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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 computationWed, 14 Dec 2011 08:48:01 -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/Dec/14/t1323870555r9intqhh00afcyh.htm/, Retrieved Wed, 01 May 2024 18:48:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154960, Retrieved Wed, 01 May 2024 18:48:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [WS 10] [2011-12-14 13:19:24] [9322e7627e8d230cdc5c9950daae258c]
- RMP     [Multiple Regression] [Ws 10 a] [2011-12-14 13:48:01] [746438a23403f20b2dde308bafe866ab] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 22.9070089772566 + 0.337244626201584Separate[t] + 0.0776091365912538Happiness[t] -0.0673773059892156`Depression `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  22.9070089772566 +  0.337244626201584Separate[t] +  0.0776091365912538Happiness[t] -0.0673773059892156`Depression
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  22.9070089772566 +  0.337244626201584Separate[t] +  0.0776091365912538Happiness[t] -0.0673773059892156`Depression
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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] = + 22.9070089772566 + 0.337244626201584Separate[t] + 0.0776091365912538Happiness[t] -0.0673773059892156`Depression `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.90700897725663.4192536.699400
Separate0.3372446262015840.0706964.77034e-062e-06
Happiness0.07760913659125380.1276640.60790.5441150.272057
`Depression `-0.06737730598921560.093423-0.72120.4718490.235925

\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) & 22.9070089772566 & 3.419253 & 6.6994 & 0 & 0 \tabularnewline
Separate & 0.337244626201584 & 0.070696 & 4.7703 & 4e-06 & 2e-06 \tabularnewline
Happiness & 0.0776091365912538 & 0.127664 & 0.6079 & 0.544115 & 0.272057 \tabularnewline
`Depression
` & -0.0673773059892156 & 0.093423 & -0.7212 & 0.471849 & 0.235925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&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]22.9070089772566[/C][C]3.419253[/C][C]6.6994[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Separate[/C][C]0.337244626201584[/C][C]0.070696[/C][C]4.7703[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0776091365912538[/C][C]0.127664[/C][C]0.6079[/C][C]0.544115[/C][C]0.272057[/C][/ROW]
[ROW][C]`Depression
`[/C][C]-0.0673773059892156[/C][C]0.093423[/C][C]-0.7212[/C][C]0.471849[/C][C]0.235925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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)22.90700897725663.4192536.699400
Separate0.3372446262015840.0706964.77034e-062e-06
Happiness0.07760913659125380.1276640.60790.5441150.272057
`Depression `-0.06737730598921560.093423-0.72120.4718490.235925






Multiple Linear Regression - Regression Statistics
Multiple R0.382028942494835
R-squared0.145946112903722
Adjusted R-squared0.12972989985759
F-TEST (value)9.00001205512868
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value1.54117388050379e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.14859943609898
Sum Squared Residuals1566.36118862245

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.382028942494835 \tabularnewline
R-squared & 0.145946112903722 \tabularnewline
Adjusted R-squared & 0.12972989985759 \tabularnewline
F-TEST (value) & 9.00001205512868 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 1.54117388050379e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.14859943609898 \tabularnewline
Sum Squared Residuals & 1566.36118862245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.382028942494835[/C][/ROW]
[ROW][C]R-squared[/C][C]0.145946112903722[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.12972989985759[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.00001205512868[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]1.54117388050379e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.14859943609898[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1566.36118862245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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 R0.382028942494835
R-squared0.145946112903722
Adjusted R-squared0.12972989985759
F-TEST (value)9.00001205512868
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value1.54117388050379e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.14859943609898
Sum Squared Residuals1566.36118862245






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14136.00030501332384.99969498667616
23934.35465110846854.64534889153152
33034.6209891129668-4.62098911296681
43134.1588636091333-3.15886360913333
53435.2118829064017-1.21188290640174
63533.27553992387451.72446007612549
73932.96581957002056.03418042997949
83435.3931930669098-1.3931930669098
93635.20093488328870.799065116711313
103736.01053684392580.989463156074208
113834.00717465166493.99282534833514
123635.30888141543050.69111858456945
133834.47600267038633.52399732961366
143936.02076867452782.97923132547217
153336.1082515454656-3.10825154546562
163234.1793272703374-2.1793272703374
173633.84208264413582.15791735586418
183836.30050972908671.69949027091327
193936.13505962530222.86494037469782
203234.0646782233075-2.06467822330754
213234.9614049586269-2.96140495862693
223133.2720106081605-2.27201060816047
233936.00030501332382.99969498667625
243736.04757675436440.952423245635601
253934.27704197187724.72295802812277
264133.79092349112567.20907650887437
273635.21116671389070.788833286109275
283335.4609284691545-2.46092846915452
293334.2569364069287-1.25693640692866
303434.0138771665529-0.0138771665528577
313132.9852089424581-1.98520894245807
322733.6970962015554-6.69709620155535
333734.00717465166492.99282534833514
343435.8080468297026-1.80804682970264
353433.23497069772190.765029302278133
363234.7388093794552-2.7388093794552
372932.7125284990427-3.71252849904267
383634.39169101890711.60830898109291
392934.0071746516649-5.00717465166486
403534.3712273577030.628772642296988
413734.25657831067312.74342168932685
423434.3345455435199-0.334545543519911
433834.40156475325363.59843524674638
443534.02410899715490.975891002845104
453832.54001778411465.45998221588538
463734.6883664189562.31163358104398
473836.61729069408421.38270930591576
483334.7963129510979-1.79631295109789
493636.0310005051299-0.0310005051298684
503833.96660542551224.03339457448779
513236.2902778984847-4.29027789848469
523233.1675933917327-1.16759339173265
533233.6769906366068-1.67699063660678
543435.7103321281628-1.71033212816281
553233.888996288921-1.88899628892096
563734.05444639270552.9455536072945
573934.73916747571074.26083252428929
582934.9310675630763-5.93106756307632
593735.30535209971651.69464790028349
603534.29397631736730.706023682632736
613031.6231854838467-1.62318548384667
623834.57371737192623.42628262807384
633434.7557437249452-0.755743724945241
643134.8538165227406-3.85381652274057
653433.64982446051470.350175539485295
663535.4200011467464-0.42000114674637
673634.41885719499921.58114280500084
683032.1353958519238-2.13539585192382
693936.6850260963292.31497390367104
703535.605914911735-0.605914911734993
713835.25843845493142.74156154506863
723135.9533913685386-4.95339136853861
733436.3477814701274-2.34778147012738
743837.22440264049820.775597359501809
753432.28038229450431.71961770549571
763932.67548858860416.32451141139593
773735.82851049090671.17148950909328
783433.22509696337530.774903036624665
792833.6498244605147-5.6498244605147
803732.41513690648274.58486309351728
813335.4708022035011-2.47080220350106
823736.29027789848470.709722101515307
833535.9428014416811-0.942801441681071
843734.46930015549832.53069984450166
853234.7289356451087-2.72893564510867
863333.5722153239235-0.572215323923451
873836.34778147012741.65221852987262
883334.6513265085174-1.65132650851742
892933.6970962015554-4.69709620155535
903333.0970448662849-0.0970448662849019
913135.4912658647051-4.49126586470513
923633.69709620155532.30290379844465
933537.0222707225305-2.02227072253054
943232.4257268333403-0.425726833340268
952933.3594934790983-4.35949347909826
963935.54841134009233.45158865990769
973734.56384363757962.43615636242037
983533.70696993590191.29303006409812
993734.95153122428042.0484687757196
1003235.5381795094903-3.53817950949027
1013835.69339778267282.30660221732722
1023734.56384363757962.43615636242037
1033635.81827866030470.181721339695322
1043232.4624086475234-0.462408647523369
1053336.1456495521597-3.14564955215973
1064033.11715043123356.88284956876653
1073835.21116671389072.78883328610927
1084136.14564955215974.85435044784027
1093634.47600267038631.52399732961366
1104336.23031929989456.76968070010551
1113034.5941810331302-4.59418103313024
1123133.8791225545744-2.87912255457443
1133237.0018070613265-5.00180706132647
1143234.1119499643482-2.11194996434819
1153734.96176305488242.03823694511757
1163734.26681014127522.73318985872481
1173336.5400396537485-3.54003965374849
1183436.6074169597377-2.60741695973771
1193335.1907030526867-2.19070305268665
1203835.12368384295292.87631615704706
1213335.305710195972-2.30571019597202
1223132.1558595131279-1.1558595131279
1233836.48253608210581.51746391789419
1243736.34742337387190.65257662612813
1253333.7845790724931-0.784579072493136
1263134.3038500517138-3.3038500517138
1273934.47917388984494.52082611015513
1284436.70513166127757.29486833872247
1293335.7812387498661-2.78123874986607
1303534.32431371291790.675686287082127
1313234.3038500517138-2.3038500517138
1322833.0497731252443-5.04977312524426
1334035.87542413569194.12457586430815
1342733.1778252223347-6.17782522233469
1353736.0779141499150.922085850084992
1363232.9855670387136-0.985567038713576
1372829.6304132184433-1.63041321844328
1383434.8538165227406-0.853816522740571
1393034.4862345009884-4.48623450098838
1403534.79631295109790.203687048902112
1413134.9109619981277-3.91096199812775
1423234.8636902570871-2.8636902570871
1433034.6717901697215-4.67179016972149
1443034.988571134719-4.988571134719
1453131.0192447568913-0.0192447568912491
1464033.86218820908446.13781179091561
1473232.4458323982888-0.445832398288839
1483635.02561104515760.974388954842391
1493233.5517516627194-1.55175166271938
1503533.11715043123351.88284956876653
1513836.49276791270781.50723208729215
1524235.24856472058486.75143527941516
1533436.3576552044739-2.35765520447391
1543536.1823313663428-1.18233136634283
1553533.79163968363661.20836031636336
1563333.1577196573861-0.157719657386119
1573633.69709620155532.30290379844465
1583235.5283057751437-3.52830577514374
1593335.7812387498661-2.78123874986607
1603434.1013600374906-0.101360037490644
1613234.4290890256012-2.4290890256012
1623434.9786974003725-0.97869740037247

