## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 08:20:42 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352121662zy422o4x4nf3enx.htm/, Retrieved Wed, 01 Feb 2023 14:59:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186042, Retrieved Wed, 01 Feb 2023 14:59:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
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]
- R PD    [Multiple Regression] [] [2012-11-05 13:20:42] [5ea595149c423d240797ea96f874e024] [Current]
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Dataseries X:
9	5	-1	6	24
11	5	-4	6	29
13	9	-6	8	29
12	10	-9	4	25
13	14	-13	8	16
15	19	-13	10	18
13	18	-10	9	13
16	16	-12	12	22
10	8	-9	9	15
14	10	-15	11	20
14	12	-14	11	19
15	13	-18	11	18
13	15	-13	11	13
8	3	-2	11	17
7	2	-1	9	17
3	-2	5	8	13
3	1	8	6	14
4	1	6	7	13
4	-1	7	8	17
0	-6	15	6	17
-4	-13	23	5	15
-14	-25	43	2	9
-18	-26	60	3	10
-8	-9	36	3	9
-1	1	28	7	14
1	3	23	8	18
2	6	23	7	18
0	2	22	7	12
1	5	22	6	16
0	5	24	6	12
-1	0	32	7	19
-3	-5	27	5	13
-3	-4	27	5	12
-3	-2	27	5	13
-4	-1	29	4	11
-8	-8	38	4	10
-9	-16	40	4	16
-13	-19	45	1	12
-18	-28	50	-1	6
-11	-11	43	3	8
-9	-4	44	4	6
-10	-9	44	3	8
-13	-12	49	2	8
-11	-10	42	1	9
-5	-2	36	4	13
-15	-13	57	3	8
-6	0	42	5	11
-6	0	39	6	8
-3	4	33	6	10
-1	7	32	6	15
-3	5	34	6	12
-4	2	37	6	13
-6	-2	38	5	12
0	6	28	6	15
-4	-3	31	5	13
-2	1	28	6	13
-2	0	30	5	16
-6	-7	39	7	14
-7	-6	38	4	12
-6	-4	39	5	15
-6	-4	38	6	14
-3	-2	37	6	19
-2	2	32	5	16
-5	-5	32	3	16
-11	-15	44	2	11
-11	-16	43	3	13
-11	-18	42	3	12
-10	-13	38	2	11
-14	-23	37	0	6
-8	-10	35	4	9
-9	-10	37	4	6
-5	-6	33	5	15
-1	-3	24	6	17
-2	-4	24	6	13
-5	-7	31	5	12
-4	-7	25	5	13
-6	-7	28	3	10
-2	-3	24	5	14
-2	0	25	5	13
-2	-5	16	5	10
-2	-3	17	3	11
2	3	11	6	12
1	2	12	6	7
-8	-7	39	4	11
-1	-1	19	6	9
1	0	14	5	13
-1	-3	15	4	12
2	4	7	5	5
2	2	12	5	13
1	3	12	4	11
-1	0	14	3	8
-2	-10	9	2	8
-2	-10	8	3	8
-1	-9	4	2	8
-8	-22	7	-1	0
-4	-16	3	0	3
-6	-18	5	-2	0
-3	-14	0	1	-1
-3	-12	-2	-2	-1
-7	-17	6	-2	-4
-9	-23	11	-2	1
-11	-28	9	-6	-1
-13	-31	17	-4	0
-11	-21	21	-2	-1
-9	-19	21	0	6
-17	-22	41	-5	0
-22	-22	57	-4	-3
-25	-25	65	-5	-3
-20	-16	68	-1	4
-24	-22	73	-2	1
-24	-21	71	-4	0
-22	-10	71	-1	-4
-19	-7	70	1	-2
-18	-5	69	1	3
-17	-4	65	-2	2
-11	7	57	1	5
-11	6	57	1	6
-12	3	57	3	6
-10	10	55	3	3
-15	0	65	1	4
-15	-2	65	1	7
-15	-1	64	0	5
-13	2	60	2	6
-8	8	43	2	1
-13	-6	47	-1	3
-9	-4	40	1	6
-7	4	31	0	0
-4	7	27	1	3
-4	3	24	1	4
-2	3	23	3	7
0	8	17	2	6
-2	3	16	0	6
-3	-3	15	0	6
1	4	8	3	6
-2	-5	5	-2	2
-1	-1	6	0	2
1	5	5	1	2
-3	0	12	-1	3
-4	-6	8	-2	-1
-9	-13	17	-1	-4
-9	-15	22	-1	4
-7	-8	24	1	5
-14	-20	36	-2	3
-12	-10	31	-5	-1
-16	-22	34	-5	-4

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Herman Ole Andreas Wold' @ wold.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 & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 10 seconds R Server 'Herman Ole Andreas Wold' @ wold.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Consumentenvertrouwen[t] = -0.000396097972544224 + 0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] + 0.270599259156973FinancieleSituatie[t] + 0.241069909023616Spaarvermogen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumentenvertrouwen[t] =  -0.000396097972544224 +  0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] +  0.270599259156973FinancieleSituatie[t] +  0.241069909023616Spaarvermogen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumentenvertrouwen[t] =  -0.000396097972544224 +  0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] +  0.270599259156973FinancieleSituatie[t] +  0.241069909023616Spaarvermogen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=1

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Estimated Regression Equation Consumentenvertrouwen[t] = -0.000396097972544224 + 0.250262710991752EcoSituatie[t] -0.250604324151446Werkloosheid[t] + 0.270599259156973FinancieleSituatie[t] + 0.241069909023616Spaarvermogen[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -0.000396097972544224 0.072453 -0.0055 0.995646 0.497823 EcoSituatie 0.250262710991752 0.003697 67.6864 0 0 Werkloosheid -0.250604324151446 0.001396 -179.5646 0 0 FinancieleSituatie 0.270599259156973 0.015893 17.026 0 0 Spaarvermogen 0.241069909023616 0.007319 32.9369 0 0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.000396097972544224 & 0.072453 & -0.0055 & 0.995646 & 0.497823 \tabularnewline
EcoSituatie & 0.250262710991752 & 0.003697 & 67.6864 & 0 & 0 \tabularnewline
Werkloosheid & -0.250604324151446 & 0.001396 & -179.5646 & 0 & 0 \tabularnewline
FinancieleSituatie & 0.270599259156973 & 0.015893 & 17.026 & 0 & 0 \tabularnewline
Spaarvermogen & 0.241069909023616 & 0.007319 & 32.9369 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.000396097972544224[/C][C]0.072453[/C][C]-0.0055[/C][C]0.995646[/C][C]0.497823[/C][/ROW]
[ROW][C]EcoSituatie[/C][C]0.250262710991752[/C][C]0.003697[/C][C]67.6864[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Werkloosheid[/C][C]-0.250604324151446[/C][C]0.001396[/C][C]-179.5646[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FinancieleSituatie[/C][C]0.270599259156973[/C][C]0.015893[/C][C]17.026[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Spaarvermogen[/C][C]0.241069909023616[/C][C]0.007319[/C][C]32.9369[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -0.000396097972544224 0.072453 -0.0055 0.995646 0.497823 EcoSituatie 0.250262710991752 0.003697 67.6864 0 0 Werkloosheid -0.250604324151446 0.001396 -179.5646 0 0 FinancieleSituatie 0.270599259156973 0.015893 17.026 0 0 Spaarvermogen 0.241069909023616 0.007319 32.9369 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.99931572168865 R-squared 0.998631911614107 Adjusted R-squared 0.99859282337451 F-TEST (value) 25548.1424057924 F-TEST (DF numerator) 4 F-TEST (DF denominator) 140 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.318839147791067 Sum Squared Residuals 14.2321763029787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99931572168865 \tabularnewline
R-squared & 0.998631911614107 \tabularnewline
F-TEST (value) & 25548.1424057924 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.318839147791067 \tabularnewline
Sum Squared Residuals & 14.2321763029787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99931572168865[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998631911614107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25548.1424057924[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/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]0.318839147791067[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.2321763029787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.99931572168865 R-squared 0.998631911614107 Adjusted R-squared 0.99859282337451 F-TEST (value) 25548.1424057924 F-TEST (DF numerator) 4 F-TEST (DF denominator) 140 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.318839147791067 Sum Squared Residuals 14.2321763029787

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 9 8.9107951526463 0.0892048473537031 2 11 10.8679576702187 0.132042329781276 3 13 12.9114156808026 0.088584319197443 4 12 11.8668146915263 0.133185308473711 5 13 12.7830506875144 0.216949312485576 6 15 15.0577025788344 -0.0577025788343662 7 13 12.5796780911132 0.420321908886778 8 16 15.5617882761161 0.438211723883926 9 10 10.3085864750915 -0.308586475091487 10 14 14.0592859054157 -0.0592859054156907 11 14 14.0681370942241 -0.0681370942241344 12 15 15.0797471927981 -0.0797471927980519 13 13 13.1219014489062 -0.121901448906249 14 8 8.32638098743378 -0.326380987433785 15 7 7.28431543397664 -0.28431543397664 16 3 3.54475974984952 -0.54475974984952 17 3 3.24360630108011 -0.24360630108011 18 4 3.77434429951636 0.225655700483642 19 4 4.25809344863285 -0.258093448632846 20 0 0.460746782148573 -0.460746782148573 21 -4 -4.04866586520946 0.048665865209463 22 -14 -14.322122111752 0.322122111752021 23 -18 -18.3209891651378 0.320989165137757 24 -8 -8.29308920766689 0.293089207666889 25 -1 -1.49788092279183 0.497880922791828 26 1 1.49054501520034 -0.490545015200343 27 2 1.97073388901863 0.0292661109813726 28 0 -0.226132084938635 0.226132084938635 29 1 1.21833642497412 -0.218336424974115 30 0 -0.247151859423241 0.247151859423241 31 -1 -1.54521138527128 0.545211385271281 32 -3 -3.53112129192846 0.531121291928459 33 -3 -3.52192848996032 0.521928489960323 34 -3 -2.7803331589532 -0.219666841046798 35 -4 -3.78401817346855 -0.215981826531454 36 -8 -8.03236597679744 0.0323659767974385 37 -9 -9.08925685889265 0.0892568588926511 38 -13 -12.8691440261905 -0.130855973809479 39 -18 -18.3621480183292 0.362148018329165 40 -11 -10.7889148077341 -0.211085192265871 41 -9 -9.49922071383357 0.499220713833567 42 -10 -10.5389937099021 0.53899370990207 43 -13 -12.8134027227915 -0.186597277208473 44 -11 -10.5881763818813 -0.411823618118739 45 -5 -5.30637133547318 0.306371335473184 46 -15 -14.7979007678379 -0.202099232162129 47 -6 -6.52101241728861 0.521012417288612 48 -6 -6.22180991274815 0.221809912748152 49 -3 -3.23499330582524 0.234993305825237 50 -1 -1.02825130358045 0.0282513035804523 51 -3 -2.7531951009377 -0.246804899062303 52 -4 -4.01472629734367 0.0147262973436749 53 -6 -5.77805063364272 -0.221949366357281 54 0 -0.276096717966423 0.276096717966423 55 -4 -4.03301316655074 0.0330131665507364 56 -2 -2.00955009097242 0.00955009097241663 57 -2 -2.30841098235318 0.308410982353185 58 -6 -6.25663017639175 0.256630176391748 59 -7 -7.0497007367667 0.0497007367667014 60 -6 -5.80597065270682 -0.194029347293179 61 -6 -5.52583697842202 -0.474163021577981 62 -3 -3.56935768716899 0.569357687168987 63 -2 -2.30909420867257 0.309094208672571 64 -5 -4.60213170392878 -0.397868296071216 65 -11 -11.5879595079387 0.587959507938708 66 -11 -10.8348788175748 -0.16512118242519 67 -11 -11.3258698244305 0.325869824430486 68 -10 -9.58380814104653 -0.41619185895347 69 -14 -13.5823789902446 -0.417621009755363 70 -8 -8.02214833535022 0.0221483353502229 71 -9 -9.24656671072396 0.246566710723963 72 -5 -4.80287012978165 -0.197129870218348 73 -1 -1.04390400223918 0.0439040022391795 74 -2 -2.2584463493254 0.258446349325397 75 -5 -5.27513391954136 0.275133919541362 76 -4 -3.53043806560907 -0.469561934390927 77 -6 -5.5466592834482 -0.453340716551796 78 -2 -2.037712988467 0.0377129884670011 79 -2 -1.77859908866681 -0.221400911333194 80 -2 -1.49768345333341 -0.502316546666593 81 -2 -1.54789096479168 -0.452109035208323 82 2 2.51017893256205 -0.510178932562046 83 1 0.803962352300767 0.196037647699233 84 -8 -7.79163768093352 -0.208362319066485 85 -1 -1.21891623168738 0.218916231687377 86 1 0.978048476999096 0.0219515230009044 87 -1 -0.535013148308197 -0.464986851691803 88 2 1.80477031783729 0.195229682162706 89 2 1.97978254728549 0.0202174527145082 90 1 1.47730618107304 -0.477306181073038 91 -1 -0.768499586432932 -0.231500413567068 92 -2 -2.2887043347502 0.288704334750201 93 -2 -1.76750075144178 -0.232499248558216 94 -1 -0.785420003001222 -0.214579996998778 95 -8 -7.53100526800819 -0.468994731991813 96 -4 -4.03320271922407 0.0332027192240696 97 -6 -6.29934503489526 0.299345034895261 98 -3 -3.47454470172372 0.474544701723719 99 -3 -3.28460840890824 0.284608408908244 100 -7 -7.26396628414942 0.263966284149419 101 -9 -8.81321462573908 -0.186785374260919 102 -11 -11.1278563870701 0.127856387070076 103 -13 -13.1012106859193 0.101210685919336 104 -11 -11.3008722633173 0.300872263317263 105 -9 -8.5716589598545 -0.428341040145502 106 -17 -17.1339493257852 0.133949325785228 107 -22 -21.5962289801222 -0.403771019877766 108 -25 -24.622450965466 -0.377549034533971 109 -20 -20.3520131392014 0.352013139201387 110 -24 -24.100420012137 0.100420012136951 111 -24 -24.1312170801799 0.131217080179871 112 -22 -21.5308091178941 -0.46919088210586 113 -19 -19.5060783244063 0.506078324406257 114 -18 -17.5495990331532 -0.450400966846773 115 -17 -17.3497867120502 0.349786712050226 116 -11 -11.0570547933876 0.0570547933876185 117 -11 -11.0662475953558 0.0662475953557519 118 -12 -11.2758372100171 -0.724162789982935 119 -10 -9.74599931184276 -0.254000688157244 120 -15 -15.0547982725651 0.0547982725650647 121 -15 -14.8321139674777 -0.167886032522279 122 -15 -15.0839860095387 0.0839860095387284 123 -13 -12.5485121526201 -0.451487847379875 124 -8 -7.99201192121312 -0.00798807878688052 125 -13 -12.8277651311271 -0.172234868872877 126 -9 -9.3086011946987 0.308601194698703 127 -7 -6.76807930270034 -0.231920697299655 128 -4 -4.02106488689148 0.021064886891484 129 -4 -4.02923284938054 0.029232849380542 130 -2 -2.5142202798443 0.514220279844301 131 0 -0.270949948157455 0.270949948157455 132 -2 -1.81285769727872 -0.187142302721283 133 -3 -3.06382963907779 0.0638296390777859 134 1 1.25403738439552 -0.254037384395519 135 -2 -2.56378997395525 0.563789973955247 136 -1 -1.27214493582574 0.272144935825736 137 1 0.750634913433197 0.249365086566803 138 -3 -2.55503751987601 -0.444962480123987 139 -4 -4.28907538447218 0.289075384472184 140 -9 -8.74896374669134 -0.251036253308663 141 -9 -8.57395151724314 -0.426048482756861 142 -7 -6.5410527612662 -0.458947238733799 143 -14 -13.8453947785027 -0.154605221497271 144 -12 -11.8658234613934 -0.13417653860664 145 -16 -16.3439986928196 0.343998692819574

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.9107951526463 & 0.0892048473537031 \tabularnewline
2 & 11 & 10.8679576702187 & 0.132042329781276 \tabularnewline
3 & 13 & 12.9114156808026 & 0.088584319197443 \tabularnewline
4 & 12 & 11.8668146915263 & 0.133185308473711 \tabularnewline
5 & 13 & 12.7830506875144 & 0.216949312485576 \tabularnewline
6 & 15 & 15.0577025788344 & -0.0577025788343662 \tabularnewline
7 & 13 & 12.5796780911132 & 0.420321908886778 \tabularnewline
8 & 16 & 15.5617882761161 & 0.438211723883926 \tabularnewline
9 & 10 & 10.3085864750915 & -0.308586475091487 \tabularnewline
10 & 14 & 14.0592859054157 & -0.0592859054156907 \tabularnewline
11 & 14 & 14.0681370942241 & -0.0681370942241344 \tabularnewline
12 & 15 & 15.0797471927981 & -0.0797471927980519 \tabularnewline
13 & 13 & 13.1219014489062 & -0.121901448906249 \tabularnewline
14 & 8 & 8.32638098743378 & -0.326380987433785 \tabularnewline
15 & 7 & 7.28431543397664 & -0.28431543397664 \tabularnewline
16 & 3 & 3.54475974984952 & -0.54475974984952 \tabularnewline
17 & 3 & 3.24360630108011 & -0.24360630108011 \tabularnewline
18 & 4 & 3.77434429951636 & 0.225655700483642 \tabularnewline
19 & 4 & 4.25809344863285 & -0.258093448632846 \tabularnewline
20 & 0 & 0.460746782148573 & -0.460746782148573 \tabularnewline
21 & -4 & -4.04866586520946 & 0.048665865209463 \tabularnewline
22 & -14 & -14.322122111752 & 0.322122111752021 \tabularnewline
23 & -18 & -18.3209891651378 & 0.320989165137757 \tabularnewline
24 & -8 & -8.29308920766689 & 0.293089207666889 \tabularnewline
25 & -1 & -1.49788092279183 & 0.497880922791828 \tabularnewline
26 & 1 & 1.49054501520034 & -0.490545015200343 \tabularnewline
27 & 2 & 1.97073388901863 & 0.0292661109813726 \tabularnewline
28 & 0 & -0.226132084938635 & 0.226132084938635 \tabularnewline
29 & 1 & 1.21833642497412 & -0.218336424974115 \tabularnewline
30 & 0 & -0.247151859423241 & 0.247151859423241 \tabularnewline
31 & -1 & -1.54521138527128 & 0.545211385271281 \tabularnewline
32 & -3 & -3.53112129192846 & 0.531121291928459 \tabularnewline
33 & -3 & -3.52192848996032 & 0.521928489960323 \tabularnewline
34 & -3 & -2.7803331589532 & -0.219666841046798 \tabularnewline
35 & -4 & -3.78401817346855 & -0.215981826531454 \tabularnewline
36 & -8 & -8.03236597679744 & 0.0323659767974385 \tabularnewline
37 & -9 & -9.08925685889265 & 0.0892568588926511 \tabularnewline
38 & -13 & -12.8691440261905 & -0.130855973809479 \tabularnewline
39 & -18 & -18.3621480183292 & 0.362148018329165 \tabularnewline
40 & -11 & -10.7889148077341 & -0.211085192265871 \tabularnewline
41 & -9 & -9.49922071383357 & 0.499220713833567 \tabularnewline
42 & -10 & -10.5389937099021 & 0.53899370990207 \tabularnewline
43 & -13 & -12.8134027227915 & -0.186597277208473 \tabularnewline
44 & -11 & -10.5881763818813 & -0.411823618118739 \tabularnewline
45 & -5 & -5.30637133547318 & 0.306371335473184 \tabularnewline
46 & -15 & -14.7979007678379 & -0.202099232162129 \tabularnewline
47 & -6 & -6.52101241728861 & 0.521012417288612 \tabularnewline
48 & -6 & -6.22180991274815 & 0.221809912748152 \tabularnewline
49 & -3 & -3.23499330582524 & 0.234993305825237 \tabularnewline
50 & -1 & -1.02825130358045 & 0.0282513035804523 \tabularnewline
51 & -3 & -2.7531951009377 & -0.246804899062303 \tabularnewline
52 & -4 & -4.01472629734367 & 0.0147262973436749 \tabularnewline
53 & -6 & -5.77805063364272 & -0.221949366357281 \tabularnewline
54 & 0 & -0.276096717966423 & 0.276096717966423 \tabularnewline
55 & -4 & -4.03301316655074 & 0.0330131665507364 \tabularnewline
56 & -2 & -2.00955009097242 & 0.00955009097241663 \tabularnewline
57 & -2 & -2.30841098235318 & 0.308410982353185 \tabularnewline
58 & -6 & -6.25663017639175 & 0.256630176391748 \tabularnewline
59 & -7 & -7.0497007367667 & 0.0497007367667014 \tabularnewline
60 & -6 & -5.80597065270682 & -0.194029347293179 \tabularnewline
61 & -6 & -5.52583697842202 & -0.474163021577981 \tabularnewline
62 & -3 & -3.56935768716899 & 0.569357687168987 \tabularnewline
63 & -2 & -2.30909420867257 & 0.309094208672571 \tabularnewline
64 & -5 & -4.60213170392878 & -0.397868296071216 \tabularnewline
65 & -11 & -11.5879595079387 & 0.587959507938708 \tabularnewline
66 & -11 & -10.8348788175748 & -0.16512118242519 \tabularnewline
67 & -11 & -11.3258698244305 & 0.325869824430486 \tabularnewline
68 & -10 & -9.58380814104653 & -0.41619185895347 \tabularnewline
69 & -14 & -13.5823789902446 & -0.417621009755363 \tabularnewline
70 & -8 & -8.02214833535022 & 0.0221483353502229 \tabularnewline
71 & -9 & -9.24656671072396 & 0.246566710723963 \tabularnewline
72 & -5 & -4.80287012978165 & -0.197129870218348 \tabularnewline
73 & -1 & -1.04390400223918 & 0.0439040022391795 \tabularnewline
74 & -2 & -2.2584463493254 & 0.258446349325397 \tabularnewline
75 & -5 & -5.27513391954136 & 0.275133919541362 \tabularnewline
76 & -4 & -3.53043806560907 & -0.469561934390927 \tabularnewline
77 & -6 & -5.5466592834482 & -0.453340716551796 \tabularnewline
78 & -2 & -2.037712988467 & 0.0377129884670011 \tabularnewline
79 & -2 & -1.77859908866681 & -0.221400911333194 \tabularnewline
80 & -2 & -1.49768345333341 & -0.502316546666593 \tabularnewline
81 & -2 & -1.54789096479168 & -0.452109035208323 \tabularnewline
82 & 2 & 2.51017893256205 & -0.510178932562046 \tabularnewline
83 & 1 & 0.803962352300767 & 0.196037647699233 \tabularnewline
84 & -8 & -7.79163768093352 & -0.208362319066485 \tabularnewline
85 & -1 & -1.21891623168738 & 0.218916231687377 \tabularnewline
86 & 1 & 0.978048476999096 & 0.0219515230009044 \tabularnewline
87 & -1 & -0.535013148308197 & -0.464986851691803 \tabularnewline
88 & 2 & 1.80477031783729 & 0.195229682162706 \tabularnewline
89 & 2 & 1.97978254728549 & 0.0202174527145082 \tabularnewline
90 & 1 & 1.47730618107304 & -0.477306181073038 \tabularnewline
91 & -1 & -0.768499586432932 & -0.231500413567068 \tabularnewline
92 & -2 & -2.2887043347502 & 0.288704334750201 \tabularnewline
93 & -2 & -1.76750075144178 & -0.232499248558216 \tabularnewline
94 & -1 & -0.785420003001222 & -0.214579996998778 \tabularnewline
95 & -8 & -7.53100526800819 & -0.468994731991813 \tabularnewline
96 & -4 & -4.03320271922407 & 0.0332027192240696 \tabularnewline
97 & -6 & -6.29934503489526 & 0.299345034895261 \tabularnewline
98 & -3 & -3.47454470172372 & 0.474544701723719 \tabularnewline
99 & -3 & -3.28460840890824 & 0.284608408908244 \tabularnewline
100 & -7 & -7.26396628414942 & 0.263966284149419 \tabularnewline
101 & -9 & -8.81321462573908 & -0.186785374260919 \tabularnewline
102 & -11 & -11.1278563870701 & 0.127856387070076 \tabularnewline
103 & -13 & -13.1012106859193 & 0.101210685919336 \tabularnewline
104 & -11 & -11.3008722633173 & 0.300872263317263 \tabularnewline
105 & -9 & -8.5716589598545 & -0.428341040145502 \tabularnewline
106 & -17 & -17.1339493257852 & 0.133949325785228 \tabularnewline
