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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 17:05:15 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t1356041533795pqx0m5ozgak2.htm/, Retrieved Thu, 31 Oct 2024 23:34:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203172, Retrieved Thu, 31 Oct 2024 23:34:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Paper chi-squared...] [2012-12-18 12:06:51] [33fe548a21de6aef2b38519618b03303]
-       [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [rfc chi kwadraat ...] [2012-12-19 22:20:44] [5681f3f0ac2340d6f296c6f0abf509cb]
- RMPD    [Two-Way ANOVA] [RFC anova deel 5] [2012-12-20 20:06:44] [5681f3f0ac2340d6f296c6f0abf509cb]
- RMP         [Multiple Regression] [RFC deel 5 paper ...] [2012-12-20 22:05:15] [b8d3d7c3406b9c8dc9154eb4f7d497b9] [Current]
- R             [Multiple Regression] [rfc deel 5 paper ...] [2012-12-20 22:37:28] [5681f3f0ac2340d6f296c6f0abf509cb]
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Dataseries X:
1	0	1
0	0	0
0	0	0
0	0	0
0	0	0
0	0	1
0	0	0
1	0	0
0	0	1
0	0	0
1	0	0
0	0	0
0	0	0
1	0	0
0	0	1
1	0	1
1	0	0
1	0	0
0	0	1
1	0	1
0	0	0
0	0	1
0	0	1
0	0	1
1	0	1
0	0	0
0	0	1
0	0	0
0	0	1
0	0	0
0	0	0
0	0	0
0	0	0
1	0	1
0	0	0
0	0	0
1	0	0
0	0	1
0	0	1
1	0	0
0	0	1
0	0	1
0	0	1
1	0	0
0	0	0
0	0	1
0	0	0
0	0	1
0	0	1
0	0	0
1	0	0
1	0	0
0	0	1
0	0	0
0	0	0
1	0	1
0	0	1
0	0	1
0	0	1
1	0	1
1	0	1
0	0	0
0	0	0
1	0	1
0	0	0
0	0	0
1	0	0
0	0	0
0	0	1
0	0	0
0	0	0
0	0	1
0	0	1
0	0	0
0	0	1
1	0	1
0	0	1
0	0	1
1	0	1
1	0	0
0	0	0
0	0	1
0	0	0
0	0	0
0	0	1
0	0	0
0	0	1
0	1	1
0	0	0
0	0	1
0	0	0
0	1	0
0	0	0
0	0	0
0	1	0
0	0	1
0	1	0
0	0	0
0	0	0
0	0	1
0	0	1
0	0	0
0	0	0
0	0	0
0	1	0
0	0	0
0	0	0
0	1	0
0	0	0
0	0	0
0	1	0
0	1	0
0	0	0
0	1	0
0	0	0
0	0	0
0	0	1
0	0	0
0	0	0
0	0	1
0	0	0
0	0	0
0	1	0
0	0	1
0	0	1
0	1	0
0	0	0
0	0	1
0	0	0
0	0	1
0	0	0
0	0	1
0	0	0
0	0	0
0	0	0
0	0	0
0	0	1
0	1	1
0	1	0
0	0	0
0	0	1
0	1	1
0	0	0
0	0	1
0	0	0
0	1	1
0	1	0
0	1	0
0	0	0
0	0	1
0	0	1
0	0	0
0	0	0
0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 12 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
T40[t] = + 0.350713250738591 -0.0361628374039664T20[t] + 0.0274233314606938Outcome[t] -0.00268687855860729t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
T40[t] =  +  0.350713250738591 -0.0361628374039664T20[t] +  0.0274233314606938Outcome[t] -0.00268687855860729t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]T40[t] =  +  0.350713250738591 -0.0361628374039664T20[t] +  0.0274233314606938Outcome[t] -0.00268687855860729t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
T40[t] = + 0.350713250738591 -0.0361628374039664T20[t] + 0.0274233314606938Outcome[t] -0.00268687855860729t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3507132507385910.0608575.762900
T20-0.03616283740396640.092549-0.39070.6965430.348272
Outcome0.02742333146069380.0561360.48850.6258960.312948
t-0.002686878558607290.00065-4.13076e-053e-05

\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) & 0.350713250738591 & 0.060857 & 5.7629 & 0 & 0 \tabularnewline
T20 & -0.0361628374039664 & 0.092549 & -0.3907 & 0.696543 & 0.348272 \tabularnewline
Outcome & 0.0274233314606938 & 0.056136 & 0.4885 & 0.625896 & 0.312948 \tabularnewline
t & -0.00268687855860729 & 0.00065 & -4.1307 & 6e-05 & 3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&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]0.350713250738591[/C][C]0.060857[/C][C]5.7629[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]T20[/C][C]-0.0361628374039664[/C][C]0.092549[/C][C]-0.3907[/C][C]0.696543[/C][C]0.348272[/C][/ROW]
[ROW][C]Outcome[/C][C]0.0274233314606938[/C][C]0.056136[/C][C]0.4885[/C][C]0.625896[/C][C]0.312948[/C][/ROW]
[ROW][C]t[/C][C]-0.00268687855860729[/C][C]0.00065[/C][C]-4.1307[/C][C]6e-05[/C][C]3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3507132507385910.0608575.762900
T20-0.03616283740396640.092549-0.39070.6965430.348272
Outcome0.02742333146069380.0561360.48850.6258960.312948
t-0.002686878558607290.00065-4.13076e-053e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.35253241119858
R-squared0.124279100945485
Adjusted R-squared0.106764682964394
F-TEST (value)7.0958167767644
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0.000172057274432036
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.337968662501995
Sum Squared Residuals17.1334225250081

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.35253241119858 \tabularnewline
R-squared & 0.124279100945485 \tabularnewline
Adjusted R-squared & 0.106764682964394 \tabularnewline
F-TEST (value) & 7.0958167767644 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0.000172057274432036 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.337968662501995 \tabularnewline
Sum Squared Residuals & 17.1334225250081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.35253241119858[/C][/ROW]
[ROW][C]R-squared[/C][C]0.124279100945485[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.106764682964394[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.0958167767644[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0.000172057274432036[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.337968662501995[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]17.1334225250081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.35253241119858
R-squared0.124279100945485
Adjusted R-squared0.106764682964394
F-TEST (value)7.0958167767644
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0.000172057274432036
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.337968662501995
Sum Squared Residuals17.1334225250081







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3754497036406790.624550296359321
200.345339493621377-0.345339493621377
300.34265261506277-0.34265261506277
400.339965736504162-0.339965736504162
500.337278857945555-0.337278857945555
600.362015310847641-0.362015310847641
700.33190510082834-0.33190510082834
810.3292182222697330.670781777730267
900.35395467517182-0.35395467517182
1000.323844465152519-0.323844465152519
1110.3211575865939110.678842413406089
1200.318470708035304-0.318470708035304
1300.315783829476697-0.315783829476697
1410.313096950918090.68690304908191
1500.337833403820176-0.337833403820176
1610.3351465252615690.664853474738431
1710.3050363152422680.694963684757732
1810.302349436683660.69765056331634
1900.327085889585747-0.327085889585747
2010.324399011027140.67560098897286
2100.294288801007838-0.294288801007838
2200.319025253909925-0.319025253909925
2300.316338375351318-0.316338375351318
2400.31365149679271-0.31365149679271
2510.3109646182341030.689035381765897
2600.280854408214802-0.280854408214802
2700.305590861116888-0.305590861116888
2800.275480651097587-0.275480651097587
2900.300217103999674-0.300217103999674
3000.270106893980373-0.270106893980373
3100.267420015421766-0.267420015421766
3200.264733136863158-0.264733136863158
3300.262046258304551-0.262046258304551
3410.2867827112066370.713217288793363
3500.256672501187336-0.256672501187336
3600.253985622628729-0.253985622628729
3710.2512987440701220.748701255929878
3800.276035196972208-0.276035196972208
3900.273348318413601-0.273348318413601
4010.24323810839430.7567618916057
4100.267974561296386-0.267974561296386
4200.265287682737779-0.265287682737779
4300.262600804179172-0.262600804179172
4410.2324905941598710.767509405840129
4500.229803715601264-0.229803715601264
4600.25454016850335-0.25454016850335
4700.224429958484049-0.224429958484049
4800.249166411386135-0.249166411386135
4900.246479532827528-0.246479532827528
5000.216369322808227-0.216369322808227
5110.213682444249620.78631755575038
5210.2109955656910130.789004434308987
5300.235732018593099-0.235732018593099
5400.205621808573798-0.205621808573798
5500.202934930015191-0.202934930015191
5610.2276713829172770.772328617082723
5700.22498450435867-0.22498450435867
5800.222297625800063-0.222297625800063
5900.219610747241455-0.219610747241455
6010.2169238686828480.783076131317152
6110.2142369901242410.785763009875759
6200.18412678010494-0.18412678010494
6300.181439901546332-0.181439901546332
6410.2061763544484190.793823645551581
6500.176066144429118-0.176066144429118
6600.17337926587051-0.17337926587051
6710.1706923873119030.829307612688097
6800.168005508753296-0.168005508753296
6900.192741961655382-0.192741961655382
7000.162631751636081-0.162631751636081
7100.159944873077474-0.159944873077474
