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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 computationFri, 18 Nov 2011 11:49:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/18/t1321634994iskztq53fsujquh.htm/, Retrieved Fri, 26 Apr 2024 05:49:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145477, Retrieved Fri, 26 Apr 2024 05:49:11 +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)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [test] [2010-11-22 21:34:59] [3635fb7041b1998c5a1332cf9de22bce]
-    D    [Multiple Regression] [Workshop 7, tutor...] [2010-11-22 21:53:32] [3635fb7041b1998c5a1332cf9de22bce]
-    D        [Multiple Regression] [] [2011-11-18 16:49:37] [f8ac047da1b1db86cbd9837decfb2b34] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	14	12
39	32	18	11
30	35	11	14
31	33	12	12
34	37	16	21
35	29	18	12
39	31	14	22
34	36	14	11
36	35	15	10
37	38	15	13
38	31	17	10
36	34	19	8
38	35	10	15
39	38	16	14
33	37	18	10
32	33	14	14
36	32	14	14
38	38	17	11
39	38	14	10
32	32	16	13
32	33	18	9.5
31	31	11	14
39	38	14	12
37	39	12	14
39	32	17	11
41	32	9	9
36	35	16	11
33	37	14	15
33	33	15	14
34	33	11	13
31	31	16	9
27	32	13	15
37	31	17	10
34	37	15	11
34	30	14	13
32	33	16	8
29	31	9	20
36	33	15	12
29	31	17	10
35	33	13	10
37	32	15	9
34	33	16	14
38	32	16	8
35	33	12	14
38	28	15	11
37	35	11	13
38	39	15	9
33	34	15	11
36	38	17	15
38	32	13	11
32	38	16	10
32	30	14	14
32	33	11	18
34	38	12	14
32	32	12	11
37	35	15	14.5
39	34	16	13
29	34	15	9
37	36	12	10
35	34	12	15
30	28	8	20
38	34	13	12
34	35	11	12
31	35	14	14
34	31	15	13
35	37	10	11
36	35	11	17
30	27	12	12
39	40	15	13
35	37	15	14
38	36	14	13
31	38	16	15
34	39	15	13
38	41	15	10
34	27	13	11
39	30	12	19
37	37	17	13
34	31	13	17
28	31	15	13
37	27	13	9
33	36	15	11
35	37	15	9
37	33	16	12
32	34	15	12
33	31	14	13
38	39	15	13
33	34	14	12
29	32	13	15
33	33	7	22
31	36	17	13
36	32	13	15
35	41	15	13
32	28	14	15
29	30	13	12.5
39	36	16	11
37	35	12	16
35	31	14	11
37	34	17	11
32	36	15	10
38	36	17	10
37	35	12	16
36	37	16	12
32	28	11	11
33	39	15	16
40	32	9	19
38	35	16	11
41	39	15	16
36	35	10	15
43	42	10	24
30	34	15	14
31	33	11	15
32	41	13	11
32	33	14	15
37	34	18	12
37	32	16	10
33	40	14	14
34	40	14	13
33	35	14	9
38	36	14	15
33	37	12	15
31	27	14	14
38	39	15	11
37	38	15	8
36	31	15	11
31	33	13	11
39	32	17	8
44	39	17	10
33	36	19	11
35	33	15	13
32	33	13	11
28	32	9	20
40	37	15	10
27	30	15	15
37	38	15	12
32	29	16	14
28	22	11	23
34	35	14	14
30	35	11	16
35	34	15	11
31	35	13	12
32	34	15	10
30	37	16	14
30	35	14	12
31	23	15	12
40	31	16	11
32	27	16	12
36	36	11	13
32	31	12	11
35	32	9	19
38	39	16	12
42	37	13	17
34	38	16	9
35	39	12	12
38	34	9	19
33	31	13	18
36	32	13	15
32	37	14	14
33	36	19	11
34	32	13	9
32	38	12	18
34	36	13	16
27	26	10	24
31	26	14	14
38	33	16	20
34	39	10	18
24	30	11	23
30	33	14	12
26	25	12	14
34	38	9	16
27	37	9	18
37	31	11	20
36	37	16	12
41	35	9	12
29	25	13	17
36	28	16	13
32	35	13	9
37	33	9	16
30	30	12	18
31	31	16	10
38	37	11	14
36	36	14	11
35	30	13	9
31	36	15	11
38	32	14	10
22	28	16	11
32	36	13	19
36	34	14	14
39	31	15	12
28	28	13	14
32	36	11	21
32	36	11	13
38	40	14	10
32	33	15	15
35	37	11	16
32	32	15	14
37	38	12	12
34	31	14	19
33	37	14	15
33	33	8	19
26	32	13	13
30	30	9	17
24	30	15	12
34	31	17	11
34	32	13	14
33	34	15	11
34	36	15	13
35	37	14	12
35	36	16	15
36	33	13	14
34	33	16	12
34	33	9	17
41	44	16	11
32	39	11	18
30	32	10	13
35	35	11	17
28	25	15	13
33	35	17	11
39	34	14	12
36	35	8	22
36	39	15	14
35	33	11	12
38	36	16	12
33	32	10	17
31	32	15	9
34	36	9	21
32	36	16	10
31	32	19	11
33	34	12	12
34	33	8	23
34	35	11	13
34	30	14	12
33	38	9	16
32	34	15	9
41	33	13	17
34	32	16	9
36	31	11	14
37	30	12	17
36	27	13	13
29	31	10	11
37	30	11	12
27	32	12	10
35	35	8	19
28	28	12	16
35	33	12	16
37	31	15	14
29	35	11	20
32	35	13	15
36	32	14	23
19	21	10	20
21	20	12	16
31	34	15	14
33	32	13	17
36	34	13	11
33	32	13	13
37	33	12	17
34	33	12	15
35	37	9	21
31	32	9	18
37	34	15	15
35	30	10	8
27	30	14	12
34	38	15	12
40	36	7	22
29	32	14	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&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 time8 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
depression[t] = + 26.4521079693122 -0.0502643542894257connected[t] -0.0104862319110606separated[t] -0.797594383277412happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
depression[t] =  +  26.4521079693122 -0.0502643542894257connected[t] -0.0104862319110606separated[t] -0.797594383277412happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]depression[t] =  +  26.4521079693122 -0.0502643542894257connected[t] -0.0104862319110606separated[t] -0.797594383277412happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&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
depression[t] = + 26.4521079693122 -0.0502643542894257connected[t] -0.0104862319110606separated[t] -0.797594383277412happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)26.45210796931221.977113.379200
connected-0.05026435428942570.051801-0.97030.3327770.166388
separated-0.01048623191106060.052944-0.19810.843150.421575
happiness-0.7975943832774120.070513-11.311400

\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) & 26.4521079693122 & 1.9771 & 13.3792 & 0 & 0 \tabularnewline
connected & -0.0502643542894257 & 0.051801 & -0.9703 & 0.332777 & 0.166388 \tabularnewline
separated & -0.0104862319110606 & 0.052944 & -0.1981 & 0.84315 & 0.421575 \tabularnewline
happiness & -0.797594383277412 & 0.070513 & -11.3114 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&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]26.4521079693122[/C][C]1.9771[/C][C]13.3792[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]connected[/C][C]-0.0502643542894257[/C][C]0.051801[/C][C]-0.9703[/C][C]0.332777[/C][C]0.166388[/C][/ROW]
[ROW][C]separated[/C][C]-0.0104862319110606[/C][C]0.052944[/C][C]-0.1981[/C][C]0.84315[/C][C]0.421575[/C][/ROW]
[ROW][C]happiness[/C][C]-0.797594383277412[/C][C]0.070513[/C][C]-11.3114[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&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)26.45210796931221.977113.379200
connected-0.05026435428942570.051801-0.97030.3327770.166388
separated-0.01048623191106060.052944-0.19810.843150.421575
happiness-0.7975943832774120.070513-11.311400






Multiple Linear Regression - Regression Statistics
Multiple R0.586044831497431
R-squared0.343448544524852
Adjusted R-squared0.335872950807831
F-TEST (value)45.3361884697153
F-TEST (DF numerator)3
F-TEST (DF denominator)260
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.82748287286659
Sum Squared Residuals2078.61144305201

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.586044831497431 \tabularnewline
R-squared & 0.343448544524852 \tabularnewline
Adjusted R-squared & 0.335872950807831 \tabularnewline
F-TEST (value) & 45.3361884697153 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 260 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.82748287286659 \tabularnewline
Sum Squared Residuals & 2078.61144305201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.586044831497431[/C][/ROW]
[ROW][C]R-squared[/C][C]0.343448544524852[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.335872950807831[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]45.3361884697153[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]260[/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]2.82748287286659[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2078.61144305201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&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 R0.586044831497431
R-squared0.343448544524852
Adjusted R-squared0.335872950807831
F-TEST (value)45.3361884697153
F-TEST (DF numerator)3
F-TEST (DF denominator)260
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.82748287286659
Sum Squared Residuals2078.61144305201






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.8264712649418-0.826471264941798
2119.79953983187731.2004601681227
31415.8036210076908-1.80362100769083
41214.9767347339461-2.97673473394612
52111.5936192103249.40638078967605
61210.03205594476821.96794405523181
72213.0004035968988.99959640310199
81113.1992942087898-2.19929420878984
91012.3116573488446-2.31165734884463
101312.2299342988220.770065701177975
111010.6578848013552-0.657884801355201
1289.13176604764605-1.13176604764605
131516.1991005566528-1.19910055665284
141411.33181120696582.66818879303424
151010.0486947980586-0.0486947980585534
161413.33128161310190.668718386898132
171413.14071042785520.859289572144774
181110.58448117797780.415518822022224
191012.9269999735206-2.92699997352059
201311.74657907845811.25342092154189
219.510.1409040799922-0.640904079992222
221415.7953015810456-1.79530158104565
231212.9269999735206-0.926999973520585
241414.6122312167432-0.612231216743199
251110.59713421515470.402865784845285
26916.8773605727952-7.87736057279515
271111.5140629655672-0.514062965567221
281513.23907233116821.7609276688318
291412.4834228755351.51657712446497
301315.6235360543553-2.62353605435525
31911.8073296646586-2.80732966465859
321514.39068399973750.609316000262532
331010.7081491556446-0.708149155644626
341112.3912135936014-1.39121359360136
351313.2622116002562-0.262211600256198
36811.736092846547-3.73609284654704
372017.49101905617932.50898094382068
381212.3326298126668-0.332629812666754
391011.11026398996-1.11026398996003
401013.978082933511-3.978082933511
41912.2928516902884-3.29285169028839
421411.63556413796822.36443586203181
43811.4449929527216-3.44499295272155
441414.7756773167884-0.775677316788414
451112.2845322636432-1.28453226364321
461315.4517705276649-2.45177052766485
47912.1691837126215-3.16918371262154
481112.472936643624-1.47293664362397
491510.68500988655664.31499011344337
501113.8377761025538-2.83777610255379
511011.6836616869917-1.68366168699174
521413.36274030883510.63725969116495
531815.72406476293412.2759352370659
541414.7735105115225-0.773510511522537
551114.9369566115678-3.93695661156775
5614.512.26139299455522.23860700544479
571311.373756134611.62624386539
58912.6739940607817-3.67399406078167
591014.6436899124764-4.64368991247638
601514.76519108487740.234808915122647
612018.26980778090051.73019221909951
621213.8168036387317-1.81680363873167
631215.6025635905331-3.60256359053313
641413.36057350356920.639426496430828
651312.45413098506770.545869014932274
661116.328921155699-5.32892115569899
671715.50203488195431.49796511804572
681215.0899164797019-3.08991647970191
691312.10843312642110.891566873578947
701412.34094923931191.65905076068806
711312.99823679163210.00176320836786771
721511.73392604128123.26607395871883
731312.37024112977920.629758870220758
741012.1482112487994-2.14821124879942
751114.0912646792668-3.09126467926679
761914.60607859536394.39392140463611
771310.64523176417832.35476823582174
781714.04931975162252.95068024837745
791312.75571711080430.244282889195719
80913.9404716163985-4.94047161639851
811112.4519641798018-1.45196417980185
82912.3409492393119-3.34094923931194
831211.48477107509990.515228924900084
841212.5232009979134-0.523200997913396
851313.3019897226346-0.301989722634563
861312.16918371262150.83081628737846
871213.2705310269014-1.27053102690138
881514.29015529115860.709844708841383
892218.86417794175433.13582205824568
901310.95730412182592.04269587817412
911513.93830481113261.06169518886736
921312.29900431166770.700995688332305
931513.38371277265721.61628722734283
9412.514.3111277549807-1.81112775498074
951111.3527836707879-0.352783670787884
961614.65417614438741.34582385561256
971113.2014610140557-2.20146101405571
981110.67669045991140.323309540088555
991012.5022285340913-2.50222853409127
1001010.6054536417999-0.605453641799898
1011614.65417614438741.34582385561256
1021211.49309050174510.5069094982549
1031115.7764959224894-4.77649592248941
1041612.42050548406873.57949451593133
1051916.92762492708462.07237507291542
1061111.4135342569884-0.41353425698837
1071612.01839064975333.98160935024674
1081516.2996292652317-1.29962926523169
1092415.87437516182838.12562483817171
1101412.62372970649221.37627029350775
1111515.7743291172235-0.774329117223528
1121114.0449861410908-3.0449861410908
1131513.33128161310191.66871838689813
114129.879096076634032.12090392336597
1151011.495257307011-1.49525730701098
1161413.2076136354350.792386364564982
1171313.1573492811456-0.157349281145593
118913.2600447949903-4.26004479499032
1191512.99823679163212.00176320836787
1201514.8342610977230.165738902276977
1211413.44446335885770.555536641142343
1221112.1691837126215-1.16918371262154
123812.229934298822-4.22993429882202
1241112.3536022764889-1.35360227648888
1251114.1791403506687-3.17914035066871
126810.5971342151547-2.59713421515471
1271010.2724088203302-0.272408820330162
128119.26158664669221.7384133533078
1291312.38289416695620.617105833043821
1301114.1288759963793-3.12887599637928
1312017.53079717855772.46920282144231
1321012.0896274678648-2.08962746786481
1331512.81646769700482.18353230299523
1341212.229934298822-0.229934298822025
1351411.77803777419132.22196222580871
1362316.04047073111356.95952926888653
1371413.20978044070090.790219559299104
1381615.80362100769080.196378992309167
1391112.3724079350451-1.37240793504512
1401214.1581678868466-2.15816788684658
1411012.5232009979134-2.5232009979134
1421411.79467662748172.20532337251835
1431213.4108378578586-1.4108378578586
1441212.6888139032245-0.688813903224488
1451111.3549504760538-0.354950476053761
1461211.79901023801340.200989761986592
1471315.4915486500432-2.49154865004322
1481114.9474428434788-3.94744284347881
1491917.17894669853171.82105330146829
1501211.37158932934410.628410670655873
1511713.58428752584083.41571247415922
152911.5831329784129-2.58313297841289
1531214.7127599253221-2.71275992532205
1541917.00718117184131.99281882815869
1551814.0995841059123.90041589408802
1561513.93830481113261.06169518886736
1571413.28933668545760.710663314542374
158119.26158664669221.7384133533078
159914.0388335197115-5.03883351971149
1601814.87403922010143.12596077989861
1611613.99688859206722.00311140793275
1622416.84638454103617.15361545896393
1631413.45494959076870.545050409231282
1642011.43450672081058.56549327918951
1651816.35821304616631.6417869538337
1662316.15763829298276.84236170701731
1671213.4318103216807-1.43181032168072
1681415.3119463606817-1.31194636068173
1691617.1662936613548-1.16629366135477
1701817.52863037329180.471369626708189
1712015.49371545530914.50628454469091
1721211.49309050174510.5069094982549
1731216.845901877062-4.84590187706197
1741714.3635589145362.63644108546396
1751311.58746658894461.41253341105535
176914.1079035325572-5.10790353255716
1771617.0679317580418-1.0679317580418
1781815.05845778396872.94154221603128
1791011.8073296646586-1.80732966465859
1801415.3805337095533-1.38053370955331
1811113.098765500211-2.09876550021098
182914.0095416292442-5.00954162924419
1831112.5524928883807-1.5524928883807
1841013.0401817192764-3.04018171927638
1851112.2911675489966-1.2911675489966
1861914.09741730064614.9025826993539
1871413.11973796403310.880262035966895
1881212.2028092136206-0.202809213620598
1891414.3823645730923-0.382364573092285
1902115.69260606720095.30739393279908
1911315.6926060672009-2.69260606720092
1921012.9562918639879-2.95629186398789
1931512.53368722982452.46631277017554
1941615.53132677242160.468673227578417
1951412.54417346173551.45582653826448
1961214.6227174486543-2.62271744865426
1971913.25172536834515.74827463165486
1981513.23907233116821.7609276688318
1991918.06658355847690.933416441523088
2001314.4409483540269-1.44094835402689
2011717.451240933801-0.451240933800959
2021212.967260759873-0.967260759873043
2031110.85894221851290.141057781487097
2041414.0388335197115-0.0388335197114888
2051112.472936643624-1.47293664362397
2061312.40169982551240.598300174487577
2071213.1385436225893-1.13854362258935
2081511.55384108794563.44615891205441
2091413.92781857922160.0721814207784232
2101211.63556413796820.364435862031807
