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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 14:20:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321989682k2x1czd56hqd3ue.htm/, Retrieved Fri, 26 Apr 2024 01:31:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146381, Retrieved Fri, 26 Apr 2024 01:31:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS 7] [2011-11-22 17:11:10] [6a3e51c0c7ab195427042dfaef1df5a0]
- R       [Multiple Regression] [WS 7 - trend] [2011-11-22 19:20:58] [9afaf62527660fe62ab98f95fea710a5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3
12	12	8	13	5
15	10	12	16	6
12	9	7	12	6
10	10	10	11	5
12	12	7	12	3
15	13	16	18	8
9	12	11	11	4
12	12	14	14	4
11	6	6	9	4
11	5	16	14	6
11	12	11	12	6
15	11	16	11	5
7	14	12	12	4
11	14	7	13	6
11	12	13	11	4
10	12	11	12	6
14	11	15	16	6
10	11	7	9	4
6	7	9	11	4
11	9	7	13	2
15	11	14	15	7
11	11	15	10	5
12	12	7	11	4
14	12	15	13	6
15	11	17	16	6
9	11	15	15	7
13	8	14	14	5
13	9	14	14	6
16	12	8	14	4
13	10	8	8	4
12	10	14	13	7
14	12	14	15	7
11	8	8	13	4
9	12	11	11	4
16	11	16	15	6
12	12	10	15	6
10	7	8	9	5
13	11	14	13	6
16	11	16	16	7
14	12	13	13	6
15	9	5	11	3
5	15	8	12	3
8	11	10	12	4
11	11	8	12	6
16	11	13	14	7
17	11	15	14	5
9	15	6	8	4
9	11	12	13	5
13	12	16	16	6
10	12	5	13	6
6	9	15	11	6
12	12	12	14	5
8	12	8	13	4
14	13	13	13	5
12	11	14	13	5
11	9	12	12	4
16	9	16	16	6
8	11	10	15	2
15	11	15	15	8
7	12	8	12	3
16	12	16	14	6
14	9	19	12	6
16	11	14	15	6
9	9	6	12	5
14	12	13	13	5
11	12	15	12	6
13	12	7	12	5
15	12	13	13	6
5	14	4	5	2
15	11	14	13	5
13	12	13	13	5
11	11	11	14	5
11	6	14	17	6
12	10	12	13	6
12	12	15	13	6
12	13	14	12	5
12	8	13	13	5
14	12	8	14	4
6	12	6	11	2
7	12	7	12	4
14	6	13	12	6
14	11	13	16	6
10	10	11	12	5
13	12	5	12	3
12	13	12	12	6
9	11	8	10	4
12	7	11	15	5
16	11	14	15	8
10	11	9	12	4
14	11	10	16	6
10	11	13	15	6
16	12	16	16	7
15	10	16	13	6
12	11	11	12	5
10	12	8	11	4
8	7	4	13	6
8	13	7	10	3
11	8	14	15	5
13	12	11	13	6
16	11	17	16	7
16	12	15	15	7
14	14	17	18	6
11	10	5	13	3
4	10	4	10	2
14	13	10	16	8
9	10	11	13	3
14	11	15	15	8
8	10	10	14	3
8	7	9	15	4
11	10	12	14	5
12	8	15	13	7
11	12	7	13	6
14	12	13	15	6
15	12	12	16	7
16	11	14	14	6
16	12	14	14	6
11	12	8	16	6
14	12	15	14	6
14	11	12	12	4
12	12	12	13	4
14	11	16	12	5
8	11	9	12	4
13	13	15	14	6
16	12	15	14	6
12	12	6	14	5
16	12	14	16	8
12	12	15	13	6
11	8	10	14	5
4	8	6	4	4
16	12	14	16	8
15	11	12	13	6
10	12	8	16	4
13	13	11	15	6
15	12	13	14	6
12	12	9	13	4
14	11	15	14	6
7	12	13	12	3
19	12	15	15	6
12	10	14	14	5
12	11	16	13	4
13	12	14	14	6
15	12	14	16	4
8	10	10	6	4
12	12	10	13	4
10	13	4	13	6
8	12	8	14	5
10	15	15	15	6
15	11	16	14	6
16	12	12	15	8
13	11	12	13	7
16	12	15	16	7
9	11	9	12	4
14	10	12	15	6
14	11	14	12	6
12	11	11	14	2

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'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.311900251104237 + 0.0962538968572747FindingFriends[t] + 0.243370280084764KnowingPeople[t] + 0.351380635996807Liked[t] + 0.627591839447788Celebrity[t] -0.000729053587325965t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.311900251104237 +  0.0962538968572747FindingFriends[t] +  0.243370280084764KnowingPeople[t] +  0.351380635996807Liked[t] +  0.627591839447788Celebrity[t] -0.000729053587325965t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.311900251104237 +  0.0962538968572747FindingFriends[t] +  0.243370280084764KnowingPeople[t] +  0.351380635996807Liked[t] +  0.627591839447788Celebrity[t] -0.000729053587325965t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&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
Popularity[t] = + 0.311900251104237 + 0.0962538968572747FindingFriends[t] + 0.243370280084764KnowingPeople[t] + 0.351380635996807Liked[t] + 0.627591839447788Celebrity[t] -0.000729053587325965t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3119002511042371.4303540.21810.827680.41384
FindingFriends0.09625389685727470.0966810.99560.3210550.160528
KnowingPeople0.2433702800847640.0616163.94980.000126e-05
Liked0.3513806359968070.0976573.59810.0004350.000217
Celebrity0.6275918394477880.1565554.00889.6e-054.8e-05
t-0.0007290535873259650.003824-0.19070.849050.424525

\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) & 0.311900251104237 & 1.430354 & 0.2181 & 0.82768 & 0.41384 \tabularnewline
FindingFriends & 0.0962538968572747 & 0.096681 & 0.9956 & 0.321055 & 0.160528 \tabularnewline
KnowingPeople & 0.243370280084764 & 0.061616 & 3.9498 & 0.00012 & 6e-05 \tabularnewline
Liked & 0.351380635996807 & 0.097657 & 3.5981 & 0.000435 & 0.000217 \tabularnewline
Celebrity & 0.627591839447788 & 0.156555 & 4.0088 & 9.6e-05 & 4.8e-05 \tabularnewline
t & -0.000729053587325965 & 0.003824 & -0.1907 & 0.84905 & 0.424525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&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]0.311900251104237[/C][C]1.430354[/C][C]0.2181[/C][C]0.82768[/C][C]0.41384[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0962538968572747[/C][C]0.096681[/C][C]0.9956[/C][C]0.321055[/C][C]0.160528[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.243370280084764[/C][C]0.061616[/C][C]3.9498[/C][C]0.00012[/C][C]6e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.351380635996807[/C][C]0.097657[/C][C]3.5981[/C][C]0.000435[/C][C]0.000217[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.627591839447788[/C][C]0.156555[/C][C]4.0088[/C][C]9.6e-05[/C][C]4.8e-05[/C][/ROW]
[ROW][C]t[/C][C]-0.000729053587325965[/C][C]0.003824[/C][C]-0.1907[/C][C]0.84905[/C][C]0.424525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&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)0.3119002511042371.4303540.21810.827680.41384
FindingFriends0.09625389685727470.0966810.99560.3210550.160528
KnowingPeople0.2433702800847640.0616163.94980.000126e-05
Liked0.3513806359968070.0976573.59810.0004350.000217
Celebrity0.6275918394477880.1565554.00889.6e-054.8e-05
t-0.0007290535873259650.003824-0.19070.849050.424525






Multiple Linear Regression - Regression Statistics
Multiple R0.706626862823286
R-squared0.499321523263479
Adjusted R-squared0.482632240705595
F-TEST (value)29.918693121269
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11226554360225
Sum Squared Residuals669.249859003394

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.706626862823286 \tabularnewline
R-squared & 0.499321523263479 \tabularnewline
Adjusted R-squared & 0.482632240705595 \tabularnewline
F-TEST (value) & 29.918693121269 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.11226554360225 \tabularnewline
Sum Squared Residuals & 669.249859003394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.706626862823286[/C][/ROW]
[ROW][C]R-squared[/C][C]0.499321523263479[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.482632240705595[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.918693121269[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.11226554360225[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.249859003394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&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 R0.706626862823286
R-squared0.499321523263479
Adjusted R-squared0.482632240705595
F-TEST (value)29.918693121269
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11226554360225
Sum Squared Residuals669.249859003394






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.420379564151.57962043584996
21211.11835861209240.88164138790758
31513.58033663256781.41966336743219
41210.86097973771221.13902026228784
51010.7076429457918-0.707642945791809
6129.265507802765972.73449219723403
71516.7976081800186-1.79760818001857
8910.5137420193814-1.51374201938135
91212.2972657140387-0.297265714038741
10118.015147858645622.98485214135438
111113.3639545679283-2.36395456792827
121112.1173901199244-1.11739011992443
131512.25828609445912.74171390554094
14711.2966264076535-4.29662640765352
151111.6856102685348-0.685610268534755
161110.99465015085230.00534984914772395
171012.1137448519878-2.1137448519878
181414.3957655658695-0.395765565869486
19108.733226140730821.26677385926918
2069.53698333187754-3.53698333187754
21118.689599104933272.31040089506673
221514.42569027488640.574309725113601
231111.6562446425042-0.656244642504229
24129.528596041645082.47140395835492
251413.43277417962510.56722582037494
261514.87667369734040.123326302659594
27914.6654152870345-5.66541528703453
281312.52598994789820.474010052101763
291313.249106630616-0.249106630615974
301610.82173390819635.17826609180369
31138.52021324491364.4797867550864
321213.6193845701623-1.61938457016225
331414.5139245822831-0.513924582283089
341110.08242147042110.917578529578899
35910.4940575725236-1.49405757252355
361614.27463224538561.72536775461442
371212.9099354081469-0.90993540814694
38109.205320654675080.794679345324915
391313.0829432524605-0.0829432524604571
401615.25068850648090.749311493519134
411412.93436876205831.06563123794168
42158.112378986884076.88762101311592
4359.7706647906915-4.7706647906915
44810.4992525492924-2.49925254929239
451111.2669666144311-0.26696661443111
461613.8134420727092.18655792729099
471713.04426990039563.95573009960437
4898.50234825804590.4976517419541
49911.9613203169699-2.96132031696988
501314.7120600280171-1.71206002801709
511010.9801159855069-0.980115985506942
52612.4215667702018-6.42156677020182
531212.4060386354747-0.406038635474658
54810.4528559861037-2.45285598610368
551412.39282406924521.60717593075476
561212.4429575020281-0.442957502028128
571110.78400761911210.215992380887871
581614.41746590874671.58253409125334
59810.2872749745773-2.28727497457734
601515.2689483581006-0.268948358100565
6179.4687801355478-2.46878013554781
621614.00055011297561.99944988702443
631413.73840893707710.261591062922901
641613.76747818477092.23252181522908
6599.94554534935272-0.945545349352724
661412.28855058292741.71144941707262
671113.0507732929606-2.05077329296056
681310.47549015924732.52450984075266
691512.91395526161322.08604473838681
7055.59398903521193-0.59398903521193
711512.43202169821822.56797830178176
721312.28417626140340.715823738596577
731112.0518333867861-1.0518333867861
741113.9816794366049-2.9816794366049
751212.4737028662899-0.473702866289919
761213.3955924466714-1.39559244667144
771212.268774534412-0.268774534412026
781211.89478635245040.105213647549632
791410.78601028241733.21398971758266
8067.98921508177449-1.98921508177449
