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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, 29 Nov 2011 11:14:47 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322583313iw5s755ukb1autg.htm/, Retrieved Thu, 31 Oct 2024 23:10:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148588, Retrieved Thu, 31 Oct 2024 23:10:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Colombia Coffee -...] [2008-02-26 10:22:06] [74be16979710d4c4e7c6647856088456]
- RMPD    [Multiple Regression] [] [2011-11-29 16:14:47] [b95d9398d16c17c54b557e3a52d461f1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12
39	32	16	11	18	11
30	35	19	15	11	14
31	33	15	6	12	12
34	37	14	13	16	21
35	29	13	10	18	12
39	31	19	12	14	22
34	36	15	14	14	11
36	35	14	12	15	10
37	38	15	6	15	13
38	31	16	10	17	10
36	34	16	12	19	8
38	35	16	12	10	15
39	38	16	11	16	14
33	37	17	15	18	10
32	33	15	12	14	14
36	32	15	10	14	14
38	38	20	12	17	11
39	38	18	11	14	10
32	32	16	12	16	13
32	33	16	11	18	7
31	31	16	12	11	14
39	38	19	13	14	12
37	39	16	11	12	14
39	32	17	9	17	11
41	32	17	13	9	9
36	35	16	10	16	11
33	37	15	14	14	15
33	33	16	12	15	14
34	33	14	10	11	13
31	28	15	12	16	9
27	32	12	8	13	15
37	31	14	10	17	10
34	37	16	12	15	11
34	30	14	12	14	13
32	33	7	7	16	8
29	31	10	6	9	20
36	33	14	12	15	12
29	31	16	10	17	10
35	33	16	10	13	10
37	32	16	10	15	9
34	33	14	12	16	14
38	32	20	15	16	8
35	33	14	10	12	14
38	28	14	10	12	11
37	35	11	12	11	13
38	39	14	13	15	9
33	34	15	11	15	11
36	38	16	11	17	15
38	32	14	12	13	11
32	38	16	14	16	10
32	30	14	10	14	14
32	33	12	12	11	18
34	38	16	13	12	14
32	32	9	5	12	11
37	32	14	6	15	12
39	34	16	12	16	13
29	34	16	12	15	9
37	36	15	11	12	10
35	34	16	10	12	15
30	28	12	7	8	20
38	34	16	12	13	12
34	35	16	14	11	12
31	35	14	11	14	14
34	31	16	12	15	13
35	37	17	13	10	11
36	35	18	14	11	17
30	27	18	11	12	12
39	40	12	12	15	13
35	37	16	12	15	14
38	36	10	8	14	13
31	38	14	11	16	15
34	39	18	14	15	13
38	41	18	14	15	10
34	27	16	12	13	11
39	30	17	9	12	19
37	37	16	13	17	13
34	31	16	11	13	17
28	31	13	12	15	13
37	27	16	12	13	9
33	36	16	12	15	11
37	38	20	12	16	10
35	37	16	12	15	9
37	33	15	12	16	12
32	34	15	11	15	12
33	31	16	10	14	13
38	39	14	9	15	13
33	34	16	12	14	12
29	32	16	12	13	15
33	33	15	12	7	22
31	36	12	9	17	13
36	32	17	15	13	15
35	41	16	12	15	13
32	28	15	12	14	15
29	30	13	12	13	10
39	36	16	10	16	11
37	35	16	13	12	16
35	31	16	9	14	11
37	34	16	12	17	11
32	36	14	10	15	10
38	36	16	14	17	10
37	35	16	11	12	16
36	37	20	15	16	12
32	28	15	11	11	11
33	39	16	11	15	16
40	32	13	12	9	19
38	35	17	12	16	11
41	39	16	12	15	16
36	35	16	11	10	15
43	42	12	7	10	24
30	34	16	12	15	14
31	33	16	14	11	15
32	41	17	11	13	11
32	33	13	11	14	15
37	34	12	10	18	12
37	32	18	13	16	10
33	40	14	13	14	14
34	40	14	8	14	13
33	35	13	11	14	9
38	36	16	12	14	15
33	37	13	11	12	15
31	27	16	13	14	14
38	39	13	12	15	11
37	38	16	14	15	8
33	31	15	13	15	11
31	33	16	15	13	11
39	32	15	10	17	8
44	39	17	11	17	10
33	36	15	9	19	11
35	33	12	11	15	13
32	33	16	10	13	11
28	32	10	11	9	20
40	37	16	8	15	10
27	30	12	11	15	15
37	38	14	12	15	12
32	29	15	12	16	14
28	22	13	9	11	23
34	35	15	11	14	14
30	35	11	10	11	16
35	34	12	8	15	11
31	35	8	9	13	12
32	34	16	8	15	10
30	34	15	9	16	14
30	35	17	15	14	12
31	23	16	11	15	12
40	31	10	8	16	11
32	27	18	13	16	12
36	36	13	12	11	13
32	31	16	12	12	11
35	32	13	9	9	19
38	39	10	7	16	12
42	37	15	13	13	17
34	38	16	9	16	9
35	39	16	6	12	12
35	34	14	8	9	19
33	31	10	8	13	18
36	32	17	15	13	15
32	37	13	6	14	14
33	36	15	9	19	11
34	32	16	11	13	9
32	35	12	8	12	18
34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&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 time7 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 19.6760915648946 + 0.330950712702927Separate[t] + 0.328569361683998Learning[t] -0.13956585228506Software[t] + 0.0538885656668382Happiness[t] -0.0357108316548837Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  19.6760915648946 +  0.330950712702927Separate[t] +  0.328569361683998Learning[t] -0.13956585228506Software[t] +  0.0538885656668382Happiness[t] -0.0357108316548837Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  19.6760915648946 +  0.330950712702927Separate[t] +  0.328569361683998Learning[t] -0.13956585228506Software[t] +  0.0538885656668382Happiness[t] -0.0357108316548837Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&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
Connected[t] = + 19.6760915648946 + 0.330950712702927Separate[t] + 0.328569361683998Learning[t] -0.13956585228506Software[t] + 0.0538885656668382Happiness[t] -0.0357108316548837Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19.67609156489463.7798595.20551e-060
Separate0.3309507127029270.0699034.73445e-062e-06
Learning0.3285693616839980.1324182.48130.0141510.007075
Software-0.139565852285060.136625-1.02150.3085860.154293
Happiness0.05388856566683820.1263430.42650.6703120.335156
Depression-0.03571083165488370.093462-0.38210.7029150.351458

\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) & 19.6760915648946 & 3.779859 & 5.2055 & 1e-06 & 0 \tabularnewline
Separate & 0.330950712702927 & 0.069903 & 4.7344 & 5e-06 & 2e-06 \tabularnewline
Learning & 0.328569361683998 & 0.132418 & 2.4813 & 0.014151 & 0.007075 \tabularnewline
Software & -0.13956585228506 & 0.136625 & -1.0215 & 0.308586 & 0.154293 \tabularnewline
Happiness & 0.0538885656668382 & 0.126343 & 0.4265 & 0.670312 & 0.335156 \tabularnewline
Depression & -0.0357108316548837 & 0.093462 & -0.3821 & 0.702915 & 0.351458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&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]19.6760915648946[/C][C]3.779859[/C][C]5.2055[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Separate[/C][C]0.330950712702927[/C][C]0.069903[/C][C]4.7344[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.328569361683998[/C][C]0.132418[/C][C]2.4813[/C][C]0.014151[/C][C]0.007075[/C][/ROW]
[ROW][C]Software[/C][C]-0.13956585228506[/C][C]0.136625[/C][C]-1.0215[/C][C]0.308586[/C][C]0.154293[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0538885656668382[/C][C]0.126343[/C][C]0.4265[/C][C]0.670312[/C][C]0.335156[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0357108316548837[/C][C]0.093462[/C][C]-0.3821[/C][C]0.702915[/C][C]0.351458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&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)19.67609156489463.7798595.20551e-060
Separate0.3309507127029270.0699034.73445e-062e-06
Learning0.3285693616839980.1324182.48130.0141510.007075
Software-0.139565852285060.136625-1.02150.3085860.154293
Happiness0.05388856566683820.1263430.42650.6703120.335156
Depression-0.03571083165488370.093462-0.38210.7029150.351458






Multiple Linear Regression - Regression Statistics
Multiple R0.423104717147008
R-squared0.179017601672049
Adjusted R-squared0.152704063264102
F-TEST (value)6.80325081700084
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value9.10900633999123e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10676170912486
Sum Squared Residuals1505.70705749637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.423104717147008 \tabularnewline
R-squared & 0.179017601672049 \tabularnewline
Adjusted R-squared & 0.152704063264102 \tabularnewline
F-TEST (value) & 6.80325081700084 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 9.10900633999123e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.10676170912486 \tabularnewline
Sum Squared Residuals & 1505.70705749637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.423104717147008[/C][/ROW]
[ROW][C]R-squared[/C][C]0.179017601672049[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.152704063264102[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.80325081700084[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]9.10900633999123e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.10676170912486[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1505.70705749637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&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 R0.423104717147008
R-squared0.179017601672049
Adjusted R-squared0.152704063264102
F-TEST (value)6.80325081700084
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value9.10900633999123e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10676170912486
Sum Squared Residuals1505.70705749637






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.17474006155425.82525993844578
23934.56557481699594.43442518300412
33035.5015191763839-5.50151917638389
43134.9067432037842-3.90674320378419
53434.8191725046899-0.819172504689881
63532.69086961446532.30913038553472
73934.47239292618884.52760707381119
83434.9265564866011-0.926556486601051
93634.6357675141061.36423248589403
103736.68745163264450.312548367355541
113834.35601222256613.64398777743394
123635.24893145074820.751068549251842
133834.84490925086543.15509074913464
143936.33636946691512.66363053308489
153336.0263451647091-3.02634516470915
163234.1057035580977-2.10570355809774
173634.05388454996491.94611545003507
183837.67210212199750.327897878002472
193937.0285743855691.97142561443104
203234.2468101700674-2.24681017006737
213235.0393688563183-3.03936885631834
223133.6107057973754-2.61070579737537
233937.00659037937311.99340962062693
243736.45176591695070.548234083049317
253935.11938731758323.88061268241684
264134.2014370464186.79856295358202
273635.5902156760560.40978432394396
283335.1146638726844-2.11466387268444
293334.4881614854486-1.48816148544858
303433.93031103563820.069688964361767
313132.7372812841912-1.7372812841912
322733.2577087721613-6.25770877216134
333733.69887349919813.30112650080194
343435.9190968312249-1.91909683122493
353432.81999288995981.18000711004015
363232.497020047314-0.497020047314041
372932.1546426197188-3.15464261971877
383633.90244442539032.09755557460965
392934.3560122225661-5.35601222256605
403534.80235938530460.197640614695444
413734.61489663559022.38510336440981
423433.88491132774740.115088672252581
433835.32094421822262.6790557817774
443533.94848876965021.05151123034981
453832.40086770110025.5991322988998
463733.3273726714223.67262732857803
473835.85571534428752.1442846557125
483334.7372411837172-1.73724118371722
493636.3545472009271-0.354547200927063
503833.49942741300864.50057258699137
513236.0605152366795-4.06051523667946
523233.0634137628751-1.06341376287508
533232.8154864494257-0.815486449425699
543435.8416834996776-1.84168349967764
553232.7796530049172-0.779653004917226
563734.40888882639782.59111117360222
573934.90871159547324.09128840452677
582934.9976663564259-5.99766635642592
593735.27318774377741.72681225622256
603534.90086737406620.0991326259337744
613031.6254747870261-1.62547478702608
623834.78275673012763.21724326987241
633434.7267986069267-0.726798606926724
643134.5786014741047-3.57860147410466
653433.86197089169760.138029108302394
663535.8386575122897-0.83865751228968
673635.20538317202030.7946168279797
683033.2089177511933-3.20891775119332
693935.5262498592883.47375014071204
703535.8119643362603-0.811964336260284
