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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 computationSun, 05 Dec 2010 12:22:18 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/05/t1291551706x1dj1frkfqw96ae.htm/, Retrieved Wed, 01 May 2024 18:48:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105358, Retrieved Wed, 01 May 2024 18:48:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
F RMPD  [Multiple Regression] [ws 7 Model 2: tijd] [2010-11-23 11:08:59] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
-    D      [Multiple Regression] [Paper: Multiple L...] [2010-12-05 12:22:18] [350231caf55a86a218fd48dc4d2e2f8b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	13	14	13	3
9	12	8	13	5
9	15	12	16	6
9	12	7	12	6
9	10	10	11	5
9	12	7	12	3
9	15	16	18	8
9	9	11	11	4
9	12	14	14	4
9	11	6	9	4
9	11	16	14	6
9	11	11	12	6
9	15	16	11	5
9	7	12	12	4
9	11	7	13	6
9	11	13	11	4
9	10	11	12	6
9	14	15	16	6
9	10	7	9	4
9	6	9	11	4
9	11	7	13	2
9	15	14	15	7
9	11	15	10	5
9	12	7	11	4
9	14	15	13	6
9	15	17	16	6
9	9	15	15	7
9	13	14	14	5
9	13	14	14	6
9	16	8	14	4
9	13	8	8	4
9	12	14	13	7
9	14	14	15	7
9	11	8	13	4
9	9	11	11	4
9	16	16	15	6
9	12	10	15	6
9	10	8	9	5
9	13	14	13	6
9	16	16	16	7
9	14	13	13	6
9	15	5	11	3
9	5	8	12	3
9	8	10	12	4
9	11	8	12	6
9	16	13	14	7
9	17	15	14	5
9	9	6	8	4
9	9	12	13	5
9	13	16	16	6
9	10	5	13	6
10	6	15	11	6
10	12	12	14	5
10	8	8	13	4
10	14	13	13	5
10	12	14	13	5
10	11	12	12	4
10	16	16	16	6
10	8	10	15	2
10	15	15	15	8
10	7	8	12	3
10	16	16	14	6
10	14	19	12	6
10	16	14	15	6
10	9	6	12	5
10	14	13	13	5
10	11	15	12	6
10	13	7	12	5
10	15	13	13	6
10	5	4	5	2
10	15	14	13	5
10	13	13	13	5
10	11	11	14	5
10	11	14	17	6
10	12	12	13	6
10	12	15	13	6
10	12	14	12	5
10	12	13	13	5
10	14	8	14	4
10	6	6	11	2
10	7	7	12	4
10	14	13	12	6
10	14	13	16	6
10	10	11	12	5
10	13	5	12	3
10	12	12	12	6
10	9	8	10	4
10	12	11	15	5
10	16	14	15	8
10	10	9	12	4
10	14	10	16	6
10	10	13	15	6
10	16	16	16	7
10	15	16	13	6
10	12	11	12	5
10	10	8	11	4
10	8	4	13	6
10	8	7	10	3
10	11	14	15	5
10	13	11	13	6
10	16	17	16	7
10	16	15	15	7
10	14	17	18	6
10	11	5	13	3
10	4	4	10	2
10	14	10	16	8
10	9	11	13	3
10	14	15	15	8
10	8	10	14	3
10	8	9	15	4
10	11	12	14	5
10	12	15	13	7
10	11	7	13	6
10	14	13	15	6
10	15	12	16	7
10	16	14	14	6
10	16	14	14	6
10	11	8	16	6
10	14	15	14	6
10	14	12	12	4
10	12	12	13	4
10	14	16	12	5
10	8	9	12	4
10	13	15	14	6
10	16	15	14	6
10	12	6	14	5
10	16	14	16	8
10	12	15	13	6
10	11	10	14	5
10	4	6	4	4
10	16	14	16	8
10	15	12	13	6
10	10	8	16	4
10	13	11	15	6
10	15	13	14	6
10	12	9	13	4
10	14	15	14	6
10	7	13	12	3
10	19	15	15	6
10	12	14	14	5
10	12	16	13	4
10	13	14	14	6
10	15	14	16	4
10	8	10	6	4
10	12	10	13	4
10	10	4	13	6
10	8	8	14	5
10	10	15	15	6
10	15	16	14	6
10	16	12	15	8
10	13	12	13	7
10	16	15	16	7
10	9	9	12	4
10	14	12	15	6
10	14	14	12	6
10	12	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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 3.49514166391521 -0.240396339657892Tijd[t] + 0.242393786561951KnowingPeople[t] + 0.365785984054008Liked[t] + 0.62160080454706Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  3.49514166391521 -0.240396339657892Tijd[t] +  0.242393786561951KnowingPeople[t] +  0.365785984054008Liked[t] +  0.62160080454706Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  3.49514166391521 -0.240396339657892Tijd[t] +  0.242393786561951KnowingPeople[t] +  0.365785984054008Liked[t] +  0.62160080454706Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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] = + 3.49514166391521 -0.240396339657892Tijd[t] + 0.242393786561951KnowingPeople[t] + 0.365785984054008Liked[t] + 0.62160080454706Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.495141663915213.539260.98750.324960.16248
Tijd-0.2403963396578920.363679-0.6610.5096110.254806
KnowingPeople0.2423937865619510.0614633.94380.0001226.1e-05
Liked0.3657859840540080.09713.76710.0002360.000118
Celebrity0.621600804547060.1564273.97380.0001095.5e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.49514166391521 & 3.53926 & 0.9875 & 0.32496 & 0.16248 \tabularnewline
Tijd & -0.240396339657892 & 0.363679 & -0.661 & 0.509611 & 0.254806 \tabularnewline
KnowingPeople & 0.242393786561951 & 0.061463 & 3.9438 & 0.000122 & 6.1e-05 \tabularnewline
Liked & 0.365785984054008 & 0.0971 & 3.7671 & 0.000236 & 0.000118 \tabularnewline
Celebrity & 0.62160080454706 & 0.156427 & 3.9738 & 0.000109 & 5.5e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&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]3.49514166391521[/C][C]3.53926[/C][C]0.9875[/C][C]0.32496[/C][C]0.16248[/C][/ROW]
[ROW][C]Tijd[/C][C]-0.240396339657892[/C][C]0.363679[/C][C]-0.661[/C][C]0.509611[/C][C]0.254806[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.242393786561951[/C][C]0.061463[/C][C]3.9438[/C][C]0.000122[/C][C]6.1e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.365785984054008[/C][C]0.0971[/C][C]3.7671[/C][C]0.000236[/C][C]0.000118[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.62160080454706[/C][C]0.156427[/C][C]3.9738[/C][C]0.000109[/C][C]5.5e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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)3.495141663915213.539260.98750.324960.16248
Tijd-0.2403963396578920.363679-0.6610.5096110.254806
KnowingPeople0.2423937865619510.0614633.94380.0001226.1e-05
Liked0.3657859840540080.09713.76710.0002360.000118
Celebrity0.621600804547060.1564273.97380.0001095.5e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.705290485738547
R-squared0.497434669273315
Adjusted R-squared0.484121680379893
F-TEST (value)37.3646123538115
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10922287268072
Sum Squared Residuals671.771990122567

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.705290485738547 \tabularnewline
R-squared & 0.497434669273315 \tabularnewline
Adjusted R-squared & 0.484121680379893 \tabularnewline
F-TEST (value) & 37.3646123538115 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10922287268072 \tabularnewline
Sum Squared Residuals & 671.771990122567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.705290485738547[/C][/ROW]
[ROW][C]R-squared[/C][C]0.497434669273315[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.484121680379893[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.3646123538115[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/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]2.10922287268072[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]671.771990122567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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 R0.705290485738547
R-squared0.497434669273315
Adjusted R-squared0.484121680379893
F-TEST (value)37.3646123538115
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10922287268072
Sum Squared Residuals671.771990122567






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.34510782520481.65489217479524
21211.13394671492720.866053285072809
31513.82248061788411.17751938211590
41211.14736774885830.852632251141696
51010.8871623199431-0.88716231994308
6129.282565335217122.71743466478288
71516.7668293413340-1.76682934133403
8910.5079553019580-1.50795530195797
91212.3324946138059-0.332494613805853
10118.56441440104022.4355855989598
111114.0604837960239-3.06048379602387
121112.1169428951061-1.11694289510610
131512.34152503931482.65847496068521
14711.1161350725739-4.11613507257393
151111.5131537329123-0.513153732912306
161110.99274287508190.00725712491812329
171012.1169428951061-2.11694289510610
181414.5496619775699-0.549661977569941
19108.806808187602151.19319181239785
20610.0231677288341-4.02316772883407
21119.026750514724071.97324948527593
221514.56308301150100.436916988498959
231111.7333452686988-0.73334526869883
24129.538380155710172.46161984428983
251413.45230402540790.547695974592085
261515.0344495506938-0.034449550693843
27914.805476798063-5.80547679806299
281312.95409541835290.0459045816470873
291313.5756962229000-0.575696222899973
301610.87813189443415.12186810556585
31138.68341599011014.31658400988990
321213.8315110433930-1.83151104339302
331414.5630830115010-0.563083011501041
341110.51234591038010.487654089619862
35910.5079553019580-1.50795530195797
361614.42626978007791.57373021992212
371212.9719070607062-0.971907060706176
38109.670802778711160.329197221288836
391313.2099102388460-0.209910238845964
401615.41365656867900.586343431321049
411412.9675164522841.03248354771599
42158.43199177803926.56800822196079
4359.52495912177907-4.52495912177907
44810.6313474994500-2.63134749945003
451111.3897615354202-0.389761535420249
461613.95490324088512.04509675911492
471713.19648920491493.80351079508514
4898.19862841698620.801371583013807
49912.103521861175-3.103521861175
501314.7920557641319-1.79205576413189
511011.0283661597884-1.02836615978840
52612.480335717642-6.48033571764201
531212.2289115055711-0.228911505571119
54810.2719495707222-2.27194957072225
551412.10551930807911.89448069192094
561212.3479130946410-0.347913094641013
571110.87573873291600.124261267083957
581614.5516594244741.448340575526
59810.2451075028600-2.24510750286005
601515.1866812629522-0.186681262952160
6179.28456278212118-2.28456278212118
621613.8200874563662.17991254363402
631413.81569684794380.18430315205618
641613.70108586729612.29891413270391
65910.0429768180914-1.04297681809140
661412.10551930807911.89448069192094
671112.8461217016960-1.84612170169602
681310.28537060465332.71462939534665
691512.72712011262612.27287988737388
7055.13288494294826-0.132884942948255
711512.34791309464102.65208690535899
721312.10551930807910.894480691920938
731111.9865177190092-0.986517719009168
741114.4326578354041-3.43265783540411
751212.4847263260642-0.484726326064171
761213.2119076857500-1.21190768575002
771211.9821271105870.0178728894129951
781212.1055193080791-0.105519308079062
791410.63773555477633.36226444522374
8067.81238842039621-1.81238842039621
8179.66376980010629-2.66376980010629
821412.36133412857211.63866587142789
831413.82447806478810.175521935211853
841011.2549457509012-1.25494575090115
85138.557381422435334.44261857756467
861212.1189403420102-0.118940342010162
8799.17459161856022-0.174591618560221
881212.3523037030632-0.352303703063177
891614.94428747639021.05571252360979
901010.1485573732302-0.148557373230189