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 36.0003050133238 & 4.99969498667616 \tabularnewline
2 & 39 & 34.3546511084685 & 4.64534889153152 \tabularnewline
3 & 30 & 34.6209891129668 & -4.62098911296681 \tabularnewline
4 & 31 & 34.1588636091333 & -3.15886360913333 \tabularnewline
5 & 34 & 35.2118829064017 & -1.21188290640174 \tabularnewline
6 & 35 & 33.2755399238745 & 1.72446007612549 \tabularnewline
7 & 39 & 32.9658195700205 & 6.03418042997949 \tabularnewline
8 & 34 & 35.3931930669098 & -1.3931930669098 \tabularnewline
9 & 36 & 35.2009348832887 & 0.799065116711313 \tabularnewline
10 & 37 & 36.0105368439258 & 0.989463156074208 \tabularnewline
11 & 38 & 34.0071746516649 & 3.99282534833514 \tabularnewline
12 & 36 & 35.3088814154305 & 0.69111858456945 \tabularnewline
13 & 38 & 34.4760026703863 & 3.52399732961366 \tabularnewline
14 & 39 & 36.0207686745278 & 2.97923132547217 \tabularnewline
15 & 33 & 36.1082515454656 & -3.10825154546562 \tabularnewline
16 & 32 & 34.1793272703374 & -2.1793272703374 \tabularnewline
17 & 36 & 33.8420826441358 & 2.15791735586418 \tabularnewline
18 & 38 & 36.3005097290867 & 1.69949027091327 \tabularnewline
19 & 39 & 36.1350596253022 & 2.86494037469782 \tabularnewline
20 & 32 & 34.0646782233075 & -2.06467822330754 \tabularnewline
21 & 32 & 34.9614049586269 & -2.96140495862693 \tabularnewline
22 & 31 & 33.2720106081605 & -2.27201060816047 \tabularnewline
23 & 39 & 36.0003050133238 & 2.99969498667625 \tabularnewline
24 & 37 & 36.0475767543644 & 0.952423245635601 \tabularnewline
25 & 39 & 34.2770419718772 & 4.72295802812277 \tabularnewline
26 & 41 & 33.7909234911256 & 7.20907650887437 \tabularnewline
27 & 36 & 35.2111667138907 & 0.788833286109275 \tabularnewline
28 & 33 & 35.4609284691545 & -2.46092846915452 \tabularnewline
29 & 33 & 34.2569364069287 & -1.25693640692866 \tabularnewline
30 & 34 & 34.0138771665529 & -0.0138771665528577 \tabularnewline
31 & 31 & 32.9852089424581 & -1.98520894245807 \tabularnewline
32 & 27 & 33.6970962015554 & -6.69709620155535 \tabularnewline
33 & 37 & 34.0071746516649 & 2.99282534833514 \tabularnewline
34 & 34 & 35.8080468297026 & -1.80804682970264 \tabularnewline
35 & 34 & 33.2349706977219 & 0.765029302278133 \tabularnewline
36 & 32 & 34.7388093794552 & -2.7388093794552 \tabularnewline
37 & 29 & 32.7125284990427 & -3.71252849904267 \tabularnewline
38 & 36 & 34.3916910189071 & 1.60830898109291 \tabularnewline
39 & 29 & 34.0071746516649 & -5.00717465166486 \tabularnewline
40 & 35 & 34.371227357703 & 0.628772642296988 \tabularnewline
41 & 37 & 34.2565783106731 & 2.74342168932685 \tabularnewline
42 & 34 & 34.3345455435199 & -0.334545543519911 \tabularnewline
43 & 38 & 34.4015647532536 & 3.59843524674638 \tabularnewline
44 & 35 & 34.0241089971549 & 0.975891002845104 \tabularnewline
45 & 38 & 32.5400177841146 & 5.45998221588538 \tabularnewline
46 & 37 & 34.688366418956 & 2.31163358104398 \tabularnewline
47 & 38 & 36.6172906940842 & 1.38270930591576 \tabularnewline
48 & 33 & 34.7963129510979 & -1.79631295109789 \tabularnewline
49 & 36 & 36.0310005051299 & -0.0310005051298684 \tabularnewline
50 & 38 & 33.9666054255122 & 4.03339457448779 \tabularnewline
51 & 32 & 36.2902778984847 & -4.29027789848469 \tabularnewline
52 & 32 & 33.1675933917327 & -1.16759339173265 \tabularnewline
53 & 32 & 33.6769906366068 & -1.67699063660678 \tabularnewline
54 & 34 & 35.7103321281628 & -1.71033212816281 \tabularnewline
55 & 32 & 33.888996288921 & -1.88899628892096 \tabularnewline
56 & 37 & 34.0544463927055 & 2.9455536072945 \tabularnewline
57 & 39 & 34.7391674757107 & 4.26083252428929 \tabularnewline
58 & 29 & 34.9310675630763 & -5.93106756307632 \tabularnewline
59 & 37 & 35.3053520997165 & 1.69464790028349 \tabularnewline
60 & 35 & 34.2939763173673 & 0.706023682632736 \tabularnewline
61 & 30 & 31.6231854838467 & -1.62318548384667 \tabularnewline
62 & 38 & 34.5737173719262 & 3.42628262807384 \tabularnewline
63 & 34 & 34.7557437249452 & -0.755743724945241 \tabularnewline
64 & 31 & 34.8538165227406 & -3.85381652274057 \tabularnewline
65 & 34 & 33.6498244605147 & 0.350175539485295 \tabularnewline
66 & 35 & 35.4200011467464 & -0.42000114674637 \tabularnewline
67 & 36 & 34.4188571949992 & 1.58114280500084 \tabularnewline
68 & 30 & 32.1353958519238 & -2.13539585192382 \tabularnewline
69 & 39 & 36.685026096329 & 2.31497390367104 \tabularnewline
70 & 35 & 35.605914911735 & -0.605914911734993 \tabularnewline
71 & 38 & 35.2584384549314 & 2.74156154506863 \tabularnewline
72 & 31 & 35.9533913685386 & -4.95339136853861 \tabularnewline
73 & 34 & 36.3477814701274 & -2.34778147012738 \tabularnewline
74 & 38 & 37.2244026404982 & 0.775597359501809 \tabularnewline
75 & 34 & 32.2803822945043 & 1.71961770549571 \tabularnewline
76 & 39 & 32.6754885886041 & 6.32451141139593 \tabularnewline
77 & 37 & 35.8285104909067 & 1.17148950909328 \tabularnewline
78 & 34 & 33.2250969633753 & 0.774903036624665 \tabularnewline
79 & 28 & 33.6498244605147 & -5.6498244605147 \tabularnewline
80 & 37 & 32.4151369064827 & 4.58486309351728 \tabularnewline
81 & 33 & 35.4708022035011 & -2.47080220350106 \tabularnewline
82 & 37 & 36.2902778984847 & 0.709722101515307 \tabularnewline
83 & 35 & 35.9428014416811 & -0.942801441681071 \tabularnewline
84 & 37 & 34.4693001554983 & 2.53069984450166 \tabularnewline
85 & 32 & 34.7289356451087 & -2.72893564510867 \tabularnewline
86 & 33 & 33.5722153239235 & -0.572215323923451 \tabularnewline
87 & 38 & 36.3477814701274 & 1.65221852987262 \tabularnewline
88 & 33 & 34.6513265085174 & -1.65132650851742 \tabularnewline
89 & 29 & 33.6970962015554 & -4.69709620155535 \tabularnewline
90 & 33 & 33.0970448662849 & -0.0970448662849019 \tabularnewline
91 & 31 & 35.4912658647051 & -4.49126586470513 \tabularnewline
92 & 36 & 33.6970962015553 & 2.30290379844465 \tabularnewline
93 & 35 & 37.0222707225305 & -2.02227072253054 \tabularnewline
94 & 32 & 32.4257268333403 & -0.425726833340268 \tabularnewline
95 & 29 & 33.3594934790983 & -4.35949347909826 \tabularnewline
96 & 39 & 35.5484113400923 & 3.45158865990769 \tabularnewline
97 & 37 & 34.5638436375796 & 2.43615636242037 \tabularnewline
98 & 35 & 33.7069699359019 & 1.29303006409812 \tabularnewline
99 & 37 & 34.9515312242804 & 2.0484687757196 \tabularnewline
100 & 32 & 35.5381795094903 & -3.53817950949027 \tabularnewline
101 & 38 & 35.6933977826728 & 2.30660221732722 \tabularnewline
102 & 37 & 34.5638436375796 & 2.43615636242037 \tabularnewline
103 & 36 & 35.8182786603047 & 0.181721339695322 \tabularnewline
104 & 32 & 32.4624086475234 & -0.462408647523369 \tabularnewline
105 & 33 & 36.1456495521597 & -3.14564955215973 \tabularnewline
106 & 40 & 33.1171504312335 & 6.88284956876653 \tabularnewline
107 & 38 & 35.2111667138907 & 2.78883328610927 \tabularnewline
108 & 41 & 36.1456495521597 & 4.85435044784027 \tabularnewline
109 & 36 & 34.4760026703863 & 1.52399732961366 \tabularnewline
110 & 43 & 36.2303192998945 & 6.76968070010551 \tabularnewline
111 & 30 & 34.5941810331302 & -4.59418103313024 \tabularnewline
112 & 31 & 33.8791225545744 & -2.87912255457443 \tabularnewline
113 & 32 & 37.0018070613265 & -5.00180706132647 \tabularnewline
114 & 32 & 34.1119499643482 & -2.11194996434819 \tabularnewline
115 & 37 & 34.9617630548824 & 2.03823694511757 \tabularnewline
116 & 37 & 34.2668101412752 & 2.73318985872481 \tabularnewline
117 & 33 & 36.5400396537485 & -3.54003965374849 \tabularnewline
118 & 34 & 36.6074169597377 & -2.60741695973771 \tabularnewline
119 & 33 & 35.1907030526867 & -2.19070305268665 \tabularnewline
120 & 38 & 35.1236838429529 & 2.87631615704706 \tabularnewline
121 & 33 & 35.305710195972 & -2.30571019597202 \tabularnewline
122 & 31 & 32.1558595131279 & -1.1558595131279 \tabularnewline
123 & 38 & 36.4825360821058 & 1.51746391789419 \tabularnewline
124 & 37 & 36.3474233738719 & 0.65257662612813 \tabularnewline
125 & 33 & 33.7845790724931 & -0.784579072493136 \tabularnewline
126 & 31 & 34.3038500517138 & -3.3038500517138 \tabularnewline
127 & 39 & 34.4791738898449 & 4.52082611015513 \tabularnewline
128 & 44 & 36.7051316612775 & 7.29486833872247 \tabularnewline
129 & 33 & 35.7812387498661 & -2.78123874986607 \tabularnewline
130 & 35 & 34.3243137129179 & 0.675686287082127 \tabularnewline
131 & 32 & 34.3038500517138 & -2.3038500517138 \tabularnewline
132 & 28 & 33.0497731252443 & -5.04977312524426 \tabularnewline
133 & 40 & 35.8754241356919 & 4.12457586430815 \tabularnewline
134 & 27 & 33.1778252223347 & -6.17782522233469 \tabularnewline
135 & 37 & 36.077914149915 & 0.922085850084992 \tabularnewline
136 & 32 & 32.9855670387136 & -0.985567038713576 \tabularnewline
137 & 28 & 29.6304132184433 & -1.63041321844328 \tabularnewline
138 & 34 & 34.8538165227406 & -0.853816522740571 \tabularnewline
139 & 30 & 34.4862345009884 & -4.48623450098838 \tabularnewline
140 & 35 & 34.7963129510979 & 0.203687048902112 \tabularnewline
141 & 31 & 34.9109619981277 & -3.91096199812775 \tabularnewline
142 & 32 & 34.8636902570871 & -2.8636902570871 \tabularnewline
143 & 30 & 34.6717901697215 & -4.67179016972149 \tabularnewline
144 & 30 & 34.988571134719 & -4.988571134719 \tabularnewline
145 & 31 & 31.0192447568913 & -0.0192447568912491 \tabularnewline
146 & 40 & 33.8621882090844 & 6.13781179091561 \tabularnewline
147 & 32 & 32.4458323982888 & -0.445832398288839 \tabularnewline
148 & 36 & 35.0256110451576 & 0.974388954842391 \tabularnewline
149 & 32 & 33.5517516627194 & -1.55175166271938 \tabularnewline
150 & 35 & 33.1171504312335 & 1.88284956876653 \tabularnewline
151 & 38 & 36.4927679127078 & 1.50723208729215 \tabularnewline
152 & 42 & 35.2485647205848 & 6.75143527941516 \tabularnewline
153 & 34 & 36.3576552044739 & -2.35765520447391 \tabularnewline
154 & 35 & 36.1823313663428 & -1.18233136634283 \tabularnewline
155 & 35 & 33.7916396836366 & 1.20836031636336 \tabularnewline
156 & 33 & 33.1577196573861 & -0.157719657386119 \tabularnewline
157 & 36 & 33.6970962015553 & 2.30290379844465 \tabularnewline
158 & 32 & 35.5283057751437 & -3.52830577514374 \tabularnewline
159 & 33 & 35.7812387498661 & -2.78123874986607 \tabularnewline
160 & 34 & 34.1013600374906 & -0.101360037490644 \tabularnewline
161 & 32 & 34.4290890256012 & -2.4290890256012 \tabularnewline
