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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 19 Nov 2013 16:23:28 -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/2013/Nov/19/t1384896246osg7wxoxsw7fe7g.htm/, Retrieved Fri, 03 May 2024 16:28:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226563, Retrieved Fri, 03 May 2024 16:28:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [workshop 7 A] [2013-11-19 21:05:31] [f661ab64c60045a179a329b9ddab9bfe]
-   P       [Multiple Regression] [Workshop 7 AA] [2013-11-19 21:23:28] [0292d86b083cfbb80712452cc5129066] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	12	12	53
39	32	11	11	83
30	35	15	14	66
31	33	6	12	67
34	37	13	21	76
35	29	10	12	78
39	31	12	22	53
34	36	14	11	80
36	35	12	10	74
37	38	9	13	76
38	31	10	10	79
36	34	12	8	54
38	35	12	15	67
39	38	11	14	54
33	37	15	10	87
32	33	12	14	58
36	32	10	14	75
38	38	12	11	88
39	38	11	10	64
32	32	12	13	57
32	33	11	9.5	66
31	31	12	14	68
39	38	13	12	54
37	39	11	14	56
39	32	12	11	86
41	32	13	9	80
36	35	10	11	76
33	37	14	15	69
33	33	12	14	78
34	33	10	13	67
31	31	12	9	80
27	32	8	15	54
37	31	10	10	71
34	37	12	11	84
34	30	12	13	74
32	33	7	8	71
29	31	9	20	63
36	33	12	12	71
29	31	10	10	76
35	33	10	10	69
37	32	10	9	74
34	33	12	14	75
38	32	15	8	54
35	33	10	14	52
38	28	10	11	69
37	35	12	13	68
38	39	13	9	65
33	34	11	11	75
36	38	11	15	74
38	32	12	11	75
32	38	14	10	72
32	30	10	14	67
32	33	12	18	63
34	38	13	14	62
32	32	5	11	63
37	35	6	14.5	76
39	34	12	13	74
29	34	12	9	67
37	36	11	10	73
35	34	10	15	70
30	28	7	20	53
38	34	12	12	77
34	35	14	12	80
31	35	11	14	52
34	31	12	13	54
35	37	13	11	80
36	35	14	17	66
30	27	11	12	73
39	40	12	13	63
35	37	12	14	69
38	36	8	13	67
31	38	11	15	54
34	39	14	13	81
38	41	14	10	69
34	27	12	11	84
39	30	9	19	80
37	37	13	13	70
34	31	11	17	69
28	31	12	13	77
37	27	12	9	54
33	36	12	11	79
35	37	12	9	71
37	33	12	12	73
32	34	11	12	72
33	31	10	13	77
38	39	9	13	75
33	34	12	12	69
29	32	12	15	54
33	33	12	22	70
31	36	9	13	73
36	32	15	15	54
35	41	12	13	77
32	28	12	15	82
29	30	12	12.5	80
39	36	10	11	80
37	35	13	16	69
35	31	9	11	78
37	34	12	11	81
32	36	10	10	76
38	36	14	10	76
37	35	11	16	73
36	37	15	12	85
32	28	11	11	66
33	39	11	16	79
40	32	12	19	68
38	35	12	11	76
41	39	12	16	71
36	35	11	15	54
43	42	7	24	46
30	34	12	14	85
31	33	14	15	74
32	41	11	11	88
32	33	11	15	38
37	34	10	12	76
37	32	13	10	86
33	40	13	14	54
34	40	8	13	67
33	35	11	9	69
38	36	12	15	90
33	37	11	15	54
31	27	13	14	76
38	39	12	11	89
37	38	14	8	76
36	31	13	11	73
31	33	15	11	79
39	32	10	8	90
44	39	11	10	74
33	36	9	11	81
35	33	11	13	72
32	33	10	11	71
28	32	11	20	66
40	37	8	10	77
27	30	11	15	65
37	38	12	12	74
32	29	12	14	85
28	22	9	23	54
34	35	11	14	63
30	35	10	16	54
35	34	8	11	64
31	35	9	12	69
32	34	8	10	54
30	37	9	14	84
30	35	15	12	86
31	23	11	12	77
40	31	8	11	89
32	27	13	12	76
36	36	12	13	60
32	31	12	11	75
35	32	9	19	73
38	39	7	12	85
42	37	13	17	79
34	38	9	9	71
35	39	6	12	72
38	34	8	19	69
33	31	8	18	78
36	32	15	15	54
32	37	6	14	69
33	36	9	11	81
34	32	11	9	84
32	38	8	18	84
34	36	8	16	69
27	26	10	24	66
31	26	8	14	81
38	33	14	20	82
34	39	10	18	72
24	30	8	23	54
30	33	11	12	78
26	25	12	14	74
34	38	12	16	82
27	37	12	18	73
37	31	5	20	55
36	37	12	12	72
41	35	10	12	78
29	25	7	17	59
36	28	12	13	72
32	35	11	9	78
37	33	8	16	68
30	30	9	18	69
31	31	10	10	67
38	37	9	14	74
36	36	12	11	54
35	30	6	9	67
31	36	15	11	70
38	32	12	10	80
22	28	12	11	89
32	36	12	19	76
36	34	11	14	74
39	31	7	12	87
28	28	7	14	54
32	36	5	21	61
32	36	12	13	38
38	40	12	10	75
32	33	3	15	69
35	37	11	16	62
32	32	10	14	72
37	38	12	12	70
34	31	9	19	79
33	37	12	15	87
33	33	9	19	62
26	32	12	13	77
30	30	12	17	69
24	30	10	12	69
34	31	9	11	75
34	32	12	14	54
33	34	8	11	72
34	36	11	13	74
35	37	11	12	85
35	36	12	15	52
36	33	10	14	70
34	33	10	12	84
34	33	12	17	64
41	44	12	11	84
32	39	11	18	87
30	32	8	13	79
35	35	12	17	67
28	25	10	13	65
33	35	11	11	85
39	34	10	12	83
36	35	8	22	61
36	39	12	14	82
35	33	12	12	76
38	36	10	12	58
33	32	12	17	72
31	32	9	9	72
34	36	9	21	38
32	36	6	10	78
31	32	10	11	54
33	34	9	12	63
34	33	9	23	66
34	35	9	13	70
34	30	6	12	71
33	38	10	16	67
32	34	6	9	58
41	33	14	17	72
34	32	10	9	72
36	31	10	14	70
37	30	6	17	76
36	27	12	13	50
29	31	12	11	72
37	30	7	12	72
27	32	8	10	88
35	35	11	19	53
28	28	3	16	58
35	33	6	16	66
37	31	10	14	82
29	35	8	20	69
32	35	9	15	68
36	32	9	23	44
19	21	8	20	56
21	20	9	16	53
31	34	7	14	70
33	32	7	17	78
36	34	6	11	71
33	32	9	13	72
37	33	10	17	68
34	33	11	15	67
35	37	12	21	75
31	32	8	18	62
37	34	11	15	67
35	30	3	8	83
27	30	11	12	64
34	38	12	12	68
40	36	7	22	62
29	32	9	12	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 15 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Sport [t] = + 72.0513612792419 + 0.165129121546017Connected[t] + 0.0912266795655202Separate[t] + 0.219254440818557Computer[t] -0.928655723897579Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Sport

[t] =  +  72.0513612792419 +  0.165129121546017Connected[t] +  0.0912266795655202Separate[t] +  0.219254440818557Computer[t] -0.928655723897579Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Sport

[t] =  +  72.0513612792419 +  0.165129121546017Connected[t] +  0.0912266795655202Separate[t] +  0.219254440818557Computer[t] -0.928655723897579Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Sport [t] = + 72.0513612792419 + 0.165129121546017Connected[t] + 0.0912266795655202Separate[t] + 0.219254440818557Computer[t] -0.928655723897579Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)72.05136127924197.5292319.569600
Connected0.1651291215460170.1805080.91480.3611460.180573
Separate0.09122667956552020.1859170.49070.6240640.312032
Computer0.2192544408185570.2694430.81370.4165460.208273
Depression-0.9286557238975790.178464-5.203600

\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) & 72.0513612792419 & 7.529231 & 9.5696 & 0 & 0 \tabularnewline
Connected & 0.165129121546017 & 0.180508 & 0.9148 & 0.361146 & 0.180573 \tabularnewline
Separate & 0.0912266795655202 & 0.185917 & 0.4907 & 0.624064 & 0.312032 \tabularnewline
Computer & 0.219254440818557 & 0.269443 & 0.8137 & 0.416546 & 0.208273 \tabularnewline
Depression & -0.928655723897579 & 0.178464 & -5.2036 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&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]72.0513612792419[/C][C]7.529231[/C][C]9.5696[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.165129121546017[/C][C]0.180508[/C][C]0.9148[/C][C]0.361146[/C][C]0.180573[/C][/ROW]
[ROW][C]Separate[/C][C]0.0912266795655202[/C][C]0.185917[/C][C]0.4907[/C][C]0.624064[/C][C]0.312032[/C][/ROW]
[ROW][C]Computer[/C][C]0.219254440818557[/C][C]0.269443[/C][C]0.8137[/C][C]0.416546[/C][C]0.208273[/C][/ROW]
[ROW][C]Depression[/C][C]-0.928655723897579[/C][C]0.178464[/C][C]-5.2036[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)72.05136127924197.5292319.569600
Connected0.1651291215460170.1805080.91480.3611460.180573
Separate0.09122667956552020.1859170.49070.6240640.312032
Computer0.2192544408185570.2694430.81370.4165460.208273
Depression-0.9286557238975790.178464-5.203600






Multiple Linear Regression - Regression Statistics
Multiple R0.344081710347458
R-squared0.118392223395632
Adjusted R-squared0.10477665927819
F-TEST (value)8.6953593971158
F-TEST (DF numerator)4
F-TEST (DF denominator)259
p-value1.3372104802345e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.83592283128316
Sum Squared Residuals25057.0528872259

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.344081710347458 \tabularnewline
R-squared & 0.118392223395632 \tabularnewline
Adjusted R-squared & 0.10477665927819 \tabularnewline
F-TEST (value) & 8.6953593971158 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 259 \tabularnewline
p-value & 1.3372104802345e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.83592283128316 \tabularnewline
Sum Squared Residuals & 25057.0528872259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.344081710347458[/C][/ROW]
[ROW][C]R-squared[/C][C]0.118392223395632[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.10477665927819[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.6953593971158[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]259[/C][/ROW]
[ROW][C]p-value[/C][C]1.3372104802345e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.83592283128316[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25057.0528872259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.344081710347458
R-squared0.118392223395632
Adjusted R-squared0.10477665927819
F-TEST (value)8.6953593971158
F-TEST (DF numerator)4
F-TEST (DF denominator)259
p-value1.3372104802345e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.83592283128316
Sum Squared Residuals25057.0528872259






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15373.7754536891699-20.7754536891699
28373.60723665176399.39276334823606
36670.4858051881278-4.48580518812783
46770.352502430971-3.35250243097095
57664.389676084522811.6103239154772
67871.52512996216726.47487003783283
75363.5200514501436-10.5200514501436
88073.80426108475166.19573891524839
97474.5334394905386-0.533439490538582
107671.52851815663284.47148184336725
117974.06028213373144.93971786626858
125476.2995242587682-22.2995242587682
136770.2204191141427-3.22041911414272
145471.3686295574643-17.3686295574643
158774.878268807487212.1217311925128
165869.9758467496332-11.9758467496332
177570.10662767461464.89337232538541
188874.208722048429613.7912779515704
196475.0832524530546-11.0832524530546
205770.8132757939652-13.8132757939652
216673.9355430663537-7.93554306635371
226869.6282642689561-1.6282642689561
235473.6644498868966-19.6644498868966
245671.1295979939378-15.1295979939378
258673.826491092582512.1735089074175
268076.23331522428823.76668477571176
277673.16627488500392.83372511499611
286970.0157357471808-1.01573574718079
297870.14097587117927.85902412882083
306770.7962518349857-3.79625183498565
318074.2715428884445.728457111556
325467.2533009751657-13.2533009751657
337173.8951530121854-2.8951530121854
348473.4569788826810.54302111732
357470.96108067792623.03891932207379
367174.4515088889258-3.45150888892585
376363.0683083600229-0.0683083600229151
387172.4936746836124-1.49367468361238
397672.57412003981733.42587996018274
406973.7473481282244-4.74734812822441
417474.9150354156485-0.915035415648504
427570.30610499272524.69389500727481
435477.1050924651849-23.1050924651849
445270.0327252326341-18.0327252326341
456972.8579463711373-3.85794637113728
466871.9126014403919-3.91260144039186
476576.3765146166088-11.3765146166088
487572.79891528161892.20108471838113
497469.94458646892874.05541353107131
507573.66136197103651.33863802896352
517274.5851119246882-2.58511192468819
526769.2636578292995-2.26365782929948
536366.2612238540428-3.26122385404284
546270.9814928313714-8.98149283137135
556371.1358061560305-8.13580615603047
567669.20409120963416.79590879036585
577472.15163300391841.84836699608162
586774.2149646840485-7.21496468404852
597374.5705408508316-1.57054085083156
607069.1952961883020.804703811697966
615362.5212485612353-9.52124856123526
627772.91515960626994.08484039373006
638072.78437868128857.21562131871149
645269.7739165463996-17.7739165463996
655471.0523073574917-17.0523073574917
668073.84136244504466.15863755495542
676668.4713583048926-2.47135830489264
687370.73628543612462.2637145638754
696372.6989930813115-9.6989930813115
706970.8361408325333-1.83614083253329
716771.2919394782292-4.29193947822917
725469.1189408611986-15.1189408611986
738172.2206296756538.77937032434699
746975.8495666926609-6.84956669266085
758472.544712087024811.4552879129752
768065.557028619815114.4429713801849
777072.3143092403415-2.31430924034146
786967.11843002108291.88156997891715
797770.06153262821566.93846737178438
805474.897410899458-20.897410899458
817973.20062308156855.79937691843153
827175.4794194520212-4.47941945202118
837372.65880380515840.3411961948416
847271.70513043617530.294869563824723
857770.44866935430866.5513306456914
867571.78487395774433.21512604225571
876972.0895139985399-3.08951399853985
885468.460576981532-14.460576981532
897062.71173007999857.28826992000146
907370.35529006822562.6447099317744
915470.2742441548098-16.2742441548098
927772.12970327469294.87029672530705
938268.59105762790813.408942372092
948070.59976293214499.40023706785509
958073.75288892920756.24711107079254
966969.3458887095177-0.345888709517681
977872.41698460437725.58301539562277
988173.67868620862157.3213137913785
997673.52564080228292.47435919771708
1007675.39343329483330.606566705166749
1017368.90737982788064.09262017211943
1028573.516344724330111.4836552756699
1036672.0864260826797-6.08642608267974
1047968.611770059958610.3882299400414
1056866.56237442294791.43762557705212
1067673.9350420097332.06495799026696
1077170.15205747314530.847942526854727
1085469.6709064302321-15.6709064302321
1094662.2304777596604-16.2304777596604
1108569.736815186106615.2631848138934
1117469.32057078582674.67942921417333
1128873.272372917031514.7276270829685
1133868.827936584917-30.827936584917
1147672.31152160308683.68847839691319
1158674.644143014206611.3558569857934
1165470.9988170689564-16.9988170689564
1176770.9963297103072-3.99632971030718
1186974.7474534089796-5.74745340897955
1199070.311645793708219.6883542062918
1205469.3579724247251-15.3579724247251
1217669.48261199151266.51738800848742
1228974.299948727995114.7000512720049
1237677.2680689802134-1.26806898021343
1247373.4591314891975-0.459131489197479
1257973.25444812223555.74555187776445
1269076.173949382638113.8260506173619
1277476.0001247403502-2.00012474035024
1288172.54285975911288.4571402408872
1297271.18063539735020.81936460264977
1307172.3233050396888-1.32330503968878
1316663.43291479967952.56708520032047
1327774.49939157257952.50060842742054
1336567.7286109384904-2.72861093849037
1347473.1149372029860.885062797013999
1358569.610940031371115.3890599686289
1365459.2961719506945-5.29617195069448
1376370.2693039110377-7.26930391103767
1385467.5322215362399-13.5322215362399
1396472.4714102022552-8.47141020225524
1406971.1927191125577-2.19271911255767
1415472.9046785615148-18.9046785615148
1428469.352731902347514.6472680976525
1438672.34311663592313.656883364077
1447770.53650783940856.46349216059146
1458973.023375771288815.9766242287112
1467671.50505256085384.49494743914625
1476071.8386989984114-11.8386989984114
1487572.57936056219492.42063943780515
1497365.07896549276217.92103450723788
1508572.275020800004812.7249791999952
1517969.42533195248129.57466804751877
1527174.747753687585-3.74775368758502
1537271.56037899454810.439621005451857
1546965.53755177571273.46244822428735
1557865.366881853183612.6331181468164
1565470.2742441548098-16.2742441548098
1576969.0252268229839-0.0252268229838924
1588172.54285975911288.4571402408872
1598474.6389024918299.36109750817099
1608465.840339488596218.1596605114038
1616967.84545582035241.15454417964764
1626658.78654826433157.21345173566848
1638168.295113107854312.7048868921457
1648265.833196017160916.1668039828391
1657266.70033329289095.29966670710912
1665459.146214460216-5.14621446021601
1677871.28364551351776.71635448648228
1687468.25525858383295.74474141616711