713834.04968312858173.95031687141834
723135.6435199118922-4.64351991189223
733436.8875836121189-2.8875836121189
743837.65661753248940.3433824675106
753432.5018125728621.49818742713801
763933.9023564106045.09764358939596
773735.81588644696381.18411355303622
783433.75091628602950.249083713970544
792832.8762628066456-4.87626280664561
803732.57323423617184.42676576382824
813335.588146118522-2.58814611852201
823737.6539243879856-0.653924387985574
833535.9905184945347-0.990518494534702
843734.28490235274122.71509764725882
853234.7015303520623-2.70153035206233
863334.0872140306009-1.08721403060089
873836.27113542680821.72886457319179
883334.8366452957944-1.83664529579443
892934.0137228097571-5.01372280975709
903333.4427969451908-0.442796945190804
913134.7289216966651-3.72892169666511
923633.92359461458592.07640538541409
933537.1714780187269-2.17147801872688
943232.4152391629282-0.415239162928223
952932.5446674575737-3.54466745757366
963935.9211663887593.07883361124103
973734.77740969825912.22259030174091
983534.29820154619570.701798453804283
993735.03402182444981.96597817555017
1003235.245849931379-3.24584993137902
1013835.45250237694052.54749762305955
1023735.05654140282921.94345859717079
1033636.8328544551177-0.832854455117698
1043232.5359826448323-0.535982644832303
1053336.5420099506414-3.54200995064143
1064032.66961713541827.3303828645818
1073835.63965333316992.36034666683008
1084136.40244409835644.59755590164363
1093634.98447510315041.01552489684958
1104336.22371856958126.7762814304188
1113034.8191121981515-4.8191121981515
1123133.9577646865562-2.95776468655622
1133237.603257764672-5.60325776467202
1143233.5524198553599-1.55241985535992
1153734.01705381629592.98294618370408
1163734.8715155361152.12848446388504
1173335.9542233330492-2.95422333304917
1183436.6877634261294-2.68776342612936
1193334.4285862706951-1.42858627069508
1203835.39141422623562.60858577376436
1213334.768445574838-1.76844557483795
1223132.3090027912791-1.30900279127912
1233835.59529017157882.4047098284212
1243736.07804833432240.921951665677608
1253333.4652573410383-0.465257341038315
1263134.0688192922244-3.06881929222437
1273934.42981523689484.57018476310525
1284437.19262143358846.80737856641159
1293335.8938285763605-2.89382857636054
1303533.34916072265251.65083927734747
1313234.7666485536497-2.76664855364967
1322831.7877640709964-3.78776407099639
1334036.51307107202013.48692892797994
1342732.284886921234-5.28488692123398
1353735.5571979889051.44280201109502
1363232.8896778386197-0.88967783861971
1372829.7437413699583-1.74374136995826
1383434.9071708357887-0.907170835788654
1393033.4993718810274-3.49937188102744
1403534.17023065552040.829769344479594
1413132.9038501062137-1.90385010621372
1423235.5202189339113-3.52021893391128
1433034.9631289589895-4.96312895898952
1443035.0774678133262-5.07746781332617
1453131.3896418740141-0.389641874014135
1464032.57412835971057.42587164028953
1473233.1453403092906-1.14534030929056
1483634.31546210749291.68453789250711
1493233.7717268580069-1.77172685800686
1503533.08831469227341.91168530772661
1513835.32558908196412.67441091803594
1524235.12891949599296.8710805040071
1533436.7940553297597-2.79405532975965
1543537.2210168416858-2.22101684168575
1553534.21835133164830.781648668351698
1563332.16248684112580.837513158874232
1573633.92359461458592.07640538541409
1583235.6097627992518-3.60976279925182
1593335.8938285763605-2.89382857636054
1603434.3675536519715-0.367553651971452
1613234.0895398496386-2.08953984963863
1623434.8743701530022-0.874370153002162

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.1747400615542 & 5.82525993844578 \tabularnewline
2 & 39 & 34.5655748169959 & 4.43442518300412 \tabularnewline
3 & 30 & 35.5015191763839 & -5.50151917638389 \tabularnewline
4 & 31 & 34.9067432037842 & -3.90674320378419 \tabularnewline
5 & 34 & 34.8191725046899 & -0.819172504689881 \tabularnewline
6 & 35 & 32.6908696144653 & 2.30913038553472 \tabularnewline
7 & 39 & 34.4723929261888 & 4.52760707381119 \tabularnewline
8 & 34 & 34.9265564866011 & -0.926556486601051 \tabularnewline
9 & 36 & 34.635767514106 & 1.36423248589403 \tabularnewline
10 & 37 & 36.6874516326445 & 0.312548367355541 \tabularnewline
11 & 38 & 34.3560122225661 & 3.64398777743394 \tabularnewline
12 & 36 & 35.2489314507482 & 0.751068549251842 \tabularnewline
13 & 38 & 34.8449092508654 & 3.15509074913464 \tabularnewline
14 & 39 & 36.3363694669151 & 2.66363053308489 \tabularnewline
15 & 33 & 36.0263451647091 & -3.02634516470915 \tabularnewline
16 & 32 & 34.1057035580977 & -2.10570355809774 \tabularnewline
17 & 36 & 34.0538845499649 & 1.94611545003507 \tabularnewline
18 & 38 & 37.6721021219975 & 0.327897878002472 \tabularnewline
19 & 39 & 37.028574385569 & 1.97142561443104 \tabularnewline
20 & 32 & 34.2468101700674 & -2.24681017006737 \tabularnewline
21 & 32 & 35.0393688563183 & -3.03936885631834 \tabularnewline
22 & 31 & 33.6107057973754 & -2.61070579737537 \tabularnewline
23 & 39 & 37.0065903793731 & 1.99340962062693 \tabularnewline
24 & 37 & 36.4517659169507 & 0.548234083049317 \tabularnewline
25 & 39 & 35.1193873175832 & 3.88061268241684 \tabularnewline
26 & 41 & 34.201437046418 & 6.79856295358202 \tabularnewline
27 & 36 & 35.590215676056 & 0.40978432394396 \tabularnewline
28 & 33 & 35.1146638726844 & -2.11466387268444 \tabularnewline
29 & 33 & 34.4881614854486 & -1.48816148544858 \tabularnewline
30 & 34 & 33.9303110356382 & 0.069688964361767 \tabularnewline
31 & 31 & 32.7372812841912 & -1.7372812841912 \tabularnewline
32 & 27 & 33.2577087721613 & -6.25770877216134 \tabularnewline
33 & 37 & 33.6988734991981 & 3.30112650080194 \tabularnewline
34 & 34 & 35.9190968312249 & -1.91909683122493 \tabularnewline
35 & 34 & 32.8199928899598 & 1.18000711004015 \tabularnewline
36 & 32 & 32.497020047314 & -0.497020047314041 \tabularnewline
37 & 29 & 32.1546426197188 & -3.15464261971877 \tabularnewline
38 & 36 & 33.9024444253903 & 2.09755557460965 \tabularnewline
39 & 29 & 34.3560122225661 & -5.35601222256605 \tabularnewline
40 & 35 & 34.8023593853046 & 0.197640614695444 \tabularnewline
41 & 37 & 34.6148966355902 & 2.38510336440981 \tabularnewline
42 & 34 & 33.8849113277474 & 0.115088672252581 \tabularnewline
43 & 38 & 35.3209442182226 & 2.6790557817774 \tabularnewline
44 & 35 & 33.9484887696502 & 1.05151123034981 \tabularnewline
45 & 38 & 32.4008677011002 & 5.5991322988998 \tabularnewline
46 & 37 & 33.327372671422 & 3.67262732857803 \tabularnewline
47 & 38 & 35.8557153442875 & 2.1442846557125 \tabularnewline
48 & 33 & 34.7372411837172 & -1.73724118371722 \tabularnewline
49 & 36 & 36.3545472009271 & -0.354547200927063 \tabularnewline
50 & 38 & 33.4994274130086 & 4.50057258699137 \tabularnewline
51 & 32 & 36.0605152366795 & -4.06051523667946 \tabularnewline
52 & 32 & 33.0634137628751 & -1.06341376287508 \tabularnewline
53 & 32 & 32.8154864494257 & -0.815486449425699 \tabularnewline
54 & 34 & 35.8416834996776 & -1.84168349967764 \tabularnewline
55 & 32 & 32.7796530049172 & -0.779653004917226 \tabularnewline
56 & 37 & 34.4088888263978 & 2.59111117360222 \tabularnewline
57 & 39 & 34.9087115954732 & 4.09128840452677 \tabularnewline
58 & 29 & 34.9976663564259 & -5.99766635642592 \tabularnewline
59 & 37 & 35.2731877437774 & 1.72681225622256 \tabularnewline
60 & 35 & 34.9008673740662 & 0.0991326259337744 \tabularnewline
61 & 30 & 31.6254747870261 & -1.62547478702608 \tabularnewline
62 & 38 & 34.7827567301276 & 3.21724326987241 \tabularnewline
63 & 34 & 34.7267986069267 & -0.726798606926724 \tabularnewline
64 & 31 & 34.5786014741047 & -3.57860147410466 \tabularnewline
65 & 34 & 33.8619708916976 & 0.138029108302394 \tabularnewline
66 & 35 & 35.8386575122897 & -0.83865751228968 \tabularnewline
67 & 36 & 35.2053831720203 & 0.7946168279797 \tabularnewline
68 & 30 & 33.2089177511933 & -3.20891775119332 \tabularnewline
69 & 39 & 35.526249859288 & 3.47375014071204 \tabularnewline
70 & 35 & 35.8119643362603 & -0.811964336260284 \tabularnewline
71 & 38 & 34.0496831285817 & 3.95031687141834 \tabularnewline
72 & 31 & 35.6435199118922 & -4.64351991189223 \tabularnewline
73 & 34 & 36.8875836121189 & -2.8875836121189 \tabularnewline
74 & 38 & 37.6566175324894 & 0.3433824675106 \tabularnewline
75 & 34 & 32.501812572862 & 1.49818742713801 \tabularnewline
76 & 39 & 33.902356410604 & 5.09764358939596 \tabularnewline
77 & 37 & 35.8158864469638 & 1.18411355303622 \tabularnewline
78 & 34 & 33.7509162860295 & 0.249083713970544 \tabularnewline
79 & 28 & 32.8762628066456 & -4.87626280664561 \tabularnewline
80 & 37 & 32.5732342361718 & 4.42676576382824 \tabularnewline
81 & 33 & 35.588146118522 & -2.58814611852201 \tabularnewline
82 & 37 & 37.6539243879856 & -0.653924387985574 \tabularnewline
83 & 35 & 35.9905184945347 & -0.990518494534702 \tabularnewline
84 & 37 & 34.2849023527412 & 2.71509764725882 \tabularnewline
85 & 32 & 34.7015303520623 & -2.70153035206233 \tabularnewline
86 & 33 & 34.0872140306009 & -1.08721403060089 \tabularnewline
87 & 38 & 36.2711354268082 & 1.72886457319179 \tabularnewline
88 & 33 & 34.8366452957944 & -1.83664529579443 \tabularnewline
89 & 29 & 34.0137228097571 & -5.01372280975709 \tabularnewline
90 & 33 & 33.4427969451908 & -0.442796945190804 \tabularnewline
91 & 31 & 34.7289216966651 & -3.72892169666511 \tabularnewline
92 & 36 & 33.9235946145859 & 2.07640538541409 \tabularnewline
93 & 35 & 37.1714780187269 & -2.17147801872688 \tabularnewline
94 & 32 & 32.4152391629282 & -0.415239162928223 \tabularnewline
95 & 29 & 32.5446674575737 & -3.54466745757366 \tabularnewline
96 & 39 & 35.921166388759 & 3.07883361124103 \tabularnewline
97 & 37 & 34.7774096982591 & 2.22259030174091 \tabularnewline
98 & 35 & 34.2982015461957 & 0.701798453804283 \tabularnewline
99 & 37 & 35.0340218244498 & 1.96597817555017 \tabularnewline
100 & 32 & 35.245849931379 & -3.24584993137902 \tabularnewline
101 & 38 & 35.4525023769405 & 2.54749762305955 \tabularnewline
102 & 37 & 35.0565414028292 & 1.94345859717079 \tabularnewline
103 & 36 & 36.8328544551177 & -0.832854455117698 \tabularnewline
104 & 32 & 32.5359826448323 & -0.535982644832303 \tabularnewline
105 & 33 & 36.5420099506414 & -3.54200995064143 \tabularnewline
106 & 40 & 32.6696171354182 & 7.3303828645818 \tabularnewline
107 & 38 & 35.6396533331699 & 2.36034666683008 \tabularnewline
108 & 41 & 36.4024440983564 & 4.59755590164363 \tabularnewline
109 & 36 & 34.9844751031504 & 1.01552489684958 \tabularnewline
110 & 43 & 36.2237185695812 & 6.7762814304188 \tabularnewline
111 & 30 & 34.8191121981515 & -4.8191121981515 \tabularnewline
112 & 31 & 33.9577646865562 & -2.95776468655622 \tabularnewline
113 & 32 & 37.603257764672 & -5.60325776467202 \tabularnewline
114 & 32 & 33.5524198553599 & -1.55241985535992 \tabularnewline
115 & 37 & 34.0170538162959 & 2.98294618370408 \tabularnewline
116 & 37 & 34.871515536115 & 2.12848446388504 \tabularnewline
117 & 33 & 35.9542233330492 & -2.95422333304917 \tabularnewline
118 & 34 & 36.6877634261294 & -2.68776342612936 \tabularnewline