107 & -22 & -21.5962289801222 & -0.403771019877766 \tabularnewline
108 & -25 & -24.622450965466 & -0.377549034533971 \tabularnewline
109 & -20 & -20.3520131392014 & 0.352013139201387 \tabularnewline
110 & -24 & -24.100420012137 & 0.100420012136951 \tabularnewline
111 & -24 & -24.1312170801799 & 0.131217080179871 \tabularnewline
112 & -22 & -21.5308091178941 & -0.46919088210586 \tabularnewline
113 & -19 & -19.5060783244063 & 0.506078324406257 \tabularnewline
114 & -18 & -17.5495990331532 & -0.450400966846773 \tabularnewline
115 & -17 & -17.3497867120502 & 0.349786712050226 \tabularnewline
116 & -11 & -11.0570547933876 & 0.0570547933876185 \tabularnewline
117 & -11 & -11.0662475953558 & 0.0662475953557519 \tabularnewline
118 & -12 & -11.2758372100171 & -0.724162789982935 \tabularnewline
119 & -10 & -9.74599931184276 & -0.254000688157244 \tabularnewline
120 & -15 & -15.0547982725651 & 0.0547982725650647 \tabularnewline
121 & -15 & -14.8321139674777 & -0.167886032522279 \tabularnewline
122 & -15 & -15.0839860095387 & 0.0839860095387284 \tabularnewline
123 & -13 & -12.5485121526201 & -0.451487847379875 \tabularnewline
124 & -8 & -7.99201192121312 & -0.00798807878688052 \tabularnewline
125 & -13 & -12.8277651311271 & -0.172234868872877 \tabularnewline
126 & -9 & -9.3086011946987 & 0.308601194698703 \tabularnewline
127 & -7 & -6.76807930270034 & -0.231920697299655 \tabularnewline
128 & -4 & -4.02106488689148 & 0.021064886891484 \tabularnewline
129 & -4 & -4.02923284938054 & 0.029232849380542 \tabularnewline
130 & -2 & -2.5142202798443 & 0.514220279844301 \tabularnewline
131 & 0 & -0.270949948157455 & 0.270949948157455 \tabularnewline
132 & -2 & -1.81285769727872 & -0.187142302721283 \tabularnewline
133 & -3 & -3.06382963907779 & 0.0638296390777859 \tabularnewline
134 & 1 & 1.25403738439552 & -0.254037384395519 \tabularnewline
135 & -2 & -2.56378997395525 & 0.563789973955247 \tabularnewline
136 & -1 & -1.27214493582574 & 0.272144935825736 \tabularnewline
137 & 1 & 0.750634913433197 & 0.249365086566803 \tabularnewline
138 & -3 & -2.55503751987601 & -0.444962480123987 \tabularnewline
139 & -4 & -4.28907538447218 & 0.289075384472184 \tabularnewline
140 & -9 & -8.74896374669134 & -0.251036253308663 \tabularnewline
141 & -9 & -8.57395151724314 & -0.426048482756861 \tabularnewline
142 & -7 & -6.5410527612662 & -0.458947238733799 \tabularnewline
143 & -14 & -13.8453947785027 & -0.154605221497271 \tabularnewline
144 & -12 & -11.8658234613934 & -0.13417653860664 \tabularnewline
145 & -16 & -16.3439986928196 & 0.343998692819574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]9[/C][C]8.9107951526463[/C][C]0.0892048473537031[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.8679576702187[/C][C]0.132042329781276[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]12.9114156808026[/C][C]0.088584319197443[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.8668146915263[/C][C]0.133185308473711[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]12.7830506875144[/C][C]0.216949312485576[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]15.0577025788344[/C][C]-0.0577025788343662[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]12.5796780911132[/C][C]0.420321908886778[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]15.5617882761161[/C][C]0.438211723883926[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.3085864750915[/C][C]-0.308586475091487[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0592859054157[/C][C]-0.0592859054156907[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0681370942241[/C][C]-0.0681370942241344[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.0797471927981[/C][C]-0.0797471927980519[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.1219014489062[/C][C]-0.121901448906249[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]8.32638098743378[/C][C]-0.326380987433785[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.28431543397664[/C][C]-0.28431543397664[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.54475974984952[/C][C]-0.54475974984952[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.24360630108011[/C][C]-0.24360630108011[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.77434429951636[/C][C]0.225655700483642[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.25809344863285[/C][C]-0.258093448632846[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.460746782148573[/C][C]-0.460746782148573[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]-4.04866586520946[/C][C]0.048665865209463[/C][/ROW]
[ROW][C]22[/C][C]-14[/C][C]-14.322122111752[/C][C]0.322122111752021[/C][/ROW]
[ROW][C]23[/C][C]-18[/C][C]-18.3209891651378[/C][C]0.320989165137757[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-8.29308920766689[/C][C]0.293089207666889[/C][/ROW]
[ROW][C]25[/C][C]-1[/C][C]-1.49788092279183[/C][C]0.497880922791828[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.49054501520034[/C][C]-0.490545015200343[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.97073388901863[/C][C]0.0292661109813726[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]-0.226132084938635[/C][C]0.226132084938635[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.21833642497412[/C][C]-0.218336424974115[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]-0.247151859423241[/C][C]0.247151859423241[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-1.54521138527128[/C][C]0.545211385271281[/C][/ROW]
[ROW][C]32[/C][C]-3[/C][C]-3.53112129192846[/C][C]0.531121291928459[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-3.52192848996032[/C][C]0.521928489960323[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]-2.7803331589532[/C][C]-0.219666841046798[/C][/ROW]
[ROW][C]35[/C][C]-4[/C][C]-3.78401817346855[/C][C]-0.215981826531454[/C][/ROW]
[ROW][C]36[/C][C]-8[/C][C]-8.03236597679744[/C][C]0.0323659767974385[/C][/ROW]
[ROW][C]37[/C][C]-9[/C][C]-9.08925685889265[/C][C]0.0892568588926511[/C][/ROW]
[ROW][C]38[/C][C]-13[/C][C]-12.8691440261905[/C][C]-0.130855973809479[/C][/ROW]
[ROW][C]39[/C][C]-18[/C][C]-18.3621480183292[/C][C]0.362148018329165[/C][/ROW]
[ROW][C]40[/C][C]-11[/C][C]-10.7889148077341[/C][C]-0.211085192265871[/C][/ROW]
[ROW][C]41[/C][C]-9[/C][C]-9.49922071383357[/C][C]0.499220713833567[/C][/ROW]
[ROW][C]42[/C][C]-10[/C][C]-10.5389937099021[/C][C]0.53899370990207[/C][/ROW]
[ROW][C]43[/C][C]-13[/C][C]-12.8134027227915[/C][C]-0.186597277208473[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-10.5881763818813[/C][C]-0.411823618118739[/C][/ROW]
[ROW][C]45[/C][C]-5[/C][C]-5.30637133547318[/C][C]0.306371335473184[/C][/ROW]
[ROW][C]46[/C][C]-15[/C][C]-14.7979007678379[/C][C]-0.202099232162129[/C][/ROW]
[ROW][C]47[/C][C]-6[/C][C]-6.52101241728861[/C][C]0.521012417288612[/C][/ROW]
[ROW][C]48[/C][C]-6[/C][C]-6.22180991274815[/C][C]0.221809912748152[/C][/ROW]
[ROW][C]49[/C][C]-3[/C][C]-3.23499330582524[/C][C]0.234993305825237[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]-1.02825130358045[/C][C]0.0282513035804523[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]-2.7531951009377[/C][C]-0.246804899062303[/C][/ROW]
[ROW][C]52[/C][C]-4[/C][C]-4.01472629734367[/C][C]0.0147262973436749[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-5.77805063364272[/C][C]-0.221949366357281[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.276096717966423[/C][C]0.276096717966423[/C][/ROW]
[ROW][C]55[/C][C]-4[/C][C]-4.03301316655074[/C][C]0.0330131665507364[/C][/ROW]
[ROW][C]56[/C][C]-2[/C][C]-2.00955009097242[/C][C]0.00955009097241663[/C][/ROW]
[ROW][C]57[/C][C]-2[/C][C]-2.30841098235318[/C][C]0.308410982353185[/C][/ROW]
[ROW][C]58[/C][C]-6[/C][C]-6.25663017639175[/C][C]0.256630176391748[/C][/ROW]
[ROW][C]59[/C][C]-7[/C][C]-7.0497007367667[/C][C]0.0497007367667014[/C][/ROW]
[ROW][C]60[/C][C]-6[/C][C]-5.80597065270682[/C][C]-0.194029347293179[/C][/ROW]
[ROW][C]61[/C][C]-6[/C][C]-5.52583697842202[/C][C]-0.474163021577981[/C][/ROW]
[ROW][C]62[/C][C]-3[/C][C]-3.56935768716899[/C][C]0.569357687168987[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-2.30909420867257[/C][C]0.309094208672571[/C][/ROW]
[ROW][C]64[/C][C]-5[/C][C]-4.60213170392878[/C][C]-0.397868296071216[/C][/ROW]
[ROW][C]65[/C][C]-11[/C][C]-11.5879595079387[/C][C]0.587959507938708[/C][/ROW]
[ROW][C]66[/C][C]-11[/C][C]-10.8348788175748[/C][C]-0.16512118242519[/C][/ROW]
[ROW][C]67[/C][C]-11[/C][C]-11.3258698244305[/C][C]0.325869824430486[/C][/ROW]
[ROW][C]68[/C][C]-10[/C][C]-9.58380814104653[/C][C]-0.41619185895347[/C][/ROW]
[ROW][C]69[/C][C]-14[/C][C]-13.5823789902446[/C][C]-0.417621009755363[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-8.02214833535022[/C][C]0.0221483353502229[/C][/ROW]
[ROW][C]71[/C][C]-9[/C][C]-9.24656671072396[/C][C]0.246566710723963[/C][/ROW]
[ROW][C]72[/C][C]-5[/C][C]-4.80287012978165[/C][C]-0.197129870218348[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-1.04390400223918[/C][C]0.0439040022391795[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-2.2584463493254[/C][C]0.258446349325397[/C][/ROW]
[ROW][C]75[/C][C]-5[/C][C]-5.27513391954136[/C][C]0.275133919541362[/C][/ROW]
[ROW][C]76[/C][C]-4[/C][C]-3.53043806560907[/C][C]-0.469561934390927[/C][/ROW]
[ROW][C]77[/C][C]-6[/C][C]-5.5466592834482[/C][C]-0.453340716551796[/C][/ROW]
[ROW][C]78[/C][C]-2[/C][C]-2.037712988467[/C][C]0.0377129884670011[/C][/ROW]
[ROW][C]79[/C][C]-2[/C][C]-1.77859908866681[/C][C]-0.221400911333194[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-1.49768345333341[/C][C]-0.502316546666593[/C][/ROW]
[ROW][C]81[/C][C]-2[/C][C]-1.54789096479168[/C][C]-0.452109035208323[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]2.51017893256205[/C][C]-0.510178932562046[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.803962352300767[/C][C]0.196037647699233[/C][/ROW]
[ROW][C]84[/C][C]-8[/C][C]-7.79163768093352[/C][C]-0.208362319066485[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.21891623168738[/C][C]0.218916231687377[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.978048476999096[/C][C]0.0219515230009044[/C][/ROW]