911413.09729670510230.902703294897706
921013.4586920807341-3.45869208073414
931615.17326022902110.82673977097894
941513.45430147231201.54569852768802
951211.25494575090120.745054249098849
96109.540377602614230.45962239738577
97810.5455760335686-2.54557603356856
9888.31059702745121-0.310597027451210
991113.0794850627490-2.07948506274903
1001312.24233253950220.75766746049778
1011615.4156540155830.584345984416989
1021614.56508045840511.4349195415949
1031415.5256251791440-1.52562517914397
104118.923167406489332.07683259351067
10546.9618148632183-2.96181486321830
1061414.3404983141964-0.340498314196413
107910.3775301258610-1.37753012586104
1081415.1866812629522-1.18668126295216
109810.5009223233531-2.5009223233531
110811.2459153253922-3.24591532539222
1111112.2289115055711-1.22891150557112
1121213.8335084902971-1.83350849029708
1131111.2727573932544-0.272757393254415
1141413.45869208073410.541307919265861
1151514.20368508277330.796314917226744
1161613.33529988324212.66470011675792
1171613.33529988324212.66470011675792
1181112.6125091319784-1.61250913197839
1191413.57769366980400.422306330195967
1201410.87573873291603.12426126708396
1211211.24152471697010.758475283029949
1221412.46691468371091.53308531628909
123810.1485573732302-2.14855737323019
1241313.5776936698040-0.577693669804033
1251613.57769366980402.42230633019597
1261210.77454878619941.22545121380059
1271615.31007346044420.689926539555782
1281213.2119076857500-1.21190768575002
1291111.7441239324472-0.744123932447217
13046.49508814111227-2.49508814111227
1311615.31007346044420.689926539555782
1321512.48472632606422.51527367393583
1331011.3693075228843-1.36930752288427
1341312.97390450761020.0260954923897635
1351513.09290609668011.90709390331987
1361210.51434335728421.48565664271580
1371413.57769366980400.422306330195967
138710.4965317149309-3.49653171493093
1391913.94347965385805.05652034614196
1401212.7136990786950-0.713699078695022
1411212.2110998632179-0.211099863217856
1421313.3352998832421-0.335299883242082
1431512.82367024225602.17632975774402
14488.1962352554681-0.196235255468090
1451210.75673714384611.24326285615385
1461010.5455760335686-0.545576033568562
147811.2593363593233-3.25933635932331
1481013.9434796538580-3.94347965385804
1491513.8200874563661.17991254363402
1501614.45949990326631.54050009673369
1511313.1063271306112-0.106327130611230
1521614.93086644245911.06913355754089
153910.1485573732302-1.14855737323019
1541413.21629829417220.783701705827813
1551412.60372791513411.39627208486594
1561210.12171530536801.87828469463201

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3451078252048 & 1.65489217479524 \tabularnewline
2 & 12 & 11.1339467149272 & 0.866053285072809 \tabularnewline
3 & 15 & 13.8224806178841 & 1.17751938211590 \tabularnewline
4 & 12 & 11.1473677488583 & 0.852632251141696 \tabularnewline
5 & 10 & 10.8871623199431 & -0.88716231994308 \tabularnewline
6 & 12 & 9.28256533521712 & 2.71743466478288 \tabularnewline
7 & 15 & 16.7668293413340 & -1.76682934133403 \tabularnewline
8 & 9 & 10.5079553019580 & -1.50795530195797 \tabularnewline
9 & 12 & 12.3324946138059 & -0.332494613805853 \tabularnewline
10 & 11 & 8.5644144010402 & 2.4355855989598 \tabularnewline
11 & 11 & 14.0604837960239 & -3.06048379602387 \tabularnewline
12 & 11 & 12.1169428951061 & -1.11694289510610 \tabularnewline
13 & 15 & 12.3415250393148 & 2.65847496068521 \tabularnewline
14 & 7 & 11.1161350725739 & -4.11613507257393 \tabularnewline
15 & 11 & 11.5131537329123 & -0.513153732912306 \tabularnewline
16 & 11 & 10.9927428750819 & 0.00725712491812329 \tabularnewline
17 & 10 & 12.1169428951061 & -2.11694289510610 \tabularnewline
18 & 14 & 14.5496619775699 & -0.549661977569941 \tabularnewline
19 & 10 & 8.80680818760215 & 1.19319181239785 \tabularnewline
20 & 6 & 10.0231677288341 & -4.02316772883407 \tabularnewline
21 & 11 & 9.02675051472407 & 1.97324948527593 \tabularnewline
22 & 15 & 14.5630830115010 & 0.436916988498959 \tabularnewline
23 & 11 & 11.7333452686988 & -0.73334526869883 \tabularnewline
24 & 12 & 9.53838015571017 & 2.46161984428983 \tabularnewline
25 & 14 & 13.4523040254079 & 0.547695974592085 \tabularnewline
26 & 15 & 15.0344495506938 & -0.034449550693843 \tabularnewline
27 & 9 & 14.805476798063 & -5.80547679806299 \tabularnewline
28 & 13 & 12.9540954183529 & 0.0459045816470873 \tabularnewline
29 & 13 & 13.5756962229000 & -0.575696222899973 \tabularnewline
30 & 16 & 10.8781318944341 & 5.12186810556585 \tabularnewline
31 & 13 & 8.6834159901101 & 4.31658400988990 \tabularnewline
32 & 12 & 13.8315110433930 & -1.83151104339302 \tabularnewline
33 & 14 & 14.5630830115010 & -0.563083011501041 \tabularnewline
34 & 11 & 10.5123459103801 & 0.487654089619862 \tabularnewline
35 & 9 & 10.5079553019580 & -1.50795530195797 \tabularnewline
36 & 16 & 14.4262697800779 & 1.57373021992212 \tabularnewline
37 & 12 & 12.9719070607062 & -0.971907060706176 \tabularnewline
38 & 10 & 9.67080277871116 & 0.329197221288836 \tabularnewline
39 & 13 & 13.2099102388460 & -0.209910238845964 \tabularnewline
40 & 16 & 15.4136565686790 & 0.586343431321049 \tabularnewline
41 & 14 & 12.967516452284 & 1.03248354771599 \tabularnewline
42 & 15 & 8.4319917780392 & 6.56800822196079 \tabularnewline
43 & 5 & 9.52495912177907 & -4.52495912177907 \tabularnewline
44 & 8 & 10.6313474994500 & -2.63134749945003 \tabularnewline
45 & 11 & 11.3897615354202 & -0.389761535420249 \tabularnewline
46 & 16 & 13.9549032408851 & 2.04509675911492 \tabularnewline
47 & 17 & 13.1964892049149 & 3.80351079508514 \tabularnewline
48 & 9 & 8.1986284169862 & 0.801371583013807 \tabularnewline
49 & 9 & 12.103521861175 & -3.103521861175 \tabularnewline
50 & 13 & 14.7920557641319 & -1.79205576413189 \tabularnewline
51 & 10 & 11.0283661597884 & -1.02836615978840 \tabularnewline
52 & 6 & 12.480335717642 & -6.48033571764201 \tabularnewline
53 & 12 & 12.2289115055711 & -0.228911505571119 \tabularnewline
54 & 8 & 10.2719495707222 & -2.27194957072225 \tabularnewline
55 & 14 & 12.1055193080791 & 1.89448069192094 \tabularnewline
56 & 12 & 12.3479130946410 & -0.347913094641013 \tabularnewline
57 & 11 & 10.8757387329160 & 0.124261267083957 \tabularnewline
58 & 16 & 14.551659424474 & 1.448340575526 \tabularnewline
59 & 8 & 10.2451075028600 & -2.24510750286005 \tabularnewline
60 & 15 & 15.1866812629522 & -0.186681262952160 \tabularnewline
61 & 7 & 9.28456278212118 & -2.28456278212118 \tabularnewline
62 & 16 & 13.820087456366 & 2.17991254363402 \tabularnewline
63 & 14 & 13.8156968479438 & 0.18430315205618 \tabularnewline
64 & 16 & 13.7010858672961 & 2.29891413270391 \tabularnewline
65 & 9 & 10.0429768180914 & -1.04297681809140 \tabularnewline
66 & 14 & 12.1055193080791 & 1.89448069192094 \tabularnewline
67 & 11 & 12.8461217016960 & -1.84612170169602 \tabularnewline
68 & 13 & 10.2853706046533 & 2.71462939534665 \tabularnewline
69 & 15 & 12.7271201126261 & 2.27287988737388 \tabularnewline
70 & 5 & 5.13288494294826 & -0.132884942948255 \tabularnewline
71 & 15 & 12.3479130946410 & 2.65208690535899 \tabularnewline
72 & 13 & 12.1055193080791 & 0.894480691920938 \tabularnewline
73 & 11 & 11.9865177190092 & -0.986517719009168 \tabularnewline
74 & 11 & 14.4326578354041 & -3.43265783540411 \tabularnewline
75 & 12 & 12.4847263260642 & -0.484726326064171 \tabularnewline
76 & 12 & 13.2119076857500 & -1.21190768575002 \tabularnewline
77 & 12 & 11.982127110587 & 0.0178728894129951 \tabularnewline
78 & 12 & 12.1055193080791 & -0.105519308079062 \tabularnewline
79 & 14 & 10.6377355547763 & 3.36226444522374 \tabularnewline
80 & 6 & 7.81238842039621 & -1.81238842039621 \tabularnewline
81 & 7 & 9.66376980010629 & -2.66376980010629 \tabularnewline
82 & 14 & 12.3613341285721 & 1.63866587142789 \tabularnewline
83 & 14 & 13.8244780647881 & 0.175521935211853 \tabularnewline
84 & 10 & 11.2549457509012 & -1.25494575090115 \tabularnewline
85 & 13 & 8.55738142243533 & 4.44261857756467 \tabularnewline
86 & 12 & 12.1189403420102 & -0.118940342010162 \tabularnewline
87 & 9 & 9.17459161856022 & -0.174591618560221 \tabularnewline
88 & 12 & 12.3523037030632 & -0.352303703063177 \tabularnewline
89 & 16 & 14.9442874763902 & 1.05571252360979 \tabularnewline
90 & 10 & 10.1485573732302 & -0.148557373230189 \tabularnewline
91 & 14 & 13.0972967051023 & 0.902703294897706 \tabularnewline
92 & 10 & 13.4586920807341 & -3.45869208073414 \tabularnewline
93 & 16 & 15.1732602290211 & 0.82673977097894 \tabularnewline
94 & 15 & 13.4543014723120 & 1.54569852768802 \tabularnewline
95 & 12 & 11.2549457509012 & 0.745054249098849 \tabularnewline
96 & 10 & 9.54037760261423 & 0.45962239738577 \tabularnewline
97 & 8 & 10.5455760335686 & -2.54557603356856 \tabularnewline
98 & 8 & 8.31059702745121 & -0.310597027451210 \tabularnewline
99 & 11 & 13.0794850627490 & -2.07948506274903 \tabularnewline
100 & 13 & 12.2423325395022 & 0.75766746049778 \tabularnewline
101 & 16 & 15.415654015583 & 0.584345984416989 \tabularnewline
102 & 16 & 14.5650804584051 & 1.4349195415949 \tabularnewline
103 & 14 & 15.5256251791440 & -1.52562517914397 \tabularnewline
104 & 11 & 8.92316740648933 & 2.07683259351067 \tabularnewline
105 & 4 & 6.9618148632183 & -2.96181486321830 \tabularnewline
106 & 14 & 14.3404983141964 & -0.340498314196413 \tabularnewline
107 & 9 & 10.3775301258610 & -1.37753012586104 \tabularnewline
108 & 14 & 15.1866812629522 & -1.18668126295216 \tabularnewline
109 & 8 & 10.5009223233531 & -2.5009223233531 \tabularnewline
110 & 8 & 11.2459153253922 & -3.24591532539222 \tabularnewline
111 & 11 & 12.2289115055711 & -1.22891150557112 \tabularnewline
112 & 12 & 13.8335084902971 & -1.83350849029708 \tabularnewline
113 & 11 & 11.2727573932544 & -0.272757393254415 \tabularnewline
114 & 14 & 13.4586920807341 & 0.541307919265861 \tabularnewline
115 & 15 & 14.2036850827733 & 0.796314917226744 \tabularnewline
116 & 16 & 13.3352998832421 & 2.66470011675792 \tabularnewline
117 & 16 & 13.3352998832421 & 2.66470011675792 \tabularnewline
118 & 11 & 12.6125091319784 & -1.61250913197839 \tabularnewline
119 & 14 & 13.5776936698040 & 0.422306330195967 \tabularnewline
120 & 14 & 10.8757387329160 & 3.12426126708396 \tabularnewline
121 & 12 & 11.2415247169701 & 0.758475283029949 \tabularnewline
