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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 computationFri, 26 Nov 2010 10:44:36 +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/26/t1290768168qr9hk2q7y828lty.htm/, Retrieved Fri, 03 May 2024 00:11:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101744, Retrieved Fri, 03 May 2024 00:11:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

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] [a9e130f95bad0a0597234e75c6380c5a]
-           [Multiple Regression] [WS 7 comp 3] [2010-11-23 19:55:17] [b659239b537e56f17142ee5c56ad6265]
-   PD          [Multiple Regression] [] [2010-11-26 10:44:36] [6fde1c772c7be11768d9b6a0344566b2] [Current]
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Dataset

Dataseries X:
Download CSV
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 time10 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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&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]10 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=101744&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 11.0492891845945 + 0.288174165510478Month[t] -0.00848639366683415Expect[t] -0.0502769907245564Criticism[t] -0.224886096320656Concerns[t] + 0.00382140890724104t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  11.0492891845945 +  0.288174165510478Month[t] -0.00848639366683415Expect[t] -0.0502769907245564Criticism[t] -0.224886096320656Concerns[t] +  0.00382140890724104t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  11.0492891845945 +  0.288174165510478Month[t] -0.00848639366683415Expect[t] -0.0502769907245564Criticism[t] -0.224886096320656Concerns[t] +  0.00382140890724104t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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
Depression[t] = + 11.0492891845945 + 0.288174165510478Month[t] -0.00848639366683415Expect[t] -0.0502769907245564Criticism[t] -0.224886096320656Concerns[t] + 0.00382140890724104t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.04928918459458.6735091.27390.2046260.102313
Month0.2881741655104780.9617140.29960.7648540.382427
Expect-0.008486393666834150.186079-0.04560.9636830.481842
Criticism-0.05027699072455640.234592-0.21430.8305860.415293
Concerns-0.2248860963206560.216168-1.04030.2998290.149914
t0.003821408907241040.0169610.22530.8220410.411021

\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) & 11.0492891845945 & 8.673509 & 1.2739 & 0.204626 & 0.102313 \tabularnewline
Month & 0.288174165510478 & 0.961714 & 0.2996 & 0.764854 & 0.382427 \tabularnewline
Expect & -0.00848639366683415 & 0.186079 & -0.0456 & 0.963683 & 0.481842 \tabularnewline
Criticism & -0.0502769907245564 & 0.234592 & -0.2143 & 0.830586 & 0.415293 \tabularnewline
Concerns & -0.224886096320656 & 0.216168 & -1.0403 & 0.299829 & 0.149914 \tabularnewline
t & 0.00382140890724104 & 0.016961 & 0.2253 & 0.822041 & 0.411021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&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]11.0492891845945[/C][C]8.673509[/C][C]1.2739[/C][C]0.204626[/C][C]0.102313[/C][/ROW]
[ROW][C]Month[/C][C]0.288174165510478[/C][C]0.961714[/C][C]0.2996[/C][C]0.764854[/C][C]0.382427[/C][/ROW]
[ROW][C]Expect[/C][C]-0.00848639366683415[/C][C]0.186079[/C][C]-0.0456[/C][C]0.963683[/C][C]0.481842[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.0502769907245564[/C][C]0.234592[/C][C]-0.2143[/C][C]0.830586[/C][C]0.415293[/C][/ROW]
[ROW][C]Concerns[/C][C]-0.224886096320656[/C][C]0.216168[/C][C]-1.0403[/C][C]0.299829[/C][C]0.149914[/C][/ROW]
[ROW][C]t[/C][C]0.00382140890724104[/C][C]0.016961[/C][C]0.2253[/C][C]0.822041[/C][C]0.411021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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)11.04928918459458.6735091.27390.2046260.102313
Month0.2881741655104780.9617140.29960.7648540.382427
Expect-0.008486393666834150.186079-0.04560.9636830.481842
Criticism-0.05027699072455640.234592-0.21430.8305860.415293
Concerns-0.2248860963206560.216168-1.04030.2998290.149914
t0.003821408907241040.0169610.22530.8220410.411021






Multiple Linear Regression - Regression Statistics
Multiple R0.162386978482884
R-squared0.0263695307808006
Adjusted R-squared-0.00544845840936947
F-TEST (value)0.828761699024885
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value0.531063059143865
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15396006269207
Sum Squared Residuals1521.96200378965

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.162386978482884 \tabularnewline
R-squared & 0.0263695307808006 \tabularnewline
Adjusted R-squared & -0.00544845840936947 \tabularnewline
F-TEST (value) & 0.828761699024885 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0.531063059143865 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.15396006269207 \tabularnewline
Sum Squared Residuals & 1521.96200378965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.162386978482884[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0263695307808006[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00544845840936947[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.828761699024885[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]0.531063059143865[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.15396006269207[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1521.96200378965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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 R0.162386978482884
R-squared0.0263695307808006
Adjusted R-squared-0.00544845840936947
F-TEST (value)0.828761699024885
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value0.531063059143865
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15396006269207
Sum Squared Residuals1521.96200378965






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.0996980801921-0.0996980801920628
21112.1693224557283-1.16932245572832
31412.53100914775221.46899085224784
41212.5150579933028-0.51505799330278
52112.48525451318098.51474548681909
61212.6383027951938-0.638302795193795
72212.52388312225919.4761168777409
81112.5496348283926-1.54963482839258
91012.5508171395344-2.55081713953443
101312.59987029279880.400129707201204
111012.1746575199548-2.17465751995476
12811.8694472156595-3.8694472156595
131512.24530691899402.75469308100595
141412.55327208237521.44672791762481
151012.7035239789839-2.70352397898385
161412.93935140557821.06064859442183
171412.39316732603321.60683267396679
181112.7149882057056-1.71498820570558
191012.4688653620587-2.46886536205866
201311.82116185353121.17883814646882
21712.1008680598889-5.10086805988887
221411.67388778997742.32611221002259
231212.2943258138605-0.29432581386046
241411.94156535308092.05843464691910
251112.5415894423309-1.54158944233088
26912.6517243921138-3.65172439211381
271112.5330615223457-1.53306152234570
281512.41823003667922.58176996332081
291412.35984691389561.64015308610436
301312.70143696630590.298563033694105
31912.3736577132498-3.37365771324979
321512.03618159487972.96381840512026
331012.2670515115815-2.2670515115815
341112.2955300519552-1.29553005195523
351312.62815573742160.371844262578402
36811.9924675063352-3.99246750633523
372011.94681397405198.05318602594808
381212.9615385554849-0.961538555484902
391011.8149119893825-1.81491198938252
401012.3184585053987-2.31845850539868
41912.0235054824564-3.02350548245640
421412.41801162335661.58198837664339
43812.6428769565181-4.64287695651809
441412.76734559586931.23265440413070
451112.5727858761186-1.57278587611857
461312.82198765510830.178012344891675
47912.5131789158748-3.51317891587483
481111.9888494720316-0.988849472031573
491512.12981292543942.87018707456061
501112.4366553536483-1.43665535364830
511012.4161369261085-2.41613692610854
521412.68999922718351.31000077281649
531812.59487075370975.40512924629033
541413.46265168321830.537348316781686
551112.1378101792355-1.13781017923552
561212.5931617768722-0.593161776872189
571312.79713181150480.202868188495186
58913.0923673843857-4.09236738438571
591013.4257917287674-3.42579172876735
601513.20465009284391.79534990715611
612013.03690251868746.96309748131257
621212.9117475911227-0.911747591122703
631212.9873860511785-0.987386051178544
641413.01586459155230.984135408447724
651313.5078076428591-0.507807642859146
661112.5710961189601-1.57109611896005
671712.38981690476934.61018309523067
681212.9855977971859-0.985597797185888
691313.5516729211498-0.551672921149793
701412.95225206747671.04774793252334
711312.5766715234620.423328476538006
721513.04299471551081.95700528448920
731312.42010901670740.579890983292626
741012.4126476465436-2.41264764654356
751112.7033159463341-1.70331594633407
761912.82026363955186.17973636044815
771313.3693659761821-0.369365976182086
781712.89323145071834.10676854928169
791313.0403628856658-0.0403628856657909
80912.6319595021560-3.63195950215603
811112.7848504965301-1.78485049653008
821013.0970588567446-3.09705885674464
83912.9417201874502-3.94172018745021
841212.9342588172864-0.934258817286393
851212.9470445931375-0.947044593137522
861312.96559331903400.0344066809660417
871313.0409077028328-0.0409077028328412
881213.2791473883898-1.27914738838976
891512.45251462892272.54748537107728
902213.09905048534098.90094951465911
911312.71842469495880.281575305041209
921513.59857677849261.40142322150737
931313.3219461168590-0.32194611685905
941512.97607134129702.02392865870296
951013.0469852406237-3.04698524062367
961112.3576518132960-1.35765181329604
971612.46659108612403.53340891387597
981112.4792229649551-1.47922296495506
991113.1334431655061-2.13344316550606
1001012.9572091976828-2.95720919768276
1011012.8527922810081-2.85279228100807
1021613.37019451331542.62980548668456
1031212.9941359960237-0.99413599602372
1041113.1138800746457-2.11388007464572
1051612.88505070397073.1149492960293
1061912.91336584634506.08663415365496
1071113.0713020870865-2.07130208708652
1081612.83855359583503.16144640416495
1091513.04520047906991.9547995209301
1102413.587741206739610.4122587932604
1111413.76657274600990.233427253990101
1121512.87885819002432.12114180997570
1131113.2860939214248-2.28609392142475
1141513.05157793310591.94842206689414
1151213.8984258132488-1.89842581324880
1161013.6810398999024-3.68103989990240
1171413.44404821181750.555951788182491
1181313.6310420684977-0.631042068497732
119913.1336915588669-4.13369155886687
1201513.54478025029861.45521974970145
1211513.10307266339791.8969273366021
1221413.7591333027990.240866697200994