162 & 34 & 34.9786974003725 & -0.97869740037247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&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]36.0003050133238[/C][C]4.99969498667616[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.3546511084685[/C][C]4.64534889153152[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.6209891129668[/C][C]-4.62098911296681[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.1588636091333[/C][C]-3.15886360913333[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.2118829064017[/C][C]-1.21188290640174[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.2755399238745[/C][C]1.72446007612549[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]32.9658195700205[/C][C]6.03418042997949[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.3931930669098[/C][C]-1.3931930669098[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.2009348832887[/C][C]0.799065116711313[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.0105368439258[/C][C]0.989463156074208[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.0071746516649[/C][C]3.99282534833514[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.3088814154305[/C][C]0.69111858456945[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.4760026703863[/C][C]3.52399732961366[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.0207686745278[/C][C]2.97923132547217[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.1082515454656[/C][C]-3.10825154546562[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.1793272703374[/C][C]-2.1793272703374[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.8420826441358[/C][C]2.15791735586418[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.3005097290867[/C][C]1.69949027091327[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.1350596253022[/C][C]2.86494037469782[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.0646782233075[/C][C]-2.06467822330754[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.9614049586269[/C][C]-2.96140495862693[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.2720106081605[/C][C]-2.27201060816047[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.0003050133238[/C][C]2.99969498667625[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.0475767543644[/C][C]0.952423245635601[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]34.2770419718772[/C][C]4.72295802812277[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.7909234911256[/C][C]7.20907650887437[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.2111667138907[/C][C]0.788833286109275[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.4609284691545[/C][C]-2.46092846915452[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.2569364069287[/C][C]-1.25693640692866[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.0138771665529[/C][C]-0.0138771665528577[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.9852089424581[/C][C]-1.98520894245807[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.6970962015554[/C][C]-6.69709620155535[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]34.0071746516649[/C][C]2.99282534833514[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.8080468297026[/C][C]-1.80804682970264[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.2349706977219[/C][C]0.765029302278133[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]34.7388093794552[/C][C]-2.7388093794552[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.7125284990427[/C][C]-3.71252849904267[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.3916910189071[/C][C]1.60830898109291[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.0071746516649[/C][C]-5.00717465166486[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.371227357703[/C][C]0.628772642296988[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.2565783106731[/C][C]2.74342168932685[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.3345455435199[/C][C]-0.334545543519911[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.4015647532536[/C][C]3.59843524674638[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.0241089971549[/C][C]0.975891002845104[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.5400177841146[/C][C]5.45998221588538[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.688366418956[/C][C]2.31163358104398[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.6172906940842[/C][C]1.38270930591576[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7963129510979[/C][C]-1.79631295109789[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.0310005051299[/C][C]-0.0310005051298684[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.9666054255122[/C][C]4.03339457448779[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.2902778984847[/C][C]-4.29027789848469[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.1675933917327[/C][C]-1.16759339173265[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.6769906366068[/C][C]-1.67699063660678[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.7103321281628[/C][C]-1.71033212816281[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.888996288921[/C][C]-1.88899628892096[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.0544463927055[/C][C]2.9455536072945[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.7391674757107[/C][C]4.26083252428929[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.9310675630763[/C][C]-5.93106756307632[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.3053520997165[/C][C]1.69464790028349[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.2939763173673[/C][C]0.706023682632736[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.6231854838467[/C][C]-1.62318548384667[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.5737173719262[/C][C]3.42628262807384[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.7557437249452[/C][C]-0.755743724945241[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.8538165227406[/C][C]-3.85381652274057[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.6498244605147[/C][C]0.350175539485295[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.4200011467464[/C][C]-0.42000114674637[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.4188571949992[/C][C]1.58114280500084[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]32.1353958519238[/C][C]-2.13539585192382[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.685026096329[/C][C]2.31497390367104[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.605914911735[/C][C]-0.605914911734993[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]35.2584384549314[/C][C]2.74156154506863[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.9533913685386[/C][C]-4.95339136853861[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.3477814701274[/C][C]-2.34778147012738[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.2244026404982[/C][C]0.775597359501809[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.2803822945043[/C][C]1.71961770549571[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]32.6754885886041[/C][C]6.32451141139593[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8285104909067[/C][C]1.17148950909328[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.2250969633753[/C][C]0.774903036624665[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.6498244605147[/C][C]-5.6498244605147[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.4151369064827[/C][C]4.58486309351728[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.4708022035011[/C][C]-2.47080220350106[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.2902778984847[/C][C]0.709722101515307[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.9428014416811[/C][C]-0.942801441681071[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.4693001554983[/C][C]2.53069984450166[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.7289356451087[/C][C]-2.72893564510867[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.5722153239235[/C][C]-0.572215323923451[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.3477814701274[/C][C]1.65221852987262[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.6513265085174[/C][C]-1.65132650851742[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.6970962015554[/C][C]-4.69709620155535[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.0970448662849[/C][C]-0.0970448662849019[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]35.4912658647051[/C][C]-4.49126586470513[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.6970962015553[/C][C]2.30290379844465[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.0222707225305[/C][C]-2.02227072253054[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.4257268333403[/C][C]-0.425726833340268[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.3594934790983[/C][C]-4.35949347909826[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.5484113400923[/C][C]3.45158865990769[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.5638436375796[/C][C]2.43615636242037[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]33.7069699359019[/C][C]1.29303006409812[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]34.9515312242804[/C][C]2.0484687757196[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.5381795094903[/C][C]-3.53817950949027[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.6933977826728[/C][C]2.30660221732722[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.5638436375796[/C][C]2.43615636242037[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]35.8182786603047[/C][C]0.181721339695322[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.4624086475234[/C][C]-0.462408647523369[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.1456495521597[/C][C]-3.14564955215973[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.1171504312335[/C][C]6.88284956876653[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.2111667138907[/C][C]2.78883328610927[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.1456495521597[/C][C]4.85435044784027[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.4760026703863[/C][C]1.52399732961366[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.2303192998945[/C][C]6.76968070010551[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.5941810331302[/C][C]-4.59418103313024[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.8791225545744[/C][C]-2.87912255457443[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.0018070613265[/C][C]-5.00180706132647[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.1119499643482[/C][C]-2.11194996434819[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.9617630548824[/C][C]2.03823694511757[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.2668101412752[/C][C]2.73318985872481[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]36.5400396537485[/C][C]-3.54003965374849[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6074169597377[/C][C]-2.60741695973771[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]35.1907030526867[/C][C]-2.19070305268665[