119 & 33 & 34.4285862706951 & -1.42858627069508 \tabularnewline
120 & 38 & 35.3914142262356 & 2.60858577376436 \tabularnewline
121 & 33 & 34.768445574838 & -1.76844557483795 \tabularnewline
122 & 31 & 32.3090027912791 & -1.30900279127912 \tabularnewline
123 & 38 & 35.5952901715788 & 2.4047098284212 \tabularnewline
124 & 37 & 36.0780483343224 & 0.921951665677608 \tabularnewline
125 & 33 & 33.4652573410383 & -0.465257341038315 \tabularnewline
126 & 31 & 34.0688192922244 & -3.06881929222437 \tabularnewline
127 & 39 & 34.4298152368948 & 4.57018476310525 \tabularnewline
128 & 44 & 37.1926214335884 & 6.80737856641159 \tabularnewline
129 & 33 & 35.8938285763605 & -2.89382857636054 \tabularnewline
130 & 35 & 33.3491607226525 & 1.65083927734747 \tabularnewline
131 & 32 & 34.7666485536497 & -2.76664855364967 \tabularnewline
132 & 28 & 31.7877640709964 & -3.78776407099639 \tabularnewline
133 & 40 & 36.5130710720201 & 3.48692892797994 \tabularnewline
134 & 27 & 32.284886921234 & -5.28488692123398 \tabularnewline
135 & 37 & 35.557197988905 & 1.44280201109502 \tabularnewline
136 & 32 & 32.8896778386197 & -0.88967783861971 \tabularnewline
137 & 28 & 29.7437413699583 & -1.74374136995826 \tabularnewline
138 & 34 & 34.9071708357887 & -0.907170835788654 \tabularnewline
139 & 30 & 33.4993718810274 & -3.49937188102744 \tabularnewline
140 & 35 & 34.1702306555204 & 0.829769344479594 \tabularnewline
141 & 31 & 32.9038501062137 & -1.90385010621372 \tabularnewline
142 & 32 & 35.5202189339113 & -3.52021893391128 \tabularnewline
143 & 30 & 34.9631289589895 & -4.96312895898952 \tabularnewline
144 & 30 & 35.0774678133262 & -5.07746781332617 \tabularnewline
145 & 31 & 31.3896418740141 & -0.389641874014135 \tabularnewline
146 & 40 & 32.5741283597105 & 7.42587164028953 \tabularnewline
147 & 32 & 33.1453403092906 & -1.14534030929056 \tabularnewline
148 & 36 & 34.3154621074929 & 1.68453789250711 \tabularnewline
149 & 32 & 33.7717268580069 & -1.77172685800686 \tabularnewline
150 & 35 & 33.0883146922734 & 1.91168530772661 \tabularnewline
151 & 38 & 35.3255890819641 & 2.67441091803594 \tabularnewline
152 & 42 & 35.1289194959929 & 6.8710805040071 \tabularnewline
153 & 34 & 36.7940553297597 & -2.79405532975965 \tabularnewline
154 & 35 & 37.2210168416858 & -2.22101684168575 \tabularnewline
155 & 35 & 34.2183513316483 & 0.781648668351698 \tabularnewline
156 & 33 & 32.1624868411258 & 0.837513158874232 \tabularnewline
157 & 36 & 33.9235946145859 & 2.07640538541409 \tabularnewline
158 & 32 & 35.6097627992518 & -3.60976279925182 \tabularnewline
159 & 33 & 35.8938285763605 & -2.89382857636054 \tabularnewline
160 & 34 & 34.3675536519715 & -0.367553651971452 \tabularnewline
161 & 32 & 34.0895398496386 & -2.08953984963863 \tabularnewline
162 & 34 & 34.8743701530022 & -0.874370153002162 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&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]41[/C][C]35.1747400615542[/C][C]5.82525993844578[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.5655748169959[/C][C]4.43442518300412[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.5015191763839[/C][C]-5.50151917638389[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.9067432037842[/C][C]-3.90674320378419[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.8191725046899[/C][C]-0.819172504689881[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.6908696144653[/C][C]2.30913038553472[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.4723929261888[/C][C]4.52760707381119[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.9265564866011[/C][C]-0.926556486601051[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.635767514106[/C][C]1.36423248589403[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.6874516326445[/C][C]0.312548367355541[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.3560122225661[/C][C]3.64398777743394[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.2489314507482[/C][C]0.751068549251842[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8449092508654[/C][C]3.15509074913464[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.3363694669151[/C][C]2.66363053308489[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.0263451647091[/C][C]-3.02634516470915[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.1057035580977[/C][C]-2.10570355809774[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.0538845499649[/C][C]1.94611545003507[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.6721021219975[/C][C]0.327897878002472[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]37.028574385569[/C][C]1.97142561443104[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.2468101700674[/C][C]-2.24681017006737[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.0393688563183[/C][C]-3.03936885631834[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.6107057973754[/C][C]-2.61070579737537[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]37.0065903793731[/C][C]1.99340962062693[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.4517659169507[/C][C]0.548234083049317[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.1193873175832[/C][C]3.88061268241684[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.201437046418[/C][C]6.79856295358202[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.590215676056[/C][C]0.40978432394396[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.1146638726844[/C][C]-2.11466387268444[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.4881614854486[/C][C]-1.48816148544858[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.9303110356382[/C][C]0.069688964361767[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.7372812841912[/C][C]-1.7372812841912[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.2577087721613[/C][C]-6.25770877216134[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6988734991981[/C][C]3.30112650080194[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.9190968312249[/C][C]-1.91909683122493[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.8199928899598[/C][C]1.18000711004015[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.497020047314[/C][C]-0.497020047314041[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.1546426197188[/C][C]-3.15464261971877[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.9024444253903[/C][C]2.09755557460965[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3560122225661[/C][C]-5.35601222256605[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.8023593853046[/C][C]0.197640614695444[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.6148966355902[/C][C]2.38510336440981[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.8849113277474[/C][C]0.115088672252581[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.3209442182226[/C][C]2.6790557817774[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.9484887696502[/C][C]1.05151123034981[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.4008677011002[/C][C]5.5991322988998[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.327372671422[/C][C]3.67262732857803[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.8557153442875[/C][C]2.1442846557125[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7372411837172[/C][C]-1.73724118371722[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.3545472009271[/C][C]-0.354547200927063[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.4994274130086[/C][C]4.50057258699137[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0605152366795[/C][C]-4.06051523667946[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0634137628751[/C][C]-1.06341376287508[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.8154864494257[/C][C]-0.815486449425699[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.8416834996776[/C][C]-1.84168349967764[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.7796530049172[/C][C]-0.779653004917226[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4088888263978[/C][C]2.59111117360222[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9087115954732[/C][C]4.09128840452677[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.9976663564259[/C][C]-5.99766635642592[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.2731877437774[/C][C]1.72681225622256[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.9008673740662[/C][C]0.0991326259337744[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.6254747870261[/C][C]-1.62547478702608[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.7827567301276[/C][C]3.21724326987241[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.7267986069267[/C][C]-0.726798606926724[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.5786014741047[/C][C]-3.57860147410466[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.8619708916976[/C][C]0.138029108302394[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.8386575122897[/C][C]-0.83865751228968[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.2053831720203[/C][C]0.7946168279797[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.2089177511933[/C][C]-3.20891775119332[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.526249859288[/C][C]3.47375014071204[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.8119643362603[/C][C]-0.811964336260284[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.0496831285817[/C][C]3.95031687141834[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.6435199118922[/C][C]-4.64351991189223[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.8875836121189[/C][C]-2.8875836121189[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6566175324894[/C][C]0.3433824675106[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.501812572862[/C][C]1.49818742713801[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.902356410604[/C][C]5.09764358939596[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8158864469638[/C][C]1.18411355303622[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7509162860295[/C][C]0.249083713970544[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.8762628066456[/C][C]-4.87626280664561[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.5732342361718[/C][C]4.42676576382824[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.588146118522[/C][C]-2.58814611852201[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.6539243879856[/C][C]-0.653924387985574[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.9905184945347[/C][C]-0.990518494534702[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.2849023527412[/C][C]2.71509764725882[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.7015303520623[/C][C]-2.70153035206233[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.0872140306009[/C][C]-1.08721403060089[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.2711354268082[/C][C]1.72886457319179[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.8366452957944[/C][C]-1.83664529579443[