[ROW][C]87[/C][C]-1[/C][C]-0.535013148308197[/C][C]-0.464986851691803[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.80477031783729[/C][C]0.195229682162706[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.97978254728549[/C][C]0.0202174527145082[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.47730618107304[/C][C]-0.477306181073038[/C][/ROW]
[ROW][C]91[/C][C]-1[/C][C]-0.768499586432932[/C][C]-0.231500413567068[/C][/ROW]
[ROW][C]92[/C][C]-2[/C][C]-2.2887043347502[/C][C]0.288704334750201[/C][/ROW]
[ROW][C]93[/C][C]-2[/C][C]-1.76750075144178[/C][C]-0.232499248558216[/C][/ROW]
[ROW][C]94[/C][C]-1[/C][C]-0.785420003001222[/C][C]-0.214579996998778[/C][/ROW]
[ROW][C]95[/C][C]-8[/C][C]-7.53100526800819[/C][C]-0.468994731991813[/C][/ROW]
[ROW][C]96[/C][C]-4[/C][C]-4.03320271922407[/C][C]0.0332027192240696[/C][/ROW]
[ROW][C]97[/C][C]-6[/C][C]-6.29934503489526[/C][C]0.299345034895261[/C][/ROW]
[ROW][C]98[/C][C]-3[/C][C]-3.47454470172372[/C][C]0.474544701723719[/C][/ROW]
[ROW][C]99[/C][C]-3[/C][C]-3.28460840890824[/C][C]0.284608408908244[/C][/ROW]
[ROW][C]100[/C][C]-7[/C][C]-7.26396628414942[/C][C]0.263966284149419[/C][/ROW]
[ROW][C]101[/C][C]-9[/C][C]-8.81321462573908[/C][C]-0.186785374260919[/C][/ROW]
[ROW][C]102[/C][C]-11[/C][C]-11.1278563870701[/C][C]0.127856387070076[/C][/ROW]
[ROW][C]103[/C][C]-13[/C][C]-13.1012106859193[/C][C]0.101210685919336[/C][/ROW]
[ROW][C]104[/C][C]-11[/C][C]-11.3008722633173[/C][C]0.300872263317263[/C][/ROW]
[ROW][C]105[/C][C]-9[/C][C]-8.5716589598545[/C][C]-0.428341040145502[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-17.1339493257852[/C][C]0.133949325785228[/C][/ROW]
[ROW][C]107[/C][C]-22[/C][C]-21.5962289801222[/C][C]-0.403771019877766[/C][/ROW]
[ROW][C]108[/C][C]-25[/C][C]-24.622450965466[/C][C]-0.377549034533971[/C][/ROW]
[ROW][C]109[/C][C]-20[/C][C]-20.3520131392014[/C][C]0.352013139201387[/C][/ROW]
[ROW][C]110[/C][C]-24[/C][C]-24.100420012137[/C][C]0.100420012136951[/C][/ROW]
[ROW][C]111[/C][C]-24[/C][C]-24.1312170801799[/C][C]0.131217080179871[/C][/ROW]
[ROW][C]112[/C][C]-22[/C][C]-21.5308091178941[/C][C]-0.46919088210586[/C][/ROW]
[ROW][C]113[/C][C]-19[/C][C]-19.5060783244063[/C][C]0.506078324406257[/C][/ROW]
[ROW][C]114[/C][C]-18[/C][C]-17.5495990331532[/C][C]-0.450400966846773[/C][/ROW]
[ROW][C]115[/C][C]-17[/C][C]-17.3497867120502[/C][C]0.349786712050226[/C][/ROW]
[ROW][C]116[/C][C]-11[/C][C]-11.0570547933876[/C][C]0.0570547933876185[/C][/ROW]
[ROW][C]117[/C][C]-11[/C][C]-11.0662475953558[/C][C]0.0662475953557519[/C][/ROW]
[ROW][C]118[/C][C]-12[/C][C]-11.2758372100171[/C][C]-0.724162789982935[/C][/ROW]
[ROW][C]119[/C][C]-10[/C][C]-9.74599931184276[/C][C]-0.254000688157244[/C][/ROW]
[ROW][C]120[/C][C]-15[/C][C]-15.0547982725651[/C][C]0.0547982725650647[/C][/ROW]
[ROW][C]121[/C][C]-15[/C][C]-14.8321139674777[/C][C]-0.167886032522279[/C][/ROW]
[ROW][C]122[/C][C]-15[/C][C]-15.0839860095387[/C][C]0.0839860095387284[/C][/ROW]
[ROW][C]123[/C][C]-13[/C][C]-12.5485121526201[/C][C]-0.451487847379875[/C][/ROW]
[ROW][C]124[/C][C]-8[/C][C]-7.99201192121312[/C][C]-0.00798807878688052[/C][/ROW]
[ROW][C]125[/C][C]-13[/C][C]-12.8277651311271[/C][C]-0.172234868872877[/C][/ROW]
[ROW][C]126[/C][C]-9[/C][C]-9.3086011946987[/C][C]0.308601194698703[/C][/ROW]
[ROW][C]127[/C][C]-7[/C][C]-6.76807930270034[/C][C]-0.231920697299655[/C][/ROW]
[ROW][C]128[/C][C]-4[/C][C]-4.02106488689148[/C][C]0.021064886891484[/C][/ROW]
[ROW][C]129[/C][C]-4[/C][C]-4.02923284938054[/C][C]0.029232849380542[/C][/ROW]
[ROW][C]130[/C][C]-2[/C][C]-2.5142202798443[/C][C]0.514220279844301[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.270949948157455[/C][C]0.270949948157455[/C][/ROW]
[ROW][C]132[/C][C]-2[/C][C]-1.81285769727872[/C][C]-0.187142302721283[/C][/ROW]
[ROW][C]133[/C][C]-3[/C][C]-3.06382963907779[/C][C]0.0638296390777859[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.25403738439552[/C][C]-0.254037384395519[/C][/ROW]
[ROW][C]135[/C][C]-2[/C][C]-2.56378997395525[/C][C]0.563789973955247[/C][/ROW]
[ROW][C]136[/C][C]-1[/C][C]-1.27214493582574[/C][C]0.272144935825736[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.750634913433197[/C][C]0.249365086566803[/C][/ROW]
[ROW][C]138[/C][C]-3[/C][C]-2.55503751987601[/C][C]-0.444962480123987[/C][/ROW]
[ROW][C]139[/C][C]-4[/C][C]-4.28907538447218[/C][C]0.289075384472184[/C][/ROW]
[ROW][C]140[/C][C]-9[/C][C]-8.74896374669134[/C][C]-0.251036253308663[/C][/ROW]
[ROW][C]141[/C][C]-9[/C][C]-8.57395151724314[/C][C]-0.426048482756861[/C][/ROW]
[ROW][C]142[/C][C]-7[/C][C]-6.5410527612662[/C][C]-0.458947238733799[/C][/ROW]
[ROW][C]143[/C][C]-14[/C][C]-13.8453947785027[/C][C]-0.154605221497271[/C][/ROW]
[ROW][C]144[/C][C]-12[/C][C]-11.8658234613934[/C][C]-0.13417653860664[/C][/ROW]
[ROW][C]145[/C][C]-16[/C][C]-16.3439986928196[/C][C]0.343998692819574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=4

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 9 8.9107951526463 0.0892048473537031 2 11 10.8679576702187 0.132042329781276 3 13 12.9114156808026 0.088584319197443 4 12 11.8668146915263 0.133185308473711 5 13 12.7830506875144 0.216949312485576 6 15 15.0577025788344 -0.0577025788343662 7 13 12.5796780911132 0.420321908886778 8 16 15.5617882761161 0.438211723883926 9 10 10.3085864750915 -0.308586475091487 10 14 14.0592859054157 -0.0592859054156907 11 14 14.0681370942241 -0.0681370942241344 12 15 15.0797471927981 -0.0797471927980519 13 13 13.1219014489062 -0.121901448906249 14 8 8.32638098743378 -0.326380987433785 15 7 7.28431543397664 -0.28431543397664 16 3 3.54475974984952 -0.54475974984952 17 3 3.24360630108011 -0.24360630108011 18 4 3.77434429951636 0.225655700483642 19 4 4.25809344863285 -0.258093448632846 20 0 0.460746782148573 -0.460746782148573 21 -4 -4.04866586520946 0.048665865209463 22 -14 -14.322122111752 0.322122111752021 23 -18 -18.3209891651378 0.320989165137757 24 -8 -8.29308920766689 0.293089207666889 25 -1 -1.49788092279183 0.497880922791828 26 1 1.49054501520034 -0.490545015200343 27 2 1.97073388901863 0.0292661109813726 28 0 -0.226132084938635 0.226132084938635 29 1 1.21833642497412 -0.218336424974115 30 0 -0.247151859423241 0.247151859423241 31 -1 -1.54521138527128 0.545211385271281 32 -3 -3.53112129192846 0.531121291928459 33 -3 -3.52192848996032 0.521928489960323 34 -3 -2.7803331589532 -0.219666841046798 35 -4 -3.78401817346855 -0.215981826531454 36 -8 -8.03236597679744 0.0323659767974385 37 -9 -9.08925685889265 0.0892568588926511 38 -13 -12.8691440261905 -0.130855973809479 39 -18 -18.3621480183292 0.362148018329165 40 -11 -10.7889148077341 -0.211085192265871 41 -9 -9.49922071383357 0.499220713833567 42 -10 -10.5389937099021 0.53899370990207 43 -13 -12.8134027227915 -0.186597277208473 44 -11 -10.5881763818813 -0.411823618118739 45 -5 -5.30637133547318 0.306371335473184 46 -15 -14.7979007678379 -0.202099232162129 47 -6 -6.52101241728861 0.521012417288612 48 -6 -6.22180991274815 0.221809912748152 49 -3 -3.23499330582524 0.234993305825237 50 -1 -1.02825130358045 0.0282513035804523 51 -3 -2.7531951009377 -0.246804899062303 52 -4 -4.01472629734367 0.0147262973436749 53 -6 -5.77805063364272 -0.221949366357281 54 0 -0.276096717966423 0.276096717966423 55 -4 -4.03301316655074 0.0330131665507364 56 -2 -2.00955009097242 0.00955009097241663 57 -2 -2.30841098235318 0.308410982353185 58 -6 -6.25663017639175 0.256630176391748 59 -7 -7.0497007367667 0.0497007367667014 60 -6 -5.80597065270682 -0.194029347293179 61 -6 -5.52583697842202 -0.474163021577981 62 -3 -3.56935768716899 0.569357687168987 63 -2 -2.30909420867257 0.309094208672571 64 -5 -4.60213170392878 -0.397868296071216 65 -11 -11.5879595079387 0.587959507938708 66 -11 -10.8348788175748 -0.16512118242519 67 -11 -11.3258698244305 0.325869824430486 68 -10 -9.58380814104653 -0.41619185895347 69 -14 -13.5823789902446 -0.417621009755363 70 -8 -8.02214833535022 0.0221483353502229 71 -9 -9.24656671072396 0.246566710723963 72 -5 -4.80287012978165 -0.197129870218348 73 -1 -1.04390400223918 0.0439040022391795 74 -2 -2.2584463493254 0.258446349325397 75 -5 -5.27513391954136 0.275133919541362 76 -4 -3.53043806560907 -0.469561934390927 77 -6 -5.5466592834482 -0.453340716551796 78 -2 -2.037712988467 0.0377129884670011 79 -2 -1.77859908866681 -0.221400911333194 80 -2 -1.49768345333341 -0.502316546666593 81 -2 -1.54789096479168 -0.452109035208323 82 2 2.51017893256205 -0.510178932562046 83 1 0.803962352300767 0.196037647699233 84 -8 -7.79163768093352 -0.208362319066485 85 -1 -1.21891623168738 0.218916231687377 86 1 0.978048476999096 0.0219515230009044 87 -1 -0.535013148308197 -0.464986851691803 88 2 1.80477031783729 0.195229682162706 89 2 1.97978254728549 0.0202174527145082 90 1 1.47730618107304 -0.477306181073038 91 -1 -0.768499586432932 -0.231500413567068 92 -2 -2.2887043347502 0.288704334750201 93 -2 -1.76750075144178 -0.232499248558216 94 -1 -0.785420003001222 -0.214579996998778 95 -8 -7.53100526800819 -0.468994731991813 96 -4 -4.03320271922407 0.0332027192240696 97 -6 -6.29934503489526 0.299345034895261 98 -3 -3.47454470172372 0.474544701723719 99 -3 -3.28460840890824 0.284608408908244 100 -7 -7.26396628414942 0.263966284149419 101 -9 -8.81321462573908 -0.186785374260919 102 -11 -11.1278563870701 0.127856387070076 103 -13 -13.1012106859193 0.101210685919336 104 -11 -11.3008722633173 0.300872263317263 105 -9 -8.5716589598545 -0.428341040145502 106 -17 -17.1339493257852 0.133949325785228 107 -22 -21.5962289801222 -0.403771019877766 108 -25 -24.622450965466 -0.377549034533971 109 -20 -20.3520131392014 0.352013139201387 110 -24 -24.100420012137 0.100420012136951 111 -24 -24.1312170801799 0.131217080179871 112 -22 -21.5308091178941 -0.46919088210586 113 -19 -19.5060783244063 0.506078324406257 114 -18 -17.5495990331532 -0.450400966846773 115 -17 -17.3497867120502 0.349786712050226 116 -11 -11.0570547933876 0.0570547933876185 117 -11 -11.0662475953558 0.0662475953557519 118 -12 -11.2758372100171 -0.724162789982935 119 -10 -9.74599931184276 -0.254000688157244 120 -15 -15.0547982725651 0.0547982725650647 121 -15 -14.8321139674777 -0.167886032522279 122 -15 -15.0839860095387 0.0839860095387284 123 -13 -12.5485121526201 -0.451487847379875 124 -8 -7.99201192121312 -0.00798807878688052 125 -13 -12.8277651311271 -0.172234868872877 126 -9 -9.3086011946987 0.308601194698703 127 -7 -6.76807930270034 -0.231920697299655 128 -4 -4.02106488689148 0.021064886891484 129 -4 -4.02923284938054 0.029232849380542 130 -2 -2.5142202798443 0.514220279844301 131 0 -0.270949948157455 0.270949948157455 132 -2 -1.81285769727872 -0.187142302721283 133 -3 -3.06382963907779 0.0638296390777859 134 1 1.25403738439552 -0.254037384395519 135 -2 -2.56378997395525 0.563789973955247 136 -1 -1.27214493582574 0.272144935825736 137 1 0.750634913433197 0.249365086566803 138 -3 -2.55503751987601 -0.444962480123987 139 -4 -4.28907538447218 0.289075384472184 140 -9 -8.74896374669134 -0.251036253308663 141 -9 -8.57395151724314 -0.426048482756861 142 -7 -6.5410527612662 -0.458947238733799 143 -14 -13.8453947785027 -0.154605221497271 144 -12 -11.8658234613934 -0.13417653860664 145 -16 -16.3439986928196 0.343998692819574