122 & 14 & 12.4669146837109 & 1.53308531628909 \tabularnewline
123 & 8 & 10.1485573732302 & -2.14855737323019 \tabularnewline
124 & 13 & 13.5776936698040 & -0.577693669804033 \tabularnewline
125 & 16 & 13.5776936698040 & 2.42230633019597 \tabularnewline
126 & 12 & 10.7745487861994 & 1.22545121380059 \tabularnewline
127 & 16 & 15.3100734604442 & 0.689926539555782 \tabularnewline
128 & 12 & 13.2119076857500 & -1.21190768575002 \tabularnewline
129 & 11 & 11.7441239324472 & -0.744123932447217 \tabularnewline
130 & 4 & 6.49508814111227 & -2.49508814111227 \tabularnewline
131 & 16 & 15.3100734604442 & 0.689926539555782 \tabularnewline
132 & 15 & 12.4847263260642 & 2.51527367393583 \tabularnewline
133 & 10 & 11.3693075228843 & -1.36930752288427 \tabularnewline
134 & 13 & 12.9739045076102 & 0.0260954923897635 \tabularnewline
135 & 15 & 13.0929060966801 & 1.90709390331987 \tabularnewline
136 & 12 & 10.5143433572842 & 1.48565664271580 \tabularnewline
137 & 14 & 13.5776936698040 & 0.422306330195967 \tabularnewline
138 & 7 & 10.4965317149309 & -3.49653171493093 \tabularnewline
139 & 19 & 13.9434796538580 & 5.05652034614196 \tabularnewline
140 & 12 & 12.7136990786950 & -0.713699078695022 \tabularnewline
141 & 12 & 12.2110998632179 & -0.211099863217856 \tabularnewline
142 & 13 & 13.3352998832421 & -0.335299883242082 \tabularnewline
143 & 15 & 12.8236702422560 & 2.17632975774402 \tabularnewline
144 & 8 & 8.1962352554681 & -0.196235255468090 \tabularnewline
145 & 12 & 10.7567371438461 & 1.24326285615385 \tabularnewline
146 & 10 & 10.5455760335686 & -0.545576033568562 \tabularnewline
147 & 8 & 11.2593363593233 & -3.25933635932331 \tabularnewline
148 & 10 & 13.9434796538580 & -3.94347965385804 \tabularnewline
149 & 15 & 13.820087456366 & 1.17991254363402 \tabularnewline
150 & 16 & 14.4594999032663 & 1.54050009673369 \tabularnewline
151 & 13 & 13.1063271306112 & -0.106327130611230 \tabularnewline
152 & 16 & 14.9308664424591 & 1.06913355754089 \tabularnewline
153 & 9 & 10.1485573732302 & -1.14855737323019 \tabularnewline
154 & 14 & 13.2162982941722 & 0.783701705827813 \tabularnewline
155 & 14 & 12.6037279151341 & 1.39627208486594 \tabularnewline
156 & 12 & 10.1217153053680 & 1.87828469463201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&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]11.3451078252048[/C][C]1.65489217479524[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.1339467149272[/C][C]0.866053285072809[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.8224806178841[/C][C]1.17751938211590[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.1473677488583[/C][C]0.852632251141696[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.8871623199431[/C][C]-0.88716231994308[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.28256533521712[/C][C]2.71743466478288[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7668293413340[/C][C]-1.76682934133403[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.5079553019580[/C][C]-1.50795530195797[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.3324946138059[/C][C]-0.332494613805853[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]8.5644144010402[/C][C]2.4355855989598[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]14.0604837960239[/C][C]-3.06048379602387[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1169428951061[/C][C]-1.11694289510610[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.3415250393148[/C][C]2.65847496068521[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.1161350725739[/C][C]-4.11613507257393[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.5131537329123[/C][C]-0.513153732912306[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9927428750819[/C][C]0.00725712491812329[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.1169428951061[/C][C]-2.11694289510610[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.5496619775699[/C][C]-0.549661977569941[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.80680818760215[/C][C]1.19319181239785[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]10.0231677288341[/C][C]-4.02316772883407[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]9.02675051472407[/C][C]1.97324948527593[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.5630830115010[/C][C]0.436916988498959[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.7333452686988[/C][C]-0.73334526869883[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.53838015571017[/C][C]2.46161984428983[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.4523040254079[/C][C]0.547695974592085[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]15.0344495506938[/C][C]-0.034449550693843[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.805476798063[/C][C]-5.80547679806299[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.9540954183529[/C][C]0.0459045816470873[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.5756962229000[/C][C]-0.575696222899973[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.8781318944341[/C][C]5.12186810556585[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.6834159901101[/C][C]4.31658400988990[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.8315110433930[/C][C]-1.83151104339302[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5630830115010[/C][C]-0.563083011501041[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.5123459103801[/C][C]0.487654089619862[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.5079553019580[/C][C]-1.50795530195797[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.4262697800779[/C][C]1.57373021992212[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.9719070607062[/C][C]-0.971907060706176[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.67080277871116[/C][C]0.329197221288836[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.2099102388460[/C][C]-0.209910238845964[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.4136565686790[/C][C]0.586343431321049[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.967516452284[/C][C]1.03248354771599[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.4319917780392[/C][C]6.56800822196079[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.52495912177907[/C][C]-4.52495912177907[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.6313474994500[/C][C]-2.63134749945003[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.3897615354202[/C][C]-0.389761535420249[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.9549032408851[/C][C]2.04509675911492[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.1964892049149[/C][C]3.80351079508514[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.1986284169862[/C][C]0.801371583013807[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]12.103521861175[/C][C]-3.103521861175[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.7920557641319[/C][C]-1.79205576413189[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.0283661597884[/C][C]-1.02836615978840[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.480335717642[/C][C]-6.48033571764201[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.2289115055711[/C][C]-0.228911505571119[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.2719495707222[/C][C]-2.27194957072225[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.1055193080791[/C][C]1.89448069192094[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.3479130946410[/C][C]-0.347913094641013[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.8757387329160[/C][C]0.124261267083957[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.551659424474[/C][C]1.448340575526[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2451075028600[/C][C]-2.24510750286005[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.1866812629522[/C][C]-0.186681262952160[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.28456278212118[/C][C]-2.28456278212118[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.820087456366[/C][C]2.17991254363402[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.8156968479438[/C][C]0.18430315205618[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7010858672961[/C][C]2.29891413270391[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.0429768180914[/C][C]-1.04297681809140[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1055193080791[/C][C]1.89448069192094[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.8461217016960[/C][C]-1.84612170169602[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.2853706046533[/C][C]2.71462939534665[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.7271201126261[/C][C]2.27287988737388[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.13288494294826[/C][C]-0.132884942948255[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.3479130946410[/C][C]2.65208690535899[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.1055193080791[/C][C]0.894480691920938[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]11.9865177190092[/C][C]-0.986517719009168[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.4326578354041[/C][C]-3.43265783540411[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4847263260642[/C][C]-0.484726326064171[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.2119076857500[/C][C]-1.21190768575002[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.982127110587[/C][C]0.0178728894129951[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.1055193080791[/C][C]-0.105519308079062[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.6377355547763[/C][C]3.36226444522374[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.81238842039621[/C][C]-1.81238842039621[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.66376980010629[/C][C]-2.66376980010629[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.3613341285721[/C][C]1.63866587142789[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8244780647881[/C][C]0.175521935211853[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2549457509012[/C][C]-1.25494575090115[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.55738142243533[/C][C]4.44261857756467[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.1189403420102[/C][C]-0.118940342010162[