1231113.2536170851395-2.25361708513953
124813.7678988557857-5.76789885578573
1251113.5379467499385-2.53794674993847
1261113.4289659507921-2.42896595079213
127813.8126672858983-5.81266728589834
1281013.4620679496071-3.46206794960712
1291113.5269711550329-2.52697115503292
1301312.81978994905490.180210050945095
1311113.2884315532309-2.28843155323088
1322013.67101015316486.32898984683515
1331013.7050153038018-3.70501530380179
1341513.79193654292871.20806345707131
1351213.4328508129706-1.43285081297064
1361413.10282608957010.897173910429943
1372313.64144430604459.35855569395548
1381413.56560703203150.434392967968494
1391613.62314657896272.37685342103729
1401113.2767904395049-2.27679043950491
1411212.9935981689107-0.993598168910745
1421012.9468219014552-2.94682190145521
1431412.96241695430861.03758304569141
1441213.5638749633897-1.56387496338973
1451213.3744334317891-1.37443343178913
1461113.1313581080207-2.1313581080207
1471212.9331061064963-0.93310610649631
1481313.5636346231142-0.563634623114218
1491113.7108356092257-2.71083560922566
1501913.38193362099735.61806637900268
1511213.5248218591114-1.52482185911138
1521713.13226852776303.86773147223695
153912.6407653140336-3.64076531403363
1541213.4977732380754-1.49777323807540
1551913.40136474179055.59863525820952
1561813.75928281963924.24071718036078
1571513.17259155646631.82740844353366
1581413.40826169542180.59173830457817
1591112.7945071521804-1.79450715218042

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.0996980801921 & -0.0996980801920628 \tabularnewline
2 & 11 & 12.1693224557283 & -1.16932245572832 \tabularnewline
3 & 14 & 12.5310091477522 & 1.46899085224784 \tabularnewline
4 & 12 & 12.5150579933028 & -0.51505799330278 \tabularnewline
5 & 21 & 12.4852545131809 & 8.51474548681909 \tabularnewline
6 & 12 & 12.6383027951938 & -0.638302795193795 \tabularnewline
7 & 22 & 12.5238831222591 & 9.4761168777409 \tabularnewline
8 & 11 & 12.5496348283926 & -1.54963482839258 \tabularnewline
9 & 10 & 12.5508171395344 & -2.55081713953443 \tabularnewline
10 & 13 & 12.5998702927988 & 0.400129707201204 \tabularnewline
11 & 10 & 12.1746575199548 & -2.17465751995476 \tabularnewline
12 & 8 & 11.8694472156595 & -3.8694472156595 \tabularnewline
13 & 15 & 12.2453069189940 & 2.75469308100595 \tabularnewline
14 & 14 & 12.5532720823752 & 1.44672791762481 \tabularnewline
15 & 10 & 12.7035239789839 & -2.70352397898385 \tabularnewline
16 & 14 & 12.9393514055782 & 1.06064859442183 \tabularnewline
17 & 14 & 12.3931673260332 & 1.60683267396679 \tabularnewline
18 & 11 & 12.7149882057056 & -1.71498820570558 \tabularnewline
19 & 10 & 12.4688653620587 & -2.46886536205866 \tabularnewline
20 & 13 & 11.8211618535312 & 1.17883814646882 \tabularnewline
21 & 7 & 12.1008680598889 & -5.10086805988887 \tabularnewline
22 & 14 & 11.6738877899774 & 2.32611221002259 \tabularnewline
23 & 12 & 12.2943258138605 & -0.29432581386046 \tabularnewline
24 & 14 & 11.9415653530809 & 2.05843464691910 \tabularnewline
25 & 11 & 12.5415894423309 & -1.54158944233088 \tabularnewline
26 & 9 & 12.6517243921138 & -3.65172439211381 \tabularnewline
27 & 11 & 12.5330615223457 & -1.53306152234570 \tabularnewline
28 & 15 & 12.4182300366792 & 2.58176996332081 \tabularnewline
29 & 14 & 12.3598469138956 & 1.64015308610436 \tabularnewline
30 & 13 & 12.7014369663059 & 0.298563033694105 \tabularnewline
31 & 9 & 12.3736577132498 & -3.37365771324979 \tabularnewline
32 & 15 & 12.0361815948797 & 2.96381840512026 \tabularnewline
33 & 10 & 12.2670515115815 & -2.2670515115815 \tabularnewline
34 & 11 & 12.2955300519552 & -1.29553005195523 \tabularnewline
35 & 13 & 12.6281557374216 & 0.371844262578402 \tabularnewline
36 & 8 & 11.9924675063352 & -3.99246750633523 \tabularnewline
37 & 20 & 11.9468139740519 & 8.05318602594808 \tabularnewline
38 & 12 & 12.9615385554849 & -0.961538555484902 \tabularnewline
39 & 10 & 11.8149119893825 & -1.81491198938252 \tabularnewline
40 & 10 & 12.3184585053987 & -2.31845850539868 \tabularnewline
41 & 9 & 12.0235054824564 & -3.02350548245640 \tabularnewline
42 & 14 & 12.4180116233566 & 1.58198837664339 \tabularnewline
43 & 8 & 12.6428769565181 & -4.64287695651809 \tabularnewline
44 & 14 & 12.7673455958693 & 1.23265440413070 \tabularnewline
45 & 11 & 12.5727858761186 & -1.57278587611857 \tabularnewline
46 & 13 & 12.8219876551083 & 0.178012344891675 \tabularnewline
47 & 9 & 12.5131789158748 & -3.51317891587483 \tabularnewline
48 & 11 & 11.9888494720316 & -0.988849472031573 \tabularnewline
49 & 15 & 12.1298129254394 & 2.87018707456061 \tabularnewline
50 & 11 & 12.4366553536483 & -1.43665535364830 \tabularnewline
51 & 10 & 12.4161369261085 & -2.41613692610854 \tabularnewline
52 & 14 & 12.6899992271835 & 1.31000077281649 \tabularnewline
53 & 18 & 12.5948707537097 & 5.40512924629033 \tabularnewline
54 & 14 & 13.4626516832183 & 0.537348316781686 \tabularnewline
55 & 11 & 12.1378101792355 & -1.13781017923552 \tabularnewline
56 & 12 & 12.5931617768722 & -0.593161776872189 \tabularnewline
57 & 13 & 12.7971318115048 & 0.202868188495186 \tabularnewline
58 & 9 & 13.0923673843857 & -4.09236738438571 \tabularnewline
59 & 10 & 13.4257917287674 & -3.42579172876735 \tabularnewline
60 & 15 & 13.2046500928439 & 1.79534990715611 \tabularnewline
61 & 20 & 13.0369025186874 & 6.96309748131257 \tabularnewline
62 & 12 & 12.9117475911227 & -0.911747591122703 \tabularnewline
63 & 12 & 12.9873860511785 & -0.987386051178544 \tabularnewline
64 & 14 & 13.0158645915523 & 0.984135408447724 \tabularnewline
65 & 13 & 13.5078076428591 & -0.507807642859146 \tabularnewline
66 & 11 & 12.5710961189601 & -1.57109611896005 \tabularnewline
67 & 17 & 12.3898169047693 & 4.61018309523067 \tabularnewline
68 & 12 & 12.9855977971859 & -0.985597797185888 \tabularnewline
69 & 13 & 13.5516729211498 & -0.551672921149793 \tabularnewline
70 & 14 & 12.9522520674767 & 1.04774793252334 \tabularnewline
71 & 13 & 12.576671523462 & 0.423328476538006 \tabularnewline
72 & 15 & 13.0429947155108 & 1.95700528448920 \tabularnewline
73 & 13 & 12.4201090167074 & 0.579890983292626 \tabularnewline
74 & 10 & 12.4126476465436 & -2.41264764654356 \tabularnewline
75 & 11 & 12.7033159463341 & -1.70331594633407 \tabularnewline
76 & 19 & 12.8202636395518 & 6.17973636044815 \tabularnewline
77 & 13 & 13.3693659761821 & -0.369365976182086 \tabularnewline
78 & 17 & 12.8932314507183 & 4.10676854928169 \tabularnewline
79 & 13 & 13.0403628856658 & -0.0403628856657909 \tabularnewline
80 & 9 & 12.6319595021560 & -3.63195950215603 \tabularnewline
81 & 11 & 12.7848504965301 & -1.78485049653008 \tabularnewline
82 & 10 & 13.0970588567446 & -3.09705885674464 \tabularnewline
83 & 9 & 12.9417201874502 & -3.94172018745021 \tabularnewline
84 & 12 & 12.9342588172864 & -0.934258817286393 \tabularnewline
85 & 12 & 12.9470445931375 & -0.947044593137522 \tabularnewline
86 & 13 & 12.9655933190340 & 0.0344066809660417 \tabularnewline
87 & 13 & 13.0409077028328 & -0.0409077028328412 \tabularnewline
88 & 12 & 13.2791473883898 & -1.27914738838976 \tabularnewline
89 & 15 & 12.4525146289227 & 2.54748537107728 \tabularnewline
90 & 22 & 13.0990504853409 & 8.90094951465911 \tabularnewline
91 & 13 & 12.7184246949588 & 0.281575305041209 \tabularnewline
92 & 15 & 13.5985767784926 & 1.40142322150737 \tabularnewline
93 & 13 & 13.3219461168590 & -0.32194611685905 \tabularnewline
94 & 15 & 12.9760713412970 & 2.02392865870296 \tabularnewline
95 & 10 & 13.0469852406237 & -3.04698524062367 \tabularnewline
96 & 11 & 12.3576518132960 & -1.35765181329604 \tabularnewline
97 & 16 & 12.4665910861240 & 3.53340891387597 \tabularnewline
98 & 11 & 12.4792229649551 & -1.47922296495506 \tabularnewline
99 & 11 & 13.1334431655061 & -2.13344316550606 \tabularnewline
100 & 10 & 12.9572091976828 & -2.95720919768276 \tabularnewline
101 & 10 & 12.8527922810081 & -2.85279228100807 \tabularnewline
102 & 16 & 13.3701945133154 & 2.62980548668456 \tabularnewline
103 & 12 & 12.9941359960237 & -0.99413599602372 \tabularnewline
104 & 11 & 13.1138800746457 & -2.11388007464572 \tabularnewline
105 & 16 & 12.8850507039707 & 3.1149492960293 \tabularnewline
106 & 19 & 12.9133658463450 & 6.08663415365496 \tabularnewline
107 & 11 & 13.0713020870865 & -2.07130208708652 \tabularnewline
108 & 16 & 12.8385535958350 & 3.16144640416495 \tabularnewline
109 & 15 & 13.0452004790699 & 1.9547995209301 \tabularnewline
110 & 24 & 13.5877412067396 & 10.4122587932604 \tabularnewline
111 & 14 & 13.7665727460099 & 0.233427253990101 \tabularnewline
112 & 15 & 12.8788581900243 & 2.12114180997570 \tabularnewline
113 & 11 & 13.2860939214248 & -2.28609392142475 \tabularnewline
114 & 15 & 13.0515779331059 & 1.94842206689414 \tabularnewline
115 & 12 & 13.8984258132488 & -1.89842581324880 \tabularnewline
116 & 10 & 13.6810398999024 & -3.68103989990240 \tabularnewline
117 & 14 & 13.4440482118175 & 0.555951788182491 \tabularnewline
118 & 13 & 13.6310420684977 & -0.631042068497732 \tabularnewline
119 & 9 & 13.1336915588669 & -4.13369155886687 \tabularnewline
120 & 15 & 13.5447802502986 & 1.45521974970145 \tabularnewline
121 & 15 & 13.1030726633979 & 1.8969273366021 \tabularnewline
122 & 14 & 13.759133302799 & 0.240866697200994 \tabularnewline
123 & 11 & 13.2536170851395 & -2.25361708513953 \tabularnewline
124 & 8 & 13.7678988557857 & -5.76789885578573 \tabularnewline
125 & 11 & 13.5379467499385 & -2.53794674993847 \tabularnewline
126 & 11 & 13.4289659507921 & -2.42896595079213 \tabularnewline
127 & 8 & 13.8126672858983 & -5.81266728589834 \tabularnewline
128 & 10 & 13.4620679496071 & -3.46206794960712 \tabularnewline
129 & 11 & 13.5269711550329 & -2.52697115503292 \tabularnewline
130 & 13 & 12.8197899490549 & 0.180210050945095 \tabularnewline
131 & 11 & 13.2884315532309 & -2.28843155323088 \tabularnewline
132 & 20 & 13.6710101531648 & 6.32898984683515 \tabularnewline
133 & 10 & 13.7050153038018 & -3.70501530380179 \tabularnewline
134 & 15 & 13.7919365429287 & 1.20806345707131 \tabularnewline
135 & 12 & 13.4328508129706 & -1.43285081297064 \tabularnewline
136 & 14 & 13.1028260895701 & 0.897173910429943 \tabularnewline
137 & 23 & 13.6414443060445 & 9.35855569395548 \tabularnewline
138 & 14 & 13.5656070320315 & 0.434392967968494 \tabularnewline
139 & 16 & 13.6231465789627 & 2.37685342103729 \tabularnewline
140 & 11 & 13.2767904395049 & -2.27679043950491 \tabularnewline
141 & 12 & 12.9935981689107 & -0.993598168910745 \tabularnewline
142 & 10 & 12.9468219014552 & -2.94682190145521 \tabularnewline
143 & 14 & 12.9624169543086 & 1.03758304569141 \tabularnewline