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.1236838429529[/C][C]2.87631615704706[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]35.305710195972[/C][C]-2.30571019597202[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.1558595131279[/C][C]-1.1558595131279[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]36.4825360821058[/C][C]1.51746391789419[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.3474233738719[/C][C]0.65257662612813[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.7845790724931[/C][C]-0.784579072493136[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.3038500517138[/C][C]-3.3038500517138[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.4791738898449[/C][C]4.52082611015513[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.7051316612775[/C][C]7.29486833872247[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.7812387498661[/C][C]-2.78123874986607[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]34.3243137129179[/C][C]0.675686287082127[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.3038500517138[/C][C]-2.3038500517138[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.0497731252443[/C][C]-5.04977312524426[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]35.8754241356919[/C][C]4.12457586430815[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]33.1778252223347[/C][C]-6.17782522233469[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]36.077914149915[/C][C]0.922085850084992[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9855670387136[/C][C]-0.985567038713576[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.6304132184433[/C][C]-1.63041321844328[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.8538165227406[/C][C]-0.853816522740571[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]34.4862345009884[/C][C]-4.48623450098838[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.7963129510979[/C][C]0.203687048902112[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]34.9109619981277[/C][C]-3.91096199812775[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.8636902570871[/C][C]-2.8636902570871[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.6717901697215[/C][C]-4.67179016972149[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.988571134719[/C][C]-4.988571134719[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.0192447568913[/C][C]-0.0192447568912491[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]33.8621882090844[/C][C]6.13781179091561[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]32.4458323982888[/C][C]-0.445832398288839[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]35.0256110451576[/C][C]0.974388954842391[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.5517516627194[/C][C]-1.55175166271938[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.1171504312335[/C][C]1.88284956876653[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]36.4927679127078[/C][C]1.50723208729215[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.2485647205848[/C][C]6.75143527941516[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.3576552044739[/C][C]-2.35765520447391[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.1823313663428[/C][C]-1.18233136634283[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.7916396836366[/C][C]1.20836031636336[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.1577196573861[/C][C]-0.157719657386119[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.6970962015553[/C][C]2.30290379844465[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.5283057751437[/C][C]-3.52830577514374[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.7812387498661[/C][C]-2.78123874986607[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.1013600374906[/C][C]-0.101360037490644[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.4290890256012[/C][C]-2.4290890256012[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.9786974003725[/C][C]-0.97869740037247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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
14136.00030501332384.99969498667616
23934.35465110846854.64534889153152
33034.6209891129668-4.62098911296681
43134.1588636091333-3.15886360913333
53435.2118829064017-1.21188290640174
63533.27553992387451.72446007612549
73932.96581957002056.03418042997949
83435.3931930669098-1.3931930669098
93635.20093488328870.799065116711313
103736.01053684392580.989463156074208
113834.00717465166493.99282534833514
123635.30888141543050.69111858456945
133834.47600267038633.52399732961366
143936.02076867452782.97923132547217
153336.1082515454656-3.10825154546562
163234.1793272703374-2.1793272703374
173633.84208264413582.15791735586418
183836.30050972908671.69949027091327
193936.13505962530222.86494037469782
203234.0646782233075-2.06467822330754
213234.9614049586269-2.96140495862693
223133.2720106081605-2.27201060816047
233936.00030501332382.99969498667625
243736.04757675436440.952423245635601
253934.27704197187724.72295802812277
264133.79092349112567.20907650887437
273635.21116671389070.788833286109275
283335.4609284691545-2.46092846915452
293334.2569364069287-1.25693640692866
303434.0138771665529-0.0138771665528577
313132.9852089424581-1.98520894245807
322733.6970962015554-6.69709620155535
333734.00717465166492.99282534833514
343435.8080468297026-1.80804682970264
353433.23497069772190.765029302278133
363234.7388093794552-2.7388093794552
372932.7125284990427-3.71252849904267
383634.39169101890711.60830898109291
392934.0071746516649-5.00717465166486
403534.3712273577030.628772642296988
413734.25657831067312.74342168932685
423434.3345455435199-0.334545543519911
433834.40156475325363.59843524674638
443534.02410899715490.975891002845104
453832.54001778411465.45998221588538
463734.6883664189562.31163358104398
473836.61729069408421.38270930591576
483334.7963129510979-1.79631295109789
493636.0310005051299-0.0310005051298684
503833.96660542551224.03339457448779
513236.2902778984847-4.29027789848469
523233.1675933917327-1.16759339173265
533233.6769906366068-1.67699063660678
543435.7103321281628-1.71033212816281
553233.888996288921-1.88899628892096
563734.05444639270552.9455536072945
573934.73916747571074.26083252428929
582934.9310675630763-5.93106756307632
593735.30535209971651.69464790028349
603534.29397631736730.706023682632736
613031.6231854838467-1.62318548384667
623834.57371737192623.42628262807384
633434.7557437249452-0.755743724945241
643134.8538165227406-3.85381652274057
653433.64982446051470.350175539485295
663535.4200011467464-0.42000114674637
673634.41885719499921.58114280500084
683032.1353958519238-2.13539585192382
693936.6850260963292.31497390367104
703535.605914911735-0.605914911734993
713835.25843845493142.74156154506863
723135.9533913685386-4.95339136853861
733436.3477814701274-2.34778147012738
743837.22440264049820.775597359501809
753432.28038229450431.71961770549571
763932.67548858860416.32451141139593
773735.82851049090671.17148950909328
783433.22509696337530.774903036624665
792833.6498244605147-5.6498244605147
803732.41513690648274.58486309351728
813335.4708022035011-2.47080220350106
823736.29027789848470.709722101515307
833535.9428014416811-0.942801441681071
843734.46930015549832.53069984450166
853234.7289356451087-2.72893564510867
863333.5722153239235-0.572215323923451
873836.34778147012741.65221852987262
883334.6513265085174-1.65132650851742
892933.6970962015554-4.69709620155535
903333.0970448662849-0.0970448662849019
913135.4912658647051-4.49126586470513
923633.69709620155532.30290379844465
933537.0222707225305-2.02227072253054
943232.4257268333403-0.425726833340268
952933.3594934790983-4.35949347909826
963935.54841134009233.45158865990769
973734.56384363757962.43615636242037
983533.70696993590191.29303006409812
993734.95153122428042.0484687757196
1003235.5381795094903-3.53817950949027
1013835.69339778267282.30660221732722
1023734.56384363757962.43615636242037
1033635.81827866030470.181721339695322
1043232.4624086475234-0.462408647523369
1053336.1456495521597-3.14564955215973
1064033.11715043123356.88284956876653
1073835.21116671389072.78883328610927
1084136.14564955215974.85435044784027
1093634.47600267038631.52399732961366
1104336.23031929989456.76968070010551
1113034.5941810331302-4.59418103313024
1123133.8791225545744-2.87912255457443
1133237.0018070613265-5.00180706132647
1143234.1119499643482-2.11194996434819
1153734.96176305488242.03823694511757
1163734.26681014127522.73318985872481
1173336.5400396537485-3.54003965374849
1183436.6074169597377-2.60741695973771
1193335.1907030526867-2.19070305268665
1203835.12368384295292.87631615704706
1213335.305710195972-2.30571019597202
1223132.1558595131279-1.1558595131279
1233836.48253608210581.51746391789419
1243736.34742337387190.65257662612813
1253333.7845790724931-0.784579072493136
1263134.3038500517138-3.3038500517138
1273934.47917388984494.52082611015513
1284436.70513166127757.29486833872247
1293335.7812387498661-2.78123874986607
1303534.32431371291790.675686287082127
1313234.3038500517138-2.3038500517138
1322833.0497731252443-5.04977312524426
1334035.87542413569194.12457586430815
1342733.1778252223347-6.17782522233469
1353736.0779141499150.922085850084992
1363232.9855670387136-0.985567038713576
1372829.6304132184433-1.63041321844328
1383434.8538165227406-0.853816522740571
1393034.4862345009884-4.48623450098838
1403534.79631295109790.203687048902112
1413134.9109619981277-3.91096199812775
1423234.8636902570871-2.8636902570871
1433034.6717901697215-4.67179016972149
1443034.988571134719-4.988571134719
1453131.0192447568913-0.0192447568912491
1464033.86218820908446.13781179091561
1473232.4458323982888-0.445832398288839
1483635.02561104515760.974388954842391
1493233.5517516627194-1.55175166271938
1503533.11715043123351.88284956876653
1513836.49276791270781.50723208729215
1524235.24856472058486.75143527941516
1533436.3576552044739-2.35765520447391
1543536.1823313663428-1.18233136634283
1553533.79163968363661.20836031636336
1563333.1577196573861-0.157719657386119
1573633.69709620155532.30290379844465
1583235.5283057751437-3.52830577514374
1593335.7812387498661-2.78123874986607
1603434.1013600374906-0.101360037490644
1613234.4290890256012-2.4290890256012
1623434.9786974003725-0.97869740037247






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9493495397262890.1013009205474230.0506504602737114
80.9055250787753610.1889498424492780.094474921224639
90.8388398525341930.3223202949316130.161160147465807
100.7541293674107670.4917412651784650.245870632589233
110.6808687837065030.6382624325869950.319131216293497
120.644299263663640.711401472672720.35570073633636
130.7274688122767550.545062375446490.272531187723245
