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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 13:32:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352140406qyxp5mwz8lep5x3.htm/, Retrieved Wed, 01 Feb 2023 16:46:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186203, Retrieved Wed, 01 Feb 2023 16:46:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS7 5] [2012-11-05 18:32:58] [0eae5e694d1d975eb250a0af7a7338a6] [Current]
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Dataseries X:
41	38	13	12	14	12	53
39	32	16	11	18	11	86
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	6	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	7	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	9	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	28	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	7	7	16	8	71
29	31	10	6	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	12	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	32	14	6	15	12	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	77
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
37	38	20	12	16	10	30
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	10	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	82
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	70
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
33	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	82
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	8	9	13	12	69
32	34	16	8	15	10	54
30	34	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
35	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	35	12	8	12	18	84
34	36	13	8	13	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=0

As an alternative you can also use a 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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.53674156126451 -0.0475663060388085Connected[t] + 0.0332648042754252Separate[t] + 0.529086781948238Learning[t] -0.0402612851467604Happiness[t] -0.0230685092860339Depression[t] -0.000217011775257963Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.53674156126451 -0.0475663060388085Connected[t] +  0.0332648042754252Separate[t] +  0.529086781948238Learning[t] -0.0402612851467604Happiness[t] -0.0230685092860339Depression[t] -0.000217011775257963Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.53674156126451 -0.0475663060388085Connected[t] +  0.0332648042754252Separate[t] +  0.529086781948238Learning[t] -0.0402612851467604Happiness[t] -0.0230685092860339Depression[t] -0.000217011775257963Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.53674156126451 -0.0475663060388085Connected[t] + 0.0332648042754252Separate[t] + 0.529086781948238Learning[t] -0.0402612851467604Happiness[t] -0.0230685092860339Depression[t] -0.000217011775257963Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.536741561264512.5528281.77710.0775060.038753
Connected-0.04756630603880850.046846-1.01540.3115060.155753
Separate0.03326480427542520.0438920.75790.4496760.224838
Learning0.5290867819482380.0667247.929500
Happiness-0.04026128514676040.075143-0.53580.5928660.296433
Depression-0.02306850928603390.055311-0.41710.6772030.338601
Belonging-0.0002170117752579630.014179-0.01530.9878090.493904

\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) & 4.53674156126451 & 2.552828 & 1.7771 & 0.077506 & 0.038753 \tabularnewline
Connected & -0.0475663060388085 & 0.046846 & -1.0154 & 0.311506 & 0.155753 \tabularnewline
Separate & 0.0332648042754252 & 0.043892 & 0.7579 & 0.449676 & 0.224838 \tabularnewline
Learning & 0.529086781948238 & 0.066724 & 7.9295 & 0 & 0 \tabularnewline
Happiness & -0.0402612851467604 & 0.075143 & -0.5358 & 0.592866 & 0.296433 \tabularnewline
Depression & -0.0230685092860339 & 0.055311 & -0.4171 & 0.677203 & 0.338601 \tabularnewline
Belonging & -0.000217011775257963 & 0.014179 & -0.0153 & 0.987809 & 0.493904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&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]4.53674156126451[/C][C]2.552828[/C][C]1.7771[/C][C]0.077506[/C][C]0.038753[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0475663060388085[/C][C]0.046846[/C][C]-1.0154[/C][C]0.311506[/C][C]0.155753[/C][/ROW]
[ROW][C]Separate[/C][C]0.0332648042754252[/C][C]0.043892[/C][C]0.7579[/C][C]0.449676[/C][C]0.224838[/C][/ROW]
[ROW][C]Learning[/C][C]0.529086781948238[/C][C]0.066724[/C][C]7.9295[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0402612851467604[/C][C]0.075143[/C][C]-0.5358[/C][C]0.592866[/C][C]0.296433[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0230685092860339[/C][C]0.055311[/C][C]-0.4171[/C][C]0.677203[/C][C]0.338601[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.000217011775257963[/C][C]0.014179[/C][C]-0.0153[/C][C]0.987809[/C][C]0.493904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=2

As an alternative you can also use a 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)4.536741561264512.5528281.77710.0775060.038753
Connected-0.04756630603880850.046846-1.01540.3115060.155753
Separate0.03326480427542520.0438920.75790.4496760.224838
Learning0.5290867819482380.0667247.929500
Happiness-0.04026128514676040.075143-0.53580.5928660.296433
Depression-0.02306850928603390.055311-0.41710.6772030.338601
Belonging-0.0002170117752579630.014179-0.01530.9878090.493904







Multiple Linear Regression - Regression Statistics
Multiple R0.551793903306914
R-squared0.30447651172668
Adjusted R-squared0.277553021858036
F-TEST (value)11.3089541219274
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.8438783833119e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82039360524009
Sum Squared Residuals513.644096089846

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.551793903306914 \tabularnewline
R-squared & 0.30447651172668 \tabularnewline
Adjusted R-squared & 0.277553021858036 \tabularnewline
F-TEST (value) & 11.3089541219274 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.8438783833119e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.82039360524009 \tabularnewline
Sum Squared Residuals & 513.644096089846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.551793903306914[/C][/ROW]
[ROW][C]R-squared[/C][C]0.30447651172668[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.277553021858036[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.3089541219274[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.8438783833119e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.82039360524009[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]513.644096089846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.551793903306914
R-squared0.30447651172668
Adjusted R-squared0.277553021858036
F-TEST (value)11.3089541219274
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.8438783833119e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82039360524009
Sum Squared Residuals513.644096089846







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.87673201389092.1232679861091
21111.2143981262762-0.214398126276164
31513.54651334297081.45348665702919
4611.3217290222382-5.32172902223825
51310.41238770913662.58761229086338
6109.696276176676440.303723823323563
71212.6828465948696-0.682846594869635
81411.21854930286232.78145069713774
91210.54517439935181.4548256006482
10611.0568497366789-5.05684973667889
111011.2935485048991-1.29354850489915
121211.4595152724630.540484727536964
131211.59569831290110.404301687098903
141111.4322483711724-0.43224837117239
151512.21805826334522.78194173665484
161211.14945623331120.850543766688827
171010.9222370047011-0.922237004701128
181213.6177276473567-1.61772764735672
191112.6610484247539-1.66104842475394
201211.58804116175180.411958838248243
211111.6772413454725-0.677241345472543
221211.77819345043510.22180654956493
231313.1461683058827-0.146168305882693
241111.721256904562-0.721256904561957
25911.7837461933712-2.78374619337116
261312.05814295169120.941857048308756
271011.539584145265-1.53958414526497
281411.20949350556022.79050649443981
291211.58637518856870.413624811431317
301010.6671360980343-0.667136098034311
311211.06074423505380.939255764946171
3289.78482343634683-1.78482343634683
331010.2846773412435-0.284677341243543
341211.73977155683810.26022844316187
351210.44503874734091.55496125265905
3676.97182931006960.028170689930404
3768.64200194427687-2.64200194427687
381210.43315880755471.56684119244535
391011.7222962945742-1.7222962945742
401011.665992289906-1.66599228990604
411011.4790557536692-1.47905575366923
421210.44102506881241.55897493118759
431513.53498403506781.4650159649322
441010.5594951741916-0.559495174191578
451010.3159885623767-0.315988562376742
46129.003489430848762.99651056915124
471310.60812261963922.39187738036076
481111.1604097740797-0.160409774079746
491111.5072772593509-0.507277259350861
501210.40748442368011.59251557631987
511411.85358033863352.14641966136646
521010.5186219325593-0.518621932559337
53129.589620646886312.41037935311369
541311.82938894775141.17061105224859
5558.09031377662165-3.09031377662165
56610.3512426383641-4.35124263836413
571211.31791742785160.682082572148437
581211.92763489295740.0723651070426507
591111.1809605467522-0.180960546752193
601011.6239588211228-1.6239588211228
6179.5952461922076-2.5952461922076
621211.50868506329090.491314936709087
631411.81273766201512.18726233798491
641110.73576743660410.264232563395859
651211.50055606587120.499443934128751
661312.42346650558240.576533494417607
671412.66282319693161.33717680306838
681112.7556647778177-1.75566477781767
69129.443807540385762.55619245961424
701211.62625489957010.373745100429911
7188.33953430347212-0.339534303472119
721110.73153674630030.268463253699653
731412.81898874603921.18101125396084
741412.7670627995961.23293720040402
751211.48764608437740.512353915622602
76911.7352770069174-2.73527700691739
771311.47345121470971.52654878529027
781111.4855494223918-0.485549422391765
791210.19370228542851.80629771457155
801211.39759453809080.602405461909221
811211.75515811747780.244841882522197
821213.7412104307933-1.74121043079329
831211.74116342244970.258836577550258
841210.87398397476681.12601602523319
851111.1855586061583-0.185558606158294
861011.5833923862259-1.58339238622588
87910.5136784647425-1.51367846474252
881211.70799140254030.292008597459742
891211.80603795206220.19396204793783
901211.19656670770830.803433292291679
9199.60858608354843-0.608586083548425
921512.00216059173872.99783940826125
931211.78064653175580.21935346824424
941210.95485542004181.04514457995818
951210.26194823794011.73805176205993
961011.4292819843229-1.42928198432293
971311.53923951580981.46076048419016
98911.5341797809451-2.53417978094508
991211.41740669092770.582593309072318
1001010.7682704042319-0.768270404231942
1011411.4605235616022.53947643839795
1021111.5383714687088-0.538371468708803