144 & 12 & 13.5638749633897 & -1.56387496338973 \tabularnewline
145 & 12 & 13.3744334317891 & -1.37443343178913 \tabularnewline
146 & 11 & 13.1313581080207 & -2.1313581080207 \tabularnewline
147 & 12 & 12.9331061064963 & -0.93310610649631 \tabularnewline
148 & 13 & 13.5636346231142 & -0.563634623114218 \tabularnewline
149 & 11 & 13.7108356092257 & -2.71083560922566 \tabularnewline
150 & 19 & 13.3819336209973 & 5.61806637900268 \tabularnewline
151 & 12 & 13.5248218591114 & -1.52482185911138 \tabularnewline
152 & 17 & 13.1322685277630 & 3.86773147223695 \tabularnewline
153 & 9 & 12.6407653140336 & -3.64076531403363 \tabularnewline
154 & 12 & 13.4977732380754 & -1.49777323807540 \tabularnewline
155 & 19 & 13.4013647417905 & 5.59863525820952 \tabularnewline
156 & 18 & 13.7592828196392 & 4.24071718036078 \tabularnewline
157 & 15 & 13.1725915564663 & 1.82740844353366 \tabularnewline
158 & 14 & 13.4082616954218 & 0.59173830457817 \tabularnewline
159 & 11 & 12.7945071521804 & -1.79450715218042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&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]12.0996980801921[/C][C]-0.0996980801920628[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.1693224557283[/C][C]-1.16932245572832[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.5310091477522[/C][C]1.46899085224784[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.5150579933028[/C][C]-0.51505799330278[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.4852545131809[/C][C]8.51474548681909[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.6383027951938[/C][C]-0.638302795193795[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.5238831222591[/C][C]9.4761168777409[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.5496348283926[/C][C]-1.54963482839258[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.5508171395344[/C][C]-2.55081713953443[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.5998702927988[/C][C]0.400129707201204[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.1746575199548[/C][C]-2.17465751995476[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]11.8694472156595[/C][C]-3.8694472156595[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2453069189940[/C][C]2.75469308100595[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.5532720823752[/C][C]1.44672791762481[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.7035239789839[/C][C]-2.70352397898385[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9393514055782[/C][C]1.06064859442183[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.3931673260332[/C][C]1.60683267396679[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.7149882057056[/C][C]-1.71498820570558[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.4688653620587[/C][C]-2.46886536205866[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.8211618535312[/C][C]1.17883814646882[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.1008680598889[/C][C]-5.10086805988887[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]11.6738877899774[/C][C]2.32611221002259[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.2943258138605[/C][C]-0.29432581386046[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]11.9415653530809[/C][C]2.05843464691910[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.5415894423309[/C][C]-1.54158944233088[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]12.6517243921138[/C][C]-3.65172439211381[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]12.5330615223457[/C][C]-1.53306152234570[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.4182300366792[/C][C]2.58176996332081[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.3598469138956[/C][C]1.64015308610436[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.7014369663059[/C][C]0.298563033694105[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.3736577132498[/C][C]-3.37365771324979[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.0361815948797[/C][C]2.96381840512026[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.2670515115815[/C][C]-2.2670515115815[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.2955300519552[/C][C]-1.29553005195523[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.6281557374216[/C][C]0.371844262578402[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]11.9924675063352[/C][C]-3.99246750633523[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]11.9468139740519[/C][C]8.05318602594808[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.9615385554849[/C][C]-0.961538555484902[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.8149119893825[/C][C]-1.81491198938252[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.3184585053987[/C][C]-2.31845850539868[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.0235054824564[/C][C]-3.02350548245640[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.4180116233566[/C][C]1.58198837664339[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.6428769565181[/C][C]-4.64287695651809[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.7673455958693[/C][C]1.23265440413070[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.5727858761186[/C][C]-1.57278587611857[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.8219876551083[/C][C]0.178012344891675[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5131789158748[/C][C]-3.51317891587483[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.9888494720316[/C][C]-0.988849472031573[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.1298129254394[/C][C]2.87018707456061[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.4366553536483[/C][C]-1.43665535364830[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.4161369261085[/C][C]-2.41613692610854[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.6899992271835[/C][C]1.31000077281649[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.5948707537097[/C][C]5.40512924629033[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.4626516832183[/C][C]0.537348316781686[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.1378101792355[/C][C]-1.13781017923552[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.5931617768722[/C][C]-0.593161776872189[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.7971318115048[/C][C]0.202868188495186[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.0923673843857[/C][C]-4.09236738438571[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.4257917287674[/C][C]-3.42579172876735[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.2046500928439[/C][C]1.79534990715611[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]13.0369025186874[/C][C]6.96309748131257[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9117475911227[/C][C]-0.911747591122703[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.9873860511785[/C][C]-0.987386051178544[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.0158645915523[/C][C]0.984135408447724[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.5078076428591[/C][C]-0.507807642859146[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.5710961189601[/C][C]-1.57109611896005[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.3898169047693[/C][C]4.61018309523067[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.9855977971859[/C][C]-0.985597797185888[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.5516729211498[/C][C]-0.551672921149793[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.9522520674767[/C][C]1.04774793252334[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.576671523462[/C][C]0.423328476538006[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0429947155108[/C][C]1.95700528448920[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.4201090167074[/C][C]0.579890983292626[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.4126476465436[/C][C]-2.41264764654356[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.7033159463341[/C][C]-1.70331594633407[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.8202636395518[/C][C]6.17973636044815[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.3693659761821[/C][C]-0.369365976182086[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.8932314507183[/C][C]4.10676854928169[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.0403628856658[/C][C]-0.0403628856657909[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.6319595021560[/C][C]-3.63195950215603[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.7848504965301[/C][C]-1.78485049653008[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.0970588567446[/C][C]-3.09705885674464[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.9417201874502[/C][C]-3.94172018745021[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9342588172864[/C][C]-0.934258817286393[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.9470445931375[/C][C]-0.947044593137522[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.9655933190340[/C][C]0.0344066809660417[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.0409077028328[/C][C]-0.0409077028328412[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.2791473883898[/C][C]-1.27914738838976[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.4525146289227[/C][C]2.54748537107728[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0990504853409[/C][C]8.90094951465911[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.7184246949588[/C][C]0.281575305041209[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5985767784926[/C][C]1.40142322150737[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.3219461168590[/C][C]-0.32194611685905[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9760713412970[/C][C]2.02392865870296[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.0469852406237[/C][C]-3.04698524062367[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.3576518132960[/C][C]-1.35765181329604[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.4665910861240[/C][C]3.53340891387597[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.4792229649551[/C][C]-1.47922296495506[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.1334431655061[/C][C]-2.13344316550606[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.9572091976828[/C][C]-2.95720919768276[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.8527922810081[/C][C]-2.85279228100807[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.3701945133154[/C][C]2.62980548668456[