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.300115363617572 0.600230727235143 0.699884636382428 9 0.362040176196489 0.724080352392978 0.637959823803511 10 0.234896154818179 0.469792309636357 0.765103845181821 11 0.141685720680266 0.283371441360531 0.858314279319734 12 0.0790469166229443 0.158093833245889 0.920953083377056 13 0.0518371827390162 0.103674365478032 0.948162817260984 14 0.0286671898257638 0.0573343796515277 0.971332810174236 15 0.0145886189478053 0.0291772378956106 0.985411381052195 16 0.00861499865101093 0.0172299973020219 0.991385001348989 17 0.00411957189457703 0.00823914378915406 0.995880428105423 18 0.0253845582670739 0.0507691165341479 0.974615441732926 19 0.0150486597944918 0.0300973195889837 0.984951340205508 20 0.0122626025212158 0.0245252050424315 0.987737397478784 21 0.0342493973210529 0.0684987946421059 0.965750602678947 22 0.0970875581910767 0.194175116382153 0.902912441808923 23 0.0700023563631878 0.140004712726376 0.929997643636812 24 0.0521308888306804 0.104261777661361 0.94786911116932 25 0.0386375009600251 0.0772750019200501 0.961362499039975 26 0.271284832056046 0.542569664112093 0.728715167943954 27 0.244720621514443 0.489441243028886 0.755279378485557 28 0.198569220499873 0.397138440999745 0.801430779500127 29 0.243430366502388 0.486860733004776 0.756569633497612 30 0.198878162020818 0.397756324041637 0.801121837979182 31 0.24156645446565 0.483132908931301 0.75843354553435 32 0.283156040974507 0.566312081949014 0.716843959025493 33 0.308137850587203 0.616275701174406 0.691862149412797 34 0.351344524301218 0.702689048602436 0.648655475698782 35 0.401244648960893 0.802489297921785 0.598755351039107 36 0.354738122205034 0.709476244410069 0.645261877794966 37 0.304005415433116 0.608010830866232 0.695994584566884 38 0.283483812128992 0.566967624257984 0.716516187871008 39 0.282866662020551 0.565733324041103 0.717133337979449 40 0.29566865079545 0.5913373015909 0.70433134920455 41 0.301011230873138 0.602022461746277 0.698988769126861 42 0.328093419843679 0.656186839687357 0.671906580156321 43 0.354436409810683 0.708872819621366 0.645563590189317 44 0.463308574513899 0.926617149027798 0.536691425486101 45 0.433386901713045 0.86677380342609 0.566613098286955 46 0.443304740952801 0.886609481905602 0.556695259047199 47 0.471294364140977 0.942588728281954 0.528705635859023 48 0.428914205297106 0.857828410594212 0.571085794702894 49 0.389979249600155 0.77995849920031 0.610020750399845 50 0.359642600241247 0.719285200482494 0.640357399758753 51 0.384009323850706 0.768018647701413 0.615990676149294 52 0.343685426939352 0.687370853878703 0.656314573060649 53 0.340440270674983 0.680880541349965 0.659559729325017 54 0.317376290188105 0.634752580376211 0.682623709811895 55 0.275606563995009 0.551213127990017 0.724393436004991 56 0.23726271389567 0.47452542779134 0.76273728610433 57 0.229338253191688 0.458676506383376 0.770661746808312 58 0.217566700851965 0.43513340170393 0.782433299148035 59 0.186031340221009 0.372062680442018 0.813968659778991 60 0.176739590931263 0.353479181862527 0.823260409068737 61 0.230310165716638 0.460620331433275 0.769689834283362 62 0.330911843400964 0.661823686801928 0.669088156599036 63 0.336419691084446 0.672839382168892 0.663580308915554 64 0.370707693991824 0.741415387983647 0.629292306008176 65 0.5140758042046 0.9718483915908 0.4859241957954 66 0.48062960120314 0.96125920240628 0.51937039879686 67 0.521973196436921 0.956053607126158 0.478026803563079 68 0.54759751200535 0.9048049759893 0.45240248799465 69 0.552940643251334 0.894118713497333 0.447059356748666 70 0.509309135321139 0.981381729357721 0.490690864678861 71 0.50145112795354 0.997097744092921 0.49854887204646 72 0.471837608441277 0.943675216882555 0.528162391558723 73 0.446280494689672 0.892560989379343 0.553719505310328 74 0.470449636396237 0.940899272792475 0.529550363603763 75 0.513226280102835 0.973547439794331 0.486773719897165 76 0.529467949531758 0.941064100936483 0.470532050468242 77 0.54775871899701 0.904482562005981 0.45224128100299 78 0.525822405081178 0.948355189837644 0.474177594918822 79 0.494429616327806 0.988859232655611 0.505570383672194 80 0.514591737085289 0.970816525829423 0.485408262914711 81 0.518277137642852 0.963445724714295 0.481722862357148 82 0.552923213236766 0.894153573526468 0.447076786763234 83 0.541635298748613 0.916729402502774 0.458364701251387 84 0.507636773965714 0.984726452068571 0.492363226034286 85 0.51394891102062 0.972102177958761 0.48605108897938 86 0.482325606963835 0.964651213927671 0.517674393036165 87 0.481469266668007 0.962938533336014 0.518530733331993 88 0.473816870713917 0.947633741427835 0.526183129286083 89 0.441183134171455 0.882366268342909 0.558816865828545 90 0.457078981936566 0.914157963873133 0.542921018063434 91 0.416479875634412 0.832959751268824 0.583520124365588 92 0.472155938190424 0.944311876380848 0.527844061809576 93 0.423410542999243 0.846821085998486 0.576589457000757 94 0.37886093991007 0.757721879820139 0.62113906008993 95 0.412087220105371 0.824174440210742 0.587912779894629 96 0.385555910810901 0.771111821621802 0.614444089189099 97 0.405302521660642 0.810605043321284 0.594697478339358 98 0.491284986578999 0.982569973157998 0.508715013421001 99 0.472887989836858 0.945775979673717 0.527112010163142 100 0.452890575507532 0.905781151015064 0.547109424492468 101 0.410275118036504 0.820550236073008 0.589724881963496 102 0.36356977213347 0.727139544266939 0.63643022786653 103 0.321939119350223 0.643878238700446 0.678060880649777 104 0.332420881021971 0.664841762043943 0.667579118978029 105 0.333643055903797 0.667286111807594 0.666356944096203 106 0.288142128679411 0.576284257358821 0.711857871320589 107 0.318639075337483 0.637278150674965 0.681360924662517 108 0.356073167385235 0.712146334770471 0.643926832614765 109 0.388813017968344 0.777626035936687 0.611186982031656 110 0.359944759875098 0.719889519750196 0.640055240124902 111 0.322884463788108 0.645768927576217 0.677115536211892 112 0.378853954951253 0.757707909902505 0.621146045048747 113 0.575795829056194 0.848408341887613 0.424204170943806 114 0.564740232447846 0.870519535104307 0.435259767552154 115 0.578239068801894 0.843521862396212 0.421760931198106 116 0.522276265962701 0.955447468074597 0.477723734037299 117 0.470535636826409 0.941071273652818 0.529464363173591 118 0.607859411081014 0.784281177837972 0.392140588918986 119 0.564906464458581 0.870187071082839 0.435093535541419 120 0.518460572945535 0.96307885410893 0.481539427054465 121 0.451758318667152 0.903516637334305 0.548241681332847 122 0.42391658224017 0.847833164480339 0.57608341775983 123 0.406554488042659 0.813108976085318 0.593445511957341 124 0.336072788489213 0.672145576978426 0.663927211510787 125 0.276579378907332 0.553158757814663 0.723420621092668 126 0.324091149919992 0.648182299839983 0.675908850080008 127 0.302597195682818 0.605194391365636 0.697402804317182 128 0.240352303730857 0.480704607461713 0.759647696269143 129 0.180102344310321 0.360204688620642 0.819897655689679 130 0.419879047523409 0.839758095046817 0.580120952476591 131 0.619456426124914 0.761087147750172 0.380543573875086 132 0.517364822506264 0.965270354987471 0.482635177493736 133 0.446480565684264 0.892961131368527 0.553519434315736 134 0.346463289424743 0.692926578849485 0.653536710575257 135 0.315267728934922 0.630535457869844 0.684732271065078 136 0.285082272741984 0.570164545483969 0.714917727258016 137 0.523293211587161 0.953413576825678 0.476706788412839

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.300115363617572 & 0.600230727235143 & 0.699884636382428 \tabularnewline
9 & 0.362040176196489 & 0.724080352392978 & 0.637959823803511 \tabularnewline
10 & 0.234896154818179 & 0.469792309636357 & 0.765103845181821 \tabularnewline
11 & 0.141685720680266 & 0.283371441360531 & 0.858314279319734 \tabularnewline
12 & 0.0790469166229443 & 0.158093833245889 & 0.920953083377056 \tabularnewline
13 & 0.0518371827390162 & 0.103674365478032 & 0.948162817260984 \tabularnewline
14 & 0.0286671898257638 & 0.0573343796515277 & 0.971332810174236 \tabularnewline
15 & 0.0145886189478053 & 0.0291772378956106 & 0.985411381052195 \tabularnewline
16 & 0.00861499865101093 & 0.0172299973020219 & 0.991385001348989 \tabularnewline
17 & 0.00411957189457703 & 0.00823914378915406 & 0.995880428105423 \tabularnewline
18 & 0.0253845582670739 & 0.0507691165341479 & 0.974615441732926 \tabularnewline
19 & 0.0150486597944918 & 0.0300973195889837 & 0.984951340205508 \tabularnewline
20 & 0.0122626025212158 & 0.0245252050424315 & 0.987737397478784 \tabularnewline
21 & 0.0342493973210529 & 0.0684987946421059 & 0.965750602678947 \tabularnewline
22 & 0.0970875581910767 & 0.194175116382153 & 0.902912441808923 \tabularnewline