7200.184681325979561-0.184681325979561
7300.181994447420953-0.181994447420953
7400.151884237401652-0.151884237401652
7500.176620690303739-0.176620690303739
7610.1739338117451310.826066188254869
7700.171246933186524-0.171246933186524
7800.168560054627917-0.168560054627917
7910.165873176069310.83412682393069
8010.1357629660500080.864237033949992
8100.133076087491401-0.133076087491401
8200.157812540393488-0.157812540393488
8300.127702330374187-0.127702330374187
8400.125015451815579-0.125015451815579
8500.149751904717666-0.149751904717666
8600.119641694698365-0.119641694698365
8700.144378147600451-0.144378147600451
8800.105528431637878-0.105528431637878
8900.111581059022543-0.111581059022543
9000.136317511924629-0.136317511924629
9100.106207301905328-0.106207301905328
9200.0673575859427546-0.0673575859427546
9300.100833544788114-0.100833544788114
9400.0981466662295064-0.0981466662295064
9500.0592969502669327-0.0592969502669327
9600.120196240572986-0.120196240572986
9700.0539231931497182-0.0539231931497182
9800.0873991519950773-0.0873991519950773
9900.08471227343647-0.08471227343647
10000.109448726338556-0.109448726338556
10100.106761847779949-0.106761847779949
10200.0766516377606481-0.0766516377606481
10300.0739647592020408-0.0739647592020408
10400.0712778806434336-0.0712778806434336
10500.0324281646808599-0.0324281646808599
10600.065904123526219-0.065904123526219
10700.0632172449676117-0.0632172449676117
10800.024367529005038-0.024367529005038
10900.0578434878503971-0.0578434878503971
11000.0551566092917899-0.0551566092917899
11100.0163068933292161-0.0163068933292161
11200.0136200147706088-0.0136200147706088
11300.047095973615968-0.047095973615968
11400.00824625765339426-0.00824625765339426
11500.0417222164987534-0.0417222164987534
11600.0390353379401461-0.0390353379401461
11700.0637717908422326-0.0637717908422326
11800.0336615808229315-0.0336615808229315
11900.0309747022643243-0.0309747022643243
12000.0557111551664108-0.0557111551664108
12100.0256009451471097-0.0256009451471097
12200.0229140665885024-0.0229140665885024
1230-0.01593564937407130.0159356493740713
12400.0449636409319816-0.0449636409319816
12500.0422767623733743-0.0422767623733743
1260-0.02399628504989320.0239962850498932
12700.00947967379546594-0.00947967379546594
12800.0342161266975525-0.0342161266975525
12900.00410591667825136-0.00410591667825136
13000.0288423695803379-0.0288423695803379
1310-0.001267840438963220.00126784043896322
13200.0234686124631233-0.0234686124631233
1330-0.00664159755617780.0066415975561778
1340-0.009328476114785090.00932847611478509
1350-0.01201535467339240.0120153546733924
1360-0.01470223323199970.0147022332319997
13700.0100342196700869-0.0100342196700869
1380-0.02881549629248680.0288154962924868
1390-0.05892570631178790.0589257063117879
1400-0.02544974746642880.0254497474664288
1410-0.0007132945643422870.000713294564342287
1420-0.0395630105269160.039563010526916
1430-0.03351038314225070.0335103831422507
1440-0.008773930240164140.00877393024016414
1450-0.03888414025946520.0388841402594652
1460-0.05031052476134510.0503105247613451
1470-0.08042073478064620.0804207347806462
1480-0.08310761333925350.0831076133392535
1490-0.04963165449389440.0496316544938944
1500-0.02489520159180790.0248952015918079
1510-0.02758208015041520.0275820801504152
1520-0.05769229016971630.0576922901697163
1530-0.06037916872832360.0603791687283236
1540-0.06306604728693080.0630660472869308

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.375449703640679 & 0.624550296359321 \tabularnewline
2 & 0 & 0.345339493621377 & -0.345339493621377 \tabularnewline
3 & 0 & 0.34265261506277 & -0.34265261506277 \tabularnewline
4 & 0 & 0.339965736504162 & -0.339965736504162 \tabularnewline
5 & 0 & 0.337278857945555 & -0.337278857945555 \tabularnewline
6 & 0 & 0.362015310847641 & -0.362015310847641 \tabularnewline
7 & 0 & 0.33190510082834 & -0.33190510082834 \tabularnewline
8 & 1 & 0.329218222269733 & 0.670781777730267 \tabularnewline
9 & 0 & 0.35395467517182 & -0.35395467517182 \tabularnewline
10 & 0 & 0.323844465152519 & -0.323844465152519 \tabularnewline
11 & 1 & 0.321157586593911 & 0.678842413406089 \tabularnewline
12 & 0 & 0.318470708035304 & -0.318470708035304 \tabularnewline
13 & 0 & 0.315783829476697 & -0.315783829476697 \tabularnewline
14 & 1 & 0.31309695091809 & 0.68690304908191 \tabularnewline
15 & 0 & 0.337833403820176 & -0.337833403820176 \tabularnewline
16 & 1 & 0.335146525261569 & 0.664853474738431 \tabularnewline
17 & 1 & 0.305036315242268 & 0.694963684757732 \tabularnewline
18 & 1 & 0.30234943668366 & 0.69765056331634 \tabularnewline
19 & 0 & 0.327085889585747 & -0.327085889585747 \tabularnewline
20 & 1 & 0.32439901102714 & 0.67560098897286 \tabularnewline
21 & 0 & 0.294288801007838 & -0.294288801007838 \tabularnewline
22 & 0 & 0.319025253909925 & -0.319025253909925 \tabularnewline
23 & 0 & 0.316338375351318 & -0.316338375351318 \tabularnewline
24 & 0 & 0.31365149679271 & -0.31365149679271 \tabularnewline
25 & 1 & 0.310964618234103 & 0.689035381765897 \tabularnewline
26 & 0 & 0.280854408214802 & -0.280854408214802 \tabularnewline
27 & 0 & 0.305590861116888 & -0.305590861116888 \tabularnewline
28 & 0 & 0.275480651097587 & -0.275480651097587 \tabularnewline
29 & 0 & 0.300217103999674 & -0.300217103999674 \tabularnewline
30 & 0 & 0.270106893980373 & -0.270106893980373 \tabularnewline
31 & 0 & 0.267420015421766 & -0.267420015421766 \tabularnewline
32 & 0 & 0.264733136863158 & -0.264733136863158 \tabularnewline
33 & 0 & 0.262046258304551 & -0.262046258304551 \tabularnewline
34 & 1 & 0.286782711206637 & 0.713217288793363 \tabularnewline
35 & 0 & 0.256672501187336 & -0.256672501187336 \tabularnewline
36 & 0 & 0.253985622628729 & -0.253985622628729 \tabularnewline
37 & 1 & 0.251298744070122 & 0.748701255929878 \tabularnewline
38 & 0 & 0.276035196972208 & -0.276035196972208 \tabularnewline
39 & 0 & 0.273348318413601 & -0.273348318413601 \tabularnewline
40 & 1 & 0.2432381083943 & 0.7567618916057 \tabularnewline
41 & 0 & 0.267974561296386 & -0.267974561296386 \tabularnewline
42 & 0 & 0.265287682737779 & -0.265287682737779 \tabularnewline
43 & 0 & 0.262600804179172 & -0.262600804179172 \tabularnewline
44 & 1 & 0.232490594159871 & 0.767509405840129 \tabularnewline
45 & 0 & 0.229803715601264 & -0.229803715601264 \tabularnewline
46 & 0 & 0.25454016850335 & -0.25454016850335 \tabularnewline
47 & 0 & 0.224429958484049 & -0.224429958484049 \tabularnewline
48 & 0 & 0.249166411386135 & -0.249166411386135 \tabularnewline
49 & 0 & 0.246479532827528 & -0.246479532827528 \tabularnewline
50 & 0 & 0.216369322808227 & -0.216369322808227 \tabularnewline
51 & 1 & 0.21368244424962 & 0.78631755575038 \tabularnewline
52 & 1 & 0.210995565691013 & 0.789004434308987 \tabularnewline
53 & 0 & 0.235732018593099 & -0.235732018593099 \tabularnewline
54 & 0 & 0.205621808573798 & -0.205621808573798 \tabularnewline
55 & 0 & 0.202934930015191 & -0.202934930015191 \tabularnewline
56 & 1 & 0.227671382917277 & 0.772328617082723 \tabularnewline
57 & 0 & 0.22498450435867 & -0.22498450435867 \tabularnewline
58 & 0 & 0.222297625800063 & -0.222297625800063 \tabularnewline
59 & 0 & 0.219610747241455 & -0.219610747241455 \tabularnewline
60 & 1 & 0.216923868682848 & 0.783076131317152 \tabularnewline
61 & 1 & 0.214236990124241 & 0.785763009875759 \tabularnewline
62 & 0 & 0.18412678010494 & -0.18412678010494 \tabularnewline
63 & 0 & 0.181439901546332 & -0.181439901546332 \tabularnewline
64 & 1 & 0.206176354448419 & 0.793823645551581 \tabularnewline
65 & 0 & 0.176066144429118 & -0.176066144429118 \tabularnewline
66 & 0 & 0.17337926587051 & -0.17337926587051 \tabularnewline
67 & 1 & 0.170692387311903 & 0.829307612688097 \tabularnewline
68 & 0 & 0.168005508753296 & -0.168005508753296 \tabularnewline
69 & 0 & 0.192741961655382 & -0.192741961655382 \tabularnewline
70 & 0 & 0.162631751636081 & -0.162631751636081 \tabularnewline
71 & 0 & 0.159944873077474 & -0.159944873077474 \tabularnewline
72 & 0 & 0.184681325979561 & -0.184681325979561 \tabularnewline
73 & 0 & 0.181994447420953 & -0.181994447420953 \tabularnewline
74 & 0 & 0.151884237401652 & -0.151884237401652 \tabularnewline
75 & 0 & 0.176620690303739 & -0.176620690303739 \tabularnewline
76 & 1 & 0.173933811745131 & 0.826066188254869 \tabularnewline
77 & 0 & 0.171246933186524 & -0.171246933186524 \tabularnewline