8179.83842062316431-2.83842062316431
821411.97557354783752.0244264521625
831413.86163652252380.13836347747623
841011.2447986284746-1.24479862847463
85138.721172009197694.27882799080231
861212.4030643314044-0.403064331404351
8799.27840141287423-0.27840141287423
881212.0072626315439-0.0072626315439181
891615.00443552398330.995564476016653
901010.2223458041906-0.222345804190629
911413.12569325357090.874306746429131
921013.503694404241-3.50369440424103
931615.30830256320990.691697436790135
941513.43333196846981.56666803153022
951211.33303293587130.666967064128684
96109.719474463442380.280525536557623
97810.2219397561188-2.22193975611881
9888.59192749759564-0.591927497595641
991112.8256077791949-1.8256077791949
1001312.40461404023660.595385959763445
1011615.44958651773870.550413482261253
1021614.70699016484241.29300983515764
1031415.8120595336817-1.81205953368174
104118.866192833320752.13380716667925
10546.94035975221045-2.94035975221045
1061414.5624489223711-0.562448922371106
107910.3242273530674-1.32422735306736
1081415.2339537859089-1.23395378590892
109810.4307796018048-2.43077960180475
110810.8768910530054-2.87689105300543
1111112.1712457336952-1.1712457336952
1121213.6119227695464-1.61192276954639
1131111.4216552232623-0.421655223262261
1141413.58390912217710.416090877822867
1151514.31878226394960.681217736050363
1161613.37818676223322.62181323776684
1171613.47371160550312.52628839449689
1181112.7155221434008-1.71552214340081
1191413.71562377841320.284376221586775
1201410.93058503682513.06941496317486
1211211.37749051609190.622509483908102
1221412.53019988943731.46980011056266
123810.1982870358089-2.19828703580887
1241313.8082324073339-0.80823240733387
1251613.71124945688932.28875054311073
1261210.89259604309131.10740395690872
1271615.4243660205190.575633979480958
1281213.3576816601305-1.35768166013049
1291111.4788744152393-0.478874415239258
13046.36326604189702-2.36326604189702
1311615.42144980616970.578550193830262
1321512.52840070866962.47159929133039
1331011.4494026606953-1.44940266069535
1341313.1788413871184-0.178841387118361
1351513.21721836084651.78278163915352
1361210.63644387202771.36355612797228
1371413.60624691698410.393753083015917
138710.6294944097475-3.62949440974753
1391914.05242334266354.94757665733649
1401212.6368437398323-0.636843739832278
1411212.2401366678272-0.240136667827161
1421313.45548526582-0.455485265819964
1431512.90233380533072.09766619466933
14488.22180947772168-0.221809477721678
1451210.87325266982651.12674733017345
1461010.7637395114835-0.763739511483486
147811.364026477927-3.36402647792696
1481014.3346235509494-4.3346235509494
1491513.84086855402091.15913144597906
1501614.56947659184421.43052340815579
1511313.1421405299582-0.142140529958208
1521615.02191812147290.978081878527131
153910.1764154281891-1.17641542818909
1541413.11886890488480.881131095115219
1551412.64699240033381.35300759966616
1561210.10854642069471.89145357930532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.42037956415 & 1.57962043584996 \tabularnewline
2 & 12 & 11.1183586120924 & 0.88164138790758 \tabularnewline
3 & 15 & 13.5803366325678 & 1.41966336743219 \tabularnewline
4 & 12 & 10.8609797377122 & 1.13902026228784 \tabularnewline
5 & 10 & 10.7076429457918 & -0.707642945791809 \tabularnewline
6 & 12 & 9.26550780276597 & 2.73449219723403 \tabularnewline
7 & 15 & 16.7976081800186 & -1.79760818001857 \tabularnewline
8 & 9 & 10.5137420193814 & -1.51374201938135 \tabularnewline
9 & 12 & 12.2972657140387 & -0.297265714038741 \tabularnewline
10 & 11 & 8.01514785864562 & 2.98485214135438 \tabularnewline
11 & 11 & 13.3639545679283 & -2.36395456792827 \tabularnewline
12 & 11 & 12.1173901199244 & -1.11739011992443 \tabularnewline
13 & 15 & 12.2582860944591 & 2.74171390554094 \tabularnewline
14 & 7 & 11.2966264076535 & -4.29662640765352 \tabularnewline
15 & 11 & 11.6856102685348 & -0.685610268534755 \tabularnewline
16 & 11 & 10.9946501508523 & 0.00534984914772395 \tabularnewline
17 & 10 & 12.1137448519878 & -2.1137448519878 \tabularnewline
18 & 14 & 14.3957655658695 & -0.395765565869486 \tabularnewline
19 & 10 & 8.73322614073082 & 1.26677385926918 \tabularnewline
20 & 6 & 9.53698333187754 & -3.53698333187754 \tabularnewline
21 & 11 & 8.68959910493327 & 2.31040089506673 \tabularnewline
22 & 15 & 14.4256902748864 & 0.574309725113601 \tabularnewline
23 & 11 & 11.6562446425042 & -0.656244642504229 \tabularnewline
24 & 12 & 9.52859604164508 & 2.47140395835492 \tabularnewline
25 & 14 & 13.4327741796251 & 0.56722582037494 \tabularnewline
26 & 15 & 14.8766736973404 & 0.123326302659594 \tabularnewline
27 & 9 & 14.6654152870345 & -5.66541528703453 \tabularnewline
28 & 13 & 12.5259899478982 & 0.474010052101763 \tabularnewline
29 & 13 & 13.249106630616 & -0.249106630615974 \tabularnewline
30 & 16 & 10.8217339081963 & 5.17826609180369 \tabularnewline
31 & 13 & 8.5202132449136 & 4.4797867550864 \tabularnewline
32 & 12 & 13.6193845701623 & -1.61938457016225 \tabularnewline
33 & 14 & 14.5139245822831 & -0.513924582283089 \tabularnewline
34 & 11 & 10.0824214704211 & 0.917578529578899 \tabularnewline
35 & 9 & 10.4940575725236 & -1.49405757252355 \tabularnewline
36 & 16 & 14.2746322453856 & 1.72536775461442 \tabularnewline
37 & 12 & 12.9099354081469 & -0.90993540814694 \tabularnewline
38 & 10 & 9.20532065467508 & 0.794679345324915 \tabularnewline
39 & 13 & 13.0829432524605 & -0.0829432524604571 \tabularnewline
40 & 16 & 15.2506885064809 & 0.749311493519134 \tabularnewline
41 & 14 & 12.9343687620583 & 1.06563123794168 \tabularnewline
42 & 15 & 8.11237898688407 & 6.88762101311592 \tabularnewline
43 & 5 & 9.7706647906915 & -4.7706647906915 \tabularnewline
44 & 8 & 10.4992525492924 & -2.49925254929239 \tabularnewline
45 & 11 & 11.2669666144311 & -0.26696661443111 \tabularnewline
46 & 16 & 13.813442072709 & 2.18655792729099 \tabularnewline
47 & 17 & 13.0442699003956 & 3.95573009960437 \tabularnewline
48 & 9 & 8.5023482580459 & 0.4976517419541 \tabularnewline
49 & 9 & 11.9613203169699 & -2.96132031696988 \tabularnewline
50 & 13 & 14.7120600280171 & -1.71206002801709 \tabularnewline
51 & 10 & 10.9801159855069 & -0.980115985506942 \tabularnewline
52 & 6 & 12.4215667702018 & -6.42156677020182 \tabularnewline
53 & 12 & 12.4060386354747 & -0.406038635474658 \tabularnewline
54 & 8 & 10.4528559861037 & -2.45285598610368 \tabularnewline
55 & 14 & 12.3928240692452 & 1.60717593075476 \tabularnewline
56 & 12 & 12.4429575020281 & -0.442957502028128 \tabularnewline
57 & 11 & 10.7840076191121 & 0.215992380887871 \tabularnewline
58 & 16 & 14.4174659087467 & 1.58253409125334 \tabularnewline
59 & 8 & 10.2872749745773 & -2.28727497457734 \tabularnewline
60 & 15 & 15.2689483581006 & -0.268948358100565 \tabularnewline
61 & 7 & 9.4687801355478 & -2.46878013554781 \tabularnewline
62 & 16 & 14.0005501129756 & 1.99944988702443 \tabularnewline
63 & 14 & 13.7384089370771 & 0.261591062922901 \tabularnewline
64 & 16 & 13.7674781847709 & 2.23252181522908 \tabularnewline
65 & 9 & 9.94554534935272 & -0.945545349352724 \tabularnewline
66 & 14 & 12.2885505829274 & 1.71144941707262 \tabularnewline
67 & 11 & 13.0507732929606 & -2.05077329296056 \tabularnewline
68 & 13 & 10.4754901592473 & 2.52450984075266 \tabularnewline
69 & 15 & 12.9139552616132 & 2.08604473838681 \tabularnewline
70 & 5 & 5.59398903521193 & -0.59398903521193 \tabularnewline
71 & 15 & 12.4320216982182 & 2.56797830178176 \tabularnewline
72 & 13 & 12.2841762614034 & 0.715823738596577 \tabularnewline
73 & 11 & 12.0518333867861 & -1.0518333867861 \tabularnewline
74 & 11 & 13.9816794366049 & -2.9816794366049 \tabularnewline
75 & 12 & 12.4737028662899 & -0.473702866289919 \tabularnewline
76 & 12 & 13.3955924466714 & -1.39559244667144 \tabularnewline
77 & 12 & 12.268774534412 & -0.268774534412026 \tabularnewline
78 & 12 & 11.8947863524504 & 0.105213647549632 \tabularnewline
79 & 14 & 10.7860102824173 & 3.21398971758266 \tabularnewline
80 & 6 & 7.98921508177449 & -1.98921508177449 \tabularnewline
81 & 7 & 9.83842062316431 & -2.83842062316431 \tabularnewline
82 & 14 & 11.9755735478375 & 2.0244264521625 \tabularnewline
83 & 14 & 13.8616365225238 & 0.13836347747623 \tabularnewline
84 & 10 & 11.2447986284746 & -1.24479862847463 \tabularnewline
85 & 13 & 8.72117200919769 & 4.27882799080231 \tabularnewline
86 & 12 & 12.4030643314044 & -0.403064331404351 \tabularnewline
87 & 9 & 9.27840141287423 & -0.27840141287423 \tabularnewline
88 & 12 & 12.0072626315439 & -0.0072626315439181 \tabularnewline
89 & 16 & 15.0044355239833 & 0.995564476016653 \tabularnewline
90 & 10 & 10.2223458041906 & -0.222345804190629 \tabularnewline
91 & 14 & 13.1256932535709 & 0.874306746429131 \tabularnewline
92 & 10 & 13.503694404241 & -3.50369440424103 \tabularnewline
93 & 16 & 15.3083025632099 & 0.691697436790135 \tabularnewline
94 & 15 & 13.4333319684698 & 1.56666803153022 \tabularnewline
95 & 12 & 11.3330329358713 & 0.666967064128684 \tabularnewline
96 & 10 & 9.71947446344238 & 0.280525536557623 \tabularnewline
97 & 8 & 10.2219397561188 & -2.22193975611881 \tabularnewline
98 & 8 & 8.59192749759564 & -0.591927497595641 \tabularnewline
99 & 11 & 12.8256077791949 & -1.8256077791949 \tabularnewline
100 & 13 & 12.4046140402366 & 0.595385959763445 \tabularnewline
101 & 16 & 15.4495865177387 & 0.550413482261253 \tabularnewline
102 & 16 & 14.7069901648424 & 1.29300983515764 \tabularnewline
103 & 14 & 15.8120595336817 & -1.81205953368174 \tabularnewline
104 & 11 & 8.86619283332075 & 2.13380716667925 \tabularnewline
105 & 4 & 6.94035975221045 & -2.94035975221045 \tabularnewline
106 & 14 & 14.5624489223711 & -0.562448922371106 \tabularnewline
107 & 9 & 10.3242273530674 & -1.32422735306736 \tabularnewline
108 & 14 & 15.2339537859089 & -1.23395378590892 \tabularnewline
109 & 8 & 10.4307796018048 & -2.43077960180475 \tabularnewline
110 & 8 & 10.8768910530054 & -2.87689105300543 \tabularnewline
111 & 11 & 12.1712457336952 & -1.1712457336952 \tabularnewline
112 & 12 & 13.6119227695464 & -1.61192276954639 \tabularnewline
113 & 11 & 11.4216552232623 & -0.421655223262261 \tabularnewline
114 & 14 & 13.5839091221771 & 0.416090877822867 \tabularnewline
115 & 15 & 14.3187822639496 & 0.681217736050363 \tabularnewline
116 & 16 & 13.3781867622332 & 2.62181323776684 \tabularnewline