1698268.904926942757613.0950730572424
1707365.80048496457487.19951503542517
1715563.5123235691168-8.51232356911683
1727272.8585814018745-0.858581401874464
1737873.06326476883644.93673523116361
1745964.8684065726854-5.86840657268542
1757271.10888556188720.891114438112797
1767874.58232428743353.41767571256646
1776868.0671631462939-0.0671631462938541
1786964.99952224979864.00047775020143
1796772.9043782829093-5.9043782829093
1807470.67376487471573.32623512528433
1815473.6960104462065-19.6960104462065
1826773.5253060501512-6.5253060501512
1837073.5281281609321-3.52812816093211
1848074.59001769493415.40998230506595
1858970.654389308038118.3456106919619
1867665.606248168841810.3937518311582
1877470.50833547456423.49166452543581
1888771.710336485026615.2896635149734
1895467.7629246615287-13.7629246615287
1906162.2141556353168-1.21415563531676
1913871.1781825122273-33.1781825122273
1927575.3198311314582-0.319831131458218
1936967.07390105836861.92609894163144
1946268.7595749439196-6.75957494391957
1957269.44611118843052.55388881156948
1967073.114937202986-3.114937202986
1977964.822609691650614.1773903083494
1988769.577226865543717.4227731344563
1996264.8399339292356-2.83993392923561
2007769.82250106468917.17749893531089
2016966.58594129615182.41405870384818
2026969.7999363047265-0.7999363047265
2037572.25185548283122.74814451716878
2045470.2148783131597-16.2148783131597
2057272.1411519591632-0.141151959163202
2067471.28918631450082.71081368549923
2078572.474197839509912.5258021604901
2085269.8162584290702-17.8162584290702
2097070.1978543541801-0.19785435418011
2108471.724907558883212.2750924411168
2116467.5201378210324-3.52013782103245
2128475.25146949046088.74853050953923
2138766.589329490617420.4106705093826
2147969.60599978759899.39400021240105
2156767.8677203017095-0.867720301709509
2166569.0756636691854-4.07566366918539
2178572.890141961184412.1098580388156
2188372.641779846178810.3582201538212
2196162.5125530404934-1.5125530404934
2208271.183723313210410.8162766867897
2217672.32854556206643.67145443793363
2225872.6591040837639-14.6591040837639
2237267.26378201992094.73621798007908
2247273.7050062455538-1.70500624555384
2253863.421431641683-25.421431641683
2267872.64862303900875.35137696099131
2275472.0669492385773-18.0669492385772
2286371.4317506760842-8.43175067608418
2296661.29044015519134.7095598448087
2307070.7594507532981-0.759450753298138
2317170.57420975691240.425790243087556
2326768.3012889395745-1.3012889395745
2335873.3948254037752-15.3948254037752
2347269.11455055349172.88544944650831
2357274.4196480510105-2.41964805101045
2367070.0154009950491-0.0154009950490697
2377666.42631850206269.5736814979374
2385071.0176588823217-21.0176588823217
2397272.0839731975568-0.0839731975568019
2407271.28885156236910.711148437630946
2418871.896579594653616.1034204053464
2425365.7911544130958-12.7911544130958
2435865.0285954504593-7.02859545045931
2446667.2983960215647-1.29839602156471
2458270.180530116595111.8194698834049
2466963.21396063746645.78603936253356
2476868.5718810624109-0.571881062410945
2484461.5294717187178-17.5294717187178
2495660.285495908089-4.28549590808898
2505364.4584048080244-11.4584048080244
2517068.80567210355991.19432789644013
2527866.167509815828111.8324901841719
2537172.1980304421641-1.19803044216414
2547270.32064159305561.67935840694444
2556867.57701630403340.422983695966611
2566769.1581948280091-2.15819482800905
2577564.335550765250210.6644492347498
2586265.1278502896571-3.12785028965707
2596769.7448088722126-2.74480887221263
2608373.79619845159319.20380154840689
2616470.5145781101831-6.51457811018311
2626872.619549838348-4.61954983834795
2636263.0450417654244-1.04504176542443
2647270.58878083076911.41121916923093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 53 & 73.7754536891699 & -20.7754536891699 \tabularnewline
2 & 83 & 73.6072366517639 & 9.39276334823606 \tabularnewline
3 & 66 & 70.4858051881278 & -4.48580518812783 \tabularnewline
4 & 67 & 70.352502430971 & -3.35250243097095 \tabularnewline
5 & 76 & 64.3896760845228 & 11.6103239154772 \tabularnewline
6 & 78 & 71.5251299621672 & 6.47487003783283 \tabularnewline
7 & 53 & 63.5200514501436 & -10.5200514501436 \tabularnewline
8 & 80 & 73.8042610847516 & 6.19573891524839 \tabularnewline
9 & 74 & 74.5334394905386 & -0.533439490538582 \tabularnewline
10 & 76 & 71.5285181566328 & 4.47148184336725 \tabularnewline
11 & 79 & 74.0602821337314 & 4.93971786626858 \tabularnewline
12 & 54 & 76.2995242587682 & -22.2995242587682 \tabularnewline
13 & 67 & 70.2204191141427 & -3.22041911414272 \tabularnewline
14 & 54 & 71.3686295574643 & -17.3686295574643 \tabularnewline
15 & 87 & 74.8782688074872 & 12.1217311925128 \tabularnewline
16 & 58 & 69.9758467496332 & -11.9758467496332 \tabularnewline
17 & 75 & 70.1066276746146 & 4.89337232538541 \tabularnewline
18 & 88 & 74.2087220484296 & 13.7912779515704 \tabularnewline
19 & 64 & 75.0832524530546 & -11.0832524530546 \tabularnewline
20 & 57 & 70.8132757939652 & -13.8132757939652 \tabularnewline
21 & 66 & 73.9355430663537 & -7.93554306635371 \tabularnewline
22 & 68 & 69.6282642689561 & -1.6282642689561 \tabularnewline
23 & 54 & 73.6644498868966 & -19.6644498868966 \tabularnewline
24 & 56 & 71.1295979939378 & -15.1295979939378 \tabularnewline
25 & 86 & 73.8264910925825 & 12.1735089074175 \tabularnewline
26 & 80 & 76.2333152242882 & 3.76668477571176 \tabularnewline
27 & 76 & 73.1662748850039 & 2.83372511499611 \tabularnewline
28 & 69 & 70.0157357471808 & -1.01573574718079 \tabularnewline
29 & 78 & 70.1409758711792 & 7.85902412882083 \tabularnewline
30 & 67 & 70.7962518349857 & -3.79625183498565 \tabularnewline
31 & 80 & 74.271542888444 & 5.728457111556 \tabularnewline
32 & 54 & 67.2533009751657 & -13.2533009751657 \tabularnewline
33 & 71 & 73.8951530121854 & -2.8951530121854 \tabularnewline
34 & 84 & 73.45697888268 & 10.54302111732 \tabularnewline
35 & 74 & 70.9610806779262 & 3.03891932207379 \tabularnewline
36 & 71 & 74.4515088889258 & -3.45150888892585 \tabularnewline
37 & 63 & 63.0683083600229 & -0.0683083600229151 \tabularnewline
38 & 71 & 72.4936746836124 & -1.49367468361238 \tabularnewline
39 & 76 & 72.5741200398173 & 3.42587996018274 \tabularnewline
40 & 69 & 73.7473481282244 & -4.74734812822441 \tabularnewline
41 & 74 & 74.9150354156485 & -0.915035415648504 \tabularnewline
42 & 75 & 70.3061049927252 & 4.69389500727481 \tabularnewline
43 & 54 & 77.1050924651849 & -23.1050924651849 \tabularnewline
44 & 52 & 70.0327252326341 & -18.0327252326341 \tabularnewline
45 & 69 & 72.8579463711373 & -3.85794637113728 \tabularnewline
46 & 68 & 71.9126014403919 & -3.91260144039186 \tabularnewline
47 & 65 & 76.3765146166088 & -11.3765146166088 \tabularnewline
48 & 75 & 72.7989152816189 & 2.20108471838113 \tabularnewline
49 & 74 & 69.9445864689287 & 4.05541353107131 \tabularnewline
50 & 75 & 73.6613619710365 & 1.33863802896352 \tabularnewline
51 & 72 & 74.5851119246882 & -2.58511192468819 \tabularnewline
52 & 67 & 69.2636578292995 & -2.26365782929948 \tabularnewline
53 & 63 & 66.2612238540428 & -3.26122385404284 \tabularnewline
54 & 62 & 70.9814928313714 & -8.98149283137135 \tabularnewline
55 & 63 & 71.1358061560305 & -8.13580615603047 \tabularnewline
56 & 76 & 69.2040912096341 & 6.79590879036585 \tabularnewline
57 & 74 & 72.1516330039184 & 1.84836699608162 \tabularnewline
58 & 67 & 74.2149646840485 & -7.21496468404852 \tabularnewline
59 & 73 & 74.5705408508316 & -1.57054085083156 \tabularnewline
60 & 70 & 69.195296188302 & 0.804703811697966 \tabularnewline
61 & 53 & 62.5212485612353 & -9.52124856123526 \tabularnewline
62 & 77 & 72.9151596062699 & 4.08484039373006 \tabularnewline
63 & 80 & 72.7843786812885 & 7.21562131871149 \tabularnewline
64 & 52 & 69.7739165463996 & -17.7739165463996 \tabularnewline
65 & 54 & 71.0523073574917 & -17.0523073574917 \tabularnewline
66 & 80 & 73.8413624450446 & 6.15863755495542 \tabularnewline
67 & 66 & 68.4713583048926 & -2.47135830489264 \tabularnewline
68 & 73 & 70.7362854361246 & 2.2637145638754 \tabularnewline
69 & 63 & 72.6989930813115 & -9.6989930813115 \tabularnewline
70 & 69 & 70.8361408325333 & -1.83614083253329 \tabularnewline
71 & 67 & 71.2919394782292 & -4.29193947822917 \tabularnewline
72 & 54 & 69.1189408611986 & -15.1189408611986 \tabularnewline
73 & 81 & 72.220629675653 & 8.77937032434699 \tabularnewline
74 & 69 & 75.8495666926609 & -6.84956669266085 \tabularnewline
75 & 84 & 72.5447120870248 & 11.4552879129752 \tabularnewline
76 & 80 & 65.5570286198151 & 14.4429713801849 \tabularnewline
77 & 70 & 72.3143092403415 & -2.31430924034146 \tabularnewline
78 & 69 & 67.1184300210829 & 1.88156997891715 \tabularnewline
79 & 77 & 70.0615326282156 & 6.93846737178438 \tabularnewline
80 & 54 & 74.897410899458 & -20.897410899458 \tabularnewline
81 & 79 & 73.2006230815685 & 5.79937691843153 \tabularnewline
82 & 71 & 75.4794194520212 & -4.47941945202118 \tabularnewline
83 & 73 & 72.6588038051584 & 0.3411961948416 \tabularnewline
84 & 72 & 71.7051304361753 & 0.294869563824723 \tabularnewline
85 & 77 & 70.4486693543086 & 6.5513306456914 \tabularnewline
86 & 75 & 71.7848739577443 & 3.21512604225571 \tabularnewline
87 & 69 & 72.0895139985399 & -3.08951399853985 \tabularnewline
88 & 54 & 68.460576981532 & -14.460576981532 \tabularnewline
89 & 70 & 62.7117300799985 & 7.28826992000146 \tabularnewline
90 & 73 & 70.3552900682256 & 2.6447099317744 \tabularnewline
91 & 54 & 70.2742441548098 & -16.2742441548098 \tabularnewline
92 & 77 & 72.1297032746929 & 4.87029672530705 \tabularnewline
93 & 82 & 68.591057627908 & 13.408942372092 \tabularnewline
94 & 80 & 70.5997629321449 & 9.40023706785509 \tabularnewline
95 & 80 & 73.7528889292075 & 6.24711107079254 \tabularnewline
96 & 69 & 69.3458887095177 & -0.345888709517681 \tabularnewline
97 & 78 & 72.4169846043772 & 5.58301539562277 \tabularnewline
98 & 81 & 73.6786862086215 & 7.3213137913785 \tabularnewline
99 & 76 & 73.5256408022829 & 2.47435919771708 \tabularnewline
100 & 76 & 75.3934332948333 & 0.606566705166749 \tabularnewline
101 & 73 & 68.9073798278806 & 4.09262017211943 \tabularnewline
102 & 85 & 73.5163447243301 & 11.4836552756699 \tabularnewline
103 & 66 & 72.0864260826797 & -6.08642608267974 \tabularnewline
104 & 79 & 68.6117700599586 & 10.3882299400414 \tabularnewline
105 & 68 & 66.5623744229479 & 1.43762557705212 \tabularnewline
106 & 76 & 73.935042009733 & 2.06495799026696 \tabularnewline
107 & 71 & 70.1520574731453 & 0.847942526854727 \tabularnewline
108 & 54 & 69.6709064302321 & -15.6709064302321 \tabularnewline
109 & 46 & 62.2304777596604 & -16.2304777596604 \tabularnewline
110 & 85 & 69.7368151861066 & 15.2631848138934 \tabularnewline
111 & 74 & 69.3205707858267 & 4.67942921417333 \tabularnewline
112 & 88 & 73.2723729170315 & 14.7276270829685 \tabularnewline
113 & 38 & 68.827936584917 & -30.827936584917 \tabularnewline
114 & 76 & 72.3115216030868 & 3.68847839691319 \tabularnewline
115 & 86 & 74.6441430142066 & 11.3558569857934 \tabularnewline
116 & 54 & 70.9988170689564 & -16.9988170689564 \tabularnewline
117 & 67 & 70.9963297103072 & -3.99632971030718 \tabularnewline
118 & 69 & 74.7474534089796 & -5.74745340897955 \tabularnewline
119 & 90 & 70.3116457937082 & 19.6883542062918 \tabularnewline
120 & 54 & 69.3579724247251 & -15.3579724247251 \tabularnewline
121 & 76 & 69.4826119915126 & 6.51738800848742 \tabularnewline
122 & 89 & 74.2999487279951 & 14.7000512720049 \tabularnewline
123 & 76 & 77.2680689802134 & -1.26806898021343 \tabularnewline
124 & 73 & 73.4591314891975 & -0.459131489197479 \tabularnewline
125 & 79 & 73.2544481222355 & 5.74555187776445 \tabularnewline
126 & 90 & 76.1739493826381 & 13.8260506173619 \tabularnewline
127 & 74 & 76.0001247403502 & -2.00012474035024 \tabularnewline
128 & 81 & 72.5428597591128 & 8.4571402408872 \tabularnewline
129 & 72 & 71.1806353973502 & 0.81936460264977 \tabularnewline
130 & 71 & 72.3233050396888 & -1.32330503968878 \tabularnewline
131 & 66 & 63.4329147996795 & 2.56708520032047 \tabularnewline
132 & 77 & 74.4993915725795 & 2.50060842742054 \tabularnewline
133 & 65 & 67.7286109384904 & -2.72861093849037 \tabularnewline
134 & 74 & 73.114937202986 & 0.885062797013999 \tabularnewline
135 & 85 & 69.6109400313711 & 15.3890599686289 \tabularnewline
136 & 54 & 59.2961719506945 & -5.29617195069448 \tabularnewline
137 & 63 & 70.2693039110377 & -7.26930391103767 \tabularnewline
138 & 54 & 67.5322215362399 & -13.5322215362399 \tabularnewline
139 & 64 & 72.4714102022552 & -8.47141020225524 \tabularnewline
140 & 69 & 71.1927191125577 & -2.19271911255767 \tabularnewline
141 & 54 & 72.9046785615148 & -18.9046785615148 \tabularnewline
142 & 84 & 69.3527319023475 & 14.6472680976525 \tabularnewline
143 & 86 & 72.343116635923 & 13.656883364077 \tabularnewline
144 & 77 & 70.5365078394085 & 6.46349216059146 \tabularnewline
145 & 89 & 73.0233757712888 & 15.9766242287112 \tabularnewline
146 & 76 & 71.5050525608538 & 4.49494743914625 \tabularnewline
147 & 60 & 71.8386989984114 & -11.8386989984114 \tabularnewline
148 & 75 & 72.5793605621949 & 2.42063943780515 \tabularnewline
149 & 73 & 65.0789654927621 & 7.92103450723788 \tabularnewline
150 & 85 & 72.2750208000048 & 12.7249791999952 \tabularnewline
151 & 79 & 69.4253319524812 & 9.57466804751877 \tabularnewline
152 & 71 & 74.747753687585 & -3.74775368758502 \tabularnewline
153 & 72 & 71.5603789945481 & 0.439621005451857 \tabularnewline
154 & 69 & 65.5375517757127 & 3.46244822428735 \tabularnewline
155 & 78 & 65.3668818531836 & 12.6331181468164 \tabularnewline
156 & 54 & 70.2742441548098 & -16.2742441548098 \tabularnewline
157 & 69 & 69.0252268229839 & -0.0252268229838924 \tabularnewline
158 & 81 & 72.5428597591128 & 8.4571402408872 \tabularnewline
159 & 84 & 74.638902491829 & 9.36109750817099 \tabularnewline
160 & 84 & 65.8403394885962 & 18.1596605114038 \tabularnewline
161 & 69 & 67.8454558203524 & 1.15454417964764 \tabularnewline
162 & 66 & 58.7865482643315 & 7.21345173566848 \tabularnewline
163 & 81 & 68.2951131078543 & 12.7048868921457 \tabularnewline
164 & 82 & 65.8331960171609 & 16.1668039828391 \tabularnewline
165 & 72 & 66.7003332928909 & 5.29966670710912 \tabularnewline
166 & 54 & 59.146214460216 & -5.14621446021601 \tabularnewline
167 & 78 & 71.2836455135177 & 6.71635448648228 \tabularnewline
168 & 74 & 68.2552585838329 & 5.74474141616711 \tabularnewline
169 & 82 & 68.9049269427576 & 13.0950730572424 \tabularnewline
170 & 73 & 65.8004849645748 & 7.19951503542517 \tabularnewline
171 & 55 & 63.5123235691168 & -8.51232356911683 \tabularnewline
172 & 72 & 72.8585814018745 & -0.858581401874464 \tabularnewline
173 & 78 & 73.0632647688364 & 4.93673523116361 \tabularnewline
174 & 59 & 64.8684065726854 & -5.86840657268542 \tabularnewline
175 & 72 & 71.1088855618872 & 0.891114438112797 \tabularnewline
176 & 78 & 74.5823242874335 & 3.41767571256646 \tabularnewline
177 & 68 & 68.0671631462939 & -0.0671631462938541 \tabularnewline
178 & 69 & 64.9995222497986 & 4.00047775020143 \tabularnewline
179 & 67 & 72.9043782829093 & -5.9043782829093 \tabularnewline
180 & 74 & 70.6737648747157 & 3.32623512528433 \tabularnewline