1031513.69743926634541.30256073365459
1041111.1713855010304-0.171385501030366
1051111.7396099838739-0.739609983873909
106129.761281178379862.23871882162014
1071211.97353831513560.026461684864413
1081211.36081562976550.639184370234495
1091111.6936520780571-0.693652078057068
11079.27131394854819-2.27131394854819
1111211.76147086385950.238529136140498
1121411.82035247904832.17964752095166
1131112.5767046911582-1.57670469115817
1141110.07255439563380.927445604366178
115109.238814827578220.761185172421776
1161312.47129538182980.528704618170188
1171310.80652482235312.19347517764686
118810.7785548371962-2.77855483719623
1191110.22320138882910.77679861117093
1201211.46292670575850.537073294241452
1211110.23509768858610.764902311413893
1221311.5226142836911.47738571630898
1231210.02769053651271.97230946348726
1241411.70127906505732.29872093494271
1251311.0610493848041.93895061519601
1261511.83101888702273.16898111297734
1271010.7939101102318-0.793910110231756
1281111.8044409436942-0.804440943694227
129911.064592171392-2.06459217139201
130119.399266028635691.60073397136431
1311011.7851886751859-1.78518867518592
132118.722182019265332.27781798073468
133811.4789613123181-3.47896131231812
134119.635384127974621.36461587202538
1351210.55126548756721.4487345124328
1361210.93066616330981.06933383669024
13799.82967036550726-0.82967036550726
1381111.1197681709081-0.119768170908117
139109.270286210115930.729713789884068
14089.48040394568525-1.48040394568525
14197.643955848454151.35604415154585
142811.7646886186332-3.76468861863324
143911.191688773214-2.19168877321399
1441512.4093527067012.59064729329904
1451111.3952137882394-0.395213788239373
14688.03891785924025-0.0389178592402469
1471312.49883598982720.50116401017276
1481210.14423019926151.85576980073846
1491211.75805230468080.241947695319202
15099.99802764969761-0.998027649697608
15178.37796844333626-1.37796844333626
1521310.7733509000332.22664909996697
153911.7817332476172-2.78173324761722
154611.8590543468075-5.85905434680751
155810.5945120872977-2.59451208729773
15688.33357342147779-0.333573421477789
1571512.00216059173872.99783940826125
158610.2219547569886-4.22195475698856
159911.064592171392-2.06459217139201
1601111.7001071243266-0.700107124326592
16189.61133172300999-1.61133172300999
162810.0876816072102-2.08768160721021

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.8767320138909 & 2.1232679861091 \tabularnewline
2 & 11 & 11.2143981262762 & -0.214398126276164 \tabularnewline
3 & 15 & 13.5465133429708 & 1.45348665702919 \tabularnewline
4 & 6 & 11.3217290222382 & -5.32172902223825 \tabularnewline
5 & 13 & 10.4123877091366 & 2.58761229086338 \tabularnewline
6 & 10 & 9.69627617667644 & 0.303723823323563 \tabularnewline
7 & 12 & 12.6828465948696 & -0.682846594869635 \tabularnewline
8 & 14 & 11.2185493028623 & 2.78145069713774 \tabularnewline
9 & 12 & 10.5451743993518 & 1.4548256006482 \tabularnewline
10 & 6 & 11.0568497366789 & -5.05684973667889 \tabularnewline
11 & 10 & 11.2935485048991 & -1.29354850489915 \tabularnewline
12 & 12 & 11.459515272463 & 0.540484727536964 \tabularnewline
13 & 12 & 11.5956983129011 & 0.404301687098903 \tabularnewline
14 & 11 & 11.4322483711724 & -0.43224837117239 \tabularnewline
15 & 15 & 12.2180582633452 & 2.78194173665484 \tabularnewline
16 & 12 & 11.1494562333112 & 0.850543766688827 \tabularnewline
17 & 10 & 10.9222370047011 & -0.922237004701128 \tabularnewline
18 & 12 & 13.6177276473567 & -1.61772764735672 \tabularnewline
19 & 11 & 12.6610484247539 & -1.66104842475394 \tabularnewline
20 & 12 & 11.5880411617518 & 0.411958838248243 \tabularnewline
21 & 11 & 11.6772413454725 & -0.677241345472543 \tabularnewline
22 & 12 & 11.7781934504351 & 0.22180654956493 \tabularnewline
23 & 13 & 13.1461683058827 & -0.146168305882693 \tabularnewline
24 & 11 & 11.721256904562 & -0.721256904561957 \tabularnewline
25 & 9 & 11.7837461933712 & -2.78374619337116 \tabularnewline
26 & 13 & 12.0581429516912 & 0.941857048308756 \tabularnewline
27 & 10 & 11.539584145265 & -1.53958414526497 \tabularnewline
28 & 14 & 11.2094935055602 & 2.79050649443981 \tabularnewline
29 & 12 & 11.5863751885687 & 0.413624811431317 \tabularnewline
30 & 10 & 10.6671360980343 & -0.667136098034311 \tabularnewline
31 & 12 & 11.0607442350538 & 0.939255764946171 \tabularnewline
32 & 8 & 9.78482343634683 & -1.78482343634683 \tabularnewline
33 & 10 & 10.2846773412435 & -0.284677341243543 \tabularnewline
34 & 12 & 11.7397715568381 & 0.26022844316187 \tabularnewline
35 & 12 & 10.4450387473409 & 1.55496125265905 \tabularnewline
36 & 7 & 6.9718293100696 & 0.028170689930404 \tabularnewline
37 & 6 & 8.64200194427687 & -2.64200194427687 \tabularnewline
38 & 12 & 10.4331588075547 & 1.56684119244535 \tabularnewline
39 & 10 & 11.7222962945742 & -1.7222962945742 \tabularnewline
40 & 10 & 11.665992289906 & -1.66599228990604 \tabularnewline
41 & 10 & 11.4790557536692 & -1.47905575366923 \tabularnewline
42 & 12 & 10.4410250688124 & 1.55897493118759 \tabularnewline
43 & 15 & 13.5349840350678 & 1.4650159649322 \tabularnewline
44 & 10 & 10.5594951741916 & -0.559495174191578 \tabularnewline
45 & 10 & 10.3159885623767 & -0.315988562376742 \tabularnewline
46 & 12 & 9.00348943084876 & 2.99651056915124 \tabularnewline
47 & 13 & 10.6081226196392 & 2.39187738036076 \tabularnewline
48 & 11 & 11.1604097740797 & -0.160409774079746 \tabularnewline
49 & 11 & 11.5072772593509 & -0.507277259350861 \tabularnewline
50 & 12 & 10.4074844236801 & 1.59251557631987 \tabularnewline
51 & 14 & 11.8535803386335 & 2.14641966136646 \tabularnewline
52 & 10 & 10.5186219325593 & -0.518621932559337 \tabularnewline
53 & 12 & 9.58962064688631 & 2.41037935311369 \tabularnewline
54 & 13 & 11.8293889477514 & 1.17061105224859 \tabularnewline
55 & 5 & 8.09031377662165 & -3.09031377662165 \tabularnewline
56 & 6 & 10.3512426383641 & -4.35124263836413 \tabularnewline
57 & 12 & 11.3179174278516 & 0.682082572148437 \tabularnewline
58 & 12 & 11.9276348929574 & 0.0723651070426507 \tabularnewline
59 & 11 & 11.1809605467522 & -0.180960546752193 \tabularnewline
60 & 10 & 11.6239588211228 & -1.6239588211228 \tabularnewline
61 & 7 & 9.5952461922076 & -2.5952461922076 \tabularnewline
62 & 12 & 11.5086850632909 & 0.491314936709087 \tabularnewline
63 & 14 & 11.8127376620151 & 2.18726233798491 \tabularnewline
64 & 11 & 10.7357674366041 & 0.264232563395859 \tabularnewline
65 & 12 & 11.5005560658712 & 0.499443934128751 \tabularnewline
66 & 13 & 12.4234665055824 & 0.576533494417607 \tabularnewline
67 & 14 & 12.6628231969316 & 1.33717680306838 \tabularnewline
68 & 11 & 12.7556647778177 & -1.75566477781767 \tabularnewline
69 & 12 & 9.44380754038576 & 2.55619245961424 \tabularnewline
70 & 12 & 11.6262548995701 & 0.373745100429911 \tabularnewline
71 & 8 & 8.33953430347212 & -0.339534303472119 \tabularnewline
72 & 11 & 10.7315367463003 & 0.268463253699653 \tabularnewline
73 & 14 & 12.8189887460392 & 1.18101125396084 \tabularnewline
74 & 14 & 12.767062799596 & 1.23293720040402 \tabularnewline
75 & 12 & 11.4876460843774 & 0.512353915622602 \tabularnewline
76 & 9 & 11.7352770069174 & -2.73527700691739 \tabularnewline
77 & 13 & 11.4734512147097 & 1.52654878529027 \tabularnewline
78 & 11 & 11.4855494223918 & -0.485549422391765 \tabularnewline
79 & 12 & 10.1937022854285 & 1.80629771457155 \tabularnewline
80 & 12 & 11.3975945380908 & 0.602405461909221 \tabularnewline
81 & 12 & 11.7551581174778 & 0.244841882522197 \tabularnewline
82 & 12 & 13.7412104307933 & -1.74121043079329 \tabularnewline
83 & 12 & 11.7411634224497 & 0.258836577550258 \tabularnewline
84 & 12 & 10.8739839747668 & 1.12601602523319 \tabularnewline
85 & 11 & 11.1855586061583 & -0.185558606158294 \tabularnewline
86 & 10 & 11.5833923862259 & -1.58339238622588 \tabularnewline
87 & 9 & 10.5136784647425 & -1.51367846474252 \tabularnewline
88 & 12 & 11.7079914025403 & 0.292008597459742 \tabularnewline
89 & 12 & 11.8060379520622 & 0.19396204793783 \tabularnewline
90 & 12 & 11.1965667077083 & 0.803433292291679 \tabularnewline
91 & 9 & 9.60858608354843 & -0.608586083548425 \tabularnewline
92 & 15 & 12.0021605917387 & 2.99783940826125 \tabularnewline
93 & 12 & 11.7806465317558 & 0.21935346824424 \tabularnewline
94 & 12 & 10.9548554200418 & 1.04514457995818 \tabularnewline
95 & 12 & 10.2619482379401 & 1.73805176205993 \tabularnewline
96 & 10 & 11.4292819843229 & -1.42928198432293 \tabularnewline
97 & 13 & 11.5392395158098 & 1.46076048419016 \tabularnewline
98 & 9 & 11.5341797809451 & -2.53417978094508 \tabularnewline
99 & 12 & 11.4174066909277 & 0.582593309072318 \tabularnewline
100 & 10 & 10.7682704042319 & -0.768270404231942 \tabularnewline
101 & 14 & 11.460523561602 & 2.53947643839795 \tabularnewline
102 & 11 & 11.5383714687088 & -0.538371468708803 \tabularnewline
103 & 15 & 13.6974392663454 & 1.30256073365459 \tabularnewline
104 & 11 & 11.1713855010304 & -0.171385501030366 \tabularnewline
105 & 11 & 11.7396099838739 & -0.739609983873909 \tabularnewline
106 & 12 & 9.76128117837986 & 2.23871882162014 \tabularnewline
107 & 12 & 11.9735383151356 & 0.026461684864413 \tabularnewline
108 & 12 & 11.3608156297655 & 0.639184370234495 \tabularnewline
109 & 11 & 11.6936520780571 & -0.693652078057068 \tabularnewline
110 & 7 & 9.27131394854819 & -2.27131394854819 \tabularnewline
111 & 12 & 11.7614708638595 & 0.238529136140498 \tabularnewline
112 & 14 & 11.8203524790483 & 2.17964752095166 \tabularnewline
113 & 11 & 12.5767046911582 & -1.57670469115817 \tabularnewline
114 & 11 & 10.0725543956338 & 0.927445604366178 \tabularnewline
115 & 10 & 9.23881482757822 & 0.761185172421776 \tabularnewline
116 & 13 & 12.4712953818298 & 0.528704618170188 \tabularnewline
117 & 13 & 10.8065248223531 & 2.19347517764686 \tabularnewline
118 & 8 & 10.7785548371962 & -2.77855483719623 \tabularnewline
119 & 11 & 10.2232013888291 & 0.77679861117093 \tabularnewline
120 & 12 & 11.4629267057585 & 0.537073294241452 \tabularnewline
121 & 11 & 10.2350976885861 & 0.764902311413893 \tabularnewline
122 & 13 & 11.522614283691 & 1.47738571630898 \tabularnewline
123 & 12 & 10.0276905365127 & 1.97230946348726 \tabularnewline
124 & 14 & 11.7012790650573 & 2.29872093494271 \tabularnewline
125 & 13 & 11.061049384804 & 1.93895061519601 \tabularnewline
126 & 15 & 11.8310188870227 & 3.16898111297734 \tabularnewline
127 & 10 & 10.7939101102318 & -0.793910110231756 \tabularnewline
128 & 11 & 11.8044409436942 & -0.804440943694227 \tabularnewline
129 & 9 & 11.064592171392 & -2.06459217139201 \tabularnewline
130 & 11 & 9.39926602863569 & 1.60073397136431 \tabularnewline
131 & 10 & 11.7851886751859 & -1.78518867518592 \tabularnewline
132 & 11 & 8.72218201926533 & 2.27781798073468 \tabularnewline
133 & 8 & 11.4789613123181 & -3.47896131231812 \tabularnewline
134 & 11 & 9.63538412797462 & 1.36461587202538 \tabularnewline