117 & 16 & 13.4737116055031 & 2.52628839449689 \tabularnewline
118 & 11 & 12.7155221434008 & -1.71552214340081 \tabularnewline
119 & 14 & 13.7156237784132 & 0.284376221586775 \tabularnewline
120 & 14 & 10.9305850368251 & 3.06941496317486 \tabularnewline
121 & 12 & 11.3774905160919 & 0.622509483908102 \tabularnewline
122 & 14 & 12.5301998894373 & 1.46980011056266 \tabularnewline
123 & 8 & 10.1982870358089 & -2.19828703580887 \tabularnewline
124 & 13 & 13.8082324073339 & -0.80823240733387 \tabularnewline
125 & 16 & 13.7112494568893 & 2.28875054311073 \tabularnewline
126 & 12 & 10.8925960430913 & 1.10740395690872 \tabularnewline
127 & 16 & 15.424366020519 & 0.575633979480958 \tabularnewline
128 & 12 & 13.3576816601305 & -1.35768166013049 \tabularnewline
129 & 11 & 11.4788744152393 & -0.478874415239258 \tabularnewline
130 & 4 & 6.36326604189702 & -2.36326604189702 \tabularnewline
131 & 16 & 15.4214498061697 & 0.578550193830262 \tabularnewline
132 & 15 & 12.5284007086696 & 2.47159929133039 \tabularnewline
133 & 10 & 11.4494026606953 & -1.44940266069535 \tabularnewline
134 & 13 & 13.1788413871184 & -0.178841387118361 \tabularnewline
135 & 15 & 13.2172183608465 & 1.78278163915352 \tabularnewline
136 & 12 & 10.6364438720277 & 1.36355612797228 \tabularnewline
137 & 14 & 13.6062469169841 & 0.393753083015917 \tabularnewline
138 & 7 & 10.6294944097475 & -3.62949440974753 \tabularnewline
139 & 19 & 14.0524233426635 & 4.94757665733649 \tabularnewline
140 & 12 & 12.6368437398323 & -0.636843739832278 \tabularnewline
141 & 12 & 12.2401366678272 & -0.240136667827161 \tabularnewline
142 & 13 & 13.45548526582 & -0.455485265819964 \tabularnewline
143 & 15 & 12.9023338053307 & 2.09766619466933 \tabularnewline
144 & 8 & 8.22180947772168 & -0.221809477721678 \tabularnewline
145 & 12 & 10.8732526698265 & 1.12674733017345 \tabularnewline
146 & 10 & 10.7637395114835 & -0.763739511483486 \tabularnewline
147 & 8 & 11.364026477927 & -3.36402647792696 \tabularnewline
148 & 10 & 14.3346235509494 & -4.3346235509494 \tabularnewline
149 & 15 & 13.8408685540209 & 1.15913144597906 \tabularnewline
150 & 16 & 14.5694765918442 & 1.43052340815579 \tabularnewline
151 & 13 & 13.1421405299582 & -0.142140529958208 \tabularnewline
152 & 16 & 15.0219181214729 & 0.978081878527131 \tabularnewline
153 & 9 & 10.1764154281891 & -1.17641542818909 \tabularnewline
154 & 14 & 13.1188689048848 & 0.881131095115219 \tabularnewline
155 & 14 & 12.6469924003338 & 1.35300759966616 \tabularnewline
156 & 12 & 10.1085464206947 & 1.89145357930532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&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]13[/C][C]11.42037956415[/C][C]1.57962043584996[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.1183586120924[/C][C]0.88164138790758[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5803366325678[/C][C]1.41966336743219[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8609797377122[/C][C]1.13902026228784[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.7076429457918[/C][C]-0.707642945791809[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.26550780276597[/C][C]2.73449219723403[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7976081800186[/C][C]-1.79760818001857[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.5137420193814[/C][C]-1.51374201938135[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.2972657140387[/C][C]-0.297265714038741[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]8.01514785864562[/C][C]2.98485214135438[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3639545679283[/C][C]-2.36395456792827[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1173901199244[/C][C]-1.11739011992443[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2582860944591[/C][C]2.74171390554094[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.2966264076535[/C][C]-4.29662640765352[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.6856102685348[/C][C]-0.685610268534755[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9946501508523[/C][C]0.00534984914772395[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.1137448519878[/C][C]-2.1137448519878[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3957655658695[/C][C]-0.395765565869486[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.73322614073082[/C][C]1.26677385926918[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.53698333187754[/C][C]-3.53698333187754[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.68959910493327[/C][C]2.31040089506673[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4256902748864[/C][C]0.574309725113601[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6562446425042[/C][C]-0.656244642504229[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.52859604164508[/C][C]2.47140395835492[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.4327741796251[/C][C]0.56722582037494[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.8766736973404[/C][C]0.123326302659594[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.6654152870345[/C][C]-5.66541528703453[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.5259899478982[/C][C]0.474010052101763[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.249106630616[/C][C]-0.249106630615974[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.8217339081963[/C][C]5.17826609180369[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.5202132449136[/C][C]4.4797867550864[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.6193845701623[/C][C]-1.61938457016225[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5139245822831[/C][C]-0.513924582283089[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0824214704211[/C][C]0.917578529578899[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4940575725236[/C][C]-1.49405757252355[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2746322453856[/C][C]1.72536775461442[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.9099354081469[/C][C]-0.90993540814694[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.20532065467508[/C][C]0.794679345324915[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.0829432524605[/C][C]-0.0829432524604571[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2506885064809[/C][C]0.749311493519134[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9343687620583[/C][C]1.06563123794168[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.11237898688407[/C][C]6.88762101311592[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.7706647906915[/C][C]-4.7706647906915[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.4992525492924[/C][C]-2.49925254929239[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2669666144311[/C][C]-0.26696661443111[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.813442072709[/C][C]2.18655792729099[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.0442699003956[/C][C]3.95573009960437[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.5023482580459[/C][C]0.4976517419541[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.9613203169699[/C][C]-2.96132031696988[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.7120600280171[/C][C]-1.71206002801709[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9801159855069[/C][C]-0.980115985506942[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4215667702018[/C][C]-6.42156677020182[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.4060386354747[/C][C]-0.406038635474658[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4528559861037[/C][C]-2.45285598610368[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3928240692452[/C][C]1.60717593075476[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4429575020281[/C][C]-0.442957502028128[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7840076191121[/C][C]0.215992380887871[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4174659087467[/C][C]1.58253409125334[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2872749745773[/C][C]-2.28727497457734[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.2689483581006[/C][C]-0.268948358100565[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.4687801355478[/C][C]-2.46878013554781[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.0005501129756[/C][C]1.99944988702443[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.7384089370771[/C][C]0.261591062922901[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7674781847709[/C][C]2.23252181522908[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.94554534935272[/C][C]-0.945545349352724[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2885505829274[/C][C]1.71144941707262[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.0507732929606[/C][C]-2.05077329296056[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4754901592473[/C][C]2.52450984075266[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9139552616132[/C][C]2.08604473838681[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.59398903521193[/C][C]-0.59398903521193[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4320216982182[/C][C]2.56797830178176[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.2841762614034[/C][C]0.715823738596577[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.0518333867861[/C][C]-1.0518333867861[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9816794366049[/C][C]-2.9816794366049[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4737028662899[/C][C]-0.473702866289919[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.3955924466714[/C][C]-1.39559244667144[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.268774534412[/C][C]-0.268774534412026[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.8947863524504[/C][C]0.105213647549632[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7860102824173[/C][C]3.21398971758266[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.98921508177449[/C][C]-1.98921508177449[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.83842062316431[/C][C]-2.83842062316431[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.9755735478375[/C][C]2.0244264521625[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8616365225238[/C][C]0.13836347747623[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2447986284746[/C][C]-1.24479862847463[