78 & 0 & 0.168560054627917 & -0.168560054627917 \tabularnewline
79 & 1 & 0.16587317606931 & 0.83412682393069 \tabularnewline
80 & 1 & 0.135762966050008 & 0.864237033949992 \tabularnewline
81 & 0 & 0.133076087491401 & -0.133076087491401 \tabularnewline
82 & 0 & 0.157812540393488 & -0.157812540393488 \tabularnewline
83 & 0 & 0.127702330374187 & -0.127702330374187 \tabularnewline
84 & 0 & 0.125015451815579 & -0.125015451815579 \tabularnewline
85 & 0 & 0.149751904717666 & -0.149751904717666 \tabularnewline
86 & 0 & 0.119641694698365 & -0.119641694698365 \tabularnewline
87 & 0 & 0.144378147600451 & -0.144378147600451 \tabularnewline
88 & 0 & 0.105528431637878 & -0.105528431637878 \tabularnewline
89 & 0 & 0.111581059022543 & -0.111581059022543 \tabularnewline
90 & 0 & 0.136317511924629 & -0.136317511924629 \tabularnewline
91 & 0 & 0.106207301905328 & -0.106207301905328 \tabularnewline
92 & 0 & 0.0673575859427546 & -0.0673575859427546 \tabularnewline
93 & 0 & 0.100833544788114 & -0.100833544788114 \tabularnewline
94 & 0 & 0.0981466662295064 & -0.0981466662295064 \tabularnewline
95 & 0 & 0.0592969502669327 & -0.0592969502669327 \tabularnewline
96 & 0 & 0.120196240572986 & -0.120196240572986 \tabularnewline
97 & 0 & 0.0539231931497182 & -0.0539231931497182 \tabularnewline
98 & 0 & 0.0873991519950773 & -0.0873991519950773 \tabularnewline
99 & 0 & 0.08471227343647 & -0.08471227343647 \tabularnewline
100 & 0 & 0.109448726338556 & -0.109448726338556 \tabularnewline
101 & 0 & 0.106761847779949 & -0.106761847779949 \tabularnewline
102 & 0 & 0.0766516377606481 & -0.0766516377606481 \tabularnewline
103 & 0 & 0.0739647592020408 & -0.0739647592020408 \tabularnewline
104 & 0 & 0.0712778806434336 & -0.0712778806434336 \tabularnewline
105 & 0 & 0.0324281646808599 & -0.0324281646808599 \tabularnewline
106 & 0 & 0.065904123526219 & -0.065904123526219 \tabularnewline
107 & 0 & 0.0632172449676117 & -0.0632172449676117 \tabularnewline
108 & 0 & 0.024367529005038 & -0.024367529005038 \tabularnewline
109 & 0 & 0.0578434878503971 & -0.0578434878503971 \tabularnewline
110 & 0 & 0.0551566092917899 & -0.0551566092917899 \tabularnewline
111 & 0 & 0.0163068933292161 & -0.0163068933292161 \tabularnewline
112 & 0 & 0.0136200147706088 & -0.0136200147706088 \tabularnewline
113 & 0 & 0.047095973615968 & -0.047095973615968 \tabularnewline
114 & 0 & 0.00824625765339426 & -0.00824625765339426 \tabularnewline
115 & 0 & 0.0417222164987534 & -0.0417222164987534 \tabularnewline
116 & 0 & 0.0390353379401461 & -0.0390353379401461 \tabularnewline
117 & 0 & 0.0637717908422326 & -0.0637717908422326 \tabularnewline
118 & 0 & 0.0336615808229315 & -0.0336615808229315 \tabularnewline
119 & 0 & 0.0309747022643243 & -0.0309747022643243 \tabularnewline
120 & 0 & 0.0557111551664108 & -0.0557111551664108 \tabularnewline
121 & 0 & 0.0256009451471097 & -0.0256009451471097 \tabularnewline
122 & 0 & 0.0229140665885024 & -0.0229140665885024 \tabularnewline
123 & 0 & -0.0159356493740713 & 0.0159356493740713 \tabularnewline
124 & 0 & 0.0449636409319816 & -0.0449636409319816 \tabularnewline
125 & 0 & 0.0422767623733743 & -0.0422767623733743 \tabularnewline
126 & 0 & -0.0239962850498932 & 0.0239962850498932 \tabularnewline
127 & 0 & 0.00947967379546594 & -0.00947967379546594 \tabularnewline
128 & 0 & 0.0342161266975525 & -0.0342161266975525 \tabularnewline
129 & 0 & 0.00410591667825136 & -0.00410591667825136 \tabularnewline
130 & 0 & 0.0288423695803379 & -0.0288423695803379 \tabularnewline
131 & 0 & -0.00126784043896322 & 0.00126784043896322 \tabularnewline
132 & 0 & 0.0234686124631233 & -0.0234686124631233 \tabularnewline
133 & 0 & -0.0066415975561778 & 0.0066415975561778 \tabularnewline
134 & 0 & -0.00932847611478509 & 0.00932847611478509 \tabularnewline
135 & 0 & -0.0120153546733924 & 0.0120153546733924 \tabularnewline
136 & 0 & -0.0147022332319997 & 0.0147022332319997 \tabularnewline
137 & 0 & 0.0100342196700869 & -0.0100342196700869 \tabularnewline
138 & 0 & -0.0288154962924868 & 0.0288154962924868 \tabularnewline
139 & 0 & -0.0589257063117879 & 0.0589257063117879 \tabularnewline
140 & 0 & -0.0254497474664288 & 0.0254497474664288 \tabularnewline
141 & 0 & -0.000713294564342287 & 0.000713294564342287 \tabularnewline
142 & 0 & -0.039563010526916 & 0.039563010526916 \tabularnewline
143 & 0 & -0.0335103831422507 & 0.0335103831422507 \tabularnewline
144 & 0 & -0.00877393024016414 & 0.00877393024016414 \tabularnewline
145 & 0 & -0.0388841402594652 & 0.0388841402594652 \tabularnewline
146 & 0 & -0.0503105247613451 & 0.0503105247613451 \tabularnewline
147 & 0 & -0.0804207347806462 & 0.0804207347806462 \tabularnewline
148 & 0 & -0.0831076133392535 & 0.0831076133392535 \tabularnewline
149 & 0 & -0.0496316544938944 & 0.0496316544938944 \tabularnewline
150 & 0 & -0.0248952015918079 & 0.0248952015918079 \tabularnewline
151 & 0 & -0.0275820801504152 & 0.0275820801504152 \tabularnewline
152 & 0 & -0.0576922901697163 & 0.0576922901697163 \tabularnewline
153 & 0 & -0.0603791687283236 & 0.0603791687283236 \tabularnewline
154 & 0 & -0.0630660472869308 & 0.0630660472869308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&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]1[/C][C]0.375449703640679[/C][C]0.624550296359321[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.345339493621377[/C][C]-0.345339493621377[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.34265261506277[/C][C]-0.34265261506277[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.339965736504162[/C][C]-0.339965736504162[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.337278857945555[/C][C]-0.337278857945555[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.362015310847641[/C][C]-0.362015310847641[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.33190510082834[/C][C]-0.33190510082834[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.329218222269733[/C][C]0.670781777730267[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.35395467517182[/C][C]-0.35395467517182[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.323844465152519[/C][C]-0.323844465152519[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.321157586593911[/C][C]0.678842413406089[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.318470708035304[/C][C]-0.318470708035304[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.315783829476697[/C][C]-0.315783829476697[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.31309695091809[/C][C]0.68690304908191[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.337833403820176[/C][C]-0.337833403820176[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.335146525261569[/C][C]0.664853474738431[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.305036315242268[/C][C]0.694963684757732[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.30234943668366[/C][C]0.69765056331634[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.327085889585747[/C][C]-0.327085889585747[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.32439901102714[/C][C]0.67560098897286[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.294288801007838[/C][C]-0.294288801007838[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.319025253909925[/C][C]-0.319025253909925[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.316338375351318[/C][C]-0.316338375351318[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.31365149679271[/C][C]-0.31365149679271[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.310964618234103[/C][C]0.689035381765897[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.280854408214802[/C][C]-0.280854408214802[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.305590861116888[/C][C]-0.305590861116888[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.275480651097587[/C][C]-0.275480651097587[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.300217103999674[/C][C]-0.300217103999674[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.270106893980373[/C][C]-0.270106893980373[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.267420015421766[/C][C]-0.267420015421766[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.264733136863158[/C][C]-0.264733136863158[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.262046258304551[/C][C]-0.262046258304551[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.286782711206637[/C][C]0.713217288793363[