140.6705041905093070.6589916189813850.329495809490693
150.7163960550443870.5672078899112260.283603944955613
160.7178895701808060.5642208596383880.282110429819194
170.6513457864175210.6973084271649570.348654213582479
180.5871126767917670.8257746464164660.412887323208233
190.5823591692115230.8352816615769530.417640830788477
200.5944949829769830.8110100340460330.405505017023017
210.6018908578784460.7962182842431090.398109142121554
220.5662998827197990.8674002345604030.433700117280201
230.5428844300550660.9142311398898680.457115569944934
240.4748363562022950.949672712404590.525163643797705
250.5168602943830120.9662794112339760.483139705616988
260.7506816503531560.4986366992936890.249318349646844
270.6984048861730810.6031902276538380.301595113826919
280.696768093050390.606463813899220.30323190694961
290.6669335051288860.6661329897422270.333066494871114
300.616646313406560.766707373186880.38335368659344
310.6060243735176880.7879512529646240.393975626482312
320.7998768145958390.4002463708083230.200123185404161
330.7839244188855830.4321511622288340.216075581114417
340.7629562915772290.4740874168455420.237043708422771
350.7180612528425470.5638774943149060.281938747157453
360.7155406334714210.5689187330571590.284459366528579
370.7268648194129780.5462703611740430.273135180587022
380.6879665078872040.6240669842255920.312033492112796
390.7602926682378110.4794146635243770.239707331762189
400.7177313102722710.5645373794554580.282268689727729
410.700483056770610.599033886458780.29951694322939
420.6540079028325430.6919841943349150.345992097167457
430.6556731499977820.6886537000044360.344326850002218
440.6103689171331510.7792621657336970.389631082866849
450.6885585941638910.6228828116722170.311441405836109
460.6599862836844290.6800274326311420.340013716315571
470.6175356482660980.7649287034678030.382464351733902
480.5919969742424010.8160060515151980.408003025757599
490.5423938168638290.9152123662723410.457606183136171
500.5583854453435860.8832291093128280.441614554656414
510.6090429853020830.7819140293958340.390957014697917
520.5715653032738980.8568693934522040.428434696726102
530.537581819776710.9248363604465790.46241818022329
540.5040467367368930.9919065265262140.495953263263107
550.4816207822165060.9632415644330120.518379217783494
560.4698232048768410.9396464097536830.530176795123159
570.5043460277945880.9913079444108240.495653972205412
580.6351220345832930.7297559308334150.364877965416707
590.6013770987031110.7972458025937780.398622901296889
600.5567150189109320.8865699621781350.443284981089068
610.5230479733986030.9539040532027940.476952026601397
620.5275181507220570.9449636985558850.472481849277943
630.4845035872431350.9690071744862690.515496412756865
640.5077229195564940.9845541608870120.492277080443506
650.4616928556491820.9233857112983650.538307144350818
660.4168684210359180.8337368420718360.583131578964082
670.3842587679581070.7685175359162130.615741232041893
680.36403251211690.7280650242338010.6359674878831
690.3425265126988090.6850530253976170.657473487301191
700.3028462760715470.6056925521430930.697153723928453
710.2916395313963880.5832790627927760.708360468603612
720.3535474797105750.707094959421150.646452520289425
730.3341284069602780.6682568139205550.665871593039723
740.2960013211238170.5920026422476330.703998678876184
750.2696457880300720.5392915760601440.730354211969928
760.3914354637626740.7828709275253480.608564536237326
770.3535704170802960.7071408341605920.646429582919704
780.3141262015909310.6282524031818630.685873798409069
790.410918101507520.821836203015040.58908189849248
800.4705068467720680.9410136935441350.529493153227932
810.4516473552052930.9032947104105860.548352644794707
820.4094598583633160.8189197167266310.590540141636684
830.3689823961318710.7379647922637420.631017603868129
840.3539343434217250.707868686843450.646065656578275
850.3423580397614520.6847160795229040.657641960238548
860.303521530109640.6070430602192810.69647846989036
870.2754800540883820.5509601081767650.724519945911618
880.2476197523417430.4952395046834850.752380247658257
890.2910974289353980.5821948578707960.708902571064602
900.2530075544688180.5060151089376370.746992445531182
910.2944404667217110.5888809334434230.705559533278289
920.277084336200250.5541686724005010.72291566379975
930.2561114657180340.5122229314360680.743888534281966
940.2215011046763940.4430022093527890.778498895323606
950.2454510194189980.4909020388379950.754548980581002
960.2521349532389110.5042699064778220.747865046761089
970.2377563525804710.4755127051609420.762243647419529
980.2123147845055820.4246295690111630.787685215494418
990.1934494159048330.3868988318096660.806550584095167
1000.196863482757270.3937269655145410.803136517242729
1010.1825466782588690.3650933565177380.817453321741131
1020.1706690326292780.3413380652585560.829330967370722
1030.1424776080922160.2849552161844330.857522391907784
1040.1202221444118650.240444288823730.879777855588135
1050.1244038768179160.2488077536358320.875596123182084
1060.2570754843199930.5141509686399870.742924515680007
1070.2493531006505780.4987062013011560.750646899349422
1080.2887002901923130.5774005803846250.711299709807687
1090.2696492442200240.5392984884400480.730350755779976
1100.4540373476453330.9080746952906660.545962652354667
1110.4995638136430790.9991276272861580.500436186356921
1120.4758837710306110.9517675420612230.524116228969389
1130.53289946282490.9342010743502010.4671005371751
1140.498844886296010.9976897725920210.501155113703989
1150.4663411271526910.9326822543053830.533658872847309
1160.4558110766028890.9116221532057780.544188923397111
1170.4609884234453080.9219768468906150.539011576554692
1180.4429196218306630.8858392436613260.557080378169337
1190.4123556593189270.8247113186378550.587644340681073
1200.4118749335254760.8237498670509510.588125066474524
1210.3794655042199160.7589310084398320.620534495780084
1220.3337732341759160.6675464683518320.666226765824084
1230.2957572300334450.5915144600668890.704242769966555
1240.2520247152165850.504049430433170.747975284783415
1250.2115930687067660.4231861374135320.788406931293234
1260.2068698769298860.4137397538597730.793130123070114
1270.2483907184298820.4967814368597650.751609281570118
1280.488388260848010.9767765216960210.51161173915199
1290.4561090797410420.9122181594820850.543890920258958
1300.4089402019247490.8178804038494970.591059798075251
1310.3703909922596960.7407819845193920.629609007740304
1320.4333031923885170.8666063847770340.566696807611483
1330.513532036804460.972935926391080.48646796319554
1340.6441407708106110.7117184583787780.355859229189389
1350.6037764594832740.7924470810334530.396223540516726
1360.5406967314011460.9186065371977080.459303268598854
1370.5332513599421760.9334972801156480.466748640057824
1380.4661964367354180.9323928734708370.533803563264582
1390.5328702558753630.9342594882492740.467129744124637
1400.4731811059681260.9463622119362520.526818894031874
1410.4753839279011260.9507678558022510.524616072098874
1420.4285027045178480.8570054090356950.571497295482152
1430.4975803722399360.9951607444798720.502419627760064
1440.5947873704326080.8104252591347840.405212629567392
1450.5218336707050520.9563326585898960.478166329294948
1460.8018097310670530.3963805378658950.198190268932947
1470.7364147612279720.5271704775440560.263585238772028
1480.6596410470213360.6807179059573290.340358952978664
1490.570908710755640.8581825784887190.42909128924436
1500.4702542474575420.9405084949150830.529745752542458
1510.4396732858653310.8793465717306620.560326714134669
1520.980605328188810.03878934362237980.0193946718111899
1530.9533080707188470.09338385856230610.0466919292811531
1540.916627806845010.1667443863099790.0833721931549897
1550.8510070951965050.2979858096069910.148992904803495

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.949349539726289 & 0.101300920547423 & 0.0506504602737114 \tabularnewline
8 & 0.905525078775361 & 0.188949842449278 & 0.094474921224639 \tabularnewline
9 & 0.838839852534193 & 0.322320294931613 & 0.161160147465807 \tabularnewline
10 & 0.754129367410767 & 0.491741265178465 & 0.245870632589233 \tabularnewline
11 & 0.680868783706503 & 0.638262432586995 & 0.319131216293497 \tabularnewline
12 & 0.64429926366364 & 0.71140147267272 & 0.35570073633636 \tabularnewline
13 & 0.727468812276755 & 0.54506237544649 & 0.272531187723245 \tabularnewline
14 & 0.670504190509307 & 0.658991618981385 & 0.329495809490693 \tabularnewline
15 & 0.716396055044387 & 0.567207889911226 & 0.283603944955613 \tabularnewline
16 & 0.717889570180806 & 0.564220859638388 & 0.282110429819194 \tabularnewline
17 & 0.651345786417521 & 0.697308427164957 & 0.348654213582479 \tabularnewline
18 & 0.587112676791767 & 0.825774646416466 & 0.412887323208233 \tabularnewline
19 & 0.582359169211523 & 0.835281661576953 & 0.417640830788477 \tabularnewline
20 & 0.594494982976983 & 0.811010034046033 & 0.405505017023017 \tabularnewline
21 & 0.601890857878446 & 0.796218284243109 & 0.398109142121554 \tabularnewline
22 & 0.566299882719799 & 0.867400234560403 & 0.433700117280201 \tabularnewline
23 & 0.542884430055066 & 0.914231139889868 & 0.457115569944934 \tabularnewline
24 & 0.474836356202295 & 0.94967271240459 & 0.525163643797705 \tabularnewline
25 & 0.516860294383012 & 0.966279411233976 & 0.483139705616988 \tabularnewline
26 & 0.750681650353156 & 0.498636699293689 & 0.249318349646844 \tabularnewline
27 & 0.698404886173081 & 0.603190227653838 & 0.301595113826919 \tabularnewline
28 & 0.69676809305039 & 0.60646381389922 & 0.30323190694961 \tabularnewline
29 & 0.666933505128886 & 0.666132989742227 & 0.333066494871114 \tabularnewline
30 & 0.61664631340656 & 0.76670737318688 & 0.38335368659344 \tabularnewline
31 & 0.606024373517688 & 0.787951252964624 & 0.393975626482312 \tabularnewline
32 & 0.799876814595839 & 0.400246370808323 & 0.200123185404161 \tabularnewline
33 & 0.783924418885583 & 0.432151162228834 & 0.216075581114417 \tabularnewline
34 & 0.762956291577229 & 0.474087416845542 & 0.237043708422771 \tabularnewline
35 & 0.718061252842547 & 0.563877494314906 & 0.281938747157453 \tabularnewline
36 & 0.715540633471421 & 0.568918733057159 & 0.284459366528579 \tabularnewline
37 & 0.726864819412978 & 0.546270361174043 & 0.273135180587022 \tabularnewline
38 & 0.687966507887204 & 0.624066984225592 & 0.312033492112796 \tabularnewline
39 & 0.760292668237811 & 0.479414663524377 & 0.239707331762189 \tabularnewline
40 & 0.717731310272271 & 0.564537379455458 & 0.282268689727729 \tabularnewline
41 & 0.70048305677061 & 0.59903388645878 & 0.29951694322939 \tabularnewline
42 & 0.654007902832543 & 0.691984194334915 & 0.345992097167457 \tabularnewline