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.0137228097571[/C][C]-5.01372280975709[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.4427969451908[/C][C]-0.442796945190804[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7289216966651[/C][C]-3.72892169666511[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.9235946145859[/C][C]2.07640538541409[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.1714780187269[/C][C]-2.17147801872688[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.4152391629282[/C][C]-0.415239162928223[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.5446674575737[/C][C]-3.54466745757366[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.921166388759[/C][C]3.07883361124103[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.7774096982591[/C][C]2.22259030174091[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.2982015461957[/C][C]0.701798453804283[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.0340218244498[/C][C]1.96597817555017[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.245849931379[/C][C]-3.24584993137902[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.4525023769405[/C][C]2.54749762305955[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.0565414028292[/C][C]1.94345859717079[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.8328544551177[/C][C]-0.832854455117698[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.5359826448323[/C][C]-0.535982644832303[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.5420099506414[/C][C]-3.54200995064143[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.6696171354182[/C][C]7.3303828645818[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6396533331699[/C][C]2.36034666683008[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.4024440983564[/C][C]4.59755590164363[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.9844751031504[/C][C]1.01552489684958[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.2237185695812[/C][C]6.7762814304188[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.8191121981515[/C][C]-4.8191121981515[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.9577646865562[/C][C]-2.95776468655622[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.603257764672[/C][C]-5.60325776467202[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.5524198553599[/C][C]-1.55241985535992[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.0170538162959[/C][C]2.98294618370408[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.871515536115[/C][C]2.12848446388504[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.9542233330492[/C][C]-2.95422333304917[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6877634261294[/C][C]-2.68776342612936[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.4285862706951[/C][C]-1.42858627069508[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.3914142262356[/C][C]2.60858577376436[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.768445574838[/C][C]-1.76844557483795[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3090027912791[/C][C]-1.30900279127912[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.5952901715788[/C][C]2.4047098284212[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.0780483343224[/C][C]0.921951665677608[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4652573410383[/C][C]-0.465257341038315[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.0688192922244[/C][C]-3.06881929222437[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.4298152368948[/C][C]4.57018476310525[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.1926214335884[/C][C]6.80737856641159[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.8938285763605[/C][C]-2.89382857636054[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.3491607226525[/C][C]1.65083927734747[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.7666485536497[/C][C]-2.76664855364967[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.7877640709964[/C][C]-3.78776407099639[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.5130710720201[/C][C]3.48692892797994[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.284886921234[/C][C]-5.28488692123398[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.557197988905[/C][C]1.44280201109502[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.8896778386197[/C][C]-0.88967783861971[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.7437413699583[/C][C]-1.74374136995826[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.9071708357887[/C][C]-0.907170835788654[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.4993718810274[/C][C]-3.49937188102744[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.1702306555204[/C][C]0.829769344479594[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.9038501062137[/C][C]-1.90385010621372[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.5202189339113[/C][C]-3.52021893391128[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.9631289589895[/C][C]-4.96312895898952[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.0774678133262[/C][C]-5.07746781332617[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.3896418740141[/C][C]-0.389641874014135[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.5741283597105[/C][C]7.42587164028953[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1453403092906[/C][C]-1.14534030929056[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.3154621074929[/C][C]1.68453789250711[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.7717268580069[/C][C]-1.77172685800686[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.0883146922734[/C][C]1.91168530772661[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.3255890819641[/C][C]2.67441091803594[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.1289194959929[/C][C]6.8710805040071[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.7940553297597[/C][C]-2.79405532975965[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.2210168416858[/C][C]-2.22101684168575[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.2183513316483[/C][C]0.781648668351698[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.1624868411258[/C][C]0.837513158874232[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.9235946145859[/C][C]2.07640538541409[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.6097627992518[/C][C]-3.60976279925182[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.8938285763605[/C][C]-2.89382857636054[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.3675536519715[/C][C]-0.367553651971452[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.0895398496386[/C][C]-2.08953984963863[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.8743701530022[/C][C]-0.874370153002162[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&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
14135.17474006155425.82525993844578
23934.56557481699594.43442518300412
33035.5015191763839-5.50151917638389
43134.9067432037842-3.90674320378419
53434.8191725046899-0.819172504689881
63532.69086961446532.30913038553472
73934.47239292618884.52760707381119
83434.9265564866011-0.926556486601051
93634.6357675141061.36423248589403
103736.68745163264450.312548367355541
113834.35601222256613.64398777743394
123635.24893145074820.751068549251842
133834.84490925086543.15509074913464
143936.33636946691512.66363053308489
153336.0263451647091-3.02634516470915
163234.1057035580977-2.10570355809774
173634.05388454996491.94611545003507
183837.67210212199750.327897878002472
193937.0285743855691.97142561443104
203234.2468101700674-2.24681017006737
213235.0393688563183-3.03936885631834
223133.6107057973754-2.61070579737537
233937.00659037937311.99340962062693
243736.45176591695070.548234083049317
253935.11938731758323.88061268241684
264134.2014370464186.79856295358202
273635.5902156760560.40978432394396
283335.1146638726844-2.11466387268444
293334.4881614854486-1.48816148544858
303433.93031103563820.069688964361767
313132.7372812841912-1.7372812841912
322733.2577087721613-6.25770877216134
333733.69887349919813.30112650080194
343435.9190968312249-1.91909683122493
353432.81999288995981.18000711004015
363232.497020047314-0.497020047314041
372932.1546426197188-3.15464261971877
383633.90244442539032.09755557460965
392934.3560122225661-5.35601222256605
403534.80235938530460.197640614695444
413734.61489663559022.38510336440981
423433.88491132774740.115088672252581
433835.32094421822262.6790557817774
443533.94848876965021.05151123034981
453832.40086770110025.5991322988998
463733.3273726714223.67262732857803
473835.85571534428752.1442846557125
483334.7372411837172-1.73724118371722
493636.3545472009271-0.354547200927063
503833.49942741300864.50057258699137
513236.0605152366795-4.06051523667946
523233.0634137628751-1.06341376287508
533232.8154864494257-0.815486449425699
543435.8416834996776-1.84168349967764
553232.7796530049172-0.779653004917226
563734.40888882639782.59111117360222
573934.90871159547324.09128840452677
582934.9976663564259-5.99766635642592
593735.27318774377741.72681225622256
603534.90086737406620.0991326259337744
613031.6254747870261-1.62547478702608
623834.78275673012763.21724326987241
633434.7267986069267-0.726798606926724
643134.5786014741047-3.57860147410466
653433.86197089169760.138029108302394
663535.8386575122897-0.83865751228968
673635.20538317202030.7946168279797
683033.2089177511933-3.20891775119332
693935.5262498592883.47375014071204
703535.8119643362603-0.811964336260284
713834.04968312858173.95031687141834
723135.6435199118922-4.64351991189223
733436.8875836121189-2.8875836121189
743837.65661753248940.3433824675106
753432.5018125728621.49818742713801
763933.9023564106045.09764358939596
773735.81588644696381.18411355303622
783433.75091628602950.249083713970544
792832.8762628066456-4.87626280664561
803732.57323423617184.42676576382824
813335.588146118522-2.58814611852201
823737.6539243879856-0.653924387985574
833535.9905184945347-0.990518494534702
843734.28490235274122.71509764725882
853234.7015303520623-2.70153035206233
863334.0872140306009-1.08721403060089
873836.27113542680821.72886457319179
883334.8366452957944-1.83664529579443
892934.0137228097571-5.01372280975709
903333.4427969451908-0.442796945190804
913134.7289216966651-3.72892169666511
923633.92359461458592.07640538541409
933537.1714780187269-2.17147801872688
943232.4152391629282-0.415239162928223
952932.5446674575737-3.54466745757366
963935.9211663887593.07883361124103
973734.77740969825912.22259030174091
983534.29820154619570.701798453804283
993735.03402182444981.96597817555017
1003235.245849931379-3.24584993137902
1013835.45250237694052.54749762305955
1023735.05654140282921.94345859717079
1033636.8328544551177-0.832854455117698
1043232.5359826448323-0.535982644832303
1053336.5420099506414-3.54200995064143
1064032.66961713541827.3303828645818
1073835.63965333316992.36034666683008
1084136.40244409835644.59755590164363