2111717.2187248209101-0.218724820910074
2121111.1683651069205-0.168365106920547
2131815.66114737146772.33885262853226
2141316.6326740867014-3.63267408670143
2151715.55229923624371.4477007637563
2161312.81863450227060.181365497729356
2171110.86726164515810.132738354841914
2181212.9689449011648-0.968944901164828
2192217.89481803178654.10518196821349
2201412.26971242120041.73028757879961
2211215.5732717000658-3.57327170006583
2221211.40304802507730.596951974922691
2231716.48188102383320.518118976166851
224912.5944378160249-3.59443781602494
2252117.18726612517693.81273387482311
2261011.7046341508139-1.70463415081386
227119.40406028291531.5959397170847
2281214.8657197934562-2.86571979345621
2292318.01631920418754.98368079581252
2301315.6025635905331-2.60256359053313
2311213.2622116002562-1.2622116002562
2321617.2165580156442-1.2165580156442
233912.5232009979134-3.5232009979134
2341713.67649680777443.32350319222555
235911.6460503698793-2.64605036987925
2361415.5439798095985-1.54397980959852
2371714.70660730394272.29339269605726
2381313.9907359706879-0.99073597068794
2391116.6934246729019-5.69342467290191
2401215.5042016872202-3.50420168722016
2411015.1882783830149-5.18827838301488
2421917.94508238607591.05491761392406
2431615.17995895636970.820041043630303
2441614.77567731678841.22432268321159
2451412.30333792219951.69666207780055
2462015.85388536198034.14611463801974
2471514.10790353255720.892096467442842
2482313.14071042785529.85928957214477
2492017.30093053490682.69906946509322
2501615.61569929168420.384300708315839
2511412.57346535220281.42653464779718
2521714.08909787400092.91090212599909
2531113.9173323473105-2.91733234731052
2541314.0890978740009-1.08909787400091
2551714.67514860820962.32485139179044
2561514.82594167107780.174058328922161
2572117.12651553897643.87348446102359
2581817.38000411568940.619995884310588
2591512.27187922646632.72812077353373
260816.4023247790764-8.40232477907642
2611213.6140620802822-1.61406208028218
2621212.3807273616903-0.380727361690302
2632218.48086876599523.51913123400484
2641213.4925609078812-1.49256090788121

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.8264712649418 & -0.826471264941798 \tabularnewline
2 & 11 & 9.7995398318773 & 1.2004601681227 \tabularnewline
3 & 14 & 15.8036210076908 & -1.80362100769083 \tabularnewline
4 & 12 & 14.9767347339461 & -2.97673473394612 \tabularnewline
5 & 21 & 11.593619210324 & 9.40638078967605 \tabularnewline
6 & 12 & 10.0320559447682 & 1.96794405523181 \tabularnewline
7 & 22 & 13.000403596898 & 8.99959640310199 \tabularnewline
8 & 11 & 13.1992942087898 & -2.19929420878984 \tabularnewline
9 & 10 & 12.3116573488446 & -2.31165734884463 \tabularnewline
10 & 13 & 12.229934298822 & 0.770065701177975 \tabularnewline
11 & 10 & 10.6578848013552 & -0.657884801355201 \tabularnewline
12 & 8 & 9.13176604764605 & -1.13176604764605 \tabularnewline
13 & 15 & 16.1991005566528 & -1.19910055665284 \tabularnewline
14 & 14 & 11.3318112069658 & 2.66818879303424 \tabularnewline
15 & 10 & 10.0486947980586 & -0.0486947980585534 \tabularnewline
16 & 14 & 13.3312816131019 & 0.668718386898132 \tabularnewline
17 & 14 & 13.1407104278552 & 0.859289572144774 \tabularnewline
18 & 11 & 10.5844811779778 & 0.415518822022224 \tabularnewline
19 & 10 & 12.9269999735206 & -2.92699997352059 \tabularnewline
20 & 13 & 11.7465790784581 & 1.25342092154189 \tabularnewline
21 & 9.5 & 10.1409040799922 & -0.640904079992222 \tabularnewline
22 & 14 & 15.7953015810456 & -1.79530158104565 \tabularnewline
23 & 12 & 12.9269999735206 & -0.926999973520585 \tabularnewline
24 & 14 & 14.6122312167432 & -0.612231216743199 \tabularnewline
25 & 11 & 10.5971342151547 & 0.402865784845285 \tabularnewline
26 & 9 & 16.8773605727952 & -7.87736057279515 \tabularnewline
27 & 11 & 11.5140629655672 & -0.514062965567221 \tabularnewline
28 & 15 & 13.2390723311682 & 1.7609276688318 \tabularnewline
29 & 14 & 12.483422875535 & 1.51657712446497 \tabularnewline
30 & 13 & 15.6235360543553 & -2.62353605435525 \tabularnewline
31 & 9 & 11.8073296646586 & -2.80732966465859 \tabularnewline
32 & 15 & 14.3906839997375 & 0.609316000262532 \tabularnewline
33 & 10 & 10.7081491556446 & -0.708149155644626 \tabularnewline
34 & 11 & 12.3912135936014 & -1.39121359360136 \tabularnewline
35 & 13 & 13.2622116002562 & -0.262211600256198 \tabularnewline
36 & 8 & 11.736092846547 & -3.73609284654704 \tabularnewline
37 & 20 & 17.4910190561793 & 2.50898094382068 \tabularnewline
38 & 12 & 12.3326298126668 & -0.332629812666754 \tabularnewline
39 & 10 & 11.11026398996 & -1.11026398996003 \tabularnewline
40 & 10 & 13.978082933511 & -3.978082933511 \tabularnewline
41 & 9 & 12.2928516902884 & -3.29285169028839 \tabularnewline
42 & 14 & 11.6355641379682 & 2.36443586203181 \tabularnewline
43 & 8 & 11.4449929527216 & -3.44499295272155 \tabularnewline
44 & 14 & 14.7756773167884 & -0.775677316788414 \tabularnewline
45 & 11 & 12.2845322636432 & -1.28453226364321 \tabularnewline
46 & 13 & 15.4517705276649 & -2.45177052766485 \tabularnewline
47 & 9 & 12.1691837126215 & -3.16918371262154 \tabularnewline
48 & 11 & 12.472936643624 & -1.47293664362397 \tabularnewline
49 & 15 & 10.6850098865566 & 4.31499011344337 \tabularnewline
50 & 11 & 13.8377761025538 & -2.83777610255379 \tabularnewline
51 & 10 & 11.6836616869917 & -1.68366168699174 \tabularnewline
52 & 14 & 13.3627403088351 & 0.63725969116495 \tabularnewline
53 & 18 & 15.7240647629341 & 2.2759352370659 \tabularnewline
54 & 14 & 14.7735105115225 & -0.773510511522537 \tabularnewline
55 & 11 & 14.9369566115678 & -3.93695661156775 \tabularnewline
56 & 14.5 & 12.2613929945552 & 2.23860700544479 \tabularnewline
57 & 13 & 11.37375613461 & 1.62624386539 \tabularnewline
58 & 9 & 12.6739940607817 & -3.67399406078167 \tabularnewline
59 & 10 & 14.6436899124764 & -4.64368991247638 \tabularnewline
60 & 15 & 14.7651910848774 & 0.234808915122647 \tabularnewline
61 & 20 & 18.2698077809005 & 1.73019221909951 \tabularnewline
62 & 12 & 13.8168036387317 & -1.81680363873167 \tabularnewline
63 & 12 & 15.6025635905331 & -3.60256359053313 \tabularnewline
64 & 14 & 13.3605735035692 & 0.639426496430828 \tabularnewline
65 & 13 & 12.4541309850677 & 0.545869014932274 \tabularnewline
66 & 11 & 16.328921155699 & -5.32892115569899 \tabularnewline
67 & 17 & 15.5020348819543 & 1.49796511804572 \tabularnewline
68 & 12 & 15.0899164797019 & -3.08991647970191 \tabularnewline
69 & 13 & 12.1084331264211 & 0.891566873578947 \tabularnewline
70 & 14 & 12.3409492393119 & 1.65905076068806 \tabularnewline
71 & 13 & 12.9982367916321 & 0.00176320836786771 \tabularnewline
72 & 15 & 11.7339260412812 & 3.26607395871883 \tabularnewline
73 & 13 & 12.3702411297792 & 0.629758870220758 \tabularnewline
74 & 10 & 12.1482112487994 & -2.14821124879942 \tabularnewline
75 & 11 & 14.0912646792668 & -3.09126467926679 \tabularnewline
76 & 19 & 14.6060785953639 & 4.39392140463611 \tabularnewline
77 & 13 & 10.6452317641783 & 2.35476823582174 \tabularnewline
78 & 17 & 14.0493197516225 & 2.95068024837745 \tabularnewline
79 & 13 & 12.7557171108043 & 0.244282889195719 \tabularnewline
80 & 9 & 13.9404716163985 & -4.94047161639851 \tabularnewline
81 & 11 & 12.4519641798018 & -1.45196417980185 \tabularnewline
82 & 9 & 12.3409492393119 & -3.34094923931194 \tabularnewline
83 & 12 & 11.4847710750999 & 0.515228924900084 \tabularnewline
84 & 12 & 12.5232009979134 & -0.523200997913396 \tabularnewline
85 & 13 & 13.3019897226346 & -0.301989722634563 \tabularnewline
86 & 13 & 12.1691837126215 & 0.83081628737846 \tabularnewline
87 & 12 & 13.2705310269014 & -1.27053102690138 \tabularnewline
88 & 15 & 14.2901552911586 & 0.709844708841383 \tabularnewline
89 & 22 & 18.8641779417543 & 3.13582205824568 \tabularnewline
90 & 13 & 10.9573041218259 & 2.04269587817412 \tabularnewline
91 & 15 & 13.9383048111326 & 1.06169518886736 \tabularnewline
92 & 13 & 12.2990043116677 & 0.700995688332305 \tabularnewline
93 & 15 & 13.3837127726572 & 1.61628722734283 \tabularnewline
94 & 12.5 & 14.3111277549807 & -1.81112775498074 \tabularnewline
95 & 11 & 11.3527836707879 & -0.352783670787884 \tabularnewline
96 & 16 & 14.6541761443874 & 1.34582385561256 \tabularnewline
97 & 11 & 13.2014610140557 & -2.20146101405571 \tabularnewline
98 & 11 & 10.6766904599114 & 0.323309540088555 \tabularnewline
99 & 10 & 12.5022285340913 & -2.50222853409127 \tabularnewline
100 & 10 & 10.6054536417999 & -0.605453641799898 \tabularnewline
101 & 16 & 14.6541761443874 & 1.34582385561256 \tabularnewline
102 & 12 & 11.4930905017451 & 0.5069094982549 \tabularnewline
103 & 11 & 15.7764959224894 & -4.77649592248941 \tabularnewline
104 & 16 & 12.4205054840687 & 3.57949451593133 \tabularnewline
105 & 19 & 16.9276249270846 & 2.07237507291542 \tabularnewline
106 & 11 & 11.4135342569884 & -0.41353425698837 \tabularnewline
107 & 16 & 12.0183906497533 & 3.98160935024674 \tabularnewline
108 & 15 & 16.2996292652317 & -1.29962926523169 \tabularnewline
109 & 24 & 15.8743751618283 & 8.12562483817171 \tabularnewline
110 & 14 & 12.6237297064922 & 1.37627029350775 \tabularnewline
111 & 15 & 15.7743291172235 & -0.774329117223528 \tabularnewline
112 & 11 & 14.0449861410908 & -3.0449861410908 \tabularnewline
113 & 15 & 13.3312816131019 & 1.66871838689813 \tabularnewline
114 & 12 & 9.87909607663403 & 2.12090392336597 \tabularnewline
115 & 10 & 11.495257307011 & -1.49525730701098 \tabularnewline
116 & 14 & 13.207613635435 & 0.792386364564982 \tabularnewline
117 & 13 & 13.1573492811456 & -0.157349281145593 \tabularnewline
118 & 9 & 13.2600447949903 & -4.26004479499032 \tabularnewline
119 & 15 & 12.9982367916321 & 2.00176320836787 \tabularnewline
120 & 15 & 14.834261097723 & 0.165738902276977 \tabularnewline
121 & 14 & 13.4444633588577 & 0.555536641142343 \tabularnewline
122 & 11 & 12.1691837126215 & -1.16918371262154 \tabularnewline
123 & 8 & 12.229934298822 & -4.22993429882202 \tabularnewline
124 & 11 & 12.3536022764889 & -1.35360227648888 \tabularnewline
125 & 11 & 14.1791403506687 & -3.17914035066871 \tabularnewline
126 & 8 & 10.5971342151547 & -2.59713421515471 \tabularnewline
127 & 10 & 10.2724088203302 & -0.272408820330162 \tabularnewline
128 & 11 & 9.2615866466922 & 1.7384133533078 \tabularnewline
129 & 13 & 12.3828941669562 & 0.617105833043821 \tabularnewline
130 & 11 & 14.1288759963793 & -3.12887599637928 \tabularnewline
131 & 20 & 17.5307971785577 & 2.46920282144231 \tabularnewline
132 & 10 & 12.0896274678648 & -2.08962746786481 \tabularnewline
133 & 15 & 12.8164676970048 & 2.18353230299523 \tabularnewline
134 & 12 & 12.229934298822 & -0.229934298822025 \tabularnewline
135 & 14 & 11.7780377741913 & 2.22196222580871 \tabularnewline
136 & 23 & 16.0404707311135 & 6.95952926888653 \tabularnewline
137 & 14 & 13.2097804407009 & 0.790219559299104 \tabularnewline
138 & 16 & 15.8036210076908 & 0.196378992309167 \tabularnewline
139 & 11 & 12.3724079350451 & -1.37240793504512 \tabularnewline
140 & 12 & 14.1581678868466 & -2.15816788684658 \tabularnewline
141 & 10 & 12.5232009979134 & -2.5232009979134 \tabularnewline
142 & 14 & 11.7946766274817 & 2.20532337251835 \tabularnewline
143 & 12 & 13.4108378578586 & -1.4108378578586 \tabularnewline
144 & 12 & 12.6888139032245 & -0.688813903224488 \tabularnewline
145 & 11 & 11.3549504760538 & -0.354950476053761 \tabularnewline
146 & 12 & 11.7990102380134 & 0.200989761986592 \tabularnewline
147 & 13 & 15.4915486500432 & -2.49154865004322 \tabularnewline
148 & 11 & 14.9474428434788 & -3.94744284347881 \tabularnewline
149 & 19 & 17.1789466985317 & 1.82105330146829 \tabularnewline
150 & 12 & 11.3715893293441 & 0.628410670655873 \tabularnewline
151 & 17 & 13.5842875258408 & 3.41571247415922 \tabularnewline
152 & 9 & 11.5831329784129 & -2.58313297841289 \tabularnewline
153 & 12 & 14.7127599253221 & -2.71275992532205 \tabularnewline
154 & 19 & 17.0071811718413 & 1.99281882815869 \tabularnewline
155 & 18 & 14.099584105912 & 3.90041589408802 \tabularnewline
156 & 15 & 13.9383048111326 & 1.06169518886736 \tabularnewline
157 & 14 & 13.2893366854576 & 0.710663314542374 \tabularnewline
158 & 11 & 9.2615866466922 & 1.7384133533078 \tabularnewline
159 & 9 & 14.0388335197115 & -5.03883351971149 \tabularnewline
160 & 18 & 14.8740392201014 & 3.12596077989861 \tabularnewline
161 & 16 & 13.9968885920672 & 2.00311140793275 \tabularnewline
162 & 24 & 16.8463845410361 & 7.15361545896393 \tabularnewline
163 & 14 & 13.4549495907687 & 0.545050409231282 \tabularnewline
164 & 20 & 11.4345067208105 & 8.56549327918951 \tabularnewline
165 & 18 & 16.3582130461663 & 1.6417869538337 \tabularnewline
166 & 23 & 16.1576382929827 & 6.84236170701731 \tabularnewline
167 & 12 & 13.4318103216807 & -1.43181032168072 \tabularnewline
168 & 14 & 15.3119463606817 & -1.31194636068173 \tabularnewline
169 & 16 & 17.1662936613548 & -1.16629366135477 \tabularnewline
170 & 18 & 17.5286303732918 & 0.471369626708189 \tabularnewline
171 & 20 & 15.4937154553091 & 4.50628454469091 \tabularnewline
172 & 12 & 11.4930905017451 & 0.5069094982549 \tabularnewline
173 & 12 & 16.845901877062 & -4.84590187706197 \tabularnewline
174 & 17 & 14.363558914536 & 2.63644108546396 \tabularnewline
175 & 13 & 11.5874665889446 & 1.41253341105535 \tabularnewline
176 & 9 & 14.1079035325572 & -5.10790353255716 \tabularnewline
177 & 16 & 17.0679317580418 & -1.0679317580418 \tabularnewline
178 & 18 & 15.0584577839687 & 2.94154221603128 \tabularnewline
179 & 10 & 11.8073296646586 & -1.80732966465859 \tabularnewline
180 & 14 & 15.3805337095533 & -1.38053370955331 \tabularnewline
181 & 11 & 13.098765500211 & -2.09876550021098 \tabularnewline
182 & 9 & 14.0095416292442 & -5.00954162924419 \tabularnewline
183 & 11 & 12.5524928883807 & -1.5524928883807 \tabularnewline
184 & 10 & 13.0401817192764 & -3.04018171927638 \tabularnewline
185 & 11 & 12.2911675489966 & -1.2911675489966 \tabularnewline
186 & 19 & 14.0974173006461 & 4.9025826993539 \tabularnewline
187 & 14 & 13.1197379640331 & 0.880262035966895 \tabularnewline
188 & 12 & 12.2028092136206 & -0.202809213620598 \tabularnewline
189 & 14 & 14.3823645730923 & -0.382364573092285 \tabularnewline
190 & 21 & 15.6926060672009 & 5.30739393279908 \tabularnewline
191 & 13 & 15.6926060672009 & -2.69260606720092 \tabularnewline
192 & 10 & 12.9562918639879 & -2.95629186398789 \tabularnewline
193 & 15 & 12.5336872298245 & 2.46631277017554 \tabularnewline
194 & 16 & 15.5313267724216 & 0.468673227578417 \tabularnewline
195 & 14 & 12.5441734617355 & 1.45582653826448 \tabularnewline
196 & 12 & 14.6227174486543 & -2.62271744865426 \tabularnewline
197 & 19 & 13.2517253683451 & 5.74827463165486 \tabularnewline
198 & 15 & 13.2390723311682 & 1.7609276688318 \tabularnewline
199 & 19 & 18.0665835584769 & 0.933416441523088 \tabularnewline
200 & 13 & 14.4409483540269 & -1.44094835402689 \tabularnewline
201 & 17 & 17.451240933801 & -0.451240933800959 \tabularnewline
202 & 12 & 12.967260759873 & -0.967260759873043 \tabularnewline
203 & 11 & 10.8589422185129 & 0.141057781487097 \tabularnewline
204 & 14 & 14.0388335197115 & -0.0388335197114888 \tabularnewline
205 & 11 & 12.472936643624 & -1.47293664362397 \tabularnewline
206 & 13 & 12.4016998255124 & 0.598300174487577 \tabularnewline
207 & 12 & 13.1385436225893 & -1.13854362258935 \tabularnewline
208 & 15 & 11.5538410879456 & 3.44615891205441 \tabularnewline
209 & 14 & 13.9278185792216 & 0.0721814207784232 \tabularnewline
210 & 12 & 11.6355641379682 & 0.364435862031807 \tabularnewline
211 & 17 & 17.2187248209101 & -0.218724820910074 \tabularnewline
212 & 11 & 11.1683651069205 & -0.168365106920547 \tabularnewline
213 & 18 & 15.6611473714677 & 2.33885262853226 \tabularnewline
214 & 13 & 16.6326740867014 & -3.63267408670143 \tabularnewline
215 & 17 & 15.5522992362437 & 1.4477007637563 \tabularnewline
216 & 13 & 12.8186345022706 & 0.181365497729356 \tabularnewline
217 & 11 & 10.8672616451581 & 0.132738354841914 \tabularnewline