181 & 54 & 73.6960104462065 & -19.6960104462065 \tabularnewline
182 & 67 & 73.5253060501512 & -6.5253060501512 \tabularnewline
183 & 70 & 73.5281281609321 & -3.52812816093211 \tabularnewline
184 & 80 & 74.5900176949341 & 5.40998230506595 \tabularnewline
185 & 89 & 70.6543893080381 & 18.3456106919619 \tabularnewline
186 & 76 & 65.6062481688418 & 10.3937518311582 \tabularnewline
187 & 74 & 70.5083354745642 & 3.49166452543581 \tabularnewline
188 & 87 & 71.7103364850266 & 15.2896635149734 \tabularnewline
189 & 54 & 67.7629246615287 & -13.7629246615287 \tabularnewline
190 & 61 & 62.2141556353168 & -1.21415563531676 \tabularnewline
191 & 38 & 71.1781825122273 & -33.1781825122273 \tabularnewline
192 & 75 & 75.3198311314582 & -0.319831131458218 \tabularnewline
193 & 69 & 67.0739010583686 & 1.92609894163144 \tabularnewline
194 & 62 & 68.7595749439196 & -6.75957494391957 \tabularnewline
195 & 72 & 69.4461111884305 & 2.55388881156948 \tabularnewline
196 & 70 & 73.114937202986 & -3.114937202986 \tabularnewline
197 & 79 & 64.8226096916506 & 14.1773903083494 \tabularnewline
198 & 87 & 69.5772268655437 & 17.4227731344563 \tabularnewline
199 & 62 & 64.8399339292356 & -2.83993392923561 \tabularnewline
200 & 77 & 69.8225010646891 & 7.17749893531089 \tabularnewline
201 & 69 & 66.5859412961518 & 2.41405870384818 \tabularnewline
202 & 69 & 69.7999363047265 & -0.7999363047265 \tabularnewline
203 & 75 & 72.2518554828312 & 2.74814451716878 \tabularnewline
204 & 54 & 70.2148783131597 & -16.2148783131597 \tabularnewline
205 & 72 & 72.1411519591632 & -0.141151959163202 \tabularnewline
206 & 74 & 71.2891863145008 & 2.71081368549923 \tabularnewline
207 & 85 & 72.4741978395099 & 12.5258021604901 \tabularnewline
208 & 52 & 69.8162584290702 & -17.8162584290702 \tabularnewline
209 & 70 & 70.1978543541801 & -0.19785435418011 \tabularnewline
210 & 84 & 71.7249075588832 & 12.2750924411168 \tabularnewline
211 & 64 & 67.5201378210324 & -3.52013782103245 \tabularnewline
212 & 84 & 75.2514694904608 & 8.74853050953923 \tabularnewline
213 & 87 & 66.5893294906174 & 20.4106705093826 \tabularnewline
214 & 79 & 69.6059997875989 & 9.39400021240105 \tabularnewline
215 & 67 & 67.8677203017095 & -0.867720301709509 \tabularnewline
216 & 65 & 69.0756636691854 & -4.07566366918539 \tabularnewline
217 & 85 & 72.8901419611844 & 12.1098580388156 \tabularnewline
218 & 83 & 72.6417798461788 & 10.3582201538212 \tabularnewline
219 & 61 & 62.5125530404934 & -1.5125530404934 \tabularnewline
220 & 82 & 71.1837233132104 & 10.8162766867897 \tabularnewline
221 & 76 & 72.3285455620664 & 3.67145443793363 \tabularnewline
222 & 58 & 72.6591040837639 & -14.6591040837639 \tabularnewline
223 & 72 & 67.2637820199209 & 4.73621798007908 \tabularnewline
224 & 72 & 73.7050062455538 & -1.70500624555384 \tabularnewline
225 & 38 & 63.421431641683 & -25.421431641683 \tabularnewline
226 & 78 & 72.6486230390087 & 5.35137696099131 \tabularnewline
227 & 54 & 72.0669492385773 & -18.0669492385772 \tabularnewline
228 & 63 & 71.4317506760842 & -8.43175067608418 \tabularnewline
229 & 66 & 61.2904401551913 & 4.7095598448087 \tabularnewline
230 & 70 & 70.7594507532981 & -0.759450753298138 \tabularnewline
231 & 71 & 70.5742097569124 & 0.425790243087556 \tabularnewline
232 & 67 & 68.3012889395745 & -1.3012889395745 \tabularnewline
233 & 58 & 73.3948254037752 & -15.3948254037752 \tabularnewline
234 & 72 & 69.1145505534917 & 2.88544944650831 \tabularnewline
235 & 72 & 74.4196480510105 & -2.41964805101045 \tabularnewline
236 & 70 & 70.0154009950491 & -0.0154009950490697 \tabularnewline
237 & 76 & 66.4263185020626 & 9.5736814979374 \tabularnewline
238 & 50 & 71.0176588823217 & -21.0176588823217 \tabularnewline
239 & 72 & 72.0839731975568 & -0.0839731975568019 \tabularnewline
240 & 72 & 71.2888515623691 & 0.711148437630946 \tabularnewline
241 & 88 & 71.8965795946536 & 16.1034204053464 \tabularnewline
242 & 53 & 65.7911544130958 & -12.7911544130958 \tabularnewline
243 & 58 & 65.0285954504593 & -7.02859545045931 \tabularnewline
244 & 66 & 67.2983960215647 & -1.29839602156471 \tabularnewline
245 & 82 & 70.1805301165951 & 11.8194698834049 \tabularnewline
246 & 69 & 63.2139606374664 & 5.78603936253356 \tabularnewline
247 & 68 & 68.5718810624109 & -0.571881062410945 \tabularnewline
248 & 44 & 61.5294717187178 & -17.5294717187178 \tabularnewline
249 & 56 & 60.285495908089 & -4.28549590808898 \tabularnewline
250 & 53 & 64.4584048080244 & -11.4584048080244 \tabularnewline
251 & 70 & 68.8056721035599 & 1.19432789644013 \tabularnewline
252 & 78 & 66.1675098158281 & 11.8324901841719 \tabularnewline
253 & 71 & 72.1980304421641 & -1.19803044216414 \tabularnewline
254 & 72 & 70.3206415930556 & 1.67935840694444 \tabularnewline
255 & 68 & 67.5770163040334 & 0.422983695966611 \tabularnewline
256 & 67 & 69.1581948280091 & -2.15819482800905 \tabularnewline
257 & 75 & 64.3355507652502 & 10.6644492347498 \tabularnewline
258 & 62 & 65.1278502896571 & -3.12785028965707 \tabularnewline
259 & 67 & 69.7448088722126 & -2.74480887221263 \tabularnewline
260 & 83 & 73.7961984515931 & 9.20380154840689 \tabularnewline
261 & 64 & 70.5145781101831 & -6.51457811018311 \tabularnewline
262 & 68 & 72.619549838348 & -4.61954983834795 \tabularnewline
263 & 62 & 63.0450417654244 & -1.04504176542443 \tabularnewline
264 & 72 & 70.5887808307691 & 1.41121916923093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&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]53[/C][C]73.7754536891699[/C][C]-20.7754536891699[/C][/ROW]
[ROW][C]2[/C][C]83[/C][C]73.6072366517639[/C][C]9.39276334823606[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]70.4858051881278[/C][C]-4.48580518812783[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]70.352502430971[/C][C]-3.35250243097095[/C][/ROW]
[ROW][C]5[/C][C]76[/C][C]64.3896760845228[/C][C]11.6103239154772[/C][/ROW]
[ROW][C]6[/C][C]78[/C][C]71.5251299621672[/C][C]6.47487003783283[/C][/ROW]
[ROW][C]7[/C][C]53[/C][C]63.5200514501436[/C][C]-10.5200514501436[/C][/ROW]
[ROW][C]8[/C][C]80[/C][C]73.8042610847516[/C][C]6.19573891524839[/C][/ROW]
[ROW][C]9[/C][C]74[/C][C]74.5334394905386[/C][C]-0.533439490538582[/C][/ROW]
[ROW][C]10[/C][C]76[/C][C]71.5285181566328[/C][C]4.47148184336725[/C][/ROW]
[ROW][C]11[/C][C]79[/C][C]74.0602821337314[/C][C]4.93971786626858[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]76.2995242587682[/C][C]-22.2995242587682[/C][/ROW]
[ROW][C]13[/C][C]67[/C][C]70.2204191141427[/C][C]-3.22041911414272[/C][/ROW]
[ROW][C]14[/C][C]54[/C][C]71.3686295574643[/C][C]-17.3686295574643[/C][/ROW]
[ROW][C]15[/C][C]87[/C][C]74.8782688074872[/C][C]12.1217311925128[/C][/ROW]
[ROW][C]16[/C][C]58[/C][C]69.9758467496332[/C][C]-11.9758467496332[/C][/ROW]
[ROW][C]17[/C][C]75[/C][C]70.1066276746146[/C][C]4.89337232538541[/C][/ROW]
[ROW][C]18[/C][C]88[/C][C]74.2087220484296[/C][C]13.7912779515704[/C][/ROW]
[ROW][C]19[/C][C]64[/C][C]75.0832524530546[/C][C]-11.0832524530546[/C][/ROW]
[ROW][C]20[/C][C]57[/C][C]70.8132757939652[/C][C]-13.8132757939652[/C][/ROW]
[ROW][C]21[/C][C]66[/C][C]73.9355430663537[/C][C]-7.93554306635371[/C][/ROW]
[ROW][C]22[/C][C]68[/C][C]69.6282642689561[/C][C]-1.6282642689561[/C][/ROW]
[ROW][C]23[/C][C]54[/C][C]73.6644498868966[/C][C]-19.6644498868966[/C][/ROW]
[ROW][C]24[/C][C]56[/C][C]71.1295979939378[/C][C]-15.1295979939378[/C][/ROW]
[ROW][C]25[/C][C]86[/C][C]73.8264910925825[/C][C]12.1735089074175[/C][/ROW]
[ROW][C]26[/C][C]80[/C][C]76.2333152242882[/C][C]3.76668477571176[/C][/ROW]
[ROW][C]27[/C][C]76[/C][C]73.1662748850039[/C][C]2.83372511499611[/C][/ROW]
[ROW][C]28[/C][C]69[/C][C]70.0157357471808[/C][C]-1.01573574718079[/C][/ROW]
[ROW][C]29[/C][C]78[/C][C]70.1409758711792[/C][C]7.85902412882083[/C][/ROW]
[ROW][C]30[/C][C]67[/C][C]70.7962518349857[/C][C]-3.79625183498565[/C][/ROW]
[ROW][C]31[/C][C]80[/C][C]74.271542888444[/C][C]5.728457111556[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]67.2533009751657[/C][C]-13.2533009751657[/C][/ROW]
[ROW][C]33[/C][C]71[/C][C]73.8951530121854[/C][C]-2.8951530121854[/C][/ROW]
[ROW][C]34[/C][C]84[/C][C]73.45697888268[/C][C]10.54302111732[/C][/ROW]
[ROW][C]35[/C][C]74[/C][C]70.9610806779262[/C][C]3.03891932207379[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]74.4515088889258[/C][C]-3.45150888892585[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]63.0683083600229[/C][C]-0.0683083600229151[/C][/ROW]
[ROW][C]38[/C][C]71[/C][C]72.4936746836124[/C][C]-1.49367468361238[/C][/ROW]
[ROW][C]39[/C][C]76[/C][C]72.5741200398173[/C][C]3.42587996018274[/C][/ROW]
[ROW][C]40[/C][C]69[/C][C]73.7473481282244[/C][C]-4.74734812822441[/C][/ROW]
[ROW][C]41[/C][C]74[/C][C]74.9150354156485[/C][C]-0.915035415648504[/C][/ROW]
[ROW][C]42[/C][C]75[/C][C]70.3061049927252[/C][C]4.69389500727481[/C][/ROW]
[ROW][C]43[/C][C]54[/C][C]77.1050924651849[/C][C]-23.1050924651849[/C][/ROW]
[ROW][C]44[/C][C]52[/C][C]70.0327252326341[/C][C]-18.0327252326341[/C][/ROW]
[ROW][C]45[/C][C]69[/C][C]72.8579463711373[/C][C]-3.85794637113728[/C][/ROW]
[ROW][C]46[/C][C]68[/C][C]71.9126014403919[/C][C]-3.91260144039186[/C][/ROW]
[ROW][C]47[/C][C]65[/C][C]76.3765146166088[/C][C]-11.3765146166088[/C][/ROW]
[ROW][C]48[/C][C]75[/C][C]72.7989152816189[/C][C]2.20108471838113[/C][/ROW]
[ROW][C]49[/C][C]74[/C][C]69.9445864689287[/C][C]4.05541353107131[/C][/ROW]
[ROW][C]50[/C][C]75[/C][C]73.6613619710365[/C][C]1.33863802896352[/C][/ROW]
[ROW][C]51[/C][C]72[/C][C]74.5851119246882[/C][C]-2.58511192468819[/C][/ROW]
[ROW][C]52[/C][C]67[/C][C]69.2636578292995[/C][C]-2.26365782929948[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]66.2612238540428[/C][C]-3.26122385404284[/C][/ROW]
[ROW][C]54[/C][C]62[/C][C]70.9814928313714[/C][C]-8.98149283137135[/C][/ROW]
[ROW][C]55[/C][C]63[/C][C]71.1358061560305[/C][C]-8.13580615603047[/C][/ROW]
[ROW][C]56[/C][C]76[/C][C]69.2040912096341[/C][C]6.79590879036585[/C][/ROW]
[ROW][C]57[/C][C]74[/C][C]72.1516330039184[/C][C]1.84836699608162[/C][/ROW]
[ROW][C]58[/C][C]67[/C][C]74.2149646840485[/C][C]-7.21496468404852[/C][/ROW]
[ROW][C]59[/C][C]73[/C][C]74.5705408508316[/C][C]-1.57054085083156[/C][/ROW]
[ROW][C]60[/C][C]70[/C][C]69.195296188302[/C][C]0.804703811697966[/C][/ROW]
[ROW][C]61[/C][C]53[/C][C]62.5212485612353[/C][C]-9.52124856123526[/C][/ROW]
[ROW][C]62[/C][C]77[/C][C]72.9151596062699[/C][C]4.08484039373006[/C][/ROW]
[ROW][C]63[/C][C]80[/C][C]72.7843786812885[/C][C]7.21562131871149[/C][/ROW]
[ROW][C]64[/C][C]52[/C][C]69.7739165463996[/C][C]-17.7739165463996[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]71.0523073574917[/C][C]-17.0523073574917[/C][/ROW]
[ROW][C]66[/C][C]80[/C][C]73.8413624450446[/C][C]6.15863755495542[/C][/ROW]
[ROW][C]67[/C][C]66[/C][C]68.4713583048926[/C][C]-2.47135830489264[/C][/ROW]
[ROW][C]68[/C][C]73[/C][C]70.7362854361246[/C][C]2.2637145638754[/C][/ROW]
[ROW][C]69[/C][C]63[/C][C]72.6989930813115[/C][C]-9.6989930813115[/C][/ROW]
[ROW][C]70[/C][C]69[/C][C]70.8361408325333[/C][C]-1.83614083253329[/C][/ROW]
[ROW][C]71[/C][C]67[/C][C]71.2919394782292[/C][C]-4.29193947822917[/C][/ROW]
[ROW][C]72[/C][C]54[/C][C]69.1189408611986[/C][C]-15.1189408611986[/C][/ROW]
[ROW][C]73[/C][C]81[/C][C]72.220629675653[/C][C]8.77937032434699[/C][/ROW]
[ROW][C]74[/C][C]69[/C][C]75.8495666926609[/C][C]-6.84956669266085[/C][/ROW]
[ROW][C]75[/C][C]84[/C][C]72.5447120870248[/C][C]11.4552879129752[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]65.5570286198151[/C][C]14.4429713801849[/C][/ROW]
[ROW][C]77[/C][C]70[/C][C]72.3143092403415[/C][C]-2.31430924034146[/C][/ROW]
[ROW][C]78[/C][C]69[/C][C]67.1184300210829[/C][C]1.88156997891715[/C][/ROW]
[ROW][C]79[/C][C]77[/C][C]70.0615326282156[/C][C]6.93846737178438[/C][/ROW]
[ROW][C]80[/C][C]54[/C][C]74.897410899458[/C][C]-20.897410899458[/C][/ROW]
[ROW][C]81[/C][C]79[/C][C]73.2006230815685[/C][C]5.79937691843153[/C][/ROW]
[ROW][C]82[/C][C]71[/C][C]75.4794194520212[/C][C]-4.47941945202118[/C][/ROW]
[ROW][C]83[/C][C]73[/C][C]72.6588038051584[/C][C]0.3411961948416[/C][/ROW]
[ROW][C]84[/C][C]72[/C][C]71.7051304361753[/C][C]0.294869563824723[/C][/ROW]
[ROW][C]85[/C][C]77[/C][C]70.4486693543086[/C][C]6.5513306456914[/C][/ROW]
[ROW][C]86[/C][C]75[/C][C]71.7848739577443[/C][C]3.21512604225571[/C][/ROW]
[ROW][C]87[/C][C]69[/C][C]72.0895139985399[/C][C]-3.08951399853985[/C][/ROW]
[ROW][C]88[/C][C]54[/C][C]68.460576981532[/C][C]-14.460576981532[/C][/ROW]
[ROW][C]89[/C][C]70[/C][C]62.7117300799985[/C][C]7.28826992000146[/C][/ROW]
[ROW][C]90[/C][C]73[/C][C]70.3552900682256[/C][C]2.6447099317744[/C][/ROW]
[ROW][C]91[/C][C]54[/C][C]70.2742441548098[/C][C]-16.2742441548098[/C][/ROW]
[ROW][C]92[/C][C]77[/C][C]72.1297032746929[/C][C]4.87029672530705[/C][/ROW]
[ROW][C]93[/C][C]82[/C][C]68.591057627908[/C][C]13.408942372092[/C][/ROW]
[ROW][C]94[/C][C]80[/C][C]70.5997629321449[/C][C]9.40023706785509[/C][/ROW]
[ROW][C]95[/C][C]80[/C][C]73.7528889292075[/C][C]6.24711107079254[/C][/ROW]
[ROW][C]96[/C][C]69[/C][C]69.3458887095177[/C][C]-0.345888709517681[/C][/ROW]
[ROW][C]97[/C][C]78[/C][C]72.4169846043772[/C][C]5.58301539562277[/C][/ROW]
[ROW][C]98[/C][C]81[/C][C]73.6786862086215[/C][C]7.3213137913785[/C][/ROW]
[ROW][C]99[/C][C]76[/C][C]73.5256408022829[/C][C]2.47435919771708[/C][/ROW]
[ROW][C]100[/C][C]76[/C][C]75.3934332948333[/C][C]0.606566705166749[/C][/ROW]
[ROW][C]101[/C][C]73[/C][C]68.9073798278806[/C][C]4.09262017211943[/C][/ROW]
[ROW][C]102[/C][C]85[/C][C]73.5163447243301[/C][C]11.4836552756699[/C][/ROW]
[ROW][C]103[/C][C]66[/C][C]72.0864260826797[/C][C]-6.08642608267974[/C][/ROW]
[ROW][C]104[/C][C]79[/C][C]68.6117700599586[/C][C]10.3882299400414[/C][/ROW]
[ROW][C]105[/C][C]68[/C][C]66.5623744229479[/C][C]1.43762557705212[/C][/ROW]
[ROW][C]106[/C][C]76[/C][C]73.935042009733[/C][C]2.06495799026696[/C][/ROW]
[ROW][C]107[/C][C]71[/C][C]70.1520574731453[/C][C]0.847942526854727[/C][/ROW]
[ROW][C]108[/C][C]54[/C][C]69.6709064302321[/C][C]-15.6709064302321[/C][/ROW]
[ROW][C]109[/C][C]46[/C][C]62.2304777596604[/C][C]-16.2304777596604[/C][/ROW]
[ROW][C]110[/C][C]85[/C][C]69.7368151861066[/C][C]15.2631848138934[/C][/ROW]
[ROW][C]111[/C][C]74[/C][C]69.3205707858267[/C][C]4.67942921417333[/C][/ROW]
[ROW][C]112[/C][C]88[/C][C]73.2723729170315[/C][C]14.7276270829685[/C][/ROW]
[ROW][C]113[/C][C]38[/C][C]68.827936584917[/C][C]-30.827936584917[/C][/ROW]
[ROW][C]114[/C][C]76[/C][C]72.3115216030868[/C][C]3.68847839691319[/C][/ROW]
[ROW][C]115[/C][C]86[/C][C]74.6441430142066[/C][C]11.3558569857934[/C][/ROW]
[ROW][C]116[/C][C]54[/C][C]70.9988170689564[/C][C]-16.9988170689564[/C][/ROW]
[ROW][C]117[/C][C]67[/C][C]70.9963297103072[/C][C]-3.99632971030718[/C][/ROW]
[ROW][C]118[/C][C]69[/C][C]74.7474534089796[/C][C]-5.74745340897955[/C][/ROW]
[ROW][C]119[/C][C]90[/C][C]70.3116457937082[/C][C]19.6883542062918[/C][/ROW]
[ROW][C]120[/C][C]54[/C][C]69.3579724247251[/C][C]-15.3579724247251[/C][/ROW]
[ROW][C]121[/C][C]76[/C][C]69.4826119915126[/C][C]6.51738800848742[/C][/ROW]
[ROW][C]122[/C][C]89[/C][C]74.2999487279951[/C][C]14.7000512720049[/C][/ROW]
[ROW][C]123[/C][C]76[/C][C]77.2680689802134[/C][C]-1.26806898021343[/C][/ROW]
[ROW][C]124[/C][C]73[/C][C]73.4591314891975[/C][C]-0.459131489197479[/C][/ROW]
