/C][C]0.76845811952334[/C][C]0.46308376095332[/C][C]0.23154188047666[/C][/ROW]
[ROW][C]138[/C][C]0.709730843463606[/C][C]0.580538313072787[/C][C]0.290269156536394[/C][/ROW]
[ROW][C]139[/C][C]0.653101299836226[/C][C]0.693797400327547[/C][C]0.346898700163773[/C][/ROW]
[ROW][C]140[/C][C]0.610071883792312[/C][C]0.779856232415376[/C][C]0.389928116207688[/C][/ROW]
[ROW][C]141[/C][C]0.546308465474531[/C][C]0.907383069050938[/C][C]0.453691534525469[/C][/ROW]
[ROW][C]142[/C][C]0.492027083953054[/C][C]0.984054167906108[/C][C]0.507972916046946[/C][/ROW]
[ROW][C]143[/C][C]0.418132524161946[/C][C]0.836265048323891[/C][C]0.581867475838054[/C][/ROW]
[ROW][C]144[/C][C]0.337276773541877[/C][C]0.674553547083753[/C][C]0.662723226458123[/C][/ROW]
[ROW][C]145[/C][C]0.330352711222166[/C][C]0.660705422444332[/C][C]0.669647288777834[/C][/ROW]
[ROW][C]146[/C][C]0.248397216102347[/C][C]0.496794432204693[/C][C]0.751602783897653[/C][/ROW]
[ROW][C]147[/C][C]0.175015640006478[/C][C]0.350031280012955[/C][C]0.824984359993522[/C][/ROW]
[ROW][C]148[/C][C]0.120747282010672[/C][C]0.241494564021345[/C][C]0.879252717989327[/C][/ROW]
[ROW][C]149[/C][C]0.225327650624310[/C][C]0.450655301248619[/C][C]0.77467234937569[/C][/ROW]
[ROW][C]150[/C][C]0.250821764923187[/C][C]0.501643529846374[/C][C]0.749178235076813[/C][/ROW]
[ROW][C]151[/C][C]0.257020965074454[/C][C]0.514041930148907[/C][C]0.742979034925546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9807716383459180.0384567233081650.0192283616540825
90.9876951778288520.02460964434229550.0123048221711478
100.9778016105866470.04439677882670530.0221983894133526
110.9635699942173620.07286001156527530.0364300057826377
120.9599843397747890.08003132045042230.0400156602252111
130.9545700212468070.09085995750638690.0454299787531934
140.9282524517589520.1434950964820950.0717475482410476
150.9356621344480910.1286757311038180.0643378655519088
160.9151309118196630.1697381763606730.0848690881803367
170.881399655300350.2372006893993000.118600344699650
180.8582284338356110.2835431323287780.141771566164389
190.8390598112450350.3218803775099290.160940188754965
200.8141401528493970.3717196943012070.185859847150603
210.8853711198333430.2292577603333130.114628880166657
220.8790513587899280.2418972824201450.120948641210072
230.8475878881764070.3048242236471860.152412111823593
240.8087671076349880.3824657847300240.191232892365012
250.7758530448392560.4482939103214870.224146955160744
260.7710377619932270.4579244760135460.228962238006773
270.7441055469952030.5117889060095940.255894453004797
280.7272512801673630.5454974396652740.272748719832637
290.6902397295644820.6195205408710360.309760270435518
300.6343534358742140.7312931282515720.365646564125786
310.679448959670990.641102080658020.32055104032901
320.6930707669845230.6138584660309530.306929233015477
330.6730043621164310.6539912757671380.326995637883569
340.6265872120021590.7468255759956820.373412787997841
350.5708137319116150.858372536176770.429186268088385
360.5800830531465920.8398338937068160.419916946853408
370.8267861086969570.3464277826060870.173213891303043
380.7914599186394520.4170801627210950.208540081360548
390.7631646649401060.4736706701197880.236835335059894
400.7433170491990770.5133659016018450.256682950800923
410.7318401769883170.5363196460233660.268159823011683
420.6988588651010520.6022822697978960.301141134898948
430.7371771127665460.5256457744669080.262822887233454
440.7023696000463570.5952607999072850.297630399953643
450.6676597189870260.6646805620259480.332340281012974
460.619604889662290.760790220675420.38039511033771
470.6241591265217690.7516817469564620.375840873478231
480.5831529493392760.8336941013214480.416847050660724
490.5703328558749940.8593342882500120.429667144125006
500.530025772100260.939948455799480.46997422789974
510.5184820201821860.9630359596356270.481517979817814
520.4777777140880260.9555554281760510.522222285911974
530.5687725113746050.862454977250790.431227488625395
540.5198159218780090.9603681562439820.480184078121991
550.4762570491930730.9525140983861470.523742950806926
560.4291544808916060.8583089617832120.570845519108394
570.3819696050850210.7639392101700420.618030394914979
580.4020609659036140.8041219318072280.597939034096386
590.3947948139025360.7895896278050710.605205186097464
600.3792359081131090.7584718162262180.620764091886891
610.5672474077670140.8655051844659720.432752592232986
620.5241341308126820.9517317383746360.475865869187318
630.4815029330962190.9630058661924370.518497066903781
640.4374482691155260.8748965382310520.562551730884474
650.3930252376919850.786050475383970.606974762308015
660.3582296756746720.7164593513493440.641770324325328
670.4063858407895080.8127716815790150.593614159210492
680.3668276224861740.7336552449723490.633172377513826
690.3259031839900470.6518063679800930.674096816009953
700.2878654901090790.5757309802181590.71213450989092
710.2494724519537200.4989449039074410.75052754804628
720.2246873857564750.4493747715129500.775312614243525