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.9941359960237[/C][C]-0.99413599602372[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.1138800746457[/C][C]-2.11388007464572[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.8850507039707[/C][C]3.1149492960293[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.9133658463450[/C][C]6.08663415365496[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]13.0713020870865[/C][C]-2.07130208708652[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.8385535958350[/C][C]3.16144640416495[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.0452004790699[/C][C]1.9547995209301[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]13.5877412067396[/C][C]10.4122587932604[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.7665727460099[/C][C]0.233427253990101[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.8788581900243[/C][C]2.12114180997570[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.2860939214248[/C][C]-2.28609392142475[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.0515779331059[/C][C]1.94842206689414[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.8984258132488[/C][C]-1.89842581324880[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.6810398999024[/C][C]-3.68103989990240[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.4440482118175[/C][C]0.555951788182491[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.6310420684977[/C][C]-0.631042068497732[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.1336915588669[/C][C]-4.13369155886687[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.5447802502986[/C][C]1.45521974970145[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.1030726633979[/C][C]1.8969273366021[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.759133302799[/C][C]0.240866697200994[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]13.2536170851395[/C][C]-2.25361708513953[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.7678988557857[/C][C]-5.76789885578573[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.5379467499385[/C][C]-2.53794674993847[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.4289659507921[/C][C]-2.42896595079213[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.8126672858983[/C][C]-5.81266728589834[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]13.4620679496071[/C][C]-3.46206794960712[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]13.5269711550329[/C][C]-2.52697115503292[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8197899490549[/C][C]0.180210050945095[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.2884315532309[/C][C]-2.28843155323088[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.6710101531648[/C][C]6.32898984683515[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.7050153038018[/C][C]-3.70501530380179[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.7919365429287[/C][C]1.20806345707131[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]13.4328508129706[/C][C]-1.43285081297064[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]13.1028260895701[/C][C]0.897173910429943[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]13.6414443060445[/C][C]9.35855569395548[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.5656070320315[/C][C]0.434392967968494[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.6231465789627[/C][C]2.37685342103729[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2767904395049[/C][C]-2.27679043950491[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.9935981689107[/C][C]-0.993598168910745[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.9468219014552[/C][C]-2.94682190145521[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.9624169543086[/C][C]1.03758304569141[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.5638749633897[/C][C]-1.56387496338973[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]13.3744334317891[/C][C]-1.37443343178913[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]13.1313581080207[/C][C]-2.1313581080207[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.9331061064963[/C][C]-0.93310610649631[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.5636346231142[/C][C]-0.563634623114218[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.7108356092257[/C][C]-2.71083560922566[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.3819336209973[/C][C]5.61806637900268[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.5248218591114[/C][C]-1.52482185911138[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.1322685277630[/C][C]3.86773147223695[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.6407653140336[/C][C]-3.64076531403363[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.4977732380754[/C][C]-1.49777323807540[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.4013647417905[/C][C]5.59863525820952[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.7592828196392[/C][C]4.24071718036078[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.1725915564663[/C][C]1.82740844353366[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4082616954218[/C][C]0.59173830457817[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.7945071521804[/C][C]-1.79450715218042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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
11212.0996980801921-0.0996980801920628
21112.1693224557283-1.16932245572832
31412.53100914775221.46899085224784
41212.5150579933028-0.51505799330278
52112.48525451318098.51474548681909
61212.6383027951938-0.638302795193795
72212.52388312225919.4761168777409
81112.5496348283926-1.54963482839258
91012.5508171395344-2.55081713953443
101312.59987029279880.400129707201204
111012.1746575199548-2.17465751995476
12811.8694472156595-3.8694472156595
131512.24530691899402.75469308100595
141412.55327208237521.44672791762481
151012.7035239789839-2.70352397898385
161412.93935140557821.06064859442183
171412.39316732603321.60683267396679
181112.7149882057056-1.71498820570558
191012.4688653620587-2.46886536205866
201311.82116185353121.17883814646882
21712.1008680598889-5.10086805988887
221411.67388778997742.32611221002259
231212.2943258138605-0.29432581386046
241411.94156535308092.05843464691910
251112.5415894423309-1.54158944233088
26912.6517243921138-3.65172439211381
271112.5330615223457-1.53306152234570
281512.41823003667922.58176996332081
291412.35984691389561.64015308610436
301312.70143696630590.298563033694105
31912.3736577132498-3.37365771324979
321512.03618159487972.96381840512026
331012.2670515115815-2.2670515115815
341112.2955300519552-1.29553005195523
351312.62815573742160.371844262578402
36811.9924675063352-3.99246750633523
372011.94681397405198.05318602594808
381212.9615385554849-0.961538555484902
391011.8149119893825-1.81491198938252
401012.3184585053987-2.31845850539868
41912.0235054824564-3.02350548245640
421412.41801162335661.58198837664339
43812.6428769565181-4.64287695651809
441412.76734559586931.23265440413070
451112.5727858761186-1.57278587611857
461312.82198765510830.178012344891675
47912.5131789158748-3.51317891587483
481111.9888494720316-0.988849472031573
491512.12981292543942.87018707456061
501112.4366553536483-1.43665535364830
511012.4161369261085-2.41613692610854
521412.68999922718351.31000077281649
531812.59487075370975.40512924629033
541413.46265168321830.537348316781686
551112.1378101792355-1.13781017923552
561212.5931617768722-0.593161776872189
571312.79713181150480.202868188495186
58913.0923673843857-4.09236738438571
591013.4257917287674-3.42579172876735
601513.20465009284391.79534990715611
612013.03690251868746.96309748131257
621212.9117475911227-0.911747591122703
631212.9873860511785-0.987386051178544
641413.01586459155230.984135408447724
651313.5078076428591-0.507807642859146
661112.5710961189601-1.57109611896005
671712.38981690476934.61018309523067
681212.9855977971859-0.985597797185888
691313.5516729211498-0.551672921149793
701412.95225206747671.04774793252334
711312.5766715234620.423328476538006
721513.04299471551081.95700528448920
731312.42010901670740.579890983292626
741012.4126476465436-2.41264764654356
751112.7033159463341-1.70331594633407
761912.82026363955186.17973636044815
771313.3693659761821-0.369365976182086
781712.89323145071834.10676854928169
791313.0403628856658-0.0403628856657909
80912.6319595021560-3.63195950215603
811112.7848504965301-1.78485049653008
821013.0970588567446-3.09705885674464
83912.9417201874502-3.94172018745021
841212.9342588172864-0.934258817286393
851212.9470445931375-0.947044593137522
861312.96559331903400.0344066809660417
871313.0409077028328-0.0409077028328412
881213.2791473883898-1.27914738838976
891512.45251462892272.54748537107728
902213.09905048534098.90094951465911
911312.71842469495880.281575305041209
921513.59857677849261.40142322150737
931313.3219461168590-0.32194611685905
941512.97607134129702.02392865870296
951013.0469852406237-3.04698524062367
961112.3576518132960-1.35765181329604
971612.46659108612403.53340891387597
981112.4792229649551-1.47922296495506
991113.1334431655061-2.13344316550606
1001012.9572091976828-2.95720919768276
1011012.8527922810081-2.85279228100807
1021613.37019451331542.62980548668456
1031212.9941359960237-0.99413599602372
1041113.1138800746457-2.11388007464572
1051612.88505070397073.1149492960293
1061912.91336584634506.08663415365496
1071113.0713020870865-2.07130208708652
1081612.83855359583503.16144640416495
1091513.04520047906991.9547995209301
1102413.587741206739610.4122587932604
1111413.76657274600990.233427253990101
1121512.87885819002432.12114180997570
1131113.2860939214248-2.28609392142475
1141513.05157793310591.94842206689414
1151213.8984258132488-1.89842581324880
1161013.6810398999024-3.68103989990240
1171413.44404821181750.555951788182491
1181313.6310420684977-0.631042068497732
119913.1336915588669-4.13369155886687
1201513.54478025029861.45521974970145
1211513.10307266339791.8969273366021
1221413.7591333027990.240866697200994
1231113.2536170851395-2.25361708513953
124813.7678988557857-5.76789885578573
1251113.5379467499385-2.53794674993847
1261113.4289659507921-2.42896595079213
127813.8126672858983-5.81266728589834
1281013.4620679496071-3.46206794960712
1291113.5269711550329-2.52697115503292
1301312.81978994905490.180210050945095
1311113.2884315532309-2.28843155323088
1322013.67101015316486.32898984683515
1331013.7050153038018-3.70501530380179
1341513.79193654292871.20806345707131
1351213.4328508129706-1.43285081297064