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.72117200919769[/C][C]4.27882799080231[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4030643314044[/C][C]-0.403064331404351[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.27840141287423[/C][C]-0.27840141287423[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0072626315439[/C][C]-0.0072626315439181[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0044355239833[/C][C]0.995564476016653[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2223458041906[/C][C]-0.222345804190629[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1256932535709[/C][C]0.874306746429131[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.503694404241[/C][C]-3.50369440424103[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3083025632099[/C][C]0.691697436790135[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4333319684698[/C][C]1.56666803153022[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.3330329358713[/C][C]0.666967064128684[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.71947446344238[/C][C]0.280525536557623[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2219397561188[/C][C]-2.22193975611881[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.59192749759564[/C][C]-0.591927497595641[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.8256077791949[/C][C]-1.8256077791949[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.4046140402366[/C][C]0.595385959763445[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4495865177387[/C][C]0.550413482261253[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7069901648424[/C][C]1.29300983515764[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.8120595336817[/C][C]-1.81205953368174[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.86619283332075[/C][C]2.13380716667925[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.94035975221045[/C][C]-2.94035975221045[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5624489223711[/C][C]-0.562448922371106[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3242273530674[/C][C]-1.32422735306736[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2339537859089[/C][C]-1.23395378590892[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4307796018048[/C][C]-2.43077960180475[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.8768910530054[/C][C]-2.87689105300543[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1712457336952[/C][C]-1.1712457336952[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6119227695464[/C][C]-1.61192276954639[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4216552232623[/C][C]-0.421655223262261[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.5839091221771[/C][C]0.416090877822867[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.3187822639496[/C][C]0.681217736050363[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3781867622332[/C][C]2.62181323776684[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4737116055031[/C][C]2.52628839449689[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7155221434008[/C][C]-1.71552214340081[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7156237784132[/C][C]0.284376221586775[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9305850368251[/C][C]3.06941496317486[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.3774905160919[/C][C]0.622509483908102[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5301998894373[/C][C]1.46980011056266[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.1982870358089[/C][C]-2.19828703580887[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.8082324073339[/C][C]-0.80823240733387[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.7112494568893[/C][C]2.28875054311073[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.8925960430913[/C][C]1.10740395690872[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.424366020519[/C][C]0.575633979480958[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.3576816601305[/C][C]-1.35768166013049[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4788744152393[/C][C]-0.478874415239258[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.36326604189702[/C][C]-2.36326604189702[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.4214498061697[/C][C]0.578550193830262[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5284007086696[/C][C]2.47159929133039[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4494026606953[/C][C]-1.44940266069535[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.1788413871184[/C][C]-0.178841387118361[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.2172183608465[/C][C]1.78278163915352[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.6364438720277[/C][C]1.36355612797228[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6062469169841[/C][C]0.393753083015917[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.6294944097475[/C][C]-3.62949440974753[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0524233426635[/C][C]4.94757665733649[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.6368437398323[/C][C]-0.636843739832278[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2401366678272[/C][C]-0.240136667827161[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.45548526582[/C][C]-0.455485265819964[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.9023338053307[/C][C]2.09766619466933[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.22180947772168[/C][C]-0.221809477721678[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.8732526698265[/C][C]1.12674733017345[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.7637395114835[/C][C]-0.763739511483486[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.364026477927[/C][C]-3.36402647792696[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3346235509494[/C][C]-4.3346235509494[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8408685540209[/C][C]1.15913144597906[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.5694765918442[/C][C]1.43052340815579[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1421405299582[/C][C]-0.142140529958208[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0219181214729[/C][C]0.978081878527131[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.1764154281891[/C][C]-1.17641542818909[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.1188689048848[/C][C]0.881131095115219[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6469924003338[/C][C]1.35300759966616[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1085464206947[/C][C]1.89145357930532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&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
11311.420379564151.57962043584996
21211.11835861209240.88164138790758
31513.58033663256781.41966336743219
41210.86097973771221.13902026228784
51010.7076429457918-0.707642945791809
6129.265507802765972.73449219723403
71516.7976081800186-1.79760818001857
8910.5137420193814-1.51374201938135
91212.2972657140387-0.297265714038741
10118.015147858645622.98485214135438
111113.3639545679283-2.36395456792827
121112.1173901199244-1.11739011992443
131512.25828609445912.74171390554094
14711.2966264076535-4.29662640765352
151111.6856102685348-0.685610268534755
161110.99465015085230.00534984914772395
171012.1137448519878-2.1137448519878
181414.3957655658695-0.395765565869486
19108.733226140730821.26677385926918
2069.53698333187754-3.53698333187754
21118.689599104933272.31040089506673
221514.42569027488640.574309725113601
231111.6562446425042-0.656244642504229
24129.528596041645082.47140395835492
251413.43277417962510.56722582037494
261514.87667369734040.123326302659594
27914.6654152870345-5.66541528703453
281312.52598994789820.474010052101763
291313.249106630616-0.249106630615974
301610.82173390819635.17826609180369
31138.52021324491364.4797867550864
321213.6193845701623-1.61938457016225
331414.5139245822831-0.513924582283089
341110.08242147042110.917578529578899
35910.4940575725236-1.49405757252355
361614.27463224538561.72536775461442
371212.9099354081469-0.90993540814694
38109.205320654675080.794679345324915
391313.0829432524605-0.0829432524604571
401615.25068850648090.749311493519134
411412.93436876205831.06563123794168
42158.112378986884076.88762101311592
4359.7706647906915-4.7706647906915
44810.4992525492924-2.49925254929239
451111.2669666144311-0.26696661443111
461613.8134420727092.18655792729099
471713.04426990039563.95573009960437
4898.50234825804590.4976517419541
49911.9613203169699-2.96132031696988
501314.7120600280171-1.71206002801709
511010.9801159855069-0.980115985506942
52612.4215667702018-6.42156677020182
531212.4060386354747-0.406038635474658
54810.4528559861037-2.45285598610368
551412.39282406924521.60717593075476
561212.4429575020281-0.442957502028128
571110.78400761911210.215992380887871
581614.41746590874671.58253409125334
59810.2872749745773-2.28727497457734
601515.2689483581006-0.268948358100565
6179.4687801355478-2.46878013554781
621614.00055011297561.99944988702443
631413.73840893707710.261591062922901
641613.76747818477092.23252181522908
6599.94554534935272-0.945545349352724
661412.28855058292741.71144941707262
671113.0507732929606-2.05077329296056
681310.47549015924732.52450984075266
691512.91395526161322.08604473838681
7055.59398903521193-0.59398903521193
711512.43202169821822.56797830178176
721312.28417626140340.715823738596577
731112.0518333867861-1.0518333867861
741113.9816794366049-2.9816794366049
751212.4737028662899-0.473702866289919
761213.3955924466714-1.39559244667144
771212.268774534412-0.268774534412026
781211.89478635245040.105213647549632
791410.78601028241733.21398971758266
8067.98921508177449-1.98921508177449
8179.83842062316431-2.83842062316431
821411.97557354783752.0244264521625
831413.86163652252380.13836347747623
841011.2447986284746-1.24479862847463
85138.721172009197694.27882799080231
861212.4030643314044-0.403064331404351
8799.27840141287423-0.27840141287423
881212.0072626315439-0.0072626315439181
891615.00443552398330.995564476016653
901010.2223458041906-0.222345804190629
911413.12569325357090.874306746429131
921013.503694404241-3.50369440424103
931615.30830256320990.691697436790135
941513.43333196846981.56666803153022
951211.33303293587130.666967064128684