135 & 12 & 10.5512654875672 & 1.4487345124328 \tabularnewline
136 & 12 & 10.9306661633098 & 1.06933383669024 \tabularnewline
137 & 9 & 9.82967036550726 & -0.82967036550726 \tabularnewline
138 & 11 & 11.1197681709081 & -0.119768170908117 \tabularnewline
139 & 10 & 9.27028621011593 & 0.729713789884068 \tabularnewline
140 & 8 & 9.48040394568525 & -1.48040394568525 \tabularnewline
141 & 9 & 7.64395584845415 & 1.35604415154585 \tabularnewline
142 & 8 & 11.7646886186332 & -3.76468861863324 \tabularnewline
143 & 9 & 11.191688773214 & -2.19168877321399 \tabularnewline
144 & 15 & 12.409352706701 & 2.59064729329904 \tabularnewline
145 & 11 & 11.3952137882394 & -0.395213788239373 \tabularnewline
146 & 8 & 8.03891785924025 & -0.0389178592402469 \tabularnewline
147 & 13 & 12.4988359898272 & 0.50116401017276 \tabularnewline
148 & 12 & 10.1442301992615 & 1.85576980073846 \tabularnewline
149 & 12 & 11.7580523046808 & 0.241947695319202 \tabularnewline
150 & 9 & 9.99802764969761 & -0.998027649697608 \tabularnewline
151 & 7 & 8.37796844333626 & -1.37796844333626 \tabularnewline
152 & 13 & 10.773350900033 & 2.22664909996697 \tabularnewline
153 & 9 & 11.7817332476172 & -2.78173324761722 \tabularnewline
154 & 6 & 11.8590543468075 & -5.85905434680751 \tabularnewline
155 & 8 & 10.5945120872977 & -2.59451208729773 \tabularnewline
156 & 8 & 8.33357342147779 & -0.333573421477789 \tabularnewline
157 & 15 & 12.0021605917387 & 2.99783940826125 \tabularnewline
158 & 6 & 10.2219547569886 & -4.22195475698856 \tabularnewline
159 & 9 & 11.064592171392 & -2.06459217139201 \tabularnewline
160 & 11 & 11.7001071243266 & -0.700107124326592 \tabularnewline
161 & 8 & 9.61133172300999 & -1.61133172300999 \tabularnewline
162 & 8 & 10.0876816072102 & -2.08768160721021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&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]9.8767320138909[/C][C]2.1232679861091[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.2143981262762[/C][C]-0.214398126276164[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5465133429708[/C][C]1.45348665702919[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.3217290222382[/C][C]-5.32172902223825[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.4123877091366[/C][C]2.58761229086338[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.69627617667644[/C][C]0.303723823323563[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.6828465948696[/C][C]-0.682846594869635[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.2185493028623[/C][C]2.78145069713774[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.5451743993518[/C][C]1.4548256006482[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0568497366789[/C][C]-5.05684973667889[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.2935485048991[/C][C]-1.29354850489915[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.459515272463[/C][C]0.540484727536964[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.5956983129011[/C][C]0.404301687098903[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.4322483711724[/C][C]-0.43224837117239[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.2180582633452[/C][C]2.78194173665484[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.1494562333112[/C][C]0.850543766688827[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.9222370047011[/C][C]-0.922237004701128[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6177276473567[/C][C]-1.61772764735672[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.6610484247539[/C][C]-1.66104842475394[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5880411617518[/C][C]0.411958838248243[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6772413454725[/C][C]-0.677241345472543[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.7781934504351[/C][C]0.22180654956493[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1461683058827[/C][C]-0.146168305882693[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.721256904562[/C][C]-0.721256904561957[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.7837461933712[/C][C]-2.78374619337116[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.0581429516912[/C][C]0.941857048308756[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.539584145265[/C][C]-1.53958414526497[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.2094935055602[/C][C]2.79050649443981[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.5863751885687[/C][C]0.413624811431317[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.6671360980343[/C][C]-0.667136098034311[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0607442350538[/C][C]0.939255764946171[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.78482343634683[/C][C]-1.78482343634683[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.2846773412435[/C][C]-0.284677341243543[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7397715568381[/C][C]0.26022844316187[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.4450387473409[/C][C]1.55496125265905[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.9718293100696[/C][C]0.028170689930404[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.64200194427687[/C][C]-2.64200194427687[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.4331588075547[/C][C]1.56684119244535[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.7222962945742[/C][C]-1.7222962945742[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.665992289906[/C][C]-1.66599228990604[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4790557536692[/C][C]-1.47905575366923[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.4410250688124[/C][C]1.55897493118759[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.5349840350678[/C][C]1.4650159649322[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5594951741916[/C][C]-0.559495174191578[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.3159885623767[/C][C]-0.315988562376742[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.00348943084876[/C][C]2.99651056915124[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.6081226196392[/C][C]2.39187738036076[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1604097740797[/C][C]-0.160409774079746[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.5072772593509[/C][C]-0.507277259350861[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.4074844236801[/C][C]1.59251557631987[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8535803386335[/C][C]2.14641966136646[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5186219325593[/C][C]-0.518621932559337[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.58962064688631[/C][C]2.41037935311369[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.8293889477514[/C][C]1.17061105224859[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.09031377662165[/C][C]-3.09031377662165[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.3512426383641[/C][C]-4.35124263836413[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3179174278516[/C][C]0.682082572148437[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9276348929574[/C][C]0.0723651070426507[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.1809605467522[/C][C]-0.180960546752193[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.6239588211228[/C][C]-1.6239588211228[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.5952461922076[/C][C]-2.5952461922076[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.5086850632909[/C][C]0.491314936709087[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.8127376620151[/C][C]2.18726233798491[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7357674366041[/C][C]0.264232563395859[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.5005560658712[/C][C]0.499443934128751[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.4234665055824[/C][C]0.576533494417607[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.6628231969316[/C][C]1.33717680306838[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.7556647778177[/C][C]-1.75566477781767[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.44380754038576[/C][C]2.55619245961424[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6262548995701[/C][C]0.373745100429911[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.33953430347212[/C][C]-0.339534303472119[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7315367463003[/C][C]0.268463253699653[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8189887460392[/C][C]1.18101125396084[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.767062799596[/C][C]1.23293720040402[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4876460843774[/C][C]0.512353915622602[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7352770069174[/C][C]-2.73527700691739[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.4734512147097[/C][C]1.52654878529027[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4855494223918[/C][C]-0.485549422391765[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.1937022854285[/C][C]1.80629771457155[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.3975945380908[/C][C]0.602405461909221[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7551581174778[/C][C]0.244841882522197[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.7412104307933[/C][C]-1.74121043079329[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7411634224497[/C][C]0.258836577550258[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.8739839747668[/C][C]1.12601602523319[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1855586061583[/C][C]-0.185558606158294[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5833923862259[/C][C]-1.58339238622588[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5136784647425[/C][C]-1.51367846474252[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.7079914025403[/C][C]0.292008597459742[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8060379520622[/C][C]0.19396204793783[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.1965667077083[/C][C]0.803433292291679[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.60858608354843[/C][C]-0.608586083548425[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.0021605917387[/C][C]2.99783940826125[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.7806465317558[/C][C]0.21935346824424[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9548554200418[/C][C]1.04514457995818[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.2619482379401[/C][C]1.73805176205993[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4292819843229[/C][C]-1.42928198432293[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5392395158098[/C][C]1.46076048419016[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.5341797809451[/C][C]-2.53417978094