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.17459161856022[/C][C]-0.174591618560221[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.3523037030632[/C][C]-0.352303703063177[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.9442874763902[/C][C]1.05571252360979[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.1485573732302[/C][C]-0.148557373230189[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.0972967051023[/C][C]0.902703294897706[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.4586920807341[/C][C]-3.45869208073414[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.1732602290211[/C][C]0.82673977097894[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4543014723120[/C][C]1.54569852768802[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.2549457509012[/C][C]0.745054249098849[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.54037760261423[/C][C]0.45962239738577[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.5455760335686[/C][C]-2.54557603356856[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.31059702745121[/C][C]-0.310597027451210[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.0794850627490[/C][C]-2.07948506274903[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.2423325395022[/C][C]0.75766746049778[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.415654015583[/C][C]0.584345984416989[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.5650804584051[/C][C]1.4349195415949[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.5256251791440[/C][C]-1.52562517914397[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.92316740648933[/C][C]2.07683259351067[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.9618148632183[/C][C]-2.96181486321830[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.3404983141964[/C][C]-0.340498314196413[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3775301258610[/C][C]-1.37753012586104[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.1866812629522[/C][C]-1.18668126295216[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.5009223233531[/C][C]-2.5009223233531[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]11.2459153253922[/C][C]-3.24591532539222[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.2289115055711[/C][C]-1.22891150557112[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.8335084902971[/C][C]-1.83350849029708[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.2727573932544[/C][C]-0.272757393254415[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.4586920807341[/C][C]0.541307919265861[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.2036850827733[/C][C]0.796314917226744[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3352998832421[/C][C]2.66470011675792[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.3352998832421[/C][C]2.66470011675792[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.6125091319784[/C][C]-1.61250913197839[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.5776936698040[/C][C]0.422306330195967[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8757387329160[/C][C]3.12426126708396[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.2415247169701[/C][C]0.758475283029949[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4669146837109[/C][C]1.53308531628909[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.1485573732302[/C][C]-2.14855737323019[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.5776936698040[/C][C]-0.577693669804033[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.5776936698040[/C][C]2.42230633019597[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.7745487861994[/C][C]1.22545121380059[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3100734604442[/C][C]0.689926539555782[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.2119076857500[/C][C]-1.21190768575002[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.7441239324472[/C][C]-0.744123932447217[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.49508814111227[/C][C]-2.49508814111227[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.3100734604442[/C][C]0.689926539555782[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.4847263260642[/C][C]2.51527367393583[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.3693075228843[/C][C]-1.36930752288427[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]12.9739045076102[/C][C]0.0260954923897635[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.0929060966801[/C][C]1.90709390331987[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.5143433572842[/C][C]1.48565664271580[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.5776936698040[/C][C]0.422306330195967[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.4965317149309[/C][C]-3.49653171493093[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.9434796538580[/C][C]5.05652034614196[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.7136990786950[/C][C]-0.713699078695022[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2110998632179[/C][C]-0.211099863217856[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.3352998832421[/C][C]-0.335299883242082[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.8236702422560[/C][C]2.17632975774402[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.1962352554681[/C][C]-0.196235255468090[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.7567371438461[/C][C]1.24326285615385[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.5455760335686[/C][C]-0.545576033568562[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.2593363593233[/C][C]-3.25933635932331[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]13.9434796538580[/C][C]-3.94347965385804[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.820087456366[/C][C]1.17991254363402[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.4594999032663[/C][C]1.54050009673369[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1063271306112[/C][C]-0.106327130611230[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.9308664424591[/C][C]1.06913355754089[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.1485573732302[/C][C]-1.14855737323019[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.2162982941722[/C][C]0.783701705827813[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6037279151341[/C][C]1.39627208486594[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1217153053680[/C][C]1.87828469463201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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
11311.34510782520481.65489217479524
21211.13394671492720.866053285072809
31513.82248061788411.17751938211590
41211.14736774885830.852632251141696
51010.8871623199431-0.88716231994308
6129.282565335217122.71743466478288
71516.7668293413340-1.76682934133403
8910.5079553019580-1.50795530195797
91212.3324946138059-0.332494613805853
10118.56441440104022.4355855989598
111114.0604837960239-3.06048379602387
121112.1169428951061-1.11694289510610
131512.34152503931482.65847496068521
14711.1161350725739-4.11613507257393
151111.5131537329123-0.513153732912306
161110.99274287508190.00725712491812329
171012.1169428951061-2.11694289510610
181414.5496619775699-0.549661977569941
19108.806808187602151.19319181239785
20610.0231677288341-4.02316772883407
21119.026750514724071.97324948527593
221514.56308301150100.436916988498959
231111.7333452686988-0.73334526869883
24129.538380155710172.46161984428983
251413.45230402540790.547695974592085
261515.0344495506938-0.034449550693843
27914.805476798063-5.80547679806299
281312.95409541835290.0459045816470873
291313.5756962229000-0.575696222899973
301610.87813189443415.12186810556585
31138.68341599011014.31658400988990
321213.8315110433930-1.83151104339302
331414.5630830115010-0.563083011501041
341110.51234591038010.487654089619862
35910.5079553019580-1.50795530195797
361614.42626978007791.57373021992212
371212.9719070607062-0.971907060706176
38109.670802778711160.329197221288836
391313.2099102388460-0.209910238845964
401615.41365656867900.586343431321049
411412.9675164522841.03248354771599
42158.43199177803926.56800822196079
4359.52495912177907-4.52495912177907
44810.6313474994500-2.63134749945003
451111.3897615354202-0.389761535420249
461613.95490324088512.04509675911492
471713.19648920491493.80351079508514
4898.19862841698620.801371583013807
49912.103521861175-3.103521861175
501314.7920557641319-1.79205576413189
511011.0283661597884-1.02836615978840
52612.480335717642-6.48033571764201
531212.2289115055711-0.228911505571119
54810.2719495707222-2.27194957072225
551412.10551930807911.89448069192094
561212.3479130946410-0.347913094641013
571110.87573873291600.124261267083957
581614.5516594244741.448340575526
59810.2451075028600-2.24510750286005
601515.1866812629522-0.186681262952160
6179.28456278212118-2.28456278212118
621613.8200874563662.17991254363402
631413.81569684794380.18430315205618
641613.70108586729612.29891413270391
65910.0429768180914-1.04297681809140
661412.10551930807911.89448069192094
671112.8461217016960-1.84612170169602
681310.28537060465332.71462939534665
691512.72712011262612.27287988737388
7055.13288494294826-0.132884942948255
711512.34791309464102.65208690535899
721312.10551930807910.894480691920938
731111.9865177190092-0.986517719009168
741114.4326578354041-3.43265783540411
751212.4847263260642-0.484726326064171
761213.2119076857500-1.21190768575002
771211.9821271105870.0178728894129951
781212.1055193080791-0.105519308079062
791410.63773555477633.36226444522374
8067.81238842039621-1.81238842039621
8179.66376980010629-2.66376980010629
821412.36133412857211.63866587142789
831413.82447806478810.175521935211853
841011.2549457509012-1.25494575090115
85138.557381422435334.44261857756467
861212.1189403420102-0.118940342010162
8799.17459161856022-0.174591618560221
881212.3523037030632-0.352303703063177
891614.94428747639021.05571252360979
901010.1485573732302-0.148557373230189
911413.09729670510230.902703294897706
921013.4586920807341-3.45869208073414
931615.17326022902110.82673977097894
941513.45430147231201.54569852768802
951211.25494575090120.745054249098849
96109.540377602614230.45962239738577
97810.5455760335686-2.54557603356856
9888.31059702745121-0.310597027451210