43 & 0.655673149997782 & 0.688653700004436 & 0.344326850002218 \tabularnewline
44 & 0.610368917133151 & 0.779262165733697 & 0.389631082866849 \tabularnewline
45 & 0.688558594163891 & 0.622882811672217 & 0.311441405836109 \tabularnewline
46 & 0.659986283684429 & 0.680027432631142 & 0.340013716315571 \tabularnewline
47 & 0.617535648266098 & 0.764928703467803 & 0.382464351733902 \tabularnewline
48 & 0.591996974242401 & 0.816006051515198 & 0.408003025757599 \tabularnewline
49 & 0.542393816863829 & 0.915212366272341 & 0.457606183136171 \tabularnewline
50 & 0.558385445343586 & 0.883229109312828 & 0.441614554656414 \tabularnewline
51 & 0.609042985302083 & 0.781914029395834 & 0.390957014697917 \tabularnewline
52 & 0.571565303273898 & 0.856869393452204 & 0.428434696726102 \tabularnewline
53 & 0.53758181977671 & 0.924836360446579 & 0.46241818022329 \tabularnewline
54 & 0.504046736736893 & 0.991906526526214 & 0.495953263263107 \tabularnewline
55 & 0.481620782216506 & 0.963241564433012 & 0.518379217783494 \tabularnewline
56 & 0.469823204876841 & 0.939646409753683 & 0.530176795123159 \tabularnewline
57 & 0.504346027794588 & 0.991307944410824 & 0.495653972205412 \tabularnewline
58 & 0.635122034583293 & 0.729755930833415 & 0.364877965416707 \tabularnewline
59 & 0.601377098703111 & 0.797245802593778 & 0.398622901296889 \tabularnewline
60 & 0.556715018910932 & 0.886569962178135 & 0.443284981089068 \tabularnewline
61 & 0.523047973398603 & 0.953904053202794 & 0.476952026601397 \tabularnewline
62 & 0.527518150722057 & 0.944963698555885 & 0.472481849277943 \tabularnewline
63 & 0.484503587243135 & 0.969007174486269 & 0.515496412756865 \tabularnewline
64 & 0.507722919556494 & 0.984554160887012 & 0.492277080443506 \tabularnewline
65 & 0.461692855649182 & 0.923385711298365 & 0.538307144350818 \tabularnewline
66 & 0.416868421035918 & 0.833736842071836 & 0.583131578964082 \tabularnewline
67 & 0.384258767958107 & 0.768517535916213 & 0.615741232041893 \tabularnewline
68 & 0.3640325121169 & 0.728065024233801 & 0.6359674878831 \tabularnewline
69 & 0.342526512698809 & 0.685053025397617 & 0.657473487301191 \tabularnewline
70 & 0.302846276071547 & 0.605692552143093 & 0.697153723928453 \tabularnewline
71 & 0.291639531396388 & 0.583279062792776 & 0.708360468603612 \tabularnewline
72 & 0.353547479710575 & 0.70709495942115 & 0.646452520289425 \tabularnewline
73 & 0.334128406960278 & 0.668256813920555 & 0.665871593039723 \tabularnewline
74 & 0.296001321123817 & 0.592002642247633 & 0.703998678876184 \tabularnewline
75 & 0.269645788030072 & 0.539291576060144 & 0.730354211969928 \tabularnewline
76 & 0.391435463762674 & 0.782870927525348 & 0.608564536237326 \tabularnewline
77 & 0.353570417080296 & 0.707140834160592 & 0.646429582919704 \tabularnewline
78 & 0.314126201590931 & 0.628252403181863 & 0.685873798409069 \tabularnewline
79 & 0.41091810150752 & 0.82183620301504 & 0.58908189849248 \tabularnewline
80 & 0.470506846772068 & 0.941013693544135 & 0.529493153227932 \tabularnewline
81 & 0.451647355205293 & 0.903294710410586 & 0.548352644794707 \tabularnewline
82 & 0.409459858363316 & 0.818919716726631 & 0.590540141636684 \tabularnewline
83 & 0.368982396131871 & 0.737964792263742 & 0.631017603868129 \tabularnewline
84 & 0.353934343421725 & 0.70786868684345 & 0.646065656578275 \tabularnewline
85 & 0.342358039761452 & 0.684716079522904 & 0.657641960238548 \tabularnewline
86 & 0.30352153010964 & 0.607043060219281 & 0.69647846989036 \tabularnewline
87 & 0.275480054088382 & 0.550960108176765 & 0.724519945911618 \tabularnewline
88 & 0.247619752341743 & 0.495239504683485 & 0.752380247658257 \tabularnewline
89 & 0.291097428935398 & 0.582194857870796 & 0.708902571064602 \tabularnewline
90 & 0.253007554468818 & 0.506015108937637 & 0.746992445531182 \tabularnewline
91 & 0.294440466721711 & 0.588880933443423 & 0.705559533278289 \tabularnewline
92 & 0.27708433620025 & 0.554168672400501 & 0.72291566379975 \tabularnewline
93 & 0.256111465718034 & 0.512222931436068 & 0.743888534281966 \tabularnewline
94 & 0.221501104676394 & 0.443002209352789 & 0.778498895323606 \tabularnewline
95 & 0.245451019418998 & 0.490902038837995 & 0.754548980581002 \tabularnewline
96 & 0.252134953238911 & 0.504269906477822 & 0.747865046761089 \tabularnewline
97 & 0.237756352580471 & 0.475512705160942 & 0.762243647419529 \tabularnewline
98 & 0.212314784505582 & 0.424629569011163 & 0.787685215494418 \tabularnewline
99 & 0.193449415904833 & 0.386898831809666 & 0.806550584095167 \tabularnewline
100 & 0.19686348275727 & 0.393726965514541 & 0.803136517242729 \tabularnewline
101 & 0.182546678258869 & 0.365093356517738 & 0.817453321741131 \tabularnewline
102 & 0.170669032629278 & 0.341338065258556 & 0.829330967370722 \tabularnewline
103 & 0.142477608092216 & 0.284955216184433 & 0.857522391907784 \tabularnewline
104 & 0.120222144411865 & 0.24044428882373 & 0.879777855588135 \tabularnewline
105 & 0.124403876817916 & 0.248807753635832 & 0.875596123182084 \tabularnewline
106 & 0.257075484319993 & 0.514150968639987 & 0.742924515680007 \tabularnewline
107 & 0.249353100650578 & 0.498706201301156 & 0.750646899349422 \tabularnewline
108 & 0.288700290192313 & 0.577400580384625 & 0.711299709807687 \tabularnewline
109 & 0.269649244220024 & 0.539298488440048 & 0.730350755779976 \tabularnewline
110 & 0.454037347645333 & 0.908074695290666 & 0.545962652354667 \tabularnewline
111 & 0.499563813643079 & 0.999127627286158 & 0.500436186356921 \tabularnewline
112 & 0.475883771030611 & 0.951767542061223 & 0.524116228969389 \tabularnewline
113 & 0.5328994628249 & 0.934201074350201 & 0.4671005371751 \tabularnewline
114 & 0.49884488629601 & 0.997689772592021 & 0.501155113703989 \tabularnewline
115 & 0.466341127152691 & 0.932682254305383 & 0.533658872847309 \tabularnewline
116 & 0.455811076602889 & 0.911622153205778 & 0.544188923397111 \tabularnewline
117 & 0.460988423445308 & 0.921976846890615 & 0.539011576554692 \tabularnewline
118 & 0.442919621830663 & 0.885839243661326 & 0.557080378169337 \tabularnewline
119 & 0.412355659318927 & 0.824711318637855 & 0.587644340681073 \tabularnewline
120 & 0.411874933525476 & 0.823749867050951 & 0.588125066474524 \tabularnewline
121 & 0.379465504219916 & 0.758931008439832 & 0.620534495780084 \tabularnewline
122 & 0.333773234175916 & 0.667546468351832 & 0.666226765824084 \tabularnewline
123 & 0.295757230033445 & 0.591514460066889 & 0.704242769966555 \tabularnewline
124 & 0.252024715216585 & 0.50404943043317 & 0.747975284783415 \tabularnewline
125 & 0.211593068706766 & 0.423186137413532 & 0.788406931293234 \tabularnewline
126 & 0.206869876929886 & 0.413739753859773 & 0.793130123070114 \tabularnewline
127 & 0.248390718429882 & 0.496781436859765 & 0.751609281570118 \tabularnewline
128 & 0.48838826084801 & 0.976776521696021 & 0.51161173915199 \tabularnewline
129 & 0.456109079741042 & 0.912218159482085 & 0.543890920258958 \tabularnewline
130 & 0.408940201924749 & 0.817880403849497 & 0.591059798075251 \tabularnewline
131 & 0.370390992259696 & 0.740781984519392 & 0.629609007740304 \tabularnewline
132 & 0.433303192388517 & 0.866606384777034 & 0.566696807611483 \tabularnewline
133 & 0.51353203680446 & 0.97293592639108 & 0.48646796319554 \tabularnewline
134 & 0.644140770810611 & 0.711718458378778 & 0.355859229189389 \tabularnewline
135 & 0.603776459483274 & 0.792447081033453 & 0.396223540516726 \tabularnewline
136 & 0.540696731401146 & 0.918606537197708 & 0.459303268598854 \tabularnewline
137 & 0.533251359942176 & 0.933497280115648 & 0.466748640057824 \tabularnewline
138 & 0.466196436735418 & 0.932392873470837 & 0.533803563264582 \tabularnewline
139 & 0.532870255875363 & 0.934259488249274 & 0.467129744124637 \tabularnewline
140 & 0.473181105968126 & 0.946362211936252 & 0.526818894031874 \tabularnewline
141 & 0.475383927901126 & 0.950767855802251 & 0.524616072098874 \tabularnewline
142 & 0.428502704517848 & 0.857005409035695 & 0.571497295482152 \tabularnewline
143 & 0.497580372239936 & 0.995160744479872 & 0.502419627760064 \tabularnewline
144 & 0.594787370432608 & 0.810425259134784 & 0.405212629567392 \tabularnewline
145 & 0.521833670705052 & 0.956332658589896 & 0.478166329294948 \tabularnewline
146 & 0.801809731067053 & 0.396380537865895 & 0.198190268932947 \tabularnewline
147 & 0.736414761227972 & 0.527170477544056 & 0.263585238772028 \tabularnewline
148 & 0.659641047021336 & 0.680717905957329 & 0.340358952978664 \tabularnewline
149 & 0.57090871075564 & 0.858182578488719 & 0.42909128924436 \tabularnewline
150 & 0.470254247457542 & 0.940508494915083 & 0.529745752542458 \tabularnewline
151 & 0.439673285865331 & 0.879346571730662 & 0.560326714134669 \tabularnewline
152 & 0.98060532818881 & 0.0387893436223798 & 0.0193946718111899 \tabularnewline
153 & 0.953308070718847 & 0.0933838585623061 & 0.0466919292811531 \tabularnewline
154 & 0.91662780684501 & 0.166744386309979 & 0.0833721931549897 \tabularnewline
155 & 0.851007095196505 & 0.297985809606991 & 0.148992904803495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&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]7[/C][C]0.949349539726289[/C][C]0.101300920547423[/C][C]0.0506504602737114[/C][/ROW]
[ROW][C]8[/C][C]0.905525078775361[/C][C]0.188949842449278[/C][C]0.094474921224639[/C][/ROW]
[ROW][C]9[/C][C]0.838839852534193[/C][C]0.322320294931613[/C][C]0.161160147465807[/C][/ROW]
[ROW][C]10[/C][C]0.754129367410767[/C][C]0.491741265178465[/C][C]0.245870632589233[/C][/ROW]
[ROW][C]11[/C][C]0.680868783706503[/C][C]0.638262432586995[/C][C]0.319131216293497[/C][/ROW]
[ROW][C]12[/C][C]0.64429926366364[/C][C]0.71140147267272[/C][C]0.35570073633636[/C][/ROW]
[ROW][C]13[/C][C]0.727468812276755[/C][C]0.54506237544649[/C][C]0.272531187723245[/C][/ROW]
[ROW][C]14[/C][C]0.670504190509307[/C][C]0.658991618981385[/C][C]0.329495809490693[/C][/ROW]
[ROW][C]15[/C][C]0.716396055044387[/C][C]0.567207889911226[/C][C]0.283603944955613[/C][/ROW]
[ROW][C]16[/C][C]0.717889570180806[/C][C]0.564220859638388[/C][C]0.282110429819194[/C][/ROW]
[ROW][C]17[/C][C]0.651345786417521[/C][C]0.697308427164957[/C][C]0.348654213582479[/C][/ROW]
[ROW][C]18[/C][C]0.587112676791767[/C][C]0.825774646416466[/C][C]0.412887323208233[/C][/ROW]
[ROW][C]19[/C][C]0.582359169211523[/C][C]0.835281661576953[/C][C]0.417640830788477[/C][/ROW]
[ROW][C]20[/C][C]0.594494982976983[/C][C]0.811010034046033[/C][C]0.405505017023017[/C][/ROW]
[ROW][C]21[/C][C]0.601890857878446[/C][C]0.796218284243109[/C][C]0.398109142121554[/C][/ROW]
[ROW][C]22[/C][C]0.566299882719799[/C][C]0.867400234560403[/C][C]0.433700117280201[/C][/ROW]
[ROW][C]23[/C][C]0.542884430055066[/C][C]0.914231139889868[/C][C]0.457115569944934[/C][/ROW]