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.256672501187336[/C][C]-0.256672501187336[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.253985622628729[/C][C]-0.253985622628729[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.251298744070122[/C][C]0.748701255929878[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.276035196972208[/C][C]-0.276035196972208[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.273348318413601[/C][C]-0.273348318413601[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.2432381083943[/C][C]0.7567618916057[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.267974561296386[/C][C]-0.267974561296386[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.265287682737779[/C][C]-0.265287682737779[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.262600804179172[/C][C]-0.262600804179172[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.232490594159871[/C][C]0.767509405840129[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.229803715601264[/C][C]-0.229803715601264[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.25454016850335[/C][C]-0.25454016850335[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.224429958484049[/C][C]-0.224429958484049[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.249166411386135[/C][C]-0.249166411386135[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.246479532827528[/C][C]-0.246479532827528[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.216369322808227[/C][C]-0.216369322808227[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.21368244424962[/C][C]0.78631755575038[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.210995565691013[/C][C]0.789004434308987[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.235732018593099[/C][C]-0.235732018593099[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.205621808573798[/C][C]-0.205621808573798[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.202934930015191[/C][C]-0.202934930015191[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.227671382917277[/C][C]0.772328617082723[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.22498450435867[/C][C]-0.22498450435867[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.222297625800063[/C][C]-0.222297625800063[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.219610747241455[/C][C]-0.219610747241455[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.216923868682848[/C][C]0.783076131317152[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.214236990124241[/C][C]0.785763009875759[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.18412678010494[/C][C]-0.18412678010494[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.181439901546332[/C][C]-0.181439901546332[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.206176354448419[/C][C]0.793823645551581[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.176066144429118[/C][C]-0.176066144429118[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.17337926587051[/C][C]-0.17337926587051[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.170692387311903[/C][C]0.829307612688097[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.168005508753296[/C][C]-0.168005508753296[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.192741961655382[/C][C]-0.192741961655382[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.162631751636081[/C][C]-0.162631751636081[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.159944873077474[/C][C]-0.159944873077474[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.184681325979561[/C][C]-0.184681325979561[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.181994447420953[/C][C]-0.181994447420953[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.151884237401652[/C][C]-0.151884237401652[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.176620690303739[/C][C]-0.176620690303739[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.173933811745131[/C][C]0.826066188254869[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.171246933186524[/C][C]-0.171246933186524[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.168560054627917[/C][C]-0.168560054627917[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.16587317606931[/C][C]0.83412682393069[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.135762966050008[/C][C]0.864237033949992[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.133076087491401[/C][C]-0.133076087491401[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.157812540393488[/C][C]-0.157812540393488[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.127702330374187[/C][C]-0.127702330374187[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.125015451815579[/C][C]-0.125015451815579[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.149751904717666[/C][C]-0.149751904717666[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.119641694698365[/C][C]-0.119641694698365[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.144378147600451[/C][C]-0.144378147600451[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.105528431637878[/C][C]-0.105528431637878[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.111581059022543[/C][C]-0.111581059022543[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.136317511924629[/C][C]-0.136317511924629[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.106207301905328[/C][C]-0.106207301905328[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.0673575859427546[/C][C]-0.0673575859427546[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.100833544788114[/C][C]-0.100833544788114[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0981466662295064[/C][C]-0.0981466662295064[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.0592969502669327[/C][C]-0.0592969502669327[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.120196240572986[/C][C]-0.120196240572986[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.0539231931497182[/C][C]-0.0539231931497182[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0873991519950773[/C][C]-0.0873991519950773[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.08471227343647[/C][C]-0.08471227343647[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.109448726338556[/C][C]-0.109448726338556[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.106761847779949[/C][C]-0.106761847779949[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0766516377606481[/C][C]-0.0766516377606481[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0739647592020408[/C][C]-0.0739647592020408[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0712778806434336[/C][C]-0.0712778806434336[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.0324281646808599[/C][C]-0.0324281646808599[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.065904123526219[/C][C]-0.065904123526219[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0632172449676117[/C][C]-0.0632172449676117[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.024367529005038[/C][C]-0.024367529005038[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0578434878503971[/C][C]-0.0578434878503971[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0551566092917899[/C][C]-0.0551566092917899[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.0163068933292161[/C][C]-0.0163068933292161[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.0136200147706088[/C][C]-0.0136200147706088[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.047095973615968[/C][C]-0.047095973615968[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.00824625765339426[/C][C]-0.00824625765339426[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0417222164987534[/C][C]-0.0417222164987534[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0390353379401461[/C][C]-0.0390353379401461[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.0637717908422326[/C][C]-0.0637717908422326[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0336615808229315[/C][C]-0.0336615808229315[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0309747022643243[/C][C]-0.0309747022643243[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]0.0557111551664108[/C][C]-0.0557111551664108[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0256009451471097[/C][C]-0.0256009451471097[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0229140665885024[/C][C]-