991113.0794850627490-2.07948506274903
1001312.24233253950220.75766746049778
1011615.4156540155830.584345984416989
1021614.56508045840511.4349195415949
1031415.5256251791440-1.52562517914397
104118.923167406489332.07683259351067
10546.9618148632183-2.96181486321830
1061414.3404983141964-0.340498314196413
107910.3775301258610-1.37753012586104
1081415.1866812629522-1.18668126295216
109810.5009223233531-2.5009223233531
110811.2459153253922-3.24591532539222
1111112.2289115055711-1.22891150557112
1121213.8335084902971-1.83350849029708
1131111.2727573932544-0.272757393254415
1141413.45869208073410.541307919265861
1151514.20368508277330.796314917226744
1161613.33529988324212.66470011675792
1171613.33529988324212.66470011675792
1181112.6125091319784-1.61250913197839
1191413.57769366980400.422306330195967
1201410.87573873291603.12426126708396
1211211.24152471697010.758475283029949
1221412.46691468371091.53308531628909
123810.1485573732302-2.14855737323019
1241313.5776936698040-0.577693669804033
1251613.57769366980402.42230633019597
1261210.77454878619941.22545121380059
1271615.31007346044420.689926539555782
1281213.2119076857500-1.21190768575002
1291111.7441239324472-0.744123932447217
13046.49508814111227-2.49508814111227
1311615.31007346044420.689926539555782
1321512.48472632606422.51527367393583
1331011.3693075228843-1.36930752288427
1341312.97390450761020.0260954923897635
1351513.09290609668011.90709390331987
1361210.51434335728421.48565664271580
1371413.57769366980400.422306330195967
138710.4965317149309-3.49653171493093
1391913.94347965385805.05652034614196
1401212.7136990786950-0.713699078695022
1411212.2110998632179-0.211099863217856
1421313.3352998832421-0.335299883242082
1431512.82367024225602.17632975774402
14488.1962352554681-0.196235255468090
1451210.75673714384611.24326285615385
1461010.5455760335686-0.545576033568562
147811.2593363593233-3.25933635932331
1481013.9434796538580-3.94347965385804
1491513.8200874563661.17991254363402
1501614.45949990326631.54050009673369
1511313.1063271306112-0.106327130611230
1521614.93086644245911.06913355754089
153910.1485573732302-1.14855737323019
1541413.21629829417220.783701705827813
1551412.60372791513411.39627208486594
1561210.12171530536801.87828469463201






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1275149572627650.2550299145255300.872485042737235
90.06353130006594710.1270626001318940.936468699934053
100.08477512208235770.1695502441647150.915224877917642
110.04338532980955460.08677065961910930.956614670190445
120.01961127082802250.0392225416560450.980388729171978
130.3465716306215250.693143261243050.653428369378475
140.6967396025812050.6065207948375890.303260397418795
150.6215007253830420.7569985492339160.378499274616958
160.5309860573130980.9380278853738050.469013942686902
170.4879259860377190.9758519720754370.512074013962281
180.4066836869244120.8133673738488250.593316313075588
190.3324685262301370.6649370524602730.667531473769863
200.599799771503740.800400456992520.40020022849626
210.5316365742899750.936726851420050.468363425710025
220.4989665906809760.9979331813619510.501033409319024
230.4297682693526710.8595365387053430.570231730647329
240.4164511487005410.8329022974010830.583548851299459
250.3833000930235950.766600186047190.616699906976405
260.3279954562988150.655990912597630.672004543701185
270.5989338988084720.8021322023830570.401066101191528
280.5382373366262640.9235253267474710.461762663373736
290.4786154445468230.9572308890936450.521384555453177
300.6487661983535750.702467603292850.351233801646425
310.7730778363494610.4538443273010790.226922163650539
320.7361237224116290.5277525551767420.263876277588371
330.694208777569020.611582444861960.30579122243098
340.6492218049057540.7015563901884930.350778195094246
350.638718227504690.722563544990620.36128177249531
360.6573037293950.685392541210.342696270605
370.6161636255807460.7676727488385090.383836374419254
380.5617739394531820.8764521210936370.438226060546818
390.5117227733966460.9765544532067080.488277226603354
400.4936747001821790.9873494003643580.506325299817821
410.4662137917917830.9324275835835650.533786208208217
420.7534774468757550.4930451062484890.246522553124245
430.9291151615306060.1417696769387880.0708848384693939
440.9435235970976110.1129528058047770.0564764029023886
450.9284635890764740.1430728218470520.0715364109235262
460.9346052315710960.1307895368578080.065394768428904
470.9697340250869220.06053194982615680.0302659749130784
480.967009976441790.06598004711642070.0329900235582103
490.970974364461390.05805127107721960.0290256355386098
500.964146485888550.07170702822289960.0358535141114498
510.9573110574936350.08537788501273030.0426889425063651
520.9846797989436320.03064040211273690.0153202010563684
530.9877279118146270.02454417637074530.0122720881853727
540.9851445033038120.02971099339237610.0148554966961881
550.9912255000853540.01754899982929230.00877449991464617
560.989001851904360.02199629619128140.0109981480956407
570.9856300626338280.02873987473234330.0143699373661716
580.9862785673896980.02744286522060480.0137214326103024
590.987690752332530.02461849533493870.0123092476674693
600.9854928729046740.02901425419065280.0145071270953264
610.985254084626630.02949183074674020.0147459153733701
620.9884509823610080.0230980352779830.0115490176389915
630.9858412443511230.02831751129775350.0141587556488767
640.9876782979979420.02464340400411540.0123217020020577
650.9841766385571920.03164672288561530.0158233614428077
660.983790986119050.03241802776190160.0162090138809508
670.9825650273993570.03486994520128650.0174349726006433
680.9863427953458180.02731440930836310.0136572046541815
690.9871929625597340.02561407488053270.0128070374402664
700.9833437488718270.03331250225634620.0166562511281731
710.9858948848065720.02821023038685540.0141051151934277
720.981947576048180.03610484790364090.0180524239518204
730.9775845219534220.04483095609315680.0224154780465784
740.9868841476373980.02623170472520460.0131158523626023
750.9826136056666120.03477278866677650.0173863943333883
760.9793503780796720.04129924384065590.0206496219203280
770.9728308172230860.05433836555382870.0271691827769143
780.9646435819921830.0707128360156330.0353564180078165
790.9784336347259520.04313273054809580.0215663652740479
800.976942917315980.04611416536803950.0230570826840197
810.9797171480569420.04056570388611640.0202828519430582
820.9774498980545040.04510020389099260.0225501019454963
830.9704187815154120.05916243696917540.0295812184845877
840.9647297587043680.07054048259126360.0352702412956318
850.991122696849520.01775460630096170.00887730315048086
860.9878421745794490.02431565084110290.0121578254205514
870.9837183780020190.03256324399596240.0162816219979812
880.9782744732975970.04345105340480540.0217255267024027
890.9729233648110180.05415327037796310.0270766351889815
900.964754481924640.07049103615072150.0352455180753608
910.9570652977459020.0858694045081960.042934702254098
920.9758310500079020.04833789998419510.0241689499920975
930.969205790209480.06158841958104130.0307942097905206
940.9639390167151540.07212196656969150.0360609832848457
950.9549825181978910.0900349636042170.0450174818021086
960.9450750364726160.1098499270547670.0549249635273837
970.9460726828906160.1078546342187690.0539273171093843
980.932651953461620.1346960930767610.0673480465383807
990.9359255482815810.1281489034368380.0640744517184188
1000.9214768552277340.1570462895445330.0785231447722663
1010.903705841388130.1925883172237400.0962941586118698
1020.8880967875491330.2238064249017340.111903212450867
1030.8939023652934110.2121952694131770.106097634706589
1040.9213019472310.1573961055380000.0786980527689998
1050.9205078767083370.1589842465833250.0794921232916627
1060.9005216005718260.1989567988563490.0994783994281743
1070.8835894426589970.2328211146820070.116410557341003
1080.8825346101099420.2349307797801150.117465389890058
1090.8860361460400420.2279277079199160.113963853959958
1100.9164465896825320.1671068206349350.0835534103174675
1110.9060606780894560.1878786438210880.0939393219105441
1120.916732749413090.1665345011738200.0832672505869099
1130.8938313873201110.2123372253597780.106168612679889
1140.8675458529044920.2649082941910170.132454147095508
1150.8375362788297720.3249274423404560.162463721170228
1160.843194908883620.3136101822327620.156805091116381
1170.8497630588304430.3004738823391130.150236941169556
1180.8366676645969080.3266646708061850.163332335403092
1190.8000007246586080.3999985506827850.199999275341392
1200.8548033277229280.2903933445541440.145196672277072
1210.8239762746796790.3520474506406420.176023725320321
1220.799281082983580.4014378340328420.200718917016421
1230.7883735981926520.4232528036146960.211626401807348
1240.7559064501223050.4881870997553890.244093549877695
1250.7508071589490710.4983856821018580.249192841050929
1260.7287374544023690.5425250911952620.271262545597631
1270.6755508494949460.6488983010101080.324449150505054
1280.6572441104237030.6855117791525940.342755889576297
1290.6013296817969720.7973406364060550.398670318203028
1300.5618664957881930.8762670084236130.438133504211807
1310.497790250608640.995580501217280.50220974939136
1320.5059264578954980.9881470842090050.494073542104502
1330.4597104248689030.9194208497378050.540289575131097
1340.3906443250902960.7812886501805930.609355674909704
1350.35498248391980.70996496783960.6450175160802
1360.3316706770238140.6633413540476280.668329322976186
1370.2649567204770790.5299134409541570.735043279522921
1380.3813062220636380.7626124441272760.618693777936362
1390.676630216746440.646739566507120.32336978325356
1400.6130693296034740.7738613407930510.386930670396526
1410.5433174124681450.913365175063710.456682587531855
1420.4561549937042110.9123099874084230.543845006295789
1430.4054717038769340.8109434077538680.594528296123066
1440.3125519907424690.6251039814849370.687448009257531
1450.2488398344987550.497679668997510.751160165501245
1460.1761232823276600.3522465646553190.82387671767234
1470.2090210682955750.4180421365911510.790978931704425
1480.8481226529627860.3037546940744270.151877347037214

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.127514957262765 & 0.255029914525530 & 0.872485042737235 \tabularnewline