1093634.98447510315041.01552489684958
1104336.22371856958126.7762814304188
1113034.8191121981515-4.8191121981515
1123133.9577646865562-2.95776468655622
1133237.603257764672-5.60325776467202
1143233.5524198553599-1.55241985535992
1153734.01705381629592.98294618370408
1163734.8715155361152.12848446388504
1173335.9542233330492-2.95422333304917
1183436.6877634261294-2.68776342612936
1193334.4285862706951-1.42858627069508
1203835.39141422623562.60858577376436
1213334.768445574838-1.76844557483795
1223132.3090027912791-1.30900279127912
1233835.59529017157882.4047098284212
1243736.07804833432240.921951665677608
1253333.4652573410383-0.465257341038315
1263134.0688192922244-3.06881929222437
1273934.42981523689484.57018476310525
1284437.19262143358846.80737856641159
1293335.8938285763605-2.89382857636054
1303533.34916072265251.65083927734747
1313234.7666485536497-2.76664855364967
1322831.7877640709964-3.78776407099639
1334036.51307107202013.48692892797994
1342732.284886921234-5.28488692123398
1353735.5571979889051.44280201109502
1363232.8896778386197-0.88967783861971
1372829.7437413699583-1.74374136995826
1383434.9071708357887-0.907170835788654
1393033.4993718810274-3.49937188102744
1403534.17023065552040.829769344479594
1413132.9038501062137-1.90385010621372
1423235.5202189339113-3.52021893391128
1433034.9631289589895-4.96312895898952
1443035.0774678133262-5.07746781332617
1453131.3896418740141-0.389641874014135
1464032.57412835971057.42587164028953
1473233.1453403092906-1.14534030929056
1483634.31546210749291.68453789250711
1493233.7717268580069-1.77172685800686
1503533.08831469227341.91168530772661
1513835.32558908196412.67441091803594
1524235.12891949599296.8710805040071
1533436.7940553297597-2.79405532975965
1543537.2210168416858-2.22101684168575
1553534.21835133164830.781648668351698
1563332.16248684112580.837513158874232
1573633.92359461458592.07640538541409
1583235.6097627992518-3.60976279925182
1593335.8938285763605-2.89382857636054
1603434.3675536519715-0.367553651971452
1613234.0895398496386-2.08953984963863
1623434.8743701530022-0.874370153002162






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9465865659363530.1068268681272930.0534134340636465
100.9029268132467820.1941463735064360.0970731867532182
110.8424948750204630.3150102499590750.157505124979537
120.82499380414440.35001239171120.1750061958556
130.8719060744417850.256187851116430.128093925558215
140.8287248040408280.3425503919183430.171275195959171
150.8298854410166870.3402291179666270.170114558983313
160.8261144223506470.3477711552987060.173885577649353
170.7671191980162410.4657616039675170.232880801983759
180.7072714143769550.5854571712460890.292728585623045
190.6839460103237120.6321079793525760.316053989676288
200.6829842635850660.6340314728298690.317015736414934
210.689994625364930.620010749270140.31000537463507
220.6458007656536890.7083984686926230.354199234346311
230.6182160368224710.7635679263550590.38178396317753
240.5476508892346620.9046982215306760.452349110765338
250.5399700735951320.9200598528097350.460029926404868
260.8007440260120250.398511947975950.199255973987975
270.7529876060194160.4940247879611690.247012393980585
280.7252962887108220.5494074225783550.274703711289178
290.6923519201593220.6152961596813560.307648079840678
300.6389809629655260.7220380740689470.361019037034474
310.6096133576985120.7807732846029760.390386642301488
320.7677199414658720.4645601170682560.232280058534128
330.7634965001387160.4730069997225680.236503499861284
340.7379038290073610.5241923419852770.262096170992639
350.6952714499927980.6094571000144040.304728550007202
360.6435730946410380.7128538107179240.356426905358962
370.6181805595455550.763638880908890.381819440454445
380.5869693543401280.8260612913197440.413030645659872
390.7106196229811710.5787607540376580.289380377018829
400.6618160734446990.6763678531106020.338183926555301
410.6318829864576960.7362340270846070.368117013542304
420.5796914294430620.8406171411138770.420308570556938
430.546251023257470.907497953485060.45374897674253
440.5006694036156320.9986611927687350.499330596384368
450.5951881831440680.8096236337118640.404811816855932
460.6139226722877380.7721546554245240.386077327712262
470.5804594569716790.8390810860566420.419540543028321
480.5518082965686670.8963834068626660.448191703431333
490.5008560716328810.9982878567342390.499143928367119
500.5336847555727670.9326304888544660.466315244427233
510.5811217036291640.8377565927416730.418878296370836
520.5405532914025170.9188934171949660.459446708597483
530.4937511827090990.9875023654181980.506248817290901
540.4626523325177710.9253046650355430.537347667482229
550.4160366263528180.8320732527056370.583963373647182
560.4010607209030240.8021214418060490.598939279096976
570.4301414894754440.8602829789508870.569858510524556
580.580104946159430.839790107681140.41989505384057
590.5455463663029120.9089072673941760.454453633697088
600.497675542826460.995351085652920.50232445717354
610.4632089680564330.9264179361128670.536791031943567
620.4603406661785590.9206813323571180.539659333821441
630.4188777976046940.8377555952093870.581122202395306
640.4301476446574340.8602952893148680.569852355342566
650.384789144171710.769578288343420.61521085582829
660.345585634178580.691171268357160.65441436582142
670.3052738288340880.6105476576681760.694726171165912
680.3223567565575270.6447135131150540.677643243442473
690.3330310225401220.6660620450802440.666968977459878
700.2943551057629020.5887102115258030.705644894237098
710.3177764809449140.6355529618898280.682223519055086
720.370357342149830.7407146842996610.62964265785017
730.3642569779051960.7285139558103920.635743022094804
740.3218455813287360.6436911626574720.678154418671264
750.2913635326417320.5827270652834650.708636467358268
760.3620820965095250.724164193019050.637917903490475
770.3245625243124050.649125048624810.675437475687595
780.2841917865766220.5683835731532450.715808213423378
790.3440436809254050.688087361850810.655956319074595
800.3984717438036350.796943487607270.601528256196365
810.3840987400759160.7681974801518310.615901259924084
820.3428876653968730.6857753307937470.657112334603127
830.3050905790162210.6101811580324410.69490942098378
840.2941892653264210.5883785306528430.705810734673579
850.2830970499265090.5661940998530180.716902950073491
860.2500248189852650.500049637970530.749975181014735
870.2260891154125340.4521782308250680.773910884587466
880.2030418705736840.4060837411473680.796958129426316
890.2550138341648420.5100276683296830.744986165835159
900.2201037668934010.4402075337868020.779896233106599
910.2363033580071590.4726067160143180.763696641992841
920.2156192428881890.4312384857763790.78438075711181
930.2003521721720710.4007043443441430.799647827827929
940.1698931409242260.3397862818484530.830106859075774
950.1735915672168860.3471831344337730.826408432783114
960.1731983896732490.3463967793464980.82680161032675
970.1585183559905580.3170367119811170.841481644009441
980.136888809387990.273777618775980.86311119061201
990.1215696419924070.2431392839848140.878430358007593
1000.1202199390759870.2404398781519730.879780060924013
1010.1109500982513350.221900196502670.889049901748665
1020.09885483207118830.1977096641423770.901145167928812
1030.08074470881440720.1614894176288140.919255291185593
1040.06619588950063150.1323917790012630.933804110499369
1050.0735902822271560.1471805644543120.926409717772844
1060.1891801200607820.3783602401215630.810819879939218
1070.17584863712460.35169727424920.8241513628754
1080.2027379613211140.4054759226422290.797262038678886
1090.1826021774529250.3652043549058490.817397822547075
1100.3420446872664120.6840893745328240.657955312733588
1110.3910022425509140.7820044851018280.608997757449086
1120.36852703492040.7370540698408010.6314729650796
1130.448362673095390.896725346190780.55163732690461
1140.4087365998945330.8174731997890650.591263400105467
1150.3907888849152540.7815777698305080.609211115084746
1160.3676436989330580.7352873978661160.632356301066942
1170.3695869746114660.7391739492229320.630413025388534
1180.3541062106120730.7082124212241470.645893789387927
1190.3166465196235940.6332930392471890.683353480376406
1200.3053772992594440.6107545985188890.694622700740556
1210.2726065707086070.5452131414172140.727393429291393
1220.2331204413008330.4662408826016650.766879558699167
1230.20664593243270.4132918648653990.7933540675673
1240.1710395393421660.3420790786843320.828960460657834
1250.1383681938375210.2767363876750420.861631806162479
1260.13677159757080.2735431951415990.8632284024292
1270.1730608295589240.3461216591178480.826939170441076
1280.3517223725205280.7034447450410550.648277627479472
1290.324081321067470.6481626421349390.67591867893253
1300.2850912189759080.5701824379518160.714908781024092
1310.2555347628476690.5110695256953380.744465237152331
1320.2920374342229750.5840748684459510.707962565777025
1330.3714960749705050.742992149941010.628503925029495
1340.5238010257844870.9523979484310260.476198974215513
1350.4686361322801780.9372722645603560.531363867719822
1360.4050069267849350.810013853569870.594993073215065
1370.3828209141920380.7656418283840760.617179085807962
1380.3188897721987620.6377795443975250.681110227801238
1390.4135897658496090.8271795316992190.586410234150391
1400.3514497721998750.7028995443997510.648550227800125
1410.5433238495377380.9133523009245230.456676150462262
1420.4776548132361660.9553096264723320.522345186763834
1430.4926223684616240.9852447369232470.507377631538376
1440.7507106733366780.4985786533266430.249289326663322
1450.6764562364974220.6470875270051560.323543763502578
1460.9598958398841450.08020832023171080.0401041601158554
1470.9431653888556610.1136692222886770.0568346111443385
1480.9666900461609080.0666199076781830.0333099538390915
1490.9578140039388330.08437199212233420.0421859960611671
1500.9238632801634810.1522734396730380.0761367198365192
1510.8688829337274540.2622341325450920.131117066272546
1520.9827785155112130.03444296897757370.0172214844887868
1530.9485257432383060.1029485135233880.0514742567616938

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.946586565936353 & 0.106826868127293 & 0.0534134340636465 \tabularnewline
10 & 0.902926813246782 & 0.194146373506436 & 0.0970731867532182 \tabularnewline
11 & 0.842494875020463 & 0.315010249959075 & 0.157505124979537 \tabularnewline
12 & 0.8249938041444 & 0.3500123917112 & 0.1750061958556 \tabularnewline
13 & 0.871906074441785 & 0.25618785111643 & 0.128093925558215 \tabularnewline
14 & 0.828724804040828 & 0.342550391918343 & 0.171275195959171 \tabularnewline
15 & 0.829885441016687 & 0.340229117966627 & 0.170114558983313 \tabularnewline
16 & 0.826114422350647 & 0.347771155298706 & 0.173885577649353 \tabularnewline
17 & 0.767119198016241 & 0.465761603967517 & 0.232880801983759 \tabularnewline
18 & 0.707271414376955 & 0.585457171246089 & 0.292728585623045 \tabularnewline
19 & 0.683946010323712 & 0.632107979352576 & 0.316053989676288 \tabularnewline
20 & 0.682984263585066 & 0.634031472829869 & 0.317015736414934 \tabularnewline