0.0229140665885024[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]-0.0159356493740713[/C][C]0.0159356493740713[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.0449636409319816[/C][C]-0.0449636409319816[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.0422767623733743[/C][C]-0.0422767623733743[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-0.0239962850498932[/C][C]0.0239962850498932[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.00947967379546594[/C][C]-0.00947967379546594[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.0342161266975525[/C][C]-0.0342161266975525[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.00410591667825136[/C][C]-0.00410591667825136[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.0288423695803379[/C][C]-0.0288423695803379[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.00126784043896322[/C][C]0.00126784043896322[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]0.0234686124631233[/C][C]-0.0234686124631233[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]-0.0066415975561778[/C][C]0.0066415975561778[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]-0.00932847611478509[/C][C]0.00932847611478509[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.0120153546733924[/C][C]0.0120153546733924[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-0.0147022332319997[/C][C]0.0147022332319997[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.0100342196700869[/C][C]-0.0100342196700869[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]-0.0288154962924868[/C][C]0.0288154962924868[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.0589257063117879[/C][C]0.0589257063117879[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.0254497474664288[/C][C]0.0254497474664288[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]-0.000713294564342287[/C][C]0.000713294564342287[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]-0.039563010526916[/C][C]0.039563010526916[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-0.0335103831422507[/C][C]0.0335103831422507[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]-0.00877393024016414[/C][C]0.00877393024016414[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]-0.0388841402594652[/C][C]0.0388841402594652[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.0503105247613451[/C][C]0.0503105247613451[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]-0.0804207347806462[/C][C]0.0804207347806462[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-0.0831076133392535[/C][C]0.0831076133392535[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0496316544938944[/C][C]0.0496316544938944[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]-0.0248952015918079[/C][C]0.0248952015918079[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.0275820801504152[/C][C]0.0275820801504152[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]-0.0576922901697163[/C][C]0.0576922901697163[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-0.0603791687283236[/C][C]0.0603791687283236[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-0.0630660472869308[/C][C]0.0630660472869308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3754497036406790.624550296359321
200.345339493621377-0.345339493621377
300.34265261506277-0.34265261506277
400.339965736504162-0.339965736504162
500.337278857945555-0.337278857945555
600.362015310847641-0.362015310847641
700.33190510082834-0.33190510082834
810.3292182222697330.670781777730267
900.35395467517182-0.35395467517182
1000.323844465152519-0.323844465152519
1110.3211575865939110.678842413406089
1200.318470708035304-0.318470708035304
1300.315783829476697-0.315783829476697
1410.313096950918090.68690304908191
1500.337833403820176-0.337833403820176
1610.3351465252615690.664853474738431
1710.3050363152422680.694963684757732
1810.302349436683660.69765056331634
1900.327085889585747-0.327085889585747
2010.324399011027140.67560098897286
2100.294288801007838-0.294288801007838
2200.319025253909925-0.319025253909925
2300.316338375351318-0.316338375351318
2400.31365149679271-0.31365149679271
2510.3109646182341030.689035381765897
2600.280854408214802-0.280854408214802
2700.305590861116888-0.305590861116888
2800.275480651097587-0.275480651097587
2900.300217103999674-0.300217103999674
3000.270106893980373-0.270106893980373
3100.267420015421766-0.267420015421766
3200.264733136863158-0.264733136863158
3300.262046258304551-0.262046258304551
3410.2867827112066370.713217288793363
3500.256672501187336-0.256672501187336
3600.253985622628729-0.253985622628729
3710.2512987440701220.748701255929878
3800.276035196972208-0.276035196972208
3900.273348318413601-0.273348318413601
4010.24323810839430.7567618916057
4100.267974561296386-0.267974561296386
4200.265287682737779-0.265287682737779
4300.262600804179172-0.262600804179172
4410.2324905941598710.767509405840129
4500.229803715601264-0.229803715601264
4600.25454016850335-0.25454016850335
4700.224429958484049-0.224429958484049
4800.249166411386135-0.249166411386135
4900.246479532827528-0.246479532827528
5000.216369322808227-0.216369322808227
5110.213682444249620.78631755575038
5210.2109955656910130.789004434308987
5300.235732018593099-0.235732018593099
5400.205621808573798-0.205621808573798
5500.202934930015191-0.202934930015191
5610.2276713829172770.772328617082723
5700.22498450435867-0.22498450435867
5800.222297625800063-0.222297625800063
5900.219610747241455-0.219610747241455
6010.2169238686828480.783076131317152
6110.2142369901242410.785763009875759
6200.18412678010494-0.18412678010494
6300.181439901546332-0.181439901546332
6410.2061763544484190.793823645551581
6500.176066144429118-0.176066144429118
6600.17337926587051-0.17337926587051
6710.1706923873119030.829307612688097
6800.168005508753296-0.168005508753296
6900.192741961655382-0.192741961655382
7000.162631751636081-0.162631751636081
7100.159944873077474-0.159944873077474
7200.184681325979561-0.184681325979561
7300.181994447420953-0.181994447420953
7400.151884237401652-0.151884237401652
7500.176620690303739-0.176620690303739
7610.1739338117451310.826066188254869
7700.171246933186524-0.171246933186524
7800.168560054627917-0.168560054627917
7910.165873176069310.83412682393069
8010.1357629660500080.864237033949992
8100.133076087491401-0.133076087491401
8200.157812540393488-0.157812540393488
8300.127702330374187-0.127702330374187
8400.125015451815579-0.125015451815579
8500.149751904717666-0.149751904717666
8600.119641694698365-0.119641694698365
8700.144378147600451-0.144378147600451
8800.105528431637878-0.105528431637878
8900.111581059022543-0.111581059022543
9000.136317511924629-0.136317511924629
9100.106207301905328-0.106207301905328
9200.0673575859427546-0.0673575859427546
9300.100833544788114-0.100833544788114
9400.0981466662295064-0.0981466662295064
9500.0592969502669327-0.0592969502669327
9600.120196240572986-0.120196240572986
9700.0539231931497182-0.0539231931497182
9800.0873991519950773-0.0873991519950773
9900.08471227343647-0.08471227343647
10000.109448726338556-0.109448726338556
10100.106761847779949-0.106761847779949
10200.0766516377606481-0.0766516377606481
10300.0739647592020408-0.0739647592020408
10400.0712778806434336-0.0712778806434336
10500.0324281646808599-0.0324281646808599
10600.065904123526219-0.065904123526219
10700.0632172449676117-0.0632172449676117
10800.024367529005038-0.024367529005038
10900.0578434878503971-0.0578434878503971
11000.0551566092917899-0.0551566092917899
11100.0163068933292161-0.0163068933292161
11200.0136200147706088-0.0136200147706088
11300.047095973615968-0.047095973615968
11400.00824625765339426-0.00824625765339426
11500.0417222164987534-0.0417222164987534
11600.0390353379401461-0.0390353379401461
11700.0637717908422326-0.0637717908422326
11800.0336615808229315-0.0336615808229315
11900.0309747022643243-0.0309747022643243
12000.0557111551664108-0.0557111551664108
12100.0256009451471097-0.0256009451471097
12200.0229140665885024-0.0229140665885024
1230-0.01593564937407130.0159356493740713
12400.0449636409319816-0.0449636409319816
12500.0422767623733743-0.0422767623733743
1260-0.02399628504989320.0239962850498932
12700.00947967379546594-0.00947967379546594
12800.0342161266975525-0.0342161266975525
12900.00410591667825136-0.00410591667825136
13000.0288423695803379-0.0288423695803379
1310-0.001267840438963220.00126784043896322