218 & 12 & 12.9689449011648 & -0.968944901164828 \tabularnewline
219 & 22 & 17.8948180317865 & 4.10518196821349 \tabularnewline
220 & 14 & 12.2697124212004 & 1.73028757879961 \tabularnewline
221 & 12 & 15.5732717000658 & -3.57327170006583 \tabularnewline
222 & 12 & 11.4030480250773 & 0.596951974922691 \tabularnewline
223 & 17 & 16.4818810238332 & 0.518118976166851 \tabularnewline
224 & 9 & 12.5944378160249 & -3.59443781602494 \tabularnewline
225 & 21 & 17.1872661251769 & 3.81273387482311 \tabularnewline
226 & 10 & 11.7046341508139 & -1.70463415081386 \tabularnewline
227 & 11 & 9.4040602829153 & 1.5959397170847 \tabularnewline
228 & 12 & 14.8657197934562 & -2.86571979345621 \tabularnewline
229 & 23 & 18.0163192041875 & 4.98368079581252 \tabularnewline
230 & 13 & 15.6025635905331 & -2.60256359053313 \tabularnewline
231 & 12 & 13.2622116002562 & -1.2622116002562 \tabularnewline
232 & 16 & 17.2165580156442 & -1.2165580156442 \tabularnewline
233 & 9 & 12.5232009979134 & -3.5232009979134 \tabularnewline
234 & 17 & 13.6764968077744 & 3.32350319222555 \tabularnewline
235 & 9 & 11.6460503698793 & -2.64605036987925 \tabularnewline
236 & 14 & 15.5439798095985 & -1.54397980959852 \tabularnewline
237 & 17 & 14.7066073039427 & 2.29339269605726 \tabularnewline
238 & 13 & 13.9907359706879 & -0.99073597068794 \tabularnewline
239 & 11 & 16.6934246729019 & -5.69342467290191 \tabularnewline
240 & 12 & 15.5042016872202 & -3.50420168722016 \tabularnewline
241 & 10 & 15.1882783830149 & -5.18827838301488 \tabularnewline
242 & 19 & 17.9450823860759 & 1.05491761392406 \tabularnewline
243 & 16 & 15.1799589563697 & 0.820041043630303 \tabularnewline
244 & 16 & 14.7756773167884 & 1.22432268321159 \tabularnewline
245 & 14 & 12.3033379221995 & 1.69666207780055 \tabularnewline
246 & 20 & 15.8538853619803 & 4.14611463801974 \tabularnewline
247 & 15 & 14.1079035325572 & 0.892096467442842 \tabularnewline
248 & 23 & 13.1407104278552 & 9.85928957214477 \tabularnewline
249 & 20 & 17.3009305349068 & 2.69906946509322 \tabularnewline
250 & 16 & 15.6156992916842 & 0.384300708315839 \tabularnewline
251 & 14 & 12.5734653522028 & 1.42653464779718 \tabularnewline
252 & 17 & 14.0890978740009 & 2.91090212599909 \tabularnewline
253 & 11 & 13.9173323473105 & -2.91733234731052 \tabularnewline
254 & 13 & 14.0890978740009 & -1.08909787400091 \tabularnewline
255 & 17 & 14.6751486082096 & 2.32485139179044 \tabularnewline
256 & 15 & 14.8259416710778 & 0.174058328922161 \tabularnewline
257 & 21 & 17.1265155389764 & 3.87348446102359 \tabularnewline
258 & 18 & 17.3800041156894 & 0.619995884310588 \tabularnewline
259 & 15 & 12.2718792264663 & 2.72812077353373 \tabularnewline
260 & 8 & 16.4023247790764 & -8.40232477907642 \tabularnewline
261 & 12 & 13.6140620802822 & -1.61406208028218 \tabularnewline
262 & 12 & 12.3807273616903 & -0.380727361690302 \tabularnewline
263 & 22 & 18.4808687659952 & 3.51913123400484 \tabularnewline
264 & 12 & 13.4925609078812 & -1.49256090788121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&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]12[/C][C]12.8264712649418[/C][C]-0.826471264941798[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.7995398318773[/C][C]1.2004601681227[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.8036210076908[/C][C]-1.80362100769083[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.9767347339461[/C][C]-2.97673473394612[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.593619210324[/C][C]9.40638078967605[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.0320559447682[/C][C]1.96794405523181[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.000403596898[/C][C]8.99959640310199[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.1992942087898[/C][C]-2.19929420878984[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.3116573488446[/C][C]-2.31165734884463[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.229934298822[/C][C]0.770065701177975[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.6578848013552[/C][C]-0.657884801355201[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.13176604764605[/C][C]-1.13176604764605[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]16.1991005566528[/C][C]-1.19910055665284[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.3318112069658[/C][C]2.66818879303424[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.0486947980586[/C][C]-0.0486947980585534[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.3312816131019[/C][C]0.668718386898132[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.1407104278552[/C][C]0.859289572144774[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.5844811779778[/C][C]0.415518822022224[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.9269999735206[/C][C]-2.92699997352059[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.7465790784581[/C][C]1.25342092154189[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]10.1409040799922[/C][C]-0.640904079992222[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.7953015810456[/C][C]-1.79530158104565[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.9269999735206[/C][C]-0.926999973520585[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.6122312167432[/C][C]-0.612231216743199[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.5971342151547[/C][C]0.402865784845285[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.8773605727952[/C][C]-7.87736057279515[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.5140629655672[/C][C]-0.514062965567221[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.2390723311682[/C][C]1.7609276688318[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.483422875535[/C][C]1.51657712446497[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.6235360543553[/C][C]-2.62353605435525[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.8073296646586[/C][C]-2.80732966465859[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.3906839997375[/C][C]0.609316000262532[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.7081491556446[/C][C]-0.708149155644626[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.3912135936014[/C][C]-1.39121359360136[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]13.2622116002562[/C][C]-0.262211600256198[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]11.736092846547[/C][C]-3.73609284654704[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.4910190561793[/C][C]2.50898094382068[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3326298126668[/C][C]-0.332629812666754[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.11026398996[/C][C]-1.11026398996003[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.978082933511[/C][C]-3.978082933511[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.2928516902884[/C][C]-3.29285169028839[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.6355641379682[/C][C]2.36443586203181[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.4449929527216[/C][C]-3.44499295272155[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.7756773167884[/C][C]-0.775677316788414[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.2845322636432[/C][C]-1.28453226364321[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.4517705276649[/C][C]-2.45177052766485[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.1691837126215[/C][C]-3.16918371262154[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.472936643624[/C][C]-1.47293664362397[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.6850098865566[/C][C]4.31499011344337[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.8377761025538[/C][C]-2.83777610255379[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.6836616869917[/C][C]-1.68366168699174[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.3627403088351[/C][C]0.63725969116495[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.7240647629341[/C][C]2.2759352370659[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.7735105115225[/C][C]-0.773510511522537[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]14.9369566115678[/C][C]-3.93695661156775[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]12.2613929945552[/C][C]2.23860700544479[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.37375613461[/C][C]1.62624386539[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.6739940607817[/C][C]-3.67399406078167[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.6436899124764[/C][C]-4.64368991247638[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.7651910848774[/C][C]0.234808915122647[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.2698077809005[/C][C]1.73019221909951[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.8168036387317[/C][C]-1.81680363873167[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]15.6025635905331[/C][C]-3.60256359053313[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.3605735035692[/C][C]0.639426496430828[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.4541309850677[/C][C]0.545869014932274[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]16.328921155699[/C][C]-5.32892115569899[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.5020348819543[/C][C]1.49796511804572[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]15.0899164797019[/C][C]-3.08991647970191[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.1084331264211[/C][C]0.891566873578947[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.3409492393119[/C][C]1.65905076068806[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.9982367916321[/C][C]0.00176320836786771[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.7339260412812[/C][C]3.26607395871883[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.3702411297792[/C][C]0.629758870220758[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.1482112487994[/C][C]-2.14821124879942[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]14.0912646792668[/C][C]-3.09126467926679[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.6060785953639[/C][C]4.39392140463611[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.6452317641783[/C][C]2.35476823582174[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.0493197516225[/C][C]2.95068024837745[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.7557171108043[/C][C]0.244282889195719[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.9404716163985[/C][C]-4.94047161639851[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.4519641798018[/C][C]-1.45196417980185[/C][/ROW]
[ROW][C]82[/C][C]9[/C][C]12.3409492393119[/C][C]-3.34094923931194[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.4847710750999[/C][C]0.515228924900084[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.5232009979134[/C][C]-0.523200997913396[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]13.3019897226346[/C][C]-0.301989722634563[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.1691837126215[/C][C]0.83081628737846[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]13.2705310269014[/C][C]-1.27053102690138[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]14.2901552911586[/C][C]0.709844708841383[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]18.8641779417543[/C][C]3.13582205824568[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]10.9573041218259[/C][C]2.04269587817412[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]13.9383048111326[/C][C]1.06169518886736[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.2990043116677[/C][C]0.700995688332305[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]13.3837127726572[/C][C]1.61628722734283[/C][/ROW]
[ROW][C]94[/C][C]12.5[/C][C]14.3111277549807[/C][C]-1.81112775498074[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]11.3527836707879[/C][C]-0.352783670787884[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.6541761443874[/C][C]1.34582385561256[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]13.2014610140557[/C][C]-2.20146101405571[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.6766904599114[/C][C]0.323309540088555[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]12.5022285340913[/C][C]-2.50222853409127[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.6054536417999[/C][C]-0.605453641799898[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6541761443874[/C][C]1.34582385561256[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.4930905017451[/C][C]0.5069094982549[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]15.7764959224894[/C][C]-4.77649592248941[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]12.4205054840687[/C][C]3.57949451593133[/C][/ROW]
[ROW][C]105[/C][C]19[/C][C]16.9276249270846[/C][C]2.07237507291542[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]11.4135342569884[/C][C]-0.41353425698837[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]12.0183906497533[/C][C]3.98160935024674[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]16.2996292652317[/C][C]-1.29962926523169[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]15.8743751618283[/C][C]8.12562483817171[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]12.6237297064922[/C][C]1.37627029350775[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]15.7743291172235[/C][C]-0.774329117223528[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.0449861410908[/C][C]-3.0449861410908[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.3312816131019[/C][C]1.66871838689813[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]9.87909607663403[/C][C]2.12090392336597[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]11.495257307011[/C][C]-1.49525730701098[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]13.207613635435[/C][C]0.792386364564982[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]13.1573492811456[/C][C]-0.157349281145593[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]13.2600447949903[/C][C]-4.26004479499032[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.9982367916321[/C][C]2.00176320836787[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]14.834261097723[/C][C]0.165738902276977[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]13.4444633588577[/C][C]0.555536641142343[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.1691837126215[/C][C]-1.16918371262154[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]12.229934298822[/C][C]-4.22993429882202[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]12.3536022764889[/C][C]-1.35360227648888[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]14.1791403506687[/C][C]-3.17914035066871[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]10.5971342151547[/C][C]-2.59713421515471[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.2724088203302[/C][C]-0.272408820330162[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.2615866466922[/C][C]1.7384133533078[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]12.3828941669562[/C][C]0.617105833043821[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]14.1288759963793[/C][C]-3.12887599637928[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]17.5307971785577[/C][C]2.46920282144231[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]12.0896274678648[/C][C]-2.08962746786481[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]12.8164676970048[/C][C]2.18353230299523[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]12.229934298822[/C][C]-0.229934298822025[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]11.7780377741913[/C][C]2.22196222580871[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]16.0404707311135[/C][C]6.95952926888653[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.2097804407009[/C][C]0.790219559299104[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]15.8036210076908[/C][C]0.196378992309167[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.3724079350451[/C][C]-1.37240793504512[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]14.1581678868466[/C][C]-2.15816788684658[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]12.5232009979134[/C][C]-2.5232009979134[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]11.7946766274817[/C][C]2.20532337251835[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]13.4108378578586[/C][C]-1.4108378578586[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.6888139032245[/C][C]-0.688813903224488[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.3549504760538[/C][C]-0.354950476053761[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.7990102380134[/C][C]0.200989761986592[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.4915486500432[/C][C]-2.49154865004322[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]14.9474428434788[/C][C]-3.94744284347881[/C][/ROW]