21 & 0.68999462536493 & 0.62001074927014 & 0.31000537463507 \tabularnewline
22 & 0.645800765653689 & 0.708398468692623 & 0.354199234346311 \tabularnewline
23 & 0.618216036822471 & 0.763567926355059 & 0.38178396317753 \tabularnewline
24 & 0.547650889234662 & 0.904698221530676 & 0.452349110765338 \tabularnewline
25 & 0.539970073595132 & 0.920059852809735 & 0.460029926404868 \tabularnewline
26 & 0.800744026012025 & 0.39851194797595 & 0.199255973987975 \tabularnewline
27 & 0.752987606019416 & 0.494024787961169 & 0.247012393980585 \tabularnewline
28 & 0.725296288710822 & 0.549407422578355 & 0.274703711289178 \tabularnewline
29 & 0.692351920159322 & 0.615296159681356 & 0.307648079840678 \tabularnewline
30 & 0.638980962965526 & 0.722038074068947 & 0.361019037034474 \tabularnewline
31 & 0.609613357698512 & 0.780773284602976 & 0.390386642301488 \tabularnewline
32 & 0.767719941465872 & 0.464560117068256 & 0.232280058534128 \tabularnewline
33 & 0.763496500138716 & 0.473006999722568 & 0.236503499861284 \tabularnewline
34 & 0.737903829007361 & 0.524192341985277 & 0.262096170992639 \tabularnewline
35 & 0.695271449992798 & 0.609457100014404 & 0.304728550007202 \tabularnewline
36 & 0.643573094641038 & 0.712853810717924 & 0.356426905358962 \tabularnewline
37 & 0.618180559545555 & 0.76363888090889 & 0.381819440454445 \tabularnewline
38 & 0.586969354340128 & 0.826061291319744 & 0.413030645659872 \tabularnewline
39 & 0.710619622981171 & 0.578760754037658 & 0.289380377018829 \tabularnewline
40 & 0.661816073444699 & 0.676367853110602 & 0.338183926555301 \tabularnewline
41 & 0.631882986457696 & 0.736234027084607 & 0.368117013542304 \tabularnewline
42 & 0.579691429443062 & 0.840617141113877 & 0.420308570556938 \tabularnewline
43 & 0.54625102325747 & 0.90749795348506 & 0.45374897674253 \tabularnewline
44 & 0.500669403615632 & 0.998661192768735 & 0.499330596384368 \tabularnewline
45 & 0.595188183144068 & 0.809623633711864 & 0.404811816855932 \tabularnewline
46 & 0.613922672287738 & 0.772154655424524 & 0.386077327712262 \tabularnewline
47 & 0.580459456971679 & 0.839081086056642 & 0.419540543028321 \tabularnewline
48 & 0.551808296568667 & 0.896383406862666 & 0.448191703431333 \tabularnewline
49 & 0.500856071632881 & 0.998287856734239 & 0.499143928367119 \tabularnewline
50 & 0.533684755572767 & 0.932630488854466 & 0.466315244427233 \tabularnewline
51 & 0.581121703629164 & 0.837756592741673 & 0.418878296370836 \tabularnewline
52 & 0.540553291402517 & 0.918893417194966 & 0.459446708597483 \tabularnewline
53 & 0.493751182709099 & 0.987502365418198 & 0.506248817290901 \tabularnewline
54 & 0.462652332517771 & 0.925304665035543 & 0.537347667482229 \tabularnewline
55 & 0.416036626352818 & 0.832073252705637 & 0.583963373647182 \tabularnewline
56 & 0.401060720903024 & 0.802121441806049 & 0.598939279096976 \tabularnewline
57 & 0.430141489475444 & 0.860282978950887 & 0.569858510524556 \tabularnewline
58 & 0.58010494615943 & 0.83979010768114 & 0.41989505384057 \tabularnewline
59 & 0.545546366302912 & 0.908907267394176 & 0.454453633697088 \tabularnewline
60 & 0.49767554282646 & 0.99535108565292 & 0.50232445717354 \tabularnewline
61 & 0.463208968056433 & 0.926417936112867 & 0.536791031943567 \tabularnewline
62 & 0.460340666178559 & 0.920681332357118 & 0.539659333821441 \tabularnewline
63 & 0.418877797604694 & 0.837755595209387 & 0.581122202395306 \tabularnewline
64 & 0.430147644657434 & 0.860295289314868 & 0.569852355342566 \tabularnewline
65 & 0.38478914417171 & 0.76957828834342 & 0.61521085582829 \tabularnewline
66 & 0.34558563417858 & 0.69117126835716 & 0.65441436582142 \tabularnewline
67 & 0.305273828834088 & 0.610547657668176 & 0.694726171165912 \tabularnewline
68 & 0.322356756557527 & 0.644713513115054 & 0.677643243442473 \tabularnewline
69 & 0.333031022540122 & 0.666062045080244 & 0.666968977459878 \tabularnewline
70 & 0.294355105762902 & 0.588710211525803 & 0.705644894237098 \tabularnewline
71 & 0.317776480944914 & 0.635552961889828 & 0.682223519055086 \tabularnewline
72 & 0.37035734214983 & 0.740714684299661 & 0.62964265785017 \tabularnewline
73 & 0.364256977905196 & 0.728513955810392 & 0.635743022094804 \tabularnewline
74 & 0.321845581328736 & 0.643691162657472 & 0.678154418671264 \tabularnewline
75 & 0.291363532641732 & 0.582727065283465 & 0.708636467358268 \tabularnewline
76 & 0.362082096509525 & 0.72416419301905 & 0.637917903490475 \tabularnewline
77 & 0.324562524312405 & 0.64912504862481 & 0.675437475687595 \tabularnewline
78 & 0.284191786576622 & 0.568383573153245 & 0.715808213423378 \tabularnewline
79 & 0.344043680925405 & 0.68808736185081 & 0.655956319074595 \tabularnewline
80 & 0.398471743803635 & 0.79694348760727 & 0.601528256196365 \tabularnewline
81 & 0.384098740075916 & 0.768197480151831 & 0.615901259924084 \tabularnewline
82 & 0.342887665396873 & 0.685775330793747 & 0.657112334603127 \tabularnewline
83 & 0.305090579016221 & 0.610181158032441 & 0.69490942098378 \tabularnewline
84 & 0.294189265326421 & 0.588378530652843 & 0.705810734673579 \tabularnewline
85 & 0.283097049926509 & 0.566194099853018 & 0.716902950073491 \tabularnewline
86 & 0.250024818985265 & 0.50004963797053 & 0.749975181014735 \tabularnewline
87 & 0.226089115412534 & 0.452178230825068 & 0.773910884587466 \tabularnewline
88 & 0.203041870573684 & 0.406083741147368 & 0.796958129426316 \tabularnewline
89 & 0.255013834164842 & 0.510027668329683 & 0.744986165835159 \tabularnewline
90 & 0.220103766893401 & 0.440207533786802 & 0.779896233106599 \tabularnewline
91 & 0.236303358007159 & 0.472606716014318 & 0.763696641992841 \tabularnewline
92 & 0.215619242888189 & 0.431238485776379 & 0.78438075711181 \tabularnewline
93 & 0.200352172172071 & 0.400704344344143 & 0.799647827827929 \tabularnewline
94 & 0.169893140924226 & 0.339786281848453 & 0.830106859075774 \tabularnewline
95 & 0.173591567216886 & 0.347183134433773 & 0.826408432783114 \tabularnewline
96 & 0.173198389673249 & 0.346396779346498 & 0.82680161032675 \tabularnewline
97 & 0.158518355990558 & 0.317036711981117 & 0.841481644009441 \tabularnewline
98 & 0.13688880938799 & 0.27377761877598 & 0.86311119061201 \tabularnewline
99 & 0.121569641992407 & 0.243139283984814 & 0.878430358007593 \tabularnewline
100 & 0.120219939075987 & 0.240439878151973 & 0.879780060924013 \tabularnewline
101 & 0.110950098251335 & 0.22190019650267 & 0.889049901748665 \tabularnewline
102 & 0.0988548320711883 & 0.197709664142377 & 0.901145167928812 \tabularnewline
103 & 0.0807447088144072 & 0.161489417628814 & 0.919255291185593 \tabularnewline
104 & 0.0661958895006315 & 0.132391779001263 & 0.933804110499369 \tabularnewline
105 & 0.073590282227156 & 0.147180564454312 & 0.926409717772844 \tabularnewline
106 & 0.189180120060782 & 0.378360240121563 & 0.810819879939218 \tabularnewline
107 & 0.1758486371246 & 0.3516972742492 & 0.8241513628754 \tabularnewline
108 & 0.202737961321114 & 0.405475922642229 & 0.797262038678886 \tabularnewline
109 & 0.182602177452925 & 0.365204354905849 & 0.817397822547075 \tabularnewline
110 & 0.342044687266412 & 0.684089374532824 & 0.657955312733588 \tabularnewline
111 & 0.391002242550914 & 0.782004485101828 & 0.608997757449086 \tabularnewline
112 & 0.3685270349204 & 0.737054069840801 & 0.6314729650796 \tabularnewline
113 & 0.44836267309539 & 0.89672534619078 & 0.55163732690461 \tabularnewline
114 & 0.408736599894533 & 0.817473199789065 & 0.591263400105467 \tabularnewline
115 & 0.390788884915254 & 0.781577769830508 & 0.609211115084746 \tabularnewline
116 & 0.367643698933058 & 0.735287397866116 & 0.632356301066942 \tabularnewline
117 & 0.369586974611466 & 0.739173949222932 & 0.630413025388534 \tabularnewline
118 & 0.354106210612073 & 0.708212421224147 & 0.645893789387927 \tabularnewline
119 & 0.316646519623594 & 0.633293039247189 & 0.683353480376406 \tabularnewline
120 & 0.305377299259444 & 0.610754598518889 & 0.694622700740556 \tabularnewline
121 & 0.272606570708607 & 0.545213141417214 & 0.727393429291393 \tabularnewline
122 & 0.233120441300833 & 0.466240882601665 & 0.766879558699167 \tabularnewline
123 & 0.2066459324327 & 0.413291864865399 & 0.7933540675673 \tabularnewline
124 & 0.171039539342166 & 0.342079078684332 & 0.828960460657834 \tabularnewline
125 & 0.138368193837521 & 0.276736387675042 & 0.861631806162479 \tabularnewline
126 & 0.1367715975708 & 0.273543195141599 & 0.8632284024292 \tabularnewline
127 & 0.173060829558924 & 0.346121659117848 & 0.826939170441076 \tabularnewline
128 & 0.351722372520528 & 0.703444745041055 & 0.648277627479472 \tabularnewline
129 & 0.32408132106747 & 0.648162642134939 & 0.67591867893253 \tabularnewline
130 & 0.285091218975908 & 0.570182437951816 & 0.714908781024092 \tabularnewline
131 & 0.255534762847669 & 0.511069525695338 & 0.744465237152331 \tabularnewline
132 & 0.292037434222975 & 0.584074868445951 & 0.707962565777025 \tabularnewline
133 & 0.371496074970505 & 0.74299214994101 & 0.628503925029495 \tabularnewline
134 & 0.523801025784487 & 0.952397948431026 & 0.476198974215513 \tabularnewline
135 & 0.468636132280178 & 0.937272264560356 & 0.531363867719822 \tabularnewline
136 & 0.405006926784935 & 0.81001385356987 & 0.594993073215065 \tabularnewline
137 & 0.382820914192038 & 0.765641828384076 & 0.617179085807962 \tabularnewline
138 & 0.318889772198762 & 0.637779544397525 & 0.681110227801238 \tabularnewline
139 & 0.413589765849609 & 0.827179531699219 & 0.586410234150391 \tabularnewline
140 & 0.351449772199875 & 0.702899544399751 & 0.648550227800125 \tabularnewline
141 & 0.543323849537738 & 0.913352300924523 & 0.456676150462262 \tabularnewline
142 & 0.477654813236166 & 0.955309626472332 & 0.522345186763834 \tabularnewline
143 & 0.492622368461624 & 0.985244736923247 & 0.507377631538376 \tabularnewline
144 & 0.750710673336678 & 0.498578653326643 & 0.249289326663322 \tabularnewline
145 & 0.676456236497422 & 0.647087527005156 & 0.323543763502578 \tabularnewline
146 & 0.959895839884145 & 0.0802083202317108 & 0.0401041601158554 \tabularnewline
147 & 0.943165388855661 & 0.113669222288677 & 0.0568346111443385 \tabularnewline
148 & 0.966690046160908 & 0.066619907678183 & 0.0333099538390915 \tabularnewline
149 & 0.957814003938833 & 0.0843719921223342 & 0.0421859960611671 \tabularnewline
150 & 0.923863280163481 & 0.152273439673038 & 0.0761367198365192 \tabularnewline
151 & 0.868882933727454 & 0.262234132545092 & 0.131117066272546 \tabularnewline
152 & 0.982778515511213 & 0.0344429689775737 & 0.0172214844887868 \tabularnewline
153 & 0.948525743238306 & 0.102948513523388 & 0.0514742567616938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.946586565936353[/C][C]0.106826868127293[/C][C]0.0534134340636465[/C][/ROW]
[ROW][C]10[/C][C]0.902926813246782[/C][C]0.194146373506436[/C][C]0.0970731867532182[/C][/ROW]
[ROW][C]11[/C][C]0.842494875020463[/C][C]0.315010249959075[/C][C]0.157505124979537[/C][/ROW]
[ROW][C]12[/C][C]0.8249938041444[/C][C]0.3500123917112[/C][C]0.1750061958556[/C][/ROW]