96109.719474463442380.280525536557623
97810.2219397561188-2.22193975611881
9888.59192749759564-0.591927497595641
991112.8256077791949-1.8256077791949
1001312.40461404023660.595385959763445
1011615.44958651773870.550413482261253
1021614.70699016484241.29300983515764
1031415.8120595336817-1.81205953368174
104118.866192833320752.13380716667925
10546.94035975221045-2.94035975221045
1061414.5624489223711-0.562448922371106
107910.3242273530674-1.32422735306736
1081415.2339537859089-1.23395378590892
109810.4307796018048-2.43077960180475
110810.8768910530054-2.87689105300543
1111112.1712457336952-1.1712457336952
1121213.6119227695464-1.61192276954639
1131111.4216552232623-0.421655223262261
1141413.58390912217710.416090877822867
1151514.31878226394960.681217736050363
1161613.37818676223322.62181323776684
1171613.47371160550312.52628839449689
1181112.7155221434008-1.71552214340081
1191413.71562377841320.284376221586775
1201410.93058503682513.06941496317486
1211211.37749051609190.622509483908102
1221412.53019988943731.46980011056266
123810.1982870358089-2.19828703580887
1241313.8082324073339-0.80823240733387
1251613.71124945688932.28875054311073
1261210.89259604309131.10740395690872
1271615.4243660205190.575633979480958
1281213.3576816601305-1.35768166013049
1291111.4788744152393-0.478874415239258
13046.36326604189702-2.36326604189702
1311615.42144980616970.578550193830262
1321512.52840070866962.47159929133039
1331011.4494026606953-1.44940266069535
1341313.1788413871184-0.178841387118361
1351513.21721836084651.78278163915352
1361210.63644387202771.36355612797228
1371413.60624691698410.393753083015917
138710.6294944097475-3.62949440974753
1391914.05242334266354.94757665733649
1401212.6368437398323-0.636843739832278
1411212.2401366678272-0.240136667827161
1421313.45548526582-0.455485265819964
1431512.90233380533072.09766619466933
14488.22180947772168-0.221809477721678
1451210.87325266982651.12674733017345
1461010.7637395114835-0.763739511483486
147811.364026477927-3.36402647792696
1481014.3346235509494-4.3346235509494
1491513.84086855402091.15913144597906
1501614.56947659184421.43052340815579
1511313.1421405299582-0.142140529958208
1521615.02191812147290.978081878527131
153910.1764154281891-1.17641542818909
1541413.11886890488480.881131095115219
1551412.64699240033381.35300759966616
1561210.10854642069471.89145357930532






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02712889731447250.0542577946289450.972871102685527
100.02373201601878250.0474640320375650.976267983981218
110.05007464839441310.1001492967888260.949925351605587
120.03368950223637960.06737900447275920.96631049776362
130.437716993543970.8754339870879390.56228300645603
140.6316563046021750.7366873907956510.368343695397825
150.5644064685689580.8711870628620850.435593531431042
160.4898538966176390.9797077932352790.510146103382361
170.4071749612637090.8143499225274180.592825038736291
180.3752283278886790.7504566557773580.624771672111321
190.3234242980174950.6468485960349910.676575701982505
200.4507550203133370.9015100406266740.549244979686663
210.4552502339366930.9105004678733860.544749766063307
220.4980670159275540.9961340318551070.501932984072446
230.4363715860395880.8727431720791760.563628413960412
240.4545691245820360.9091382491640720.545430875417964
250.4303559148050240.8607118296100480.569644085194976
260.3761378032845510.7522756065691020.623862196715449
270.6071415742230350.7857168515539290.392858425776964
280.5582137086585040.8835725826829920.441786291341496
290.5018775261643080.9962449476713850.498122473835692
300.6806149782735140.6387700434529720.319385021726486
310.7848748240737090.4302503518525810.215125175926291
320.7475318694581150.504936261083770.252468130541885
330.6991622362990250.601675527401950.300837763700975
340.6635366045134890.6729267909730230.336463395486511
350.6696571087682650.660685782463470.330342891231735
360.6770310108730860.6459379782538280.322968989126914
370.6441962705471470.7116074589057060.355803729452853
380.5944451175840880.8111097648318240.405554882415912
390.540247519348870.919504961302260.45975248065113
400.5109253844648880.9781492310702240.489074615535112
410.4702767250187360.9405534500374710.529723274981264
420.735932527574330.5281349448513390.26406747242567
430.9498969266616530.1002061466766950.0501030733383473
440.9618637636256220.07627247274875580.0381362363743779
450.9504794200293230.09904115994135310.0495205799706766
460.9561215624413710.08775687511725810.0438784375586291
470.9780050416388570.04398991672228540.0219949583611427
480.9709778518565920.05804429628681550.0290221481434077
490.9798699712083410.04026005758331860.0201300287916593
500.9768820692080470.04623586158390690.0231179307919534
510.9721887475291550.05562250494168990.0278112524708449
520.9973138774368590.005372245126281980.00268612256314099
530.9961461927906140.00770761441877220.0038538072093861
540.9969098573422690.006180285315461380.00309014265773069
550.9967891747919470.006421650416106410.00321082520805321
560.995474626740240.009050746519520080.00452537325976004
570.9936557628918140.01268847421637140.00634423710818569
580.9929661867861520.01406762642769510.00703381321384755
590.9945914439920650.01081711201586960.0054085560079348
600.9929280109472370.01414397810552690.00707198905276343
610.9937939122155120.01241217556897650.00620608778448824
620.9944798961762630.01104020764747420.00552010382373709
630.9926749290423060.01465014191538780.00732507095769389
640.9931953130628560.01360937387428710.00680468693714357
650.991362425086320.01727514982736090.00863757491368044
660.9906063599090930.01878728018181460.00939364009090729
670.9905151144312320.01896977113753710.00948488556876853
680.9920123112137370.01597537757252580.00798768878626288
690.9921592761377870.01568144772442580.0078407238622129
700.9894755957305780.02104880853884450.0105244042694222
710.991146581644350.01770683671129960.00885341835564978
720.9883631806332680.02327363873346350.0116368193667318
730.9853943801996670.02921123960066680.0146056198003334
740.989423409464030.02115318107193930.0105765905359696
750.9857333151283550.02853336974329060.0142666848716453
760.9832710566093790.03345788678124280.0167289433906214
770.9779924896479920.04401502070401560.0220075103520078
780.9709913896960490.05801722060790110.0290086103039505
790.9817652488847430.03646950223051470.0182347511152574
800.9808862149799350.03822757004013040.0191137850200652
810.9840648936251040.03187021274979290.0159351063748964
820.9850011894587430.02999762108251430.0149988105412572
830.9799193938455660.04016121230886830.0200806061544342
840.975301719085280.04939656182943920.0246982809147196
850.9935872347096980.01282553058060370.00641276529030183
860.9911351101146010.01772977977079730.00886488988539866
870.9880204490500210.02395910189995890.0119795509499795
880.9843528530299840.0312942939400330.0156471469700165
890.9805943928199380.03881121436012480.0194056071800624
900.9745712936747530.05085741265049310.0254287063252466
910.9701027085906160.05979458281876720.0298972914093836
920.9817541493793390.03649170124132250.0182458506206613
930.9764739227590290.04705215448194210.023526077240971
940.9736869171707830.05262616565843490.0263130828292174
950.967493542405080.06501291518984030.0325064575949201
960.9599840934920060.08003181301598710.0400159065079936
970.9562319242184290.08753615156314250.0437680757815713
980.9446532935513370.1106934128973260.0553467064486631
990.9395514308045070.1208971383909870.0604485691954934
1000.9271862288156380.1456275423687250.0728137711843623
1010.909654431578330.180691136843340.0903455684216701
1020.8974776614051540.2050446771896920.102522338594846
1030.8990616546144610.2018766907710790.100938345385539
1040.9348483751247250.1303032497505490.0651516248752747
1050.9313246359837920.1373507280324160.0686753640162082
1060.9125493563558660.1749012872882690.0874506436441343
1070.8930246516619960.2139506966760080.106975348338004
1080.8843853500420890.2312292999158230.115614649957911
1090.8804341743548570.2391316512902850.119565825645143
1100.9022293013752040.1955413972495920.0977706986247961
1110.8930334521576350.213933095684730.106966547842365
1120.9279799681029920.1440400637940160.0720200318970082
1130.906831134639830.1863377307203390.0931688653601697
1140.8837075208867270.2325849582265450.116292479113273
1150.856362593656430.287274812687140.14363740634357
1160.8519843457937220.2960313084125560.148015654206278
1170.8569680719491580.2860638561016840.143031928050842
1180.850915776108260.2981684477834790.14908422389174
1190.8165680625414680.3668638749170650.183431937458532
1200.8634311813469330.2731376373061340.136568818653067
1210.8368959783772140.3262080432455730.163104021622786
1220.8152185525962030.3695628948075930.184781447403797
1230.8021976817511860.3956046364976270.197802318248814
1240.7615172467187930.4769655065624150.238482753281207
1250.7639329777113380.4721340445773240.236067022288662
1260.7547771027604880.4904457944790240.245222897239512
1270.7011668072798280.5976663854403430.298833192720172
1280.6643111428464810.6713777143070380.335688857153519
1290.6792176729310690.6415646541378610.320782327068931
1300.6809763611633670.6380472776732660.319023638836633
1310.6294336847444620.7411326305110770.370566315255538
1320.6013179625406320.7973640749187370.398682037459368
1330.5885623634079120.8228752731841760.411437636592088
1340.513980516509780.9720389669804410.48601948349022
1350.4714562936929490.9429125873858980.528543706307051
1360.4614565555723920.9229131111447850.538543444427608
1370.3862474605728060.7724949211456130.613752539427194
1380.4582887563736840.9165775127473680.541711243626316
1390.8190632535879580.3618734928240830.180936746412042
1400.8259053894498170.3481892211003660.174094610550183
1410.7893785805205820.4212428389588360.210621419479418
1420.7197478789426480.5605042421147050.280252121057352
1430.6574406502340460.6851186995319090.342559349765954
1440.542451151397970.915097697204060.45754884860203
1450.6207548605703980.7584902788592030.379245139429602
1460.7729885932620240.4540228134759530.227011406737976
1470.6304131595792420.7391736808415160.369586840420758

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0271288973144725 & 0.054257794628945 & 0.972871102685527 \tabularnewline
10 & 0.0237320160187825 & 0.047464032037565 & 0.976267983981218 \tabularnewline