[ROW][C]149[/C][C]19[/C][C]17.1789466985317[/C][C]1.82105330146829[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.3715893293441[/C][C]0.628410670655873[/C][/ROW]
[ROW][C]151[/C][C]17[/C][C]13.5842875258408[/C][C]3.41571247415922[/C][/ROW]
[ROW][C]152[/C][C]9[/C][C]11.5831329784129[/C][C]-2.58313297841289[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.7127599253221[/C][C]-2.71275992532205[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]17.0071811718413[/C][C]1.99281882815869[/C][/ROW]
[ROW][C]155[/C][C]18[/C][C]14.099584105912[/C][C]3.90041589408802[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]13.9383048111326[/C][C]1.06169518886736[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.2893366854576[/C][C]0.710663314542374[/C][/ROW]
[ROW][C]158[/C][C]11[/C][C]9.2615866466922[/C][C]1.7384133533078[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]14.0388335197115[/C][C]-5.03883351971149[/C][/ROW]
[ROW][C]160[/C][C]18[/C][C]14.8740392201014[/C][C]3.12596077989861[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]13.9968885920672[/C][C]2.00311140793275[/C][/ROW]
[ROW][C]162[/C][C]24[/C][C]16.8463845410361[/C][C]7.15361545896393[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]13.4549495907687[/C][C]0.545050409231282[/C][/ROW]
[ROW][C]164[/C][C]20[/C][C]11.4345067208105[/C][C]8.56549327918951[/C][/ROW]
[ROW][C]165[/C][C]18[/C][C]16.3582130461663[/C][C]1.6417869538337[/C][/ROW]
[ROW][C]166[/C][C]23[/C][C]16.1576382929827[/C][C]6.84236170701731[/C][/ROW]
[ROW][C]167[/C][C]12[/C][C]13.4318103216807[/C][C]-1.43181032168072[/C][/ROW]
[ROW][C]168[/C][C]14[/C][C]15.3119463606817[/C][C]-1.31194636068173[/C][/ROW]
[ROW][C]169[/C][C]16[/C][C]17.1662936613548[/C][C]-1.16629366135477[/C][/ROW]
[ROW][C]170[/C][C]18[/C][C]17.5286303732918[/C][C]0.471369626708189[/C][/ROW]
[ROW][C]171[/C][C]20[/C][C]15.4937154553091[/C][C]4.50628454469091[/C][/ROW]
[ROW][C]172[/C][C]12[/C][C]11.4930905017451[/C][C]0.5069094982549[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]16.845901877062[/C][C]-4.84590187706197[/C][/ROW]
[ROW][C]174[/C][C]17[/C][C]14.363558914536[/C][C]2.63644108546396[/C][/ROW]
[ROW][C]175[/C][C]13[/C][C]11.5874665889446[/C][C]1.41253341105535[/C][/ROW]
[ROW][C]176[/C][C]9[/C][C]14.1079035325572[/C][C]-5.10790353255716[/C][/ROW]
[ROW][C]177[/C][C]16[/C][C]17.0679317580418[/C][C]-1.0679317580418[/C][/ROW]
[ROW][C]178[/C][C]18[/C][C]15.0584577839687[/C][C]2.94154221603128[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]11.8073296646586[/C][C]-1.80732966465859[/C][/ROW]
[ROW][C]180[/C][C]14[/C][C]15.3805337095533[/C][C]-1.38053370955331[/C][/ROW]
[ROW][C]181[/C][C]11[/C][C]13.098765500211[/C][C]-2.09876550021098[/C][/ROW]
[ROW][C]182[/C][C]9[/C][C]14.0095416292442[/C][C]-5.00954162924419[/C][/ROW]
[ROW][C]183[/C][C]11[/C][C]12.5524928883807[/C][C]-1.5524928883807[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]13.0401817192764[/C][C]-3.04018171927638[/C][/ROW]
[ROW][C]185[/C][C]11[/C][C]12.2911675489966[/C][C]-1.2911675489966[/C][/ROW]
[ROW][C]186[/C][C]19[/C][C]14.0974173006461[/C][C]4.9025826993539[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]13.1197379640331[/C][C]0.880262035966895[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.2028092136206[/C][C]-0.202809213620598[/C][/ROW]
[ROW][C]189[/C][C]14[/C][C]14.3823645730923[/C][C]-0.382364573092285[/C][/ROW]
[ROW][C]190[/C][C]21[/C][C]15.6926060672009[/C][C]5.30739393279908[/C][/ROW]
[ROW][C]191[/C][C]13[/C][C]15.6926060672009[/C][C]-2.69260606720092[/C][/ROW]
[ROW][C]192[/C][C]10[/C][C]12.9562918639879[/C][C]-2.95629186398789[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]12.5336872298245[/C][C]2.46631277017554[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]15.5313267724216[/C][C]0.468673227578417[/C][/ROW]
[ROW][C]195[/C][C]14[/C][C]12.5441734617355[/C][C]1.45582653826448[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]14.6227174486543[/C][C]-2.62271744865426[/C][/ROW]
[ROW][C]197[/C][C]19[/C][C]13.2517253683451[/C][C]5.74827463165486[/C][/ROW]
[ROW][C]198[/C][C]15[/C][C]13.2390723311682[/C][C]1.7609276688318[/C][/ROW]
[ROW][C]199[/C][C]19[/C][C]18.0665835584769[/C][C]0.933416441523088[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]14.4409483540269[/C][C]-1.44094835402689[/C][/ROW]
[ROW][C]201[/C][C]17[/C][C]17.451240933801[/C][C]-0.451240933800959[/C][/ROW]
[ROW][C]202[/C][C]12[/C][C]12.967260759873[/C][C]-0.967260759873043[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]10.8589422185129[/C][C]0.141057781487097[/C][/ROW]
[ROW][C]204[/C][C]14[/C][C]14.0388335197115[/C][C]-0.0388335197114888[/C][/ROW]
[ROW][C]205[/C][C]11[/C][C]12.472936643624[/C][C]-1.47293664362397[/C][/ROW]
[ROW][C]206[/C][C]13[/C][C]12.4016998255124[/C][C]0.598300174487577[/C][/ROW]
[ROW][C]207[/C][C]12[/C][C]13.1385436225893[/C][C]-1.13854362258935[/C][/ROW]
[ROW][C]208[/C][C]15[/C][C]11.5538410879456[/C][C]3.44615891205441[/C][/ROW]
[ROW][C]209[/C][C]14[/C][C]13.9278185792216[/C][C]0.0721814207784232[/C][/ROW]
[ROW][C]210[/C][C]12[/C][C]11.6355641379682[/C][C]0.364435862031807[/C][/ROW]
[ROW][C]211[/C][C]17[/C][C]17.2187248209101[/C][C]-0.218724820910074[/C][/ROW]
[ROW][C]212[/C][C]11[/C][C]11.1683651069205[/C][C]-0.168365106920547[/C][/ROW]
[ROW][C]213[/C][C]18[/C][C]15.6611473714677[/C][C]2.33885262853226[/C][/ROW]
[ROW][C]214[/C][C]13[/C][C]16.6326740867014[/C][C]-3.63267408670143[/C][/ROW]
[ROW][C]215[/C][C]17[/C][C]15.5522992362437[/C][C]1.4477007637563[/C][/ROW]
[ROW][C]216[/C][C]13[/C][C]12.8186345022706[/C][C]0.181365497729356[/C][/ROW]
[ROW][C]217[/C][C]11[/C][C]10.8672616451581[/C][C]0.132738354841914[/C][/ROW]
[ROW][C]218[/C][C]12[/C][C]12.9689449011648[/C][C]-0.968944901164828[/C][/ROW]
[ROW][C]219[/C][C]22[/C][C]17.8948180317865[/C][C]4.10518196821349[/C][/ROW]
[ROW][C]220[/C][C]14[/C][C]12.2697124212004[/C][C]1.73028757879961[/C][/ROW]
[ROW][C]221[/C][C]12[/C][C]15.5732717000658[/C][C]-3.57327170006583[/C][/ROW]
[ROW][C]222[/C][C]12[/C][C]11.4030480250773[/C][C]0.596951974922691[/C][/ROW]
[ROW][C]223[/C][C]17[/C][C]16.4818810238332[/C][C]0.518118976166851[/C][/ROW]
[ROW][C]224[/C][C]9[/C][C]12.5944378160249[/C][C]-3.59443781602494[/C][/ROW]
[ROW][C]225[/C][C]21[/C][C]17.1872661251769[/C][C]3.81273387482311[/C][/ROW]
[ROW][C]226[/C][C]10[/C][C]11.7046341508139[/C][C]-1.70463415081386[/C][/ROW]
[ROW][C]227[/C][C]11[/C][C]9.4040602829153[/C][C]1.5959397170847[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]14.8657197934562[/C][C]-2.86571979345621[/C][/ROW]
[ROW][C]229[/C][C]23[/C][C]18.0163192041875[/C][C]4.98368079581252[/C][/ROW]
[ROW][C]230[/C][C]13[/C][C]15.6025635905331[/C][C]-2.60256359053313[/C][/ROW]
[ROW][C]231[/C][C]12[/C][C]13.2622116002562[/C][C]-1.2622116002562[/C][/ROW]
[ROW][C]232[/C][C]16[/C][C]17.2165580156442[/C][C]-1.2165580156442[/C][/ROW]
[ROW][C]233[/C][C]9[/C][C]12.5232009979134[/C][C]-3.5232009979134[/C][/ROW]
[ROW][C]234[/C][C]17[/C][C]13.6764968077744[/C][C]3.32350319222555[/C][/ROW]
[ROW][C]235[/C][C]9[/C][C]11.6460503698793[/C][C]-2.64605036987925[/C][/ROW]
[ROW][C]236[/C][C]14[/C][C]15.5439798095985[/C][C]-1.54397980959852[/C][/ROW]
[ROW][C]237[/C][C]17[/C][C]14.7066073039427[/C][C]2.29339269605726[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]13.9907359706879[/C][C]-0.99073597068794[/C][/ROW]
[ROW][C]239[/C][C]11[/C][C]16.6934246729019[/C][C]-5.69342467290191[/C][/ROW]
[ROW][C]240[/C][C]12[/C][C]15.5042016872202[/C][C]-3.50420168722016[/C][/ROW]
[ROW][C]241[/C][C]10[/C][C]15.1882783830149[/C][C]-5.18827838301488[/C][/ROW]
[ROW][C]242[/C][C]19[/C][C]17.9450823860759[/C][C]1.05491761392406[/C][/ROW]
[ROW][C]243[/C][C]16[/C][C]15.1799589563697[/C][C]0.820041043630303[/C][/ROW]
[ROW][C]244[/C][C]16[/C][C]14.7756773167884[/C][C]1.22432268321159[/C][/ROW]
[ROW][C]245[/C][C]14[/C][C]12.3033379221995[/C][C]1.69666207780055[/C][/ROW]
[ROW][C]246[/C][C]20[/C][C]15.8538853619803[/C][C]4.14611463801974[/C][/ROW]
[ROW][C]247[/C][C]15[/C][C]14.1079035325572[/C][C]0.892096467442842[/C][/ROW]
[ROW][C]248[/C][C]23[/C][C]13.1407104278552[/C][C]9.85928957214477[/C][/ROW]
[ROW][C]249[/C][C]20[/C][C]17.3009305349068[/C][C]2.69906946509322[/C][/ROW]
[ROW][C]250[/C][C]16[/C][C]15.6156992916842[/C][C]0.384300708315839[/C][/ROW]
[ROW][C]251[/C][C]14[/C][C]12.5734653522028[/C][C]1.42653464779718[/C][/ROW]
[ROW][C]252[/C][C]17[/C][C]14.0890978740009[/C][C]2.91090212599909[/C][/ROW]
[ROW][C]253[/C][C]11[/C][C]13.9173323473105[/C][C]-2.91733234731052[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]14.0890978740009[/C][C]-1.08909787400091[/C][/ROW]
[ROW][C]255[/C][C]17[/C][C]14.6751486082096[/C][C]2.32485139179044[/C][/ROW]
[ROW][C]256[/C][C]15[/C][C]14.8259416710778[/C][C]0.174058328922161[/C][/ROW]
[ROW][C]257[/C][C]21[/C][C]17.1265155389764[/C][C]3.87348446102359[/C][/ROW]
[ROW][C]258[/C][C]18[/C][C]17.3800041156894[/C][C]0.619995884310588[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.2718792264663[/C][C]2.72812077353373[/C][/ROW]
[ROW][C]260[/C][C]8[/C][C]16.4023247790764[/C][C]-8.40232477907642[/C][/ROW]
[ROW][C]261[/C][C]12[/C][C]13.6140620802822[/C][C]-1.61406208028218[/C][/ROW]
[ROW][C]262[/C][C]12[/C][C]12.3807273616903[/C][C]-0.380727361690302[/C][/ROW]
[ROW][C]263[/C][C]22[/C][C]18.4808687659952[/C][C]3.51913123400484[/C][/ROW]
[ROW][C]264[/C][C]12[/C][C]13.4925609078812[/C][C]-1.49256090788121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&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
11212.8264712649418-0.826471264941798
2119.79953983187731.2004601681227
31415.8036210076908-1.80362100769083
41214.9767347339461-2.97673473394612
52111.5936192103249.40638078967605
61210.03205594476821.96794405523181
72213.0004035968988.99959640310199
81113.1992942087898-2.19929420878984
91012.3116573488446-2.31165734884463
101312.2299342988220.770065701177975
111010.6578848013552-0.657884801355201
1289.13176604764605-1.13176604764605
131516.1991005566528-1.19910055665284
141411.33181120696582.66818879303424
151010.0486947980586-0.0486947980585534
161413.33128161310190.668718386898132
171413.14071042785520.859289572144774
181110.58448117797780.415518822022224
191012.9269999735206-2.92699997352059
201311.74657907845811.25342092154189
219.510.1409040799922-0.640904079992222
221415.7953015810456-1.79530158104565
231212.9269999735206-0.926999973520585
241414.6122312167432-0.612231216743199
251110.59713421515470.402865784845285
26916.8773605727952-7.87736057279515
271111.5140629655672-0.514062965567221
281513.23907233116821.7609276688318
291412.4834228755351.51657712446497
301315.6235360543553-2.62353605435525
31911.8073296646586-2.80732966465859
321514.39068399973750.609316000262532
331010.7081491556446-0.708149155644626
341112.3912135936014-1.39121359360136
351313.2622116002562-0.262211600256198
36811.736092846547-3.73609284654704
372017.49101905617932.50898094382068
381212.3326298126668-0.332629812666754
391011.11026398996-1.11026398996003
401013.978082933511-3.978082933511
41912.2928516902884-3.29285169028839
421411.63556413796822.36443586203181
43811.4449929527216-3.44499295272155
441414.7756773167884-0.775677316788414
451112.2845322636432-1.28453226364321
461315.4517705276649-2.45177052766485
47912.1691837126215-3.16918371262154
481112.472936643624-1.47293664362397
491510.68500988655664.31499011344337
501113.8377761025538-2.83777610255379
511011.6836616869917-1.68366168699174
521413.36274030883510.63725969116495
531815.72406476293412.2759352370659
541414.7735105115225-0.773510511522537
551114.9369566115678-3.93695661156775
5614.512.26139299455522.23860700544479
571311.373756134611.62624386539
58912.6739940607817-3.67399406078167
591014.6436899124764-4.64368991247638
601514.76519108487740.234808915122647
612018.26980778090051.73019221909951
621213.8168036387317-1.81680363873167
631215.6025635905331-3.60256359053313
641413.36057350356920.639426496430828
651312.45413098506770.545869014932274
661116.328921155699-5.32892115569899
671715.50203488195431.49796511804572
681215.0899164797019-3.08991647970191
691312.10843312642110.891566873578947
701412.34094923931191.65905076068806
711312.99823679163210.00176320836786771
721511.73392604128123.26607395871883
731312.37024112977920.629758870220758
741012.1482112487994-2.14821124879942
751114.0912646792668-3.09126467926679
761914.60607859536394.39392140463611
771310.64523176417832.35476823582174
781714.04931975162252.95068024837745
791312.75571711080430.244282889195719
80913.9404716163985-4.94047161639851
811112.4519641798018-1.45196417980185
82912.3409492393119-3.34094923931194
831211.48477107509990.515228924900084
841212.5232009979134-0.523200997913396
851313.3019897226346-0.301989722634563
861312.16918371262150.83081628737846
871213.2705310269014-1.27053102690138
881514.29015529115860.709844708841383
892218.86417794175433.13582205824568
901310.95730412182592.04269587817412
911513.93830481113261.06169518886736
921312.29900431166770.700995688332305
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1001010.6054536417999-0.605453641799898
1011614.65417614438741.34582385561256
1021211.49309050174510.5069094982549
1031115.7764959224894-4.77649592248941
1041612.42050548406873.57949451593133
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1092415.87437516182838.12562483817171
1101412.62372970649221.37627029350775
1111515.7743291172235-0.774329117223528
1121114.0449861410908-3.0449861410908
1131513.33128161310191.66871838689813
114129.879096076634032.12090392336597
1151011.495257307011-1.49525730701098
1161413.2076136354350.792386364564982
1171313.1573492811456-0.157349281145593
118913.2600447949903-4.26004479499032
1191512.99823679163212.00176320836787
1201514.8342610977230.165738902276977
1211413.44446335885770.555536641142343
1221112.1691837126215-1.16918371262154
123812.229934298822-4.22993429882202
1241112.3536022764889-1.35360227648888
1251114.1791403506687-3.17914035066871
126810.5971342151547-2.59713421515471
1271010.2724088203302-0.272408820330162
128119.26158664669221.7384133533078
1291312.38289416695620.617105833043821
1301114.1288759963793-3.12887599637928
1312017.53079717855772.46920282144231
1321012.0896274678648-2.08962746786481
1331512.81646769700482.18353230299523
1341212.229934298822-0.229934298822025
1351411.77803777419132.22196222580871
1362316.04047073111356.95952926888653
1371413.20978044070090.790219559299104
1381615.80362100769080.196378992309167
1391112.3724079350451-1.37240793504512
1401214.1581678868466-2.15816788684658
1411012.5232009979134-2.5232009979134
1421411.79467662748172.20532337251835
1431213.4108378578586-1.4108378578586
1441212.6888139032245-0.688813903224488
1451111.3549504760538-0.354950476053761
1461211.79901023801340.200989761986592
1471315.4915486500432-2.49154865004322
1481114.9474428434788-3.94744284347881
1491917.17894669853171.82105330146829
1501211.37158932934410.628410670655873
1511713.58428752584083.41571247415922