508[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4174066909277[/C][C]0.582593309072318[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7682704042319[/C][C]-0.768270404231942[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.460523561602[/C][C]2.53947643839795[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.5383714687088[/C][C]-0.538371468708803[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.6974392663454[/C][C]1.30256073365459[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.1713855010304[/C][C]-0.171385501030366[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.7396099838739[/C][C]-0.739609983873909[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.76128117837986[/C][C]2.23871882162014[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.9735383151356[/C][C]0.026461684864413[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.3608156297655[/C][C]0.639184370234495[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.6936520780571[/C][C]-0.693652078057068[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.27131394854819[/C][C]-2.27131394854819[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.7614708638595[/C][C]0.238529136140498[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.8203524790483[/C][C]2.17964752095166[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5767046911582[/C][C]-1.57670469115817[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.0725543956338[/C][C]0.927445604366178[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.23881482757822[/C][C]0.761185172421776[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4712953818298[/C][C]0.528704618170188[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.8065248223531[/C][C]2.19347517764686[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7785548371962[/C][C]-2.77855483719623[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.2232013888291[/C][C]0.77679861117093[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4629267057585[/C][C]0.537073294241452[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.2350976885861[/C][C]0.764902311413893[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.522614283691[/C][C]1.47738571630898[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.0276905365127[/C][C]1.97230946348726[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.7012790650573[/C][C]2.29872093494271[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.061049384804[/C][C]1.93895061519601[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.8310188870227[/C][C]3.16898111297734[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.7939101102318[/C][C]-0.793910110231756[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8044409436942[/C][C]-0.804440943694227[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.064592171392[/C][C]-2.06459217139201[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.39926602863569[/C][C]1.60073397136431[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.7851886751859[/C][C]-1.78518867518592[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.72218201926533[/C][C]2.27781798073468[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.4789613123181[/C][C]-3.47896131231812[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.63538412797462[/C][C]1.36461587202538[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.5512654875672[/C][C]1.4487345124328[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.9306661633098[/C][C]1.06933383669024[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.82967036550726[/C][C]-0.82967036550726[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1197681709081[/C][C]-0.119768170908117[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.27028621011593[/C][C]0.729713789884068[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.48040394568525[/C][C]-1.48040394568525[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.64395584845415[/C][C]1.35604415154585[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.7646886186332[/C][C]-3.76468861863324[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.191688773214[/C][C]-2.19168877321399[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.409352706701[/C][C]2.59064729329904[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.3952137882394[/C][C]-0.395213788239373[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.03891785924025[/C][C]-0.0389178592402469[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.4988359898272[/C][C]0.50116401017276[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.1442301992615[/C][C]1.85576980073846[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.7580523046808[/C][C]0.241947695319202[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.99802764969761[/C][C]-0.998027649697608[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.37796844333626[/C][C]-1.37796844333626[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.773350900033[/C][C]2.22664909996697[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.7817332476172[/C][C]-2.78173324761722[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.8590543468075[/C][C]-5.85905434680751[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.5945120872977[/C][C]-2.59451208729773[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.33357342147779[/C][C]-0.333573421477789[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.0021605917387[/C][C]2.99783940826125[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2219547569886[/C][C]-4.22195475698856[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.064592171392[/C][C]-2.06459217139201[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.7001071243266[/C][C]-0.700107124326592[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.61133172300999[/C][C]-1.61133172300999[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.0876816072102[/C][C]-2.08768160721021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.87673201389092.1232679861091
21111.2143981262762-0.214398126276164
31513.54651334297081.45348665702919
4611.3217290222382-5.32172902223825
51310.41238770913662.58761229086338
6109.696276176676440.303723823323563
71212.6828465948696-0.682846594869635
81411.21854930286232.78145069713774
91210.54517439935181.4548256006482
10611.0568497366789-5.05684973667889
111011.2935485048991-1.29354850489915
121211.4595152724630.540484727536964
131211.59569831290110.404301687098903
141111.4322483711724-0.43224837117239
151512.21805826334522.78194173665484
161211.14945623331120.850543766688827
171010.9222370047011-0.922237004701128
181213.6177276473567-1.61772764735672
191112.6610484247539-1.66104842475394
201211.58804116175180.411958838248243
211111.6772413454725-0.677241345472543
221211.77819345043510.22180654956493
231313.1461683058827-0.146168305882693
241111.721256904562-0.721256904561957
25911.7837461933712-2.78374619337116
261312.05814295169120.941857048308756
271011.539584145265-1.53958414526497
281411.20949350556022.79050649443981
291211.58637518856870.413624811431317
301010.6671360980343-0.667136098034311
311211.06074423505380.939255764946171
3289.78482343634683-1.78482343634683
331010.2846773412435-0.284677341243543
341211.73977155683810.26022844316187
351210.44503874734091.55496125265905
3676.97182931006960.028170689930404
3768.64200194427687-2.64200194427687
381210.43315880755471.56684119244535
391011.7222962945742-1.7222962945742
401011.665992289906-1.66599228990604
411011.4790557536692-1.47905575366923
421210.44102506881241.55897493118759
431513.53498403506781.4650159649322
441010.5594951741916-0.559495174191578
451010.3159885623767-0.315988562376742
46129.003489430848762.99651056915124
471310.60812261963922.39187738036076
481111.1604097740797-0.160409774079746
491111.5072772593509-0.507277259350861
501210.40748442368011.59251557631987
511411.85358033863352.14641966136646
521010.5186219325593-0.518621932559337
53129.589620646886312.41037935311369
541311.82938894775141.17061105224859
5558.09031377662165-3.09031377662165
56610.3512426383641-4.35124263836413
571211.31791742785160.682082572148437
581211.92763489295740.0723651070426507
591111.1809605467522-0.180960546752193
601011.6239588211228-1.6239588211228
6179.5952461922076-2.5952461922076
621211.50868506329090.491314936709087
631411.81273766201512.18726233798491
641110.73576743660410.264232563395859
651211.50055606587120.499443934128751
661312.42346650558240.576533494417607
671412.66282319693161.33717680306838
681112.7556647778177-1.75566477781767
69129.443807540385762.55619245961424
701211.62625489957010.373745100429911
7188.33953430347212-0.339534303472119
721110.73153674630030.268463253699653
731412.81898874603921.18101125396084
741412.7670627995961.23293720040402
751211.48764608437740.512353915622602
76911.7352770069174-2.73527700691739
771311.47345121470971.52654878529027
781111.4855494223918-0.485549422391765
791210.19370228542851.80629771457155
801211.39759453809080.602405461909221
811211.75515811747780.244841882522197
821213.7412104307933-1.74121043079329
831211.74116342244970.258836577550258
841210.87398397476681.12601602523319
851111.1855586061583-0.185558606158294
861011.5833923862259-1.58339238622588
87910.5136784647425-1.51367846474252
881211.70799140254030.292008597459742
891211.80603795206220.19396204793783
901211.19656670770830.803433292291679
9199.60858608354843-0.608586083548425
921512.00216059173872.99783940826125
931211.78064653175580.21935346824424
941210.95485542004181.04514457995818
951210.26194823794011.73805176205993
961011.4292819843229-1.42928198432293
971311.53923951580981.46076048419016
98911.5341797809451-2.53417978094508
991211.41740669092770.582593309072318
1001010.7682704042319-0.768270404231942
1011411.4605235616022.53947643839795
1021111.5383714687088-0.538371468708803
1031513.69743926634541.30256073365459
1041111.1713855010304-0.171385501030366
1051111.7396099838739-0.739609983873909
106129.761281178379862.23871882162014
1071211.97353831513560.026461684864413
1081211.36081562976550.639184370234495
1091111.6936520780571-0.693652078057068
11079.27131394854819-2.27131394854819
1111211.76147086385950.238529136140498
1121411.82035247904832.17964752095166
1131112.5767046911582-1.57670469115817
1141110.07255439563380.927445604366178
115109.238814827578220.761185172421776
1161312.47129538182980.528704618170188
1171310.80652482235312.19347517764686
118810.7785548371962-2.77855483719623
1191110.22320138882910.77679861117093
1201211.46292670575850.537073294241452
1211110.23509768858610.764902311413893
1221311.5226142836911.47738571630898
1231210.02769053651271.97230946348726
1241411.70127906505732.29872093494271
1251311.0610493848041.93895061519601
1261511.83101888702273.16898111297734
1271010.7939101102318-0.793910110231756
1281111.8044409436942-0.804440943694227
