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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Nov 2012 12:29:50 -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/19/t1353346250761m880m2zjq3v1.htm/, Retrieved Sat, 27 Apr 2024 17:44:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190680, Retrieved Sat, 27 Apr 2024 17:44:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 Multiple Regr...] [2012-11-19 17:29:50] [933e9ab295d38e240eca0a457ef09371] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Depression [t] = + 24.1568693472891 -0.048698971906455Connected[t] + 0.0212697039878104Separate[t] -0.73207250482067Happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression
[t] =  +  24.1568693472891 -0.048698971906455Connected[t] +  0.0212697039878104Separate[t] -0.73207250482067Happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression
[t] =  +  24.1568693472891 -0.048698971906455Connected[t] +  0.0212697039878104Separate[t] -0.73207250482067Happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Depression [t] = + 24.1568693472891 -0.048698971906455Connected[t] + 0.0212697039878104Separate[t] -0.73207250482067Happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.15686934728912.6753069.029600
Connected-0.0486989719064550.067524-0.72120.4718490.235925
Separate0.02126970398781040.0642640.3310.7411020.370551
Happiness-0.732072504820670.091736-7.980200

\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) & 24.1568693472891 & 2.675306 & 9.0296 & 0 & 0 \tabularnewline
Connected & -0.048698971906455 & 0.067524 & -0.7212 & 0.471849 & 0.235925 \tabularnewline
Separate & 0.0212697039878104 & 0.064264 & 0.331 & 0.741102 & 0.370551 \tabularnewline
Happiness & -0.73207250482067 & 0.091736 & -7.9802 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&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]24.1568693472891[/C][C]2.675306[/C][C]9.0296[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]-0.048698971906455[/C][C]0.067524[/C][C]-0.7212[/C][C]0.471849[/C][C]0.235925[/C][/ROW]
[ROW][C]Separate[/C][C]0.0212697039878104[/C][C]0.064264[/C][C]0.331[/C][C]0.741102[/C][C]0.370551[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.73207250482067[/C][C]0.091736[/C][C]-7.9802[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.15686934728912.6753069.029600
Connected-0.0486989719064550.067524-0.72120.4718490.235925
Separate0.02126970398781040.0642640.3310.7411020.370551
Happiness-0.732072504820670.091736-7.980200






Multiple Linear Regression - Regression Statistics
Multiple R0.546383336681775
R-squared0.29853475060351
Adjusted R-squared0.285215790171931
F-TEST (value)22.4142681508154
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value3.81550346872928e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.67682813705154
Sum Squared Residuals1132.13460229911

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.546383336681775 \tabularnewline
R-squared & 0.29853475060351 \tabularnewline
Adjusted R-squared & 0.285215790171931 \tabularnewline
F-TEST (value) & 22.4142681508154 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 3.81550346872928e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.67682813705154 \tabularnewline
Sum Squared Residuals & 1132.13460229911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.546383336681775[/C][/ROW]
[ROW][C]R-squared[/C][C]0.29853475060351[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.285215790171931[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.4142681508154[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]3.81550346872928e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.67682813705154[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1132.13460229911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.546383336681775
R-squared0.29853475060351
Adjusted R-squared0.285215790171931
F-TEST (value)22.4142681508154
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value3.81550346872928e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.67682813705154
Sum Squared Residuals1132.13460229911