11 & 0.0500746483944131 & 0.100149296788826 & 0.949925351605587 \tabularnewline
12 & 0.0336895022363796 & 0.0673790044727592 & 0.96631049776362 \tabularnewline
13 & 0.43771699354397 & 0.875433987087939 & 0.56228300645603 \tabularnewline
14 & 0.631656304602175 & 0.736687390795651 & 0.368343695397825 \tabularnewline
15 & 0.564406468568958 & 0.871187062862085 & 0.435593531431042 \tabularnewline
16 & 0.489853896617639 & 0.979707793235279 & 0.510146103382361 \tabularnewline
17 & 0.407174961263709 & 0.814349922527418 & 0.592825038736291 \tabularnewline
18 & 0.375228327888679 & 0.750456655777358 & 0.624771672111321 \tabularnewline
19 & 0.323424298017495 & 0.646848596034991 & 0.676575701982505 \tabularnewline
20 & 0.450755020313337 & 0.901510040626674 & 0.549244979686663 \tabularnewline
21 & 0.455250233936693 & 0.910500467873386 & 0.544749766063307 \tabularnewline
22 & 0.498067015927554 & 0.996134031855107 & 0.501932984072446 \tabularnewline
23 & 0.436371586039588 & 0.872743172079176 & 0.563628413960412 \tabularnewline
24 & 0.454569124582036 & 0.909138249164072 & 0.545430875417964 \tabularnewline
25 & 0.430355914805024 & 0.860711829610048 & 0.569644085194976 \tabularnewline
26 & 0.376137803284551 & 0.752275606569102 & 0.623862196715449 \tabularnewline
27 & 0.607141574223035 & 0.785716851553929 & 0.392858425776964 \tabularnewline
28 & 0.558213708658504 & 0.883572582682992 & 0.441786291341496 \tabularnewline
29 & 0.501877526164308 & 0.996244947671385 & 0.498122473835692 \tabularnewline
30 & 0.680614978273514 & 0.638770043452972 & 0.319385021726486 \tabularnewline
31 & 0.784874824073709 & 0.430250351852581 & 0.215125175926291 \tabularnewline
32 & 0.747531869458115 & 0.50493626108377 & 0.252468130541885 \tabularnewline
33 & 0.699162236299025 & 0.60167552740195 & 0.300837763700975 \tabularnewline
34 & 0.663536604513489 & 0.672926790973023 & 0.336463395486511 \tabularnewline
35 & 0.669657108768265 & 0.66068578246347 & 0.330342891231735 \tabularnewline
36 & 0.677031010873086 & 0.645937978253828 & 0.322968989126914 \tabularnewline
37 & 0.644196270547147 & 0.711607458905706 & 0.355803729452853 \tabularnewline
38 & 0.594445117584088 & 0.811109764831824 & 0.405554882415912 \tabularnewline
39 & 0.54024751934887 & 0.91950496130226 & 0.45975248065113 \tabularnewline
40 & 0.510925384464888 & 0.978149231070224 & 0.489074615535112 \tabularnewline
41 & 0.470276725018736 & 0.940553450037471 & 0.529723274981264 \tabularnewline
42 & 0.73593252757433 & 0.528134944851339 & 0.26406747242567 \tabularnewline
43 & 0.949896926661653 & 0.100206146676695 & 0.0501030733383473 \tabularnewline
44 & 0.961863763625622 & 0.0762724727487558 & 0.0381362363743779 \tabularnewline
45 & 0.950479420029323 & 0.0990411599413531 & 0.0495205799706766 \tabularnewline
46 & 0.956121562441371 & 0.0877568751172581 & 0.0438784375586291 \tabularnewline
47 & 0.978005041638857 & 0.0439899167222854 & 0.0219949583611427 \tabularnewline
48 & 0.970977851856592 & 0.0580442962868155 & 0.0290221481434077 \tabularnewline
49 & 0.979869971208341 & 0.0402600575833186 & 0.0201300287916593 \tabularnewline
50 & 0.976882069208047 & 0.0462358615839069 & 0.0231179307919534 \tabularnewline
51 & 0.972188747529155 & 0.0556225049416899 & 0.0278112524708449 \tabularnewline
52 & 0.997313877436859 & 0.00537224512628198 & 0.00268612256314099 \tabularnewline
53 & 0.996146192790614 & 0.0077076144187722 & 0.0038538072093861 \tabularnewline
54 & 0.996909857342269 & 0.00618028531546138 & 0.00309014265773069 \tabularnewline
55 & 0.996789174791947 & 0.00642165041610641 & 0.00321082520805321 \tabularnewline
56 & 0.99547462674024 & 0.00905074651952008 & 0.00452537325976004 \tabularnewline
57 & 0.993655762891814 & 0.0126884742163714 & 0.00634423710818569 \tabularnewline
58 & 0.992966186786152 & 0.0140676264276951 & 0.00703381321384755 \tabularnewline
59 & 0.994591443992065 & 0.0108171120158696 & 0.0054085560079348 \tabularnewline
60 & 0.992928010947237 & 0.0141439781055269 & 0.00707198905276343 \tabularnewline
61 & 0.993793912215512 & 0.0124121755689765 & 0.00620608778448824 \tabularnewline
62 & 0.994479896176263 & 0.0110402076474742 & 0.00552010382373709 \tabularnewline
63 & 0.992674929042306 & 0.0146501419153878 & 0.00732507095769389 \tabularnewline
64 & 0.993195313062856 & 0.0136093738742871 & 0.00680468693714357 \tabularnewline
65 & 0.99136242508632 & 0.0172751498273609 & 0.00863757491368044 \tabularnewline
66 & 0.990606359909093 & 0.0187872801818146 & 0.00939364009090729 \tabularnewline
67 & 0.990515114431232 & 0.0189697711375371 & 0.00948488556876853 \tabularnewline
68 & 0.992012311213737 & 0.0159753775725258 & 0.00798768878626288 \tabularnewline
69 & 0.992159276137787 & 0.0156814477244258 & 0.0078407238622129 \tabularnewline
70 & 0.989475595730578 & 0.0210488085388445 & 0.0105244042694222 \tabularnewline
71 & 0.99114658164435 & 0.0177068367112996 & 0.00885341835564978 \tabularnewline
72 & 0.988363180633268 & 0.0232736387334635 & 0.0116368193667318 \tabularnewline
73 & 0.985394380199667 & 0.0292112396006668 & 0.0146056198003334 \tabularnewline
74 & 0.98942340946403 & 0.0211531810719393 & 0.0105765905359696 \tabularnewline
75 & 0.985733315128355 & 0.0285333697432906 & 0.0142666848716453 \tabularnewline
76 & 0.983271056609379 & 0.0334578867812428 & 0.0167289433906214 \tabularnewline
77 & 0.977992489647992 & 0.0440150207040156 & 0.0220075103520078 \tabularnewline
78 & 0.970991389696049 & 0.0580172206079011 & 0.0290086103039505 \tabularnewline
79 & 0.981765248884743 & 0.0364695022305147 & 0.0182347511152574 \tabularnewline
80 & 0.980886214979935 & 0.0382275700401304 & 0.0191137850200652 \tabularnewline
81 & 0.984064893625104 & 0.0318702127497929 & 0.0159351063748964 \tabularnewline
82 & 0.985001189458743 & 0.0299976210825143 & 0.0149988105412572 \tabularnewline
83 & 0.979919393845566 & 0.0401612123088683 & 0.0200806061544342 \tabularnewline
84 & 0.97530171908528 & 0.0493965618294392 & 0.0246982809147196 \tabularnewline
85 & 0.993587234709698 & 0.0128255305806037 & 0.00641276529030183 \tabularnewline
86 & 0.991135110114601 & 0.0177297797707973 & 0.00886488988539866 \tabularnewline
87 & 0.988020449050021 & 0.0239591018999589 & 0.0119795509499795 \tabularnewline
88 & 0.984352853029984 & 0.031294293940033 & 0.0156471469700165 \tabularnewline
89 & 0.980594392819938 & 0.0388112143601248 & 0.0194056071800624 \tabularnewline
90 & 0.974571293674753 & 0.0508574126504931 & 0.0254287063252466 \tabularnewline
91 & 0.970102708590616 & 0.0597945828187672 & 0.0298972914093836 \tabularnewline
92 & 0.981754149379339 & 0.0364917012413225 & 0.0182458506206613 \tabularnewline
93 & 0.976473922759029 & 0.0470521544819421 & 0.023526077240971 \tabularnewline
94 & 0.973686917170783 & 0.0526261656584349 & 0.0263130828292174 \tabularnewline
95 & 0.96749354240508 & 0.0650129151898403 & 0.0325064575949201 \tabularnewline
96 & 0.959984093492006 & 0.0800318130159871 & 0.0400159065079936 \tabularnewline
97 & 0.956231924218429 & 0.0875361515631425 & 0.0437680757815713 \tabularnewline
98 & 0.944653293551337 & 0.110693412897326 & 0.0553467064486631 \tabularnewline
99 & 0.939551430804507 & 0.120897138390987 & 0.0604485691954934 \tabularnewline
100 & 0.927186228815638 & 0.145627542368725 & 0.0728137711843623 \tabularnewline
101 & 0.90965443157833 & 0.18069113684334 & 0.0903455684216701 \tabularnewline
102 & 0.897477661405154 & 0.205044677189692 & 0.102522338594846 \tabularnewline
103 & 0.899061654614461 & 0.201876690771079 & 0.100938345385539 \tabularnewline
104 & 0.934848375124725 & 0.130303249750549 & 0.0651516248752747 \tabularnewline
105 & 0.931324635983792 & 0.137350728032416 & 0.0686753640162082 \tabularnewline
106 & 0.912549356355866 & 0.174901287288269 & 0.0874506436441343 \tabularnewline
107 & 0.893024651661996 & 0.213950696676008 & 0.106975348338004 \tabularnewline
108 & 0.884385350042089 & 0.231229299915823 & 0.115614649957911 \tabularnewline
109 & 0.880434174354857 & 0.239131651290285 & 0.119565825645143 \tabularnewline
110 & 0.902229301375204 & 0.195541397249592 & 0.0977706986247961 \tabularnewline
111 & 0.893033452157635 & 0.21393309568473 & 0.106966547842365 \tabularnewline
112 & 0.927979968102992 & 0.144040063794016 & 0.0720200318970082 \tabularnewline
113 & 0.90683113463983 & 0.186337730720339 & 0.0931688653601697 \tabularnewline
114 & 0.883707520886727 & 0.232584958226545 & 0.116292479113273 \tabularnewline
115 & 0.85636259365643 & 0.28727481268714 & 0.14363740634357 \tabularnewline
116 & 0.851984345793722 & 0.296031308412556 & 0.148015654206278 \tabularnewline
117 & 0.856968071949158 & 0.286063856101684 & 0.143031928050842 \tabularnewline
118 & 0.85091577610826 & 0.298168447783479 & 0.14908422389174 \tabularnewline
119 & 0.816568062541468 & 0.366863874917065 & 0.183431937458532 \tabularnewline
120 & 0.863431181346933 & 0.273137637306134 & 0.136568818653067 \tabularnewline
121 & 0.836895978377214 & 0.326208043245573 & 0.163104021622786 \tabularnewline
122 & 0.815218552596203 & 0.369562894807593 & 0.184781447403797 \tabularnewline
123 & 0.802197681751186 & 0.395604636497627 & 0.197802318248814 \tabularnewline
124 & 0.761517246718793 & 0.476965506562415 & 0.238482753281207 \tabularnewline
125 & 0.763932977711338 & 0.472134044577324 & 0.236067022288662 \tabularnewline
126 & 0.754777102760488 & 0.490445794479024 & 0.245222897239512 \tabularnewline
127 & 0.701166807279828 & 0.597666385440343 & 0.298833192720172 \tabularnewline
128 & 0.664311142846481 & 0.671377714307038 & 0.335688857153519 \tabularnewline
129 & 0.679217672931069 & 0.641564654137861 & 0.320782327068931 \tabularnewline
130 & 0.680976361163367 & 0.638047277673266 & 0.319023638836633 \tabularnewline
131 & 0.629433684744462 & 0.741132630511077 & 0.370566315255538 \tabularnewline
132 & 0.601317962540632 & 0.797364074918737 & 0.398682037459368 \tabularnewline
133 & 0.588562363407912 & 0.822875273184176 & 0.411437636592088 \tabularnewline
134 & 0.51398051650978 & 0.972038966980441 & 0.48601948349022 \tabularnewline
135 & 0.471456293692949 & 0.942912587385898 & 0.528543706307051 \tabularnewline
136 & 0.461456555572392 & 0.922913111144785 & 0.538543444427608 \tabularnewline
137 & 0.386247460572806 & 0.772494921145613 & 0.613752539427194 \tabularnewline
138 & 0.458288756373684 & 0.916577512747368 & 0.541711243626316 \tabularnewline