730.1917934314762960.3835868629525930.808206568523704
740.1797489423967540.3594978847935090.820251057603246
750.1597421070433920.3194842140867840.840257892956608
760.2465074287461440.4930148574922880.753492571253856
770.2130075854384190.4260151708768380.786992414561581
780.2312021944911930.4624043889823860.768797805508807
790.1981860953099000.3963721906198010.8018139046901
800.2110563808012440.4221127616024880.788943619198756
810.1905084304535570.3810168609071140.809491569546443
820.1920851887905080.3841703775810170.807914811209492
830.2137014145578440.4274028291156880.786298585442156
840.1861521737129020.3723043474258030.813847826287098
850.1598148332348880.3196296664697760.840185166765112
860.1356540477582870.2713080955165740.864345952241713
870.1127559338048640.2255118676097270.887244066195136
880.09649273969805970.1929854793961190.90350726030194
890.08864493804555450.1772898760911090.911355061954445
900.275190743214180.550381486428360.72480925678582
910.2375852288597860.4751704577195720.762414771140214
920.2082641119203290.4165282238406580.791735888079671
930.1766616229648730.3533232459297450.823338377035127
940.1572889635776920.3145779271553830.842711036422308
950.1544766001748860.3089532003497730.845523399825114
960.1349953016279340.2699906032558690.865004698372066
970.1395561212244310.2791122424488620.860443878775569
980.1204140156380860.2408280312761720.879585984361914
990.1096297052819640.2192594105639280.890370294718036
1000.111158658138070.222317316276140.88884134186193
1010.1157947648702860.2315895297405720.884205235129714
1020.1020180433639880.2040360867279750.897981956636013
1030.09556489326963850.1911297865392770.904435106730362
1040.1074628010119490.2149256020238980.892537198988051
1050.09413347971980850.1882669594396170.905866520280192
1060.1281709350623210.2563418701246410.87182906493768
1070.1274085407518610.2548170815037210.87259145924814
1080.1108365602335720.2216731204671440.889163439766428
1090.0957010341922650.191402068384530.904298965807735
1100.4169827505309480.8339655010618960.583017249469052
1110.3694246301781370.7388492603562750.630575369821863
1120.3407676554119760.6815353108239530.659232344588024
1130.3184310281573040.6368620563146070.681568971842696
1140.2834845215059190.5669690430118390.71651547849408
1150.2558027075852520.5116054151705050.744197292414748
1160.2844719433736890.5689438867473790.71552805662631
1170.2414561597015190.4829123194030380.758543840298481
1180.2041497160248030.4082994320496060.795850283975197
1190.2321146986831290.4642293973662580.767885301316871
1200.2052185562413900.4104371124827810.79478144375861
1210.2103392721451490.4206785442902980.78966072785485
1220.1727325644310270.3454651288620540.827267435568973
1230.1545213003184960.3090426006369920.845478699681504
1240.2148148670895280.4296297341790560.785185132910472
1250.192699444913650.38539888982730.80730055508635
1260.1923518751613530.3847037503227070.807648124838647
1270.3500408142555190.7000816285110380.649959185744481
1280.3705989715922700.7411979431845410.62940102840773
1290.3968293662848670.7936587325697330.603170633715133
1300.3783597175768740.7567194351537470.621640282423126
1310.3515852092433460.7031704184866930.648414790756654
1320.4391215502277450.878243100455490.560878449772255
1330.563410023230290.8731799535394210.436589976769711
1340.5034837380485210.9930325239029590.496516261951479
1350.4684680292223880.9369360584447760.531531970777612
1360.4026585868725260.8053171737450520.597341413127474
1370.768458119523340.463083760953320.23154188047666
1380.7097308434636060.5805383130727870.290269156536394
1390.6531012998362260.6937974003275470.346898700163773
1400.6100718837923120.7798562324153760.389928116207688
1410.5463084654745310.9073830690509380.453691534525469
1420.4920270839530540.9840541679061080.507972916046946
1430.4181325241619460.8362650483238910.581867475838054
1440.3372767735418770.6745535470837530.662723226458123
1450.3303527112221660.6607054224443320.669647288777834
1460.2483972161023470.4967944322046930.751602783897653
1470.1750156400064780.3500312800129550.824984359993522
1480.1207472820106720.2414945640213450.879252717989327
1490.2253276506243100.4506553012486190.77467234937569
1500.2508217649231870.5016435298463740.749178235076813
1510.2570209650744540.5140419301489070.742979034925546






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0208333333333333OK
10% type I error level60.0416666666666667OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0208333333333333 & OK \tabularnewline
10% type I error level & 6 & 0.0416666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99323&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0208333333333333[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99323&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99323&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0208333333333333OK
10% type I error level60.0416666666666667OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}