13200.0234686124631233-0.0234686124631233
1330-0.00664159755617780.0066415975561778
1340-0.009328476114785090.00932847611478509
1350-0.01201535467339240.0120153546733924
1360-0.01470223323199970.0147022332319997
13700.0100342196700869-0.0100342196700869
1380-0.02881549629248680.0288154962924868
1390-0.05892570631178790.0589257063117879
1400-0.02544974746642880.0254497474664288
1410-0.0007132945643422870.000713294564342287
1420-0.0395630105269160.039563010526916
1430-0.03351038314225070.0335103831422507
1440-0.008773930240164140.00877393024016414
1450-0.03888414025946520.0388841402594652
1460-0.05031052476134510.0503105247613451
1470-0.08042073478064620.0804207347806462
1480-0.08310761333925350.0831076133392535
1490-0.04963165449389440.0496316544938944
1500-0.02489520159180790.0248952015918079
1510-0.02758208015041520.0275820801504152
1520-0.05769229016971630.0576922901697163
1530-0.06037916872832360.0603791687283236
1540-0.06306604728693080.0630660472869308







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5097213934884420.9805572130231170.490278606511558
80.9799585641097030.0400828717805940.020041435890297
90.9769595717921380.04608085641572330.0230404282078617
100.9588751977377090.08224960452458240.0411248022622912
110.9914202018024650.0171595963950690.00857979819753451
120.9886112522201850.02277749555962910.0113887477798146
130.9835168311341310.03296633773173890.0164831688658694
140.9943048209457840.0113903581084320.00569517905421599
150.9940909786995710.01181804260085820.00590902130042909
160.9967467943934490.00650641121310220.0032532056065511
170.9977896186323880.004420762735224460.00221038136761223
180.9980789159059180.003842168188163890.00192108409408194
190.999053653658030.001892692683939850.000946346341969926
200.9992683731474110.001463253705178620.000731626852589309
210.9996486200132460.0007027599735073050.000351379986753653
220.9997616671887590.0004766656224824580.000238332811241229
230.9997848831729240.0004302336541520060.000215116827076003
240.9997711482458390.0004577035083228910.000228851754161445
250.9999077486080130.0001845027839739019.22513919869507e-05
260.9999157762586770.0001684474826469948.42237413234968e-05
270.9999093434989780.0001813130020446459.06565010223226e-05
280.999891154617450.0002176907651003580.000108845382550179
290.9998661879893230.0002676240213536250.000133812010676812
300.9998203236494970.0003593527010057430.000179676350502871
310.9997517371855050.0004965256289902320.000248262814495116
320.9996540447216360.0006919105567287850.000345955278364393
330.9995193922508230.0009612154983539570.000480607749176978
340.9998891638438630.0002216723122748690.000110836156137435
350.9998471683222230.0003056633555538870.000152831677776944
360.999790392373150.000419215253699670.000209607626849835
370.9999656828647146.86342705723194e-053.43171352861597e-05
380.9999584479450878.31041098255924e-054.15520549127962e-05
390.9999479672963480.0001040654073034925.20327036517459e-05
400.9999915351669521.69296660960139e-058.46483304800697e-06
410.999989426199312.1147601379528e-051.0573800689764e-05
420.9999865541825442.6891634911928e-051.3445817455964e-05
430.9999827972620583.44054758835142e-051.72027379417571e-05
440.9999974824357545.03512849193623e-062.51756424596811e-06
450.9999967200910446.55981791290844e-063.27990895645422e-06
460.999995796985128.40602976000252e-064.20301488000126e-06
470.9999943886104541.1222779091266e-055.611389545633e-06
480.9999928445163451.43109673106893e-057.15548365534467e-06
490.9999910096125721.7980774855589e-058.99038742779452e-06
500.9999880126011952.39747976094039e-051.19873988047019e-05
510.9999987060096332.58798073385789e-061.29399036692894e-06
520.999999892521642.14956720156394e-071.07478360078197e-07
530.9999998637609192.72478161802076e-071.36239080901038e-07
540.9999998153632733.69273454931954e-071.84636727465977e-07
550.9999997456521765.08695648116012e-072.54347824058006e-07
560.9999999825008033.49983938566259e-081.74991969283129e-08
570.9999999774061724.51876561223649e-082.25938280611824e-08
580.9999999713438085.73123840065366e-082.86561920032683e-08
590.999999964572597.08548209825798e-083.54274104912899e-08
600.9999999982979693.404062155238e-091.702031077619e-09
610.9999999999594958.10108796766148e-114.05054398383074e-11
620.9999999999384051.23189155905446e-106.1594577952723e-11
630.9999999999043491.91302766348407e-109.56513831742037e-11
640.9999999999993071.38686246761266e-126.93431233806331e-13
650.9999999999988342.33148899838872e-121.16574449919436e-12
660.9999999999980153.96972822904183e-121.98486411452092e-12
670.9999999999999992.44165909617749e-151.22082954808875e-15
680.9999999999999984.74821226299196e-152.37410613149598e-15
690.9999999999999968.39288881031583e-154.19644440515792e-15
700.9999999999999921.66168719179671e-148.30843595898353e-15
710.9999999999999833.30638518560132e-141.65319259280066e-14
720.9999999999999715.85214752828837e-142.92607376414419e-14
730.9999999999999491.02082332554329e-135.10411662771646e-14
740.9999999999998992.01410739080789e-131.00705369540395e-13
750.9999999999998293.41917690671135e-131.70958845335568e-13
7611.93275695583768e-179.66378477918841e-18
7714.17060471513881e-172.0853023575694e-17
7818.82382903944297e-174.41191451972148e-17
7914.34257540085541e-252.17128770042771e-25
80100
81100
82100
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.509721393488442 & 0.980557213023117 & 0.490278606511558 \tabularnewline
8 & 0.979958564109703 & 0.040082871780594 & 0.020041435890297 \tabularnewline
9 & 0.976959571792138 & 0.0460808564157233 & 0.0230404282078617 \tabularnewline
10 & 0.958875197737709 & 0.0822496045245824 & 0.0411248022622912 \tabularnewline
11 & 0.991420201802465 & 0.017159596395069 & 0.00857979819753451 \tabularnewline
12 & 0.988611252220185 & 0.0227774955596291 & 0.0113887477798146 \tabularnewline
13 & 0.983516831134131 & 0.0329663377317389 & 0.0164831688658694 \tabularnewline
14 & 0.994304820945784 & 0.011390358108432 & 0.00569517905421599 \tabularnewline
15 & 0.994090978699571 & 0.0118180426008582 & 0.00590902130042909 \tabularnewline
16 & 0.996746794393449 & 0.0065064112131022 & 0.0032532056065511 \tabularnewline
17 & 0.997789618632388 & 0.00442076273522446 & 0.00221038136761223 \tabularnewline
18 & 0.998078915905918 & 0.00384216818816389 & 0.00192108409408194 \tabularnewline
19 & 0.99905365365803 & 0.00189269268393985 & 0.000946346341969926 \tabularnewline
20 & 0.999268373147411 & 0.00146325370517862 & 0.000731626852589309 \tabularnewline
21 & 0.999648620013246 & 0.000702759973507305 & 0.000351379986753653 \tabularnewline
22 & 0.999761667188759 & 0.000476665622482458 & 0.000238332811241229 \tabularnewline
23 & 0.999784883172924 & 0.000430233654152006 & 0.000215116827076003 \tabularnewline
24 & 0.999771148245839 & 0.000457703508322891 & 0.000228851754161445 \tabularnewline
25 & 0.999907748608013 & 0.000184502783973901 & 9.22513919869507e-05 \tabularnewline
26 & 0.999915776258677 & 0.000168447482646994 & 8.42237413234968e-05 \tabularnewline
27 & 0.999909343498978 & 0.000181313002044645 & 9.06565010223226e-05 \tabularnewline
28 & 0.99989115461745 & 0.000217690765100358 & 0.000108845382550179 \tabularnewline
29 & 0.999866187989323 & 0.000267624021353625 & 0.000133812010676812 \tabularnewline
30 & 0.999820323649497 & 0.000359352701005743 & 0.000179676350502871 \tabularnewline
31 & 0.999751737185505 & 0.000496525628990232 & 0.000248262814495116 \tabularnewline
32 & 0.999654044721636 & 0.000691910556728785 & 0.000345955278364393 \tabularnewline
33 & 0.999519392250823 & 0.000961215498353957 & 0.000480607749176978 \tabularnewline
34 & 0.999889163843863 & 0.000221672312274869 & 0.000110836156137435 \tabularnewline
35 & 0.999847168322223 & 0.000305663355553887 & 0.000152831677776944 \tabularnewline
36 & 0.99979039237315 & 0.00041921525369967 & 0.000209607626849835 \tabularnewline
37 & 0.999965682864714 & 6.86342705723194e-05 & 3.43171352861597e-05 \tabularnewline
38 & 0.999958447945087 & 8.31041098255924e-05 & 4.15520549127962e-05 \tabularnewline
39 & 0.999947967296348 & 0.000104065407303492 & 5.20327036517459e-05 \tabularnewline
40 & 0.999991535166952 & 1.69296660960139e-05 & 8.46483304800697e-06 \tabularnewline
41 & 0.99998942619931 & 2.1147601379528e-05 & 1.0573800689764e-05 \tabularnewline
42 & 0.999986554182544 & 2.6891634911928e-05 & 1.3445817455964e-05 \tabularnewline