1361413.10282608957010.897173910429943
1372313.64144430604459.35855569395548
1381413.56560703203150.434392967968494
1391613.62314657896272.37685342103729
1401113.2767904395049-2.27679043950491
1411212.9935981689107-0.993598168910745
1421012.9468219014552-2.94682190145521
1431412.96241695430861.03758304569141
1441213.5638749633897-1.56387496338973
1451213.3744334317891-1.37443343178913
1461113.1313581080207-2.1313581080207
1471212.9331061064963-0.93310610649631
1481313.5636346231142-0.563634623114218
1491113.7108356092257-2.71083560922566
1501913.38193362099735.61806637900268
1511213.5248218591114-1.52482185911138
1521713.13226852776303.86773147223695
153912.6407653140336-3.64076531403363
1541213.4977732380754-1.49777323807540
1551913.40136474179055.59863525820952
1561813.75928281963924.24071718036078
1571513.17259155646631.82740844353366
1581413.40826169542180.59173830457817
1591112.7945071521804-1.79450715218042






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9947123495256120.01057530094877620.00528765047438808
100.988135933483030.02372813303394190.0118640665169709
110.975380403682240.04923919263552090.0246195963177605
120.963796051582390.07240789683522120.0362039484176106
130.97055746093380.05888507813240170.0294425390662009
140.9508063958997290.09838720820054230.0491936041002712
150.943978867907980.1120422641840410.0560211320920207
160.9176141651639240.1647716696721520.082385834836076
170.8972665920271230.2054668159457550.102733407972877
180.8613794824453730.2772410351092540.138620517554627
190.8212773036861820.3574453926276360.178722696313818
200.8250045757191880.3499908485616240.174995424280812
210.8497420351279920.3005159297440170.150257964872009
220.8604515530899150.2790968938201710.139548446910085
230.8464238240079520.3071523519840970.153576175992048
240.8172662825614150.365467434877170.182733717438585
250.7706923804027750.4586152391944490.229307619597225
260.7373230650906890.5253538698186220.262676934909311
270.6863275884301830.6273448231396330.313672411569817
280.7140956005455810.5718087989088390.285904399454419
290.6939352455777510.6121295088444990.306064754422249
300.6377462527582510.7245074944834970.362253747241749
310.6393967498163010.7212065003673990.360603250183699
320.694437022531050.61112595493790.30556297746895
330.6638495404954350.672300919009130.336150459504565
340.6093685480664280.7812629038671440.390631451933572
350.5591442708099990.8817114583800020.440855729190001
360.5425037903523620.9149924192952770.457496209647638
370.836249509683190.3275009806336210.163750490316810
380.800059397370730.3998812052585410.199940602629271
390.7669923381398060.4660153237203880.233007661860194
400.7367169183924360.5265661632151280.263283081607564
410.7154366224650930.5691267550698140.284563377534907
420.6968288277572170.6063423444855650.303171172242783
430.7115803206820430.5768393586359140.288419679317957
440.6866488669137520.6267022661724960.313351133086248
450.6429684154168520.7140631691662960.357031584583148
460.6036946341473880.7926107317052240.396305365852612
470.5899652341655380.8200695316689230.410034765834462
480.5420857992765460.9158284014469090.457914200723454
490.5500363976485730.8999272047028530.449963602351427
500.5073186181909150.9853627636181710.492681381809085
510.4821413534945040.9642827069890090.517858646505496
520.4605087786026250.921017557205250.539491221397375
530.5789712331197040.8420575337605920.421028766880296
540.5302643669903420.9394712660193170.469735633009658
550.4835201578090450.967040315618090.516479842190955
560.4356594783402430.8713189566804870.564340521659757
570.3879520969127550.775904193825510.612047903087245
580.4049897218539050.809979443707810.595010278146095
590.3946100888098110.7892201776196210.60538991119019
600.3812983565785640.7625967131571280.618701643421436
610.5781901506438090.8436196987123830.421809849356191
620.5337052698434930.9325894603130150.466294730156507
630.4893294467872230.9786588935744450.510670553212777
640.4464003338052550.892800667610510.553599666194745
650.4006370979028920.8012741958057850.599362902097108
660.3643575483886360.7287150967772720.635642451611364
670.4175927888326060.8351855776652120.582407211167394
680.375562714435820.751125428871640.62443728556418
690.332926681560670.665853363121340.66707331843933
700.2957480835815230.5914961671630470.704251916418477
710.2571704684480450.514340936896090.742829531551955
720.2344198070813880.4688396141627750.765580192918612
730.2014323885301660.4028647770603320.798567611469834
740.1851900125942340.3703800251884690.814809987405766
750.1627940696828670.3255881393657350.837205930317133
760.2587950868549030.5175901737098060.741204913145097
770.2233136762408970.4466273524817940.776686323759103
780.2479528315479520.4959056630959050.752047168452048
790.2136193306366540.4272386612733090.786380669363346
800.2212624244078680.4425248488157370.778737575592132
810.1975816062911340.3951632125822670.802418393708866
820.1949320083213990.3898640166427980.805067991678601
830.2107682341408410.4215364682816820.78923176585916
840.1820400997298050.3640801994596110.817959900270195
850.1549412357487470.3098824714974940.845058764251253
860.1312116903404010.2624233806808020.8687883096596
870.1087305620778930.2174611241557860.891269437922107
880.09161663794538670.1832332758907730.908383362054613
890.0861733192373610.1723466384747220.913826680762639
900.2777025814264370.5554051628528740.722297418573563
910.2398241269194080.4796482538388160.760175873080592
920.2101451085988300.4202902171976610.78985489140117
930.1780657414092770.3561314828185550.821934258590723
940.1589651493518800.3179302987037600.84103485064812
950.1549199156922140.3098398313844270.845080084307786
960.1336610378638530.2673220757277050.866338962136147
970.1405169263585720.2810338527171450.859483073641428
980.1199262261512050.2398524523024090.880073773848795
990.1082226675121770.2164453350243530.891777332487823
1000.1078511217322130.2157022434644250.892148878267787
1010.1099488059841940.2198976119683880.890051194015806
1020.09777616576948370.1955523315389670.902223834230516
1030.0907090635415060.1814181270830120.909290936458494
1040.1008942652761110.2017885305522210.89910573472389
1050.08915290069728850.1783058013945770.910847099302711
1060.1226488775210780.2452977550421560.877351122478922
1070.1215889787361410.2431779574722820.878411021263859
1080.1055636745386900.2111273490773800.89443632546131
1090.09348760226245890.1869752045249180.906512397737541
1100.4598213328607260.9196426657214520.540178667139274
1110.4151047883482460.8302095766964910.584895211651754
1120.4254451011390620.8508902022781250.574554898860938
1130.3975175050642880.7950350101285770.602482494935712
1140.4037933430601740.8075866861203480.596206656939826
1150.3642544541147920.7285089082295840.635745545885208
1160.3685070309838110.7370140619676220.631492969016189
1170.327207498802720.654414997605440.67279250119728
1180.2857135975892680.5714271951785360.714286402410732
1190.2808799263647910.5617598527295820.719120073635209
1200.2696517118001760.5393034236003520.730348288199824
1210.3411009195536410.6822018391072810.65889908044636
1220.2974197362665680.5948394725331360.702580263733432
1230.2569463062264870.5138926124529750.743053693773513
1240.3043085181254490.6086170362508970.695691481874551
1250.2641517004110540.5283034008221080.735848299588946
1260.2332632556277360.4665265112554710.766736744372264
1270.3560305412055710.7120610824111430.643969458794429
1280.3530753063995970.7061506127991950.646924693600403
1290.3599898199737770.7199796399475530.640010180026223
1300.3587749712417690.7175499424835390.64122502875823
1310.319123140439370.638246280878740.68087685956063
1320.4319429575304510.8638859150609020.568057042469549
1330.5214520222709560.957095955458090.478547977729045
1340.4569553390980750.9139106781961490.543044660901925
1350.4150337287174880.8300674574349750.584966271282512
1360.3496153245919560.6992306491839120.650384675408044
1370.7799786957281580.4400426085436850.220021304271842
1380.7174289543985210.5651420912029580.282571045601479
1390.6988504365461220.6022991269077560.301149563453878
1400.6336433744812060.7327132510375880.366356625518794
1410.5976904738354980.8046190523290030.402309526164502
1420.5146257587135340.9707484825729320.485374241286466
1430.4298051202915310.8596102405830610.570194879708469
1440.3391329051954570.6782658103909130.660867094804543
1450.274854709197330.549709418394660.72514529080267
1460.1964615969026260.3929231938052530.803538403097374
1470.1301543762801190.2603087525602370.869845623719881
1480.07959579830783350.1591915966156670.920404201692167
1490.1586750786625260.3173501573250520.841324921337474
1500.1539951102018160.3079902204036320.846004889798184

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.994712349525612 & 0.0105753009487762 & 0.00528765047438808 \tabularnewline
10 & 0.98813593348303 & 0.0237281330339419 & 0.0118640665169709 \tabularnewline
11 & 0.97538040368224 & 0.0492391926355209 & 0.0246195963177605 \tabularnewline
12 & 0.96379605158239 & 0.0724078968352212 & 0.0362039484176106 \tabularnewline
13 & 0.9705574609338 & 0.0588850781324017 & 0.0294425390662009 \tabularnewline
14 & 0.950806395899729 & 0.0983872082005423 & 0.0491936041002712 \tabularnewline
15 & 0.94397886790798 & 0.112042264184041 & 0.0560211320920207 \tabularnewline
16 & 0.917614165163924 & 0.164771669672152 & 0.082385834836076 \tabularnewline
17 & 0.897266592027123 & 0.205466815945755 & 0.102733407972877 \tabularnewline
18 & 0.861379482445373 & 0.277241035109254 & 0.138620517554627 \tabularnewline
19 & 0.821277303686182 & 0.357445392627636 & 0.178722696313818 \tabularnewline
20 & 0.825004575719188 & 0.349990848561624 & 0.174995424280812 \tabularnewline
21 & 0.849742035127992 & 0.300515929744017 & 0.150257964872009 \tabularnewline
22 & 0.860451553089915 & 0.279096893820171 & 0.139548446910085 \tabularnewline
23 & 0.846423824007952 & 0.307152351984097 & 0.153576175992048 \tabularnewline
24 & 0.817266282561415 & 0.36546743487717 & 0.182733717438585 \tabularnewline
25 & 0.770692380402775 & 0.458615239194449 & 0.229307619597225 \tabularnewline
26 & 0.737323065090689 & 0.525353869818622 & 0.262676934909311 \tabularnewline
27 & 0.686327588430183 & 0.627344823139633 & 0.313672411569817 \tabularnewline
28 & 0.714095600545581 & 0.571808798908839 & 0.285904399454419 \tabularnewline
29 & 0.693935245577751 & 0.612129508844499 & 0.306064754422249 \tabularnewline
30 & 0.637746252758251 & 0.724507494483497 & 0.362253747241749 \tabularnewline