23 & 0.0700023563631878 & 0.140004712726376 & 0.929997643636812 \tabularnewline
24 & 0.0521308888306804 & 0.104261777661361 & 0.94786911116932 \tabularnewline
25 & 0.0386375009600251 & 0.0772750019200501 & 0.961362499039975 \tabularnewline
26 & 0.271284832056046 & 0.542569664112093 & 0.728715167943954 \tabularnewline
27 & 0.244720621514443 & 0.489441243028886 & 0.755279378485557 \tabularnewline
28 & 0.198569220499873 & 0.397138440999745 & 0.801430779500127 \tabularnewline
29 & 0.243430366502388 & 0.486860733004776 & 0.756569633497612 \tabularnewline
30 & 0.198878162020818 & 0.397756324041637 & 0.801121837979182 \tabularnewline
31 & 0.24156645446565 & 0.483132908931301 & 0.75843354553435 \tabularnewline
32 & 0.283156040974507 & 0.566312081949014 & 0.716843959025493 \tabularnewline
33 & 0.308137850587203 & 0.616275701174406 & 0.691862149412797 \tabularnewline
34 & 0.351344524301218 & 0.702689048602436 & 0.648655475698782 \tabularnewline
35 & 0.401244648960893 & 0.802489297921785 & 0.598755351039107 \tabularnewline
36 & 0.354738122205034 & 0.709476244410069 & 0.645261877794966 \tabularnewline
37 & 0.304005415433116 & 0.608010830866232 & 0.695994584566884 \tabularnewline
38 & 0.283483812128992 & 0.566967624257984 & 0.716516187871008 \tabularnewline
39 & 0.282866662020551 & 0.565733324041103 & 0.717133337979449 \tabularnewline
40 & 0.29566865079545 & 0.5913373015909 & 0.70433134920455 \tabularnewline
41 & 0.301011230873138 & 0.602022461746277 & 0.698988769126861 \tabularnewline
42 & 0.328093419843679 & 0.656186839687357 & 0.671906580156321 \tabularnewline
43 & 0.354436409810683 & 0.708872819621366 & 0.645563590189317 \tabularnewline
44 & 0.463308574513899 & 0.926617149027798 & 0.536691425486101 \tabularnewline
45 & 0.433386901713045 & 0.86677380342609 & 0.566613098286955 \tabularnewline
46 & 0.443304740952801 & 0.886609481905602 & 0.556695259047199 \tabularnewline
47 & 0.471294364140977 & 0.942588728281954 & 0.528705635859023 \tabularnewline
48 & 0.428914205297106 & 0.857828410594212 & 0.571085794702894 \tabularnewline
49 & 0.389979249600155 & 0.77995849920031 & 0.610020750399845 \tabularnewline
50 & 0.359642600241247 & 0.719285200482494 & 0.640357399758753 \tabularnewline
51 & 0.384009323850706 & 0.768018647701413 & 0.615990676149294 \tabularnewline
52 & 0.343685426939352 & 0.687370853878703 & 0.656314573060649 \tabularnewline
53 & 0.340440270674983 & 0.680880541349965 & 0.659559729325017 \tabularnewline
54 & 0.317376290188105 & 0.634752580376211 & 0.682623709811895 \tabularnewline
55 & 0.275606563995009 & 0.551213127990017 & 0.724393436004991 \tabularnewline
56 & 0.23726271389567 & 0.47452542779134 & 0.76273728610433 \tabularnewline
57 & 0.229338253191688 & 0.458676506383376 & 0.770661746808312 \tabularnewline
58 & 0.217566700851965 & 0.43513340170393 & 0.782433299148035 \tabularnewline
59 & 0.186031340221009 & 0.372062680442018 & 0.813968659778991 \tabularnewline
60 & 0.176739590931263 & 0.353479181862527 & 0.823260409068737 \tabularnewline
61 & 0.230310165716638 & 0.460620331433275 & 0.769689834283362 \tabularnewline
62 & 0.330911843400964 & 0.661823686801928 & 0.669088156599036 \tabularnewline
63 & 0.336419691084446 & 0.672839382168892 & 0.663580308915554 \tabularnewline
64 & 0.370707693991824 & 0.741415387983647 & 0.629292306008176 \tabularnewline
65 & 0.5140758042046 & 0.9718483915908 & 0.4859241957954 \tabularnewline
66 & 0.48062960120314 & 0.96125920240628 & 0.51937039879686 \tabularnewline
67 & 0.521973196436921 & 0.956053607126158 & 0.478026803563079 \tabularnewline
68 & 0.54759751200535 & 0.9048049759893 & 0.45240248799465 \tabularnewline
69 & 0.552940643251334 & 0.894118713497333 & 0.447059356748666 \tabularnewline
70 & 0.509309135321139 & 0.981381729357721 & 0.490690864678861 \tabularnewline
71 & 0.50145112795354 & 0.997097744092921 & 0.49854887204646 \tabularnewline
72 & 0.471837608441277 & 0.943675216882555 & 0.528162391558723 \tabularnewline
73 & 0.446280494689672 & 0.892560989379343 & 0.553719505310328 \tabularnewline
74 & 0.470449636396237 & 0.940899272792475 & 0.529550363603763 \tabularnewline
75 & 0.513226280102835 & 0.973547439794331 & 0.486773719897165 \tabularnewline
76 & 0.529467949531758 & 0.941064100936483 & 0.470532050468242 \tabularnewline
77 & 0.54775871899701 & 0.904482562005981 & 0.45224128100299 \tabularnewline
78 & 0.525822405081178 & 0.948355189837644 & 0.474177594918822 \tabularnewline
79 & 0.494429616327806 & 0.988859232655611 & 0.505570383672194 \tabularnewline
80 & 0.514591737085289 & 0.970816525829423 & 0.485408262914711 \tabularnewline
81 & 0.518277137642852 & 0.963445724714295 & 0.481722862357148 \tabularnewline
82 & 0.552923213236766 & 0.894153573526468 & 0.447076786763234 \tabularnewline
83 & 0.541635298748613 & 0.916729402502774 & 0.458364701251387 \tabularnewline
84 & 0.507636773965714 & 0.984726452068571 & 0.492363226034286 \tabularnewline
85 & 0.51394891102062 & 0.972102177958761 & 0.48605108897938 \tabularnewline
86 & 0.482325606963835 & 0.964651213927671 & 0.517674393036165 \tabularnewline
87 & 0.481469266668007 & 0.962938533336014 & 0.518530733331993 \tabularnewline
88 & 0.473816870713917 & 0.947633741427835 & 0.526183129286083 \tabularnewline
89 & 0.441183134171455 & 0.882366268342909 & 0.558816865828545 \tabularnewline
90 & 0.457078981936566 & 0.914157963873133 & 0.542921018063434 \tabularnewline
91 & 0.416479875634412 & 0.832959751268824 & 0.583520124365588 \tabularnewline
92 & 0.472155938190424 & 0.944311876380848 & 0.527844061809576 \tabularnewline
93 & 0.423410542999243 & 0.846821085998486 & 0.576589457000757 \tabularnewline
94 & 0.37886093991007 & 0.757721879820139 & 0.62113906008993 \tabularnewline
95 & 0.412087220105371 & 0.824174440210742 & 0.587912779894629 \tabularnewline
96 & 0.385555910810901 & 0.771111821621802 & 0.614444089189099 \tabularnewline
97 & 0.405302521660642 & 0.810605043321284 & 0.594697478339358 \tabularnewline
98 & 0.491284986578999 & 0.982569973157998 & 0.508715013421001 \tabularnewline
99 & 0.472887989836858 & 0.945775979673717 & 0.527112010163142 \tabularnewline
100 & 0.452890575507532 & 0.905781151015064 & 0.547109424492468 \tabularnewline
101 & 0.410275118036504 & 0.820550236073008 & 0.589724881963496 \tabularnewline
102 & 0.36356977213347 & 0.727139544266939 & 0.63643022786653 \tabularnewline
103 & 0.321939119350223 & 0.643878238700446 & 0.678060880649777 \tabularnewline
104 & 0.332420881021971 & 0.664841762043943 & 0.667579118978029 \tabularnewline
105 & 0.333643055903797 & 0.667286111807594 & 0.666356944096203 \tabularnewline
106 & 0.288142128679411 & 0.576284257358821 & 0.711857871320589 \tabularnewline
107 & 0.318639075337483 & 0.637278150674965 & 0.681360924662517 \tabularnewline
108 & 0.356073167385235 & 0.712146334770471 & 0.643926832614765 \tabularnewline
109 & 0.388813017968344 & 0.777626035936687 & 0.611186982031656 \tabularnewline
110 & 0.359944759875098 & 0.719889519750196 & 0.640055240124902 \tabularnewline
111 & 0.322884463788108 & 0.645768927576217 & 0.677115536211892 \tabularnewline
112 & 0.378853954951253 & 0.757707909902505 & 0.621146045048747 \tabularnewline
113 & 0.575795829056194 & 0.848408341887613 & 0.424204170943806 \tabularnewline
114 & 0.564740232447846 & 0.870519535104307 & 0.435259767552154 \tabularnewline
115 & 0.578239068801894 & 0.843521862396212 & 0.421760931198106 \tabularnewline
116 & 0.522276265962701 & 0.955447468074597 & 0.477723734037299 \tabularnewline
117 & 0.470535636826409 & 0.941071273652818 & 0.529464363173591 \tabularnewline
118 & 0.607859411081014 & 0.784281177837972 & 0.392140588918986 \tabularnewline
119 & 0.564906464458581 & 0.870187071082839 & 0.435093535541419 \tabularnewline
120 & 0.518460572945535 & 0.96307885410893 & 0.481539427054465 \tabularnewline
121 & 0.451758318667152 & 0.903516637334305 & 0.548241681332847 \tabularnewline
122 & 0.42391658224017 & 0.847833164480339 & 0.57608341775983 \tabularnewline
123 & 0.406554488042659 & 0.813108976085318 & 0.593445511957341 \tabularnewline
124 & 0.336072788489213 & 0.672145576978426 & 0.663927211510787 \tabularnewline
125 & 0.276579378907332 & 0.553158757814663 & 0.723420621092668 \tabularnewline
126 & 0.324091149919992 & 0.648182299839983 & 0.675908850080008 \tabularnewline
127 & 0.302597195682818 & 0.605194391365636 & 0.697402804317182 \tabularnewline
128 & 0.240352303730857 & 0.480704607461713 & 0.759647696269143 \tabularnewline
129 & 0.180102344310321 & 0.360204688620642 & 0.819897655689679 \tabularnewline
130 & 0.419879047523409 & 0.839758095046817 & 0.580120952476591 \tabularnewline
131 & 0.619456426124914 & 0.761087147750172 & 0.380543573875086 \tabularnewline
132 & 0.517364822506264 & 0.965270354987471 & 0.482635177493736 \tabularnewline
133 & 0.446480565684264 & 0.892961131368527 & 0.553519434315736 \tabularnewline
134 & 0.346463289424743 & 0.692926578849485 & 0.653536710575257 \tabularnewline
135 & 0.315267728934922 & 0.630535457869844 & 0.684732271065078 \tabularnewline
136 & 0.285082272741984 & 0.570164545483969 & 0.714917727258016 \tabularnewline
137 & 0.523293211587161 & 0.953413576825678 & 0.476706788412839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.300115363617572[/C][C]0.600230727235143[/C][C]0.699884636382428[/C][/ROW]
[ROW][C]9[/C][C]0.362040176196489[/C][C]0.724080352392978[/C][C]0.637959823803511[/C][/ROW]
[ROW][C]10[/C][C]0.234896154818179[/C][C]0.469792309636357[/C][C]0.765103845181821[/C][/ROW]
[ROW][C]11[/C][C]0.141685720680266[/C][C]0.283371441360531[/C][C]0.858314279319734[/C][/ROW]
[ROW][C]12[/C][C]0.0790469166229443[/C][C]0.158093833245889[/C][C]0.920953083377056[/C][/ROW]
[ROW][C]13[/C][C]0.0518371827390162[/C][C]0.103674365478032[/C][C]0.948162817260984[/C][/ROW]
[ROW][C]14[/C][C]0.0286671898257638[/C][C]0.0573343796515277[/C][C]0.971332810174236[/C][/ROW]
[ROW][C]15[/C][C]0.0145886189478053[/C][C]0.0291772378956106[/C][C]0.985411381052195[/C][/ROW]
[ROW][C]16[/C][C]0.00861499865101093[/C][C]0.0172299973020219[/C][C]0.991385001348989[/C][/ROW]