[ROW][C]13[/C][C]0.871906074441785[/C][C]0.25618785111643[/C][C]0.128093925558215[/C][/ROW]
[ROW][C]14[/C][C]0.828724804040828[/C][C]0.342550391918343[/C][C]0.171275195959171[/C][/ROW]
[ROW][C]15[/C][C]0.829885441016687[/C][C]0.340229117966627[/C][C]0.170114558983313[/C][/ROW]
[ROW][C]16[/C][C]0.826114422350647[/C][C]0.347771155298706[/C][C]0.173885577649353[/C][/ROW]
[ROW][C]17[/C][C]0.767119198016241[/C][C]0.465761603967517[/C][C]0.232880801983759[/C][/ROW]
[ROW][C]18[/C][C]0.707271414376955[/C][C]0.585457171246089[/C][C]0.292728585623045[/C][/ROW]
[ROW][C]19[/C][C]0.683946010323712[/C][C]0.632107979352576[/C][C]0.316053989676288[/C][/ROW]
[ROW][C]20[/C][C]0.682984263585066[/C][C]0.634031472829869[/C][C]0.317015736414934[/C][/ROW]
[ROW][C]21[/C][C]0.68999462536493[/C][C]0.62001074927014[/C][C]0.31000537463507[/C][/ROW]
[ROW][C]22[/C][C]0.645800765653689[/C][C]0.708398468692623[/C][C]0.354199234346311[/C][/ROW]
[ROW][C]23[/C][C]0.618216036822471[/C][C]0.763567926355059[/C][C]0.38178396317753[/C][/ROW]
[ROW][C]24[/C][C]0.547650889234662[/C][C]0.904698221530676[/C][C]0.452349110765338[/C][/ROW]
[ROW][C]25[/C][C]0.539970073595132[/C][C]0.920059852809735[/C][C]0.460029926404868[/C][/ROW]
[ROW][C]26[/C][C]0.800744026012025[/C][C]0.39851194797595[/C][C]0.199255973987975[/C][/ROW]
[ROW][C]27[/C][C]0.752987606019416[/C][C]0.494024787961169[/C][C]0.247012393980585[/C][/ROW]
[ROW][C]28[/C][C]0.725296288710822[/C][C]0.549407422578355[/C][C]0.274703711289178[/C][/ROW]
[ROW][C]29[/C][C]0.692351920159322[/C][C]0.615296159681356[/C][C]0.307648079840678[/C][/ROW]
[ROW][C]30[/C][C]0.638980962965526[/C][C]0.722038074068947[/C][C]0.361019037034474[/C][/ROW]
[ROW][C]31[/C][C]0.609613357698512[/C][C]0.780773284602976[/C][C]0.390386642301488[/C][/ROW]
[ROW][C]32[/C][C]0.767719941465872[/C][C]0.464560117068256[/C][C]0.232280058534128[/C][/ROW]
[ROW][C]33[/C][C]0.763496500138716[/C][C]0.473006999722568[/C][C]0.236503499861284[/C][/ROW]
[ROW][C]34[/C][C]0.737903829007361[/C][C]0.524192341985277[/C][C]0.262096170992639[/C][/ROW]
[ROW][C]35[/C][C]0.695271449992798[/C][C]0.609457100014404[/C][C]0.304728550007202[/C][/ROW]
[ROW][C]36[/C][C]0.643573094641038[/C][C]0.712853810717924[/C][C]0.356426905358962[/C][/ROW]
[ROW][C]37[/C][C]0.618180559545555[/C][C]0.76363888090889[/C][C]0.381819440454445[/C][/ROW]
[ROW][C]38[/C][C]0.586969354340128[/C][C]0.826061291319744[/C][C]0.413030645659872[/C][/ROW]
[ROW][C]39[/C][C]0.710619622981171[/C][C]0.578760754037658[/C][C]0.289380377018829[/C][/ROW]
[ROW][C]40[/C][C]0.661816073444699[/C][C]0.676367853110602[/C][C]0.338183926555301[/C][/ROW]
[ROW][C]41[/C][C]0.631882986457696[/C][C]0.736234027084607[/C][C]0.368117013542304[/C][/ROW]
[ROW][C]42[/C][C]0.579691429443062[/C][C]0.840617141113877[/C][C]0.420308570556938[/C][/ROW]
[ROW][C]43[/C][C]0.54625102325747[/C][C]0.90749795348506[/C][C]0.45374897674253[/C][/ROW]
[ROW][C]44[/C][C]0.500669403615632[/C][C]0.998661192768735[/C][C]0.499330596384368[/C][/ROW]
[ROW][C]45[/C][C]0.595188183144068[/C][C]0.809623633711864[/C][C]0.404811816855932[/C][/ROW]
[ROW][C]46[/C][C]0.613922672287738[/C][C]0.772154655424524[/C][C]0.386077327712262[/C][/ROW]
[ROW][C]47[/C][C]0.580459456971679[/C][C]0.839081086056642[/C][C]0.419540543028321[/C][/ROW]
[ROW][C]48[/C][C]0.551808296568667[/C][C]0.896383406862666[/C][C]0.448191703431333[/C][/ROW]
[ROW][C]49[/C][C]0.500856071632881[/C][C]0.998287856734239[/C][C]0.499143928367119[/C][/ROW]
[ROW][C]50[/C][C]0.533684755572767[/C][C]0.932630488854466[/C][C]0.466315244427233[/C][/ROW]
[ROW][C]51[/C][C]0.581121703629164[/C][C]0.837756592741673[/C][C]0.418878296370836[/C][/ROW]
[ROW][C]52[/C][C]0.540553291402517[/C][C]0.918893417194966[/C][C]0.459446708597483[/C][/ROW]
[ROW][C]53[/C][C]0.493751182709099[/C][C]0.987502365418198[/C][C]0.506248817290901[/C][/ROW]
[ROW][C]54[/C][C]0.462652332517771[/C][C]0.925304665035543[/C][C]0.537347667482229[/C][/ROW]
[ROW][C]55[/C][C]0.416036626352818[/C][C]0.832073252705637[/C][C]0.583963373647182[/C][/ROW]
[ROW][C]56[/C][C]0.401060720903024[/C][C]0.802121441806049[/C][C]0.598939279096976[/C][/ROW]
[ROW][C]57[/C][C]0.430141489475444[/C][C]0.860282978950887[/C][C]0.569858510524556[/C][/ROW]
[ROW][C]58[/C][C]0.58010494615943[/C][C]0.83979010768114[/C][C]0.41989505384057[/C][/ROW]
[ROW][C]59[/C][C]0.545546366302912[/C][C]0.908907267394176[/C][C]0.454453633697088[/C][/ROW]
[ROW][C]60[/C][C]0.49767554282646[/C][C]0.99535108565292[/C][C]0.50232445717354[/C][/ROW]
[ROW][C]61[/C][C]0.463208968056433[/C][C]0.926417936112867[/C][C]0.536791031943567[/C][/ROW]
[ROW][C]62[/C][C]0.460340666178559[/C][C]0.920681332357118[/C][C]0.539659333821441[/C][/ROW]
[ROW][C]63[/C][C]0.418877797604694[/C][C]0.837755595209387[/C][C]0.581122202395306[/C][/ROW]
[ROW][C]64[/C][C]0.430147644657434[/C][C]0.860295289314868[/C][C]0.569852355342566[/C][/ROW]
[ROW][C]65[/C][C]0.38478914417171[/C][C]0.76957828834342[/C][C]0.61521085582829[/C][/ROW]
[ROW][C]66[/C][C]0.34558563417858[/C][C]0.69117126835716[/C][C]0.65441436582142[/C][/ROW]
[ROW][C]67[/C][C]0.305273828834088[/C][C]0.610547657668176[/C][C]0.694726171165912[/C][/ROW]
[ROW][C]68[/C][C]0.322356756557527[/C][C]0.644713513115054[/C][C]0.677643243442473[/C][/ROW]
[ROW][C]69[/C][C]0.333031022540122[/C][C]0.666062045080244[/C][C]0.666968977459878[/C][/ROW]
[ROW][C]70[/C][C]0.294355105762902[/C][C]0.588710211525803[/C][C]0.705644894237098[/C][/ROW]
[ROW][C]71[/C][C]0.317776480944914[/C][C]0.635552961889828[/C][C]0.682223519055086[/C][/ROW]
[ROW][C]72[/C][C]0.37035734214983[/C][C]0.740714684299661[/C][C]0.62964265785017[/C][/ROW]
[ROW][C]73[/C][C]0.364256977905196[/C][C]0.728513955810392[/C][C]0.635743022094804[/C][/ROW]
[ROW][C]74[/C][C]0.321845581328736[/C][C]0.643691162657472[/C][C]0.678154418671264[/C][/ROW]
[ROW][C]75[/C][C]0.291363532641732[/C][C]0.582727065283465[/C][C]0.708636467358268[/C][/ROW]
[ROW][C]76[/C][C]0.362082096509525[/C][C]0.72416419301905[/C][C]0.637917903490475[/C][/ROW]
[ROW][C]77[/C][C]0.324562524312405[/C][C]0.64912504862481[/C][C]0.675437475687595[/C][/ROW]
[ROW][C]78[/C][C]0.284191786576622[/C][C]0.568383573153245[/C][C]0.715808213423378[/C][/ROW]
[ROW][C]79[/C][C]0.344043680925405[/C][C]0.68808736185081[/C][C]0.655956319074595[/C][/ROW]
[ROW][C]80[/C][C]0.398471743803635[/C][C]0.79694348760727[/C][C]0.601528256196365[/C][/ROW]
[ROW][C]81[/C][C]0.384098740075916[/C][C]0.768197480151831[/C][C]0.615901259924084[/C][/ROW]
[ROW][C]82[/C][C]0.342887665396873[/C][C]0.685775330793747[/C][C]0.657112334603127[/C][/ROW]
[ROW][C]83[/C][C]0.305090579016221[/C][C]0.610181158032441[/C][C]0.69490942098378[/C][/ROW]
[ROW][C]84[/C][C]0.294189265326421[/C][C]0.588378530652843[/C][C]0.705810734673579[/C][/ROW]
[ROW][C]85[/C][C]0.283097049926509[/C][C]0.566194099853018[/C][C]0.716902950073491[/C][/ROW]
[ROW][C]86[/C][C]0.250024818985265[/C][C]0.50004963797053[/C][C]0.749975181014735[/C][/ROW]
[ROW][C]87[/C][C]0.226089115412534[/C][C]0.452178230825068[/C][C]0.773910884587466[/C][/ROW]
[ROW][C]88[/C][C]0.203041870573684[/C][C]0.406083741147368[/C][C]0.796958129426316[/C][/ROW]
[ROW][C]89[/C][C]0.255013834164842[/C][C]0.510027668329683[/C][C]0.744986165835159[/C][/ROW]
[ROW][C]90[/C][C]0.220103766893401[/C][C]0.440207533786802[/C][C]0.779896233106599[/C][/ROW]
[ROW][C]91[/C][C]0.236303358007159[/C][C]0.472606716014318[/C][C]0.763696641992841[/C][/ROW]
[ROW][C]92[/C][C]0.215619242888189[/C][C]0.431238485776379[/C][C]0.78438075711181[/C][/ROW]
[ROW][C]93[/C][C]0.200352172172071[/C][C]0.400704344344143[/C][C]0.799647827827929[/C][/ROW]
[ROW][C]94[/C][C]0.169893140924226[/C][C]0.339786281848453[/C][C]0.830106859075774[/C][/ROW]
[ROW][C]95[/C][C]0.173591567216886[/C][C]0.347183134433773[/C][C]0.826408432783114[/C][/ROW]
[ROW][C]96[/C][C]0.173198389673249[/C][C]0.346396779346498[/C][C]0.82680161032675[/C][/ROW]
[ROW][C]97[/C][C]0.158518355990558[/C][C]0.317036711981117[/C][C]0.841481644009441[/C][/ROW]
[ROW][C]98[/C][C]0.13688880938799[/C][C]0.27377761877598[/C][C]0.86311119061201[/C][/ROW]
[ROW][C]99[/C][C]0.121569641992407[/C][C]0.243139283984814[/C][C]0.878430358007593[/C][/ROW]
[ROW][C]100[/C][C]0.120219939075987[/C][C]0.240439878151973[/C][C]0.879780060924013[/C][/ROW]
[ROW][C]101[/C][C]0.110950098251335[/C][C]0.22190019650267[/C][C]0.889049901748665[/C][/ROW]
[ROW][C]102[/C][C]0.0988548320711883[/C][C]0.197709664142377[/C][C]0.901145167928812[/C][/ROW]
[ROW][C]103[/C][C]0.0807447088144072[/C][C]0.161489417628814[/C][C]0.919255291185593[/C][/ROW]
[ROW][C]104[/C][C]0.0661958895006315[/C][C]0.132391779001263[/C][C]0.933804110499369[/C][/ROW]
[ROW][C]105[/C][C]0.073590282227156[/C][C]0.147180564454312[/C][C]0.926409717772844[/C][/ROW]
[ROW][C]106[/C][C]0.189180120060782[/C][C]0.378360240121563[/C][C]0.810819879939218[/C][/ROW]
[ROW][C]107[/C][C]0.1758486371246[/C][C]0.3516972742492[/C][C]0.8241513628754[/C][/ROW]
[ROW][C]108[/C][C]0.202737961321114[/C][C]0.405475922642229[/C][C]0.797262038678886[/C][/ROW]
[ROW][C]109[/C][C]0.182602177452925[/C][C]0.365204354905849[/C][C]0.817397822547075[/C][/ROW]
[ROW][C]110[/C][C]0.342044687266412[/C][C]0.684089374532824[/C][C]0.657955312733588[/C][/ROW]
[ROW][C]111[/C][C]0.391002242550914[/C][C]0.782004485101828[/C][C]0.608997757449086[/C][/ROW]
[ROW][C]112[/C][C]0.3685270349204[/C][C]0.737054069840801[/C][C]0.6314729650796[/C][/ROW]
[ROW][C]113[/C][C]0.44836267309539[/C][C]0.89672534619078[/C][C]0.55163732690461[/C][/ROW]
[ROW][C]114[/C][C]0.408736599894533[/C][C]0.817473199789065[/C][C]0.591263400105467[/C][/ROW]
[ROW][C]115[/C][C]0.390788884915254[/C][C]0.781577769830508[/C][C]0.609211115084746[/C][/ROW]
[ROW][C]116[/C][C]0.367643698933058[/C][C]0.735287397866116[/C][C]0.632356301066942[/C][/ROW]
[ROW][C]117[/C][C]0.369586974611466[/C][C]0.739173949222932[/C][C]0.630413025388534[/C][/ROW]
[ROW][C]118[/C][C]0.354106210612073[/C][C]0.708212421224147[/C][C]0.645893789387927[/C][/ROW]
[ROW][C]119[/C][C]0.316646519623594[/C][C]0.633293039247189[/C][C]0.683353480376406[/C][/ROW]
[ROW][C]120[/C][C]0.305377299259444[/C][C]0.610754598518889[/C][C]0.694622700740556[/C][/ROW]
[ROW][C]121[/C][C]0.272606570708607[/C][C]0.545213141417214[/C][C]0.727393429291393[/C][/ROW]
[ROW][C]122[/C][C]0.233120441300833[/C][C]0.466240882601665[/C][C]0.766879558699167[/C][/ROW]
[ROW][C]123[/C][C]0.2066459324327[/C][C]0.413291864865399[/C][C]0.7933540675673[/C][/ROW]
[ROW][C]124[/C][C]0.171039539342166[/C][C]0.342079078684332[/C][C]0.828960460657834[/C][/ROW]
[ROW][C]125[/C][C]0.138368193837521[/C][C]0.276736387675042[/C][C]0.861631806162479[/C][/ROW]
[ROW][C]126[/C][C]0.1367715975708[/C][C]0.273543195141599[/C][C]0.8632284024292[/C][/ROW]
[ROW][C]127[/C][C]0.173060829558924[/C][C]0.346121659117848[/C][C]0.826939170441076[/C][/ROW]
[ROW][C]128[/C][C]0.351722372520528[/C][C]0.703444745041055[/C][C]0.648277627479472[/C][/ROW]
[ROW][C]129[/C][C]0.32408132106747[/C][C]0.648162642134939[/C][C]0.67591867893253[/C][/ROW]
[ROW][C]130[/C][C]0.285091218975908[/C][C]0.570182437951816[/C][C]0.714908781024092[/C][/ROW]