129911.064592171392-2.06459217139201
130119.399266028635691.60073397136431
1311011.7851886751859-1.78518867518592
132118.722182019265332.27781798073468
133811.4789613123181-3.47896131231812
134119.635384127974621.36461587202538
1351210.55126548756721.4487345124328
1361210.93066616330981.06933383669024
13799.82967036550726-0.82967036550726
1381111.1197681709081-0.119768170908117
139109.270286210115930.729713789884068
14089.48040394568525-1.48040394568525
14197.643955848454151.35604415154585
142811.7646886186332-3.76468861863324
143911.191688773214-2.19168877321399
1441512.4093527067012.59064729329904
1451111.3952137882394-0.395213788239373
14688.03891785924025-0.0389178592402469
1471312.49883598982720.50116401017276
1481210.14423019926151.85576980073846
1491211.75805230468080.241947695319202
15099.99802764969761-0.998027649697608
15178.37796844333626-1.37796844333626
1521310.7733509000332.22664909996697
153911.7817332476172-2.78173324761722
154611.8590543468075-5.85905434680751
155810.5945120872977-2.59451208729773
15688.33357342147779-0.333573421477789
1571512.00216059173872.99783940826125
158610.2219547569886-4.22195475698856
159911.064592171392-2.06459217139201
1601111.7001071243266-0.700107124326592
16189.61133172300999-1.61133172300999
162810.0876816072102-2.08768160721021







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9998627020595950.0002745958808092590.00013729794040463
110.9996438447248740.000712310550251860.00035615527512593
120.9995218532913690.0009562934172616910.000478146708630846
130.999090141665670.001819716668659510.000909858334329755
140.9983309227593510.003338154481297470.00166907724064873
150.997996087072910.004007825854179160.00200391292708958
160.9966198460262020.006760307947595950.00338015397379797
170.993935415952820.01212916809435920.00606458404717959
180.9925730536739730.01485389265205330.00742694632602664
190.9888888539557830.02222229208843470.0111111460442173
200.9819773505801720.03604529883965570.0180226494198279
210.9735045005691710.05299099886165720.0264954994308286
220.9624049832039590.07519003359208130.0375950167960407
230.9467435096932020.1065129806135970.0532564903067985
240.9282288221965110.1435423556069780.0717711778034888
250.9255653744769490.1488692510461030.0744346255230514
260.9436760357431590.1126479285136820.0563239642568411
270.9331964110628470.1336071778743070.0668035889371534
280.9396414845288660.1207170309422670.0603585154711336
290.9188609706741530.1622780586516930.0811390293258467
300.8983990982099410.2032018035801190.101600901790059
310.879636743056550.24072651388690.12036325694345
320.8969823339233030.2060353321533950.103017666076697
330.8678453545014580.2643092909970840.132154645498542
340.8335367878317290.3329264243365410.166463212168271
350.8225018608884580.3549962782230840.177498139111542
360.7856479854148570.4287040291702850.214352014585142
370.8257014333702320.3485971332595360.174298566629768
380.8169937831879870.3660124336240260.183006216812013
390.8065339790477160.3869320419045690.193466020952284
400.7878029142357660.4243941715284690.212197085764234
410.763100740351020.4737985192979590.23689925964898
420.7472466601328380.5055066797343240.252753339867162
430.7481947280729850.503610543854030.251805271927015
440.705620587760730.5887588244785390.29437941223927
450.6613930638488710.6772138723022580.338606936151129
460.7286934305453960.5426131389092090.271306569454604
470.73483145414570.53033709170860.2651685458543
480.6908411244973870.6183177510052260.309158875502613
490.6530259423876520.6939481152246960.346974057612348
500.6390331881560660.7219336236878680.360966811843934
510.6445092360558970.7109815278882070.355490763944103
520.597901623160190.804196753679620.40209837683981
530.6259187495157610.7481625009684770.374081250484239
540.5909433524705470.8181132950589050.409056647529453
550.6852891776269340.6294216447461330.314710822373066
560.8443068005020540.3113863989958920.155693199497946
570.8171487053284040.3657025893431920.182851294671596
580.783222446999480.433555106001040.21677755300052
590.7468227770564790.5063544458870410.253177222943521
600.7353607161193710.5292785677612570.264639283880629
610.7532104093762260.4935791812475490.246789590623774
620.7170672638804430.5658654722391140.282932736119557
630.7316965384361170.5366069231277660.268303461563883
640.6914399988629890.6171200022740230.308560001137011
650.6568997754274680.6862004491450630.343100224572532
660.6152677450214850.7694645099570310.384732254978515
670.5948057700970650.8103884598058690.405194229902935
680.5774897304856040.8450205390287910.422510269514396
690.5961077951420960.8077844097158070.403892204857904
700.5523224976628040.8953550046743920.447677502337196
710.5121729959085710.9756540081828570.487827004091429
720.4690660467560890.9381320935121780.530933953243911
730.4406887931890450.8813775863780910.559311206810955
740.4166662560949770.8333325121899540.583333743905023
750.3906429053750010.7812858107500020.609357094624999
760.4405788250941830.8811576501883650.559421174905817
770.4267835712215570.8535671424431140.573216428778443
780.3860338307705580.7720676615411160.613966169229442
790.3908912222749040.7817824445498080.609108777725096
800.3683144929691940.7366289859383890.631685507030806
810.3276255523003670.6552511046007340.672374447699633
820.3242418161231760.6484836322463520.675758183876824
830.2859020423288170.5718040846576330.714097957671183
840.2608375558340340.5216751116680690.739162444165966
850.224950440821810.449900881643620.77504955917819
860.2170867754599190.4341735509198390.782913224540081
870.2167938932213070.4335877864426140.783206106778693
880.1847964457651340.3695928915302680.815203554234866
890.1566769563911960.3133539127823930.843323043608804
900.1357111634879640.2714223269759270.864288836512036
910.1159480484618550.231896096923710.884051951538145
920.1620771352795190.3241542705590390.837922864720481
930.1413088540909680.2826177081819370.858691145909032
940.1259972475414910.2519944950829810.874002752458509
950.1216591707152010.2433183414304010.878340829284799
960.1132108465877670.2264216931755340.886789153412233
970.1044940318490410.2089880636980810.895505968150959
980.1289368274969280.2578736549938570.871063172503072
990.107609915635870.215219831271740.89239008436413
1000.09085601966152320.1817120393230460.909143980338477
1010.1115753416305250.223150683261050.888424658369475
1020.09231257100570930.1846251420114190.907687428994291
1030.08806149301995720.1761229860399140.911938506980043
1040.074218633470430.148437266940860.92578136652957
1050.06253472172978510.125069443459570.937465278270215
1060.06364992225210490.127299844504210.936350077747895
1070.05012750583500440.1002550116700090.949872494164996
1080.04432144056049950.0886428811209990.955678559439501
1090.03533076305352450.07066152610704910.964669236946475
1100.03665618474473160.07331236948946320.963343815255268
1110.02880204752711750.0576040950542350.971197952472883
1120.03221318778872110.06442637557744220.967786812211279
1130.02881519849788090.05763039699576180.971184801502119
1140.02313137788629030.04626275577258050.97686862211371
1150.01822135651527010.03644271303054020.98177864348473
1160.0137660621489450.02753212429789010.986233937851055
1170.02205201497401480.04410402994802960.977947985025985
1180.02522013807025350.05044027614050710.974779861929746
1190.0193749987732640.03874999754652790.980625001226736
1200.01502528154842320.03005056309684640.984974718451577
1210.01254553427026710.02509106854053420.987454465729733
1220.01047970038092480.02095940076184970.989520299619075
1230.01235574071768590.02471148143537180.987644259282314
1240.01945482664204790.03890965328409580.980545173357952
1250.02053953187025840.04107906374051670.979460468129742
1260.04897786137035270.09795572274070540.951022138629647
1270.0374363621339260.0748727242678520.962563637866074
1280.02873529873248830.05747059746497650.971264701267512
1290.02403465674791410.04806931349582810.975965343252086
1300.02257273033574210.04514546067148410.977427269664258
1310.01831186285733380.03662372571466760.981688137142666
1320.02083404651176790.04166809302353580.979165953488232
1330.03294417106975860.06588834213951710.967055828930241
1340.03574486737403950.07148973474807890.964255132625961
1350.03969923919800750.0793984783960150.960300760801993
1360.03388490198177510.06776980396355020.966115098018225
1370.02918491664687170.05836983329374340.970815083353128
1380.02109371752269570.04218743504539130.978906282477304
1390.02106917730090270.04213835460180540.978930822699097
1400.01497113714894590.02994227429789170.985028862851054
1410.04530952408013540.09061904816027080.954690475919865
1420.05053176383586690.1010635276717340.949468236164133
1430.03721930835477920.07443861670955840.962780691645221
1440.364361668391490.728723336782980.63563833160851
1450.3720360779464030.7440721558928070.627963922053597
1460.6356954088575860.7286091822848270.364304591142414
1470.6190564554986690.7618870890026610.380943544501331
1480.8283936428844450.3432127142311110.171606357115556
1490.8161740731143870.3676518537712260.183825926885613
1500.7259188699551190.5481622600897620.274081130044881
1510.6197858314618650.760428337076270.380214168538135
1520.6008637056480580.7982725887038840.399136294351942

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.999862702059595 & 0.000274595880809259 & 0.00013729794040463 \tabularnewline
11 & 0.999643844724874 & 0.00071231055025186 & 0.00035615527512593 \tabularnewline
12 & 0.999521853291369 & 0.000956293417261691 & 0.000478146708630846 \tabularnewline
13 & 0.99909014166567 & 0.00181971666865951 & 0.000909858334329755 \tabularnewline
14 & 0.998330922759351 & 0.00333815448129747 & 0.00166907724064873 \tabularnewline
15 & 0.99799608707291 & 0.00400782585417916 & 0.00200391292708958 \tabularnewline
16 & 0.996619846026202 & 0.00676030794759595 & 0.00338015397379797 \tabularnewline
17 & 0.99393541595282 & 0.0121291680943592 & 0.00606458404717959 \tabularnewline
18 & 0.992573053673973 & 0.0148538926520533 & 0.00742694632602664 \tabularnewline
19 & 0.988888853955783 & 0.0222222920884347 & 0.0111111460442173 \tabularnewline
20 & 0.981977350580172 & 0.0360452988396557 & 0.0180226494198279 \tabularnewline
21 & 0.973504500569171 & 0.0529909988616572 & 0.0264954994308286 \tabularnewline
22 & 0.962404983203959 & 0.0751900335920813 & 0.0375950167960407 \tabularnewline
23 & 0.946743509693202 & 0.106512980613597 & 0.0532564903067985 \tabularnewline
24 & 0.928228822196511 & 0.143542355606978 & 0.0717711778034888 \tabularnewline
25 & 0.925565374476949 & 0.148869251046103 & 0.0744346255230514 \tabularnewline
26 & 0.943676035743159 & 0.112647928513682 & 0.0563239642568411 \tabularnewline
27 & 0.933196411062847 & 0.133607177874307 & 0.0668035889371534 \tabularnewline