152911.5831329784129-2.58313297841289
1531214.7127599253221-2.71275992532205
1541917.00718117184131.99281882815869
1551814.0995841059123.90041589408802
1561513.93830481113261.06169518886736
1571413.28933668545760.710663314542374
158119.26158664669221.7384133533078
159914.0388335197115-5.03883351971149
1601814.87403922010143.12596077989861
1611613.99688859206722.00311140793275
1622416.84638454103617.15361545896393
1631413.45494959076870.545050409231282
1642011.43450672081058.56549327918951
1651816.35821304616631.6417869538337
1662316.15763829298276.84236170701731
1671213.4318103216807-1.43181032168072
1681415.3119463606817-1.31194636068173
1691617.1662936613548-1.16629366135477
1701817.52863037329180.471369626708189
1712015.49371545530914.50628454469091
1721211.49309050174510.5069094982549
1731216.845901877062-4.84590187706197
1741714.3635589145362.63644108546396
1751311.58746658894461.41253341105535
176914.1079035325572-5.10790353255716
1771617.0679317580418-1.0679317580418
1781815.05845778396872.94154221603128
1791011.8073296646586-1.80732966465859
1801415.3805337095533-1.38053370955331
1811113.098765500211-2.09876550021098
182914.0095416292442-5.00954162924419
1831112.5524928883807-1.5524928883807
1841013.0401817192764-3.04018171927638
1851112.2911675489966-1.2911675489966
1861914.09741730064614.9025826993539
1871413.11973796403310.880262035966895
1881212.2028092136206-0.202809213620598
1891414.3823645730923-0.382364573092285
1902115.69260606720095.30739393279908
1911315.6926060672009-2.69260606720092
1921012.9562918639879-2.95629186398789
1931512.53368722982452.46631277017554
1941615.53132677242160.468673227578417
1951412.54417346173551.45582653826448
1961214.6227174486543-2.62271744865426
1971913.25172536834515.74827463165486
1981513.23907233116821.7609276688318
1991918.06658355847690.933416441523088
2001314.4409483540269-1.44094835402689
2011717.451240933801-0.451240933800959
2021212.967260759873-0.967260759873043
2031110.85894221851290.141057781487097
2041414.0388335197115-0.0388335197114888
2051112.472936643624-1.47293664362397
2061312.40169982551240.598300174487577
2071213.1385436225893-1.13854362258935
2081511.55384108794563.44615891205441
2091413.92781857922160.0721814207784232
2101211.63556413796820.364435862031807
2111717.2187248209101-0.218724820910074
2121111.1683651069205-0.168365106920547
2131815.66114737146772.33885262853226
2141316.6326740867014-3.63267408670143
2151715.55229923624371.4477007637563
2161312.81863450227060.181365497729356
2171110.86726164515810.132738354841914
2181212.9689449011648-0.968944901164828
2192217.89481803178654.10518196821349
2201412.26971242120041.73028757879961
2211215.5732717000658-3.57327170006583
2221211.40304802507730.596951974922691
2231716.48188102383320.518118976166851
224912.5944378160249-3.59443781602494
2252117.18726612517693.81273387482311
2261011.7046341508139-1.70463415081386
227119.40406028291531.5959397170847
2281214.8657197934562-2.86571979345621
2292318.01631920418754.98368079581252
2301315.6025635905331-2.60256359053313
2311213.2622116002562-1.2622116002562
2321617.2165580156442-1.2165580156442
233912.5232009979134-3.5232009979134
2341713.67649680777443.32350319222555
235911.6460503698793-2.64605036987925
2361415.5439798095985-1.54397980959852
2371714.70660730394272.29339269605726
2381313.9907359706879-0.99073597068794
2391116.6934246729019-5.69342467290191
2401215.5042016872202-3.50420168722016
2411015.1882783830149-5.18827838301488
2421917.94508238607591.05491761392406
2431615.17995895636970.820041043630303
2441614.77567731678841.22432268321159
2451412.30333792219951.69666207780055
2462015.85388536198034.14611463801974
2471514.10790353255720.892096467442842
2482313.14071042785529.85928957214477
2492017.30093053490682.69906946509322
2501615.61569929168420.384300708315839
2511412.57346535220281.42653464779718
2521714.08909787400092.91090212599909
2531113.9173323473105-2.91733234731052
2541314.0890978740009-1.08909787400091
2551714.67514860820962.32485139179044
2561514.82594167107780.174058328922161
2572117.12651553897643.87348446102359
2581817.38000411568940.619995884310588
2591512.27187922646632.72812077353373
260816.4023247790764-8.40232477907642
2611213.6140620802822-1.61406208028218
2621212.3807273616903-0.380727361690302
2632218.48086876599523.51913123400484
2641213.4925609078812-1.49256090788121






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.999071398803720.00185720239255940.0009286011962797
80.9987841821696670.002431635660665830.00121581783033291
90.9987828855942850.002434228811430280.00121711440571514
100.9972870215184170.005425956963166330.00271297848158317
110.9969446566984670.006110686603066640.00305534330153332
120.996092298101130.007815403797741840.00390770189887092
130.9935282388863650.01294352222727070.00647176111363536
140.9896878171711660.02062436565766830.0103121828288341
150.983025336561220.03394932687755860.0169746634387793
160.9738414435338270.05231711293234660.0261585564661733
170.960137532652850.0797249346942990.0398624673471495
180.9430462001003630.1139075997992750.0569537998996374
190.9457501396976360.1084997206047290.0542498603023643
200.9247425026135330.1505149947729340.075257497386467
210.903707832293170.1925843354136610.0962921677068303
220.8769304784369910.2461390431260180.123069521563009
230.8452276279492440.3095447441015130.154772372050756
240.8038941824947550.392211635010490.196105817505245
250.7670736284926410.4658527430147180.232926371507359
260.9019940496615760.1960119006768480.098005950338424
270.877316964508780.2453660709824390.122683035491219
280.8585647248819730.2828705502360550.141435275118027
290.8297415140752240.3405169718495510.170258485924776
300.7985295860396610.4029408279206780.201470413960339
310.8097404557496740.3805190885006520.190259544250326
320.7775026007334180.4449947985331640.222497399266582
330.7433565356589390.5132869286821220.256643464341061
340.7128765587947030.5742468824105950.287123441205297
350.665716316546960.6685673669060810.33428368345304
360.7126547580494670.5746904839010670.287345241950533
370.7671933860788440.4656132278423110.232806613921156
380.7260503799085310.5478992401829380.273949620091469
390.6981352271109480.6037295457781040.301864772889052
400.7130766543044440.5738466913911130.286923345695556
410.7147497785169660.5705004429660680.285250221483034
420.6981234976693030.6037530046613950.301876502330697
430.7081440700768730.5837118598462550.291855929923127
440.6668073374840640.6663853250318710.333192662515936
450.6239160895632220.7521678208735560.376083910436778
460.5883290431510820.8233419136978360.411670956848918
470.5948093978121960.8103812043756090.405190602187804
480.5593940994972810.8812118010054370.440605900502719
490.5975980869209410.8048038261581170.402401913079059
500.5706571783512330.8586856432975340.429342821648767
510.5547728373170770.8904543253658460.445227162682923
520.517533662011840.964932675976320.48246633798816
530.5355923783977290.9288152432045420.464407621602271
540.4915497593838240.9830995187676470.508450240616176
550.5036983106271970.9926033787456060.496301689372803
560.4951120630743550.990224126148710.504887936925645
570.4692425283007870.9384850566015740.530757471699213
580.5028122473071220.9943755053857570.497187752692878
590.5361659507557780.9276680984884440.463834049244222
600.5035420111162230.9929159777675540.496457988883777
610.5296559350465020.9406881299069960.470344064953498
620.4949852480762010.9899704961524030.505014751923799
630.491955366178190.983910732356380.50804463382181
640.4542250161043220.9084500322086440.545774983895678
650.4156779646013230.8313559292026460.584322035398677
660.463199754419010.926399508838020.53680024558099
670.4632199698277940.9264399396555880.536780030172206
680.4552405800462770.9104811600925540.544759419953723
690.4222209109921120.8444418219842230.577779089007888
700.3988423077878640.7976846155757280.601157692212136
710.3618103117327470.7236206234654940.638189688267253
720.3656681545706260.7313363091412520.634331845429374
730.3296893016018450.6593786032036890.670310698398155
740.3144238513429370.6288477026858730.685576148657063
750.3056153807298080.6112307614596170.694384619270192
760.4129527827256010.8259055654512020.587047217274399
770.3959389512708830.7918779025417660.604061048729117
780.4165899171307590.8331798342615170.583410082869241
790.3790020236734110.7580040473468220.620997976326589
800.4335086419671540.8670172839343080.566491358032846
810.4059268822343190.8118537644686390.59407311776568
820.4205241265836770.8410482531673540.579475873416323
830.3844334913456220.7688669826912440.615566508654378
840.3492175975536760.6984351951073510.650782402446324
850.3148104165793860.6296208331587720.685189583420614
860.2853104179514450.570620835902890.714689582048555
870.2580330844325710.5160661688651430.741966915567429
880.2331805039122490.4663610078244970.766819496087751
890.2897425579673030.5794851159346050.710257442032697
900.2707496504371650.541499300874330.729250349562835
910.2499224712248890.4998449424497780.750077528775111
920.2225713656875880.4451427313751760.777428634312412
930.2074090341413910.4148180682827820.792590965858609
940.1899103473634380.3798206947268750.810089652636562
950.1659067206450390.3318134412900770.834093279354961
960.1541611335715490.3083222671430970.845838866428452
970.1435900656370640.2871801312741280.856409934362936
980.1236680168174790.2473360336349570.876331983182521
990.1203373609986740.2406747219973480.879662639001326
1000.1039411420053790.2078822840107580.89605885799462
1010.09530414600095540.1906082920019110.904695853999045
1020.080951894948160.161903789896320.91904810505184
1030.1011581759798960.2023163519597930.898841824020104
1040.1115703354176390.2231406708352780.888429664582361
1050.1162369430061420.2324738860122840.883763056993858
1060.099745076702570.199490153405140.90025492329743
1070.1160248812290450.2320497624580910.883975118770955
1080.1016624536111310.2033249072222630.898337546388869
1090.2699372674343360.5398745348686720.730062732565664
1100.2500921774122770.5001843548245550.749907822587722
1110.2237517304988320.4475034609976640.776248269501168
1120.2307244194501280.4614488389002560.769275580549872
1130.2166610845793230.4333221691586450.783338915420677
1140.2040747619848530.4081495239697070.795925238015147
1150.1860707122468970.3721414244937950.813929287753103
1160.1648671736763490.3297343473526990.835132826323651
1170.1440952467943280.2881904935886550.855904753205672
1180.1691770226898980.3383540453797960.830822977310102
1190.1578951102215620.3157902204431250.842104889778438
1200.1376222088634810.2752444177269610.86237779113652
1210.1227785261765040.2455570523530080.877221473823496
1220.1097386105347070.2194772210694130.890261389465293
1230.1349046316513360.2698092633026720.865095368348664
1240.1201692607005050.240338521401010.879830739299495
1250.1216589809423130.2433179618846260.878341019057687
1260.1203290533446710.2406581066893410.87967094665533
1270.1047873534030570.2095747068061130.895212646596943
1280.09487094965992140.1897418993198430.905129050340079
1290.08166428859195570.1633285771839110.918335711408044
1300.08274650286179670.1654930057235930.917253497138203
1310.0856558686429560.1713117372859120.914344131357044
1320.08037347965859140.1607469593171830.919626520341409
1330.07741522203884940.1548304440776990.92258477796115
1340.06547115043817760.1309423008763550.934528849561822
1350.06230906682847070.1246181336569410.93769093317153
1360.150181066326380.3003621326527590.84981893367362
1370.132072410448580.264144820897160.86792758955142
1380.1144008896936760.2288017793873520.885599110306324
1390.1018149097533650.203629819506730.898185090246635
1400.09482479571189910.1896495914237980.905175204288101
1410.0916673279612210.1833346559224420.908332672038779
1420.08594353104161510.171887062083230.914056468958385
1430.07572149896144460.1514429979228890.924278501038555
1440.0646615540030.1293231080060.935338445997
1450.05452129639936830.1090425927987370.945478703600632
1460.04545978192289060.09091956384578120.95454021807711
1470.0432351445962180.0864702891924360.956764855403782
1480.05090774971898260.1018154994379650.949092250281017
1490.04674395023502060.09348790047004130.95325604976498
1500.03895944687247090.07791889374494190.96104055312753
1510.04215428949620580.08430857899241160.957845710503794
1520.04084892795820720.08169785591641430.959151072041793
1530.04000911168227110.08001822336454230.959990888317729
1540.0366595523496130.0733191046992260.963340447650387
1550.04350794400032320.08701588800064630.956492055999677
1560.03686595242055090.07373190484110190.96313404757945
1570.0306124703677830.06122494073556610.969387529632217
1580.0267538656971460.0535077313942920.973246134302854
1590.0408203237950770.0816406475901540.959179676204923
1600.04269639694861160.08539279389722310.957303603051388
1610.03872182361639160.07744364723278320.961278176383608
1620.0984299645286950.196859929057390.901570035471305
1630.08405841548508480.168116830970170.915941584514915
1640.251067438641410.502134877282820.74893256135859
1650.2317032296448660.4634064592897330.768296770355134
1660.3821888518471880.7643777036943760.617811148152812
1670.3557739404736220.7115478809472440.644226059526378
1680.3287353240494940.6574706480989890.671264675950506
1690.3019767367538530.6039534735077070.698023263246147
1700.2715177562755910.5430355125511820.728482243724409
1710.3159283961468860.6318567922937710.684071603853114
1720.2846514846765330.5693029693530660.715348515323467
1730.3541268913777230.7082537827554460.645873108622277
1740.3511277754659830.7022555509319660.648872224534017
1750.3264373581049960.6528747162099920.673562641895004
1760.4051696299813590.8103392599627180.594830370018641
1770.3769055860202410.7538111720404820.623094413979759
1780.38055788081810.7611157616361990.6194421191819
1790.357030764730920.714061529461840.64296923526908
1800.3346889345667270.6693778691334550.665311065433273
1810.3216160204003340.6432320408006680.678383979599666
1820.3978396736585510.7956793473171010.602160326341449
1830.3722688591927140.7445377183854280.627731140807286
1840.3846842271641970.7693684543283940.615315772835803
1850.3532824349311360.7065648698622730.646717565068864
1860.4227464922812560.8454929845625130.577253507718744
1870.3871126071972450.774225214394490.612887392802755
1880.3523587373165380.7047174746330760.647641262683462
1890.3169511163932630.6339022327865260.683048883606737
1900.4067866534773320.8135733069546630.593213346522668
1910.4018617743560630.8037235487121260.598138225643937
1920.4162400248710130.8324800497420270.583759975128987
1930.4064891090481950.812978218096390.593510890951805
1940.3685556014903380.7371112029806750.631444398509662
1950.3411881514041630.6823763028083260.658811848595837
1960.3477369627996160.6954739255992330.652263037200384
1970.4645588622882070.9291177245764140.535441137711793
1980.4378441912421250.875688382484250.562155808757875
1990.3998828742825390.7997657485650780.600117125717461
2000.365845244089410.7316904881788190.63415475591059
2010.3282016675165420.6564033350330830.671798332483458
2020.2935694476504840.5871388953009680.706430552349516
2030.2588883541708960.5177767083417930.741111645829104
2040.2259414179312030.4518828358624050.774058582068797
2050.2025494451669620.4050988903339240.797450554833038
2060.1746598671941540.3493197343883070.825340132805847
2070.154198574380870.3083971487617390.84580142561913
2080.1629481493982370.3258962987964750.837051850601763
2090.1378167708144470.2756335416288940.862183229185553
2100.1158679330766370.2317358661532730.884132066923363
2110.09696745953223510.193934919064470.903032540467765
2120.0810702775326750.162140555065350.918929722467325
2130.0733808822216220.1467617644432440.926619117778378
2140.08165895240944910.1633179048188980.918341047590551
2150.06798138179730480.135962763594610.932018618202695