43 & 0.999982797262058 & 3.44054758835142e-05 & 1.72027379417571e-05 \tabularnewline
44 & 0.999997482435754 & 5.03512849193623e-06 & 2.51756424596811e-06 \tabularnewline
45 & 0.999996720091044 & 6.55981791290844e-06 & 3.27990895645422e-06 \tabularnewline
46 & 0.99999579698512 & 8.40602976000252e-06 & 4.20301488000126e-06 \tabularnewline
47 & 0.999994388610454 & 1.1222779091266e-05 & 5.611389545633e-06 \tabularnewline
48 & 0.999992844516345 & 1.43109673106893e-05 & 7.15548365534467e-06 \tabularnewline
49 & 0.999991009612572 & 1.7980774855589e-05 & 8.99038742779452e-06 \tabularnewline
50 & 0.999988012601195 & 2.39747976094039e-05 & 1.19873988047019e-05 \tabularnewline
51 & 0.999998706009633 & 2.58798073385789e-06 & 1.29399036692894e-06 \tabularnewline
52 & 0.99999989252164 & 2.14956720156394e-07 & 1.07478360078197e-07 \tabularnewline
53 & 0.999999863760919 & 2.72478161802076e-07 & 1.36239080901038e-07 \tabularnewline
54 & 0.999999815363273 & 3.69273454931954e-07 & 1.84636727465977e-07 \tabularnewline
55 & 0.999999745652176 & 5.08695648116012e-07 & 2.54347824058006e-07 \tabularnewline
56 & 0.999999982500803 & 3.49983938566259e-08 & 1.74991969283129e-08 \tabularnewline
57 & 0.999999977406172 & 4.51876561223649e-08 & 2.25938280611824e-08 \tabularnewline
58 & 0.999999971343808 & 5.73123840065366e-08 & 2.86561920032683e-08 \tabularnewline
59 & 0.99999996457259 & 7.08548209825798e-08 & 3.54274104912899e-08 \tabularnewline
60 & 0.999999998297969 & 3.404062155238e-09 & 1.702031077619e-09 \tabularnewline
61 & 0.999999999959495 & 8.10108796766148e-11 & 4.05054398383074e-11 \tabularnewline
62 & 0.999999999938405 & 1.23189155905446e-10 & 6.1594577952723e-11 \tabularnewline
63 & 0.999999999904349 & 1.91302766348407e-10 & 9.56513831742037e-11 \tabularnewline
64 & 0.999999999999307 & 1.38686246761266e-12 & 6.93431233806331e-13 \tabularnewline
65 & 0.999999999998834 & 2.33148899838872e-12 & 1.16574449919436e-12 \tabularnewline
66 & 0.999999999998015 & 3.96972822904183e-12 & 1.98486411452092e-12 \tabularnewline
67 & 0.999999999999999 & 2.44165909617749e-15 & 1.22082954808875e-15 \tabularnewline
68 & 0.999999999999998 & 4.74821226299196e-15 & 2.37410613149598e-15 \tabularnewline
69 & 0.999999999999996 & 8.39288881031583e-15 & 4.19644440515792e-15 \tabularnewline
70 & 0.999999999999992 & 1.66168719179671e-14 & 8.30843595898353e-15 \tabularnewline
71 & 0.999999999999983 & 3.30638518560132e-14 & 1.65319259280066e-14 \tabularnewline
72 & 0.999999999999971 & 5.85214752828837e-14 & 2.92607376414419e-14 \tabularnewline
73 & 0.999999999999949 & 1.02082332554329e-13 & 5.10411662771646e-14 \tabularnewline
74 & 0.999999999999899 & 2.01410739080789e-13 & 1.00705369540395e-13 \tabularnewline
75 & 0.999999999999829 & 3.41917690671135e-13 & 1.70958845335568e-13 \tabularnewline
76 & 1 & 1.93275695583768e-17 & 9.66378477918841e-18 \tabularnewline
77 & 1 & 4.17060471513881e-17 & 2.0853023575694e-17 \tabularnewline
78 & 1 & 8.82382903944297e-17 & 4.41191451972148e-17 \tabularnewline
79 & 1 & 4.34257540085541e-25 & 2.17128770042771e-25 \tabularnewline
80 & 1 & 0 & 0 \tabularnewline
81 & 1 & 0 & 0 \tabularnewline
82 & 1 & 0 & 0 \tabularnewline
83 & 1 & 0 & 0 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 0 & 0 \tabularnewline
132 & 1 & 0 & 0 \tabularnewline
133 & 1 & 0 & 0 \tabularnewline
134 & 1 & 0 & 0 \tabularnewline
135 & 1 & 0 & 0 \tabularnewline
136 & 1 & 0 & 0 \tabularnewline
137 & 1 & 0 & 0 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 0 & 0 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 0 & 0 \tabularnewline
142 & 1 & 0 & 0 \tabularnewline
143 & 1 & 0 & 0 \tabularnewline
144 & 1 & 0 & 0 \tabularnewline
145 & 1 & 0 & 0 \tabularnewline
146 & 1 & 0 & 0 \tabularnewline
147 & 1 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&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]0.509721393488442[/C][C]0.980557213023117[/C][C]0.490278606511558[/C][/ROW]
[ROW][C]8[/C][C]0.979958564109703[/C][C]0.040082871780594[/C][C]0.020041435890297[/C][/ROW]
[ROW][C]9[/C][C]0.976959571792138[/C][C]0.0460808564157233[/C][C]0.0230404282078617[/C][/ROW]
[ROW][C]10[/C][C]0.958875197737709[/C][C]0.0822496045245824[/C][C]0.0411248022622912[/C][/ROW]
[ROW][C]11[/C][C]0.991420201802465[/C][C]0.017159596395069[/C][C]0.00857979819753451[/C][/ROW]
[ROW][C]12[/C][C]0.988611252220185[/C][C]0.0227774955596291[/C][C]0.0113887477798146[/C][/ROW]
[ROW][C]13[/C][C]0.983516831134131[/C][C]0.0329663377317389[/C][C]0.0164831688658694[/C][/ROW]
[ROW][C]14[/C][C]0.994304820945784[/C][C]0.011390358108432[/C][C]0.00569517905421599[/C][/ROW]
[ROW][C]15[/C][C]0.994090978699571[/C][C]0.0118180426008582[/C][C]0.00590902130042909[/C][/ROW]
[ROW][C]16[/C][C]0.996746794393449[/C][C]0.0065064112131022[/C][C]0.0032532056065511[/C][/ROW]
[ROW][C]17[/C][C]0.997789618632388[/C][C]0.00442076273522446[/C][C]0.00221038136761223[/C][/ROW]
[ROW][C]18[/C][C]0.998078915905918[/C][C]0.00384216818816389[/C][C]0.00192108409408194[/C][/ROW]
[ROW][C]19[/C][C]0.99905365365803[/C][C]0.00189269268393985[/C][C]0.000946346341969926[/C][/ROW]
[ROW][C]20[/C][C]0.999268373147411[/C][C]0.00146325370517862[/C][C]0.000731626852589309[/C][/ROW]
[ROW][C]21[/C][C]0.999648620013246[/C][C]0.000702759973507305[/C][C]0.000351379986753653[/C][/ROW]
[ROW][C]22[/C][C]0.999761667188759[/C][C]0.000476665622482458[/C][C]0.000238332811241229[/C][/ROW]
[ROW][C]23[/C][C]0.999784883172924[/C][C]0.000430233654152006[/C][C]0.000215116827076003[/C][/ROW]
[ROW][C]24[/C][C]0.999771148245839[/C][C]0.000457703508322891[/C][C]0.000228851754161445[/C][/ROW]
[ROW][C]25[/C][C]0.999907748608013[/C][C]0.000184502783973901[/C][C]9.22513919869507e-05[/C][/ROW]
[ROW][C]26[/C][C]0.999915776258677[/C][C]0.000168447482646994[/C][C]8.42237413234968e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999909343498978[/C][C]0.000181313002044645[/C][C]9.06565010223226e-05[/C][/ROW]
[ROW][C]28[/C][C]0.99989115461745[/C][C]0.000217690765100358[/C][C]0.000108845382550179[/C][/ROW]
[ROW][C]29[/C][C]0.999866187989323[/C][C]0.000267624021353625[/C][C]0.000133812010676812[/C][/ROW]
[ROW][C]30[/C][C]0.999820323649497[/C][C]0.000359352701005743[/C][C]0.000179676350502871[/C][/ROW]
[ROW][C]31[/C][C]0.999751737185505[/C][C]0.000496525628990232[/C][C]0.000248262814495116[/C][/ROW]
[ROW][C]32[/C][C]0.999654044721636[/C][C]0.000691910556728785[/C][C]0.000345955278364393[/C][/ROW]
[ROW][C]33[/C][C]0.999519392250823[/C][C]0.000961215498353957[/C][C]0.000480607749176978[/C][/ROW]
[ROW][C]34[/C][C]0.999889163843863[/C][C]0.000221672312274869[/C][C]0.000110836156137435[/C][/ROW]
[ROW][C]35[/C][C]0.999847168322223[/C][C]0.000305663355553887[/C][C]0.000152831677776944[/C][/ROW]
[ROW][C]36[/C][C]0.99979039237315[/C][C]0.00041921525369967[/C][C]0.000209607626849835[/C][/ROW]
[ROW][C]37[/C][C]0.999965682864714[/C][C]6.86342705723194e-05[/C][C]3.43171352861597e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999958447945087[/C][C]8.31041098255924e-05[/C][C]4.15520549127962e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999947967296348[/C][C]0.000104065407303492[/C][C]5.20327036517459e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999991535166952[/C][C]1.69296660960139e-05[/C][C]8.46483304800697e-06[/C][/ROW]
[ROW][C]41[/C][C]0.99998942619931[/C][C]2.1147601379528e-05[/C][C]1.0573800689764e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999986554182544[/C][C]2.6891634911928e-05[/C][C]1.3445817455964e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999982797262058[/C][C]3.44054758835142e-05[/C][C]1.72027379417571e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999997482435754[/C][C]5.03512849193623e-06[/C][C]2.51756424596811e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999996720091044[/C][C]6.55981791290844e-06[/C][C]3.27990895645422e-06[/C][/ROW]
[ROW][C]46[/C][C]0.99999579698512[/C][C]8.40602976000252e-06[/C][C]4.20301488000126e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999994388610454[/C][C]1.1222779091266e-05[/C][C]5.611389545633e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999992844516345[/C][C]1.43109673106893e-05[/C][C]7.15548365534467e-06[/C][/ROW]
[ROW][C]49[/C][C]0.999991009612572[/C][C]1.7980774855589e-05[/C][C]8.99038742779452e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999988012601195[/C][C]2.39747976094039e-05[/C][C]1.19873988047019e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999998706009633[/C][C]2.58798073385789e-06[/C][C]1.29399036692894e-06[/C][/ROW]