28 & 0.939641484528866 & 0.120717030942267 & 0.0603585154711336 \tabularnewline
29 & 0.918860970674153 & 0.162278058651693 & 0.0811390293258467 \tabularnewline
30 & 0.898399098209941 & 0.203201803580119 & 0.101600901790059 \tabularnewline
31 & 0.87963674305655 & 0.2407265138869 & 0.12036325694345 \tabularnewline
32 & 0.896982333923303 & 0.206035332153395 & 0.103017666076697 \tabularnewline
33 & 0.867845354501458 & 0.264309290997084 & 0.132154645498542 \tabularnewline
34 & 0.833536787831729 & 0.332926424336541 & 0.166463212168271 \tabularnewline
35 & 0.822501860888458 & 0.354996278223084 & 0.177498139111542 \tabularnewline
36 & 0.785647985414857 & 0.428704029170285 & 0.214352014585142 \tabularnewline
37 & 0.825701433370232 & 0.348597133259536 & 0.174298566629768 \tabularnewline
38 & 0.816993783187987 & 0.366012433624026 & 0.183006216812013 \tabularnewline
39 & 0.806533979047716 & 0.386932041904569 & 0.193466020952284 \tabularnewline
40 & 0.787802914235766 & 0.424394171528469 & 0.212197085764234 \tabularnewline
41 & 0.76310074035102 & 0.473798519297959 & 0.23689925964898 \tabularnewline
42 & 0.747246660132838 & 0.505506679734324 & 0.252753339867162 \tabularnewline
43 & 0.748194728072985 & 0.50361054385403 & 0.251805271927015 \tabularnewline
44 & 0.70562058776073 & 0.588758824478539 & 0.29437941223927 \tabularnewline
45 & 0.661393063848871 & 0.677213872302258 & 0.338606936151129 \tabularnewline
46 & 0.728693430545396 & 0.542613138909209 & 0.271306569454604 \tabularnewline
47 & 0.7348314541457 & 0.5303370917086 & 0.2651685458543 \tabularnewline
48 & 0.690841124497387 & 0.618317751005226 & 0.309158875502613 \tabularnewline
49 & 0.653025942387652 & 0.693948115224696 & 0.346974057612348 \tabularnewline
50 & 0.639033188156066 & 0.721933623687868 & 0.360966811843934 \tabularnewline
51 & 0.644509236055897 & 0.710981527888207 & 0.355490763944103 \tabularnewline
52 & 0.59790162316019 & 0.80419675367962 & 0.40209837683981 \tabularnewline
53 & 0.625918749515761 & 0.748162500968477 & 0.374081250484239 \tabularnewline
54 & 0.590943352470547 & 0.818113295058905 & 0.409056647529453 \tabularnewline
55 & 0.685289177626934 & 0.629421644746133 & 0.314710822373066 \tabularnewline
56 & 0.844306800502054 & 0.311386398995892 & 0.155693199497946 \tabularnewline
57 & 0.817148705328404 & 0.365702589343192 & 0.182851294671596 \tabularnewline
58 & 0.78322244699948 & 0.43355510600104 & 0.21677755300052 \tabularnewline
59 & 0.746822777056479 & 0.506354445887041 & 0.253177222943521 \tabularnewline
60 & 0.735360716119371 & 0.529278567761257 & 0.264639283880629 \tabularnewline
61 & 0.753210409376226 & 0.493579181247549 & 0.246789590623774 \tabularnewline
62 & 0.717067263880443 & 0.565865472239114 & 0.282932736119557 \tabularnewline
63 & 0.731696538436117 & 0.536606923127766 & 0.268303461563883 \tabularnewline
64 & 0.691439998862989 & 0.617120002274023 & 0.308560001137011 \tabularnewline
65 & 0.656899775427468 & 0.686200449145063 & 0.343100224572532 \tabularnewline
66 & 0.615267745021485 & 0.769464509957031 & 0.384732254978515 \tabularnewline
67 & 0.594805770097065 & 0.810388459805869 & 0.405194229902935 \tabularnewline
68 & 0.577489730485604 & 0.845020539028791 & 0.422510269514396 \tabularnewline
69 & 0.596107795142096 & 0.807784409715807 & 0.403892204857904 \tabularnewline
70 & 0.552322497662804 & 0.895355004674392 & 0.447677502337196 \tabularnewline
71 & 0.512172995908571 & 0.975654008182857 & 0.487827004091429 \tabularnewline
72 & 0.469066046756089 & 0.938132093512178 & 0.530933953243911 \tabularnewline
73 & 0.440688793189045 & 0.881377586378091 & 0.559311206810955 \tabularnewline
74 & 0.416666256094977 & 0.833332512189954 & 0.583333743905023 \tabularnewline
75 & 0.390642905375001 & 0.781285810750002 & 0.609357094624999 \tabularnewline
76 & 0.440578825094183 & 0.881157650188365 & 0.559421174905817 \tabularnewline
77 & 0.426783571221557 & 0.853567142443114 & 0.573216428778443 \tabularnewline
78 & 0.386033830770558 & 0.772067661541116 & 0.613966169229442 \tabularnewline
79 & 0.390891222274904 & 0.781782444549808 & 0.609108777725096 \tabularnewline
80 & 0.368314492969194 & 0.736628985938389 & 0.631685507030806 \tabularnewline
81 & 0.327625552300367 & 0.655251104600734 & 0.672374447699633 \tabularnewline
82 & 0.324241816123176 & 0.648483632246352 & 0.675758183876824 \tabularnewline
83 & 0.285902042328817 & 0.571804084657633 & 0.714097957671183 \tabularnewline
84 & 0.260837555834034 & 0.521675111668069 & 0.739162444165966 \tabularnewline
85 & 0.22495044082181 & 0.44990088164362 & 0.77504955917819 \tabularnewline
86 & 0.217086775459919 & 0.434173550919839 & 0.782913224540081 \tabularnewline
87 & 0.216793893221307 & 0.433587786442614 & 0.783206106778693 \tabularnewline
88 & 0.184796445765134 & 0.369592891530268 & 0.815203554234866 \tabularnewline
89 & 0.156676956391196 & 0.313353912782393 & 0.843323043608804 \tabularnewline
90 & 0.135711163487964 & 0.271422326975927 & 0.864288836512036 \tabularnewline
91 & 0.115948048461855 & 0.23189609692371 & 0.884051951538145 \tabularnewline
92 & 0.162077135279519 & 0.324154270559039 & 0.837922864720481 \tabularnewline
93 & 0.141308854090968 & 0.282617708181937 & 0.858691145909032 \tabularnewline
94 & 0.125997247541491 & 0.251994495082981 & 0.874002752458509 \tabularnewline
95 & 0.121659170715201 & 0.243318341430401 & 0.878340829284799 \tabularnewline
96 & 0.113210846587767 & 0.226421693175534 & 0.886789153412233 \tabularnewline
97 & 0.104494031849041 & 0.208988063698081 & 0.895505968150959 \tabularnewline
98 & 0.128936827496928 & 0.257873654993857 & 0.871063172503072 \tabularnewline
99 & 0.10760991563587 & 0.21521983127174 & 0.89239008436413 \tabularnewline
100 & 0.0908560196615232 & 0.181712039323046 & 0.909143980338477 \tabularnewline
101 & 0.111575341630525 & 0.22315068326105 & 0.888424658369475 \tabularnewline
102 & 0.0923125710057093 & 0.184625142011419 & 0.907687428994291 \tabularnewline
103 & 0.0880614930199572 & 0.176122986039914 & 0.911938506980043 \tabularnewline
104 & 0.07421863347043 & 0.14843726694086 & 0.92578136652957 \tabularnewline
105 & 0.0625347217297851 & 0.12506944345957 & 0.937465278270215 \tabularnewline
106 & 0.0636499222521049 & 0.12729984450421 & 0.936350077747895 \tabularnewline
107 & 0.0501275058350044 & 0.100255011670009 & 0.949872494164996 \tabularnewline
108 & 0.0443214405604995 & 0.088642881120999 & 0.955678559439501 \tabularnewline
109 & 0.0353307630535245 & 0.0706615261070491 & 0.964669236946475 \tabularnewline
110 & 0.0366561847447316 & 0.0733123694894632 & 0.963343815255268 \tabularnewline
111 & 0.0288020475271175 & 0.057604095054235 & 0.971197952472883 \tabularnewline
112 & 0.0322131877887211 & 0.0644263755774422 & 0.967786812211279 \tabularnewline
113 & 0.0288151984978809 & 0.0576303969957618 & 0.971184801502119 \tabularnewline
114 & 0.0231313778862903 & 0.0462627557725805 & 0.97686862211371 \tabularnewline
115 & 0.0182213565152701 & 0.0364427130305402 & 0.98177864348473 \tabularnewline
116 & 0.013766062148945 & 0.0275321242978901 & 0.986233937851055 \tabularnewline
117 & 0.0220520149740148 & 0.0441040299480296 & 0.977947985025985 \tabularnewline
118 & 0.0252201380702535 & 0.0504402761405071 & 0.974779861929746 \tabularnewline
119 & 0.019374998773264 & 0.0387499975465279 & 0.980625001226736 \tabularnewline
120 & 0.0150252815484232 & 0.0300505630968464 & 0.984974718451577 \tabularnewline
121 & 0.0125455342702671 & 0.0250910685405342 & 0.987454465729733 \tabularnewline
122 & 0.0104797003809248 & 0.0209594007618497 & 0.989520299619075 \tabularnewline
123 & 0.0123557407176859 & 0.0247114814353718 & 0.987644259282314 \tabularnewline
124 & 0.0194548266420479 & 0.0389096532840958 & 0.980545173357952 \tabularnewline
125 & 0.0205395318702584 & 0.0410790637405167 & 0.979460468129742 \tabularnewline
126 & 0.0489778613703527 & 0.0979557227407054 & 0.951022138629647 \tabularnewline
127 & 0.037436362133926 & 0.074872724267852 & 0.962563637866074 \tabularnewline
128 & 0.0287352987324883 & 0.0574705974649765 & 0.971264701267512 \tabularnewline
129 & 0.0240346567479141 & 0.0480693134958281 & 0.975965343252086 \tabularnewline
130 & 0.0225727303357421 & 0.0451454606714841 & 0.977427269664258 \tabularnewline
131 & 0.0183118628573338 & 0.0366237257146676 & 0.981688137142666 \tabularnewline
132 & 0.0208340465117679 & 0.0416680930235358 & 0.979165953488232 \tabularnewline
133 & 0.0329441710697586 & 0.0658883421395171 & 0.967055828930241 \tabularnewline
134 & 0.0357448673740395 & 0.0714897347480789 & 0.964255132625961 \tabularnewline
135 & 0.0396992391980075 & 0.079398478396015 & 0.960300760801993 \tabularnewline
136 & 0.0338849019817751 & 0.0677698039635502 & 0.966115098018225 \tabularnewline
137 & 0.0291849166468717 & 0.0583698332937434 & 0.970815083353128 \tabularnewline
138 & 0.0210937175226957 & 0.0421874350453913 & 0.978906282477304 \tabularnewline
139 & 0.0210691773009027 & 0.0421383546018054 & 0.978930822699097 \tabularnewline
140 & 0.0149711371489459 & 0.0299422742978917 & 0.985028862851054 \tabularnewline
141 & 0.0453095240801354 & 0.0906190481602708 & 0.954690475919865 \tabularnewline
142 & 0.0505317638358669 & 0.101063527671734 & 0.949468236164133 \tabularnewline
143 & 0.0372193083547792 & 0.0744386167095584 & 0.962780691645221 \tabularnewline
144 & 0.36436166839149 & 0.72872333678298 & 0.63563833160851 \tabularnewline
145 & 0.372036077946403 & 0.744072155892807 & 0.627963922053597 \tabularnewline
146 & 0.635695408857586 & 0.728609182284827 & 0.364304591142414 \tabularnewline
147 & 0.619056455498669 & 0.761887089002661 & 0.380943544501331 \tabularnewline
148 & 0.828393642884445 & 0.343212714231111 & 0.171606357115556 \tabularnewline
149 & 0.816174073114387 & 0.367651853771226 & 0.183825926885613 \tabularnewline
150 & 0.725918869955119 & 0.548162260089762 & 0.274081130044881 \tabularnewline
151 & 0.619785831461865 & 0.76042833707627 & 0.380214168538135 \tabularnewline
152 & 0.600863705648058 & 0.798272588703884 & 0.399136294351942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.999862702059595[/C][C]0.000274595880809259[/C][C]0.00013729794040463[/C][/ROW]
[ROW][C]11[/C][C]0.999643844724874[/C][C]0.00071231055025186[/C][C]0.00035615527512593[/C][/ROW]
[ROW][C]12[/C][C]0.999521853291369[/C][C]0.000956293417261691[/C][C]0.000478146708630846[/C][/ROW]
[ROW][C]13[/C][C]0.99909014166567[/C][C]0.00181971666865951[/C][C]0.000909858334329755[/C][/ROW]
[ROW][C]14[/C][C]0.998330922759351[/C][C]0.00333815448129747[/C][C]0.00166907724064873[/C][/ROW]
[ROW][C]15[/C][C]0.99799608707291[/C][C]0.00400782585417916[/C][C]0.00200391292708958[/C][/ROW]
[ROW][C]16[/C][C]0.996619846026202[/C][C]0.00676030794759595[/C][C]0.00338015397379797[/C][/ROW]
[ROW][C]17[/C][C]0.99393541595282[/C][C]0.0121291680943592[/C][C]0.00606458404717959[/C][/ROW]