2160.05571333865532330.1114266773106470.944286661344677
2170.04436427122475750.0887285424495150.955635728775242
2180.03664054338072380.07328108676144760.963359456619276
2190.03964750068786620.07929500137573240.960352499312134
2200.03303205816553320.06606411633106640.966967941834467
2210.03928444285019480.07856888570038960.960715557149805
2220.03036475303174410.06072950606348820.969635246968256
2230.02308726833528240.04617453667056470.976912731664718
2240.0242684219353490.04853684387069790.975731578064651
2250.02645868346961260.05291736693922520.973541316530387
2260.02127919740319230.04255839480638470.978720802596808
2270.01785730225034440.03571460450068880.982142697749656
2280.01736817350010420.03473634700020840.982631826499896
2290.0256430765595080.0512861531190160.974356923440492
2300.02412699340862070.04825398681724130.97587300659138
2310.01891995544934640.03783991089869280.981080044550654
2320.01465238058179860.02930476116359720.985347619418201
2330.01721048453436810.03442096906873620.982789515465632
2340.01552404408163260.03104808816326510.984475955918367
2350.0158480795115480.03169615902309610.984151920488452
2360.01254708529843290.02509417059686580.987452914701567
2370.009884220748125460.01976844149625090.990115779251875
2380.007077741722261670.01415548344452330.992922258277738
2390.01698984213164240.03397968426328480.983010157868358
2400.02307434311546070.04614868623092150.97692565688454
2410.05066274268700620.1013254853740120.949337257312994
2420.03635174681540430.07270349363080860.963648253184596
2430.0252067989393730.05041359787874610.974793201060627
2440.01701892380177130.03403784760354270.982981076198229
2450.01156821740132080.02313643480264170.988431782598679
2460.01129406716000860.02258813432001730.988705932839991
2470.006998654938044180.01399730987608840.993001345061956
2480.1840934486949740.3681868973899470.815906551305026
2490.1736665092383480.3473330184766950.826333490761652
2500.2810375130006590.5620750260013190.718962486999341
2510.2166817020867610.4333634041735220.783318297913239
2520.2828287269935440.5656574539870890.717171273006456
2530.2886266968330440.5772533936660880.711373303166956
2540.2015061318534990.4030122637069980.798493868146501
2550.1904910211178380.3809820422356750.809508978882162
2560.119754351031650.23950870206330.88024564896835
2570.06495841501809730.1299168300361950.935041584981903

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.99907139880372 & 0.0018572023925594 & 0.0009286011962797 \tabularnewline
8 & 0.998784182169667 & 0.00243163566066583 & 0.00121581783033291 \tabularnewline
9 & 0.998782885594285 & 0.00243422881143028 & 0.00121711440571514 \tabularnewline
10 & 0.997287021518417 & 0.00542595696316633 & 0.00271297848158317 \tabularnewline
11 & 0.996944656698467 & 0.00611068660306664 & 0.00305534330153332 \tabularnewline
12 & 0.99609229810113 & 0.00781540379774184 & 0.00390770189887092 \tabularnewline
13 & 0.993528238886365 & 0.0129435222272707 & 0.00647176111363536 \tabularnewline
14 & 0.989687817171166 & 0.0206243656576683 & 0.0103121828288341 \tabularnewline
15 & 0.98302533656122 & 0.0339493268775586 & 0.0169746634387793 \tabularnewline
16 & 0.973841443533827 & 0.0523171129323466 & 0.0261585564661733 \tabularnewline
17 & 0.96013753265285 & 0.079724934694299 & 0.0398624673471495 \tabularnewline
18 & 0.943046200100363 & 0.113907599799275 & 0.0569537998996374 \tabularnewline
19 & 0.945750139697636 & 0.108499720604729 & 0.0542498603023643 \tabularnewline
20 & 0.924742502613533 & 0.150514994772934 & 0.075257497386467 \tabularnewline
21 & 0.90370783229317 & 0.192584335413661 & 0.0962921677068303 \tabularnewline
22 & 0.876930478436991 & 0.246139043126018 & 0.123069521563009 \tabularnewline
23 & 0.845227627949244 & 0.309544744101513 & 0.154772372050756 \tabularnewline
24 & 0.803894182494755 & 0.39221163501049 & 0.196105817505245 \tabularnewline
25 & 0.767073628492641 & 0.465852743014718 & 0.232926371507359 \tabularnewline
26 & 0.901994049661576 & 0.196011900676848 & 0.098005950338424 \tabularnewline
27 & 0.87731696450878 & 0.245366070982439 & 0.122683035491219 \tabularnewline
28 & 0.858564724881973 & 0.282870550236055 & 0.141435275118027 \tabularnewline
29 & 0.829741514075224 & 0.340516971849551 & 0.170258485924776 \tabularnewline
30 & 0.798529586039661 & 0.402940827920678 & 0.201470413960339 \tabularnewline
31 & 0.809740455749674 & 0.380519088500652 & 0.190259544250326 \tabularnewline
32 & 0.777502600733418 & 0.444994798533164 & 0.222497399266582 \tabularnewline
33 & 0.743356535658939 & 0.513286928682122 & 0.256643464341061 \tabularnewline
34 & 0.712876558794703 & 0.574246882410595 & 0.287123441205297 \tabularnewline
35 & 0.66571631654696 & 0.668567366906081 & 0.33428368345304 \tabularnewline
36 & 0.712654758049467 & 0.574690483901067 & 0.287345241950533 \tabularnewline
37 & 0.767193386078844 & 0.465613227842311 & 0.232806613921156 \tabularnewline
38 & 0.726050379908531 & 0.547899240182938 & 0.273949620091469 \tabularnewline
39 & 0.698135227110948 & 0.603729545778104 & 0.301864772889052 \tabularnewline
40 & 0.713076654304444 & 0.573846691391113 & 0.286923345695556 \tabularnewline
41 & 0.714749778516966 & 0.570500442966068 & 0.285250221483034 \tabularnewline
42 & 0.698123497669303 & 0.603753004661395 & 0.301876502330697 \tabularnewline
43 & 0.708144070076873 & 0.583711859846255 & 0.291855929923127 \tabularnewline
44 & 0.666807337484064 & 0.666385325031871 & 0.333192662515936 \tabularnewline
45 & 0.623916089563222 & 0.752167820873556 & 0.376083910436778 \tabularnewline
46 & 0.588329043151082 & 0.823341913697836 & 0.411670956848918 \tabularnewline
47 & 0.594809397812196 & 0.810381204375609 & 0.405190602187804 \tabularnewline
48 & 0.559394099497281 & 0.881211801005437 & 0.440605900502719 \tabularnewline
49 & 0.597598086920941 & 0.804803826158117 & 0.402401913079059 \tabularnewline
50 & 0.570657178351233 & 0.858685643297534 & 0.429342821648767 \tabularnewline
51 & 0.554772837317077 & 0.890454325365846 & 0.445227162682923 \tabularnewline
52 & 0.51753366201184 & 0.96493267597632 & 0.48246633798816 \tabularnewline
53 & 0.535592378397729 & 0.928815243204542 & 0.464407621602271 \tabularnewline
54 & 0.491549759383824 & 0.983099518767647 & 0.508450240616176 \tabularnewline
55 & 0.503698310627197 & 0.992603378745606 & 0.496301689372803 \tabularnewline
56 & 0.495112063074355 & 0.99022412614871 & 0.504887936925645 \tabularnewline
57 & 0.469242528300787 & 0.938485056601574 & 0.530757471699213 \tabularnewline
58 & 0.502812247307122 & 0.994375505385757 & 0.497187752692878 \tabularnewline
59 & 0.536165950755778 & 0.927668098488444 & 0.463834049244222 \tabularnewline
60 & 0.503542011116223 & 0.992915977767554 & 0.496457988883777 \tabularnewline
61 & 0.529655935046502 & 0.940688129906996 & 0.470344064953498 \tabularnewline
62 & 0.494985248076201 & 0.989970496152403 & 0.505014751923799 \tabularnewline
63 & 0.49195536617819 & 0.98391073235638 & 0.50804463382181 \tabularnewline
64 & 0.454225016104322 & 0.908450032208644 & 0.545774983895678 \tabularnewline
65 & 0.415677964601323 & 0.831355929202646 & 0.584322035398677 \tabularnewline
66 & 0.46319975441901 & 0.92639950883802 & 0.53680024558099 \tabularnewline
67 & 0.463219969827794 & 0.926439939655588 & 0.536780030172206 \tabularnewline
68 & 0.455240580046277 & 0.910481160092554 & 0.544759419953723 \tabularnewline
69 & 0.422220910992112 & 0.844441821984223 & 0.577779089007888 \tabularnewline
70 & 0.398842307787864 & 0.797684615575728 & 0.601157692212136 \tabularnewline
71 & 0.361810311732747 & 0.723620623465494 & 0.638189688267253 \tabularnewline
72 & 0.365668154570626 & 0.731336309141252 & 0.634331845429374 \tabularnewline
73 & 0.329689301601845 & 0.659378603203689 & 0.670310698398155 \tabularnewline
74 & 0.314423851342937 & 0.628847702685873 & 0.685576148657063 \tabularnewline
75 & 0.305615380729808 & 0.611230761459617 & 0.694384619270192 \tabularnewline
76 & 0.412952782725601 & 0.825905565451202 & 0.587047217274399 \tabularnewline
77 & 0.395938951270883 & 0.791877902541766 & 0.604061048729117 \tabularnewline
78 & 0.416589917130759 & 0.833179834261517 & 0.583410082869241 \tabularnewline
79 & 0.379002023673411 & 0.758004047346822 & 0.620997976326589 \tabularnewline
80 & 0.433508641967154 & 0.867017283934308 & 0.566491358032846 \tabularnewline
81 & 0.405926882234319 & 0.811853764468639 & 0.59407311776568 \tabularnewline
82 & 0.420524126583677 & 0.841048253167354 & 0.579475873416323 \tabularnewline
83 & 0.384433491345622 & 0.768866982691244 & 0.615566508654378 \tabularnewline
84 & 0.349217597553676 & 0.698435195107351 & 0.650782402446324 \tabularnewline
85 & 0.314810416579386 & 0.629620833158772 & 0.685189583420614 \tabularnewline
86 & 0.285310417951445 & 0.57062083590289 & 0.714689582048555 \tabularnewline
87 & 0.258033084432571 & 0.516066168865143 & 0.741966915567429 \tabularnewline
88 & 0.233180503912249 & 0.466361007824497 & 0.766819496087751 \tabularnewline
89 & 0.289742557967303 & 0.579485115934605 & 0.710257442032697 \tabularnewline
90 & 0.270749650437165 & 0.54149930087433 & 0.729250349562835 \tabularnewline
91 & 0.249922471224889 & 0.499844942449778 & 0.750077528775111 \tabularnewline
92 & 0.222571365687588 & 0.445142731375176 & 0.777428634312412 \tabularnewline
93 & 0.207409034141391 & 0.414818068282782 & 0.792590965858609 \tabularnewline
94 & 0.189910347363438 & 0.379820694726875 & 0.810089652636562 \tabularnewline
95 & 0.165906720645039 & 0.331813441290077 & 0.834093279354961 \tabularnewline
96 & 0.154161133571549 & 0.308322267143097 & 0.845838866428452 \tabularnewline
97 & 0.143590065637064 & 0.287180131274128 & 0.856409934362936 \tabularnewline
98 & 0.123668016817479 & 0.247336033634957 & 0.876331983182521 \tabularnewline
99 & 0.120337360998674 & 0.240674721997348 & 0.879662639001326 \tabularnewline
100 & 0.103941142005379 & 0.207882284010758 & 0.89605885799462 \tabularnewline
101 & 0.0953041460009554 & 0.190608292001911 & 0.904695853999045 \tabularnewline
102 & 0.08095189494816 & 0.16190378989632 & 0.91904810505184 \tabularnewline
103 & 0.101158175979896 & 0.202316351959793 & 0.898841824020104 \tabularnewline
104 & 0.111570335417639 & 0.223140670835278 & 0.888429664582361 \tabularnewline
105 & 0.116236943006142 & 0.232473886012284 & 0.883763056993858 \tabularnewline
106 & 0.09974507670257 & 0.19949015340514 & 0.90025492329743 \tabularnewline
107 & 0.116024881229045 & 0.232049762458091 & 0.883975118770955 \tabularnewline
108 & 0.101662453611131 & 0.203324907222263 & 0.898337546388869 \tabularnewline
109 & 0.269937267434336 & 0.539874534868672 & 0.730062732565664 \tabularnewline
110 & 0.250092177412277 & 0.500184354824555 & 0.749907822587722 \tabularnewline
111 & 0.223751730498832 & 0.447503460997664 & 0.776248269501168 \tabularnewline
112 & 0.230724419450128 & 0.461448838900256 & 0.769275580549872 \tabularnewline
113 & 0.216661084579323 & 0.433322169158645 & 0.783338915420677 \tabularnewline
114 & 0.204074761984853 & 0.408149523969707 & 0.795925238015147 \tabularnewline
115 & 0.186070712246897 & 0.372141424493795 & 0.813929287753103 \tabularnewline
116 & 0.164867173676349 & 0.329734347352699 & 0.835132826323651 \tabularnewline
117 & 0.144095246794328 & 0.288190493588655 & 0.855904753205672 \tabularnewline
118 & 0.169177022689898 & 0.338354045379796 & 0.830822977310102 \tabularnewline
119 & 0.157895110221562 & 0.315790220443125 & 0.842104889778438 \tabularnewline
120 & 0.137622208863481 & 0.275244417726961 & 0.86237779113652 \tabularnewline
121 & 0.122778526176504 & 0.245557052353008 & 0.877221473823496 \tabularnewline
122 & 0.109738610534707 & 0.219477221069413 & 0.890261389465293 \tabularnewline
123 & 0.134904631651336 & 0.269809263302672 & 0.865095368348664 \tabularnewline
124 & 0.120169260700505 & 0.24033852140101 & 0.879830739299495 \tabularnewline
125 & 0.121658980942313 & 0.243317961884626 & 0.878341019057687 \tabularnewline
126 & 0.120329053344671 & 0.240658106689341 & 0.87967094665533 \tabularnewline
127 & 0.104787353403057 & 0.209574706806113 & 0.895212646596943 \tabularnewline
128 & 0.0948709496599214 & 0.189741899319843 & 0.905129050340079 \tabularnewline
129 & 0.0816642885919557 & 0.163328577183911 & 0.918335711408044 \tabularnewline
130 & 0.0827465028617967 & 0.165493005723593 & 0.917253497138203 \tabularnewline
131 & 0.085655868642956 & 0.171311737285912 & 0.914344131357044 \tabularnewline
132 & 0.0803734796585914 & 0.160746959317183 & 0.919626520341409 \tabularnewline
133 & 0.0774152220388494 & 0.154830444077699 & 0.92258477796115 \tabularnewline
134 & 0.0654711504381776 & 0.130942300876355 & 0.934528849561822 \tabularnewline
135 & 0.0623090668284707 & 0.124618133656941 & 0.93769093317153 \tabularnewline
136 & 0.15018106632638 & 0.300362132652759 & 0.84981893367362 \tabularnewline
137 & 0.13207241044858 & 0.26414482089716 & 0.86792758955142 \tabularnewline
138 & 0.114400889693676 & 0.228801779387352 & 0.885599110306324 \tabularnewline
139 & 0.101814909753365 & 0.20362981950673 & 0.898185090246635 \tabularnewline
140 & 0.0948247957118991 & 0.189649591423798 & 0.905175204288101 \tabularnewline
141 & 0.091667327961221 & 0.183334655922442 & 0.908332672038779 \tabularnewline
142 & 0.0859435310416151 & 0.17188706208323 & 0.914056468958385 \tabularnewline
143 & 0.0757214989614446 & 0.151442997922889 & 0.924278501038555 \tabularnewline
144 & 0.064661554003 & 0.129323108006 & 0.935338445997 \tabularnewline
145 & 0.0545212963993683 & 0.109042592798737 & 0.945478703600632 \tabularnewline
146 & 0.0454597819228906 & 0.0909195638457812 & 0.95454021807711 \tabularnewline
147 & 0.043235144596218 & 0.086470289192436 & 0.956764855403782 \tabularnewline
148 & 0.0509077497189826 & 0.101815499437965 & 0.949092250281017 \tabularnewline
149 & 0.0467439502350206 & 0.0934879004700413 & 0.95325604976498 \tabularnewline
150 & 0.0389594468724709 & 0.0779188937449419 & 0.96104055312753 \tabularnewline
151 & 0.0421542894962058 & 0.0843085789924116 & 0.957845710503794 \tabularnewline
152 & 0.0408489279582072 & 0.0816978559164143 & 0.959151072041793 \tabularnewline
153 & 0.0400091116822711 & 0.0800182233645423 & 0.959990888317729 \tabularnewline
154 & 0.036659552349613 & 0.073319104699226 & 0.963340447650387 \tabularnewline
155 & 0.0435079440003232 & 0.0870158880006463 & 0.956492055999677 \tabularnewline
156 & 0.0368659524205509 & 0.0737319048411019 & 0.96313404757945 \tabularnewline
157 & 0.030612470367783 & 0.0612249407355661 & 0.969387529632217 \tabularnewline
158 & 0.026753865697146 & 0.053507731394292 & 0.973246134302854 \tabularnewline
159 & 0.040820323795077 & 0.081640647590154 & 0.959179676204923 \tabularnewline
160 & 0.0426963969486116 & 0.0853927938972231 & 0.957303603051388 \tabularnewline
161 & 0.0387218236163916 & 0.0774436472327832 & 0.961278176383608 \tabularnewline
162 & 0.098429964528695 & 0.19685992905739 & 0.901570035471305 \tabularnewline
163 & 0.0840584154850848 & 0.16811683097017 & 0.915941584514915 \tabularnewline
164 & 0.25106743864141 & 0.50213487728282 & 0.74893256135859 \tabularnewline
165 & 0.231703229644866 & 0.463406459289733 & 0.768296770355134 \tabularnewline
166 & 0.382188851847188 & 0.764377703694376 & 0.617811148152812 \tabularnewline
167 & 0.355773940473622 & 0.711547880947244 & 0.644226059526378 \tabularnewline
168 & 0.328735324049494 & 0.657470648098989 & 0.671264675950506 \tabularnewline
169 & 0.301976736753853 & 0.603953473507707 & 0.698023263246147 \tabularnewline
170 & 0.271517756275591 & 0.543035512551182 & 0.728482243724409 \tabularnewline
171 & 0.315928396146886 & 0.631856792293771 & 0.684071603853114 \tabularnewline
172 & 0.284651484676533 & 0.569302969353066 & 0.715348515323467 \tabularnewline
173 & 0.354126891377723 & 0.708253782755446 & 0.645873108622277 \tabularnewline
174 & 0.351127775465983 & 0.702255550931966 & 0.648872224534017 \tabularnewline