[ROW][C]125[/C][C]79[/C][C]73.2544481222355[/C][C]5.74555187776445[/C][/ROW]
[ROW][C]126[/C][C]90[/C][C]76.1739493826381[/C][C]13.8260506173619[/C][/ROW]
[ROW][C]127[/C][C]74[/C][C]76.0001247403502[/C][C]-2.00012474035024[/C][/ROW]
[ROW][C]128[/C][C]81[/C][C]72.5428597591128[/C][C]8.4571402408872[/C][/ROW]
[ROW][C]129[/C][C]72[/C][C]71.1806353973502[/C][C]0.81936460264977[/C][/ROW]
[ROW][C]130[/C][C]71[/C][C]72.3233050396888[/C][C]-1.32330503968878[/C][/ROW]
[ROW][C]131[/C][C]66[/C][C]63.4329147996795[/C][C]2.56708520032047[/C][/ROW]
[ROW][C]132[/C][C]77[/C][C]74.4993915725795[/C][C]2.50060842742054[/C][/ROW]
[ROW][C]133[/C][C]65[/C][C]67.7286109384904[/C][C]-2.72861093849037[/C][/ROW]
[ROW][C]134[/C][C]74[/C][C]73.114937202986[/C][C]0.885062797013999[/C][/ROW]
[ROW][C]135[/C][C]85[/C][C]69.6109400313711[/C][C]15.3890599686289[/C][/ROW]
[ROW][C]136[/C][C]54[/C][C]59.2961719506945[/C][C]-5.29617195069448[/C][/ROW]
[ROW][C]137[/C][C]63[/C][C]70.2693039110377[/C][C]-7.26930391103767[/C][/ROW]
[ROW][C]138[/C][C]54[/C][C]67.5322215362399[/C][C]-13.5322215362399[/C][/ROW]
[ROW][C]139[/C][C]64[/C][C]72.4714102022552[/C][C]-8.47141020225524[/C][/ROW]
[ROW][C]140[/C][C]69[/C][C]71.1927191125577[/C][C]-2.19271911255767[/C][/ROW]
[ROW][C]141[/C][C]54[/C][C]72.9046785615148[/C][C]-18.9046785615148[/C][/ROW]
[ROW][C]142[/C][C]84[/C][C]69.3527319023475[/C][C]14.6472680976525[/C][/ROW]
[ROW][C]143[/C][C]86[/C][C]72.343116635923[/C][C]13.656883364077[/C][/ROW]
[ROW][C]144[/C][C]77[/C][C]70.5365078394085[/C][C]6.46349216059146[/C][/ROW]
[ROW][C]145[/C][C]89[/C][C]73.0233757712888[/C][C]15.9766242287112[/C][/ROW]
[ROW][C]146[/C][C]76[/C][C]71.5050525608538[/C][C]4.49494743914625[/C][/ROW]
[ROW][C]147[/C][C]60[/C][C]71.8386989984114[/C][C]-11.8386989984114[/C][/ROW]
[ROW][C]148[/C][C]75[/C][C]72.5793605621949[/C][C]2.42063943780515[/C][/ROW]
[ROW][C]149[/C][C]73[/C][C]65.0789654927621[/C][C]7.92103450723788[/C][/ROW]
[ROW][C]150[/C][C]85[/C][C]72.2750208000048[/C][C]12.7249791999952[/C][/ROW]
[ROW][C]151[/C][C]79[/C][C]69.4253319524812[/C][C]9.57466804751877[/C][/ROW]
[ROW][C]152[/C][C]71[/C][C]74.747753687585[/C][C]-3.74775368758502[/C][/ROW]
[ROW][C]153[/C][C]72[/C][C]71.5603789945481[/C][C]0.439621005451857[/C][/ROW]
[ROW][C]154[/C][C]69[/C][C]65.5375517757127[/C][C]3.46244822428735[/C][/ROW]
[ROW][C]155[/C][C]78[/C][C]65.3668818531836[/C][C]12.6331181468164[/C][/ROW]
[ROW][C]156[/C][C]54[/C][C]70.2742441548098[/C][C]-16.2742441548098[/C][/ROW]
[ROW][C]157[/C][C]69[/C][C]69.0252268229839[/C][C]-0.0252268229838924[/C][/ROW]
[ROW][C]158[/C][C]81[/C][C]72.5428597591128[/C][C]8.4571402408872[/C][/ROW]
[ROW][C]159[/C][C]84[/C][C]74.638902491829[/C][C]9.36109750817099[/C][/ROW]
[ROW][C]160[/C][C]84[/C][C]65.8403394885962[/C][C]18.1596605114038[/C][/ROW]
[ROW][C]161[/C][C]69[/C][C]67.8454558203524[/C][C]1.15454417964764[/C][/ROW]
[ROW][C]162[/C][C]66[/C][C]58.7865482643315[/C][C]7.21345173566848[/C][/ROW]
[ROW][C]163[/C][C]81[/C][C]68.2951131078543[/C][C]12.7048868921457[/C][/ROW]
[ROW][C]164[/C][C]82[/C][C]65.8331960171609[/C][C]16.1668039828391[/C][/ROW]
[ROW][C]165[/C][C]72[/C][C]66.7003332928909[/C][C]5.29966670710912[/C][/ROW]
[ROW][C]166[/C][C]54[/C][C]59.146214460216[/C][C]-5.14621446021601[/C][/ROW]
[ROW][C]167[/C][C]78[/C][C]71.2836455135177[/C][C]6.71635448648228[/C][/ROW]
[ROW][C]168[/C][C]74[/C][C]68.2552585838329[/C][C]5.74474141616711[/C][/ROW]
[ROW][C]169[/C][C]82[/C][C]68.9049269427576[/C][C]13.0950730572424[/C][/ROW]
[ROW][C]170[/C][C]73[/C][C]65.8004849645748[/C][C]7.19951503542517[/C][/ROW]
[ROW][C]171[/C][C]55[/C][C]63.5123235691168[/C][C]-8.51232356911683[/C][/ROW]
[ROW][C]172[/C][C]72[/C][C]72.8585814018745[/C][C]-0.858581401874464[/C][/ROW]
[ROW][C]173[/C][C]78[/C][C]73.0632647688364[/C][C]4.93673523116361[/C][/ROW]
[ROW][C]174[/C][C]59[/C][C]64.8684065726854[/C][C]-5.86840657268542[/C][/ROW]
[ROW][C]175[/C][C]72[/C][C]71.1088855618872[/C][C]0.891114438112797[/C][/ROW]
[ROW][C]176[/C][C]78[/C][C]74.5823242874335[/C][C]3.41767571256646[/C][/ROW]
[ROW][C]177[/C][C]68[/C][C]68.0671631462939[/C][C]-0.0671631462938541[/C][/ROW]
[ROW][C]178[/C][C]69[/C][C]64.9995222497986[/C][C]4.00047775020143[/C][/ROW]
[ROW][C]179[/C][C]67[/C][C]72.9043782829093[/C][C]-5.9043782829093[/C][/ROW]
[ROW][C]180[/C][C]74[/C][C]70.6737648747157[/C][C]3.32623512528433[/C][/ROW]
[ROW][C]181[/C][C]54[/C][C]73.6960104462065[/C][C]-19.6960104462065[/C][/ROW]
[ROW][C]182[/C][C]67[/C][C]73.5253060501512[/C][C]-6.5253060501512[/C][/ROW]
[ROW][C]183[/C][C]70[/C][C]73.5281281609321[/C][C]-3.52812816093211[/C][/ROW]
[ROW][C]184[/C][C]80[/C][C]74.5900176949341[/C][C]5.40998230506595[/C][/ROW]
[ROW][C]185[/C][C]89[/C][C]70.6543893080381[/C][C]18.3456106919619[/C][/ROW]
[ROW][C]186[/C][C]76[/C][C]65.6062481688418[/C][C]10.3937518311582[/C][/ROW]
[ROW][C]187[/C][C]74[/C][C]70.5083354745642[/C][C]3.49166452543581[/C][/ROW]
[ROW][C]188[/C][C]87[/C][C]71.7103364850266[/C][C]15.2896635149734[/C][/ROW]
[ROW][C]189[/C][C]54[/C][C]67.7629246615287[/C][C]-13.7629246615287[/C][/ROW]
[ROW][C]190[/C][C]61[/C][C]62.2141556353168[/C][C]-1.21415563531676[/C][/ROW]
[ROW][C]191[/C][C]38[/C][C]71.1781825122273[/C][C]-33.1781825122273[/C][/ROW]
[ROW][C]192[/C][C]75[/C][C]75.3198311314582[/C][C]-0.319831131458218[/C][/ROW]
[ROW][C]193[/C][C]69[/C][C]67.0739010583686[/C][C]1.92609894163144[/C][/ROW]
[ROW][C]194[/C][C]62[/C][C]68.7595749439196[/C][C]-6.75957494391957[/C][/ROW]
[ROW][C]195[/C][C]72[/C][C]69.4461111884305[/C][C]2.55388881156948[/C][/ROW]
[ROW][C]196[/C][C]70[/C][C]73.114937202986[/C][C]-3.114937202986[/C][/ROW]
[ROW][C]197[/C][C]79[/C][C]64.8226096916506[/C][C]14.1773903083494[/C][/ROW]
[ROW][C]198[/C][C]87[/C][C]69.5772268655437[/C][C]17.4227731344563[/C][/ROW]
[ROW][C]199[/C][C]62[/C][C]64.8399339292356[/C][C]-2.83993392923561[/C][/ROW]
[ROW][C]200[/C][C]77[/C][C]69.8225010646891[/C][C]7.17749893531089[/C][/ROW]
[ROW][C]201[/C][C]69[/C][C]66.5859412961518[/C][C]2.41405870384818[/C][/ROW]
[ROW][C]202[/C][C]69[/C][C]69.7999363047265[/C][C]-0.7999363047265[/C][/ROW]
[ROW][C]203[/C][C]75[/C][C]72.2518554828312[/C][C]2.74814451716878[/C][/ROW]
[ROW][C]204[/C][C]54[/C][C]70.2148783131597[/C][C]-16.2148783131597[/C][/ROW]
[ROW][C]205[/C][C]72[/C][C]72.1411519591632[/C][C]-0.141151959163202[/C][/ROW]
[ROW][C]206[/C][C]74[/C][C]71.2891863145008[/C][C]2.71081368549923[/C][/ROW]
[ROW][C]207[/C][C]85[/C][C]72.4741978395099[/C][C]12.5258021604901[/C][/ROW]
[ROW][C]208[/C][C]52[/C][C]69.8162584290702[/C][C]-17.8162584290702[/C][/ROW]
[ROW][C]209[/C][C]70[/C][C]70.1978543541801[/C][C]-0.19785435418011[/C][/ROW]
[ROW][C]210[/C][C]84[/C][C]71.7249075588832[/C][C]12.2750924411168[/C][/ROW]
[ROW][C]211[/C][C]64[/C][C]67.5201378210324[/C][C]-3.52013782103245[/C][/ROW]
[ROW][C]212[/C][C]84[/C][C]75.2514694904608[/C][C]8.74853050953923[/C][/ROW]
[ROW][C]213[/C][C]87[/C][C]66.5893294906174[/C][C]20.4106705093826[/C][/ROW]
[ROW][C]214[/C][C]79[/C][C]69.6059997875989[/C][C]9.39400021240105[/C][/ROW]
[ROW][C]215[/C][C]67[/C][C]67.8677203017095[/C][C]-0.867720301709509[/C][/ROW]
[ROW][C]216[/C][C]65[/C][C]69.0756636691854[/C][C]-4.07566366918539[/C][/ROW]
[ROW][C]217[/C][C]85[/C][C]72.8901419611844[/C][C]12.1098580388156[/C][/ROW]
[ROW][C]218[/C][C]83[/C][C]72.6417798461788[/C][C]10.3582201538212[/C][/ROW]
[ROW][C]219[/C][C]61[/C][C]62.5125530404934[/C][C]-1.5125530404934[/C][/ROW]
[ROW][C]220[/C][C]82[/C][C]71.1837233132104[/C][C]10.8162766867897[/C][/ROW]
[ROW][C]221[/C][C]76[/C][C]72.3285455620664[/C][C]3.67145443793363[/C][/ROW]
[ROW][C]222[/C][C]58[/C][C]72.6591040837639[/C][C]-14.6591040837639[/C][/ROW]
[ROW][C]223[/C][C]72[/C][C]67.2637820199209[/C][C]4.73621798007908[/C][/ROW]
[ROW][C]224[/C][C]72[/C][C]73.7050062455538[/C][C]-1.70500624555384[/C][/ROW]
[ROW][C]225[/C][C]38[/C][C]63.421431641683[/C][C]-25.421431641683[/C][/ROW]
[ROW][C]226[/C][C]78[/C][C]72.6486230390087[/C][C]5.35137696099131[/C][/ROW]
[ROW][C]227[/C][C]54[/C][C]72.0669492385773[/C][C]-18.0669492385772[/C][/ROW]
[ROW][C]228[/C][C]63[/C][C]71.4317506760842[/C][C]-8.43175067608418[/C][/ROW]
[ROW][C]229[/C][C]66[/C][C]61.2904401551913[/C][C]4.7095598448087[/C][/ROW]
[ROW][C]230[/C][C]70[/C][C]70.7594507532981[/C][C]-0.759450753298138[/C][/ROW]
[ROW][C]231[/C][C]71[/C][C]70.5742097569124[/C][C]0.425790243087556[/C][/ROW]
[ROW][C]232[/C][C]67[/C][C]68.3012889395745[/C][C]-1.3012889395745[/C][/ROW]
[ROW][C]233[/C][C]58[/C][C]73.3948254037752[/C][C]-15.3948254037752[/C][/ROW]
[ROW][C]234[/C][C]72[/C][C]69.1145505534917[/C][C]2.88544944650831[/C][/ROW]
[ROW][C]235[/C][C]72[/C][C]74.4196480510105[/C][C]-2.41964805101045[/C][/ROW]
[ROW][C]236[/C][C]70[/C][C]70.0154009950491[/C][C]-0.0154009950490697[/C][/ROW]
[ROW][C]237[/C][C]76[/C][C]66.4263185020626[/C][C]9.5736814979374[/C][/ROW]
[ROW][C]238[/C][C]50[/C][C]71.0176588823217[/C][C]-21.0176588823217[/C][/ROW]
[ROW][C]239[/C][C]72[/C][C]72.0839731975568[/C][C]-0.0839731975568019[/C][/ROW]
[ROW][C]240[/C][C]72[/C][C]71.2888515623691[/C][C]0.711148437630946[/C][/ROW]
[ROW][C]241[/C][C]88[/C][C]71.8965795946536[/C][C]16.1034204053464[/C][/ROW]
[ROW][C]242[/C][C]53[/C][C]65.7911544130958[/C][C]-12.7911544130958[/C][/ROW]
[ROW][C]243[/C][C]58[/C][C]65.0285954504593[/C][C]-7.02859545045931[/C][/ROW]
[ROW][C]244[/C][C]66[/C][C]67.2983960215647[/C][C]-1.29839602156471[/C][/ROW]
[ROW][C]245[/C][C]82[/C][C]70.1805301165951[/C][C]11.8194698834049[/C][/ROW]
[ROW][C]246[/C][C]69[/C][C]63.2139606374664[/C][C]5.78603936253356[/C][/ROW]
[ROW][C]247[/C][C]68[/C][C]68.5718810624109[/C][C]-0.571881062410945[/C][/ROW]
[ROW][C]248[/C][C]44[/C][C]61.5294717187178[/C][C]-17.5294717187178[/C][/ROW]
[ROW][C]249[/C][C]56[/C][C]60.285495908089[/C][C]-4.28549590808898[/C][/ROW]
[ROW][C]250[/C][C]53[/C][C]64.4584048080244[/C][C]-11.4584048080244[/C][/ROW]
[ROW][C]251[/C][C]70[/C][C]68.8056721035599[/C][C]1.19432789644013[/C][/ROW]
[ROW][C]252[/C][C]78[/C][C]66.1675098158281[/C][C]11.8324901841719[/C][/ROW]
[ROW][C]253[/C][C]71[/C][C]72.1980304421641[/C][C]-1.19803044216414[/C][/ROW]
[ROW][C]254[/C][C]72[/C][C]70.3206415930556[/C][C]1.67935840694444[/C][/ROW]
[ROW][C]255[/C][C]68[/C][C]67.5770163040334[/C][C]0.422983695966611[/C][/ROW]
[ROW][C]256[/C][C]67[/C][C]69.1581948280091[/C][C]-2.15819482800905[/C][/ROW]
[ROW][C]257[/C][C]75[/C][C]64.3355507652502[/C][C]10.6644492347498[/C][/ROW]
[ROW][C]258[/C][C]62[/C][C]65.1278502896571[/C][C]-3.12785028965707[/C][/ROW]
[ROW][C]259[/C][C]67[/C][C]69.7448088722126[/C][C]-2.74480887221263[/C][/ROW]
[ROW][C]260[/C][C]83[/C][C]73.7961984515931[/C][C]9.20380154840689[/C][/ROW]
[ROW][C]261[/C][C]64[/C][C]70.5145781101831[/C][C]-6.51457811018311[/C][/ROW]
[ROW][C]262[/C][C]68[/C][C]72.619549838348[/C][C]-4.61954983834795[/C][/ROW]
[ROW][C]263[/C][C]62[/C][C]63.0450417654244[/C][C]-1.04504176542443[/C][/ROW]
[ROW][C]264[/C][C]72[/C][C]70.5887808307691[/C][C]1.41121916923093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15373.7754536891699-20.7754536891699
28373.60723665176399.39276334823606
36670.4858051881278-4.48580518812783
46770.352502430971-3.35250243097095
57664.389676084522811.6103239154772
67871.52512996216726.47487003783283
75363.5200514501436-10.5200514501436
88073.80426108475166.19573891524839
97474.5334394905386-0.533439490538582
107671.52851815663284.47148184336725
117974.06028213373144.93971786626858
125476.2995242587682-22.2995242587682
136770.2204191141427-3.22041911414272
145471.3686295574643-17.3686295574643
158774.878268807487212.1217311925128
165869.9758467496332-11.9758467496332
177570.10662767461464.89337232538541
188874.208722048429613.7912779515704
196475.0832524530546-11.0832524530546
205770.8132757939652-13.8132757939652
216673.9355430663537-7.93554306635371
226869.6282642689561-1.6282642689561
235473.6644498868966-19.6644498868966
245671.1295979939378-15.1295979939378
258673.826491092582512.1735089074175
268076.23331522428823.76668477571176
277673.16627488500392.83372511499611
286970.0157357471808-1.01573574718079
297870.14097587117927.85902412882083
306770.7962518349857-3.79625183498565
318074.2715428884445.728457111556
325467.2533009751657-13.2533009751657
337173.8951530121854-2.8951530121854
348473.4569788826810.54302111732
357470.96108067792623.03891932207379
367174.4515088889258-3.45150888892585
376363.0683083600229-0.0683083600229151
387172.4936746836124-1.49367468361238
397672.57412003981733.42587996018274
406973.7473481282244-4.74734812822441
417474.9150354156485-0.915035415648504
427570.30610499272524.69389500727481
435477.1050924651849-23.1050924651849
445270.0327252326341-18.0327252326341
456972.8579463711373-3.85794637113728
466871.9126014403919-3.91260144039186
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1927575.3198311314582-0.319831131458218
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1996264.8399339292356-2.83993392923561
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2016966.58594129615182.41405870384818
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2037572.25185548283122.74814451716878
2045470.2148783131597-16.2148783131597
2057272.1411519591632-0.141151959163202
2067471.28918631450082.71081368549923
2078572.474197839509912.5258021604901
2085269.8162584290702-17.8162584290702
2097070.1978543541801-0.19785435418011
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2116467.5201378210324-3.52013782103245
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2166569.0756636691854-4.07566366918539
2178572.890141961184412.1098580388156
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2196162.5125530404934-1.5125530404934
2208271.183723313210410.8162766867897
2217672.32854556206643.67145443793363
2225872.6591040837639-14.6591040837639
2237267.26378201992094.73621798007908
2247273.7050062455538-1.70500624555384
2253863.421431641683-25.421431641683
2267872.64862303900875.35137696099131
2275472.0669492385773-18.0669492385772
2286371.4317506760842-8.43175067608418
2296661.29044015519134.7095598448087
2307070.7594507532981-0.759450753298138
2317170.57420975691240.425790243087556
2326768.3012889395745-1.3012889395745
2335873.3948254037752-15.3948254037752
2347269.11455055349172.88544944650831
2357274.4196480510105-2.41964805101045
2367070.0154009950491-0.0154009950490697
2377666.42631850206269.5736814979374
2385071.0176588823217-21.0176588823217
2397272.0839731975568-0.0839731975568019
2407271.28885156236910.711148437630946
2418871.896579594653616.1034204053464
2425365.7911544130958-12.7911544130958
2435865.0285954504593-7.02859545045931
2446667.2983960215647-1.29839602156471
2458270.180530116595111.8194698834049
2466963.21396063746645.78603936253356
2476868.5718810624109-0.571881062410945
2484461.5294717187178-17.5294717187178
2495660.285495908089-4.28549590808898
2505364.4584048080244-11.4584048080244
2517068.80567210355991.19432789644013
2527866.167509815828111.8324901841719
2537172.1980304421641-1.19803044216414
2547270.32064159305561.67935840694444
2556867.57701630403340.422983695966611
2566769.1581948280091-2.15819482800905
2577564.335550765250210.6644492347498
2586265.1278502896571-3.12785028965707