31 & 0.639396749816301 & 0.721206500367399 & 0.360603250183699 \tabularnewline
32 & 0.69443702253105 & 0.6111259549379 & 0.30556297746895 \tabularnewline
33 & 0.663849540495435 & 0.67230091900913 & 0.336150459504565 \tabularnewline
34 & 0.609368548066428 & 0.781262903867144 & 0.390631451933572 \tabularnewline
35 & 0.559144270809999 & 0.881711458380002 & 0.440855729190001 \tabularnewline
36 & 0.542503790352362 & 0.914992419295277 & 0.457496209647638 \tabularnewline
37 & 0.83624950968319 & 0.327500980633621 & 0.163750490316810 \tabularnewline
38 & 0.80005939737073 & 0.399881205258541 & 0.199940602629271 \tabularnewline
39 & 0.766992338139806 & 0.466015323720388 & 0.233007661860194 \tabularnewline
40 & 0.736716918392436 & 0.526566163215128 & 0.263283081607564 \tabularnewline
41 & 0.715436622465093 & 0.569126755069814 & 0.284563377534907 \tabularnewline
42 & 0.696828827757217 & 0.606342344485565 & 0.303171172242783 \tabularnewline
43 & 0.711580320682043 & 0.576839358635914 & 0.288419679317957 \tabularnewline
44 & 0.686648866913752 & 0.626702266172496 & 0.313351133086248 \tabularnewline
45 & 0.642968415416852 & 0.714063169166296 & 0.357031584583148 \tabularnewline
46 & 0.603694634147388 & 0.792610731705224 & 0.396305365852612 \tabularnewline
47 & 0.589965234165538 & 0.820069531668923 & 0.410034765834462 \tabularnewline
48 & 0.542085799276546 & 0.915828401446909 & 0.457914200723454 \tabularnewline
49 & 0.550036397648573 & 0.899927204702853 & 0.449963602351427 \tabularnewline
50 & 0.507318618190915 & 0.985362763618171 & 0.492681381809085 \tabularnewline
51 & 0.482141353494504 & 0.964282706989009 & 0.517858646505496 \tabularnewline
52 & 0.460508778602625 & 0.92101755720525 & 0.539491221397375 \tabularnewline
53 & 0.578971233119704 & 0.842057533760592 & 0.421028766880296 \tabularnewline
54 & 0.530264366990342 & 0.939471266019317 & 0.469735633009658 \tabularnewline
55 & 0.483520157809045 & 0.96704031561809 & 0.516479842190955 \tabularnewline
56 & 0.435659478340243 & 0.871318956680487 & 0.564340521659757 \tabularnewline
57 & 0.387952096912755 & 0.77590419382551 & 0.612047903087245 \tabularnewline
58 & 0.404989721853905 & 0.80997944370781 & 0.595010278146095 \tabularnewline
59 & 0.394610088809811 & 0.789220177619621 & 0.60538991119019 \tabularnewline
60 & 0.381298356578564 & 0.762596713157128 & 0.618701643421436 \tabularnewline
61 & 0.578190150643809 & 0.843619698712383 & 0.421809849356191 \tabularnewline
62 & 0.533705269843493 & 0.932589460313015 & 0.466294730156507 \tabularnewline
63 & 0.489329446787223 & 0.978658893574445 & 0.510670553212777 \tabularnewline
64 & 0.446400333805255 & 0.89280066761051 & 0.553599666194745 \tabularnewline
65 & 0.400637097902892 & 0.801274195805785 & 0.599362902097108 \tabularnewline
66 & 0.364357548388636 & 0.728715096777272 & 0.635642451611364 \tabularnewline
67 & 0.417592788832606 & 0.835185577665212 & 0.582407211167394 \tabularnewline
68 & 0.37556271443582 & 0.75112542887164 & 0.62443728556418 \tabularnewline
69 & 0.33292668156067 & 0.66585336312134 & 0.66707331843933 \tabularnewline
70 & 0.295748083581523 & 0.591496167163047 & 0.704251916418477 \tabularnewline
71 & 0.257170468448045 & 0.51434093689609 & 0.742829531551955 \tabularnewline
72 & 0.234419807081388 & 0.468839614162775 & 0.765580192918612 \tabularnewline
73 & 0.201432388530166 & 0.402864777060332 & 0.798567611469834 \tabularnewline
74 & 0.185190012594234 & 0.370380025188469 & 0.814809987405766 \tabularnewline
75 & 0.162794069682867 & 0.325588139365735 & 0.837205930317133 \tabularnewline
76 & 0.258795086854903 & 0.517590173709806 & 0.741204913145097 \tabularnewline
77 & 0.223313676240897 & 0.446627352481794 & 0.776686323759103 \tabularnewline
78 & 0.247952831547952 & 0.495905663095905 & 0.752047168452048 \tabularnewline
79 & 0.213619330636654 & 0.427238661273309 & 0.786380669363346 \tabularnewline
80 & 0.221262424407868 & 0.442524848815737 & 0.778737575592132 \tabularnewline
81 & 0.197581606291134 & 0.395163212582267 & 0.802418393708866 \tabularnewline
82 & 0.194932008321399 & 0.389864016642798 & 0.805067991678601 \tabularnewline
83 & 0.210768234140841 & 0.421536468281682 & 0.78923176585916 \tabularnewline
84 & 0.182040099729805 & 0.364080199459611 & 0.817959900270195 \tabularnewline
85 & 0.154941235748747 & 0.309882471497494 & 0.845058764251253 \tabularnewline
86 & 0.131211690340401 & 0.262423380680802 & 0.8687883096596 \tabularnewline
87 & 0.108730562077893 & 0.217461124155786 & 0.891269437922107 \tabularnewline
88 & 0.0916166379453867 & 0.183233275890773 & 0.908383362054613 \tabularnewline
89 & 0.086173319237361 & 0.172346638474722 & 0.913826680762639 \tabularnewline
90 & 0.277702581426437 & 0.555405162852874 & 0.722297418573563 \tabularnewline
91 & 0.239824126919408 & 0.479648253838816 & 0.760175873080592 \tabularnewline
92 & 0.210145108598830 & 0.420290217197661 & 0.78985489140117 \tabularnewline
93 & 0.178065741409277 & 0.356131482818555 & 0.821934258590723 \tabularnewline
94 & 0.158965149351880 & 0.317930298703760 & 0.84103485064812 \tabularnewline
95 & 0.154919915692214 & 0.309839831384427 & 0.845080084307786 \tabularnewline
96 & 0.133661037863853 & 0.267322075727705 & 0.866338962136147 \tabularnewline
97 & 0.140516926358572 & 0.281033852717145 & 0.859483073641428 \tabularnewline
98 & 0.119926226151205 & 0.239852452302409 & 0.880073773848795 \tabularnewline
99 & 0.108222667512177 & 0.216445335024353 & 0.891777332487823 \tabularnewline
100 & 0.107851121732213 & 0.215702243464425 & 0.892148878267787 \tabularnewline
101 & 0.109948805984194 & 0.219897611968388 & 0.890051194015806 \tabularnewline
102 & 0.0977761657694837 & 0.195552331538967 & 0.902223834230516 \tabularnewline
103 & 0.090709063541506 & 0.181418127083012 & 0.909290936458494 \tabularnewline
104 & 0.100894265276111 & 0.201788530552221 & 0.89910573472389 \tabularnewline
105 & 0.0891529006972885 & 0.178305801394577 & 0.910847099302711 \tabularnewline
106 & 0.122648877521078 & 0.245297755042156 & 0.877351122478922 \tabularnewline
107 & 0.121588978736141 & 0.243177957472282 & 0.878411021263859 \tabularnewline
108 & 0.105563674538690 & 0.211127349077380 & 0.89443632546131 \tabularnewline
109 & 0.0934876022624589 & 0.186975204524918 & 0.906512397737541 \tabularnewline
110 & 0.459821332860726 & 0.919642665721452 & 0.540178667139274 \tabularnewline
111 & 0.415104788348246 & 0.830209576696491 & 0.584895211651754 \tabularnewline
112 & 0.425445101139062 & 0.850890202278125 & 0.574554898860938 \tabularnewline
113 & 0.397517505064288 & 0.795035010128577 & 0.602482494935712 \tabularnewline
114 & 0.403793343060174 & 0.807586686120348 & 0.596206656939826 \tabularnewline
115 & 0.364254454114792 & 0.728508908229584 & 0.635745545885208 \tabularnewline
116 & 0.368507030983811 & 0.737014061967622 & 0.631492969016189 \tabularnewline
117 & 0.32720749880272 & 0.65441499760544 & 0.67279250119728 \tabularnewline
118 & 0.285713597589268 & 0.571427195178536 & 0.714286402410732 \tabularnewline
119 & 0.280879926364791 & 0.561759852729582 & 0.719120073635209 \tabularnewline
120 & 0.269651711800176 & 0.539303423600352 & 0.730348288199824 \tabularnewline
121 & 0.341100919553641 & 0.682201839107281 & 0.65889908044636 \tabularnewline
122 & 0.297419736266568 & 0.594839472533136 & 0.702580263733432 \tabularnewline
123 & 0.256946306226487 & 0.513892612452975 & 0.743053693773513 \tabularnewline
124 & 0.304308518125449 & 0.608617036250897 & 0.695691481874551 \tabularnewline
125 & 0.264151700411054 & 0.528303400822108 & 0.735848299588946 \tabularnewline
126 & 0.233263255627736 & 0.466526511255471 & 0.766736744372264 \tabularnewline
127 & 0.356030541205571 & 0.712061082411143 & 0.643969458794429 \tabularnewline
128 & 0.353075306399597 & 0.706150612799195 & 0.646924693600403 \tabularnewline
129 & 0.359989819973777 & 0.719979639947553 & 0.640010180026223 \tabularnewline
130 & 0.358774971241769 & 0.717549942483539 & 0.64122502875823 \tabularnewline
131 & 0.31912314043937 & 0.63824628087874 & 0.68087685956063 \tabularnewline
132 & 0.431942957530451 & 0.863885915060902 & 0.568057042469549 \tabularnewline
133 & 0.521452022270956 & 0.95709595545809 & 0.478547977729045 \tabularnewline
134 & 0.456955339098075 & 0.913910678196149 & 0.543044660901925 \tabularnewline
135 & 0.415033728717488 & 0.830067457434975 & 0.584966271282512 \tabularnewline
136 & 0.349615324591956 & 0.699230649183912 & 0.650384675408044 \tabularnewline
137 & 0.779978695728158 & 0.440042608543685 & 0.220021304271842 \tabularnewline
138 & 0.717428954398521 & 0.565142091202958 & 0.282571045601479 \tabularnewline
139 & 0.698850436546122 & 0.602299126907756 & 0.301149563453878 \tabularnewline
140 & 0.633643374481206 & 0.732713251037588 & 0.366356625518794 \tabularnewline
141 & 0.597690473835498 & 0.804619052329003 & 0.402309526164502 \tabularnewline
142 & 0.514625758713534 & 0.970748482572932 & 0.485374241286466 \tabularnewline
143 & 0.429805120291531 & 0.859610240583061 & 0.570194879708469 \tabularnewline
144 & 0.339132905195457 & 0.678265810390913 & 0.660867094804543 \tabularnewline
145 & 0.27485470919733 & 0.54970941839466 & 0.72514529080267 \tabularnewline
146 & 0.196461596902626 & 0.392923193805253 & 0.803538403097374 \tabularnewline
147 & 0.130154376280119 & 0.260308752560237 & 0.869845623719881 \tabularnewline
148 & 0.0795957983078335 & 0.159191596615667 & 0.920404201692167 \tabularnewline
149 & 0.158675078662526 & 0.317350157325052 & 0.841324921337474 \tabularnewline
150 & 0.153995110201816 & 0.307990220403632 & 0.846004889798184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&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]9[/C][C]0.994712349525612[/C][C]0.0105753009487762[/C][C]0.00528765047438808[/C][/ROW]
[ROW][C]10[/C][C]0.98813593348303[/C][C]0.0237281330339419[/C][C]0.0118640665169709[/C][/ROW]
[ROW][C]11[/C][C]0.97538040368224[/C][C]0.0492391926355209[/C][C]0.0246195963177605[/C][/ROW]
[ROW][C]12[/C][C]0.96379605158239[/C][C]0.0724078968352212[/C][C]0.0362039484176106[/C][/ROW]
[ROW][C]13[/C][C]0.9705574609338[/C][C]0.0588850781324017[/C][C]0.0294425390662009[/C][/ROW]
[ROW][C]14[/C][C]0.950806395899729[/C][C]0.0983872082005423[/C][C]0.0491936041002712[/C][/ROW]
[ROW][C]15[/C][C]0.94397886790798[/C][C]0.112042264184041[/C][C]0.0560211320920207[/C][/ROW]
[ROW][C]16[/C][C]0.917614165163924[/C][C]0.164771669672152[/C][C]0.082385834836076[/C][/ROW]
[ROW][C]17[/C][C]0.897266592027123[/C][C]0.205466815945755[/C][C]0.102733407972877[/C][/ROW]
[ROW][C]18[/C][C]0.861379482445373[/C][C]0.277241035109254[/C][C]0.138620517554627[/C][/ROW]