9 & 0.0635313000659471 & 0.127062600131894 & 0.936468699934053 \tabularnewline
10 & 0.0847751220823577 & 0.169550244164715 & 0.915224877917642 \tabularnewline
11 & 0.0433853298095546 & 0.0867706596191093 & 0.956614670190445 \tabularnewline
12 & 0.0196112708280225 & 0.039222541656045 & 0.980388729171978 \tabularnewline
13 & 0.346571630621525 & 0.69314326124305 & 0.653428369378475 \tabularnewline
14 & 0.696739602581205 & 0.606520794837589 & 0.303260397418795 \tabularnewline
15 & 0.621500725383042 & 0.756998549233916 & 0.378499274616958 \tabularnewline
16 & 0.530986057313098 & 0.938027885373805 & 0.469013942686902 \tabularnewline
17 & 0.487925986037719 & 0.975851972075437 & 0.512074013962281 \tabularnewline
18 & 0.406683686924412 & 0.813367373848825 & 0.593316313075588 \tabularnewline
19 & 0.332468526230137 & 0.664937052460273 & 0.667531473769863 \tabularnewline
20 & 0.59979977150374 & 0.80040045699252 & 0.40020022849626 \tabularnewline
21 & 0.531636574289975 & 0.93672685142005 & 0.468363425710025 \tabularnewline
22 & 0.498966590680976 & 0.997933181361951 & 0.501033409319024 \tabularnewline
23 & 0.429768269352671 & 0.859536538705343 & 0.570231730647329 \tabularnewline
24 & 0.416451148700541 & 0.832902297401083 & 0.583548851299459 \tabularnewline
25 & 0.383300093023595 & 0.76660018604719 & 0.616699906976405 \tabularnewline
26 & 0.327995456298815 & 0.65599091259763 & 0.672004543701185 \tabularnewline
27 & 0.598933898808472 & 0.802132202383057 & 0.401066101191528 \tabularnewline
28 & 0.538237336626264 & 0.923525326747471 & 0.461762663373736 \tabularnewline
29 & 0.478615444546823 & 0.957230889093645 & 0.521384555453177 \tabularnewline
30 & 0.648766198353575 & 0.70246760329285 & 0.351233801646425 \tabularnewline
31 & 0.773077836349461 & 0.453844327301079 & 0.226922163650539 \tabularnewline
32 & 0.736123722411629 & 0.527752555176742 & 0.263876277588371 \tabularnewline
33 & 0.69420877756902 & 0.61158244486196 & 0.30579122243098 \tabularnewline
34 & 0.649221804905754 & 0.701556390188493 & 0.350778195094246 \tabularnewline
35 & 0.63871822750469 & 0.72256354499062 & 0.36128177249531 \tabularnewline
36 & 0.657303729395 & 0.68539254121 & 0.342696270605 \tabularnewline
37 & 0.616163625580746 & 0.767672748838509 & 0.383836374419254 \tabularnewline
38 & 0.561773939453182 & 0.876452121093637 & 0.438226060546818 \tabularnewline
39 & 0.511722773396646 & 0.976554453206708 & 0.488277226603354 \tabularnewline
40 & 0.493674700182179 & 0.987349400364358 & 0.506325299817821 \tabularnewline
41 & 0.466213791791783 & 0.932427583583565 & 0.533786208208217 \tabularnewline
42 & 0.753477446875755 & 0.493045106248489 & 0.246522553124245 \tabularnewline
43 & 0.929115161530606 & 0.141769676938788 & 0.0708848384693939 \tabularnewline
44 & 0.943523597097611 & 0.112952805804777 & 0.0564764029023886 \tabularnewline
45 & 0.928463589076474 & 0.143072821847052 & 0.0715364109235262 \tabularnewline
46 & 0.934605231571096 & 0.130789536857808 & 0.065394768428904 \tabularnewline
47 & 0.969734025086922 & 0.0605319498261568 & 0.0302659749130784 \tabularnewline
48 & 0.96700997644179 & 0.0659800471164207 & 0.0329900235582103 \tabularnewline
49 & 0.97097436446139 & 0.0580512710772196 & 0.0290256355386098 \tabularnewline
50 & 0.96414648588855 & 0.0717070282228996 & 0.0358535141114498 \tabularnewline
51 & 0.957311057493635 & 0.0853778850127303 & 0.0426889425063651 \tabularnewline
52 & 0.984679798943632 & 0.0306404021127369 & 0.0153202010563684 \tabularnewline
53 & 0.987727911814627 & 0.0245441763707453 & 0.0122720881853727 \tabularnewline
54 & 0.985144503303812 & 0.0297109933923761 & 0.0148554966961881 \tabularnewline
55 & 0.991225500085354 & 0.0175489998292923 & 0.00877449991464617 \tabularnewline
56 & 0.98900185190436 & 0.0219962961912814 & 0.0109981480956407 \tabularnewline
57 & 0.985630062633828 & 0.0287398747323433 & 0.0143699373661716 \tabularnewline
58 & 0.986278567389698 & 0.0274428652206048 & 0.0137214326103024 \tabularnewline
59 & 0.98769075233253 & 0.0246184953349387 & 0.0123092476674693 \tabularnewline
60 & 0.985492872904674 & 0.0290142541906528 & 0.0145071270953264 \tabularnewline
61 & 0.98525408462663 & 0.0294918307467402 & 0.0147459153733701 \tabularnewline
62 & 0.988450982361008 & 0.023098035277983 & 0.0115490176389915 \tabularnewline
63 & 0.985841244351123 & 0.0283175112977535 & 0.0141587556488767 \tabularnewline
64 & 0.987678297997942 & 0.0246434040041154 & 0.0123217020020577 \tabularnewline
65 & 0.984176638557192 & 0.0316467228856153 & 0.0158233614428077 \tabularnewline
66 & 0.98379098611905 & 0.0324180277619016 & 0.0162090138809508 \tabularnewline
67 & 0.982565027399357 & 0.0348699452012865 & 0.0174349726006433 \tabularnewline
68 & 0.986342795345818 & 0.0273144093083631 & 0.0136572046541815 \tabularnewline
69 & 0.987192962559734 & 0.0256140748805327 & 0.0128070374402664 \tabularnewline
70 & 0.983343748871827 & 0.0333125022563462 & 0.0166562511281731 \tabularnewline
71 & 0.985894884806572 & 0.0282102303868554 & 0.0141051151934277 \tabularnewline
72 & 0.98194757604818 & 0.0361048479036409 & 0.0180524239518204 \tabularnewline
73 & 0.977584521953422 & 0.0448309560931568 & 0.0224154780465784 \tabularnewline
74 & 0.986884147637398 & 0.0262317047252046 & 0.0131158523626023 \tabularnewline
75 & 0.982613605666612 & 0.0347727886667765 & 0.0173863943333883 \tabularnewline
76 & 0.979350378079672 & 0.0412992438406559 & 0.0206496219203280 \tabularnewline
77 & 0.972830817223086 & 0.0543383655538287 & 0.0271691827769143 \tabularnewline
78 & 0.964643581992183 & 0.070712836015633 & 0.0353564180078165 \tabularnewline
79 & 0.978433634725952 & 0.0431327305480958 & 0.0215663652740479 \tabularnewline
80 & 0.97694291731598 & 0.0461141653680395 & 0.0230570826840197 \tabularnewline
81 & 0.979717148056942 & 0.0405657038861164 & 0.0202828519430582 \tabularnewline
82 & 0.977449898054504 & 0.0451002038909926 & 0.0225501019454963 \tabularnewline
83 & 0.970418781515412 & 0.0591624369691754 & 0.0295812184845877 \tabularnewline
84 & 0.964729758704368 & 0.0705404825912636 & 0.0352702412956318 \tabularnewline
85 & 0.99112269684952 & 0.0177546063009617 & 0.00887730315048086 \tabularnewline
86 & 0.987842174579449 & 0.0243156508411029 & 0.0121578254205514 \tabularnewline
87 & 0.983718378002019 & 0.0325632439959624 & 0.0162816219979812 \tabularnewline
88 & 0.978274473297597 & 0.0434510534048054 & 0.0217255267024027 \tabularnewline
89 & 0.972923364811018 & 0.0541532703779631 & 0.0270766351889815 \tabularnewline
90 & 0.96475448192464 & 0.0704910361507215 & 0.0352455180753608 \tabularnewline
91 & 0.957065297745902 & 0.085869404508196 & 0.042934702254098 \tabularnewline
92 & 0.975831050007902 & 0.0483378999841951 & 0.0241689499920975 \tabularnewline
93 & 0.96920579020948 & 0.0615884195810413 & 0.0307942097905206 \tabularnewline
94 & 0.963939016715154 & 0.0721219665696915 & 0.0360609832848457 \tabularnewline
95 & 0.954982518197891 & 0.090034963604217 & 0.0450174818021086 \tabularnewline
96 & 0.945075036472616 & 0.109849927054767 & 0.0549249635273837 \tabularnewline
97 & 0.946072682890616 & 0.107854634218769 & 0.0539273171093843 \tabularnewline
98 & 0.93265195346162 & 0.134696093076761 & 0.0673480465383807 \tabularnewline
99 & 0.935925548281581 & 0.128148903436838 & 0.0640744517184188 \tabularnewline
100 & 0.921476855227734 & 0.157046289544533 & 0.0785231447722663 \tabularnewline
101 & 0.90370584138813 & 0.192588317223740 & 0.0962941586118698 \tabularnewline
102 & 0.888096787549133 & 0.223806424901734 & 0.111903212450867 \tabularnewline
103 & 0.893902365293411 & 0.212195269413177 & 0.106097634706589 \tabularnewline
104 & 0.921301947231 & 0.157396105538000 & 0.0786980527689998 \tabularnewline
105 & 0.920507876708337 & 0.158984246583325 & 0.0794921232916627 \tabularnewline
106 & 0.900521600571826 & 0.198956798856349 & 0.0994783994281743 \tabularnewline
107 & 0.883589442658997 & 0.232821114682007 & 0.116410557341003 \tabularnewline
108 & 0.882534610109942 & 0.234930779780115 & 0.117465389890058 \tabularnewline
109 & 0.886036146040042 & 0.227927707919916 & 0.113963853959958 \tabularnewline
110 & 0.916446589682532 & 0.167106820634935 & 0.0835534103174675 \tabularnewline
111 & 0.906060678089456 & 0.187878643821088 & 0.0939393219105441 \tabularnewline
112 & 0.91673274941309 & 0.166534501173820 & 0.0832672505869099 \tabularnewline
113 & 0.893831387320111 & 0.212337225359778 & 0.106168612679889 \tabularnewline
114 & 0.867545852904492 & 0.264908294191017 & 0.132454147095508 \tabularnewline
115 & 0.837536278829772 & 0.324927442340456 & 0.162463721170228 \tabularnewline
116 & 0.84319490888362 & 0.313610182232762 & 0.156805091116381 \tabularnewline
117 & 0.849763058830443 & 0.300473882339113 & 0.150236941169556 \tabularnewline
118 & 0.836667664596908 & 0.326664670806185 & 0.163332335403092 \tabularnewline
119 & 0.800000724658608 & 0.399998550682785 & 0.199999275341392 \tabularnewline
120 & 0.854803327722928 & 0.290393344554144 & 0.145196672277072 \tabularnewline
121 & 0.823976274679679 & 0.352047450640642 & 0.176023725320321 \tabularnewline
122 & 0.79928108298358 & 0.401437834032842 & 0.200718917016421 \tabularnewline
123 & 0.788373598192652 & 0.423252803614696 & 0.211626401807348 \tabularnewline
124 & 0.755906450122305 & 0.488187099755389 & 0.244093549877695 \tabularnewline
125 & 0.750807158949071 & 0.498385682101858 & 0.249192841050929 \tabularnewline
126 & 0.728737454402369 & 0.542525091195262 & 0.271262545597631 \tabularnewline
127 & 0.675550849494946 & 0.648898301010108 & 0.324449150505054 \tabularnewline
128 & 0.657244110423703 & 0.685511779152594 & 0.342755889576297 \tabularnewline
129 & 0.601329681796972 & 0.797340636406055 & 0.398670318203028 \tabularnewline
130 & 0.561866495788193 & 0.876267008423613 & 0.438133504211807 \tabularnewline
131 & 0.49779025060864 & 0.99558050121728 & 0.50220974939136 \tabularnewline
132 & 0.505926457895498 & 0.988147084209005 & 0.494073542104502 \tabularnewline
133 & 0.459710424868903 & 0.919420849737805 & 0.540289575131097 \tabularnewline
134 & 0.390644325090296 & 0.781288650180593 & 0.609355674909704 \tabularnewline
135 & 0.3549824839198 & 0.7099649678396 & 0.6450175160802 \tabularnewline
136 & 0.331670677023814 & 0.663341354047628 & 0.668329322976186 \tabularnewline