2596769.7448088722126-2.74480887221263
2608373.79619845159319.20380154840689
2616470.5145781101831-6.51457811018311
2626872.619549838348-4.61954983834795
2636263.0450417654244-1.04504176542443
2647270.58878083076911.41121916923093






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9296069728577870.1407860542844250.0703930271422126
90.8665511639553690.2668976720892620.133448836044631
100.8467806898099110.3064386203801770.153219310190089
110.7803930772756170.4392138454487660.219606922724383
120.9210978388701320.1578043222597350.0789021611298676
130.8778825896182130.2442348207635740.122117410381787
140.8746718867376060.2506562265247890.125328113262394
150.8930892778816860.2138214442366280.106910722118314
160.9219604146751630.1560791706496740.0780395853248369
170.8995750114810370.2008499770379250.100424988518963
180.9442347628646840.1115304742706320.0557652371353159
190.9309844332361780.1380311335276440.0690155667638219
200.9476476570250820.1047046859498350.0523523429749175
210.9353605059945630.1292789880108740.0646394940054369
220.9110568896911010.1778862206177980.088943110308899
230.9357977143094590.1284045713810810.0642022856905407
240.9342154442246870.1315691115506250.0657845557753127
250.9483848351475050.103230329704990.051615164852495
260.9324340178942670.1351319642114660.0675659821057328
270.9183559003177130.1632881993645730.0816440996822867
280.8948315874164140.2103368251671710.105168412583586
290.8842740200160170.2314519599679660.115725979983983
300.8552916220896050.2894167558207910.144708377910395
310.8243280774694360.3513438450611290.175671922530564
320.8278580682190570.3442838635618850.172141931780943
330.7928337496763480.4143325006473050.207166250323652
340.821462614829960.3570747703400790.17853738517004
350.7846889865731060.4306220268537880.215311013426894
360.7441597396355440.5116805207289120.255840260364456
370.7034223976256950.593155204748610.296577602374305
380.6569311555485230.6861376889029550.343068844451477
390.6115666571839570.7768666856320860.388433342816043
400.5671262141289310.8657475717421390.432873785871069
410.5161719341010940.9676561317978110.483828065898906
420.4787097293707440.9574194587414870.521290270629256
430.715782979435360.5684340411292810.28421702056464
440.776285027260820.4474299454783610.22371497273918
450.7418934975520940.5162130048958130.258106502447906
460.7033569893755050.5932860212489890.296643010624495
470.68673191975140.6265361604972010.3132680802486
480.6506166845852110.6987666308295770.349383315414789
490.635287728679270.729424542641460.36471227132073
500.5960747684033630.8078504631932740.403925231596637
510.5516875706814140.8966248586371710.448312429318586
520.5079619529686610.9840760940626770.492038047031339
530.4656389719526960.9312779439053930.534361028047304
540.4410246666530320.8820493333060630.558975333346968
550.4114667618544240.8229335237088470.588533238145576
560.422031824401580.844063648803160.57796817559842
570.3874303399126010.7748606798252020.612569660087399
580.3607446777781070.7214893555562140.639255322221893
590.3226774291649330.6453548583298660.677322570835067
600.2872389536435430.5744779072870860.712761046356457
610.2849048115668440.5698096231336890.715095188433155
620.2627659995707010.5255319991414020.737234000429299
630.2557574704872490.5115149409744970.744242529512751
640.3158181028641830.6316362057283650.684181897135817
650.3846417287679880.7692834575359750.615358271232012
660.3739372192596260.7478744385192520.626062780740374
670.3360957136653050.6721914273306110.663904286334695
680.3023996033565420.6047992067130840.697600396643458
690.2866113774383420.5732227548766840.713388622561658
700.2539437205940220.5078874411880440.746056279405978
710.2242964687810980.4485929375621960.775703531218902
720.243059017141050.48611803428210.75694098285895
730.2557519221428510.5115038442857010.744248077857149
740.2335678836652270.4671357673304550.766432116334772
750.2409538482616880.4819076965233760.759046151738312
760.2887569448174440.5775138896348890.711243055182556
770.2571386995081250.514277399016250.742861300491875
780.2275168247047220.4550336494094440.772483175295278
790.2161155178322110.4322310356644230.783884482167789
800.353513208472310.707026416944620.64648679152769
810.3409647378850860.6819294757701720.659035262114914
820.3107176085450240.6214352170900480.689282391454976
830.2785807507984180.5571615015968370.721419249201582
840.2487612292041110.4975224584082230.751238770795889
850.2363193093237560.4726386186475120.763680690676244
860.2202169690308090.4404339380616180.779783030969191
870.1947609884912010.3895219769824020.805239011508799
880.2243823320370260.4487646640740510.775617667962974
890.2125689808705750.4251379617411510.787431019129425
900.1928042947768170.3856085895536340.807195705223183
910.2438963569144980.4877927138289960.756103643085502
920.2296477558516960.4592955117033920.770352244148304
930.2575551632380770.5151103264761540.742444836761923
940.2567186706241580.5134373412483170.743281329375842
950.2467917431455710.4935834862911430.753208256854429
960.2188229511459460.4376459022918920.781177048854054
970.2029039984432950.405807996886590.797096001556705
980.1955362120687560.3910724241375130.804463787931244
990.1749380999346920.3498761998693840.825061900065308
1000.1539103683640530.3078207367281050.846089631635947
1010.1377386255090510.2754772510181020.862261374490949
1020.1499568086731050.2999136173462110.850043191326895
1030.1375286062304080.2750572124608170.862471393769592
1040.1437171982794880.2874343965589770.856282801720512
1050.1241323616516070.2482647233032150.875867638348393
1060.1080109025068530.2160218050137050.891989097493147
1070.09235306637095520.184706132741910.907646933629045
1080.1178224966421930.2356449932843870.882177503357807
1090.1433300313208590.2866600626417190.856669968679141
1100.1754316182675540.3508632365351090.824568381732446
1110.1565930671006550.3131861342013110.843406932899345
1120.1912300318607930.3824600637215850.808769968139207
1130.4698969882690020.9397939765380040.530103011730998
1140.4419648225631090.8839296451262190.558035177436891
1150.4516747417272460.9033494834544930.548325258272754
1160.5220286867522150.955942626495570.477971313247785
1170.4914503848759060.9829007697518130.508549615124094
1180.4686104230262820.9372208460525640.531389576973718
1190.5775740941736540.8448518116526910.422425905826346
1200.6250109599953980.7499780800092040.374989040004602
1210.6030469618427570.7939060763144870.396953038157243
1220.6454697634798690.7090604730402610.354530236520131
1230.6139495581453410.7721008837093190.386050441854659
1240.5799157469508510.8401685060982990.420084253049149
1250.5531803288397610.8936393423204780.446819671160239
1260.5861187973592920.8277624052814160.413881202640708
1270.5541520608069570.8916958783860860.445847939193043
1280.5477182677798710.9045634644402570.452281732220129
1290.5127819238079610.9744361523840790.487218076192039
1300.4780810149218230.9561620298436460.521918985078177
1310.4463873118926530.8927746237853070.553612688107347
1320.4152162176701880.8304324353403750.584783782329812
1330.3833097404236160.7666194808472320.616690259576384
1340.3508403582471740.7016807164943470.649159641752826
1350.3966728107809360.7933456215618720.603327189219064
1360.370084285650110.740168571300220.62991571434989
1370.3552805972586160.7105611945172320.644719402741384
1380.3829967987525540.7659935975051080.617003201247446
1390.3729098967758960.7458197935517920.627090103224104
1400.3420781704242210.6841563408484420.657921829575779
1410.4312400210947840.8624800421895680.568759978905216
1420.4793279305030340.9586558610060670.520672069496966
1430.5032192956009930.9935614087980140.496780704399007
1440.4847903591569490.9695807183138970.515209640843051
1450.5480531996811890.9038936006376220.451946800318811
1460.5215360354065510.9569279291868970.478463964593449
1470.5409349637790060.9181300724419890.459065036220994
1480.5072186659692230.9855626680615530.492781334030777
1490.4981470531026970.9962941062053930.501852946897303
1500.5226362266362990.9547275467274020.477363773363701
1510.5191941486252650.9616117027494690.480805851374735
1520.4918471412440650.983694282488130.508152858755935
1530.4595339359535060.9190678719070110.540466064046494
1540.4292694563716460.8585389127432930.570730543628354
1550.4552095409924860.9104190819849710.544790459007514
1560.516966184015590.966067631968820.48303381598441
1570.4824090059587990.9648180119175990.517590994041201
1580.4690516514234730.9381033028469450.530948348576527
1590.4636060483805870.9272120967611750.536393951619413
1600.5409135941366920.9181728117266170.459086405863308
1610.5049179424446480.9901641151107050.495082057555352
1620.4916234468497360.9832468936994710.508376553150264
1630.5229643182410120.9540713635179750.477035681758988
1640.5919218375332340.8161563249335330.408078162466766
1650.5632119420018160.8735761159963680.436788057998184
1660.5364286523607550.9271426952784910.463571347639245
1670.5145374594549540.9709250810900930.485462540545046
1680.4984158670647620.9968317341295250.501584132935238
1690.5196044756448140.9607910487103720.480395524355186
1700.4983370469469040.9966740938938070.501662953053096
1710.4865797682704220.9731595365408440.513420231729578
1720.4501165517107630.9002331034215260.549883448289237
1730.4223746029518890.8447492059037770.577625397048111
1740.3950564343987390.7901128687974780.604943565601261
1750.3632525510449850.7265051020899710.636747448955015
1760.3315283742535430.6630567485070860.668471625746457
1770.2979610552783620.5959221105567240.702038944721638
1780.2731465972385480.5462931944770960.726853402761452
1790.2520390759926570.5040781519853130.747960924007343
1800.224921524727580.4498430494551610.77507847527242
1810.3207707474025450.6415414948050890.679229252597455
1820.3024399364318180.6048798728636370.697560063568182
1830.2753406775915970.5506813551831930.724659322408403
1840.2541040082705220.5082080165410440.745895991729478
1850.3507000372750520.7014000745501050.649299962724948
1860.3581239435898520.7162478871797030.641876056410148
1870.3271264049811860.6542528099623720.672873595018814
1880.3764373129598680.7528746259197360.623562687040132
1890.4020259772946350.8040519545892690.597974022705365
1900.3678230356986210.7356460713972420.632176964301379
1910.7771058681964250.4457882636071510.222894131803575
1920.7504482867871150.499103426425770.249551713212885
1930.7173764459415230.5652471081169540.282623554058477
1940.7073106570012350.585378685997530.292689342998765
1950.6739303094111080.6521393811777850.326069690588892
1960.6477982512386260.7044034975227470.352201748761374
1970.7135643213510310.5728713572979380.286435678648969
1980.7702503847376070.4594992305247870.229749615262393
1990.7384957751462110.5230084497075770.261504224853789
2000.7232280897413810.5535438205172380.276771910258619
2010.6995435926456870.6009128147086260.300456407354313
2020.661278553487430.677442893025140.33872144651257
2030.6260617431643660.7478765136712680.373938256835634
2040.677652154426330.644695691147340.32234784557367
2050.6390484465353610.7219031069292790.360951553464639
2060.5982137143955150.803572571208970.401786285604485
2070.6076523445710070.7846953108579860.392347655428993
2080.7127583282412580.5744833435174850.287241671758742
2090.6730375956412190.6539248087175630.326962404358781
2100.6959810976699510.6080378046600980.304018902330049
2110.6572625942688190.6854748114623620.342737405731181
2120.6243623540820150.7512752918359710.375637645917985
2130.7430574240382290.5138851519235430.256942575961771
2140.7449591831902250.5100816336195490.255040816809775
2150.7054384524188040.5891230951623920.294561547581196
2160.6647982975948430.6704034048103130.335201702405157
2170.6917128336129930.6165743327740130.308287166387007
2180.7017069804267120.5965860391465760.298293019573288
2190.6578546787697180.6842906424605640.342145321230282
2200.6792777026988250.641444594602350.320722297301175
2210.6549826458196990.6900347083606010.345017354180301
2220.7039603992586530.5920792014826950.296039600741347
2230.6957217214428430.6085565571143140.304278278557157
2240.6486934021104180.7026131957791650.351306597889582
2250.8818691472360.2362617055280.118130852764
2260.8563760565510180.2872478868979640.143623943448982
2270.9101753328463270.1796493343073470.0898246671536733
2280.9071786914888920.1856426170222160.0928213085111081
2290.8925817008620880.2148365982758240.107418299137912
2300.8639898181898870.2720203636202260.136010181810113
2310.8290910600191170.3418178799617660.170908939980883
2320.7928686362438380.4142627275123250.207131363756162
2330.9171979770346070.1656040459307870.0828020229653934
2340.9238745701612040.1522508596775920.0761254298387961
2350.9012799187838430.1974401624323130.0987200812161567
2360.8743319718363660.2513360563272690.125668028163634
2370.9004211665297380.1991576669405250.0995788334702623
2380.9333283973696490.1333432052607030.0666716026303514
2390.9073012285253120.1853975429493770.0926987714746884
2400.8736540799448020.2526918401103960.126345920055198
2410.9128060271644390.1743879456711220.0871939728355611
2420.9334970670514040.1330058658971920.066502932948596
2430.9251933734739050.149613253052190.0748066265260952
2440.8990118614614660.2019762770770690.100988138538534
2450.9576163145366070.08476737092678570.0423836854633929
2460.93466751365110.13066497269780.0653324863488999
2470.9039155809690420.1921688380619170.0960844190309584
2480.9793790407133330.04124191857333430.0206209592866671
2490.9626411459729240.07471770805415130.0373588540270757
2500.9524636315167530.09507273696649360.0475363684832468
2510.9183614009751350.163277198049730.0816385990248649
2520.9303104799112190.1393790401775610.0696895200887807
2530.892418239759430.2151635204811410.10758176024057
2540.8142925117733220.3714149764533550.185707488226678
2550.6927818470143380.6144363059713250.307218152985662
2560.5244733940315810.9510532119368380.475526605968419

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.929606972857787 & 0.140786054284425 & 0.0703930271422126 \tabularnewline
9 & 0.866551163955369 & 0.266897672089262 & 0.133448836044631 \tabularnewline
10 & 0.846780689809911 & 0.306438620380177 & 0.153219310190089 \tabularnewline
11 & 0.780393077275617 & 0.439213845448766 & 0.219606922724383 \tabularnewline
12 & 0.921097838870132 & 0.157804322259735 & 0.0789021611298676 \tabularnewline
13 & 0.877882589618213 & 0.244234820763574 & 0.122117410381787 \tabularnewline
14 & 0.874671886737606 & 0.250656226524789 & 0.125328113262394 \tabularnewline
15 & 0.893089277881686 & 0.213821444236628 & 0.106910722118314 \tabularnewline
16 & 0.921960414675163 & 0.156079170649674 & 0.0780395853248369 \tabularnewline
17 & 0.899575011481037 & 0.200849977037925 & 0.100424988518963 \tabularnewline
18 & 0.944234762864684 & 0.111530474270632 & 0.0557652371353159 \tabularnewline
19 & 0.930984433236178 & 0.138031133527644 & 0.0690155667638219 \tabularnewline
20 & 0.947647657025082 & 0.104704685949835 & 0.0523523429749175 \tabularnewline
21 & 0.935360505994563 & 0.129278988010874 & 0.0646394940054369 \tabularnewline