[ROW][C]17[/C][C]0.00411957189457703[/C][C]0.00823914378915406[/C][C]0.995880428105423[/C][/ROW]
[ROW][C]18[/C][C]0.0253845582670739[/C][C]0.0507691165341479[/C][C]0.974615441732926[/C][/ROW]
[ROW][C]19[/C][C]0.0150486597944918[/C][C]0.0300973195889837[/C][C]0.984951340205508[/C][/ROW]
[ROW][C]20[/C][C]0.0122626025212158[/C][C]0.0245252050424315[/C][C]0.987737397478784[/C][/ROW]
[ROW][C]21[/C][C]0.0342493973210529[/C][C]0.0684987946421059[/C][C]0.965750602678947[/C][/ROW]
[ROW][C]22[/C][C]0.0970875581910767[/C][C]0.194175116382153[/C][C]0.902912441808923[/C][/ROW]
[ROW][C]23[/C][C]0.0700023563631878[/C][C]0.140004712726376[/C][C]0.929997643636812[/C][/ROW]
[ROW][C]24[/C][C]0.0521308888306804[/C][C]0.104261777661361[/C][C]0.94786911116932[/C][/ROW]
[ROW][C]25[/C][C]0.0386375009600251[/C][C]0.0772750019200501[/C][C]0.961362499039975[/C][/ROW]
[ROW][C]26[/C][C]0.271284832056046[/C][C]0.542569664112093[/C][C]0.728715167943954[/C][/ROW]
[ROW][C]27[/C][C]0.244720621514443[/C][C]0.489441243028886[/C][C]0.755279378485557[/C][/ROW]
[ROW][C]28[/C][C]0.198569220499873[/C][C]0.397138440999745[/C][C]0.801430779500127[/C][/ROW]
[ROW][C]29[/C][C]0.243430366502388[/C][C]0.486860733004776[/C][C]0.756569633497612[/C][/ROW]
[ROW][C]30[/C][C]0.198878162020818[/C][C]0.397756324041637[/C][C]0.801121837979182[/C][/ROW]
[ROW][C]31[/C][C]0.24156645446565[/C][C]0.483132908931301[/C][C]0.75843354553435[/C][/ROW]
[ROW][C]32[/C][C]0.283156040974507[/C][C]0.566312081949014[/C][C]0.716843959025493[/C][/ROW]
[ROW][C]33[/C][C]0.308137850587203[/C][C]0.616275701174406[/C][C]0.691862149412797[/C][/ROW]
[ROW][C]34[/C][C]0.351344524301218[/C][C]0.702689048602436[/C][C]0.648655475698782[/C][/ROW]
[ROW][C]35[/C][C]0.401244648960893[/C][C]0.802489297921785[/C][C]0.598755351039107[/C][/ROW]
[ROW][C]36[/C][C]0.354738122205034[/C][C]0.709476244410069[/C][C]0.645261877794966[/C][/ROW]
[ROW][C]37[/C][C]0.304005415433116[/C][C]0.608010830866232[/C][C]0.695994584566884[/C][/ROW]
[ROW][C]38[/C][C]0.283483812128992[/C][C]0.566967624257984[/C][C]0.716516187871008[/C][/ROW]
[ROW][C]39[/C][C]0.282866662020551[/C][C]0.565733324041103[/C][C]0.717133337979449[/C][/ROW]
[ROW][C]40[/C][C]0.29566865079545[/C][C]0.5913373015909[/C][C]0.70433134920455[/C][/ROW]
[ROW][C]41[/C][C]0.301011230873138[/C][C]0.602022461746277[/C][C]0.698988769126861[/C][/ROW]
[ROW][C]42[/C][C]0.328093419843679[/C][C]0.656186839687357[/C][C]0.671906580156321[/C][/ROW]
[ROW][C]43[/C][C]0.354436409810683[/C][C]0.708872819621366[/C][C]0.645563590189317[/C][/ROW]
[ROW][C]44[/C][C]0.463308574513899[/C][C]0.926617149027798[/C][C]0.536691425486101[/C][/ROW]
[ROW][C]45[/C][C]0.433386901713045[/C][C]0.86677380342609[/C][C]0.566613098286955[/C][/ROW]
[ROW][C]46[/C][C]0.443304740952801[/C][C]0.886609481905602[/C][C]0.556695259047199[/C][/ROW]
[ROW][C]47[/C][C]0.471294364140977[/C][C]0.942588728281954[/C][C]0.528705635859023[/C][/ROW]
[ROW][C]48[/C][C]0.428914205297106[/C][C]0.857828410594212[/C][C]0.571085794702894[/C][/ROW]
[ROW][C]49[/C][C]0.389979249600155[/C][C]0.77995849920031[/C][C]0.610020750399845[/C][/ROW]
[ROW][C]50[/C][C]0.359642600241247[/C][C]0.719285200482494[/C][C]0.640357399758753[/C][/ROW]
[ROW][C]51[/C][C]0.384009323850706[/C][C]0.768018647701413[/C][C]0.615990676149294[/C][/ROW]
[ROW][C]52[/C][C]0.343685426939352[/C][C]0.687370853878703[/C][C]0.656314573060649[/C][/ROW]
[ROW][C]53[/C][C]0.340440270674983[/C][C]0.680880541349965[/C][C]0.659559729325017[/C][/ROW]
[ROW][C]54[/C][C]0.317376290188105[/C][C]0.634752580376211[/C][C]0.682623709811895[/C][/ROW]
[ROW][C]55[/C][C]0.275606563995009[/C][C]0.551213127990017[/C][C]0.724393436004991[/C][/ROW]
[ROW][C]56[/C][C]0.23726271389567[/C][C]0.47452542779134[/C][C]0.76273728610433[/C][/ROW]
[ROW][C]57[/C][C]0.229338253191688[/C][C]0.458676506383376[/C][C]0.770661746808312[/C][/ROW]
[ROW][C]58[/C][C]0.217566700851965[/C][C]0.43513340170393[/C][C]0.782433299148035[/C][/ROW]
[ROW][C]59[/C][C]0.186031340221009[/C][C]0.372062680442018[/C][C]0.813968659778991[/C][/ROW]
[ROW][C]60[/C][C]0.176739590931263[/C][C]0.353479181862527[/C][C]0.823260409068737[/C][/ROW]
[ROW][C]61[/C][C]0.230310165716638[/C][C]0.460620331433275[/C][C]0.769689834283362[/C][/ROW]
[ROW][C]62[/C][C]0.330911843400964[/C][C]0.661823686801928[/C][C]0.669088156599036[/C][/ROW]
[ROW][C]63[/C][C]0.336419691084446[/C][C]0.672839382168892[/C][C]0.663580308915554[/C][/ROW]
[ROW][C]64[/C][C]0.370707693991824[/C][C]0.741415387983647[/C][C]0.629292306008176[/C][/ROW]
[ROW][C]65[/C][C]0.5140758042046[/C][C]0.9718483915908[/C][C]0.4859241957954[/C][/ROW]
[ROW][C]66[/C][C]0.48062960120314[/C][C]0.96125920240628[/C][C]0.51937039879686[/C][/ROW]
[ROW][C]67[/C][C]0.521973196436921[/C][C]0.956053607126158[/C][C]0.478026803563079[/C][/ROW]
[ROW][C]68[/C][C]0.54759751200535[/C][C]0.9048049759893[/C][C]0.45240248799465[/C][/ROW]
[ROW][C]69[/C][C]0.552940643251334[/C][C]0.894118713497333[/C][C]0.447059356748666[/C][/ROW]
[ROW][C]70[/C][C]0.509309135321139[/C][C]0.981381729357721[/C][C]0.490690864678861[/C][/ROW]
[ROW][C]71[/C][C]0.50145112795354[/C][C]0.997097744092921[/C][C]0.49854887204646[/C][/ROW]
[ROW][C]72[/C][C]0.471837608441277[/C][C]0.943675216882555[/C][C]0.528162391558723[/C][/ROW]
[ROW][C]73[/C][C]0.446280494689672[/C][C]0.892560989379343[/C][C]0.553719505310328[/C][/ROW]
[ROW][C]74[/C][C]0.470449636396237[/C][C]0.940899272792475[/C][C]0.529550363603763[/C][/ROW]
[ROW][C]75[/C][C]0.513226280102835[/C][C]0.973547439794331[/C][C]0.486773719897165[/C][/ROW]
[ROW][C]76[/C][C]0.529467949531758[/C][C]0.941064100936483[/C][C]0.470532050468242[/C][/ROW]
[ROW][C]77[/C][C]0.54775871899701[/C][C]0.904482562005981[/C][C]0.45224128100299[/C][/ROW]
[ROW][C]78[/C][C]0.525822405081178[/C][C]0.948355189837644[/C][C]0.474177594918822[/C][/ROW]
[ROW][C]79[/C][C]0.494429616327806[/C][C]0.988859232655611[/C][C]0.505570383672194[/C][/ROW]
[ROW][C]80[/C][C]0.514591737085289[/C][C]0.970816525829423[/C][C]0.485408262914711[/C][/ROW]
[ROW][C]81[/C][C]0.518277137642852[/C][C]0.963445724714295[/C][C]0.481722862357148[/C][/ROW]
[ROW][C]82[/C][C]0.552923213236766[/C][C]0.894153573526468[/C][C]0.447076786763234[/C][/ROW]
[ROW][C]83[/C][C]0.541635298748613[/C][C]0.916729402502774[/C][C]0.458364701251387[/C][/ROW]
[ROW][C]84[/C][C]0.507636773965714[/C][C]0.984726452068571[/C][C]0.492363226034286[/C][/ROW]
[ROW][C]85[/C][C]0.51394891102062[/C][C]0.972102177958761[/C][C]0.48605108897938[/C][/ROW]
[ROW][C]86[/C][C]0.482325606963835[/C][C]0.964651213927671[/C][C]0.517674393036165[/C][/ROW]
[ROW][C]87[/C][C]0.481469266668007[/C][C]0.962938533336014[/C][C]0.518530733331993[/C][/ROW]
[ROW][C]88[/C][C]0.473816870713917[/C][C]0.947633741427835[/C][C]0.526183129286083[/C][/ROW]
[ROW][C]89[/C][C]0.441183134171455[/C][C]0.882366268342909[/C][C]0.558816865828545[/C][/ROW]
[ROW][C]90[/C][C]0.457078981936566[/C][C]0.914157963873133[/C][C]0.542921018063434[/C][/ROW]
[ROW][C]91[/C][C]0.416479875634412[/C][C]0.832959751268824[/C][C]0.583520124365588[/C][/ROW]
[ROW][C]92[/C][C]0.472155938190424[/C][C]0.944311876380848[/C][C]0.527844061809576[/C][/ROW]
[ROW][C]93[/C][C]0.423410542999243[/C][C]0.846821085998486[/C][C]0.576589457000757[/C][/ROW]
[ROW][C]94[/C][C]0.37886093991007[/C][C]0.757721879820139[/C][C]0.62113906008993[/C][/ROW]
[ROW][C]95[/C][C]0.412087220105371[/C][C]0.824174440210742[/C][C]0.587912779894629[/C][/ROW]
[ROW][C]96[/C][C]0.385555910810901[/C][C]0.771111821621802[/C][C]0.614444089189099[/C][/ROW]
[ROW][C]97[/C][C]0.405302521660642[/C][C]0.810605043321284[/C][C]0.594697478339358[/C][/ROW]
[ROW][C]98[/C][C]0.491284986578999[/C][C]0.982569973157998[/C][C]0.508715013421001[/C][/ROW]
[ROW][C]99[/C][C]0.472887989836858[/C][C]0.945775979673717[/C][C]0.527112010163142[/C][/ROW]
[ROW][C]100[/C][C]0.452890575507532[/C][C]0.905781151015064[/C][C]0.547109424492468[/C][/ROW]
[ROW][C]101[/C][C]0.410275118036504[/C][C]0.820550236073008[/C][C]0.589724881963496[/C][/ROW]
[ROW][C]102[/C][C]0.36356977213347[/C][C]0.727139544266939[/C][C]0.63643022786653[/C][/ROW]
[ROW][C]103[/C][C]0.321939119350223[/C][C]0.643878238700446[/C][C]0.678060880649777[/C][/ROW]
[ROW][C]104[/C][C]0.332420881021971[/C][C]0.664841762043943[/C][C]0.667579118978029[/C][/ROW]
[ROW][C]105[/C][C]0.333643055903797[/C][C]0.667286111807594[/C][C]0.666356944096203[/C][/ROW]
[ROW][C]106[/C][C]0.288142128679411[/C][C]0.576284257358821[/C][C]0.711857871320589[/C][/ROW]
[ROW][C]107[/C][C]0.318639075337483[/C][C]0.637278150674965[/C][C]0.681360924662517[/C][/ROW]
[ROW][C]108[/C][C]0.356073167385235[/C][C]0.712146334770471[/C][C]0.643926832614765[/C][/ROW]
[ROW][C]109[/C][C]0.388813017968344[/C][C]0.777626035936687[/C][C]0.611186982031656[/C][/ROW]
[ROW][C]110[/C][C]0.359944759875098[/C][C]0.719889519750196[/C][C]0.640055240124902[/C][/ROW]
[ROW][C]111[/C][C]0.322884463788108[/C][C]0.645768927576217[/C][C]0.677115536211892[/C][/ROW]
[ROW][C]112[/C][C]0.378853954951253[/C][C]0.757707909902505[/C][C]0.621146045048747[/C][/ROW]
[ROW][C]113[/C][C]0.575795829056194[/C][C]0.848408341887613[/C][C]0.424204170943806[/C][/ROW]
[ROW][C]114[/C][C]0.564740232447846[/C][C]0.870519535104307[/C][C]0.435259767552154[/C][/ROW]
[ROW][C]115[/C][C]0.578239068801894[/C][C]0.843521862396212[/C][C]0.421760931198106[/C][/ROW]
[ROW][C]116[/C][C]0.522276265962701[/C][C]0.955447468074597[/C][C]0.477723734037299[/C][/ROW]
[ROW][C]117[/C][C]0.470535636826409[/C][C]0.941071273652818[/C][C]0.529464363173591[/C][/ROW]
[ROW][C]118[/C][C]0.607859411081014[/C][C]0.784281177837972[/C][C]0.392140588918986[/C][/ROW]
[ROW][C]119[/C][C]0.564906464458581[/C][C]0.870187071082839[/C][C]0.435093535541419[/C][/ROW]
[ROW][C]120[/C][C]0.518460572945535[/C][C]0.96307885410893[/C][C]0.481539427054465[/C][/ROW]
[ROW][C]121[/C][C]0.451758318667152[/C][C]0.903516637334305[/C][C]0.548241681332847[/C][/ROW]
[ROW][C]122[/C][C]0.42391658224017[/C][C]0.847833164480339[/C][C]0.57608341775983[/C][/ROW]
[ROW][C]123[/C][C]0.406554488042659[/C][C]0.813108976085318[/C][C]0.593445511957341[/C][/ROW]