175 & 0.326437358104996 & 0.652874716209992 & 0.673562641895004 \tabularnewline
176 & 0.405169629981359 & 0.810339259962718 & 0.594830370018641 \tabularnewline
177 & 0.376905586020241 & 0.753811172040482 & 0.623094413979759 \tabularnewline
178 & 0.3805578808181 & 0.761115761636199 & 0.6194421191819 \tabularnewline
179 & 0.35703076473092 & 0.71406152946184 & 0.64296923526908 \tabularnewline
180 & 0.334688934566727 & 0.669377869133455 & 0.665311065433273 \tabularnewline
181 & 0.321616020400334 & 0.643232040800668 & 0.678383979599666 \tabularnewline
182 & 0.397839673658551 & 0.795679347317101 & 0.602160326341449 \tabularnewline
183 & 0.372268859192714 & 0.744537718385428 & 0.627731140807286 \tabularnewline
184 & 0.384684227164197 & 0.769368454328394 & 0.615315772835803 \tabularnewline
185 & 0.353282434931136 & 0.706564869862273 & 0.646717565068864 \tabularnewline
186 & 0.422746492281256 & 0.845492984562513 & 0.577253507718744 \tabularnewline
187 & 0.387112607197245 & 0.77422521439449 & 0.612887392802755 \tabularnewline
188 & 0.352358737316538 & 0.704717474633076 & 0.647641262683462 \tabularnewline
189 & 0.316951116393263 & 0.633902232786526 & 0.683048883606737 \tabularnewline
190 & 0.406786653477332 & 0.813573306954663 & 0.593213346522668 \tabularnewline
191 & 0.401861774356063 & 0.803723548712126 & 0.598138225643937 \tabularnewline
192 & 0.416240024871013 & 0.832480049742027 & 0.583759975128987 \tabularnewline
193 & 0.406489109048195 & 0.81297821809639 & 0.593510890951805 \tabularnewline
194 & 0.368555601490338 & 0.737111202980675 & 0.631444398509662 \tabularnewline
195 & 0.341188151404163 & 0.682376302808326 & 0.658811848595837 \tabularnewline
196 & 0.347736962799616 & 0.695473925599233 & 0.652263037200384 \tabularnewline
197 & 0.464558862288207 & 0.929117724576414 & 0.535441137711793 \tabularnewline
198 & 0.437844191242125 & 0.87568838248425 & 0.562155808757875 \tabularnewline
199 & 0.399882874282539 & 0.799765748565078 & 0.600117125717461 \tabularnewline
200 & 0.36584524408941 & 0.731690488178819 & 0.63415475591059 \tabularnewline
201 & 0.328201667516542 & 0.656403335033083 & 0.671798332483458 \tabularnewline
202 & 0.293569447650484 & 0.587138895300968 & 0.706430552349516 \tabularnewline
203 & 0.258888354170896 & 0.517776708341793 & 0.741111645829104 \tabularnewline
204 & 0.225941417931203 & 0.451882835862405 & 0.774058582068797 \tabularnewline
205 & 0.202549445166962 & 0.405098890333924 & 0.797450554833038 \tabularnewline
206 & 0.174659867194154 & 0.349319734388307 & 0.825340132805847 \tabularnewline
207 & 0.15419857438087 & 0.308397148761739 & 0.84580142561913 \tabularnewline
208 & 0.162948149398237 & 0.325896298796475 & 0.837051850601763 \tabularnewline
209 & 0.137816770814447 & 0.275633541628894 & 0.862183229185553 \tabularnewline
210 & 0.115867933076637 & 0.231735866153273 & 0.884132066923363 \tabularnewline
211 & 0.0969674595322351 & 0.19393491906447 & 0.903032540467765 \tabularnewline
212 & 0.081070277532675 & 0.16214055506535 & 0.918929722467325 \tabularnewline
213 & 0.073380882221622 & 0.146761764443244 & 0.926619117778378 \tabularnewline
214 & 0.0816589524094491 & 0.163317904818898 & 0.918341047590551 \tabularnewline
215 & 0.0679813817973048 & 0.13596276359461 & 0.932018618202695 \tabularnewline
216 & 0.0557133386553233 & 0.111426677310647 & 0.944286661344677 \tabularnewline
217 & 0.0443642712247575 & 0.088728542449515 & 0.955635728775242 \tabularnewline
218 & 0.0366405433807238 & 0.0732810867614476 & 0.963359456619276 \tabularnewline
219 & 0.0396475006878662 & 0.0792950013757324 & 0.960352499312134 \tabularnewline
220 & 0.0330320581655332 & 0.0660641163310664 & 0.966967941834467 \tabularnewline
221 & 0.0392844428501948 & 0.0785688857003896 & 0.960715557149805 \tabularnewline
222 & 0.0303647530317441 & 0.0607295060634882 & 0.969635246968256 \tabularnewline
223 & 0.0230872683352824 & 0.0461745366705647 & 0.976912731664718 \tabularnewline
224 & 0.024268421935349 & 0.0485368438706979 & 0.975731578064651 \tabularnewline
225 & 0.0264586834696126 & 0.0529173669392252 & 0.973541316530387 \tabularnewline
226 & 0.0212791974031923 & 0.0425583948063847 & 0.978720802596808 \tabularnewline
227 & 0.0178573022503444 & 0.0357146045006888 & 0.982142697749656 \tabularnewline
228 & 0.0173681735001042 & 0.0347363470002084 & 0.982631826499896 \tabularnewline
229 & 0.025643076559508 & 0.051286153119016 & 0.974356923440492 \tabularnewline
230 & 0.0241269934086207 & 0.0482539868172413 & 0.97587300659138 \tabularnewline
231 & 0.0189199554493464 & 0.0378399108986928 & 0.981080044550654 \tabularnewline
232 & 0.0146523805817986 & 0.0293047611635972 & 0.985347619418201 \tabularnewline
233 & 0.0172104845343681 & 0.0344209690687362 & 0.982789515465632 \tabularnewline
234 & 0.0155240440816326 & 0.0310480881632651 & 0.984475955918367 \tabularnewline
235 & 0.015848079511548 & 0.0316961590230961 & 0.984151920488452 \tabularnewline
236 & 0.0125470852984329 & 0.0250941705968658 & 0.987452914701567 \tabularnewline
237 & 0.00988422074812546 & 0.0197684414962509 & 0.990115779251875 \tabularnewline
238 & 0.00707774172226167 & 0.0141554834445233 & 0.992922258277738 \tabularnewline
239 & 0.0169898421316424 & 0.0339796842632848 & 0.983010157868358 \tabularnewline
240 & 0.0230743431154607 & 0.0461486862309215 & 0.97692565688454 \tabularnewline
241 & 0.0506627426870062 & 0.101325485374012 & 0.949337257312994 \tabularnewline
242 & 0.0363517468154043 & 0.0727034936308086 & 0.963648253184596 \tabularnewline
243 & 0.025206798939373 & 0.0504135978787461 & 0.974793201060627 \tabularnewline
244 & 0.0170189238017713 & 0.0340378476035427 & 0.982981076198229 \tabularnewline
245 & 0.0115682174013208 & 0.0231364348026417 & 0.988431782598679 \tabularnewline
246 & 0.0112940671600086 & 0.0225881343200173 & 0.988705932839991 \tabularnewline
247 & 0.00699865493804418 & 0.0139973098760884 & 0.993001345061956 \tabularnewline
248 & 0.184093448694974 & 0.368186897389947 & 0.815906551305026 \tabularnewline
249 & 0.173666509238348 & 0.347333018476695 & 0.826333490761652 \tabularnewline
250 & 0.281037513000659 & 0.562075026001319 & 0.718962486999341 \tabularnewline
251 & 0.216681702086761 & 0.433363404173522 & 0.783318297913239 \tabularnewline
252 & 0.282828726993544 & 0.565657453987089 & 0.717171273006456 \tabularnewline
253 & 0.288626696833044 & 0.577253393666088 & 0.711373303166956 \tabularnewline
254 & 0.201506131853499 & 0.403012263706998 & 0.798493868146501 \tabularnewline
255 & 0.190491021117838 & 0.380982042235675 & 0.809508978882162 \tabularnewline
256 & 0.11975435103165 & 0.2395087020633 & 0.88024564896835 \tabularnewline
257 & 0.0649584150180973 & 0.129916830036195 & 0.935041584981903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.99907139880372[/C][C]0.0018572023925594[/C][C]0.0009286011962797[/C][/ROW]
[ROW][C]8[/C][C]0.998784182169667[/C][C]0.00243163566066583[/C][C]0.00121581783033291[/C][/ROW]
[ROW][C]9[/C][C]0.998782885594285[/C][C]0.00243422881143028[/C][C]0.00121711440571514[/C][/ROW]
[ROW][C]10[/C][C]0.997287021518417[/C][C]0.00542595696316633[/C][C]0.00271297848158317[/C][/ROW]
[ROW][C]11[/C][C]0.996944656698467[/C][C]0.00611068660306664[/C][C]0.00305534330153332[/C][/ROW]
[ROW][C]12[/C][C]0.99609229810113[/C][C]0.00781540379774184[/C][C]0.00390770189887092[/C][/ROW]
[ROW][C]13[/C][C]0.993528238886365[/C][C]0.0129435222272707[/C][C]0.00647176111363536[/C][/ROW]
[ROW][C]14[/C][C]0.989687817171166[/C][C]0.0206243656576683[/C][C]0.0103121828288341[/C][/ROW]
[ROW][C]15[/C][C]0.98302533656122[/C][C]0.0339493268775586[/C][C]0.0169746634387793[/C][/ROW]
[ROW][C]16[/C][C]0.973841443533827[/C][C]0.0523171129323466[/C][C]0.0261585564661733[/C][/ROW]
[ROW][C]17[/C][C]0.96013753265285[/C][C]0.079724934694299[/C][C]0.0398624673471495[/C][/ROW]
[ROW][C]18[/C][C]0.943046200100363[/C][C]0.113907599799275[/C][C]0.0569537998996374[/C][/ROW]
[ROW][C]19[/C][C]0.945750139697636[/C][C]0.108499720604729[/C][C]0.0542498603023643[/C][/ROW]
[ROW][C]20[/C][C]0.924742502613533[/C][C]0.150514994772934[/C][C]0.075257497386467[/C][/ROW]
[ROW][C]21[/C][C]0.90370783229317[/C][C]0.192584335413661[/C][C]0.0962921677068303[/C][/ROW]
[ROW][C]22[/C][C]0.876930478436991[/C][C]0.246139043126018[/C][C]0.123069521563009[/C][/ROW]
[ROW][C]23[/C][C]0.845227627949244[/C][C]0.309544744101513[/C][C]0.154772372050756[/C][/ROW]
[ROW][C]24[/C][C]0.803894182494755[/C][C]0.39221163501049[/C][C]0.196105817505245[/C][/ROW]
[ROW][C]25[/C][C]0.767073628492641[/C][C]0.465852743014718[/C][C]0.232926371507359[/C][/ROW]
[ROW][C]26[/C][C]0.901994049661576[/C][C]0.196011900676848[/C][C]0.098005950338424[/C][/ROW]
[ROW][C]27[/C][C]0.87731696450878[/C][C]0.245366070982439[/C][C]0.122683035491219[/C][/ROW]
[ROW][C]28[/C][C]0.858564724881973[/C][C]0.282870550236055[/C][C]0.141435275118027[/C][/ROW]
[ROW][C]29[/C][C]0.829741514075224[/C][C]0.340516971849551[/C][C]0.170258485924776[/C][/ROW]
[ROW][C]30[/C][C]0.798529586039661[/C][C]0.402940827920678[/C][C]0.201470413960339[/C][/ROW]
[ROW][C]31[/C][C]0.809740455749674[/C][C]0.380519088500652[/C][C]0.190259544250326[/C][/ROW]
[ROW][C]32[/C][C]0.777502600733418[/C][C]0.444994798533164[/C][C]0.222497399266582[/C][/ROW]
[ROW][C]33[/C][C]0.743356535658939[/C][C]0.513286928682122[/C][C]0.256643464341061[/C][/ROW]
[ROW][C]34[/C][C]0.712876558794703[/C][C]0.574246882410595[/C][C]0.287123441205297[/C][/ROW]
[ROW][C]35[/C][C]0.66571631654696[/C][C]0.668567366906081[/C][C]0.33428368345304[/C][/ROW]
[ROW][C]36[/C][C]0.712654758049467[/C][C]0.574690483901067[/C][C]0.287345241950533[/C][/ROW]
[ROW][C]37[/C][C]0.767193386078844[/C][C]0.465613227842311[/C][C]0.232806613921156[/C][/ROW]
[ROW][C]38[/C][C]0.726050379908531[/C][C]0.547899240182938[/C][C]0.273949620091469[/C][/ROW]
[ROW][C]39[/C][C]0.698135227110948[/C][C]0.603729545778104[/C][C]0.301864772889052[/C][/ROW]
[ROW][C]40[/C][C]0.713076654304444[/C][C]0.573846691391113[/C][C]0.286923345695556[/C][/ROW]
[ROW][C]41[/C][C]0.714749778516966[/C][C]0.570500442966068[/C][C]0.285250221483034[/C][/ROW]
[ROW][C]42[/C][C]0.698123497669303[/C][C]0.603753004661395[/C][C]0.301876502330697[/C][/ROW]
[ROW][C]43[/C][C]0.708144070076873[/C][C]0.583711859846255[/C][C]0.291855929923127[/C][/ROW]
[ROW][C]44[/C][C]0.666807337484064[/C][C]0.666385325031871[/C][C]0.333192662515936[/C][/ROW]
[ROW][C]45[/C][C]0.623916089563222[/C][C]0.752167820873556[/C][C]0.376083910436778[/C][/ROW]
[ROW][C]46[/C][C]0.588329043151082[/C][C]0.823341913697836[/C][C]0.411670956848918[/C][/ROW]
[ROW][C]47[/C][C]0.594809397812196[/C][C]0.810381204375609[/C][C]0.405190602187804[/C][/ROW]
[ROW][C]48[/C][C]0.559394099497281[/C][C]0.881211801005437[/C][C]0.440605900502719[/C][/ROW]
[ROW][C]49[/C][C]0.597598086920941[/C][C]0.804803826158117[/C][C]0.402401913079059[/C][/ROW]
[ROW][C]50[/C][C]0.570657178351233[/C][C]0.858685643297534[/C][C]0.429342821648767[/C][/ROW]
[ROW][C]51[/C][C]0.554772837317077[/C][C]0.890454325365846[/C][C]0.445227162682923[/C][/ROW]
[ROW][C]52[/C][C]0.51753366201184[/C][C]0.96493267597632[/C][C]0.48246633798816[/C][/ROW]
[ROW][C]53[/C][C]0.535592378397729[/C][C]0.928815243204542[/C][C]0.464407621602271[/C][/ROW]
[ROW][C]54[/C][C]0.491549759383824[/C][C]0.983099518767647[/C][C]0.508450240616176[/C][/ROW]
[ROW][C]55[/C][C]0.503698310627197[/C][C]0.992603378745606[/C][C]0.496301689372803[/C][/ROW]
[ROW][C]56[/C][C]0.495112063074355[/C][C]0.99022412614871[/C][C]0.504887936925645[/C][/ROW]
[ROW][C]57[/C][C]0.469242528300787[/C][C]0.938485056601574[/C][C]0.530757471699213[/C][/ROW]
[ROW][C]58[/C][C]0.502812247307122[/C][C]0.994375505385757[/C][C]0.497187752692878[/C][/ROW]
[ROW][C]59[/C][C]0.536165950755778[/C][C]0.927668098488444[/C][C]0.463834049244222[/C][/ROW]
[ROW][C]60[/C][C]0.503542011116223[/C][C]0.992915977767554[/C][C]0.496457988883777[/C][/ROW]
[ROW][C]61[/C][C]0.529655935046502[/C][C]0.940688129906996[/C][C]0.470344064953498[/C][/ROW]
[ROW][C]62[/C][C]0.494985248076201[/C][C]0.989970496152403[/C][C]0.505014751923799[/C][/ROW]
[ROW][C]63[/C][C]0.49195536617819[/C][C]0.98391073235638[/C][C]0.50804463382181[/C][/ROW]
[ROW][C]64[/C][C]0.454225016104322[/C][C]0.908450032208644[/C][C]0.545774983895678[/C][/ROW]
[ROW][C]65[/C][C]0.415677964601323[/C][C]0.831355929202646[/C][C]0.584322035398677[/C][/ROW]
[ROW][C]66[/C][C]0.46319975441901[/C][C]0.92639950883802[/C][C]0.53680024558099[/C][/ROW]
[ROW][C]67[/C][C]0.463219969827794[/C][C]0.926439939655588[/C][C]0.536780030172206[/C][/ROW]
[ROW][C]68[/C][C]0.455240580046277[/C][C]0.910481160092554[/C][C]0.544759419953723[/C][/ROW]
[ROW][C]69[/C][C]0.422220910992112[/C][C]0.844441821984223[/C][C]0.577779089007888[/C][/ROW]
[ROW][C]70[/C][C]0.398842307787864[/C][C]0.797684615575728[/C][C]0.601157692212136[/C][/ROW]
[ROW][C]71[/C][C]0.361810311732747[/C][C]0.723620623465494[/C][C]0.638189688267253[/C][/ROW]
[ROW][C]72[/C][C]0.365668154570626[/C][C]0.731336309141252[/C][C]0.634331845429374[/C][/ROW]
[ROW][C]73[/C][C]0.329689301601845[/C][C]0.659378603203689[/C][C]0.670310698398155[/C][/ROW]
[ROW][C]74[/C][C]0.314423851342937[/C][C]0.628847702685873[/C][C]0.685576148657063[/C][/ROW]
[ROW][C]75[/C][C]0.305615380729808[/C][C]0.611230761459617[/C][C]0.694384619270192[/C][/ROW]
[ROW][C]76[/C][C]0.412952782725601[/C][C]0.825905565451202[/C][C]0.587047217274399[/C][/ROW]
[ROW][C]77[/C][C]0.395938951270883[/C][C]0.791877902541766[/C][C]0.604061048729117[/C][/ROW]
[ROW][C]78[/C][C]0.416589917130759[/C][C]0.833179834261517[/C][C]0.583410082869241[/C][/ROW]
[ROW][C]79[/C][C]0.379002023673411[/C][C]0.758004047346822[/C][C]0.620997976326589[/C][/ROW]
[ROW][C]80[/C][C]0.433508641967154[/C][C]0.867017283934308[/C][C]0.566491358032846[/C][/ROW]
[ROW][C]81[/C][C]0.405926882234319[/C][C]0.811853764468639[/C][C]0.59407311776568[/C][/ROW]
[ROW][C]82[/C][C]0.420524126583677[/C][C]0.841048253167354[/C][C]0.579475873416323[/C][/ROW]
[ROW][C]83[/C][C]0.384433491345622[/C][C]0.768866982691244[/C][C]0.615566508654378[/C][/ROW]
[ROW][C]84[/C][C]0.349217597553676[/C][C]0.698435195107351[/C][C]0.650782402446324[/C][/ROW]
[ROW][C]85[/C][C]0.314810416579386[/C][C]0.629620833158772[/C][C]0.685189583420614[/C][/ROW]
[ROW][C]86[/C][C]0.285310417951445[/C][C]0.57062083590289[/C][C]0.714689582048555[/C][/ROW]
[ROW][C]87[/C][C]0.258033084432571[/C][C]0.516066168865143[/C][C]0.741966915567429[/C][/ROW]
[ROW][C]88[/C][C]0.233180503912249[/C][C]0.466361007824497[/C][C]0.766819496087751[/C][/ROW]
[ROW][C]89[/C][C]0.289742557967303[/C][C]0.579485115934605[/C][C]0.710257442032697[/C][/ROW]
[ROW][C]90[/C][C]0.270749650437165[/C][C]0.54149930087433[/C][C]0.729250349562835[/C][/ROW]
[ROW][C]91[/C][C]0.249922471224889[/C][C]0.499844942449778[/C][C]0.750077528775111[/C][/ROW]
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[ROW][C]236[/C][C]0.0125470852984329[/C][C]0.0250941705968658[/C][C]0.987452914701567[/C][/ROW]
[ROW][C]237[/C][C]0.00988422074812546[/C][C]0.0197684414962509[/C][C]0.990115779251875[/C][/ROW]
[ROW][C]238[/C][C]0.00707774172226167[/C][C]0.0141554834445233[/C][C]0.992922258277738[/C][/ROW]
[ROW][C]239[/C][C]0.0169898421316424[/C][C]0.0339796842632848[/C][C]0.983010157868358[/C][/ROW]
[ROW][C]240[/C][C]0.0230743431154607[/C][C]0.0461486862309215[/C][C]0.97692565688454[/C][/ROW]
[ROW][C]241[/C][C]0.0506627426870062[/C][C]0.101325485374012[/C][C]0.949337257312994[/C][/ROW]
[ROW][C]242[/C][C]0.0363517468154043[/C][C]0.0727034936308086[/C][C]0.963648253184596[/C][/ROW]
[ROW][C]243[/C][C]0.025206798939373[/C][C]0.0504135978787461[/C][C]0.974793201060627[/C][/ROW]
[ROW][C]244[/C][C]0.0170189238017713[/C][C]0.0340378476035427[/C][C]0.982981076198229[/C][/ROW]
[ROW][C]245[/C][C]0.0115682174013208[/C][C]0.0231364348026417[/C][C]0.988431782598679[/C][/ROW]
[ROW][C]246[/C][C]0.0112940671600086[/C][C]0.0225881343200173[/C][C]0.988705932839991[/C][/ROW]
[ROW][C]247[/C][C]0.00699865493804418[/C][C]0.0139973098760884[/C][C]0.993001345061956[/C][/ROW]