22 & 0.911056889691101 & 0.177886220617798 & 0.088943110308899 \tabularnewline
23 & 0.935797714309459 & 0.128404571381081 & 0.0642022856905407 \tabularnewline
24 & 0.934215444224687 & 0.131569111550625 & 0.0657845557753127 \tabularnewline
25 & 0.948384835147505 & 0.10323032970499 & 0.051615164852495 \tabularnewline
26 & 0.932434017894267 & 0.135131964211466 & 0.0675659821057328 \tabularnewline
27 & 0.918355900317713 & 0.163288199364573 & 0.0816440996822867 \tabularnewline
28 & 0.894831587416414 & 0.210336825167171 & 0.105168412583586 \tabularnewline
29 & 0.884274020016017 & 0.231451959967966 & 0.115725979983983 \tabularnewline
30 & 0.855291622089605 & 0.289416755820791 & 0.144708377910395 \tabularnewline
31 & 0.824328077469436 & 0.351343845061129 & 0.175671922530564 \tabularnewline
32 & 0.827858068219057 & 0.344283863561885 & 0.172141931780943 \tabularnewline
33 & 0.792833749676348 & 0.414332500647305 & 0.207166250323652 \tabularnewline
34 & 0.82146261482996 & 0.357074770340079 & 0.17853738517004 \tabularnewline
35 & 0.784688986573106 & 0.430622026853788 & 0.215311013426894 \tabularnewline
36 & 0.744159739635544 & 0.511680520728912 & 0.255840260364456 \tabularnewline
37 & 0.703422397625695 & 0.59315520474861 & 0.296577602374305 \tabularnewline
38 & 0.656931155548523 & 0.686137688902955 & 0.343068844451477 \tabularnewline
39 & 0.611566657183957 & 0.776866685632086 & 0.388433342816043 \tabularnewline
40 & 0.567126214128931 & 0.865747571742139 & 0.432873785871069 \tabularnewline
41 & 0.516171934101094 & 0.967656131797811 & 0.483828065898906 \tabularnewline
42 & 0.478709729370744 & 0.957419458741487 & 0.521290270629256 \tabularnewline
43 & 0.71578297943536 & 0.568434041129281 & 0.28421702056464 \tabularnewline
44 & 0.77628502726082 & 0.447429945478361 & 0.22371497273918 \tabularnewline
45 & 0.741893497552094 & 0.516213004895813 & 0.258106502447906 \tabularnewline
46 & 0.703356989375505 & 0.593286021248989 & 0.296643010624495 \tabularnewline
47 & 0.6867319197514 & 0.626536160497201 & 0.3132680802486 \tabularnewline
48 & 0.650616684585211 & 0.698766630829577 & 0.349383315414789 \tabularnewline
49 & 0.63528772867927 & 0.72942454264146 & 0.36471227132073 \tabularnewline
50 & 0.596074768403363 & 0.807850463193274 & 0.403925231596637 \tabularnewline
51 & 0.551687570681414 & 0.896624858637171 & 0.448312429318586 \tabularnewline
52 & 0.507961952968661 & 0.984076094062677 & 0.492038047031339 \tabularnewline
53 & 0.465638971952696 & 0.931277943905393 & 0.534361028047304 \tabularnewline
54 & 0.441024666653032 & 0.882049333306063 & 0.558975333346968 \tabularnewline
55 & 0.411466761854424 & 0.822933523708847 & 0.588533238145576 \tabularnewline
56 & 0.42203182440158 & 0.84406364880316 & 0.57796817559842 \tabularnewline
57 & 0.387430339912601 & 0.774860679825202 & 0.612569660087399 \tabularnewline
58 & 0.360744677778107 & 0.721489355556214 & 0.639255322221893 \tabularnewline
59 & 0.322677429164933 & 0.645354858329866 & 0.677322570835067 \tabularnewline
60 & 0.287238953643543 & 0.574477907287086 & 0.712761046356457 \tabularnewline
61 & 0.284904811566844 & 0.569809623133689 & 0.715095188433155 \tabularnewline
62 & 0.262765999570701 & 0.525531999141402 & 0.737234000429299 \tabularnewline
63 & 0.255757470487249 & 0.511514940974497 & 0.744242529512751 \tabularnewline
64 & 0.315818102864183 & 0.631636205728365 & 0.684181897135817 \tabularnewline
65 & 0.384641728767988 & 0.769283457535975 & 0.615358271232012 \tabularnewline
66 & 0.373937219259626 & 0.747874438519252 & 0.626062780740374 \tabularnewline
67 & 0.336095713665305 & 0.672191427330611 & 0.663904286334695 \tabularnewline
68 & 0.302399603356542 & 0.604799206713084 & 0.697600396643458 \tabularnewline
69 & 0.286611377438342 & 0.573222754876684 & 0.713388622561658 \tabularnewline
70 & 0.253943720594022 & 0.507887441188044 & 0.746056279405978 \tabularnewline
71 & 0.224296468781098 & 0.448592937562196 & 0.775703531218902 \tabularnewline
72 & 0.24305901714105 & 0.4861180342821 & 0.75694098285895 \tabularnewline
73 & 0.255751922142851 & 0.511503844285701 & 0.744248077857149 \tabularnewline
74 & 0.233567883665227 & 0.467135767330455 & 0.766432116334772 \tabularnewline
75 & 0.240953848261688 & 0.481907696523376 & 0.759046151738312 \tabularnewline
76 & 0.288756944817444 & 0.577513889634889 & 0.711243055182556 \tabularnewline
77 & 0.257138699508125 & 0.51427739901625 & 0.742861300491875 \tabularnewline
78 & 0.227516824704722 & 0.455033649409444 & 0.772483175295278 \tabularnewline
79 & 0.216115517832211 & 0.432231035664423 & 0.783884482167789 \tabularnewline
80 & 0.35351320847231 & 0.70702641694462 & 0.64648679152769 \tabularnewline
81 & 0.340964737885086 & 0.681929475770172 & 0.659035262114914 \tabularnewline
82 & 0.310717608545024 & 0.621435217090048 & 0.689282391454976 \tabularnewline
83 & 0.278580750798418 & 0.557161501596837 & 0.721419249201582 \tabularnewline
84 & 0.248761229204111 & 0.497522458408223 & 0.751238770795889 \tabularnewline
85 & 0.236319309323756 & 0.472638618647512 & 0.763680690676244 \tabularnewline
86 & 0.220216969030809 & 0.440433938061618 & 0.779783030969191 \tabularnewline
87 & 0.194760988491201 & 0.389521976982402 & 0.805239011508799 \tabularnewline
88 & 0.224382332037026 & 0.448764664074051 & 0.775617667962974 \tabularnewline
89 & 0.212568980870575 & 0.425137961741151 & 0.787431019129425 \tabularnewline
90 & 0.192804294776817 & 0.385608589553634 & 0.807195705223183 \tabularnewline
91 & 0.243896356914498 & 0.487792713828996 & 0.756103643085502 \tabularnewline
92 & 0.229647755851696 & 0.459295511703392 & 0.770352244148304 \tabularnewline
93 & 0.257555163238077 & 0.515110326476154 & 0.742444836761923 \tabularnewline
94 & 0.256718670624158 & 0.513437341248317 & 0.743281329375842 \tabularnewline
95 & 0.246791743145571 & 0.493583486291143 & 0.753208256854429 \tabularnewline
96 & 0.218822951145946 & 0.437645902291892 & 0.781177048854054 \tabularnewline
97 & 0.202903998443295 & 0.40580799688659 & 0.797096001556705 \tabularnewline
98 & 0.195536212068756 & 0.391072424137513 & 0.804463787931244 \tabularnewline
99 & 0.174938099934692 & 0.349876199869384 & 0.825061900065308 \tabularnewline
100 & 0.153910368364053 & 0.307820736728105 & 0.846089631635947 \tabularnewline
101 & 0.137738625509051 & 0.275477251018102 & 0.862261374490949 \tabularnewline
102 & 0.149956808673105 & 0.299913617346211 & 0.850043191326895 \tabularnewline
103 & 0.137528606230408 & 0.275057212460817 & 0.862471393769592 \tabularnewline
104 & 0.143717198279488 & 0.287434396558977 & 0.856282801720512 \tabularnewline
105 & 0.124132361651607 & 0.248264723303215 & 0.875867638348393 \tabularnewline
106 & 0.108010902506853 & 0.216021805013705 & 0.891989097493147 \tabularnewline
107 & 0.0923530663709552 & 0.18470613274191 & 0.907646933629045 \tabularnewline
108 & 0.117822496642193 & 0.235644993284387 & 0.882177503357807 \tabularnewline
109 & 0.143330031320859 & 0.286660062641719 & 0.856669968679141 \tabularnewline
110 & 0.175431618267554 & 0.350863236535109 & 0.824568381732446 \tabularnewline
111 & 0.156593067100655 & 0.313186134201311 & 0.843406932899345 \tabularnewline
112 & 0.191230031860793 & 0.382460063721585 & 0.808769968139207 \tabularnewline
113 & 0.469896988269002 & 0.939793976538004 & 0.530103011730998 \tabularnewline
114 & 0.441964822563109 & 0.883929645126219 & 0.558035177436891 \tabularnewline
115 & 0.451674741727246 & 0.903349483454493 & 0.548325258272754 \tabularnewline
116 & 0.522028686752215 & 0.95594262649557 & 0.477971313247785 \tabularnewline
117 & 0.491450384875906 & 0.982900769751813 & 0.508549615124094 \tabularnewline
118 & 0.468610423026282 & 0.937220846052564 & 0.531389576973718 \tabularnewline
119 & 0.577574094173654 & 0.844851811652691 & 0.422425905826346 \tabularnewline
120 & 0.625010959995398 & 0.749978080009204 & 0.374989040004602 \tabularnewline
121 & 0.603046961842757 & 0.793906076314487 & 0.396953038157243 \tabularnewline
122 & 0.645469763479869 & 0.709060473040261 & 0.354530236520131 \tabularnewline
123 & 0.613949558145341 & 0.772100883709319 & 0.386050441854659 \tabularnewline
124 & 0.579915746950851 & 0.840168506098299 & 0.420084253049149 \tabularnewline
125 & 0.553180328839761 & 0.893639342320478 & 0.446819671160239 \tabularnewline
126 & 0.586118797359292 & 0.827762405281416 & 0.413881202640708 \tabularnewline
127 & 0.554152060806957 & 0.891695878386086 & 0.445847939193043 \tabularnewline
128 & 0.547718267779871 & 0.904563464440257 & 0.452281732220129 \tabularnewline
129 & 0.512781923807961 & 0.974436152384079 & 0.487218076192039 \tabularnewline
130 & 0.478081014921823 & 0.956162029843646 & 0.521918985078177 \tabularnewline
131 & 0.446387311892653 & 0.892774623785307 & 0.553612688107347 \tabularnewline
132 & 0.415216217670188 & 0.830432435340375 & 0.584783782329812 \tabularnewline
133 & 0.383309740423616 & 0.766619480847232 & 0.616690259576384 \tabularnewline
134 & 0.350840358247174 & 0.701680716494347 & 0.649159641752826 \tabularnewline
135 & 0.396672810780936 & 0.793345621561872 & 0.603327189219064 \tabularnewline
136 & 0.37008428565011 & 0.74016857130022 & 0.62991571434989 \tabularnewline
137 & 0.355280597258616 & 0.710561194517232 & 0.644719402741384 \tabularnewline
138 & 0.382996798752554 & 0.765993597505108 & 0.617003201247446 \tabularnewline
139 & 0.372909896775896 & 0.745819793551792 & 0.627090103224104 \tabularnewline
140 & 0.342078170424221 & 0.684156340848442 & 0.657921829575779 \tabularnewline
141 & 0.431240021094784 & 0.862480042189568 & 0.568759978905216 \tabularnewline
142 & 0.479327930503034 & 0.958655861006067 & 0.520672069496966 \tabularnewline
143 & 0.503219295600993 & 0.993561408798014 & 0.496780704399007 \tabularnewline
144 & 0.484790359156949 & 0.969580718313897 & 0.515209640843051 \tabularnewline
145 & 0.548053199681189 & 0.903893600637622 & 0.451946800318811 \tabularnewline
146 & 0.521536035406551 & 0.956927929186897 & 0.478463964593449 \tabularnewline
147 & 0.540934963779006 & 0.918130072441989 & 0.459065036220994 \tabularnewline
148 & 0.507218665969223 & 0.985562668061553 & 0.492781334030777 \tabularnewline
149 & 0.498147053102697 & 0.996294106205393 & 0.501852946897303 \tabularnewline
150 & 0.522636226636299 & 0.954727546727402 & 0.477363773363701 \tabularnewline
151 & 0.519194148625265 & 0.961611702749469 & 0.480805851374735 \tabularnewline
152 & 0.491847141244065 & 0.98369428248813 & 0.508152858755935 \tabularnewline
153 & 0.459533935953506 & 0.919067871907011 & 0.540466064046494 \tabularnewline
154 & 0.429269456371646 & 0.858538912743293 & 0.570730543628354 \tabularnewline
155 & 0.455209540992486 & 0.910419081984971 & 0.544790459007514 \tabularnewline
156 & 0.51696618401559 & 0.96606763196882 & 0.48303381598441 \tabularnewline
157 & 0.482409005958799 & 0.964818011917599 & 0.517590994041201 \tabularnewline
158 & 0.469051651423473 & 0.938103302846945 & 0.530948348576527 \tabularnewline
159 & 0.463606048380587 & 0.927212096761175 & 0.536393951619413 \tabularnewline
160 & 0.540913594136692 & 0.918172811726617 & 0.459086405863308 \tabularnewline
161 & 0.504917942444648 & 0.990164115110705 & 0.495082057555352 \tabularnewline
162 & 0.491623446849736 & 0.983246893699471 & 0.508376553150264 \tabularnewline
163 & 0.522964318241012 & 0.954071363517975 & 0.477035681758988 \tabularnewline
164 & 0.591921837533234 & 0.816156324933533 & 0.408078162466766 \tabularnewline
165 & 0.563211942001816 & 0.873576115996368 & 0.436788057998184 \tabularnewline
166 & 0.536428652360755 & 0.927142695278491 & 0.463571347639245 \tabularnewline
167 & 0.514537459454954 & 0.970925081090093 & 0.485462540545046 \tabularnewline
168 & 0.498415867064762 & 0.996831734129525 & 0.501584132935238 \tabularnewline
169 & 0.519604475644814 & 0.960791048710372 & 0.480395524355186 \tabularnewline
170 & 0.498337046946904 & 0.996674093893807 & 0.501662953053096 \tabularnewline
171 & 0.486579768270422 & 0.973159536540844 & 0.513420231729578 \tabularnewline
172 & 0.450116551710763 & 0.900233103421526 & 0.549883448289237 \tabularnewline
173 & 0.422374602951889 & 0.844749205903777 & 0.577625397048111 \tabularnewline
174 & 0.395056434398739 & 0.790112868797478 & 0.604943565601261 \tabularnewline
175 & 0.363252551044985 & 0.726505102089971 & 0.636747448955015 \tabularnewline
176 & 0.331528374253543 & 0.663056748507086 & 0.668471625746457 \tabularnewline
177 & 0.297961055278362 & 0.595922110556724 & 0.702038944721638 \tabularnewline
178 & 0.273146597238548 & 0.546293194477096 & 0.726853402761452 \tabularnewline
179 & 0.252039075992657 & 0.504078151985313 & 0.747960924007343 \tabularnewline
180 & 0.22492152472758 & 0.449843049455161 & 0.77507847527242 \tabularnewline
181 & 0.320770747402545 & 0.641541494805089 & 0.679229252597455 \tabularnewline
182 & 0.302439936431818 & 0.604879872863637 & 0.697560063568182 \tabularnewline
183 & 0.275340677591597 & 0.550681355183193 & 0.724659322408403 \tabularnewline
184 & 0.254104008270522 & 0.508208016541044 & 0.745895991729478 \tabularnewline
185 & 0.350700037275052 & 0.701400074550105 & 0.649299962724948 \tabularnewline
186 & 0.358123943589852 & 0.716247887179703 & 0.641876056410148 \tabularnewline
187 & 0.327126404981186 & 0.654252809962372 & 0.672873595018814 \tabularnewline
188 & 0.376437312959868 & 0.752874625919736 & 0.623562687040132 \tabularnewline
189 & 0.402025977294635 & 0.804051954589269 & 0.597974022705365 \tabularnewline
190 & 0.367823035698621 & 0.735646071397242 & 0.632176964301379 \tabularnewline
191 & 0.777105868196425 & 0.445788263607151 & 0.222894131803575 \tabularnewline
192 & 0.750448286787115 & 0.49910342642577 & 0.249551713212885 \tabularnewline
193 & 0.717376445941523 & 0.565247108116954 & 0.282623554058477 \tabularnewline
194 & 0.707310657001235 & 0.58537868599753 & 0.292689342998765 \tabularnewline
195 & 0.673930309411108 & 0.652139381177785 & 0.326069690588892 \tabularnewline
196 & 0.647798251238626 & 0.704403497522747 & 0.352201748761374 \tabularnewline
197 & 0.713564321351031 & 0.572871357297938 & 0.286435678648969 \tabularnewline
198 & 0.770250384737607 & 0.459499230524787 & 0.229749615262393 \tabularnewline
199 & 0.738495775146211 & 0.523008449707577 & 0.261504224853789 \tabularnewline
200 & 0.723228089741381 & 0.553543820517238 & 0.276771910258619 \tabularnewline
201 & 0.699543592645687 & 0.600912814708626 & 0.300456407354313 \tabularnewline
202 & 0.66127855348743 & 0.67744289302514 & 0.33872144651257 \tabularnewline
203 & 0.626061743164366 & 0.747876513671268 & 0.373938256835634 \tabularnewline
204 & 0.67765215442633 & 0.64469569114734 & 0.32234784557367 \tabularnewline
205 & 0.639048446535361 & 0.721903106929279 & 0.360951553464639 \tabularnewline
206 & 0.598213714395515 & 0.80357257120897 & 0.401786285604485 \tabularnewline
207 & 0.607652344571007 & 0.784695310857986 & 0.392347655428993 \tabularnewline
208 & 0.712758328241258 & 0.574483343517485 & 0.287241671758742 \tabularnewline
209 & 0.673037595641219 & 0.653924808717563 & 0.326962404358781 \tabularnewline
210 & 0.695981097669951 & 0.608037804660098 & 0.304018902330049 \tabularnewline
211 & 0.657262594268819 & 0.685474811462362 & 0.342737405731181 \tabularnewline