[ROW][C]19[/C][C]0.821277303686182[/C][C]0.357445392627636[/C][C]0.178722696313818[/C][/ROW]
[ROW][C]20[/C][C]0.825004575719188[/C][C]0.349990848561624[/C][C]0.174995424280812[/C][/ROW]
[ROW][C]21[/C][C]0.849742035127992[/C][C]0.300515929744017[/C][C]0.150257964872009[/C][/ROW]
[ROW][C]22[/C][C]0.860451553089915[/C][C]0.279096893820171[/C][C]0.139548446910085[/C][/ROW]
[ROW][C]23[/C][C]0.846423824007952[/C][C]0.307152351984097[/C][C]0.153576175992048[/C][/ROW]
[ROW][C]24[/C][C]0.817266282561415[/C][C]0.36546743487717[/C][C]0.182733717438585[/C][/ROW]
[ROW][C]25[/C][C]0.770692380402775[/C][C]0.458615239194449[/C][C]0.229307619597225[/C][/ROW]
[ROW][C]26[/C][C]0.737323065090689[/C][C]0.525353869818622[/C][C]0.262676934909311[/C][/ROW]
[ROW][C]27[/C][C]0.686327588430183[/C][C]0.627344823139633[/C][C]0.313672411569817[/C][/ROW]
[ROW][C]28[/C][C]0.714095600545581[/C][C]0.571808798908839[/C][C]0.285904399454419[/C][/ROW]
[ROW][C]29[/C][C]0.693935245577751[/C][C]0.612129508844499[/C][C]0.306064754422249[/C][/ROW]
[ROW][C]30[/C][C]0.637746252758251[/C][C]0.724507494483497[/C][C]0.362253747241749[/C][/ROW]
[ROW][C]31[/C][C]0.639396749816301[/C][C]0.721206500367399[/C][C]0.360603250183699[/C][/ROW]
[ROW][C]32[/C][C]0.69443702253105[/C][C]0.6111259549379[/C][C]0.30556297746895[/C][/ROW]
[ROW][C]33[/C][C]0.663849540495435[/C][C]0.67230091900913[/C][C]0.336150459504565[/C][/ROW]
[ROW][C]34[/C][C]0.609368548066428[/C][C]0.781262903867144[/C][C]0.390631451933572[/C][/ROW]
[ROW][C]35[/C][C]0.559144270809999[/C][C]0.881711458380002[/C][C]0.440855729190001[/C][/ROW]
[ROW][C]36[/C][C]0.542503790352362[/C][C]0.914992419295277[/C][C]0.457496209647638[/C][/ROW]
[ROW][C]37[/C][C]0.83624950968319[/C][C]0.327500980633621[/C][C]0.163750490316810[/C][/ROW]
[ROW][C]38[/C][C]0.80005939737073[/C][C]0.399881205258541[/C][C]0.199940602629271[/C][/ROW]
[ROW][C]39[/C][C]0.766992338139806[/C][C]0.466015323720388[/C][C]0.233007661860194[/C][/ROW]
[ROW][C]40[/C][C]0.736716918392436[/C][C]0.526566163215128[/C][C]0.263283081607564[/C][/ROW]
[ROW][C]41[/C][C]0.715436622465093[/C][C]0.569126755069814[/C][C]0.284563377534907[/C][/ROW]
[ROW][C]42[/C][C]0.696828827757217[/C][C]0.606342344485565[/C][C]0.303171172242783[/C][/ROW]
[ROW][C]43[/C][C]0.711580320682043[/C][C]0.576839358635914[/C][C]0.288419679317957[/C][/ROW]
[ROW][C]44[/C][C]0.686648866913752[/C][C]0.626702266172496[/C][C]0.313351133086248[/C][/ROW]
[ROW][C]45[/C][C]0.642968415416852[/C][C]0.714063169166296[/C][C]0.357031584583148[/C][/ROW]
[ROW][C]46[/C][C]0.603694634147388[/C][C]0.792610731705224[/C][C]0.396305365852612[/C][/ROW]
[ROW][C]47[/C][C]0.589965234165538[/C][C]0.820069531668923[/C][C]0.410034765834462[/C][/ROW]
[ROW][C]48[/C][C]0.542085799276546[/C][C]0.915828401446909[/C][C]0.457914200723454[/C][/ROW]
[ROW][C]49[/C][C]0.550036397648573[/C][C]0.899927204702853[/C][C]0.449963602351427[/C][/ROW]
[ROW][C]50[/C][C]0.507318618190915[/C][C]0.985362763618171[/C][C]0.492681381809085[/C][/ROW]
[ROW][C]51[/C][C]0.482141353494504[/C][C]0.964282706989009[/C][C]0.517858646505496[/C][/ROW]
[ROW][C]52[/C][C]0.460508778602625[/C][C]0.92101755720525[/C][C]0.539491221397375[/C][/ROW]
[ROW][C]53[/C][C]0.578971233119704[/C][C]0.842057533760592[/C][C]0.421028766880296[/C][/ROW]
[ROW][C]54[/C][C]0.530264366990342[/C][C]0.939471266019317[/C][C]0.469735633009658[/C][/ROW]
[ROW][C]55[/C][C]0.483520157809045[/C][C]0.96704031561809[/C][C]0.516479842190955[/C][/ROW]
[ROW][C]56[/C][C]0.435659478340243[/C][C]0.871318956680487[/C][C]0.564340521659757[/C][/ROW]
[ROW][C]57[/C][C]0.387952096912755[/C][C]0.77590419382551[/C][C]0.612047903087245[/C][/ROW]
[ROW][C]58[/C][C]0.404989721853905[/C][C]0.80997944370781[/C][C]0.595010278146095[/C][/ROW]
[ROW][C]59[/C][C]0.394610088809811[/C][C]0.789220177619621[/C][C]0.60538991119019[/C][/ROW]
[ROW][C]60[/C][C]0.381298356578564[/C][C]0.762596713157128[/C][C]0.618701643421436[/C][/ROW]
[ROW][C]61[/C][C]0.578190150643809[/C][C]0.843619698712383[/C][C]0.421809849356191[/C][/ROW]
[ROW][C]62[/C][C]0.533705269843493[/C][C]0.932589460313015[/C][C]0.466294730156507[/C][/ROW]
[ROW][C]63[/C][C]0.489329446787223[/C][C]0.978658893574445[/C][C]0.510670553212777[/C][/ROW]
[ROW][C]64[/C][C]0.446400333805255[/C][C]0.89280066761051[/C][C]0.553599666194745[/C][/ROW]
[ROW][C]65[/C][C]0.400637097902892[/C][C]0.801274195805785[/C][C]0.599362902097108[/C][/ROW]
[ROW][C]66[/C][C]0.364357548388636[/C][C]0.728715096777272[/C][C]0.635642451611364[/C][/ROW]
[ROW][C]67[/C][C]0.417592788832606[/C][C]0.835185577665212[/C][C]0.582407211167394[/C][/ROW]
[ROW][C]68[/C][C]0.37556271443582[/C][C]0.75112542887164[/C][C]0.62443728556418[/C][/ROW]
[ROW][C]69[/C][C]0.33292668156067[/C][C]0.66585336312134[/C][C]0.66707331843933[/C][/ROW]
[ROW][C]70[/C][C]0.295748083581523[/C][C]0.591496167163047[/C][C]0.704251916418477[/C][/ROW]
[ROW][C]71[/C][C]0.257170468448045[/C][C]0.51434093689609[/C][C]0.742829531551955[/C][/ROW]
[ROW][C]72[/C][C]0.234419807081388[/C][C]0.468839614162775[/C][C]0.765580192918612[/C][/ROW]
[ROW][C]73[/C][C]0.201432388530166[/C][C]0.402864777060332[/C][C]0.798567611469834[/C][/ROW]
[ROW][C]74[/C][C]0.185190012594234[/C][C]0.370380025188469[/C][C]0.814809987405766[/C][/ROW]
[ROW][C]75[/C][C]0.162794069682867[/C][C]0.325588139365735[/C][C]0.837205930317133[/C][/ROW]
[ROW][C]76[/C][C]0.258795086854903[/C][C]0.517590173709806[/C][C]0.741204913145097[/C][/ROW]
[ROW][C]77[/C][C]0.223313676240897[/C][C]0.446627352481794[/C][C]0.776686323759103[/C][/ROW]
[ROW][C]78[/C][C]0.247952831547952[/C][C]0.495905663095905[/C][C]0.752047168452048[/C][/ROW]
[ROW][C]79[/C][C]0.213619330636654[/C][C]0.427238661273309[/C][C]0.786380669363346[/C][/ROW]
[ROW][C]80[/C][C]0.221262424407868[/C][C]0.442524848815737[/C][C]0.778737575592132[/C][/ROW]
[ROW][C]81[/C][C]0.197581606291134[/C][C]0.395163212582267[/C][C]0.802418393708866[/C][/ROW]
[ROW][C]82[/C][C]0.194932008321399[/C][C]0.389864016642798[/C][C]0.805067991678601[/C][/ROW]
[ROW][C]83[/C][C]0.210768234140841[/C][C]0.421536468281682[/C][C]0.78923176585916[/C][/ROW]
[ROW][C]84[/C][C]0.182040099729805[/C][C]0.364080199459611[/C][C]0.817959900270195[/C][/ROW]
[ROW][C]85[/C][C]0.154941235748747[/C][C]0.309882471497494[/C][C]0.845058764251253[/C][/ROW]
[ROW][C]86[/C][C]0.131211690340401[/C][C]0.262423380680802[/C][C]0.8687883096596[/C][/ROW]
[ROW][C]87[/C][C]0.108730562077893[/C][C]0.217461124155786[/C][C]0.891269437922107[/C][/ROW]
[ROW][C]88[/C][C]0.0916166379453867[/C][C]0.183233275890773[/C][C]0.908383362054613[/C][/ROW]
[ROW][C]89[/C][C]0.086173319237361[/C][C]0.172346638474722[/C][C]0.913826680762639[/C][/ROW]
[ROW][C]90[/C][C]0.277702581426437[/C][C]0.555405162852874[/C][C]0.722297418573563[/C][/ROW]
[ROW][C]91[/C][C]0.239824126919408[/C][C]0.479648253838816[/C][C]0.760175873080592[/C][/ROW]
[ROW][C]92[/C][C]0.210145108598830[/C][C]0.420290217197661[/C][C]0.78985489140117[/C][/ROW]
[ROW][C]93[/C][C]0.178065741409277[/C][C]0.356131482818555[/C][C]0.821934258590723[/C][/ROW]
[ROW][C]94[/C][C]0.158965149351880[/C][C]0.317930298703760[/C][C]0.84103485064812[/C][/ROW]
[ROW][C]95[/C][C]0.154919915692214[/C][C]0.309839831384427[/C][C]0.845080084307786[/C][/ROW]
[ROW][C]96[/C][C]0.133661037863853[/C][C]0.267322075727705[/C][C]0.866338962136147[/C][/ROW]
[ROW][C]97[/C][C]0.140516926358572[/C][C]0.281033852717145[/C][C]0.859483073641428[/C][/ROW]
[ROW][C]98[/C][C]0.119926226151205[/C][C]0.239852452302409[/C][C]0.880073773848795[/C][/ROW]
[ROW][C]99[/C][C]0.108222667512177[/C][C]0.216445335024353[/C][C]0.891777332487823[/C][/ROW]
[ROW][C]100[/C][C]0.107851121732213[/C][C]0.215702243464425[/C][C]0.892148878267787[/C][/ROW]
[ROW][C]101[/C][C]0.109948805984194[/C][C]0.219897611968388[/C][C]0.890051194015806[/C][/ROW]
[ROW][C]102[/C][C]0.0977761657694837[/C][C]0.195552331538967[/C][C]0.902223834230516[/C][/ROW]
[ROW][C]103[/C][C]0.090709063541506[/C][C]0.181418127083012[/C][C]0.909290936458494[/C][/ROW]
[ROW][C]104[/C][C]0.100894265276111[/C][C]0.201788530552221[/C][C]0.89910573472389[/C][/ROW]
[ROW][C]105[/C][C]0.0891529006972885[/C][C]0.178305801394577[/C][C]0.910847099302711[/C][/ROW]
[ROW][C]106[/C][C]0.122648877521078[/C][C]0.245297755042156[/C][C]0.877351122478922[/C][/ROW]
[ROW][C]107[/C][C]0.121588978736141[/C][C]0.243177957472282[/C][C]0.878411021263859[/C][/ROW]
[ROW][C]108[/C][C]0.105563674538690[/C][C]0.211127349077380[/C][C]0.89443632546131[/C][/ROW]
[ROW][C]109[/C][C]0.0934876022624589[/C][C]0.186975204524918[/C][C]0.906512397737541[/C][/ROW]
[ROW][C]110[/C][C]0.459821332860726[/C][C]0.919642665721452[/C][C]0.540178667139274[/C][/ROW]
[ROW][C]111[/C][C]0.415104788348246[/C][C]0.830209576696491[/C][C]0.584895211651754[/C][/ROW]
[ROW][C]112[/C][C]0.425445101139062[/C][C]0.850890202278125[/C][C]0.574554898860938[/C][/ROW]
[ROW][C]113[/C][C]0.397517505064288[/C][C]0.795035010128577[/C][C]0.602482494935712[/C][/ROW]
[ROW][C]114[/C][C]0.403793343060174[/C][C]0.807586686120348[/C][C]0.596206656939826[/C][/ROW]
[ROW][C]115[/C][C]0.364254454114792[/C][C]0.728508908229584[/C][C]0.635745545885208[/C][/ROW]
[ROW][C]116[/C][C]0.368507030983811[/C][C]0.737014061967622[/C][C]0.631492969016189[/C][/ROW]
[ROW][C]117[/C][C]0.32720749880272[/C][C]0.65441499760544[/C][C]0.67279250119728[/C][/ROW]
[ROW][C]118[/C][C]0.285713597589268[/C][C]0.571427195178536[/C][C]0.714286402410732[/C][/ROW]
[ROW][C]119[/C][C]0.280879926364791[/C][C]0.561759852729582[/C][C]0.719120073635209[/C][/ROW]
[ROW][C]120[/C][C]0.269651711800176[/C][C]0.539303423600352[/C][C]0.730348288199824[/C][/ROW]
[ROW][C]121[/C][C]0.341100919553641[/C][C]0.682201839107281[/C][C]0.65889908044636[/C][/ROW]
[ROW][C]122[/C][C]0.297419736266568[/C][C]0.594839472533136[/C][C]0.702580263733432[/C][/ROW]
[ROW][C]123[/C][C]0.256946306226487[/C][C]0.513892612452975[/C][C]0.743053693773513[/C][/ROW]
[ROW][C]124[/C][C]0.304308518125449[/C][C]0.608617036250897[/C][C]0.695691481874551[/C][/ROW]
[ROW][C]125[/C][C]0.264151700411054[/C][C]0.528303400822108[/C][C]0.735848299588946[/C][/ROW]
[ROW][C]126[/C][C]0.233263255627736[/C][C]0.466526511255471[/C][C]0.766736744372264[/C][/ROW]
[ROW][C]127[/C][C]0.356030541205571[/C][C]0.712061082411143[/C][C]0.643969458794429[/C][/ROW]
[ROW][C]128[/C][C]0.353075306399597[/C][C]0.706150612799195[/C][C]0.646924693600403[/C][/ROW]
[ROW][C]129[/C][C]0.359989819973777[/C][C]0.719979639947553[/C][C]0.640010180026223[/C][/ROW]
[ROW][C]130[/C][C]0.358774971241769[/C][C]0.717549942483539[/C][C]0.64122502875823[/C][/ROW]