139 & 0.819063253587958 & 0.361873492824083 & 0.180936746412042 \tabularnewline
140 & 0.825905389449817 & 0.348189221100366 & 0.174094610550183 \tabularnewline
141 & 0.789378580520582 & 0.421242838958836 & 0.210621419479418 \tabularnewline
142 & 0.719747878942648 & 0.560504242114705 & 0.280252121057352 \tabularnewline
143 & 0.657440650234046 & 0.685118699531909 & 0.342559349765954 \tabularnewline
144 & 0.54245115139797 & 0.91509769720406 & 0.45754884860203 \tabularnewline
145 & 0.620754860570398 & 0.758490278859203 & 0.379245139429602 \tabularnewline
146 & 0.772988593262024 & 0.454022813475953 & 0.227011406737976 \tabularnewline
147 & 0.630413159579242 & 0.739173680841516 & 0.369586840420758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0271288973144725[/C][C]0.054257794628945[/C][C]0.972871102685527[/C][/ROW]
[ROW][C]10[/C][C]0.0237320160187825[/C][C]0.047464032037565[/C][C]0.976267983981218[/C][/ROW]
[ROW][C]11[/C][C]0.0500746483944131[/C][C]0.100149296788826[/C][C]0.949925351605587[/C][/ROW]
[ROW][C]12[/C][C]0.0336895022363796[/C][C]0.0673790044727592[/C][C]0.96631049776362[/C][/ROW]
[ROW][C]13[/C][C]0.43771699354397[/C][C]0.875433987087939[/C][C]0.56228300645603[/C][/ROW]
[ROW][C]14[/C][C]0.631656304602175[/C][C]0.736687390795651[/C][C]0.368343695397825[/C][/ROW]
[ROW][C]15[/C][C]0.564406468568958[/C][C]0.871187062862085[/C][C]0.435593531431042[/C][/ROW]
[ROW][C]16[/C][C]0.489853896617639[/C][C]0.979707793235279[/C][C]0.510146103382361[/C][/ROW]
[ROW][C]17[/C][C]0.407174961263709[/C][C]0.814349922527418[/C][C]0.592825038736291[/C][/ROW]
[ROW][C]18[/C][C]0.375228327888679[/C][C]0.750456655777358[/C][C]0.624771672111321[/C][/ROW]
[ROW][C]19[/C][C]0.323424298017495[/C][C]0.646848596034991[/C][C]0.676575701982505[/C][/ROW]
[ROW][C]20[/C][C]0.450755020313337[/C][C]0.901510040626674[/C][C]0.549244979686663[/C][/ROW]
[ROW][C]21[/C][C]0.455250233936693[/C][C]0.910500467873386[/C][C]0.544749766063307[/C][/ROW]
[ROW][C]22[/C][C]0.498067015927554[/C][C]0.996134031855107[/C][C]0.501932984072446[/C][/ROW]
[ROW][C]23[/C][C]0.436371586039588[/C][C]0.872743172079176[/C][C]0.563628413960412[/C][/ROW]
[ROW][C]24[/C][C]0.454569124582036[/C][C]0.909138249164072[/C][C]0.545430875417964[/C][/ROW]
[ROW][C]25[/C][C]0.430355914805024[/C][C]0.860711829610048[/C][C]0.569644085194976[/C][/ROW]
[ROW][C]26[/C][C]0.376137803284551[/C][C]0.752275606569102[/C][C]0.623862196715449[/C][/ROW]
[ROW][C]27[/C][C]0.607141574223035[/C][C]0.785716851553929[/C][C]0.392858425776964[/C][/ROW]
[ROW][C]28[/C][C]0.558213708658504[/C][C]0.883572582682992[/C][C]0.441786291341496[/C][/ROW]
[ROW][C]29[/C][C]0.501877526164308[/C][C]0.996244947671385[/C][C]0.498122473835692[/C][/ROW]
[ROW][C]30[/C][C]0.680614978273514[/C][C]0.638770043452972[/C][C]0.319385021726486[/C][/ROW]
[ROW][C]31[/C][C]0.784874824073709[/C][C]0.430250351852581[/C][C]0.215125175926291[/C][/ROW]
[ROW][C]32[/C][C]0.747531869458115[/C][C]0.50493626108377[/C][C]0.252468130541885[/C][/ROW]
[ROW][C]33[/C][C]0.699162236299025[/C][C]0.60167552740195[/C][C]0.300837763700975[/C][/ROW]
[ROW][C]34[/C][C]0.663536604513489[/C][C]0.672926790973023[/C][C]0.336463395486511[/C][/ROW]
[ROW][C]35[/C][C]0.669657108768265[/C][C]0.66068578246347[/C][C]0.330342891231735[/C][/ROW]
[ROW][C]36[/C][C]0.677031010873086[/C][C]0.645937978253828[/C][C]0.322968989126914[/C][/ROW]
[ROW][C]37[/C][C]0.644196270547147[/C][C]0.711607458905706[/C][C]0.355803729452853[/C][/ROW]
[ROW][C]38[/C][C]0.594445117584088[/C][C]0.811109764831824[/C][C]0.405554882415912[/C][/ROW]
[ROW][C]39[/C][C]0.54024751934887[/C][C]0.91950496130226[/C][C]0.45975248065113[/C][/ROW]
[ROW][C]40[/C][C]0.510925384464888[/C][C]0.978149231070224[/C][C]0.489074615535112[/C][/ROW]
[ROW][C]41[/C][C]0.470276725018736[/C][C]0.940553450037471[/C][C]0.529723274981264[/C][/ROW]
[ROW][C]42[/C][C]0.73593252757433[/C][C]0.528134944851339[/C][C]0.26406747242567[/C][/ROW]
[ROW][C]43[/C][C]0.949896926661653[/C][C]0.100206146676695[/C][C]0.0501030733383473[/C][/ROW]
[ROW][C]44[/C][C]0.961863763625622[/C][C]0.0762724727487558[/C][C]0.0381362363743779[/C][/ROW]
[ROW][C]45[/C][C]0.950479420029323[/C][C]0.0990411599413531[/C][C]0.0495205799706766[/C][/ROW]
[ROW][C]46[/C][C]0.956121562441371[/C][C]0.0877568751172581[/C][C]0.0438784375586291[/C][/ROW]
[ROW][C]47[/C][C]0.978005041638857[/C][C]0.0439899167222854[/C][C]0.0219949583611427[/C][/ROW]
[ROW][C]48[/C][C]0.970977851856592[/C][C]0.0580442962868155[/C][C]0.0290221481434077[/C][/ROW]
[ROW][C]49[/C][C]0.979869971208341[/C][C]0.0402600575833186[/C][C]0.0201300287916593[/C][/ROW]
[ROW][C]50[/C][C]0.976882069208047[/C][C]0.0462358615839069[/C][C]0.0231179307919534[/C][/ROW]
[ROW][C]51[/C][C]0.972188747529155[/C][C]0.0556225049416899[/C][C]0.0278112524708449[/C][/ROW]
[ROW][C]52[/C][C]0.997313877436859[/C][C]0.00537224512628198[/C][C]0.00268612256314099[/C][/ROW]
[ROW][C]53[/C][C]0.996146192790614[/C][C]0.0077076144187722[/C][C]0.0038538072093861[/C][/ROW]
[ROW][C]54[/C][C]0.996909857342269[/C][C]0.00618028531546138[/C][C]0.00309014265773069[/C][/ROW]
[ROW][C]55[/C][C]0.996789174791947[/C][C]0.00642165041610641[/C][C]0.00321082520805321[/C][/ROW]
[ROW][C]56[/C][C]0.99547462674024[/C][C]0.00905074651952008[/C][C]0.00452537325976004[/C][/ROW]
[ROW][C]57[/C][C]0.993655762891814[/C][C]0.0126884742163714[/C][C]0.00634423710818569[/C][/ROW]
[ROW][C]58[/C][C]0.992966186786152[/C][C]0.0140676264276951[/C][C]0.00703381321384755[/C][/ROW]
[ROW][C]59[/C][C]0.994591443992065[/C][C]0.0108171120158696[/C][C]0.0054085560079348[/C][/ROW]
[ROW][C]60[/C][C]0.992928010947237[/C][C]0.0141439781055269[/C][C]0.00707198905276343[/C][/ROW]
[ROW][C]61[/C][C]0.993793912215512[/C][C]0.0124121755689765[/C][C]0.00620608778448824[/C][/ROW]
[ROW][C]62[/C][C]0.994479896176263[/C][C]0.0110402076474742[/C][C]0.00552010382373709[/C][/ROW]
[ROW][C]63[/C][C]0.992674929042306[/C][C]0.0146501419153878[/C][C]0.00732507095769389[/C][/ROW]
[ROW][C]64[/C][C]0.993195313062856[/C][C]0.0136093738742871[/C][C]0.00680468693714357[/C][/ROW]
[ROW][C]65[/C][C]0.99136242508632[/C][C]0.0172751498273609[/C][C]0.00863757491368044[/C][/ROW]
[ROW][C]66[/C][C]0.990606359909093[/C][C]0.0187872801818146[/C][C]0.00939364009090729[/C][/ROW]
[ROW][C]67[/C][C]0.990515114431232[/C][C]0.0189697711375371[/C][C]0.00948488556876853[/C][/ROW]
[ROW][C]68[/C][C]0.992012311213737[/C][C]0.0159753775725258[/C][C]0.00798768878626288[/C][/ROW]
[ROW][C]69[/C][C]0.992159276137787[/C][C]0.0156814477244258[/C][C]0.0078407238622129[/C][/ROW]
[ROW][C]70[/C][C]0.989475595730578[/C][C]0.0210488085388445[/C][C]0.0105244042694222[/C][/ROW]
[ROW][C]71[/C][C]0.99114658164435[/C][C]0.0177068367112996[/C][C]0.00885341835564978[/C][/ROW]
[ROW][C]72[/C][C]0.988363180633268[/C][C]0.0232736387334635[/C][C]0.0116368193667318[/C][/ROW]
[ROW][C]73[/C][C]0.985394380199667[/C][C]0.0292112396006668[/C][C]0.0146056198003334[/C][/ROW]
[ROW][C]74[/C][C]0.98942340946403[/C][C]0.0211531810719393[/C][C]0.0105765905359696[/C][/ROW]
[ROW][C]75[/C][C]0.985733315128355[/C][C]0.0285333697432906[/C][C]0.0142666848716453[/C][/ROW]
[ROW][C]76[/C][C]0.983271056609379[/C][C]0.0334578867812428[/C][C]0.0167289433906214[/C][/ROW]
[ROW][C]77[/C][C]0.977992489647992[/C][C]0.0440150207040156[/C][C]0.0220075103520078[/C][/ROW]
[ROW][C]78[/C][C]0.970991389696049[/C][C]0.0580172206079011[/C][C]0.0290086103039505[/C][/ROW]
[ROW][C]79[/C][C]0.981765248884743[/C][C]0.0364695022305147[/C][C]0.0182347511152574[/C][/ROW]
[ROW][C]80[/C][C]0.980886214979935[/C][C]0.0382275700401304[/C][C]0.0191137850200652[/C][/ROW]
[ROW][C]81[/C][C]0.984064893625104[/C][C]0.0318702127497929[/C][C]0.0159351063748964[/C][/ROW]
[ROW][C]82[/C][C]0.985001189458743[/C][C]0.0299976210825143[/C][C]0.0149988105412572[/C][/ROW]
[ROW][C]83[/C][C]0.979919393845566[/C][C]0.0401612123088683[/C][C]0.0200806061544342[/C][/ROW]
[ROW][C]84[/C][C]0.97530171908528[/C][C]0.0493965618294392[/C][C]0.0246982809147196[/C][/ROW]
[ROW][C]85[/C][C]0.993587234709698[/C][C]0.0128255305806037[/C][C]0.00641276529030183[/C][/ROW]
[ROW][C]86[/C][C]0.991135110114601[/C][C]0.0177297797707973[/C][C]0.00886488988539866[/C][/ROW]
[ROW][C]87[/C][C]0.988020449050021[/C][C]0.0239591018999589[/C][C]0.0119795509499795[/C][/ROW]
[ROW][C]88[/C][C]0.984352853029984[/C][C]0.031294293940033[/C][C]0.0156471469700165[/C][/ROW]
[ROW][C]89[/C][C]0.980594392819938[/C][C]0.0388112143601248[/C][C]0.0194056071800624[/C][/ROW]
[ROW][C]90[/C][C]0.974571293674753[/C][C]0.0508574126504931[/C][C]0.0254287063252466[/C][/ROW]
[ROW][C]91[/C][C]0.970102708590616[/C][C]0.0597945828187672[/C][C]0.0298972914093836[/C][/ROW]
[ROW][C]92[/C][C]0.981754149379339[/C][C]0.0364917012413225[/C][C]0.0182458506206613[/C][/ROW]
[ROW][C]93[/C][C]0.976473922759029[/C][C]0.0470521544819421[/C][C]0.023526077240971[/C][/ROW]
[ROW][C]94[/C][C]0.973686917170783[/C][C]0.0526261656584349[/C][C]0.0263130828292174[/C][/ROW]
[ROW][C]95[/C][C]0.96749354240508[/C][C]0.0650129151898403[/C][C]0.0325064575949201[/C][/ROW]
[ROW][C]96[/C][C]0.959984093492006[/C][C]0.0800318130159871[/C][C]0.0400159065079936[/C][/ROW]
[ROW][C]97[/C][C]0.956231924218429[/C][C]0.0875361515631425[/C][C]0.0437680757815713[/C][/ROW]
[ROW][C]98[/C][C]0.944653293551337[/C][C]0.110693412897326[/C][C]0.0553467064486631[/C][/ROW]
[ROW][C]99[/C][C]0.939551430804507[/C][C]0.120897138390987[/C][C]0.0604485691954934[/C][/ROW]
[ROW][C]100[/C][C]0.927186228815638[/C][C]0.145627542368725[/C][C]0.0728137711843623[/C][/ROW]
[ROW][C]101[/C][C]0.90965443157833[/C][C]0.18069113684334[/C][C]0.0903455684216701[/C][/ROW]
[ROW][C]102[/C][C]0.897477661405154[/C][C]0.205044677189692[/C][C]0.102522338594846[/C][/ROW]
[ROW][C]103[/C][C]0.899061654614461[/C][C]0.201876690771079[/C][C]0.100938345385539[/C][/ROW]
[ROW][C]104[/C][C]0.934848375124725[/C][C]0.130303249750549[/C][C]0.0651516248752747[/C][/ROW]
[ROW][C]105[/C][C]0.931324635983792[/C][C]0.137350728032416[/C][C]0.0686753640162082[/C][/ROW]
[ROW][C]106[/C][C]0.912549356355866[/C][C]0.174901287288269[/C][C]0.0874506436441343[/C][/ROW]
[ROW][C]107[/C][C]0.893024651661996[/C][C]0.213950696676008[/C][C]0.106975348338004[/C][/ROW]
[ROW][C]108[/C][C]0.884385350042089[/C][C]0.231229299915823[/C][C]0.115614649957911[/C][/ROW]