[ROW][C]52[/C][C]0.99999989252164[/C][C]2.14956720156394e-07[/C][C]1.07478360078197e-07[/C][/ROW]
[ROW][C]53[/C][C]0.999999863760919[/C][C]2.72478161802076e-07[/C][C]1.36239080901038e-07[/C][/ROW]
[ROW][C]54[/C][C]0.999999815363273[/C][C]3.69273454931954e-07[/C][C]1.84636727465977e-07[/C][/ROW]
[ROW][C]55[/C][C]0.999999745652176[/C][C]5.08695648116012e-07[/C][C]2.54347824058006e-07[/C][/ROW]
[ROW][C]56[/C][C]0.999999982500803[/C][C]3.49983938566259e-08[/C][C]1.74991969283129e-08[/C][/ROW]
[ROW][C]57[/C][C]0.999999977406172[/C][C]4.51876561223649e-08[/C][C]2.25938280611824e-08[/C][/ROW]
[ROW][C]58[/C][C]0.999999971343808[/C][C]5.73123840065366e-08[/C][C]2.86561920032683e-08[/C][/ROW]
[ROW][C]59[/C][C]0.99999996457259[/C][C]7.08548209825798e-08[/C][C]3.54274104912899e-08[/C][/ROW]
[ROW][C]60[/C][C]0.999999998297969[/C][C]3.404062155238e-09[/C][C]1.702031077619e-09[/C][/ROW]
[ROW][C]61[/C][C]0.999999999959495[/C][C]8.10108796766148e-11[/C][C]4.05054398383074e-11[/C][/ROW]
[ROW][C]62[/C][C]0.999999999938405[/C][C]1.23189155905446e-10[/C][C]6.1594577952723e-11[/C][/ROW]
[ROW][C]63[/C][C]0.999999999904349[/C][C]1.91302766348407e-10[/C][C]9.56513831742037e-11[/C][/ROW]
[ROW][C]64[/C][C]0.999999999999307[/C][C]1.38686246761266e-12[/C][C]6.93431233806331e-13[/C][/ROW]
[ROW][C]65[/C][C]0.999999999998834[/C][C]2.33148899838872e-12[/C][C]1.16574449919436e-12[/C][/ROW]
[ROW][C]66[/C][C]0.999999999998015[/C][C]3.96972822904183e-12[/C][C]1.98486411452092e-12[/C][/ROW]
[ROW][C]67[/C][C]0.999999999999999[/C][C]2.44165909617749e-15[/C][C]1.22082954808875e-15[/C][/ROW]
[ROW][C]68[/C][C]0.999999999999998[/C][C]4.74821226299196e-15[/C][C]2.37410613149598e-15[/C][/ROW]
[ROW][C]69[/C][C]0.999999999999996[/C][C]8.39288881031583e-15[/C][C]4.19644440515792e-15[/C][/ROW]
[ROW][C]70[/C][C]0.999999999999992[/C][C]1.66168719179671e-14[/C][C]8.30843595898353e-15[/C][/ROW]
[ROW][C]71[/C][C]0.999999999999983[/C][C]3.30638518560132e-14[/C][C]1.65319259280066e-14[/C][/ROW]
[ROW][C]72[/C][C]0.999999999999971[/C][C]5.85214752828837e-14[/C][C]2.92607376414419e-14[/C][/ROW]
[ROW][C]73[/C][C]0.999999999999949[/C][C]1.02082332554329e-13[/C][C]5.10411662771646e-14[/C][/ROW]
[ROW][C]74[/C][C]0.999999999999899[/C][C]2.01410739080789e-13[/C][C]1.00705369540395e-13[/C][/ROW]
[ROW][C]75[/C][C]0.999999999999829[/C][C]3.41917690671135e-13[/C][C]1.70958845335568e-13[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.93275695583768e-17[/C][C]9.66378477918841e-18[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]4.17060471513881e-17[/C][C]2.0853023575694e-17[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]8.82382903944297e-17[/C][C]4.41191451972148e-17[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]4.34257540085541e-25[/C][C]2.17128770042771e-25[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5097213934884420.9805572130231170.490278606511558
80.9799585641097030.0400828717805940.020041435890297
90.9769595717921380.04608085641572330.0230404282078617
100.9588751977377090.08224960452458240.0411248022622912
110.9914202018024650.0171595963950690.00857979819753451
120.9886112522201850.02277749555962910.0113887477798146
130.9835168311341310.03296633773173890.0164831688658694
140.9943048209457840.0113903581084320.00569517905421599
150.9940909786995710.01181804260085820.00590902130042909
160.9967467943934490.00650641121310220.0032532056065511
170.9977896186323880.004420762735224460.00221038136761223
180.9980789159059180.003842168188163890.00192108409408194
190.999053653658030.001892692683939850.000946346341969926
200.9992683731474110.001463253705178620.000731626852589309
210.9996486200132460.0007027599735073050.000351379986753653
220.9997616671887590.0004766656224824580.000238332811241229
230.9997848831729240.0004302336541520060.000215116827076003
240.9997711482458390.0004577035083228910.000228851754161445
250.9999077486080130.0001845027839739019.22513919869507e-05
260.9999157762586770.0001684474826469948.42237413234968e-05
270.9999093434989780.0001813130020446459.06565010223226e-05
280.999891154617450.0002176907651003580.000108845382550179
290.9998661879893230.0002676240213536250.000133812010676812
300.9998203236494970.0003593527010057430.000179676350502871
310.9997517371855050.0004965256289902320.000248262814495116
320.9996540447216360.0006919105567287850.000345955278364393
330.9995193922508230.0009612154983539570.000480607749176978
340.9998891638438630.0002216723122748690.000110836156137435
350.9998471683222230.0003056633555538870.000152831677776944
360.999790392373150.000419215253699670.000209607626849835
370.9999656828647146.86342705723194e-053.43171352861597e-05
380.9999584479450878.31041098255924e-054.15520549127962e-05
390.9999479672963480.0001040654073034925.20327036517459e-05
400.9999915351669521.69296660960139e-058.46483304800697e-06
410.999989426199312.1147601379528e-051.0573800689764e-05
420.9999865541825442.6891634911928e-051.3445817455964e-05
430.9999827972620583.44054758835142e-051.72027379417571e-05
440.9999974824357545.03512849193623e-062.51756424596811e-06
450.9999967200910446.55981791290844e-063.27990895645422e-06
460.999995796985128.40602976000252e-064.20301488000126e-06
470.9999943886104541.1222779091266e-055.611389545633e-06
480.9999928445163451.43109673106893e-057.15548365534467e-06
490.9999910096125721.7980774855589e-058.99038742779452e-06
500.9999880126011952.39747976094039e-051.19873988047019e-05
510.9999987060096332.58798073385789e-061.29399036692894e-06
520.999999892521642.14956720156394e-071.07478360078197e-07
530.9999998637609192.72478161802076e-071.36239080901038e-07
540.9999998153632733.69273454931954e-071.84636727465977e-07
550.9999997456521765.08695648116012e-072.54347824058006e-07
560.9999999825008033.49983938566259e-081.74991969283129e-08
570.9999999774061724.51876561223649e-082.25938280611824e-08
580.9999999713438085.73123840065366e-082.86561920032683e-08
590.999999964572597.08548209825798e-083.54274104912899e-08
600.9999999982979693.404062155238e-091.702031077619e-09
610.9999999999594958.10108796766148e-114.05054398383074e-11
620.9999999999384051.23189155905446e-106.1594577952723e-11
630.9999999999043491.91302766348407e-109.56513831742037e-11
640.9999999999993071.38686246761266e-126.93431233806331e-13
650.9999999999988342.33148899838872e-121.16574449919436e-12
660.9999999999980153.96972822904183e-121.98486411452092e-12
670.9999999999999992.44165909617749e-151.22082954808875e-15
680.9999999999999984.74821226299196e-152.37410613149598e-15
690.9999999999999968.39288881031583e-154.19644440515792e-15
700.9999999999999921.66168719179671e-148.30843595898353e-15
710.9999999999999833.30638518560132e-141.65319259280066e-14
720.9999999999999715.85214752828837e-142.92607376414419e-14
730.9999999999999491.02082332554329e-135.10411662771646e-14
740.9999999999998992.01410739080789e-131.00705369540395e-13
750.9999999999998293.41917690671135e-131.70958845335568e-13
7611.93275695583768e-179.66378477918841e-18
7714.17060471513881e-172.0853023575694e-17
7818.82382903944297e-174.41191451972148e-17
7914.34257540085541e-252.17128770042771e-25
80100
81100
82100
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.936170212765957NOK
5% type I error level1390.985815602836879NOK
10% type I error level1400.99290780141844NOK

\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 & 132 & 0.936170212765957 & NOK \tabularnewline
5% type I error level & 139 & 0.985815602836879 & NOK \tabularnewline
10% type I error level & 140 & 0.99290780141844 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203172&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]132[/C][C]0.936170212765957[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]139[/C][C]0.985815602836879[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]140[/C][C]0.99290780141844[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203172&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203172&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.936170212765957NOK
5% type I error level1390.985815602836879NOK
10% type I error level1400.99290780141844NOK



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = Exact Pearson Chi-Squared by Simulation ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}