137 & 0.264956720477079 & 0.529913440954157 & 0.735043279522921 \tabularnewline
138 & 0.381306222063638 & 0.762612444127276 & 0.618693777936362 \tabularnewline
139 & 0.67663021674644 & 0.64673956650712 & 0.32336978325356 \tabularnewline
140 & 0.613069329603474 & 0.773861340793051 & 0.386930670396526 \tabularnewline
141 & 0.543317412468145 & 0.91336517506371 & 0.456682587531855 \tabularnewline
142 & 0.456154993704211 & 0.912309987408423 & 0.543845006295789 \tabularnewline
143 & 0.405471703876934 & 0.810943407753868 & 0.594528296123066 \tabularnewline
144 & 0.312551990742469 & 0.625103981484937 & 0.687448009257531 \tabularnewline
145 & 0.248839834498755 & 0.49767966899751 & 0.751160165501245 \tabularnewline
146 & 0.176123282327660 & 0.352246564655319 & 0.82387671767234 \tabularnewline
147 & 0.209021068295575 & 0.418042136591151 & 0.790978931704425 \tabularnewline
148 & 0.848122652962786 & 0.303754694074427 & 0.151877347037214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.127514957262765[/C][C]0.255029914525530[/C][C]0.872485042737235[/C][/ROW]
[ROW][C]9[/C][C]0.0635313000659471[/C][C]0.127062600131894[/C][C]0.936468699934053[/C][/ROW]
[ROW][C]10[/C][C]0.0847751220823577[/C][C]0.169550244164715[/C][C]0.915224877917642[/C][/ROW]
[ROW][C]11[/C][C]0.0433853298095546[/C][C]0.0867706596191093[/C][C]0.956614670190445[/C][/ROW]
[ROW][C]12[/C][C]0.0196112708280225[/C][C]0.039222541656045[/C][C]0.980388729171978[/C][/ROW]
[ROW][C]13[/C][C]0.346571630621525[/C][C]0.69314326124305[/C][C]0.653428369378475[/C][/ROW]
[ROW][C]14[/C][C]0.696739602581205[/C][C]0.606520794837589[/C][C]0.303260397418795[/C][/ROW]
[ROW][C]15[/C][C]0.621500725383042[/C][C]0.756998549233916[/C][C]0.378499274616958[/C][/ROW]
[ROW][C]16[/C][C]0.530986057313098[/C][C]0.938027885373805[/C][C]0.469013942686902[/C][/ROW]
[ROW][C]17[/C][C]0.487925986037719[/C][C]0.975851972075437[/C][C]0.512074013962281[/C][/ROW]
[ROW][C]18[/C][C]0.406683686924412[/C][C]0.813367373848825[/C][C]0.593316313075588[/C][/ROW]
[ROW][C]19[/C][C]0.332468526230137[/C][C]0.664937052460273[/C][C]0.667531473769863[/C][/ROW]
[ROW][C]20[/C][C]0.59979977150374[/C][C]0.80040045699252[/C][C]0.40020022849626[/C][/ROW]
[ROW][C]21[/C][C]0.531636574289975[/C][C]0.93672685142005[/C][C]0.468363425710025[/C][/ROW]
[ROW][C]22[/C][C]0.498966590680976[/C][C]0.997933181361951[/C][C]0.501033409319024[/C][/ROW]
[ROW][C]23[/C][C]0.429768269352671[/C][C]0.859536538705343[/C][C]0.570231730647329[/C][/ROW]
[ROW][C]24[/C][C]0.416451148700541[/C][C]0.832902297401083[/C][C]0.583548851299459[/C][/ROW]
[ROW][C]25[/C][C]0.383300093023595[/C][C]0.76660018604719[/C][C]0.616699906976405[/C][/ROW]
[ROW][C]26[/C][C]0.327995456298815[/C][C]0.65599091259763[/C][C]0.672004543701185[/C][/ROW]
[ROW][C]27[/C][C]0.598933898808472[/C][C]0.802132202383057[/C][C]0.401066101191528[/C][/ROW]
[ROW][C]28[/C][C]0.538237336626264[/C][C]0.923525326747471[/C][C]0.461762663373736[/C][/ROW]
[ROW][C]29[/C][C]0.478615444546823[/C][C]0.957230889093645[/C][C]0.521384555453177[/C][/ROW]
[ROW][C]30[/C][C]0.648766198353575[/C][C]0.70246760329285[/C][C]0.351233801646425[/C][/ROW]
[ROW][C]31[/C][C]0.773077836349461[/C][C]0.453844327301079[/C][C]0.226922163650539[/C][/ROW]
[ROW][C]32[/C][C]0.736123722411629[/C][C]0.527752555176742[/C][C]0.263876277588371[/C][/ROW]
[ROW][C]33[/C][C]0.69420877756902[/C][C]0.61158244486196[/C][C]0.30579122243098[/C][/ROW]
[ROW][C]34[/C][C]0.649221804905754[/C][C]0.701556390188493[/C][C]0.350778195094246[/C][/ROW]
[ROW][C]35[/C][C]0.63871822750469[/C][C]0.72256354499062[/C][C]0.36128177249531[/C][/ROW]
[ROW][C]36[/C][C]0.657303729395[/C][C]0.68539254121[/C][C]0.342696270605[/C][/ROW]
[ROW][C]37[/C][C]0.616163625580746[/C][C]0.767672748838509[/C][C]0.383836374419254[/C][/ROW]
[ROW][C]38[/C][C]0.561773939453182[/C][C]0.876452121093637[/C][C]0.438226060546818[/C][/ROW]
[ROW][C]39[/C][C]0.511722773396646[/C][C]0.976554453206708[/C][C]0.488277226603354[/C][/ROW]
[ROW][C]40[/C][C]0.493674700182179[/C][C]0.987349400364358[/C][C]0.506325299817821[/C][/ROW]
[ROW][C]41[/C][C]0.466213791791783[/C][C]0.932427583583565[/C][C]0.533786208208217[/C][/ROW]
[ROW][C]42[/C][C]0.753477446875755[/C][C]0.493045106248489[/C][C]0.246522553124245[/C][/ROW]
[ROW][C]43[/C][C]0.929115161530606[/C][C]0.141769676938788[/C][C]0.0708848384693939[/C][/ROW]
[ROW][C]44[/C][C]0.943523597097611[/C][C]0.112952805804777[/C][C]0.0564764029023886[/C][/ROW]
[ROW][C]45[/C][C]0.928463589076474[/C][C]0.143072821847052[/C][C]0.0715364109235262[/C][/ROW]
[ROW][C]46[/C][C]0.934605231571096[/C][C]0.130789536857808[/C][C]0.065394768428904[/C][/ROW]
[ROW][C]47[/C][C]0.969734025086922[/C][C]0.0605319498261568[/C][C]0.0302659749130784[/C][/ROW]
[ROW][C]48[/C][C]0.96700997644179[/C][C]0.0659800471164207[/C][C]0.0329900235582103[/C][/ROW]
[ROW][C]49[/C][C]0.97097436446139[/C][C]0.0580512710772196[/C][C]0.0290256355386098[/C][/ROW]
[ROW][C]50[/C][C]0.96414648588855[/C][C]0.0717070282228996[/C][C]0.0358535141114498[/C][/ROW]
[ROW][C]51[/C][C]0.957311057493635[/C][C]0.0853778850127303[/C][C]0.0426889425063651[/C][/ROW]
[ROW][C]52[/C][C]0.984679798943632[/C][C]0.0306404021127369[/C][C]0.0153202010563684[/C][/ROW]
[ROW][C]53[/C][C]0.987727911814627[/C][C]0.0245441763707453[/C][C]0.0122720881853727[/C][/ROW]
[ROW][C]54[/C][C]0.985144503303812[/C][C]0.0297109933923761[/C][C]0.0148554966961881[/C][/ROW]
[ROW][C]55[/C][C]0.991225500085354[/C][C]0.0175489998292923[/C][C]0.00877449991464617[/C][/ROW]
[ROW][C]56[/C][C]0.98900185190436[/C][C]0.0219962961912814[/C][C]0.0109981480956407[/C][/ROW]
[ROW][C]57[/C][C]0.985630062633828[/C][C]0.0287398747323433[/C][C]0.0143699373661716[/C][/ROW]
[ROW][C]58[/C][C]0.986278567389698[/C][C]0.0274428652206048[/C][C]0.0137214326103024[/C][/ROW]
[ROW][C]59[/C][C]0.98769075233253[/C][C]0.0246184953349387[/C][C]0.0123092476674693[/C][/ROW]
[ROW][C]60[/C][C]0.985492872904674[/C][C]0.0290142541906528[/C][C]0.0145071270953264[/C][/ROW]
[ROW][C]61[/C][C]0.98525408462663[/C][C]0.0294918307467402[/C][C]0.0147459153733701[/C][/ROW]
[ROW][C]62[/C][C]0.988450982361008[/C][C]0.023098035277983[/C][C]0.0115490176389915[/C][/ROW]
[ROW][C]63[/C][C]0.985841244351123[/C][C]0.0283175112977535[/C][C]0.0141587556488767[/C][/ROW]
[ROW][C]64[/C][C]0.987678297997942[/C][C]0.0246434040041154[/C][C]0.0123217020020577[/C][/ROW]
[ROW][C]65[/C][C]0.984176638557192[/C][C]0.0316467228856153[/C][C]0.0158233614428077[/C][/ROW]
[ROW][C]66[/C][C]0.98379098611905[/C][C]0.0324180277619016[/C][C]0.0162090138809508[/C][/ROW]
[ROW][C]67[/C][C]0.982565027399357[/C][C]0.0348699452012865[/C][C]0.0174349726006433[/C][/ROW]
[ROW][C]68[/C][C]0.986342795345818[/C][C]0.0273144093083631[/C][C]0.0136572046541815[/C][/ROW]
[ROW][C]69[/C][C]0.987192962559734[/C][C]0.0256140748805327[/C][C]0.0128070374402664[/C][/ROW]
[ROW][C]70[/C][C]0.983343748871827[/C][C]0.0333125022563462[/C][C]0.0166562511281731[/C][/ROW]
[ROW][C]71[/C][C]0.985894884806572[/C][C]0.0282102303868554[/C][C]0.0141051151934277[/C][/ROW]
[ROW][C]72[/C][C]0.98194757604818[/C][C]0.0361048479036409[/C][C]0.0180524239518204[/C][/ROW]
[ROW][C]73[/C][C]0.977584521953422[/C][C]0.0448309560931568[/C][C]0.0224154780465784[/C][/ROW]
[ROW][C]74[/C][C]0.986884147637398[/C][C]0.0262317047252046[/C][C]0.0131158523626023[/C][/ROW]
[ROW][C]75[/C][C]0.982613605666612[/C][C]0.0347727886667765[/C][C]0.0173863943333883[/C][/ROW]
[ROW][C]76[/C][C]0.979350378079672[/C][C]0.0412992438406559[/C][C]0.0206496219203280[/C][/ROW]
[ROW][C]77[/C][C]0.972830817223086[/C][C]0.0543383655538287[/C][C]0.0271691827769143[/C][/ROW]
[ROW][C]78[/C][C]0.964643581992183[/C][C]0.070712836015633[/C][C]0.0353564180078165[/C][/ROW]
[ROW][C]79[/C][C]0.978433634725952[/C][C]0.0431327305480958[/C][C]0.0215663652740479[/C][/ROW]
[ROW][C]80[/C][C]0.97694291731598[/C][C]0.0461141653680395[/C][C]0.0230570826840197[/C][/ROW]
[ROW][C]81[/C][C]0.979717148056942[/C][C]0.0405657038861164[/C][C]0.0202828519430582[/C][/ROW]
[ROW][C]82[/C][C]0.977449898054504[/C][C]0.0451002038909926[/C][C]0.0225501019454963[/C][/ROW]
[ROW][C]83[/C][C]0.970418781515412[/C][C]0.0591624369691754[/C][C]0.0295812184845877[/C][/ROW]
[ROW][C]84[/C][C]0.964729758704368[/C][C]0.0705404825912636[/C][C]0.0352702412956318[/C][/ROW]
[ROW][C]85[/C][C]0.99112269684952[/C][C]0.0177546063009617[/C][C]0.00887730315048086[/C][/ROW]
[ROW][C]86[/C][C]0.987842174579449[/C][C]0.0243156508411029[/C][C]0.0121578254205514[/C][/ROW]
[ROW][C]87[/C][C]0.983718378002019[/C][C]0.0325632439959624[/C][C]0.0162816219979812[/C][/ROW]
[ROW][C]88[/C][C]0.978274473297597[/C][C]0.0434510534048054[/C][C]0.0217255267024027[/C][/ROW]
[ROW][C]89[/C][C]0.972923364811018[/C][C]0.0541532703779631[/C][C]0.0270766351889815[/C][/ROW]
[ROW][C]90[/C][C]0.96475448192464[/C][C]0.0704910361507215[/C][C]0.0352455180753608[/C][/ROW]
[ROW][C]91[/C][C]0.957065297745902[/C][C]0.085869404508196[/C][C]0.042934702254098[/C][/ROW]
[ROW][C]92[/C][C]0.975831050007902[/C][C]0.0483378999841951[/C][C]0.0241689499920975[/C][/ROW]
[ROW][C]93[/C][C]0.96920579020948[/C][C]0.0615884195810413[/C][C]0.0307942097905206[/C][/ROW]
[ROW][C]94[/C][C]0.963939016715154[/C][C]0.0721219665696915[/C][C]0.0360609832848457[/C][/ROW]
[ROW][C]95[/C][C]0.954982518197891[/C][C]0.090034963604217[/C][C]0.0450174818021086[/C][/ROW]
[ROW][C]96[/C][C]0.945075036472616[/C][C]0.109849927054767[/C][C]0.0549249635273837[/C][/ROW]
[ROW][C]97[/C][C]0.946072682890616[/C][C]0.107854634218769[/C][C]0.0539273171093843[/C][/ROW]
[ROW][C]98[/C][C]0.93265195346162[/C][C]0.134696093076761[/C][C]0.0673480465383807[/C][/ROW]
[ROW][C]99[/C][C]0.935925548281581[/C][C]0.128148903436838[/C][C]0.0640744517184188[/C][/ROW]
[ROW][C]100[/C][C]0.921476855227734[/C][C]0.157046289544533[/C][C]0.0785231447722663[/C][/ROW]
[ROW][C]101[/C][C]0.90370584138813[/C][C]0.192588317223740[/C][C]0.0962941586118698[/C][/ROW]
[ROW][C]102[/C][C]0.888096787549133[/C][C]0.223806424901734[/C][C]0.111903212450867[/C][/ROW]
[ROW][C]103[/C][C]0.893902365293411[/C][C]0.212195269413177[/C][C]0.106097634706589[/C][/ROW]
[ROW][C]104[/C][C]0.921301947231[/C][C]0.157396105538000[/C][C]0.0786980527689998[/C][/ROW]
[ROW][C]105[/C][C]0.920507876708337[/C][C]0.158984246583325[/C][C]0.0794921232916627[/C][/ROW]