212 & 0.624362354082015 & 0.751275291835971 & 0.375637645917985 \tabularnewline
213 & 0.743057424038229 & 0.513885151923543 & 0.256942575961771 \tabularnewline
214 & 0.744959183190225 & 0.510081633619549 & 0.255040816809775 \tabularnewline
215 & 0.705438452418804 & 0.589123095162392 & 0.294561547581196 \tabularnewline
216 & 0.664798297594843 & 0.670403404810313 & 0.335201702405157 \tabularnewline
217 & 0.691712833612993 & 0.616574332774013 & 0.308287166387007 \tabularnewline
218 & 0.701706980426712 & 0.596586039146576 & 0.298293019573288 \tabularnewline
219 & 0.657854678769718 & 0.684290642460564 & 0.342145321230282 \tabularnewline
220 & 0.679277702698825 & 0.64144459460235 & 0.320722297301175 \tabularnewline
221 & 0.654982645819699 & 0.690034708360601 & 0.345017354180301 \tabularnewline
222 & 0.703960399258653 & 0.592079201482695 & 0.296039600741347 \tabularnewline
223 & 0.695721721442843 & 0.608556557114314 & 0.304278278557157 \tabularnewline
224 & 0.648693402110418 & 0.702613195779165 & 0.351306597889582 \tabularnewline
225 & 0.881869147236 & 0.236261705528 & 0.118130852764 \tabularnewline
226 & 0.856376056551018 & 0.287247886897964 & 0.143623943448982 \tabularnewline
227 & 0.910175332846327 & 0.179649334307347 & 0.0898246671536733 \tabularnewline
228 & 0.907178691488892 & 0.185642617022216 & 0.0928213085111081 \tabularnewline
229 & 0.892581700862088 & 0.214836598275824 & 0.107418299137912 \tabularnewline
230 & 0.863989818189887 & 0.272020363620226 & 0.136010181810113 \tabularnewline
231 & 0.829091060019117 & 0.341817879961766 & 0.170908939980883 \tabularnewline
232 & 0.792868636243838 & 0.414262727512325 & 0.207131363756162 \tabularnewline
233 & 0.917197977034607 & 0.165604045930787 & 0.0828020229653934 \tabularnewline
234 & 0.923874570161204 & 0.152250859677592 & 0.0761254298387961 \tabularnewline
235 & 0.901279918783843 & 0.197440162432313 & 0.0987200812161567 \tabularnewline
236 & 0.874331971836366 & 0.251336056327269 & 0.125668028163634 \tabularnewline
237 & 0.900421166529738 & 0.199157666940525 & 0.0995788334702623 \tabularnewline
238 & 0.933328397369649 & 0.133343205260703 & 0.0666716026303514 \tabularnewline
239 & 0.907301228525312 & 0.185397542949377 & 0.0926987714746884 \tabularnewline
240 & 0.873654079944802 & 0.252691840110396 & 0.126345920055198 \tabularnewline
241 & 0.912806027164439 & 0.174387945671122 & 0.0871939728355611 \tabularnewline
242 & 0.933497067051404 & 0.133005865897192 & 0.066502932948596 \tabularnewline
243 & 0.925193373473905 & 0.14961325305219 & 0.0748066265260952 \tabularnewline
244 & 0.899011861461466 & 0.201976277077069 & 0.100988138538534 \tabularnewline
245 & 0.957616314536607 & 0.0847673709267857 & 0.0423836854633929 \tabularnewline
246 & 0.9346675136511 & 0.1306649726978 & 0.0653324863488999 \tabularnewline
247 & 0.903915580969042 & 0.192168838061917 & 0.0960844190309584 \tabularnewline
248 & 0.979379040713333 & 0.0412419185733343 & 0.0206209592866671 \tabularnewline
249 & 0.962641145972924 & 0.0747177080541513 & 0.0373588540270757 \tabularnewline
250 & 0.952463631516753 & 0.0950727369664936 & 0.0475363684832468 \tabularnewline
251 & 0.918361400975135 & 0.16327719804973 & 0.0816385990248649 \tabularnewline
252 & 0.930310479911219 & 0.139379040177561 & 0.0696895200887807 \tabularnewline
253 & 0.89241823975943 & 0.215163520481141 & 0.10758176024057 \tabularnewline
254 & 0.814292511773322 & 0.371414976453355 & 0.185707488226678 \tabularnewline
255 & 0.692781847014338 & 0.614436305971325 & 0.307218152985662 \tabularnewline
256 & 0.524473394031581 & 0.951053211936838 & 0.475526605968419 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.929606972857787[/C][C]0.140786054284425[/C][C]0.0703930271422126[/C][/ROW]
[ROW][C]9[/C][C]0.866551163955369[/C][C]0.266897672089262[/C][C]0.133448836044631[/C][/ROW]
[ROW][C]10[/C][C]0.846780689809911[/C][C]0.306438620380177[/C][C]0.153219310190089[/C][/ROW]
[ROW][C]11[/C][C]0.780393077275617[/C][C]0.439213845448766[/C][C]0.219606922724383[/C][/ROW]
[ROW][C]12[/C][C]0.921097838870132[/C][C]0.157804322259735[/C][C]0.0789021611298676[/C][/ROW]
[ROW][C]13[/C][C]0.877882589618213[/C][C]0.244234820763574[/C][C]0.122117410381787[/C][/ROW]
[ROW][C]14[/C][C]0.874671886737606[/C][C]0.250656226524789[/C][C]0.125328113262394[/C][/ROW]
[ROW][C]15[/C][C]0.893089277881686[/C][C]0.213821444236628[/C][C]0.106910722118314[/C][/ROW]
[ROW][C]16[/C][C]0.921960414675163[/C][C]0.156079170649674[/C][C]0.0780395853248369[/C][/ROW]
[ROW][C]17[/C][C]0.899575011481037[/C][C]0.200849977037925[/C][C]0.100424988518963[/C][/ROW]
[ROW][C]18[/C][C]0.944234762864684[/C][C]0.111530474270632[/C][C]0.0557652371353159[/C][/ROW]
[ROW][C]19[/C][C]0.930984433236178[/C][C]0.138031133527644[/C][C]0.0690155667638219[/C][/ROW]
[ROW][C]20[/C][C]0.947647657025082[/C][C]0.104704685949835[/C][C]0.0523523429749175[/C][/ROW]
[ROW][C]21[/C][C]0.935360505994563[/C][C]0.129278988010874[/C][C]0.0646394940054369[/C][/ROW]
[ROW][C]22[/C][C]0.911056889691101[/C][C]0.177886220617798[/C][C]0.088943110308899[/C][/ROW]
[ROW][C]23[/C][C]0.935797714309459[/C][C]0.128404571381081[/C][C]0.0642022856905407[/C][/ROW]
[ROW][C]24[/C][C]0.934215444224687[/C][C]0.131569111550625[/C][C]0.0657845557753127[/C][/ROW]
[ROW][C]25[/C][C]0.948384835147505[/C][C]0.10323032970499[/C][C]0.051615164852495[/C][/ROW]
[ROW][C]26[/C][C]0.932434017894267[/C][C]0.135131964211466[/C][C]0.0675659821057328[/C][/ROW]
[ROW][C]27[/C][C]0.918355900317713[/C][C]0.163288199364573[/C][C]0.0816440996822867[/C][/ROW]
[ROW][C]28[/C][C]0.894831587416414[/C][C]0.210336825167171[/C][C]0.105168412583586[/C][/ROW]
[ROW][C]29[/C][C]0.884274020016017[/C][C]0.231451959967966[/C][C]0.115725979983983[/C][/ROW]
[ROW][C]30[/C][C]0.855291622089605[/C][C]0.289416755820791[/C][C]0.144708377910395[/C][/ROW]
[ROW][C]31[/C][C]0.824328077469436[/C][C]0.351343845061129[/C][C]0.175671922530564[/C][/ROW]
[ROW][C]32[/C][C]0.827858068219057[/C][C]0.344283863561885[/C][C]0.172141931780943[/C][/ROW]
[ROW][C]33[/C][C]0.792833749676348[/C][C]0.414332500647305[/C][C]0.207166250323652[/C][/ROW]
[ROW][C]34[/C][C]0.82146261482996[/C][C]0.357074770340079[/C][C]0.17853738517004[/C][/ROW]
[ROW][C]35[/C][C]0.784688986573106[/C][C]0.430622026853788[/C][C]0.215311013426894[/C][/ROW]
[ROW][C]36[/C][C]0.744159739635544[/C][C]0.511680520728912[/C][C]0.255840260364456[/C][/ROW]
[ROW][C]37[/C][C]0.703422397625695[/C][C]0.59315520474861[/C][C]0.296577602374305[/C][/ROW]
[ROW][C]38[/C][C]0.656931155548523[/C][C]0.686137688902955[/C][C]0.343068844451477[/C][/ROW]
[ROW][C]39[/C][C]0.611566657183957[/C][C]0.776866685632086[/C][C]0.388433342816043[/C][/ROW]
[ROW][C]40[/C][C]0.567126214128931[/C][C]0.865747571742139[/C][C]0.432873785871069[/C][/ROW]
[ROW][C]41[/C][C]0.516171934101094[/C][C]0.967656131797811[/C][C]0.483828065898906[/C][/ROW]
[ROW][C]42[/C][C]0.478709729370744[/C][C]0.957419458741487[/C][C]0.521290270629256[/C][/ROW]
[ROW][C]43[/C][C]0.71578297943536[/C][C]0.568434041129281[/C][C]0.28421702056464[/C][/ROW]
[ROW][C]44[/C][C]0.77628502726082[/C][C]0.447429945478361[/C][C]0.22371497273918[/C][/ROW]
[ROW][C]45[/C][C]0.741893497552094[/C][C]0.516213004895813[/C][C]0.258106502447906[/C][/ROW]
[ROW][C]46[/C][C]0.703356989375505[/C][C]0.593286021248989[/C][C]0.296643010624495[/C][/ROW]
[ROW][C]47[/C][C]0.6867319197514[/C][C]0.626536160497201[/C][C]0.3132680802486[/C][/ROW]
[ROW][C]48[/C][C]0.650616684585211[/C][C]0.698766630829577[/C][C]0.349383315414789[/C][/ROW]
[ROW][C]49[/C][C]0.63528772867927[/C][C]0.72942454264146[/C][C]0.36471227132073[/C][/ROW]
[ROW][C]50[/C][C]0.596074768403363[/C][C]0.807850463193274[/C][C]0.403925231596637[/C][/ROW]
[ROW][C]51[/C][C]0.551687570681414[/C][C]0.896624858637171[/C][C]0.448312429318586[/C][/ROW]
[ROW][C]52[/C][C]0.507961952968661[/C][C]0.984076094062677[/C][C]0.492038047031339[/C][/ROW]
[ROW][C]53[/C][C]0.465638971952696[/C][C]0.931277943905393[/C][C]0.534361028047304[/C][/ROW]
[ROW][C]54[/C][C]0.441024666653032[/C][C]0.882049333306063[/C][C]0.558975333346968[/C][/ROW]
[ROW][C]55[/C][C]0.411466761854424[/C][C]0.822933523708847[/C][C]0.588533238145576[/C][/ROW]
[ROW][C]56[/C][C]0.42203182440158[/C][C]0.84406364880316[/C][C]0.57796817559842[/C][/ROW]
[ROW][C]57[/C][C]0.387430339912601[/C][C]0.774860679825202[/C][C]0.612569660087399[/C][/ROW]
[ROW][C]58[/C][C]0.360744677778107[/C][C]0.721489355556214[/C][C]0.639255322221893[/C][/ROW]
[ROW][C]59[/C][C]0.322677429164933[/C][C]0.645354858329866[/C][C]0.677322570835067[/C][/ROW]
[ROW][C]60[/C][C]0.287238953643543[/C][C]0.574477907287086[/C][C]0.712761046356457[/C][/ROW]
[ROW][C]61[/C][C]0.284904811566844[/C][C]0.569809623133689[/C][C]0.715095188433155[/C][/ROW]
[ROW][C]62[/C][C]0.262765999570701[/C][C]0.525531999141402[/C][C]0.737234000429299[/C][/ROW]
[ROW][C]63[/C][C]0.255757470487249[/C][C]0.511514940974497[/C][C]0.744242529512751[/C][/ROW]
[ROW][C]64[/C][C]0.315818102864183[/C][C]0.631636205728365[/C][C]0.684181897135817[/C][/ROW]
[ROW][C]65[/C][C]0.384641728767988[/C][C]0.769283457535975[/C][C]0.615358271232012[/C][/ROW]
[ROW][C]66[/C][C]0.373937219259626[/C][C]0.747874438519252[/C][C]0.626062780740374[/C][/ROW]
[ROW][C]67[/C][C]0.336095713665305[/C][C]0.672191427330611[/C][C]0.663904286334695[/C][/ROW]
[ROW][C]68[/C][C]0.302399603356542[/C][C]0.604799206713084[/C][C]0.697600396643458[/C][/ROW]
[ROW][C]69[/C][C]0.286611377438342[/C][C]0.573222754876684[/C][C]0.713388622561658[/C][/ROW]
[ROW][C]70[/C][C]0.253943720594022[/C][C]0.507887441188044[/C][C]0.746056279405978[/C][/ROW]
[ROW][C]71[/C][C]0.224296468781098[/C][C]0.448592937562196[/C][C]0.775703531218902[/C][/ROW]
[ROW][C]72[/C][C]0.24305901714105[/C][C]0.4861180342821[/C][C]0.75694098285895[/C][/ROW]
[ROW][C]73[/C][C]0.255751922142851[/C][C]0.511503844285701[/C][C]0.744248077857149[/C][/ROW]
[ROW][C]74[/C][C]0.233567883665227[/C][C]0.467135767330455[/C][C]0.766432116334772[/C][/ROW]
[ROW][C]75[/C][C]0.240953848261688[/C][C]0.481907696523376[/C][C]0.759046151738312[/C][/ROW]
[ROW][C]76[/C][C]0.288756944817444[/C][C]0.577513889634889[/C][C]0.711243055182556[/C][/ROW]
[ROW][C]77[/C][C]0.257138699508125[/C][C]0.51427739901625[/C][C]0.742861300491875[/C][/ROW]
[ROW][C]78[/C][C]0.227516824704722[/C][C]0.455033649409444[/C][C]0.772483175295278[/C][/ROW]
[ROW][C]79[/C][C]0.216115517832211[/C][C]0.432231035664423[/C][C]0.783884482167789[/C][/ROW]
[ROW][C]80[/C][C]0.35351320847231[/C][C]0.70702641694462[/C][C]0.64648679152769[/C][/ROW]
[ROW][C]81[/C][C]0.340964737885086[/C][C]0.681929475770172[/C][C]0.659035262114914[/C][/ROW]
[ROW][C]82[/C][C]0.310717608545024[/C][C]0.621435217090048[/C][C]0.689282391454976[/C][/ROW]
[ROW][C]83[/C][C]0.278580750798418[/C][C]0.557161501596837[/C][C]0.721419249201582[/C][/ROW]
[ROW][C]84[/C][C]0.248761229204111[/C][C]0.497522458408223[/C][C]0.751238770795889[/C][/ROW]
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[ROW][C]127[/C][C]0.554152060806957[/C][C]0.891695878386086[/C][C]0.445847939193043[/C][/ROW]
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[ROW][C]130[/C][C]0.478081014921823[/C][C]0.956162029843646[/C][C]0.521918985078177[/C][/ROW]
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[ROW][C]187[/C][C]0.327126404981186[/C][C]0.654252809962372[/C][C]0.672873595018814[/C][/ROW]
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[ROW][C]189[/C][C]0.402025977294635[/C][C]0.804051954589269[/C][C]0.597974022705365[/C][/ROW]
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[ROW][C]208[/C][C]0.712758328241258[/C][C]0.574483343517485[/C][C]0.287241671758742[/C][/ROW]
[ROW][C]209[/C][C]0.673037595641219[/C][C]0.653924808717563[/C][C]0.326962404358781[/C][/ROW]
[ROW][C]210[/C][C]0.695981097669951[/C][C]0.608037804660098[/C][C]0.304018902330049[/C][/ROW]
[ROW][C]211[/C][C]0.657262594268819[/C][C]0.685474811462362[/C][C]0.342737405731181[/C][/ROW]
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[ROW][C]217[/C][C]0.691712833612993[/C][C]0.616574332774013[/C][C]0.308287166387007[/C][/ROW]
[ROW][C]218[/C][C]0.701706980426712[/C][C]0.596586039146576[/C][C]0.298293019573288[/C][/ROW]
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[ROW][C]220[/C][C]0.679277702698825[/C][C]0.64144459460235[/C][C]0.320722297301175[/C][/ROW]
[ROW][C]221[/C][C]0.654982645819699[/C][C]0.690034708360601[/C][C]0.345017354180301[/C][/ROW]
[ROW][C]222[/C][C]0.703960399258653[/C][C]0.592079201482695[/C][C]0.296039600741347[/C][/ROW]
[ROW][C]223[/C][C]0.695721721442843[/C][C]0.608556557114314[/C][C]0.304278278557157[/C][/ROW]
[ROW][C]224[/C][C]0.648693402110418[/C][C]0.702613195779165[/C][C]0.351306597889582[/C][/ROW]
[ROW][C]225[/C][C]0.881869147236[/C][C]0.236261705528[/C][C]0.118130852764[/C][/ROW]
[ROW][C]226[/C][C]0.856376056551018[/C][C]0.287247886897964[/C][C]0.143623943448982[/C][/ROW]
[ROW][C]227[/C][C]0.910175332846327[/C][C]0.179649334307347[/C][C]0.0898246671536733[/C][/ROW]
[ROW][C]228[/C][C]0.907178691488892[/C][C]0.185642617022216[/C][C]0.0928213085111081[/C][/ROW]
[ROW][C]229[/C][C]0.892581700862088[/C][C]0.214836598275824[/C][C]0.107418299137912[/C][/ROW]
[ROW][C]230[/C][C]0.863989818189887[/C][C]0.272020363620226[/C][C]0.136010181810113[/C][/ROW]
[ROW][C]231[/C][C]0.829091060019117[/C][C]0.341817879961766[/C][C]0.170908939980883[/C][/ROW]
[ROW][C]232[/C][C]0.792868636243838[/C][C]0.414262727512325[/C][C]0.207131363756162[/C][/ROW]
[ROW][C]233[/C][C]0.917197977034607[/C][C]0.165604045930787[/C][C]0.0828020229653934[/C][/ROW]
[ROW][C]234[/C][C]0.923874570161204[/C][C]0.152250859677592[/C][C]0.0761254298387961[/C][/ROW]
[ROW][C]235[/C][C]0.901279918783843[/C][C]0.197440162432313[/C][C]0.0987200812161567[/C][/ROW]
[ROW][C]236[/C][C]0.874331971836366[/C][C]0.251336056327269[/C][C]0.125668028163634[/C][/ROW]
[ROW][C]237[/C][C]0.900421166529738[/C][C]0.199157666940525[/C][C]0.0995788334702623[/C][/ROW]
[ROW][C]238[/C][C]0.933328397369649[/C][C]0.133343205260703[/C][C]0.0666716026303514[/C][/ROW]
[ROW][C]239[/C][C]0.907301228525312[/C][C]0.185397542949377[/C][C]0.0926987714746884[/C][/ROW]
[ROW][C]240[/C][C]0.873654079944802[/C][C]0.252691840110396[/C][C]0.126345920055198[/C][/ROW]
[ROW][C]241[/C][C]0.912806027164439[/C][C]0.174387945671122[/C][C]0.0871939728355611[/C][/ROW]
[ROW][C]242[/C][C]0.933497067051404[/C][C]0.133005865897192[/C][C]0.066502932948596[/C][/ROW]
[ROW][C]243[/C][C]0.925193373473905[/C][C]0.14961325305219[/C][C]0.0748066265260952[/C][/ROW]
[ROW][C]244[/C][C]0.899011861461466[/C][C]0.201976277077069[/C][C]0.100988138538534[/C][/ROW]
[ROW][C]245[/C][C]0.957616314536607[/C][C]0.0847673709267857[/C][C]0.0423836854633929[/C][/ROW]
[ROW][C]246[/C][C]0.9346675136511[/C][C]0.1306649726978[/C][C]0.0653324863488999[/C][/ROW]
[ROW][C]247[/C][C]0.903915580969042[/C][C]0.192168838061917[/C][C]0.0960844190309584[/C][/ROW]
[ROW][C]248[/C][C]0.979379040713333[/C][C]0.0412419185733343[/C][C]0.0206209592866671[/C][/ROW]
[ROW][C]249[/C][C]0.962641145972924[/C][C]0.0747177080541513[/C][C]0.0373588540270757[/C][/ROW]
[ROW][C]250[/C][C]0.952463631516753[/C][C]0.0950727369664936[/C][C]0.0475363684832468[/C][/ROW]