[ROW][C]248[/C][C]0.184093448694974[/C][C]0.368186897389947[/C][C]0.815906551305026[/C][/ROW]
[ROW][C]249[/C][C]0.173666509238348[/C][C]0.347333018476695[/C][C]0.826333490761652[/C][/ROW]
[ROW][C]250[/C][C]0.281037513000659[/C][C]0.562075026001319[/C][C]0.718962486999341[/C][/ROW]
[ROW][C]251[/C][C]0.216681702086761[/C][C]0.433363404173522[/C][C]0.783318297913239[/C][/ROW]
[ROW][C]252[/C][C]0.282828726993544[/C][C]0.565657453987089[/C][C]0.717171273006456[/C][/ROW]
[ROW][C]253[/C][C]0.288626696833044[/C][C]0.577253393666088[/C][C]0.711373303166956[/C][/ROW]
[ROW][C]254[/C][C]0.201506131853499[/C][C]0.403012263706998[/C][C]0.798493868146501[/C][/ROW]
[ROW][C]255[/C][C]0.190491021117838[/C][C]0.380982042235675[/C][C]0.809508978882162[/C][/ROW]
[ROW][C]256[/C][C]0.11975435103165[/C][C]0.2395087020633[/C][C]0.88024564896835[/C][/ROW]
[ROW][C]257[/C][C]0.0649584150180973[/C][C]0.129916830036195[/C][C]0.935041584981903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.999071398803720.00185720239255940.0009286011962797
80.9987841821696670.002431635660665830.00121581783033291
90.9987828855942850.002434228811430280.00121711440571514
100.9972870215184170.005425956963166330.00271297848158317
110.9969446566984670.006110686603066640.00305534330153332
120.996092298101130.007815403797741840.00390770189887092
130.9935282388863650.01294352222727070.00647176111363536
140.9896878171711660.02062436565766830.0103121828288341
150.983025336561220.03394932687755860.0169746634387793
160.9738414435338270.05231711293234660.0261585564661733
170.960137532652850.0797249346942990.0398624673471495
180.9430462001003630.1139075997992750.0569537998996374
190.9457501396976360.1084997206047290.0542498603023643
200.9247425026135330.1505149947729340.075257497386467
210.903707832293170.1925843354136610.0962921677068303
220.8769304784369910.2461390431260180.123069521563009
230.8452276279492440.3095447441015130.154772372050756
240.8038941824947550.392211635010490.196105817505245
250.7670736284926410.4658527430147180.232926371507359
260.9019940496615760.1960119006768480.098005950338424
270.877316964508780.2453660709824390.122683035491219
280.8585647248819730.2828705502360550.141435275118027
290.8297415140752240.3405169718495510.170258485924776
300.7985295860396610.4029408279206780.201470413960339
310.8097404557496740.3805190885006520.190259544250326
320.7775026007334180.4449947985331640.222497399266582
330.7433565356589390.5132869286821220.256643464341061
340.7128765587947030.5742468824105950.287123441205297
350.665716316546960.6685673669060810.33428368345304
360.7126547580494670.5746904839010670.287345241950533
370.7671933860788440.4656132278423110.232806613921156
380.7260503799085310.5478992401829380.273949620091469
390.6981352271109480.6037295457781040.301864772889052
400.7130766543044440.5738466913911130.286923345695556
410.7147497785169660.5705004429660680.285250221483034
420.6981234976693030.6037530046613950.301876502330697
430.7081440700768730.5837118598462550.291855929923127
440.6668073374840640.6663853250318710.333192662515936
450.6239160895632220.7521678208735560.376083910436778
460.5883290431510820.8233419136978360.411670956848918
470.5948093978121960.8103812043756090.405190602187804
480.5593940994972810.8812118010054370.440605900502719
490.5975980869209410.8048038261581170.402401913079059
500.5706571783512330.8586856432975340.429342821648767
510.5547728373170770.8904543253658460.445227162682923
520.517533662011840.964932675976320.48246633798816
530.5355923783977290.9288152432045420.464407621602271
540.4915497593838240.9830995187676470.508450240616176
550.5036983106271970.9926033787456060.496301689372803
560.4951120630743550.990224126148710.504887936925645
570.4692425283007870.9384850566015740.530757471699213
580.5028122473071220.9943755053857570.497187752692878
590.5361659507557780.9276680984884440.463834049244222
600.5035420111162230.9929159777675540.496457988883777
610.5296559350465020.9406881299069960.470344064953498
620.4949852480762010.9899704961524030.505014751923799
630.491955366178190.983910732356380.50804463382181
640.4542250161043220.9084500322086440.545774983895678
650.4156779646013230.8313559292026460.584322035398677
660.463199754419010.926399508838020.53680024558099
670.4632199698277940.9264399396555880.536780030172206
680.4552405800462770.9104811600925540.544759419953723
690.4222209109921120.8444418219842230.577779089007888
700.3988423077878640.7976846155757280.601157692212136
710.3618103117327470.7236206234654940.638189688267253
720.3656681545706260.7313363091412520.634331845429374
730.3296893016018450.6593786032036890.670310698398155
740.3144238513429370.6288477026858730.685576148657063
750.3056153807298080.6112307614596170.694384619270192
760.4129527827256010.8259055654512020.587047217274399
770.3959389512708830.7918779025417660.604061048729117
780.4165899171307590.8331798342615170.583410082869241
790.3790020236734110.7580040473468220.620997976326589
800.4335086419671540.8670172839343080.566491358032846
810.4059268822343190.8118537644686390.59407311776568
820.4205241265836770.8410482531673540.579475873416323
830.3844334913456220.7688669826912440.615566508654378
840.3492175975536760.6984351951073510.650782402446324
850.3148104165793860.6296208331587720.685189583420614
860.2853104179514450.570620835902890.714689582048555
870.2580330844325710.5160661688651430.741966915567429
880.2331805039122490.4663610078244970.766819496087751
890.2897425579673030.5794851159346050.710257442032697
900.2707496504371650.541499300874330.729250349562835
910.2499224712248890.4998449424497780.750077528775111
920.2225713656875880.4451427313751760.777428634312412
930.2074090341413910.4148180682827820.792590965858609
940.1899103473634380.3798206947268750.810089652636562
950.1659067206450390.3318134412900770.834093279354961
960.1541611335715490.3083222671430970.845838866428452
970.1435900656370640.2871801312741280.856409934362936
980.1236680168174790.2473360336349570.876331983182521
990.1203373609986740.2406747219973480.879662639001326
1000.1039411420053790.2078822840107580.89605885799462
1010.09530414600095540.1906082920019110.904695853999045
1020.080951894948160.161903789896320.91904810505184
1030.1011581759798960.2023163519597930.898841824020104
1040.1115703354176390.2231406708352780.888429664582361
1050.1162369430061420.2324738860122840.883763056993858
1060.099745076702570.199490153405140.90025492329743
1070.1160248812290450.2320497624580910.883975118770955
1080.1016624536111310.2033249072222630.898337546388869
1090.2699372674343360.5398745348686720.730062732565664
1100.2500921774122770.5001843548245550.749907822587722
1110.2237517304988320.4475034609976640.776248269501168
1120.2307244194501280.4614488389002560.769275580549872
1130.2166610845793230.4333221691586450.783338915420677
1140.2040747619848530.4081495239697070.795925238015147
1150.1860707122468970.3721414244937950.813929287753103
1160.1648671736763490.3297343473526990.835132826323651
1170.1440952467943280.2881904935886550.855904753205672
1180.1691770226898980.3383540453797960.830822977310102
1190.1578951102215620.3157902204431250.842104889778438
1200.1376222088634810.2752444177269610.86237779113652
1210.1227785261765040.2455570523530080.877221473823496
1220.1097386105347070.2194772210694130.890261389465293
1230.1349046316513360.2698092633026720.865095368348664
1240.1201692607005050.240338521401010.879830739299495
1250.1216589809423130.2433179618846260.878341019057687
1260.1203290533446710.2406581066893410.87967094665533
1270.1047873534030570.2095747068061130.895212646596943
1280.09487094965992140.1897418993198430.905129050340079
1290.08166428859195570.1633285771839110.918335711408044
1300.08274650286179670.1654930057235930.917253497138203
1310.0856558686429560.1713117372859120.914344131357044
1320.08037347965859140.1607469593171830.919626520341409
1330.07741522203884940.1548304440776990.92258477796115
1340.06547115043817760.1309423008763550.934528849561822
1350.06230906682847070.1246181336569410.93769093317153
1360.150181066326380.3003621326527590.84981893367362
1370.132072410448580.264144820897160.86792758955142
1380.1144008896936760.2288017793873520.885599110306324
1390.1018149097533650.203629819506730.898185090246635
1400.09482479571189910.1896495914237980.905175204288101
1410.0916673279612210.1833346559224420.908332672038779
1420.08594353104161510.171887062083230.914056468958385
1430.07572149896144460.1514429979228890.924278501038555
1440.0646615540030.1293231080060.935338445997
1450.05452129639936830.1090425927987370.945478703600632
1460.04545978192289060.09091956384578120.95454021807711
1470.0432351445962180.0864702891924360.956764855403782
1480.05090774971898260.1018154994379650.949092250281017
1490.04674395023502060.09348790047004130.95325604976498
1500.03895944687247090.07791889374494190.96104055312753
1510.04215428949620580.08430857899241160.957845710503794
1520.04084892795820720.08169785591641430.959151072041793
1530.04000911168227110.08001822336454230.959990888317729
1540.0366595523496130.0733191046992260.963340447650387
1550.04350794400032320.08701588800064630.956492055999677
1560.03686595242055090.07373190484110190.96313404757945
1570.0306124703677830.06122494073556610.969387529632217
1580.0267538656971460.0535077313942920.973246134302854
1590.0408203237950770.0816406475901540.959179676204923
1600.04269639694861160.08539279389722310.957303603051388
1610.03872182361639160.07744364723278320.961278176383608
1620.0984299645286950.196859929057390.901570035471305
1630.08405841548508480.168116830970170.915941584514915
1640.251067438641410.502134877282820.74893256135859
1650.2317032296448660.4634064592897330.768296770355134
1660.3821888518471880.7643777036943760.617811148152812
1670.3557739404736220.7115478809472440.644226059526378
1680.3287353240494940.6574706480989890.671264675950506
1690.3019767367538530.6039534735077070.698023263246147
1700.2715177562755910.5430355125511820.728482243724409
1710.3159283961468860.6318567922937710.684071603853114
1720.2846514846765330.5693029693530660.715348515323467
1730.3541268913777230.7082537827554460.645873108622277
1740.3511277754659830.7022555509319660.648872224534017
1750.3264373581049960.6528747162099920.673562641895004
1760.4051696299813590.8103392599627180.594830370018641
1770.3769055860202410.7538111720404820.623094413979759
1780.38055788081810.7611157616361990.6194421191819
1790.357030764730920.714061529461840.64296923526908
1800.3346889345667270.6693778691334550.665311065433273
1810.3216160204003340.6432320408006680.678383979599666
1820.3978396736585510.7956793473171010.602160326341449
1830.3722688591927140.7445377183854280.627731140807286
1840.3846842271641970.7693684543283940.615315772835803
1850.3532824349311360.7065648698622730.646717565068864
1860.4227464922812560.8454929845625130.577253507718744
1870.3871126071972450.774225214394490.612887392802755
1880.3523587373165380.7047174746330760.647641262683462
1890.3169511163932630.6339022327865260.683048883606737
1900.4067866534773320.8135733069546630.593213346522668
1910.4018617743560630.8037235487121260.598138225643937
1920.4162400248710130.8324800497420270.583759975128987
1930.4064891090481950.812978218096390.593510890951805
1940.3685556014903380.7371112029806750.631444398509662
1950.3411881514041630.6823763028083260.658811848595837
1960.3477369627996160.6954739255992330.652263037200384
1970.4645588622882070.9291177245764140.535441137711793
1980.4378441912421250.875688382484250.562155808757875
1990.3998828742825390.7997657485650780.600117125717461
2000.365845244089410.7316904881788190.63415475591059
2010.3282016675165420.6564033350330830.671798332483458
2020.2935694476504840.5871388953009680.706430552349516
2030.2588883541708960.5177767083417930.741111645829104
2040.2259414179312030.4518828358624050.774058582068797
2050.2025494451669620.4050988903339240.797450554833038
2060.1746598671941540.3493197343883070.825340132805847
2070.154198574380870.3083971487617390.84580142561913
2080.1629481493982370.3258962987964750.837051850601763
2090.1378167708144470.2756335416288940.862183229185553
2100.1158679330766370.2317358661532730.884132066923363
2110.09696745953223510.193934919064470.903032540467765
2120.0810702775326750.162140555065350.918929722467325
2130.0733808822216220.1467617644432440.926619117778378
2140.08165895240944910.1633179048188980.918341047590551
2150.06798138179730480.135962763594610.932018618202695
2160.05571333865532330.1114266773106470.944286661344677
2170.04436427122475750.0887285424495150.955635728775242
2180.03664054338072380.07328108676144760.963359456619276
2190.03964750068786620.07929500137573240.960352499312134
2200.03303205816553320.06606411633106640.966967941834467
2210.03928444285019480.07856888570038960.960715557149805
2220.03036475303174410.06072950606348820.969635246968256
2230.02308726833528240.04617453667056470.976912731664718
2240.0242684219353490.04853684387069790.975731578064651
2250.02645868346961260.05291736693922520.973541316530387
2260.02127919740319230.04255839480638470.978720802596808
2270.01785730225034440.03571460450068880.982142697749656
2280.01736817350010420.03473634700020840.982631826499896
2290.0256430765595080.0512861531190160.974356923440492
2300.02412699340862070.04825398681724130.97587300659138
2310.01891995544934640.03783991089869280.981080044550654
2320.01465238058179860.02930476116359720.985347619418201
2330.01721048453436810.03442096906873620.982789515465632
2340.01552404408163260.03104808816326510.984475955918367
2350.0158480795115480.03169615902309610.984151920488452
2360.01254708529843290.02509417059686580.987452914701567
2370.009884220748125460.01976844149625090.990115779251875
2380.007077741722261670.01415548344452330.992922258277738
2390.01698984213164240.03397968426328480.983010157868358
2400.02307434311546070.04614868623092150.97692565688454
2410.05066274268700620.1013254853740120.949337257312994
2420.03635174681540430.07270349363080860.963648253184596
2430.0252067989393730.05041359787874610.974793201060627
2440.01701892380177130.03403784760354270.982981076198229
2450.01156821740132080.02313643480264170.988431782598679
2460.01129406716000860.02258813432001730.988705932839991
2470.006998654938044180.01399730987608840.993001345061956
2480.1840934486949740.3681868973899470.815906551305026
2490.1736665092383480.3473330184766950.826333490761652
2500.2810375130006590.5620750260013190.718962486999341
2510.2166817020867610.4333634041735220.783318297913239
2520.2828287269935440.5656574539870890.717171273006456
2530.2886266968330440.5772533936660880.711373303166956
2540.2015061318534990.4030122637069980.798493868146501
2550.1904910211178380.3809820422356750.809508978882162
2560.119754351031650.23950870206330.88024564896835
2570.06495841501809730.1299168300361950.935041584981903






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.0239043824701195NOK
5% type I error level290.115537848605578NOK
10% type I error level560.223107569721116NOK

\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 & 6 & 0.0239043824701195 & NOK \tabularnewline
5% type I error level & 29 & 0.115537848605578 & NOK \tabularnewline
10% type I error level & 56 & 0.223107569721116 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145477&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]6[/C][C]0.0239043824701195[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.115537848605578[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.223107569721116[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145477&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145477&T=6

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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 level60.0239043824701195NOK
5% type I error level290.115537848605578NOK
10% type I error level560.223107569721116NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}