[ROW][C]24[/C][C]0.474836356202295[/C][C]0.94967271240459[/C][C]0.525163643797705[/C][/ROW]
[ROW][C]25[/C][C]0.516860294383012[/C][C]0.966279411233976[/C][C]0.483139705616988[/C][/ROW]
[ROW][C]26[/C][C]0.750681650353156[/C][C]0.498636699293689[/C][C]0.249318349646844[/C][/ROW]
[ROW][C]27[/C][C]0.698404886173081[/C][C]0.603190227653838[/C][C]0.301595113826919[/C][/ROW]
[ROW][C]28[/C][C]0.69676809305039[/C][C]0.60646381389922[/C][C]0.30323190694961[/C][/ROW]
[ROW][C]29[/C][C]0.666933505128886[/C][C]0.666132989742227[/C][C]0.333066494871114[/C][/ROW]
[ROW][C]30[/C][C]0.61664631340656[/C][C]0.76670737318688[/C][C]0.38335368659344[/C][/ROW]
[ROW][C]31[/C][C]0.606024373517688[/C][C]0.787951252964624[/C][C]0.393975626482312[/C][/ROW]
[ROW][C]32[/C][C]0.799876814595839[/C][C]0.400246370808323[/C][C]0.200123185404161[/C][/ROW]
[ROW][C]33[/C][C]0.783924418885583[/C][C]0.432151162228834[/C][C]0.216075581114417[/C][/ROW]
[ROW][C]34[/C][C]0.762956291577229[/C][C]0.474087416845542[/C][C]0.237043708422771[/C][/ROW]
[ROW][C]35[/C][C]0.718061252842547[/C][C]0.563877494314906[/C][C]0.281938747157453[/C][/ROW]
[ROW][C]36[/C][C]0.715540633471421[/C][C]0.568918733057159[/C][C]0.284459366528579[/C][/ROW]
[ROW][C]37[/C][C]0.726864819412978[/C][C]0.546270361174043[/C][C]0.273135180587022[/C][/ROW]
[ROW][C]38[/C][C]0.687966507887204[/C][C]0.624066984225592[/C][C]0.312033492112796[/C][/ROW]
[ROW][C]39[/C][C]0.760292668237811[/C][C]0.479414663524377[/C][C]0.239707331762189[/C][/ROW]
[ROW][C]40[/C][C]0.717731310272271[/C][C]0.564537379455458[/C][C]0.282268689727729[/C][/ROW]
[ROW][C]41[/C][C]0.70048305677061[/C][C]0.59903388645878[/C][C]0.29951694322939[/C][/ROW]
[ROW][C]42[/C][C]0.654007902832543[/C][C]0.691984194334915[/C][C]0.345992097167457[/C][/ROW]
[ROW][C]43[/C][C]0.655673149997782[/C][C]0.688653700004436[/C][C]0.344326850002218[/C][/ROW]
[ROW][C]44[/C][C]0.610368917133151[/C][C]0.779262165733697[/C][C]0.389631082866849[/C][/ROW]
[ROW][C]45[/C][C]0.688558594163891[/C][C]0.622882811672217[/C][C]0.311441405836109[/C][/ROW]
[ROW][C]46[/C][C]0.659986283684429[/C][C]0.680027432631142[/C][C]0.340013716315571[/C][/ROW]
[ROW][C]47[/C][C]0.617535648266098[/C][C]0.764928703467803[/C][C]0.382464351733902[/C][/ROW]
[ROW][C]48[/C][C]0.591996974242401[/C][C]0.816006051515198[/C][C]0.408003025757599[/C][/ROW]
[ROW][C]49[/C][C]0.542393816863829[/C][C]0.915212366272341[/C][C]0.457606183136171[/C][/ROW]
[ROW][C]50[/C][C]0.558385445343586[/C][C]0.883229109312828[/C][C]0.441614554656414[/C][/ROW]
[ROW][C]51[/C][C]0.609042985302083[/C][C]0.781914029395834[/C][C]0.390957014697917[/C][/ROW]
[ROW][C]52[/C][C]0.571565303273898[/C][C]0.856869393452204[/C][C]0.428434696726102[/C][/ROW]
[ROW][C]53[/C][C]0.53758181977671[/C][C]0.924836360446579[/C][C]0.46241818022329[/C][/ROW]
[ROW][C]54[/C][C]0.504046736736893[/C][C]0.991906526526214[/C][C]0.495953263263107[/C][/ROW]
[ROW][C]55[/C][C]0.481620782216506[/C][C]0.963241564433012[/C][C]0.518379217783494[/C][/ROW]
[ROW][C]56[/C][C]0.469823204876841[/C][C]0.939646409753683[/C][C]0.530176795123159[/C][/ROW]
[ROW][C]57[/C][C]0.504346027794588[/C][C]0.991307944410824[/C][C]0.495653972205412[/C][/ROW]
[ROW][C]58[/C][C]0.635122034583293[/C][C]0.729755930833415[/C][C]0.364877965416707[/C][/ROW]
[ROW][C]59[/C][C]0.601377098703111[/C][C]0.797245802593778[/C][C]0.398622901296889[/C][/ROW]
[ROW][C]60[/C][C]0.556715018910932[/C][C]0.886569962178135[/C][C]0.443284981089068[/C][/ROW]
[ROW][C]61[/C][C]0.523047973398603[/C][C]0.953904053202794[/C][C]0.476952026601397[/C][/ROW]
[ROW][C]62[/C][C]0.527518150722057[/C][C]0.944963698555885[/C][C]0.472481849277943[/C][/ROW]
[ROW][C]63[/C][C]0.484503587243135[/C][C]0.969007174486269[/C][C]0.515496412756865[/C][/ROW]
[ROW][C]64[/C][C]0.507722919556494[/C][C]0.984554160887012[/C][C]0.492277080443506[/C][/ROW]
[ROW][C]65[/C][C]0.461692855649182[/C][C]0.923385711298365[/C][C]0.538307144350818[/C][/ROW]
[ROW][C]66[/C][C]0.416868421035918[/C][C]0.833736842071836[/C][C]0.583131578964082[/C][/ROW]
[ROW][C]67[/C][C]0.384258767958107[/C][C]0.768517535916213[/C][C]0.615741232041893[/C][/ROW]
[ROW][C]68[/C][C]0.3640325121169[/C][C]0.728065024233801[/C][C]0.6359674878831[/C][/ROW]
[ROW][C]69[/C][C]0.342526512698809[/C][C]0.685053025397617[/C][C]0.657473487301191[/C][/ROW]
[ROW][C]70[/C][C]0.302846276071547[/C][C]0.605692552143093[/C][C]0.697153723928453[/C][/ROW]
[ROW][C]71[/C][C]0.291639531396388[/C][C]0.583279062792776[/C][C]0.708360468603612[/C][/ROW]
[ROW][C]72[/C][C]0.353547479710575[/C][C]0.70709495942115[/C][C]0.646452520289425[/C][/ROW]
[ROW][C]73[/C][C]0.334128406960278[/C][C]0.668256813920555[/C][C]0.665871593039723[/C][/ROW]
[ROW][C]74[/C][C]0.296001321123817[/C][C]0.592002642247633[/C][C]0.703998678876184[/C][/ROW]
[ROW][C]75[/C][C]0.269645788030072[/C][C]0.539291576060144[/C][C]0.730354211969928[/C][/ROW]
[ROW][C]76[/C][C]0.391435463762674[/C][C]0.782870927525348[/C][C]0.608564536237326[/C][/ROW]
[ROW][C]77[/C][C]0.353570417080296[/C][C]0.707140834160592[/C][C]0.646429582919704[/C][/ROW]
[ROW][C]78[/C][C]0.314126201590931[/C][C]0.628252403181863[/C][C]0.685873798409069[/C][/ROW]
[ROW][C]79[/C][C]0.41091810150752[/C][C]0.82183620301504[/C][C]0.58908189849248[/C][/ROW]
[ROW][C]80[/C][C]0.470506846772068[/C][C]0.941013693544135[/C][C]0.529493153227932[/C][/ROW]
[ROW][C]81[/C][C]0.451647355205293[/C][C]0.903294710410586[/C][C]0.548352644794707[/C][/ROW]
[ROW][C]82[/C][C]0.409459858363316[/C][C]0.818919716726631[/C][C]0.590540141636684[/C][/ROW]
[ROW][C]83[/C][C]0.368982396131871[/C][C]0.737964792263742[/C][C]0.631017603868129[/C][/ROW]
[ROW][C]84[/C][C]0.353934343421725[/C][C]0.70786868684345[/C][C]0.646065656578275[/C][/ROW]
[ROW][C]85[/C][C]0.342358039761452[/C][C]0.684716079522904[/C][C]0.657641960238548[/C][/ROW]
[ROW][C]86[/C][C]0.30352153010964[/C][C]0.607043060219281[/C][C]0.69647846989036[/C][/ROW]
[ROW][C]87[/C][C]0.275480054088382[/C][C]0.550960108176765[/C][C]0.724519945911618[/C][/ROW]
[ROW][C]88[/C][C]0.247619752341743[/C][C]0.495239504683485[/C][C]0.752380247658257[/C][/ROW]
[ROW][C]89[/C][C]0.291097428935398[/C][C]0.582194857870796[/C][C]0.708902571064602[/C][/ROW]
[ROW][C]90[/C][C]0.253007554468818[/C][C]0.506015108937637[/C][C]0.746992445531182[/C][/ROW]
[ROW][C]91[/C][C]0.294440466721711[/C][C]0.588880933443423[/C][C]0.705559533278289[/C][/ROW]
[ROW][C]92[/C][C]0.27708433620025[/C][C]0.554168672400501[/C][C]0.72291566379975[/C][/ROW]
[ROW][C]93[/C][C]0.256111465718034[/C][C]0.512222931436068[/C][C]0.743888534281966[/C][/ROW]
[ROW][C]94[/C][C]0.221501104676394[/C][C]0.443002209352789[/C][C]0.778498895323606[/C][/ROW]
[ROW][C]95[/C][C]0.245451019418998[/C][C]0.490902038837995[/C][C]0.754548980581002[/C][/ROW]
[ROW][C]96[/C][C]0.252134953238911[/C][C]0.504269906477822[/C][C]0.747865046761089[/C][/ROW]
[ROW][C]97[/C][C]0.237756352580471[/C][C]0.475512705160942[/C][C]0.762243647419529[/C][/ROW]
[ROW][C]98[/C][C]0.212314784505582[/C][C]0.424629569011163[/C][C]0.787685215494418[/C][/ROW]
[ROW][C]99[/C][C]0.193449415904833[/C][C]0.386898831809666[/C][C]0.806550584095167[/C][/ROW]
[ROW][C]100[/C][C]0.19686348275727[/C][C]0.393726965514541[/C][C]0.803136517242729[/C][/ROW]
[ROW][C]101[/C][C]0.182546678258869[/C][C]0.365093356517738[/C][C]0.817453321741131[/C][/ROW]
[ROW][C]102[/C][C]0.170669032629278[/C][C]0.341338065258556[/C][C]0.829330967370722[/C][/ROW]
[ROW][C]103[/C][C]0.142477608092216[/C][C]0.284955216184433[/C][C]0.857522391907784[/C][/ROW]
[ROW][C]104[/C][C]0.120222144411865[/C][C]0.24044428882373[/C][C]0.879777855588135[/C][/ROW]
[ROW][C]105[/C][C]0.124403876817916[/C][C]0.248807753635832[/C][C]0.875596123182084[/C][/ROW]
[ROW][C]106[/C][C]0.257075484319993[/C][C]0.514150968639987[/C][C]0.742924515680007[/C][/ROW]
[ROW][C]107[/C][C]0.249353100650578[/C][C]0.498706201301156[/C][C]0.750646899349422[/C][/ROW]
[ROW][C]108[/C][C]0.288700290192313[/C][C]0.577400580384625[/C][C]0.711299709807687[/C][/ROW]
[ROW][C]109[/C][C]0.269649244220024[/C][C]0.539298488440048[/C][C]0.730350755779976[/C][/ROW]
[ROW][C]110[/C][C]0.454037347645333[/C][C]0.908074695290666[/C][C]0.545962652354667[/C][/ROW]
[ROW][C]111[/C][C]0.499563813643079[/C][C]0.999127627286158[/C][C]0.500436186356921[/C][/ROW]
[ROW][C]112[/C][C]0.475883771030611[/C][C]0.951767542061223[/C][C]0.524116228969389[/C][/ROW]
[ROW][C]113[/C][C]0.5328994628249[/C][C]0.934201074350201[/C][C]0.4671005371751[/C][/ROW]
[ROW][C]114[/C][C]0.49884488629601[/C][C]0.997689772592021[/C][C]0.501155113703989[/C][/ROW]
[ROW][C]115[/C][C]0.466341127152691[/C][C]0.932682254305383[/C][C]0.533658872847309[/C][/ROW]
[ROW][C]116[/C][C]0.455811076602889[/C][C]0.911622153205778[/C][C]0.544188923397111[/C][/ROW]
[ROW][C]117[/C][C]0.460988423445308[/C][C]0.921976846890615[/C][C]0.539011576554692[/C][/ROW]
[ROW][C]118[/C][C]0.442919621830663[/C][C]0.885839243661326[/C][C]0.557080378169337[/C][/ROW]
[ROW][C]119[/C][C]0.412355659318927[/C][C]0.824711318637855[/C][C]0.587644340681073[/C][/ROW]
[ROW][C]120[/C][C]0.411874933525476[/C][C]0.823749867050951[/C][C]0.588125066474524[/C][/ROW]
[ROW][C]121[/C][C]0.379465504219916[/C][C]0.758931008439832[/C][C]0.620534495780084[/C][/ROW]
[ROW][C]122[/C][C]0.333773234175916[/C][C]0.667546468351832[/C][C]0.666226765824084[/C][/ROW]
[ROW][C]123[/C][C]0.295757230033445[/C][C]0.591514460066889[/C][C]0.704242769966555[/C][/ROW]
[ROW][C]124[/C][C]0.252024715216585[/C][C]0.50404943043317[/C][C]0.747975284783415[/C][/ROW]
[ROW][C]125[/C][C]0.211593068706766[/C][C]0.423186137413532[/C][C]0.788406931293234[/C][/ROW]
[ROW][C]126[/C][C]0.206869876929886[/C][C]0.413739753859773[/C][C]0.793130123070114[/C][/ROW]
[ROW][C]127[/C][C]0.248390718429882[/C][C]0.496781436859765[/C][C]0.751609281570118[/C][/ROW]
[ROW][C]128[/C][C]0.48838826084801[/C][C]0.976776521696021[/C][C]0.51161173915199[/C][/ROW]
[ROW][C]129[/C][C]0.456109079741042[/C][C]0.912218159482085[/C][C]0.543890920258958[/C][/ROW]
[ROW][C]130[/C][C]0.408940201924749[/C][C]0.817880403849497[/C][C]0.591059798075251[/C][/ROW]
[ROW][C]131[/C][C]0.370390992259696[/C][C]0.740781984519392[/C][C]0.629609007740304[/C][/ROW]
[ROW][C]132[/C][C]0.433303192388517[/C][C]0.866606384777034[/C][C]0.566696807611483[/C][/ROW]
[ROW][C]133[/C][C]0.51353203680446[/C][C]0.97293592639108[/C][C]0.48646796319554[/C][/ROW]
[ROW][C]134[/C][C]0.644140770810611[/C][C]0.711718458378778[/C][C]0.355859229189389[/C][/ROW]
[ROW][C]135[/C][C]0.603776459483274[/C][C]0.792447081033453[/C][C]0.396223540516726[/C][/ROW]
[ROW][C]136[/C][C]0.540696731401146[/C][C]0.918606537197708[/C][C]0.459303268598854[/C][/ROW]
[ROW][C]137[/C][C]0.533251359942176[/C][C]0.933497280115648[/C][C]0.466748640057824[/C][/ROW]
[ROW][C]138[/C][C]0.466196436735418[/C][C]0.932392873470837[/C][C]0.533803563264582[/C][/ROW]