[ROW][C]124[/C][C]0.336072788489213[/C][C]0.672145576978426[/C][C]0.663927211510787[/C][/ROW]
[ROW][C]125[/C][C]0.276579378907332[/C][C]0.553158757814663[/C][C]0.723420621092668[/C][/ROW]
[ROW][C]126[/C][C]0.324091149919992[/C][C]0.648182299839983[/C][C]0.675908850080008[/C][/ROW]
[ROW][C]127[/C][C]0.302597195682818[/C][C]0.605194391365636[/C][C]0.697402804317182[/C][/ROW]
[ROW][C]128[/C][C]0.240352303730857[/C][C]0.480704607461713[/C][C]0.759647696269143[/C][/ROW]
[ROW][C]129[/C][C]0.180102344310321[/C][C]0.360204688620642[/C][C]0.819897655689679[/C][/ROW]
[ROW][C]130[/C][C]0.419879047523409[/C][C]0.839758095046817[/C][C]0.580120952476591[/C][/ROW]
[ROW][C]131[/C][C]0.619456426124914[/C][C]0.761087147750172[/C][C]0.380543573875086[/C][/ROW]
[ROW][C]132[/C][C]0.517364822506264[/C][C]0.965270354987471[/C][C]0.482635177493736[/C][/ROW]
[ROW][C]133[/C][C]0.446480565684264[/C][C]0.892961131368527[/C][C]0.553519434315736[/C][/ROW]
[ROW][C]134[/C][C]0.346463289424743[/C][C]0.692926578849485[/C][C]0.653536710575257[/C][/ROW]
[ROW][C]135[/C][C]0.315267728934922[/C][C]0.630535457869844[/C][C]0.684732271065078[/C][/ROW]
[ROW][C]136[/C][C]0.285082272741984[/C][C]0.570164545483969[/C][C]0.714917727258016[/C][/ROW]
[ROW][C]137[/C][C]0.523293211587161[/C][C]0.953413576825678[/C][C]0.476706788412839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=5

As an alternative you can also use a QR Code:

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

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.300115363617572 0.600230727235143 0.699884636382428 9 0.362040176196489 0.724080352392978 0.637959823803511 10 0.234896154818179 0.469792309636357 0.765103845181821 11 0.141685720680266 0.283371441360531 0.858314279319734 12 0.0790469166229443 0.158093833245889 0.920953083377056 13 0.0518371827390162 0.103674365478032 0.948162817260984 14 0.0286671898257638 0.0573343796515277 0.971332810174236 15 0.0145886189478053 0.0291772378956106 0.985411381052195 16 0.00861499865101093 0.0172299973020219 0.991385001348989 17 0.00411957189457703 0.00823914378915406 0.995880428105423 18 0.0253845582670739 0.0507691165341479 0.974615441732926 19 0.0150486597944918 0.0300973195889837 0.984951340205508 20 0.0122626025212158 0.0245252050424315 0.987737397478784 21 0.0342493973210529 0.0684987946421059 0.965750602678947 22 0.0970875581910767 0.194175116382153 0.902912441808923 23 0.0700023563631878 0.140004712726376 0.929997643636812 24 0.0521308888306804 0.104261777661361 0.94786911116932 25 0.0386375009600251 0.0772750019200501 0.961362499039975 26 0.271284832056046 0.542569664112093 0.728715167943954 27 0.244720621514443 0.489441243028886 0.755279378485557 28 0.198569220499873 0.397138440999745 0.801430779500127 29 0.243430366502388 0.486860733004776 0.756569633497612 30 0.198878162020818 0.397756324041637 0.801121837979182 31 0.24156645446565 0.483132908931301 0.75843354553435 32 0.283156040974507 0.566312081949014 0.716843959025493 33 0.308137850587203 0.616275701174406 0.691862149412797 34 0.351344524301218 0.702689048602436 0.648655475698782 35 0.401244648960893 0.802489297921785 0.598755351039107 36 0.354738122205034 0.709476244410069 0.645261877794966 37 0.304005415433116 0.608010830866232 0.695994584566884 38 0.283483812128992 0.566967624257984 0.716516187871008 39 0.282866662020551 0.565733324041103 0.717133337979449 40 0.29566865079545 0.5913373015909 0.70433134920455 41 0.301011230873138 0.602022461746277 0.698988769126861 42 0.328093419843679 0.656186839687357 0.671906580156321 43 0.354436409810683 0.708872819621366 0.645563590189317 44 0.463308574513899 0.926617149027798 0.536691425486101 45 0.433386901713045 0.86677380342609 0.566613098286955 46 0.443304740952801 0.886609481905602 0.556695259047199 47 0.471294364140977 0.942588728281954 0.528705635859023 48 0.428914205297106 0.857828410594212 0.571085794702894 49 0.389979249600155 0.77995849920031 0.610020750399845 50 0.359642600241247 0.719285200482494 0.640357399758753 51 0.384009323850706 0.768018647701413 0.615990676149294 52 0.343685426939352 0.687370853878703 0.656314573060649 53 0.340440270674983 0.680880541349965 0.659559729325017 54 0.317376290188105 0.634752580376211 0.682623709811895 55 0.275606563995009 0.551213127990017 0.724393436004991 56 0.23726271389567 0.47452542779134 0.76273728610433 57 0.229338253191688 0.458676506383376 0.770661746808312 58 0.217566700851965 0.43513340170393 0.782433299148035 59 0.186031340221009 0.372062680442018 0.813968659778991 60 0.176739590931263 0.353479181862527 0.823260409068737 61 0.230310165716638 0.460620331433275 0.769689834283362 62 0.330911843400964 0.661823686801928 0.669088156599036 63 0.336419691084446 0.672839382168892 0.663580308915554 64 0.370707693991824 0.741415387983647 0.629292306008176 65 0.5140758042046 0.9718483915908 0.4859241957954 66 0.48062960120314 0.96125920240628 0.51937039879686 67 0.521973196436921 0.956053607126158 0.478026803563079 68 0.54759751200535 0.9048049759893 0.45240248799465 69 0.552940643251334 0.894118713497333 0.447059356748666 70 0.509309135321139 0.981381729357721 0.490690864678861 71 0.50145112795354 0.997097744092921 0.49854887204646 72 0.471837608441277 0.943675216882555 0.528162391558723 73 0.446280494689672 0.892560989379343 0.553719505310328 74 0.470449636396237 0.940899272792475 0.529550363603763 75 0.513226280102835 0.973547439794331 0.486773719897165 76 0.529467949531758 0.941064100936483 0.470532050468242 77 0.54775871899701 0.904482562005981 0.45224128100299 78 0.525822405081178 0.948355189837644 0.474177594918822 79 0.494429616327806 0.988859232655611 0.505570383672194 80 0.514591737085289 0.970816525829423 0.485408262914711 81 0.518277137642852 0.963445724714295 0.481722862357148 82 0.552923213236766 0.894153573526468 0.447076786763234 83 0.541635298748613 0.916729402502774 0.458364701251387 84 0.507636773965714 0.984726452068571 0.492363226034286 85 0.51394891102062 0.972102177958761 0.48605108897938 86 0.482325606963835 0.964651213927671 0.517674393036165 87 0.481469266668007 0.962938533336014 0.518530733331993 88 0.473816870713917 0.947633741427835 0.526183129286083 89 0.441183134171455 0.882366268342909 0.558816865828545 90 0.457078981936566 0.914157963873133 0.542921018063434 91 0.416479875634412 0.832959751268824 0.583520124365588 92 0.472155938190424 0.944311876380848 0.527844061809576 93 0.423410542999243 0.846821085998486 0.576589457000757 94 0.37886093991007 0.757721879820139 0.62113906008993 95 0.412087220105371 0.824174440210742 0.587912779894629 96 0.385555910810901 0.771111821621802 0.614444089189099 97 0.405302521660642 0.810605043321284 0.594697478339358 98 0.491284986578999 0.982569973157998 0.508715013421001 99 0.472887989836858 0.945775979673717 0.527112010163142 100 0.452890575507532 0.905781151015064 0.547109424492468 101 0.410275118036504 0.820550236073008 0.589724881963496 102 0.36356977213347 0.727139544266939 0.63643022786653 103 0.321939119350223 0.643878238700446 0.678060880649777 104 0.332420881021971 0.664841762043943 0.667579118978029 105 0.333643055903797 0.667286111807594 0.666356944096203 106 0.288142128679411 0.576284257358821 0.711857871320589 107 0.318639075337483 0.637278150674965 0.681360924662517 108 0.356073167385235 0.712146334770471 0.643926832614765 109 0.388813017968344 0.777626035936687 0.611186982031656 110 0.359944759875098 0.719889519750196 0.640055240124902 111 0.322884463788108 0.645768927576217 0.677115536211892 112 0.378853954951253 0.757707909902505 0.621146045048747 113 0.575795829056194 0.848408341887613 0.424204170943806 114 0.564740232447846 0.870519535104307 0.435259767552154 115 0.578239068801894 0.843521862396212 0.421760931198106 116 0.522276265962701 0.955447468074597 0.477723734037299 117 0.470535636826409 0.941071273652818 0.529464363173591 118 0.607859411081014 0.784281177837972 0.392140588918986 119 0.564906464458581 0.870187071082839 0.435093535541419 120 0.518460572945535 0.96307885410893 0.481539427054465 121 0.451758318667152 0.903516637334305 0.548241681332847 122 0.42391658224017 0.847833164480339 0.57608341775983 123 0.406554488042659 0.813108976085318 0.593445511957341 124 0.336072788489213 0.672145576978426 0.663927211510787 125 0.276579378907332 0.553158757814663 0.723420621092668 126 0.324091149919992 0.648182299839983 0.675908850080008 127 0.302597195682818 0.605194391365636 0.697402804317182 128 0.240352303730857 0.480704607461713 0.759647696269143 129 0.180102344310321 0.360204688620642 0.819897655689679 130 0.419879047523409 0.839758095046817 0.580120952476591 131 0.619456426124914 0.761087147750172 0.380543573875086 132 0.517364822506264 0.965270354987471 0.482635177493736 133 0.446480565684264 0.892961131368527 0.553519434315736 134 0.346463289424743 0.692926578849485 0.653536710575257 135 0.315267728934922 0.630535457869844 0.684732271065078 136 0.285082272741984 0.570164545483969 0.714917727258016 137 0.523293211587161 0.953413576825678 0.476706788412839

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.00769230769230769 OK 5% type I error level 5 0.0384615384615385 OK 10% type I error level 9 0.0692307692307692 OK

\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 & 1 & 0.00769230769230769 & OK \tabularnewline
5% type I error level & 5 & 0.0384615384615385 & OK \tabularnewline
10% type I error level & 9 & 0.0692307692307692 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186042&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]1[/C][C]0.00769230769230769[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.0384615384615385[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0692307692307692[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186042&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186042&T=6

As an alternative you can also use a QR Code:

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.00769230769230769 OK 5% type I error level 5 0.0384615384615385 OK 10% type I error level 9 0.0692307692307692 OK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}