[ROW][C]251[/C][C]0.918361400975135[/C][C]0.16327719804973[/C][C]0.0816385990248649[/C][/ROW]
[ROW][C]252[/C][C]0.930310479911219[/C][C]0.139379040177561[/C][C]0.0696895200887807[/C][/ROW]
[ROW][C]253[/C][C]0.89241823975943[/C][C]0.215163520481141[/C][C]0.10758176024057[/C][/ROW]
[ROW][C]254[/C][C]0.814292511773322[/C][C]0.371414976453355[/C][C]0.185707488226678[/C][/ROW]
[ROW][C]255[/C][C]0.692781847014338[/C][C]0.614436305971325[/C][C]0.307218152985662[/C][/ROW]
[ROW][C]256[/C][C]0.524473394031581[/C][C]0.951053211936838[/C][C]0.475526605968419[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9296069728577870.1407860542844250.0703930271422126
90.8665511639553690.2668976720892620.133448836044631
100.8467806898099110.3064386203801770.153219310190089
110.7803930772756170.4392138454487660.219606922724383
120.9210978388701320.1578043222597350.0789021611298676
130.8778825896182130.2442348207635740.122117410381787
140.8746718867376060.2506562265247890.125328113262394
150.8930892778816860.2138214442366280.106910722118314
160.9219604146751630.1560791706496740.0780395853248369
170.8995750114810370.2008499770379250.100424988518963
180.9442347628646840.1115304742706320.0557652371353159
190.9309844332361780.1380311335276440.0690155667638219
200.9476476570250820.1047046859498350.0523523429749175
210.9353605059945630.1292789880108740.0646394940054369
220.9110568896911010.1778862206177980.088943110308899
230.9357977143094590.1284045713810810.0642022856905407
240.9342154442246870.1315691115506250.0657845557753127
250.9483848351475050.103230329704990.051615164852495
260.9324340178942670.1351319642114660.0675659821057328
270.9183559003177130.1632881993645730.0816440996822867
280.8948315874164140.2103368251671710.105168412583586
290.8842740200160170.2314519599679660.115725979983983
300.8552916220896050.2894167558207910.144708377910395
310.8243280774694360.3513438450611290.175671922530564
320.8278580682190570.3442838635618850.172141931780943
330.7928337496763480.4143325006473050.207166250323652
340.821462614829960.3570747703400790.17853738517004
350.7846889865731060.4306220268537880.215311013426894
360.7441597396355440.5116805207289120.255840260364456
370.7034223976256950.593155204748610.296577602374305
380.6569311555485230.6861376889029550.343068844451477
390.6115666571839570.7768666856320860.388433342816043
400.5671262141289310.8657475717421390.432873785871069
410.5161719341010940.9676561317978110.483828065898906
420.4787097293707440.9574194587414870.521290270629256
430.715782979435360.5684340411292810.28421702056464
440.776285027260820.4474299454783610.22371497273918
450.7418934975520940.5162130048958130.258106502447906
460.7033569893755050.5932860212489890.296643010624495
470.68673191975140.6265361604972010.3132680802486
480.6506166845852110.6987666308295770.349383315414789
490.635287728679270.729424542641460.36471227132073
500.5960747684033630.8078504631932740.403925231596637
510.5516875706814140.8966248586371710.448312429318586
520.5079619529686610.9840760940626770.492038047031339
530.4656389719526960.9312779439053930.534361028047304
540.4410246666530320.8820493333060630.558975333346968
550.4114667618544240.8229335237088470.588533238145576
560.422031824401580.844063648803160.57796817559842
570.3874303399126010.7748606798252020.612569660087399
580.3607446777781070.7214893555562140.639255322221893
590.3226774291649330.6453548583298660.677322570835067
600.2872389536435430.5744779072870860.712761046356457
610.2849048115668440.5698096231336890.715095188433155
620.2627659995707010.5255319991414020.737234000429299
630.2557574704872490.5115149409744970.744242529512751
640.3158181028641830.6316362057283650.684181897135817
650.3846417287679880.7692834575359750.615358271232012
660.3739372192596260.7478744385192520.626062780740374
670.3360957136653050.6721914273306110.663904286334695
680.3023996033565420.6047992067130840.697600396643458
690.2866113774383420.5732227548766840.713388622561658
700.2539437205940220.5078874411880440.746056279405978
710.2242964687810980.4485929375621960.775703531218902
720.243059017141050.48611803428210.75694098285895
730.2557519221428510.5115038442857010.744248077857149
740.2335678836652270.4671357673304550.766432116334772
750.2409538482616880.4819076965233760.759046151738312
760.2887569448174440.5775138896348890.711243055182556
770.2571386995081250.514277399016250.742861300491875
780.2275168247047220.4550336494094440.772483175295278
790.2161155178322110.4322310356644230.783884482167789
800.353513208472310.707026416944620.64648679152769
810.3409647378850860.6819294757701720.659035262114914
820.3107176085450240.6214352170900480.689282391454976
830.2785807507984180.5571615015968370.721419249201582
840.2487612292041110.4975224584082230.751238770795889
850.2363193093237560.4726386186475120.763680690676244
860.2202169690308090.4404339380616180.779783030969191
870.1947609884912010.3895219769824020.805239011508799
880.2243823320370260.4487646640740510.775617667962974
890.2125689808705750.4251379617411510.787431019129425
900.1928042947768170.3856085895536340.807195705223183
910.2438963569144980.4877927138289960.756103643085502
920.2296477558516960.4592955117033920.770352244148304
930.2575551632380770.5151103264761540.742444836761923
940.2567186706241580.5134373412483170.743281329375842
950.2467917431455710.4935834862911430.753208256854429
960.2188229511459460.4376459022918920.781177048854054
970.2029039984432950.405807996886590.797096001556705
980.1955362120687560.3910724241375130.804463787931244
990.1749380999346920.3498761998693840.825061900065308
1000.1539103683640530.3078207367281050.846089631635947
1010.1377386255090510.2754772510181020.862261374490949
1020.1499568086731050.2999136173462110.850043191326895
1030.1375286062304080.2750572124608170.862471393769592
1040.1437171982794880.2874343965589770.856282801720512
1050.1241323616516070.2482647233032150.875867638348393
1060.1080109025068530.2160218050137050.891989097493147
1070.09235306637095520.184706132741910.907646933629045
1080.1178224966421930.2356449932843870.882177503357807
1090.1433300313208590.2866600626417190.856669968679141
1100.1754316182675540.3508632365351090.824568381732446
1110.1565930671006550.3131861342013110.843406932899345
1120.1912300318607930.3824600637215850.808769968139207
1130.4698969882690020.9397939765380040.530103011730998
1140.4419648225631090.8839296451262190.558035177436891
1150.4516747417272460.9033494834544930.548325258272754
1160.5220286867522150.955942626495570.477971313247785
1170.4914503848759060.9829007697518130.508549615124094
1180.4686104230262820.9372208460525640.531389576973718
1190.5775740941736540.8448518116526910.422425905826346
1200.6250109599953980.7499780800092040.374989040004602
1210.6030469618427570.7939060763144870.396953038157243
1220.6454697634798690.7090604730402610.354530236520131
1230.6139495581453410.7721008837093190.386050441854659
1240.5799157469508510.8401685060982990.420084253049149
1250.5531803288397610.8936393423204780.446819671160239
1260.5861187973592920.8277624052814160.413881202640708
1270.5541520608069570.8916958783860860.445847939193043
1280.5477182677798710.9045634644402570.452281732220129
1290.5127819238079610.9744361523840790.487218076192039
1300.4780810149218230.9561620298436460.521918985078177
1310.4463873118926530.8927746237853070.553612688107347
1320.4152162176701880.8304324353403750.584783782329812
1330.3833097404236160.7666194808472320.616690259576384
1340.3508403582471740.7016807164943470.649159641752826
1350.3966728107809360.7933456215618720.603327189219064
1360.370084285650110.740168571300220.62991571434989
1370.3552805972586160.7105611945172320.644719402741384
1380.3829967987525540.7659935975051080.617003201247446
1390.3729098967758960.7458197935517920.627090103224104
1400.3420781704242210.6841563408484420.657921829575779
1410.4312400210947840.8624800421895680.568759978905216
1420.4793279305030340.9586558610060670.520672069496966
1430.5032192956009930.9935614087980140.496780704399007
1440.4847903591569490.9695807183138970.515209640843051
1450.5480531996811890.9038936006376220.451946800318811
1460.5215360354065510.9569279291868970.478463964593449
1470.5409349637790060.9181300724419890.459065036220994
1480.5072186659692230.9855626680615530.492781334030777
1490.4981470531026970.9962941062053930.501852946897303
1500.5226362266362990.9547275467274020.477363773363701
1510.5191941486252650.9616117027494690.480805851374735
1520.4918471412440650.983694282488130.508152858755935
1530.4595339359535060.9190678719070110.540466064046494
1540.4292694563716460.8585389127432930.570730543628354
1550.4552095409924860.9104190819849710.544790459007514
1560.516966184015590.966067631968820.48303381598441
1570.4824090059587990.9648180119175990.517590994041201
1580.4690516514234730.9381033028469450.530948348576527
1590.4636060483805870.9272120967611750.536393951619413
1600.5409135941366920.9181728117266170.459086405863308
1610.5049179424446480.9901641151107050.495082057555352
1620.4916234468497360.9832468936994710.508376553150264
1630.5229643182410120.9540713635179750.477035681758988
1640.5919218375332340.8161563249335330.408078162466766
1650.5632119420018160.8735761159963680.436788057998184
1660.5364286523607550.9271426952784910.463571347639245
1670.5145374594549540.9709250810900930.485462540545046
1680.4984158670647620.9968317341295250.501584132935238
1690.5196044756448140.9607910487103720.480395524355186
1700.4983370469469040.9966740938938070.501662953053096
1710.4865797682704220.9731595365408440.513420231729578
1720.4501165517107630.9002331034215260.549883448289237
1730.4223746029518890.8447492059037770.577625397048111
1740.3950564343987390.7901128687974780.604943565601261
1750.3632525510449850.7265051020899710.636747448955015
1760.3315283742535430.6630567485070860.668471625746457
1770.2979610552783620.5959221105567240.702038944721638
1780.2731465972385480.5462931944770960.726853402761452
1790.2520390759926570.5040781519853130.747960924007343
1800.224921524727580.4498430494551610.77507847527242
1810.3207707474025450.6415414948050890.679229252597455
1820.3024399364318180.6048798728636370.697560063568182
1830.2753406775915970.5506813551831930.724659322408403
1840.2541040082705220.5082080165410440.745895991729478
1850.3507000372750520.7014000745501050.649299962724948
1860.3581239435898520.7162478871797030.641876056410148
1870.3271264049811860.6542528099623720.672873595018814
1880.3764373129598680.7528746259197360.623562687040132
1890.4020259772946350.8040519545892690.597974022705365
1900.3678230356986210.7356460713972420.632176964301379
1910.7771058681964250.4457882636071510.222894131803575
1920.7504482867871150.499103426425770.249551713212885
1930.7173764459415230.5652471081169540.282623554058477
1940.7073106570012350.585378685997530.292689342998765
1950.6739303094111080.6521393811777850.326069690588892
1960.6477982512386260.7044034975227470.352201748761374
1970.7135643213510310.5728713572979380.286435678648969
1980.7702503847376070.4594992305247870.229749615262393
1990.7384957751462110.5230084497075770.261504224853789
2000.7232280897413810.5535438205172380.276771910258619
2010.6995435926456870.6009128147086260.300456407354313
2020.661278553487430.677442893025140.33872144651257
2030.6260617431643660.7478765136712680.373938256835634
2040.677652154426330.644695691147340.32234784557367
2050.6390484465353610.7219031069292790.360951553464639
2060.5982137143955150.803572571208970.401786285604485
2070.6076523445710070.7846953108579860.392347655428993
2080.7127583282412580.5744833435174850.287241671758742
2090.6730375956412190.6539248087175630.326962404358781
2100.6959810976699510.6080378046600980.304018902330049
2110.6572625942688190.6854748114623620.342737405731181
2120.6243623540820150.7512752918359710.375637645917985
2130.7430574240382290.5138851519235430.256942575961771
2140.7449591831902250.5100816336195490.255040816809775
2150.7054384524188040.5891230951623920.294561547581196
2160.6647982975948430.6704034048103130.335201702405157
2170.6917128336129930.6165743327740130.308287166387007
2180.7017069804267120.5965860391465760.298293019573288
2190.6578546787697180.6842906424605640.342145321230282
2200.6792777026988250.641444594602350.320722297301175
2210.6549826458196990.6900347083606010.345017354180301
2220.7039603992586530.5920792014826950.296039600741347
2230.6957217214428430.6085565571143140.304278278557157
2240.6486934021104180.7026131957791650.351306597889582
2250.8818691472360.2362617055280.118130852764
2260.8563760565510180.2872478868979640.143623943448982
2270.9101753328463270.1796493343073470.0898246671536733
2280.9071786914888920.1856426170222160.0928213085111081
2290.8925817008620880.2148365982758240.107418299137912
2300.8639898181898870.2720203636202260.136010181810113
2310.8290910600191170.3418178799617660.170908939980883
2320.7928686362438380.4142627275123250.207131363756162
2330.9171979770346070.1656040459307870.0828020229653934
2340.9238745701612040.1522508596775920.0761254298387961
2350.9012799187838430.1974401624323130.0987200812161567
2360.8743319718363660.2513360563272690.125668028163634
2370.9004211665297380.1991576669405250.0995788334702623
2380.9333283973696490.1333432052607030.0666716026303514
2390.9073012285253120.1853975429493770.0926987714746884
2400.8736540799448020.2526918401103960.126345920055198
2410.9128060271644390.1743879456711220.0871939728355611
2420.9334970670514040.1330058658971920.066502932948596
2430.9251933734739050.149613253052190.0748066265260952
2440.8990118614614660.2019762770770690.100988138538534
2450.9576163145366070.08476737092678570.0423836854633929
2460.93466751365110.13066497269780.0653324863488999
2470.9039155809690420.1921688380619170.0960844190309584
2480.9793790407133330.04124191857333430.0206209592866671
2490.9626411459729240.07471770805415130.0373588540270757
2500.9524636315167530.09507273696649360.0475363684832468
2510.9183614009751350.163277198049730.0816385990248649
2520.9303104799112190.1393790401775610.0696895200887807
2530.892418239759430.2151635204811410.10758176024057
2540.8142925117733220.3714149764533550.185707488226678
2550.6927818470143380.6144363059713250.307218152985662
2560.5244733940315810.9510532119368380.475526605968419






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00401606425702811OK
10% type I error level40.0160642570281124OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00401606425702811 & OK \tabularnewline
10% type I error level & 4 & 0.0160642570281124 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226563&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00401606425702811[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0160642570281124[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226563&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226563&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00401606425702811OK
10% type I error level40.0160642570281124OK


Figures (Output of Computation)

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Input Parameters & R Code

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