[ROW][C]109[/C][C]0.880434174354857[/C][C]0.239131651290285[/C][C]0.119565825645143[/C][/ROW]
[ROW][C]110[/C][C]0.902229301375204[/C][C]0.195541397249592[/C][C]0.0977706986247961[/C][/ROW]
[ROW][C]111[/C][C]0.893033452157635[/C][C]0.21393309568473[/C][C]0.106966547842365[/C][/ROW]
[ROW][C]112[/C][C]0.927979968102992[/C][C]0.144040063794016[/C][C]0.0720200318970082[/C][/ROW]
[ROW][C]113[/C][C]0.90683113463983[/C][C]0.186337730720339[/C][C]0.0931688653601697[/C][/ROW]
[ROW][C]114[/C][C]0.883707520886727[/C][C]0.232584958226545[/C][C]0.116292479113273[/C][/ROW]
[ROW][C]115[/C][C]0.85636259365643[/C][C]0.28727481268714[/C][C]0.14363740634357[/C][/ROW]
[ROW][C]116[/C][C]0.851984345793722[/C][C]0.296031308412556[/C][C]0.148015654206278[/C][/ROW]
[ROW][C]117[/C][C]0.856968071949158[/C][C]0.286063856101684[/C][C]0.143031928050842[/C][/ROW]
[ROW][C]118[/C][C]0.85091577610826[/C][C]0.298168447783479[/C][C]0.14908422389174[/C][/ROW]
[ROW][C]119[/C][C]0.816568062541468[/C][C]0.366863874917065[/C][C]0.183431937458532[/C][/ROW]
[ROW][C]120[/C][C]0.863431181346933[/C][C]0.273137637306134[/C][C]0.136568818653067[/C][/ROW]
[ROW][C]121[/C][C]0.836895978377214[/C][C]0.326208043245573[/C][C]0.163104021622786[/C][/ROW]
[ROW][C]122[/C][C]0.815218552596203[/C][C]0.369562894807593[/C][C]0.184781447403797[/C][/ROW]
[ROW][C]123[/C][C]0.802197681751186[/C][C]0.395604636497627[/C][C]0.197802318248814[/C][/ROW]
[ROW][C]124[/C][C]0.761517246718793[/C][C]0.476965506562415[/C][C]0.238482753281207[/C][/ROW]
[ROW][C]125[/C][C]0.763932977711338[/C][C]0.472134044577324[/C][C]0.236067022288662[/C][/ROW]
[ROW][C]126[/C][C]0.754777102760488[/C][C]0.490445794479024[/C][C]0.245222897239512[/C][/ROW]
[ROW][C]127[/C][C]0.701166807279828[/C][C]0.597666385440343[/C][C]0.298833192720172[/C][/ROW]
[ROW][C]128[/C][C]0.664311142846481[/C][C]0.671377714307038[/C][C]0.335688857153519[/C][/ROW]
[ROW][C]129[/C][C]0.679217672931069[/C][C]0.641564654137861[/C][C]0.320782327068931[/C][/ROW]
[ROW][C]130[/C][C]0.680976361163367[/C][C]0.638047277673266[/C][C]0.319023638836633[/C][/ROW]
[ROW][C]131[/C][C]0.629433684744462[/C][C]0.741132630511077[/C][C]0.370566315255538[/C][/ROW]
[ROW][C]132[/C][C]0.601317962540632[/C][C]0.797364074918737[/C][C]0.398682037459368[/C][/ROW]
[ROW][C]133[/C][C]0.588562363407912[/C][C]0.822875273184176[/C][C]0.411437636592088[/C][/ROW]
[ROW][C]134[/C][C]0.51398051650978[/C][C]0.972038966980441[/C][C]0.48601948349022[/C][/ROW]
[ROW][C]135[/C][C]0.471456293692949[/C][C]0.942912587385898[/C][C]0.528543706307051[/C][/ROW]
[ROW][C]136[/C][C]0.461456555572392[/C][C]0.922913111144785[/C][C]0.538543444427608[/C][/ROW]
[ROW][C]137[/C][C]0.386247460572806[/C][C]0.772494921145613[/C][C]0.613752539427194[/C][/ROW]
[ROW][C]138[/C][C]0.458288756373684[/C][C]0.916577512747368[/C][C]0.541711243626316[/C][/ROW]
[ROW][C]139[/C][C]0.819063253587958[/C][C]0.361873492824083[/C][C]0.180936746412042[/C][/ROW]
[ROW][C]140[/C][C]0.825905389449817[/C][C]0.348189221100366[/C][C]0.174094610550183[/C][/ROW]
[ROW][C]141[/C][C]0.789378580520582[/C][C]0.421242838958836[/C][C]0.210621419479418[/C][/ROW]
[ROW][C]142[/C][C]0.719747878942648[/C][C]0.560504242114705[/C][C]0.280252121057352[/C][/ROW]
[ROW][C]143[/C][C]0.657440650234046[/C][C]0.685118699531909[/C][C]0.342559349765954[/C][/ROW]
[ROW][C]144[/C][C]0.54245115139797[/C][C]0.91509769720406[/C][C]0.45754884860203[/C][/ROW]
[ROW][C]145[/C][C]0.620754860570398[/C][C]0.758490278859203[/C][C]0.379245139429602[/C][/ROW]
[ROW][C]146[/C][C]0.772988593262024[/C][C]0.454022813475953[/C][C]0.227011406737976[/C][/ROW]
[ROW][C]147[/C][C]0.630413159579242[/C][C]0.739173680841516[/C][C]0.369586840420758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02712889731447250.0542577946289450.972871102685527
100.02373201601878250.0474640320375650.976267983981218
110.05007464839441310.1001492967888260.949925351605587
120.03368950223637960.06737900447275920.96631049776362
130.437716993543970.8754339870879390.56228300645603
140.6316563046021750.7366873907956510.368343695397825
150.5644064685689580.8711870628620850.435593531431042
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170.4071749612637090.8143499225274180.592825038736291
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190.3234242980174950.6468485960349910.676575701982505
200.4507550203133370.9015100406266740.549244979686663
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230.4363715860395880.8727431720791760.563628413960412
240.4545691245820360.9091382491640720.545430875417964
250.4303559148050240.8607118296100480.569644085194976
260.3761378032845510.7522756065691020.623862196715449
270.6071415742230350.7857168515539290.392858425776964
280.5582137086585040.8835725826829920.441786291341496
290.5018775261643080.9962449476713850.498122473835692
300.6806149782735140.6387700434529720.319385021726486
310.7848748240737090.4302503518525810.215125175926291
320.7475318694581150.504936261083770.252468130541885
330.6991622362990250.601675527401950.300837763700975
340.6635366045134890.6729267909730230.336463395486511
350.6696571087682650.660685782463470.330342891231735
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370.6441962705471470.7116074589057060.355803729452853
380.5944451175840880.8111097648318240.405554882415912
390.540247519348870.919504961302260.45975248065113
400.5109253844648880.9781492310702240.489074615535112
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420.735932527574330.5281349448513390.26406747242567
430.9498969266616530.1002061466766950.0501030733383473
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460.9561215624413710.08775687511725810.0438784375586291
470.9780050416388570.04398991672228540.0219949583611427
480.9709778518565920.05804429628681550.0290221481434077
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510.9721887475291550.05562250494168990.0278112524708449
520.9973138774368590.005372245126281980.00268612256314099
530.9961461927906140.00770761441877220.0038538072093861
540.9969098573422690.006180285315461380.00309014265773069
550.9967891747919470.006421650416106410.00321082520805321
560.995474626740240.009050746519520080.00452537325976004
570.9936557628918140.01268847421637140.00634423710818569
580.9929661867861520.01406762642769510.00703381321384755
590.9945914439920650.01081711201586960.0054085560079348
600.9929280109472370.01414397810552690.00707198905276343
610.9937939122155120.01241217556897650.00620608778448824
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650.991362425086320.01727514982736090.00863757491368044
660.9906063599090930.01878728018181460.00939364009090729
670.9905151144312320.01896977113753710.00948488556876853
680.9920123112137370.01597537757252580.00798768878626288
690.9921592761377870.01568144772442580.0078407238622129
700.9894755957305780.02104880853884450.0105244042694222
710.991146581644350.01770683671129960.00885341835564978
720.9883631806332680.02327363873346350.0116368193667318
730.9853943801996670.02921123960066680.0146056198003334
740.989423409464030.02115318107193930.0105765905359696
750.9857333151283550.02853336974329060.0142666848716453
760.9832710566093790.03345788678124280.0167289433906214
770.9779924896479920.04401502070401560.0220075103520078
780.9709913896960490.05801722060790110.0290086103039505
790.9817652488847430.03646950223051470.0182347511152574
800.9808862149799350.03822757004013040.0191137850200652
810.9840648936251040.03187021274979290.0159351063748964
820.9850011894587430.02999762108251430.0149988105412572
830.9799193938455660.04016121230886830.0200806061544342
840.975301719085280.04939656182943920.0246982809147196
850.9935872347096980.01282553058060370.00641276529030183
860.9911351101146010.01772977977079730.00886488988539866
870.9880204490500210.02395910189995890.0119795509499795
880.9843528530299840.0312942939400330.0156471469700165
890.9805943928199380.03881121436012480.0194056071800624
900.9745712936747530.05085741265049310.0254287063252466
910.9701027085906160.05979458281876720.0298972914093836
920.9817541493793390.03649170124132250.0182458506206613
930.9764739227590290.04705215448194210.023526077240971
940.9736869171707830.05262616565843490.0263130828292174
950.967493542405080.06501291518984030.0325064575949201
960.9599840934920060.08003181301598710.0400159065079936
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980.9446532935513370.1106934128973260.0553467064486631
990.9395514308045070.1208971383909870.0604485691954934
1000.9271862288156380.1456275423687250.0728137711843623
1010.909654431578330.180691136843340.0903455684216701
1020.8974776614051540.2050446771896920.102522338594846
1030.8990616546144610.2018766907710790.100938345385539
1040.9348483751247250.1303032497505490.0651516248752747
1050.9313246359837920.1373507280324160.0686753640162082
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1070.8930246516619960.2139506966760080.106975348338004
1080.8843853500420890.2312292999158230.115614649957911
1090.8804341743548570.2391316512902850.119565825645143
1100.9022293013752040.1955413972495920.0977706986247961
1110.8930334521576350.213933095684730.106966547842365
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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0359712230215827NOK
5% type I error level430.309352517985612NOK
10% type I error level570.410071942446043NOK

\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 & 5 & 0.0359712230215827 & NOK \tabularnewline
5% type I error level & 43 & 0.309352517985612 & NOK \tabularnewline
10% type I error level & 57 & 0.410071942446043 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146381&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]5[/C][C]0.0359712230215827[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.309352517985612[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.410071942446043[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146381&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146381&T=6

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0359712230215827NOK
5% type I error level430.309352517985612NOK
10% type I error level570.410071942446043NOK


Figures (Output of Computation)

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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 = 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')
}