[ROW][C]18[/C][C]0.992573053673973[/C][C]0.0148538926520533[/C][C]0.00742694632602664[/C][/ROW]
[ROW][C]19[/C][C]0.988888853955783[/C][C]0.0222222920884347[/C][C]0.0111111460442173[/C][/ROW]
[ROW][C]20[/C][C]0.981977350580172[/C][C]0.0360452988396557[/C][C]0.0180226494198279[/C][/ROW]
[ROW][C]21[/C][C]0.973504500569171[/C][C]0.0529909988616572[/C][C]0.0264954994308286[/C][/ROW]
[ROW][C]22[/C][C]0.962404983203959[/C][C]0.0751900335920813[/C][C]0.0375950167960407[/C][/ROW]
[ROW][C]23[/C][C]0.946743509693202[/C][C]0.106512980613597[/C][C]0.0532564903067985[/C][/ROW]
[ROW][C]24[/C][C]0.928228822196511[/C][C]0.143542355606978[/C][C]0.0717711778034888[/C][/ROW]
[ROW][C]25[/C][C]0.925565374476949[/C][C]0.148869251046103[/C][C]0.0744346255230514[/C][/ROW]
[ROW][C]26[/C][C]0.943676035743159[/C][C]0.112647928513682[/C][C]0.0563239642568411[/C][/ROW]
[ROW][C]27[/C][C]0.933196411062847[/C][C]0.133607177874307[/C][C]0.0668035889371534[/C][/ROW]
[ROW][C]28[/C][C]0.939641484528866[/C][C]0.120717030942267[/C][C]0.0603585154711336[/C][/ROW]
[ROW][C]29[/C][C]0.918860970674153[/C][C]0.162278058651693[/C][C]0.0811390293258467[/C][/ROW]
[ROW][C]30[/C][C]0.898399098209941[/C][C]0.203201803580119[/C][C]0.101600901790059[/C][/ROW]
[ROW][C]31[/C][C]0.87963674305655[/C][C]0.2407265138869[/C][C]0.12036325694345[/C][/ROW]
[ROW][C]32[/C][C]0.896982333923303[/C][C]0.206035332153395[/C][C]0.103017666076697[/C][/ROW]
[ROW][C]33[/C][C]0.867845354501458[/C][C]0.264309290997084[/C][C]0.132154645498542[/C][/ROW]
[ROW][C]34[/C][C]0.833536787831729[/C][C]0.332926424336541[/C][C]0.166463212168271[/C][/ROW]
[ROW][C]35[/C][C]0.822501860888458[/C][C]0.354996278223084[/C][C]0.177498139111542[/C][/ROW]
[ROW][C]36[/C][C]0.785647985414857[/C][C]0.428704029170285[/C][C]0.214352014585142[/C][/ROW]
[ROW][C]37[/C][C]0.825701433370232[/C][C]0.348597133259536[/C][C]0.174298566629768[/C][/ROW]
[ROW][C]38[/C][C]0.816993783187987[/C][C]0.366012433624026[/C][C]0.183006216812013[/C][/ROW]
[ROW][C]39[/C][C]0.806533979047716[/C][C]0.386932041904569[/C][C]0.193466020952284[/C][/ROW]
[ROW][C]40[/C][C]0.787802914235766[/C][C]0.424394171528469[/C][C]0.212197085764234[/C][/ROW]
[ROW][C]41[/C][C]0.76310074035102[/C][C]0.473798519297959[/C][C]0.23689925964898[/C][/ROW]
[ROW][C]42[/C][C]0.747246660132838[/C][C]0.505506679734324[/C][C]0.252753339867162[/C][/ROW]
[ROW][C]43[/C][C]0.748194728072985[/C][C]0.50361054385403[/C][C]0.251805271927015[/C][/ROW]
[ROW][C]44[/C][C]0.70562058776073[/C][C]0.588758824478539[/C][C]0.29437941223927[/C][/ROW]
[ROW][C]45[/C][C]0.661393063848871[/C][C]0.677213872302258[/C][C]0.338606936151129[/C][/ROW]
[ROW][C]46[/C][C]0.728693430545396[/C][C]0.542613138909209[/C][C]0.271306569454604[/C][/ROW]
[ROW][C]47[/C][C]0.7348314541457[/C][C]0.5303370917086[/C][C]0.2651685458543[/C][/ROW]
[ROW][C]48[/C][C]0.690841124497387[/C][C]0.618317751005226[/C][C]0.309158875502613[/C][/ROW]
[ROW][C]49[/C][C]0.653025942387652[/C][C]0.693948115224696[/C][C]0.346974057612348[/C][/ROW]
[ROW][C]50[/C][C]0.639033188156066[/C][C]0.721933623687868[/C][C]0.360966811843934[/C][/ROW]
[ROW][C]51[/C][C]0.644509236055897[/C][C]0.710981527888207[/C][C]0.355490763944103[/C][/ROW]
[ROW][C]52[/C][C]0.59790162316019[/C][C]0.80419675367962[/C][C]0.40209837683981[/C][/ROW]
[ROW][C]53[/C][C]0.625918749515761[/C][C]0.748162500968477[/C][C]0.374081250484239[/C][/ROW]
[ROW][C]54[/C][C]0.590943352470547[/C][C]0.818113295058905[/C][C]0.409056647529453[/C][/ROW]
[ROW][C]55[/C][C]0.685289177626934[/C][C]0.629421644746133[/C][C]0.314710822373066[/C][/ROW]
[ROW][C]56[/C][C]0.844306800502054[/C][C]0.311386398995892[/C][C]0.155693199497946[/C][/ROW]
[ROW][C]57[/C][C]0.817148705328404[/C][C]0.365702589343192[/C][C]0.182851294671596[/C][/ROW]
[ROW][C]58[/C][C]0.78322244699948[/C][C]0.43355510600104[/C][C]0.21677755300052[/C][/ROW]
[ROW][C]59[/C][C]0.746822777056479[/C][C]0.506354445887041[/C][C]0.253177222943521[/C][/ROW]
[ROW][C]60[/C][C]0.735360716119371[/C][C]0.529278567761257[/C][C]0.264639283880629[/C][/ROW]
[ROW][C]61[/C][C]0.753210409376226[/C][C]0.493579181247549[/C][C]0.246789590623774[/C][/ROW]
[ROW][C]62[/C][C]0.717067263880443[/C][C]0.565865472239114[/C][C]0.282932736119557[/C][/ROW]
[ROW][C]63[/C][C]0.731696538436117[/C][C]0.536606923127766[/C][C]0.268303461563883[/C][/ROW]
[ROW][C]64[/C][C]0.691439998862989[/C][C]0.617120002274023[/C][C]0.308560001137011[/C][/ROW]
[ROW][C]65[/C][C]0.656899775427468[/C][C]0.686200449145063[/C][C]0.343100224572532[/C][/ROW]
[ROW][C]66[/C][C]0.615267745021485[/C][C]0.769464509957031[/C][C]0.384732254978515[/C][/ROW]
[ROW][C]67[/C][C]0.594805770097065[/C][C]0.810388459805869[/C][C]0.405194229902935[/C][/ROW]
[ROW][C]68[/C][C]0.577489730485604[/C][C]0.845020539028791[/C][C]0.422510269514396[/C][/ROW]
[ROW][C]69[/C][C]0.596107795142096[/C][C]0.807784409715807[/C][C]0.403892204857904[/C][/ROW]
[ROW][C]70[/C][C]0.552322497662804[/C][C]0.895355004674392[/C][C]0.447677502337196[/C][/ROW]
[ROW][C]71[/C][C]0.512172995908571[/C][C]0.975654008182857[/C][C]0.487827004091429[/C][/ROW]
[ROW][C]72[/C][C]0.469066046756089[/C][C]0.938132093512178[/C][C]0.530933953243911[/C][/ROW]
[ROW][C]73[/C][C]0.440688793189045[/C][C]0.881377586378091[/C][C]0.559311206810955[/C][/ROW]
[ROW][C]74[/C][C]0.416666256094977[/C][C]0.833332512189954[/C][C]0.583333743905023[/C][/ROW]
[ROW][C]75[/C][C]0.390642905375001[/C][C]0.781285810750002[/C][C]0.609357094624999[/C][/ROW]
[ROW][C]76[/C][C]0.440578825094183[/C][C]0.881157650188365[/C][C]0.559421174905817[/C][/ROW]
[ROW][C]77[/C][C]0.426783571221557[/C][C]0.853567142443114[/C][C]0.573216428778443[/C][/ROW]
[ROW][C]78[/C][C]0.386033830770558[/C][C]0.772067661541116[/C][C]0.613966169229442[/C][/ROW]
[ROW][C]79[/C][C]0.390891222274904[/C][C]0.781782444549808[/C][C]0.609108777725096[/C][/ROW]
[ROW][C]80[/C][C]0.368314492969194[/C][C]0.736628985938389[/C][C]0.631685507030806[/C][/ROW]
[ROW][C]81[/C][C]0.327625552300367[/C][C]0.655251104600734[/C][C]0.672374447699633[/C][/ROW]
[ROW][C]82[/C][C]0.324241816123176[/C][C]0.648483632246352[/C][C]0.675758183876824[/C][/ROW]
[ROW][C]83[/C][C]0.285902042328817[/C][C]0.571804084657633[/C][C]0.714097957671183[/C][/ROW]
[ROW][C]84[/C][C]0.260837555834034[/C][C]0.521675111668069[/C][C]0.739162444165966[/C][/ROW]
[ROW][C]85[/C][C]0.22495044082181[/C][C]0.44990088164362[/C][C]0.77504955917819[/C][/ROW]
[ROW][C]86[/C][C]0.217086775459919[/C][C]0.434173550919839[/C][C]0.782913224540081[/C][/ROW]
[ROW][C]87[/C][C]0.216793893221307[/C][C]0.433587786442614[/C][C]0.783206106778693[/C][/ROW]
[ROW][C]88[/C][C]0.184796445765134[/C][C]0.369592891530268[/C][C]0.815203554234866[/C][/ROW]
[ROW][C]89[/C][C]0.156676956391196[/C][C]0.313353912782393[/C][C]0.843323043608804[/C][/ROW]
[ROW][C]90[/C][C]0.135711163487964[/C][C]0.271422326975927[/C][C]0.864288836512036[/C][/ROW]
[ROW][C]91[/C][C]0.115948048461855[/C][C]0.23189609692371[/C][C]0.884051951538145[/C][/ROW]
[ROW][C]92[/C][C]0.162077135279519[/C][C]0.324154270559039[/C][C]0.837922864720481[/C][/ROW]
[ROW][C]93[/C][C]0.141308854090968[/C][C]0.282617708181937[/C][C]0.858691145909032[/C][/ROW]
[ROW][C]94[/C][C]0.125997247541491[/C][C]0.251994495082981[/C][C]0.874002752458509[/C][/ROW]
[ROW][C]95[/C][C]0.121659170715201[/C][C]0.243318341430401[/C][C]0.878340829284799[/C][/ROW]
[ROW][C]96[/C][C]0.113210846587767[/C][C]0.226421693175534[/C][C]0.886789153412233[/C][/ROW]
[ROW][C]97[/C][C]0.104494031849041[/C][C]0.208988063698081[/C][C]0.895505968150959[/C][/ROW]
[ROW][C]98[/C][C]0.128936827496928[/C][C]0.257873654993857[/C][C]0.871063172503072[/C][/ROW]
[ROW][C]99[/C][C]0.10760991563587[/C][C]0.21521983127174[/C][C]0.89239008436413[/C][/ROW]
[ROW][C]100[/C][C]0.0908560196615232[/C][C]0.181712039323046[/C][C]0.909143980338477[/C][/ROW]
[ROW][C]101[/C][C]0.111575341630525[/C][C]0.22315068326105[/C][C]0.888424658369475[/C][/ROW]
[ROW][C]102[/C][C]0.0923125710057093[/C][C]0.184625142011419[/C][C]0.907687428994291[/C][/ROW]
[ROW][C]103[/C][C]0.0880614930199572[/C][C]0.176122986039914[/C][C]0.911938506980043[/C][/ROW]
[ROW][C]104[/C][C]0.07421863347043[/C][C]0.14843726694086[/C][C]0.92578136652957[/C][/ROW]
[ROW][C]105[/C][C]0.0625347217297851[/C][C]0.12506944345957[/C][C]0.937465278270215[/C][/ROW]
[ROW][C]106[/C][C]0.0636499222521049[/C][C]0.12729984450421[/C][C]0.936350077747895[/C][/ROW]
[ROW][C]107[/C][C]0.0501275058350044[/C][C]0.100255011670009[/C][C]0.949872494164996[/C][/ROW]
[ROW][C]108[/C][C]0.0443214405604995[/C][C]0.088642881120999[/C][C]0.955678559439501[/C][/ROW]
[ROW][C]109[/C][C]0.0353307630535245[/C][C]0.0706615261070491[/C][C]0.964669236946475[/C][/ROW]
[ROW][C]110[/C][C]0.0366561847447316[/C][C]0.0733123694894632[/C][C]0.963343815255268[/C][/ROW]
[ROW][C]111[/C][C]0.0288020475271175[/C][C]0.057604095054235[/C][C]0.971197952472883[/C][/ROW]
[ROW][C]112[/C][C]0.0322131877887211[/C][C]0.0644263755774422[/C][C]0.967786812211279[/C][/ROW]
[ROW][C]113[/C][C]0.0288151984978809[/C][C]0.0576303969957618[/C][C]0.971184801502119[/C][/ROW]
[ROW][C]114[/C][C]0.0231313778862903[/C][C]0.0462627557725805[/C][C]0.97686862211371[/C][/ROW]
[ROW][C]115[/C][C]0.0182213565152701[/C][C]0.0364427130305402[/C][C]0.98177864348473[/C][/ROW]
[ROW][C]116[/C][C]0.013766062148945[/C][C]0.0275321242978901[/C][C]0.986233937851055[/C][/ROW]
[ROW][C]117[/C][C]0.0220520149740148[/C][C]0.0441040299480296[/C][C]0.977947985025985[/C][/ROW]
[ROW][C]118[/C][C]0.0252201380702535[/C][C]0.0504402761405071[/C][C]0.974779861929746[/C][/ROW]
[ROW][C]119[/C][C]0.019374998773264[/C][C]0.0387499975465279[/C][C]0.980625001226736[/C][/ROW]
[ROW][C]120[/C][C]0.0150252815484232[/C][C]0.0300505630968464[/C][C]0.984974718451577[/C][/ROW]
[ROW][C]121[/C][C]0.0125455342702671[/C][C]0.0250910685405342[/C][C]0.987454465729733[/C][/ROW]
[ROW][C]122[/C][C]0.0104797003809248[/C][C]0.0209594007618497[/C][C]0.989520299619075[/C][/ROW]
[ROW][C]123[/C][C]0.0123557407176859[/C][C]0.0247114814353718[/C][C]0.987644259282314[/C][/ROW]
[ROW][C]124[/C][C]0.0194548266420479[/C][C]0.0389096532840958[/C][C]0.980545173357952[/C][/ROW]
[ROW][C]125[/C][C]0.0205395318702584[/C][C]0.0410790637405167[/C][C]0.979460468129742[/C][/ROW]
[ROW][C]126[/C][C]0.0489778613703527[/C][C]0.0979557227407054[/C][C]0.951022138629647[/C][/ROW]
[ROW][C]127[/C][C]0.037436362133926[/C][C]0.074872724267852[/C][C]0.962563637866074[/C][/ROW]
[ROW][C]128[/C][C]0.0287352987324883[/C][C]0.0574705974649765[/C][C]0.971264701267512[/C][/ROW]
[ROW][C]129[/C][C]0.0240346567479141[/C][C]0.0480693134958281[/C][C]0.975965343252086[/C][/ROW]
[ROW][C]130[/C][C]0.0225727303357421[/C][C]0.0451454606714841[/C][C]0.977427269664258[/C][/ROW]
[ROW][C]131[/C][C]0.0183118628573338[/C][C]0.0366237257146676[/C][C]0.981688137142666[/C][/ROW]
[ROW][C]132[/C][C]0.0208340465117679[/C][C]0.0416680930235358[/C][C]0.979165953488232[/C][/ROW]