[ROW][C]106[/C][C]0.900521600571826[/C][C]0.198956798856349[/C][C]0.0994783994281743[/C][/ROW]
[ROW][C]107[/C][C]0.883589442658997[/C][C]0.232821114682007[/C][C]0.116410557341003[/C][/ROW]
[ROW][C]108[/C][C]0.882534610109942[/C][C]0.234930779780115[/C][C]0.117465389890058[/C][/ROW]
[ROW][C]109[/C][C]0.886036146040042[/C][C]0.227927707919916[/C][C]0.113963853959958[/C][/ROW]
[ROW][C]110[/C][C]0.916446589682532[/C][C]0.167106820634935[/C][C]0.0835534103174675[/C][/ROW]
[ROW][C]111[/C][C]0.906060678089456[/C][C]0.187878643821088[/C][C]0.0939393219105441[/C][/ROW]
[ROW][C]112[/C][C]0.91673274941309[/C][C]0.166534501173820[/C][C]0.0832672505869099[/C][/ROW]
[ROW][C]113[/C][C]0.893831387320111[/C][C]0.212337225359778[/C][C]0.106168612679889[/C][/ROW]
[ROW][C]114[/C][C]0.867545852904492[/C][C]0.264908294191017[/C][C]0.132454147095508[/C][/ROW]
[ROW][C]115[/C][C]0.837536278829772[/C][C]0.324927442340456[/C][C]0.162463721170228[/C][/ROW]
[ROW][C]116[/C][C]0.84319490888362[/C][C]0.313610182232762[/C][C]0.156805091116381[/C][/ROW]
[ROW][C]117[/C][C]0.849763058830443[/C][C]0.300473882339113[/C][C]0.150236941169556[/C][/ROW]
[ROW][C]118[/C][C]0.836667664596908[/C][C]0.326664670806185[/C][C]0.163332335403092[/C][/ROW]
[ROW][C]119[/C][C]0.800000724658608[/C][C]0.399998550682785[/C][C]0.199999275341392[/C][/ROW]
[ROW][C]120[/C][C]0.854803327722928[/C][C]0.290393344554144[/C][C]0.145196672277072[/C][/ROW]
[ROW][C]121[/C][C]0.823976274679679[/C][C]0.352047450640642[/C][C]0.176023725320321[/C][/ROW]
[ROW][C]122[/C][C]0.79928108298358[/C][C]0.401437834032842[/C][C]0.200718917016421[/C][/ROW]
[ROW][C]123[/C][C]0.788373598192652[/C][C]0.423252803614696[/C][C]0.211626401807348[/C][/ROW]
[ROW][C]124[/C][C]0.755906450122305[/C][C]0.488187099755389[/C][C]0.244093549877695[/C][/ROW]
[ROW][C]125[/C][C]0.750807158949071[/C][C]0.498385682101858[/C][C]0.249192841050929[/C][/ROW]
[ROW][C]126[/C][C]0.728737454402369[/C][C]0.542525091195262[/C][C]0.271262545597631[/C][/ROW]
[ROW][C]127[/C][C]0.675550849494946[/C][C]0.648898301010108[/C][C]0.324449150505054[/C][/ROW]
[ROW][C]128[/C][C]0.657244110423703[/C][C]0.685511779152594[/C][C]0.342755889576297[/C][/ROW]
[ROW][C]129[/C][C]0.601329681796972[/C][C]0.797340636406055[/C][C]0.398670318203028[/C][/ROW]
[ROW][C]130[/C][C]0.561866495788193[/C][C]0.876267008423613[/C][C]0.438133504211807[/C][/ROW]
[ROW][C]131[/C][C]0.49779025060864[/C][C]0.99558050121728[/C][C]0.50220974939136[/C][/ROW]
[ROW][C]132[/C][C]0.505926457895498[/C][C]0.988147084209005[/C][C]0.494073542104502[/C][/ROW]
[ROW][C]133[/C][C]0.459710424868903[/C][C]0.919420849737805[/C][C]0.540289575131097[/C][/ROW]
[ROW][C]134[/C][C]0.390644325090296[/C][C]0.781288650180593[/C][C]0.609355674909704[/C][/ROW]
[ROW][C]135[/C][C]0.3549824839198[/C][C]0.7099649678396[/C][C]0.6450175160802[/C][/ROW]
[ROW][C]136[/C][C]0.331670677023814[/C][C]0.663341354047628[/C][C]0.668329322976186[/C][/ROW]
[ROW][C]137[/C][C]0.264956720477079[/C][C]0.529913440954157[/C][C]0.735043279522921[/C][/ROW]
[ROW][C]138[/C][C]0.381306222063638[/C][C]0.762612444127276[/C][C]0.618693777936362[/C][/ROW]
[ROW][C]139[/C][C]0.67663021674644[/C][C]0.64673956650712[/C][C]0.32336978325356[/C][/ROW]
[ROW][C]140[/C][C]0.613069329603474[/C][C]0.773861340793051[/C][C]0.386930670396526[/C][/ROW]
[ROW][C]141[/C][C]0.543317412468145[/C][C]0.91336517506371[/C][C]0.456682587531855[/C][/ROW]
[ROW][C]142[/C][C]0.456154993704211[/C][C]0.912309987408423[/C][C]0.543845006295789[/C][/ROW]
[ROW][C]143[/C][C]0.405471703876934[/C][C]0.810943407753868[/C][C]0.594528296123066[/C][/ROW]
[ROW][C]144[/C][C]0.312551990742469[/C][C]0.625103981484937[/C][C]0.687448009257531[/C][/ROW]
[ROW][C]145[/C][C]0.248839834498755[/C][C]0.49767966899751[/C][C]0.751160165501245[/C][/ROW]
[ROW][C]146[/C][C]0.176123282327660[/C][C]0.352246564655319[/C][C]0.82387671767234[/C][/ROW]
[ROW][C]147[/C][C]0.209021068295575[/C][C]0.418042136591151[/C][C]0.790978931704425[/C][/ROW]
[ROW][C]148[/C][C]0.848122652962786[/C][C]0.303754694074427[/C][C]0.151877347037214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1275149572627650.2550299145255300.872485042737235
90.06353130006594710.1270626001318940.936468699934053
100.08477512208235770.1695502441647150.915224877917642
110.04338532980955460.08677065961910930.956614670190445
120.01961127082802250.0392225416560450.980388729171978
130.3465716306215250.693143261243050.653428369378475
140.6967396025812050.6065207948375890.303260397418795
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190.3324685262301370.6649370524602730.667531473769863
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220.4989665906809760.9979331813619510.501033409319024
230.4297682693526710.8595365387053430.570231730647329
240.4164511487005410.8329022974010830.583548851299459
250.3833000930235950.766600186047190.616699906976405
260.3279954562988150.655990912597630.672004543701185
270.5989338988084720.8021322023830570.401066101191528
280.5382373366262640.9235253267474710.461762663373736
290.4786154445468230.9572308890936450.521384555453177
300.6487661983535750.702467603292850.351233801646425
310.7730778363494610.4538443273010790.226922163650539
320.7361237224116290.5277525551767420.263876277588371
330.694208777569020.611582444861960.30579122243098
340.6492218049057540.7015563901884930.350778195094246
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360.6573037293950.685392541210.342696270605
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390.5117227733966460.9765544532067080.488277226603354
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420.7534774468757550.4930451062484890.246522553124245
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450.9284635890764740.1430728218470520.0715364109235262
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480.967009976441790.06598004711642070.0329900235582103
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510.9573110574936350.08537788501273030.0426889425063651
520.9846797989436320.03064040211273690.0153202010563684
530.9877279118146270.02454417637074530.0122720881853727
540.9851445033038120.02971099339237610.0148554966961881
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560.989001851904360.02199629619128140.0109981480956407
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580.9862785673896980.02744286522060480.0137214326103024
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600.9854928729046740.02901425419065280.0145071270953264
610.985254084626630.02949183074674020.0147459153733701
620.9884509823610080.0230980352779830.0115490176389915
630.9858412443511230.02831751129775350.0141587556488767
640.9876782979979420.02464340400411540.0123217020020577
650.9841766385571920.03164672288561530.0158233614428077
660.983790986119050.03241802776190160.0162090138809508
670.9825650273993570.03486994520128650.0174349726006433
680.9863427953458180.02731440930836310.0136572046541815
690.9871929625597340.02561407488053270.0128070374402664
700.9833437488718270.03331250225634620.0166562511281731
710.9858948848065720.02821023038685540.0141051151934277
720.981947576048180.03610484790364090.0180524239518204
730.9775845219534220.04483095609315680.0224154780465784
740.9868841476373980.02623170472520460.0131158523626023
750.9826136056666120.03477278866677650.0173863943333883
760.9793503780796720.04129924384065590.0206496219203280
770.9728308172230860.05433836555382870.0271691827769143
780.9646435819921830.0707128360156330.0353564180078165
790.9784336347259520.04313273054809580.0215663652740479
800.976942917315980.04611416536803950.0230570826840197
810.9797171480569420.04056570388611640.0202828519430582
820.9774498980545040.04510020389099260.0225501019454963
830.9704187815154120.05916243696917540.0295812184845877
840.9647297587043680.07054048259126360.0352702412956318
850.991122696849520.01775460630096170.00887730315048086
860.9878421745794490.02431565084110290.0121578254205514
870.9837183780020190.03256324399596240.0162816219979812
880.9782744732975970.04345105340480540.0217255267024027
890.9729233648110180.05415327037796310.0270766351889815
900.964754481924640.07049103615072150.0352455180753608
910.9570652977459020.0858694045081960.042934702254098
920.9758310500079020.04833789998419510.0241689499920975
930.969205790209480.06158841958104130.0307942097905206
940.9639390167151540.07212196656969150.0360609832848457
950.9549825181978910.0900349636042170.0450174818021086
960.9450750364726160.1098499270547670.0549249635273837
970.9460726828906160.1078546342187690.0539273171093843
980.932651953461620.1346960930767610.0673480465383807
990.9359255482815810.1281489034368380.0640744517184188
1000.9214768552277340.1570462895445330.0785231447722663
1010.903705841388130.1925883172237400.0962941586118698
1020.8880967875491330.2238064249017340.111903212450867
1030.8939023652934110.2121952694131770.106097634706589
1040.9213019472310.1573961055380000.0786980527689998
1050.9205078767083370.1589842465833250.0794921232916627
1060.9005216005718260.1989567988563490.0994783994281743
1070.8835894426589970.2328211146820070.116410557341003
1080.8825346101099420.2349307797801150.117465389890058
1090.8860361460400420.2279277079199160.113963853959958
1100.9164465896825320.1671068206349350.0835534103174675
1110.9060606780894560.1878786438210880.0939393219105441
1120.916732749413090.1665345011738200.0832672505869099
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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level350.24822695035461NOK
10% type I error level510.361702127659574NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 35 & 0.24822695035461 & NOK \tabularnewline
10% type I error level & 51 & 0.361702127659574 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105358&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.24822695035461[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]0.361702127659574[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105358&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105358&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 level00OK
5% type I error level350.24822695035461NOK
10% type I error level510.361702127659574NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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