[ROW][C]139[/C][C]0.532870255875363[/C][C]0.934259488249274[/C][C]0.467129744124637[/C][/ROW]
[ROW][C]140[/C][C]0.473181105968126[/C][C]0.946362211936252[/C][C]0.526818894031874[/C][/ROW]
[ROW][C]141[/C][C]0.475383927901126[/C][C]0.950767855802251[/C][C]0.524616072098874[/C][/ROW]
[ROW][C]142[/C][C]0.428502704517848[/C][C]0.857005409035695[/C][C]0.571497295482152[/C][/ROW]
[ROW][C]143[/C][C]0.497580372239936[/C][C]0.995160744479872[/C][C]0.502419627760064[/C][/ROW]
[ROW][C]144[/C][C]0.594787370432608[/C][C]0.810425259134784[/C][C]0.405212629567392[/C][/ROW]
[ROW][C]145[/C][C]0.521833670705052[/C][C]0.956332658589896[/C][C]0.478166329294948[/C][/ROW]
[ROW][C]146[/C][C]0.801809731067053[/C][C]0.396380537865895[/C][C]0.198190268932947[/C][/ROW]
[ROW][C]147[/C][C]0.736414761227972[/C][C]0.527170477544056[/C][C]0.263585238772028[/C][/ROW]
[ROW][C]148[/C][C]0.659641047021336[/C][C]0.680717905957329[/C][C]0.340358952978664[/C][/ROW]
[ROW][C]149[/C][C]0.57090871075564[/C][C]0.858182578488719[/C][C]0.42909128924436[/C][/ROW]
[ROW][C]150[/C][C]0.470254247457542[/C][C]0.940508494915083[/C][C]0.529745752542458[/C][/ROW]
[ROW][C]151[/C][C]0.439673285865331[/C][C]0.879346571730662[/C][C]0.560326714134669[/C][/ROW]
[ROW][C]152[/C][C]0.98060532818881[/C][C]0.0387893436223798[/C][C]0.0193946718111899[/C][/ROW]
[ROW][C]153[/C][C]0.953308070718847[/C][C]0.0933838585623061[/C][C]0.0466919292811531[/C][/ROW]
[ROW][C]154[/C][C]0.91662780684501[/C][C]0.166744386309979[/C][C]0.0833721931549897[/C][/ROW]
[ROW][C]155[/C][C]0.851007095196505[/C][C]0.297985809606991[/C][C]0.148992904803495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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
70.9493495397262890.1013009205474230.0506504602737114
80.9055250787753610.1889498424492780.094474921224639
90.8388398525341930.3223202949316130.161160147465807
100.7541293674107670.4917412651784650.245870632589233
110.6808687837065030.6382624325869950.319131216293497
120.644299263663640.711401472672720.35570073633636
130.7274688122767550.545062375446490.272531187723245
140.6705041905093070.6589916189813850.329495809490693
150.7163960550443870.5672078899112260.283603944955613
160.7178895701808060.5642208596383880.282110429819194
170.6513457864175210.6973084271649570.348654213582479
180.5871126767917670.8257746464164660.412887323208233
190.5823591692115230.8352816615769530.417640830788477
200.5944949829769830.8110100340460330.405505017023017
210.6018908578784460.7962182842431090.398109142121554
220.5662998827197990.8674002345604030.433700117280201
230.5428844300550660.9142311398898680.457115569944934
240.4748363562022950.949672712404590.525163643797705
250.5168602943830120.9662794112339760.483139705616988
260.7506816503531560.4986366992936890.249318349646844
270.6984048861730810.6031902276538380.301595113826919
280.696768093050390.606463813899220.30323190694961
290.6669335051288860.6661329897422270.333066494871114
300.616646313406560.766707373186880.38335368659344
310.6060243735176880.7879512529646240.393975626482312
320.7998768145958390.4002463708083230.200123185404161
330.7839244188855830.4321511622288340.216075581114417
340.7629562915772290.4740874168455420.237043708422771
350.7180612528425470.5638774943149060.281938747157453
360.7155406334714210.5689187330571590.284459366528579
370.7268648194129780.5462703611740430.273135180587022
380.6879665078872040.6240669842255920.312033492112796
390.7602926682378110.4794146635243770.239707331762189
400.7177313102722710.5645373794554580.282268689727729
410.700483056770610.599033886458780.29951694322939
420.6540079028325430.6919841943349150.345992097167457
430.6556731499977820.6886537000044360.344326850002218
440.6103689171331510.7792621657336970.389631082866849
450.6885585941638910.6228828116722170.311441405836109
460.6599862836844290.6800274326311420.340013716315571
470.6175356482660980.7649287034678030.382464351733902
480.5919969742424010.8160060515151980.408003025757599
490.5423938168638290.9152123662723410.457606183136171
500.5583854453435860.8832291093128280.441614554656414
510.6090429853020830.7819140293958340.390957014697917
520.5715653032738980.8568693934522040.428434696726102
530.537581819776710.9248363604465790.46241818022329
540.5040467367368930.9919065265262140.495953263263107
550.4816207822165060.9632415644330120.518379217783494
560.4698232048768410.9396464097536830.530176795123159
570.5043460277945880.9913079444108240.495653972205412
580.6351220345832930.7297559308334150.364877965416707
590.6013770987031110.7972458025937780.398622901296889
600.5567150189109320.8865699621781350.443284981089068
610.5230479733986030.9539040532027940.476952026601397
620.5275181507220570.9449636985558850.472481849277943
630.4845035872431350.9690071744862690.515496412756865
640.5077229195564940.9845541608870120.492277080443506
650.4616928556491820.9233857112983650.538307144350818
660.4168684210359180.8337368420718360.583131578964082
670.3842587679581070.7685175359162130.615741232041893
680.36403251211690.7280650242338010.6359674878831
690.3425265126988090.6850530253976170.657473487301191
700.3028462760715470.6056925521430930.697153723928453
710.2916395313963880.5832790627927760.708360468603612
720.3535474797105750.707094959421150.646452520289425
730.3341284069602780.6682568139205550.665871593039723
740.2960013211238170.5920026422476330.703998678876184
750.2696457880300720.5392915760601440.730354211969928
760.3914354637626740.7828709275253480.608564536237326
770.3535704170802960.7071408341605920.646429582919704
780.3141262015909310.6282524031818630.685873798409069
790.410918101507520.821836203015040.58908189849248
800.4705068467720680.9410136935441350.529493153227932
810.4516473552052930.9032947104105860.548352644794707
820.4094598583633160.8189197167266310.590540141636684
830.3689823961318710.7379647922637420.631017603868129
840.3539343434217250.707868686843450.646065656578275
850.3423580397614520.6847160795229040.657641960238548
860.303521530109640.6070430602192810.69647846989036
870.2754800540883820.5509601081767650.724519945911618
880.2476197523417430.4952395046834850.752380247658257
890.2910974289353980.5821948578707960.708902571064602
900.2530075544688180.5060151089376370.746992445531182
910.2944404667217110.5888809334434230.705559533278289
920.277084336200250.5541686724005010.72291566379975
930.2561114657180340.5122229314360680.743888534281966
940.2215011046763940.4430022093527890.778498895323606
950.2454510194189980.4909020388379950.754548980581002
960.2521349532389110.5042699064778220.747865046761089
970.2377563525804710.4755127051609420.762243647419529
980.2123147845055820.4246295690111630.787685215494418
990.1934494159048330.3868988318096660.806550584095167
1000.196863482757270.3937269655145410.803136517242729
1010.1825466782588690.3650933565177380.817453321741131
1020.1706690326292780.3413380652585560.829330967370722
1030.1424776080922160.2849552161844330.857522391907784
1040.1202221444118650.240444288823730.879777855588135
1050.1244038768179160.2488077536358320.875596123182084
1060.2570754843199930.5141509686399870.742924515680007
1070.2493531006505780.4987062013011560.750646899349422
1080.2887002901923130.5774005803846250.711299709807687
1090.2696492442200240.5392984884400480.730350755779976
1100.4540373476453330.9080746952906660.545962652354667
1110.4995638136430790.9991276272861580.500436186356921
1120.4758837710306110.9517675420612230.524116228969389
1130.53289946282490.9342010743502010.4671005371751
1140.498844886296010.9976897725920210.501155113703989
1150.4663411271526910.9326822543053830.533658872847309
1160.4558110766028890.9116221532057780.544188923397111
1170.4609884234453080.9219768468906150.539011576554692
1180.4429196218306630.8858392436613260.557080378169337
1190.4123556593189270.8247113186378550.587644340681073
1200.4118749335254760.8237498670509510.588125066474524
1210.3794655042199160.7589310084398320.620534495780084
1220.3337732341759160.6675464683518320.666226765824084
1230.2957572300334450.5915144600668890.704242769966555
1240.2520247152165850.504049430433170.747975284783415
1250.2115930687067660.4231861374135320.788406931293234
1260.2068698769298860.4137397538597730.793130123070114
1270.2483907184298820.4967814368597650.751609281570118
1280.488388260848010.9767765216960210.51161173915199
1290.4561090797410420.9122181594820850.543890920258958
1300.4089402019247490.8178804038494970.591059798075251
1310.3703909922596960.7407819845193920.629609007740304
1320.4333031923885170.8666063847770340.566696807611483
1330.513532036804460.972935926391080.48646796319554
1340.6441407708106110.7117184583787780.355859229189389
1350.6037764594832740.7924470810334530.396223540516726
1360.5406967314011460.9186065371977080.459303268598854
1370.5332513599421760.9334972801156480.466748640057824
1380.4661964367354180.9323928734708370.533803563264582
1390.5328702558753630.9342594882492740.467129744124637
1400.4731811059681260.9463622119362520.526818894031874
1410.4753839279011260.9507678558022510.524616072098874
1420.4285027045178480.8570054090356950.571497295482152
1430.4975803722399360.9951607444798720.502419627760064
1440.5947873704326080.8104252591347840.405212629567392
1450.5218336707050520.9563326585898960.478166329294948
1460.8018097310670530.3963805378658950.198190268932947
1470.7364147612279720.5271704775440560.263585238772028
1480.6596410470213360.6807179059573290.340358952978664
1490.570908710755640.8581825784887190.42909128924436
1500.4702542474575420.9405084949150830.529745752542458
1510.4396732858653310.8793465717306620.560326714134669
1520.980605328188810.03878934362237980.0193946718111899
1530.9533080707188470.09338385856230610.0466919292811531
1540.916627806845010.1667443863099790.0833721931549897
1550.8510070951965050.2979858096069910.148992904803495






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00671140939597315OK
10% type I error level20.0134228187919463OK

\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 & 0.00671140939597315 & OK \tabularnewline
10% type I error level & 2 & 0.0134228187919463 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154960&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]0.00671140939597315[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0134228187919463[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154960&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154960&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 level10.00671140939597315OK
10% type I error level20.0134228187919463OK


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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}