[ROW][C]133[/C][C]0.0329441710697586[/C][C]0.0658883421395171[/C][C]0.967055828930241[/C][/ROW]
[ROW][C]134[/C][C]0.0357448673740395[/C][C]0.0714897347480789[/C][C]0.964255132625961[/C][/ROW]
[ROW][C]135[/C][C]0.0396992391980075[/C][C]0.079398478396015[/C][C]0.960300760801993[/C][/ROW]
[ROW][C]136[/C][C]0.0338849019817751[/C][C]0.0677698039635502[/C][C]0.966115098018225[/C][/ROW]
[ROW][C]137[/C][C]0.0291849166468717[/C][C]0.0583698332937434[/C][C]0.970815083353128[/C][/ROW]
[ROW][C]138[/C][C]0.0210937175226957[/C][C]0.0421874350453913[/C][C]0.978906282477304[/C][/ROW]
[ROW][C]139[/C][C]0.0210691773009027[/C][C]0.0421383546018054[/C][C]0.978930822699097[/C][/ROW]
[ROW][C]140[/C][C]0.0149711371489459[/C][C]0.0299422742978917[/C][C]0.985028862851054[/C][/ROW]
[ROW][C]141[/C][C]0.0453095240801354[/C][C]0.0906190481602708[/C][C]0.954690475919865[/C][/ROW]
[ROW][C]142[/C][C]0.0505317638358669[/C][C]0.101063527671734[/C][C]0.949468236164133[/C][/ROW]
[ROW][C]143[/C][C]0.0372193083547792[/C][C]0.0744386167095584[/C][C]0.962780691645221[/C][/ROW]
[ROW][C]144[/C][C]0.36436166839149[/C][C]0.72872333678298[/C][C]0.63563833160851[/C][/ROW]
[ROW][C]145[/C][C]0.372036077946403[/C][C]0.744072155892807[/C][C]0.627963922053597[/C][/ROW]
[ROW][C]146[/C][C]0.635695408857586[/C][C]0.728609182284827[/C][C]0.364304591142414[/C][/ROW]
[ROW][C]147[/C][C]0.619056455498669[/C][C]0.761887089002661[/C][C]0.380943544501331[/C][/ROW]
[ROW][C]148[/C][C]0.828393642884445[/C][C]0.343212714231111[/C][C]0.171606357115556[/C][/ROW]
[ROW][C]149[/C][C]0.816174073114387[/C][C]0.367651853771226[/C][C]0.183825926885613[/C][/ROW]
[ROW][C]150[/C][C]0.725918869955119[/C][C]0.548162260089762[/C][C]0.274081130044881[/C][/ROW]
[ROW][C]151[/C][C]0.619785831461865[/C][C]0.76042833707627[/C][C]0.380214168538135[/C][/ROW]
[ROW][C]152[/C][C]0.600863705648058[/C][C]0.798272588703884[/C][C]0.399136294351942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9998627020595950.0002745958808092590.00013729794040463
110.9996438447248740.000712310550251860.00035615527512593
120.9995218532913690.0009562934172616910.000478146708630846
130.999090141665670.001819716668659510.000909858334329755
140.9983309227593510.003338154481297470.00166907724064873
150.997996087072910.004007825854179160.00200391292708958
160.9966198460262020.006760307947595950.00338015397379797
170.993935415952820.01212916809435920.00606458404717959
180.9925730536739730.01485389265205330.00742694632602664
190.9888888539557830.02222229208843470.0111111460442173
200.9819773505801720.03604529883965570.0180226494198279
210.9735045005691710.05299099886165720.0264954994308286
220.9624049832039590.07519003359208130.0375950167960407
230.9467435096932020.1065129806135970.0532564903067985
240.9282288221965110.1435423556069780.0717711778034888
250.9255653744769490.1488692510461030.0744346255230514
260.9436760357431590.1126479285136820.0563239642568411
270.9331964110628470.1336071778743070.0668035889371534
280.9396414845288660.1207170309422670.0603585154711336
290.9188609706741530.1622780586516930.0811390293258467
300.8983990982099410.2032018035801190.101600901790059
310.879636743056550.24072651388690.12036325694345
320.8969823339233030.2060353321533950.103017666076697
330.8678453545014580.2643092909970840.132154645498542
340.8335367878317290.3329264243365410.166463212168271
350.8225018608884580.3549962782230840.177498139111542
360.7856479854148570.4287040291702850.214352014585142
370.8257014333702320.3485971332595360.174298566629768
380.8169937831879870.3660124336240260.183006216812013
390.8065339790477160.3869320419045690.193466020952284
400.7878029142357660.4243941715284690.212197085764234
410.763100740351020.4737985192979590.23689925964898
420.7472466601328380.5055066797343240.252753339867162
430.7481947280729850.503610543854030.251805271927015
440.705620587760730.5887588244785390.29437941223927
450.6613930638488710.6772138723022580.338606936151129
460.7286934305453960.5426131389092090.271306569454604
470.73483145414570.53033709170860.2651685458543
480.6908411244973870.6183177510052260.309158875502613
490.6530259423876520.6939481152246960.346974057612348
500.6390331881560660.7219336236878680.360966811843934
510.6445092360558970.7109815278882070.355490763944103
520.597901623160190.804196753679620.40209837683981
530.6259187495157610.7481625009684770.374081250484239
540.5909433524705470.8181132950589050.409056647529453
550.6852891776269340.6294216447461330.314710822373066
560.8443068005020540.3113863989958920.155693199497946
570.8171487053284040.3657025893431920.182851294671596
580.783222446999480.433555106001040.21677755300052
590.7468227770564790.5063544458870410.253177222943521
600.7353607161193710.5292785677612570.264639283880629
610.7532104093762260.4935791812475490.246789590623774
620.7170672638804430.5658654722391140.282932736119557
630.7316965384361170.5366069231277660.268303461563883
640.6914399988629890.6171200022740230.308560001137011
650.6568997754274680.6862004491450630.343100224572532
660.6152677450214850.7694645099570310.384732254978515
670.5948057700970650.8103884598058690.405194229902935
680.5774897304856040.8450205390287910.422510269514396
690.5961077951420960.8077844097158070.403892204857904
700.5523224976628040.8953550046743920.447677502337196
710.5121729959085710.9756540081828570.487827004091429
720.4690660467560890.9381320935121780.530933953243911
730.4406887931890450.8813775863780910.559311206810955
740.4166662560949770.8333325121899540.583333743905023
750.3906429053750010.7812858107500020.609357094624999
760.4405788250941830.8811576501883650.559421174905817
770.4267835712215570.8535671424431140.573216428778443
780.3860338307705580.7720676615411160.613966169229442
790.3908912222749040.7817824445498080.609108777725096
800.3683144929691940.7366289859383890.631685507030806
810.3276255523003670.6552511046007340.672374447699633
820.3242418161231760.6484836322463520.675758183876824
830.2859020423288170.5718040846576330.714097957671183
840.2608375558340340.5216751116680690.739162444165966
850.224950440821810.449900881643620.77504955917819
860.2170867754599190.4341735509198390.782913224540081
870.2167938932213070.4335877864426140.783206106778693
880.1847964457651340.3695928915302680.815203554234866
890.1566769563911960.3133539127823930.843323043608804
900.1357111634879640.2714223269759270.864288836512036
910.1159480484618550.231896096923710.884051951538145
920.1620771352795190.3241542705590390.837922864720481
930.1413088540909680.2826177081819370.858691145909032
940.1259972475414910.2519944950829810.874002752458509
950.1216591707152010.2433183414304010.878340829284799
960.1132108465877670.2264216931755340.886789153412233
970.1044940318490410.2089880636980810.895505968150959
980.1289368274969280.2578736549938570.871063172503072
990.107609915635870.215219831271740.89239008436413
1000.09085601966152320.1817120393230460.909143980338477
1010.1115753416305250.223150683261050.888424658369475
1020.09231257100570930.1846251420114190.907687428994291
1030.08806149301995720.1761229860399140.911938506980043
1040.074218633470430.148437266940860.92578136652957
1050.06253472172978510.125069443459570.937465278270215
1060.06364992225210490.127299844504210.936350077747895
1070.05012750583500440.1002550116700090.949872494164996
1080.04432144056049950.0886428811209990.955678559439501
1090.03533076305352450.07066152610704910.964669236946475
1100.03665618474473160.07331236948946320.963343815255268
1110.02880204752711750.0576040950542350.971197952472883
1120.03221318778872110.06442637557744220.967786812211279
1130.02881519849788090.05763039699576180.971184801502119
1140.02313137788629030.04626275577258050.97686862211371
1150.01822135651527010.03644271303054020.98177864348473
1160.0137660621489450.02753212429789010.986233937851055
1170.02205201497401480.04410402994802960.977947985025985
1180.02522013807025350.05044027614050710.974779861929746
1190.0193749987732640.03874999754652790.980625001226736
1200.01502528154842320.03005056309684640.984974718451577
1210.01254553427026710.02509106854053420.987454465729733
1220.01047970038092480.02095940076184970.989520299619075
1230.01235574071768590.02471148143537180.987644259282314
1240.01945482664204790.03890965328409580.980545173357952
1250.02053953187025840.04107906374051670.979460468129742
1260.04897786137035270.09795572274070540.951022138629647
1270.0374363621339260.0748727242678520.962563637866074
1280.02873529873248830.05747059746497650.971264701267512
1290.02403465674791410.04806931349582810.975965343252086
1300.02257273033574210.04514546067148410.977427269664258
1310.01831186285733380.03662372571466760.981688137142666
1320.02083404651176790.04166809302353580.979165953488232
1330.03294417106975860.06588834213951710.967055828930241
1340.03574486737403950.07148973474807890.964255132625961
1350.03969923919800750.0793984783960150.960300760801993
1360.03388490198177510.06776980396355020.966115098018225
1370.02918491664687170.05836983329374340.970815083353128
1380.02109371752269570.04218743504539130.978906282477304
1390.02106917730090270.04213835460180540.978930822699097
1400.01497113714894590.02994227429789170.985028862851054
1410.04530952408013540.09061904816027080.954690475919865
1420.05053176383586690.1010635276717340.949468236164133
1430.03721930835477920.07443861670955840.962780691645221
1440.364361668391490.728723336782980.63563833160851
1450.3720360779464030.7440721558928070.627963922053597
1460.6356954088575860.7286091822848270.364304591142414
1470.6190564554986690.7618870890026610.380943544501331
1480.8283936428844450.3432127142311110.171606357115556
1490.8161740731143870.3676518537712260.183825926885613
1500.7259188699551190.5481622600897620.274081130044881
1510.6197858314618650.760428337076270.380214168538135
1520.6008637056480580.7982725887038840.399136294351942







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level290.202797202797203NOK
10% type I error level480.335664335664336NOK

\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 & 7 & 0.048951048951049 & NOK \tabularnewline
5% type I error level & 29 & 0.202797202797203 & NOK \tabularnewline
10% type I error level & 48 & 0.335664335664336 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186203&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]7[/C][C]0.048951048951049[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.202797202797203[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.335664335664336[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186203&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186203&T=6

As an alternative you can also use a 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 level70.048951048951049NOK
5% type I error level290.202797202797203NOK
10% type I error level480.335664335664336NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}