[ROW][C]131[/C][C]0.31912314043937[/C][C]0.63824628087874[/C][C]0.68087685956063[/C][/ROW]
[ROW][C]132[/C][C]0.431942957530451[/C][C]0.863885915060902[/C][C]0.568057042469549[/C][/ROW]
[ROW][C]133[/C][C]0.521452022270956[/C][C]0.95709595545809[/C][C]0.478547977729045[/C][/ROW]
[ROW][C]134[/C][C]0.456955339098075[/C][C]0.913910678196149[/C][C]0.543044660901925[/C][/ROW]
[ROW][C]135[/C][C]0.415033728717488[/C][C]0.830067457434975[/C][C]0.584966271282512[/C][/ROW]
[ROW][C]136[/C][C]0.349615324591956[/C][C]0.699230649183912[/C][C]0.650384675408044[/C][/ROW]
[ROW][C]137[/C][C]0.779978695728158[/C][C]0.440042608543685[/C][C]0.220021304271842[/C][/ROW]
[ROW][C]138[/C][C]0.717428954398521[/C][C]0.565142091202958[/C][C]0.282571045601479[/C][/ROW]
[ROW][C]139[/C][C]0.698850436546122[/C][C]0.602299126907756[/C][C]0.301149563453878[/C][/ROW]
[ROW][C]140[/C][C]0.633643374481206[/C][C]0.732713251037588[/C][C]0.366356625518794[/C][/ROW]
[ROW][C]141[/C][C]0.597690473835498[/C][C]0.804619052329003[/C][C]0.402309526164502[/C][/ROW]
[ROW][C]142[/C][C]0.514625758713534[/C][C]0.970748482572932[/C][C]0.485374241286466[/C][/ROW]
[ROW][C]143[/C][C]0.429805120291531[/C][C]0.859610240583061[/C][C]0.570194879708469[/C][/ROW]
[ROW][C]144[/C][C]0.339132905195457[/C][C]0.678265810390913[/C][C]0.660867094804543[/C][/ROW]
[ROW][C]145[/C][C]0.27485470919733[/C][C]0.54970941839466[/C][C]0.72514529080267[/C][/ROW]
[ROW][C]146[/C][C]0.196461596902626[/C][C]0.392923193805253[/C][C]0.803538403097374[/C][/ROW]
[ROW][C]147[/C][C]0.130154376280119[/C][C]0.260308752560237[/C][C]0.869845623719881[/C][/ROW]
[ROW][C]148[/C][C]0.0795957983078335[/C][C]0.159191596615667[/C][C]0.920404201692167[/C][/ROW]
[ROW][C]149[/C][C]0.158675078662526[/C][C]0.317350157325052[/C][C]0.841324921337474[/C][/ROW]
[ROW][C]150[/C][C]0.153995110201816[/C][C]0.307990220403632[/C][C]0.846004889798184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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
90.9947123495256120.01057530094877620.00528765047438808
100.988135933483030.02372813303394190.0118640665169709
110.975380403682240.04923919263552090.0246195963177605
120.963796051582390.07240789683522120.0362039484176106
130.97055746093380.05888507813240170.0294425390662009
140.9508063958997290.09838720820054230.0491936041002712
150.943978867907980.1120422641840410.0560211320920207
160.9176141651639240.1647716696721520.082385834836076
170.8972665920271230.2054668159457550.102733407972877
180.8613794824453730.2772410351092540.138620517554627
190.8212773036861820.3574453926276360.178722696313818
200.8250045757191880.3499908485616240.174995424280812
210.8497420351279920.3005159297440170.150257964872009
220.8604515530899150.2790968938201710.139548446910085
230.8464238240079520.3071523519840970.153576175992048
240.8172662825614150.365467434877170.182733717438585
250.7706923804027750.4586152391944490.229307619597225
260.7373230650906890.5253538698186220.262676934909311
270.6863275884301830.6273448231396330.313672411569817
280.7140956005455810.5718087989088390.285904399454419
290.6939352455777510.6121295088444990.306064754422249
300.6377462527582510.7245074944834970.362253747241749
310.6393967498163010.7212065003673990.360603250183699
320.694437022531050.61112595493790.30556297746895
330.6638495404954350.672300919009130.336150459504565
340.6093685480664280.7812629038671440.390631451933572
350.5591442708099990.8817114583800020.440855729190001
360.5425037903523620.9149924192952770.457496209647638
370.836249509683190.3275009806336210.163750490316810
380.800059397370730.3998812052585410.199940602629271
390.7669923381398060.4660153237203880.233007661860194
400.7367169183924360.5265661632151280.263283081607564
410.7154366224650930.5691267550698140.284563377534907
420.6968288277572170.6063423444855650.303171172242783
430.7115803206820430.5768393586359140.288419679317957
440.6866488669137520.6267022661724960.313351133086248
450.6429684154168520.7140631691662960.357031584583148
460.6036946341473880.7926107317052240.396305365852612
470.5899652341655380.8200695316689230.410034765834462
480.5420857992765460.9158284014469090.457914200723454
490.5500363976485730.8999272047028530.449963602351427
500.5073186181909150.9853627636181710.492681381809085
510.4821413534945040.9642827069890090.517858646505496
520.4605087786026250.921017557205250.539491221397375
530.5789712331197040.8420575337605920.421028766880296
540.5302643669903420.9394712660193170.469735633009658
550.4835201578090450.967040315618090.516479842190955
560.4356594783402430.8713189566804870.564340521659757
570.3879520969127550.775904193825510.612047903087245
580.4049897218539050.809979443707810.595010278146095
590.3946100888098110.7892201776196210.60538991119019
600.3812983565785640.7625967131571280.618701643421436
610.5781901506438090.8436196987123830.421809849356191
620.5337052698434930.9325894603130150.466294730156507
630.4893294467872230.9786588935744450.510670553212777
640.4464003338052550.892800667610510.553599666194745
650.4006370979028920.8012741958057850.599362902097108
660.3643575483886360.7287150967772720.635642451611364
670.4175927888326060.8351855776652120.582407211167394
680.375562714435820.751125428871640.62443728556418
690.332926681560670.665853363121340.66707331843933
700.2957480835815230.5914961671630470.704251916418477
710.2571704684480450.514340936896090.742829531551955
720.2344198070813880.4688396141627750.765580192918612
730.2014323885301660.4028647770603320.798567611469834
740.1851900125942340.3703800251884690.814809987405766
750.1627940696828670.3255881393657350.837205930317133
760.2587950868549030.5175901737098060.741204913145097
770.2233136762408970.4466273524817940.776686323759103
780.2479528315479520.4959056630959050.752047168452048
790.2136193306366540.4272386612733090.786380669363346
800.2212624244078680.4425248488157370.778737575592132
810.1975816062911340.3951632125822670.802418393708866
820.1949320083213990.3898640166427980.805067991678601
830.2107682341408410.4215364682816820.78923176585916
840.1820400997298050.3640801994596110.817959900270195
850.1549412357487470.3098824714974940.845058764251253
860.1312116903404010.2624233806808020.8687883096596
870.1087305620778930.2174611241557860.891269437922107
880.09161663794538670.1832332758907730.908383362054613
890.0861733192373610.1723466384747220.913826680762639
900.2777025814264370.5554051628528740.722297418573563
910.2398241269194080.4796482538388160.760175873080592
920.2101451085988300.4202902171976610.78985489140117
930.1780657414092770.3561314828185550.821934258590723
940.1589651493518800.3179302987037600.84103485064812
950.1549199156922140.3098398313844270.845080084307786
960.1336610378638530.2673220757277050.866338962136147
970.1405169263585720.2810338527171450.859483073641428
980.1199262261512050.2398524523024090.880073773848795
990.1082226675121770.2164453350243530.891777332487823
1000.1078511217322130.2157022434644250.892148878267787
1010.1099488059841940.2198976119683880.890051194015806
1020.09777616576948370.1955523315389670.902223834230516
1030.0907090635415060.1814181270830120.909290936458494
1040.1008942652761110.2017885305522210.89910573472389
1050.08915290069728850.1783058013945770.910847099302711
1060.1226488775210780.2452977550421560.877351122478922
1070.1215889787361410.2431779574722820.878411021263859
1080.1055636745386900.2111273490773800.89443632546131
1090.09348760226245890.1869752045249180.906512397737541
1100.4598213328607260.9196426657214520.540178667139274
1110.4151047883482460.8302095766964910.584895211651754
1120.4254451011390620.8508902022781250.574554898860938
1130.3975175050642880.7950350101285770.602482494935712
1140.4037933430601740.8075866861203480.596206656939826
1150.3642544541147920.7285089082295840.635745545885208
1160.3685070309838110.7370140619676220.631492969016189
1170.327207498802720.654414997605440.67279250119728
1180.2857135975892680.5714271951785360.714286402410732
1190.2808799263647910.5617598527295820.719120073635209
1200.2696517118001760.5393034236003520.730348288199824
1210.3411009195536410.6822018391072810.65889908044636
1220.2974197362665680.5948394725331360.702580263733432
1230.2569463062264870.5138926124529750.743053693773513
1240.3043085181254490.6086170362508970.695691481874551
1250.2641517004110540.5283034008221080.735848299588946
1260.2332632556277360.4665265112554710.766736744372264
1270.3560305412055710.7120610824111430.643969458794429
1280.3530753063995970.7061506127991950.646924693600403
1290.3599898199737770.7199796399475530.640010180026223
1300.3587749712417690.7175499424835390.64122502875823
1310.319123140439370.638246280878740.68087685956063
1320.4319429575304510.8638859150609020.568057042469549
1330.5214520222709560.957095955458090.478547977729045
1340.4569553390980750.9139106781961490.543044660901925
1350.4150337287174880.8300674574349750.584966271282512
1360.3496153245919560.6992306491839120.650384675408044
1370.7799786957281580.4400426085436850.220021304271842
1380.7174289543985210.5651420912029580.282571045601479
1390.6988504365461220.6022991269077560.301149563453878
1400.6336433744812060.7327132510375880.366356625518794
1410.5976904738354980.8046190523290030.402309526164502
1420.5146257587135340.9707484825729320.485374241286466
1430.4298051202915310.8596102405830610.570194879708469
1440.3391329051954570.6782658103909130.660867094804543
1450.274854709197330.549709418394660.72514529080267
1460.1964615969026260.3929231938052530.803538403097374
1470.1301543762801190.2603087525602370.869845623719881
1480.07959579830783350.1591915966156670.920404201692167
1490.1586750786625260.3173501573250520.841324921337474
1500.1539951102018160.3079902204036320.846004889798184






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0211267605633803OK
10% type I error level60.0422535211267606OK

\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 & 0.0211267605633803 & OK \tabularnewline
10% type I error level & 6 & 0.0422535211267606 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101744&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]0.0211267605633803[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0422535211267606[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101744&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101744&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 level30.0211267605633803OK
10% type I error level60.0422535211267606OK


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 = Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}