[ROW][C]131[/C][C]0.255534762847669[/C][C]0.511069525695338[/C][C]0.744465237152331[/C][/ROW]
[ROW][C]132[/C][C]0.292037434222975[/C][C]0.584074868445951[/C][C]0.707962565777025[/C][/ROW]
[ROW][C]133[/C][C]0.371496074970505[/C][C]0.74299214994101[/C][C]0.628503925029495[/C][/ROW]
[ROW][C]134[/C][C]0.523801025784487[/C][C]0.952397948431026[/C][C]0.476198974215513[/C][/ROW]
[ROW][C]135[/C][C]0.468636132280178[/C][C]0.937272264560356[/C][C]0.531363867719822[/C][/ROW]
[ROW][C]136[/C][C]0.405006926784935[/C][C]0.81001385356987[/C][C]0.594993073215065[/C][/ROW]
[ROW][C]137[/C][C]0.382820914192038[/C][C]0.765641828384076[/C][C]0.617179085807962[/C][/ROW]
[ROW][C]138[/C][C]0.318889772198762[/C][C]0.637779544397525[/C][C]0.681110227801238[/C][/ROW]
[ROW][C]139[/C][C]0.413589765849609[/C][C]0.827179531699219[/C][C]0.586410234150391[/C][/ROW]
[ROW][C]140[/C][C]0.351449772199875[/C][C]0.702899544399751[/C][C]0.648550227800125[/C][/ROW]
[ROW][C]141[/C][C]0.543323849537738[/C][C]0.913352300924523[/C][C]0.456676150462262[/C][/ROW]
[ROW][C]142[/C][C]0.477654813236166[/C][C]0.955309626472332[/C][C]0.522345186763834[/C][/ROW]
[ROW][C]143[/C][C]0.492622368461624[/C][C]0.985244736923247[/C][C]0.507377631538376[/C][/ROW]
[ROW][C]144[/C][C]0.750710673336678[/C][C]0.498578653326643[/C][C]0.249289326663322[/C][/ROW]
[ROW][C]145[/C][C]0.676456236497422[/C][C]0.647087527005156[/C][C]0.323543763502578[/C][/ROW]
[ROW][C]146[/C][C]0.959895839884145[/C][C]0.0802083202317108[/C][C]0.0401041601158554[/C][/ROW]
[ROW][C]147[/C][C]0.943165388855661[/C][C]0.113669222288677[/C][C]0.0568346111443385[/C][/ROW]
[ROW][C]148[/C][C]0.966690046160908[/C][C]0.066619907678183[/C][C]0.0333099538390915[/C][/ROW]
[ROW][C]149[/C][C]0.957814003938833[/C][C]0.0843719921223342[/C][C]0.0421859960611671[/C][/ROW]
[ROW][C]150[/C][C]0.923863280163481[/C][C]0.152273439673038[/C][C]0.0761367198365192[/C][/ROW]
[ROW][C]151[/C][C]0.868882933727454[/C][C]0.262234132545092[/C][C]0.131117066272546[/C][/ROW]
[ROW][C]152[/C][C]0.982778515511213[/C][C]0.0344429689775737[/C][C]0.0172214844887868[/C][/ROW]
[ROW][C]153[/C][C]0.948525743238306[/C][C]0.102948513523388[/C][C]0.0514742567616938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9465865659363530.1068268681272930.0534134340636465
100.9029268132467820.1941463735064360.0970731867532182
110.8424948750204630.3150102499590750.157505124979537
120.82499380414440.35001239171120.1750061958556
130.8719060744417850.256187851116430.128093925558215
140.8287248040408280.3425503919183430.171275195959171
150.8298854410166870.3402291179666270.170114558983313
160.8261144223506470.3477711552987060.173885577649353
170.7671191980162410.4657616039675170.232880801983759
180.7072714143769550.5854571712460890.292728585623045
190.6839460103237120.6321079793525760.316053989676288
200.6829842635850660.6340314728298690.317015736414934
210.689994625364930.620010749270140.31000537463507
220.6458007656536890.7083984686926230.354199234346311
230.6182160368224710.7635679263550590.38178396317753
240.5476508892346620.9046982215306760.452349110765338
250.5399700735951320.9200598528097350.460029926404868
260.8007440260120250.398511947975950.199255973987975
270.7529876060194160.4940247879611690.247012393980585
280.7252962887108220.5494074225783550.274703711289178
290.6923519201593220.6152961596813560.307648079840678
300.6389809629655260.7220380740689470.361019037034474
310.6096133576985120.7807732846029760.390386642301488
320.7677199414658720.4645601170682560.232280058534128
330.7634965001387160.4730069997225680.236503499861284
340.7379038290073610.5241923419852770.262096170992639
350.6952714499927980.6094571000144040.304728550007202
360.6435730946410380.7128538107179240.356426905358962
370.6181805595455550.763638880908890.381819440454445
380.5869693543401280.8260612913197440.413030645659872
390.7106196229811710.5787607540376580.289380377018829
400.6618160734446990.6763678531106020.338183926555301
410.6318829864576960.7362340270846070.368117013542304
420.5796914294430620.8406171411138770.420308570556938
430.546251023257470.907497953485060.45374897674253
440.5006694036156320.9986611927687350.499330596384368
450.5951881831440680.8096236337118640.404811816855932
460.6139226722877380.7721546554245240.386077327712262
470.5804594569716790.8390810860566420.419540543028321
480.5518082965686670.8963834068626660.448191703431333
490.5008560716328810.9982878567342390.499143928367119
500.5336847555727670.9326304888544660.466315244427233
510.5811217036291640.8377565927416730.418878296370836
520.5405532914025170.9188934171949660.459446708597483
530.4937511827090990.9875023654181980.506248817290901
540.4626523325177710.9253046650355430.537347667482229
550.4160366263528180.8320732527056370.583963373647182
560.4010607209030240.8021214418060490.598939279096976
570.4301414894754440.8602829789508870.569858510524556
580.580104946159430.839790107681140.41989505384057
590.5455463663029120.9089072673941760.454453633697088
600.497675542826460.995351085652920.50232445717354
610.4632089680564330.9264179361128670.536791031943567
620.4603406661785590.9206813323571180.539659333821441
630.4188777976046940.8377555952093870.581122202395306
640.4301476446574340.8602952893148680.569852355342566
650.384789144171710.769578288343420.61521085582829
660.345585634178580.691171268357160.65441436582142
670.3052738288340880.6105476576681760.694726171165912
680.3223567565575270.6447135131150540.677643243442473
690.3330310225401220.6660620450802440.666968977459878
700.2943551057629020.5887102115258030.705644894237098
710.3177764809449140.6355529618898280.682223519055086
720.370357342149830.7407146842996610.62964265785017
730.3642569779051960.7285139558103920.635743022094804
740.3218455813287360.6436911626574720.678154418671264
750.2913635326417320.5827270652834650.708636467358268
760.3620820965095250.724164193019050.637917903490475
770.3245625243124050.649125048624810.675437475687595
780.2841917865766220.5683835731532450.715808213423378
790.3440436809254050.688087361850810.655956319074595
800.3984717438036350.796943487607270.601528256196365
810.3840987400759160.7681974801518310.615901259924084
820.3428876653968730.6857753307937470.657112334603127
830.3050905790162210.6101811580324410.69490942098378
840.2941892653264210.5883785306528430.705810734673579
850.2830970499265090.5661940998530180.716902950073491
860.2500248189852650.500049637970530.749975181014735
870.2260891154125340.4521782308250680.773910884587466
880.2030418705736840.4060837411473680.796958129426316
890.2550138341648420.5100276683296830.744986165835159
900.2201037668934010.4402075337868020.779896233106599
910.2363033580071590.4726067160143180.763696641992841
920.2156192428881890.4312384857763790.78438075711181
930.2003521721720710.4007043443441430.799647827827929
940.1698931409242260.3397862818484530.830106859075774
950.1735915672168860.3471831344337730.826408432783114
960.1731983896732490.3463967793464980.82680161032675
970.1585183559905580.3170367119811170.841481644009441
980.136888809387990.273777618775980.86311119061201
990.1215696419924070.2431392839848140.878430358007593
1000.1202199390759870.2404398781519730.879780060924013
1010.1109500982513350.221900196502670.889049901748665
1020.09885483207118830.1977096641423770.901145167928812
1030.08074470881440720.1614894176288140.919255291185593
1040.06619588950063150.1323917790012630.933804110499369
1050.0735902822271560.1471805644543120.926409717772844
1060.1891801200607820.3783602401215630.810819879939218
1070.17584863712460.35169727424920.8241513628754
1080.2027379613211140.4054759226422290.797262038678886
1090.1826021774529250.3652043549058490.817397822547075
1100.3420446872664120.6840893745328240.657955312733588
1110.3910022425509140.7820044851018280.608997757449086
1120.36852703492040.7370540698408010.6314729650796
1130.448362673095390.896725346190780.55163732690461
1140.4087365998945330.8174731997890650.591263400105467
1150.3907888849152540.7815777698305080.609211115084746
1160.3676436989330580.7352873978661160.632356301066942
1170.3695869746114660.7391739492229320.630413025388534
1180.3541062106120730.7082124212241470.645893789387927
1190.3166465196235940.6332930392471890.683353480376406
1200.3053772992594440.6107545985188890.694622700740556
1210.2726065707086070.5452131414172140.727393429291393
1220.2331204413008330.4662408826016650.766879558699167
1230.20664593243270.4132918648653990.7933540675673
1240.1710395393421660.3420790786843320.828960460657834
1250.1383681938375210.2767363876750420.861631806162479
1260.13677159757080.2735431951415990.8632284024292
1270.1730608295589240.3461216591178480.826939170441076
1280.3517223725205280.7034447450410550.648277627479472
1290.324081321067470.6481626421349390.67591867893253
1300.2850912189759080.5701824379518160.714908781024092
1310.2555347628476690.5110695256953380.744465237152331
1320.2920374342229750.5840748684459510.707962565777025
1330.3714960749705050.742992149941010.628503925029495
1340.5238010257844870.9523979484310260.476198974215513
1350.4686361322801780.9372722645603560.531363867719822
1360.4050069267849350.810013853569870.594993073215065
1370.3828209141920380.7656418283840760.617179085807962
1380.3188897721987620.6377795443975250.681110227801238
1390.4135897658496090.8271795316992190.586410234150391
1400.3514497721998750.7028995443997510.648550227800125
1410.5433238495377380.9133523009245230.456676150462262
1420.4776548132361660.9553096264723320.522345186763834
1430.4926223684616240.9852447369232470.507377631538376
1440.7507106733366780.4985786533266430.249289326663322
1450.6764562364974220.6470875270051560.323543763502578
1460.9598958398841450.08020832023171080.0401041601158554
1470.9431653888556610.1136692222886770.0568346111443385
1480.9666900461609080.0666199076781830.0333099538390915
1490.9578140039388330.08437199212233420.0421859960611671
1500.9238632801634810.1522734396730380.0761367198365192
1510.8688829337274540.2622341325450920.131117066272546
1520.9827785155112130.03444296897757370.0172214844887868
1530.9485257432383060.1029485135233880.0514742567616938






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00689655172413793OK
10% type I error level40.0275862068965517OK

\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 & 0.00689655172413793 & OK \tabularnewline
10% type I error level & 4 & 0.0275862068965517 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148588&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]0.00689655172413793[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0275862068965517[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148588&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148588&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00689655172413793OK
10% type I error level40.0275862068965517OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ; par8 = terrain.colors ;
Parameters (R input):
par1 = 1 ; 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')
}