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.7194451831719-0.719445183171919
2119.760934883775221.23906511622478
31415.3875422766414-1.38754227664143
41214.5642313919387-2.56423139193869
52111.57492327288799.42507672711211
6129.891921659437612.10807834056239
72212.66795519907019.33204480092991
81113.0177985785414-2.01779857854142
91012.16705842592-2.16705842592003
101312.1821685659770.817831434022999
111010.5204366565145-0.520436656514534
1289.21749870264953-1.21749870264953
131515.7300230062105-0.730023006210466
141411.35269811734342.64730188265658
151010.159477235153-0.159477235153001
161413.05138741039090.948612589609106
171412.83532181877731.16467818122274
181110.66932458442920.330675415570794
191012.8168431269848-2.81684312698476
201311.56597269676171.43402730323826
21710.1230973911082-3.12309739110821
221415.2537644887837-1.25376448878374
231212.8168431269848-0.816843126984761
241414.3996557844268-0.399655784426821
251110.49300738859590.506992611404111
26916.2521894833483-7.25218948334834
271111.4349859210994-0.434985921099355
281513.08776725443571.91223274556432
291412.27061593366381.72938406633623
301315.15020698104-2.15020698103999
31911.529592852717-2.52959285271696
321514.0056850707560.994314929243971
331010.569135628421-0.569135628420989
341112.3069957777086-1.30699577770856
351312.89018035461460.109819645385447
36811.5872424007496-3.58724240074956
372016.8153074422383.18469255776201
381212.1245190179444-0.124519017944405
391010.9587274036726-0.958727403672629
401013.6373629994922-3.6373629994922
41912.0545503420501-3.05455034205014
421411.48984445693662.51015554306336
43811.273778865323-3.27377886532301
441414.3694355043129-0.36943550431287
451114.1169900686545-3.11699006865445
461315.0466494732962-2.04664947329625
47912.1547392980584-3.15473929805836
481112.2918856376516-1.29188563765158
491510.76672252824214.23327747175788
501113.469996379785-2.46999637978502
511011.6935909206886-1.69359092068861
521412.98757829842751.01242170157254
531815.24760492485292.7523950751471
541414.5244829961584-0.524482996158376
551114.4942627160444-3.49426271604442
561212.0545503420501-0.0545503420501392
571311.26761930139221.73238069860782
58912.4866815252774-3.4866815252774
591014.3358466724634-4.33584667246339
601514.39070520830070.609294791699321
612017.43487186318882.56512813681123
621213.5125357877606-1.51253578776064
631215.1927463890156-3.19274638901561
641413.1426257902730.857374209727029
651312.17937755378170.820622446218306
661115.9186593299054-4.91865932990545
671715.09534844520271.9046515547973
681214.4853121399183-2.48531213991828
691312.12731003013970.872689969860288
701412.25829680580211.7417031941979
711312.82300269091560.176997309084405
721511.74228989259513.25771010740494
731312.34953518568420.650464814315823
741012.197278706034-2.19727870603398
751113.5584437474718-2.55844374747179
761914.11083050472364.88916949527638
771310.69675385234792.30324614765215
781713.6435225634233.35647743657697
791312.47157138522040.528428614779576
80913.4123468317524-4.41234683175243
811112.3344250456272-1.3344250456272
821011.4500960611563-1.45009606115633
83912.2582968058021-3.2582968058021
841211.34374754121730.656252458782721
851212.340584609558-0.340584609558035
861312.96014903050880.0398509694911812
871312.15473929805840.845260701941643
881213.0239581424722-1.02395814247225
891513.90828712694311.09171287305688
902218.12719597222913.87280402777087
911310.96767797979882.03232202020123
921513.56739432359791.43260567640207
931312.34337562175330.656624378246658
941512.94503889045182.05496110954816
951013.8657477189675-3.8657477189675
961111.3101587093678-0.310158709367801
971614.31457696847561.68542303152442
981112.8627510866959-1.86275108669591
991110.63294474038440.36705525961558
1001012.3831240175337-2.38312401753366
1011010.6267851764536-0.626785176453586
1021614.31457696847561.68542303152442
1031211.4775253290750.522474670925024
1041115.1412564049139-4.14125640491385
1051612.39823415759063.60176584240937
1061916.30088845525482.69911154474521
1071111.3375879772864-0.337587977286445
1081612.0086423823393.99135761766101
1091515.8274209500234-0.827420950023374
1102415.63541607459298.36458392540714
1111412.43798255337091.56201744662906
1121515.2963038967594-0.296303896759359
1131113.953617547114-2.95361754711405
1141513.05138741039091.94861258960911
115129.900872235563752.09912776443625
1161011.3224778372295-1.32247783722947
1171413.15157636639910.848423633600888
1181313.1028773944927-0.102877394492657
119913.0452278464601-4.04522784646006
1201512.82300269091562.17699730908441
1211514.5519122640770.44808773592298
1221412.97246815837051.02753184162951
1231112.1547392980584-1.15473929805836
124812.182168565977-4.182168565977
1251112.2280765256881-1.22807652568815
1261113.832158887118-2.83215888711802
127810.4930073885959-2.49300738859589
1281010.3984004569783-0.398400456978286
129119.406135026344521.59386497365548
1301312.17321798985090.826782010149141
1311113.7834599152116-2.78345991521156
1322016.88527611813233.11472388186775
1331012.0148019462698-2.01480194626983
1341512.49900065313912.50099934686093
1351212.182168565977-0.182168565977001
1361411.50216358479832.49783641520169
1372315.20843406861287.79156593138719
1381412.99652887455361.00347112544639
1391615.38754227664140.612457723358565
1401112.1944876938387-1.19448769383867
1411213.8746982950936-1.87469829509364
1421012.340584609558-2.34058460955803
1431411.70591004855032.29408995144972
1441213.1913247621794-1.19132476217943
1451212.1553168375986-0.155316837598576
1461111.1551112175223-0.155111217522294
1471211.45962417682270.540375823177307
1481315.1166181491905-2.11661814919051
1491114.4729930120566-3.47299301205661
1501916.54438331478712.45561668521293
1511211.42266679323770.577333206762313
1521713.38154901209833.61845098790174
153911.5961929768757-2.5961929768757
1541214.4970537282397-2.49705372823973
1551916.58692272276272.41307727723731
1561813.69222153532954.30777846467051
1571513.56739432359791.43260567640207
1581413.13646622634210.863533773657864
159119.406135026344521.59386497365548
160913.6647922674108-4.66479226741084
1611814.55807182800793.44192817199215
1621613.74987108336212.25012891663791

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.7194451831719 & -0.719445183171919 \tabularnewline
2 & 11 & 9.76093488377522 & 1.23906511622478 \tabularnewline
3 & 14 & 15.3875422766414 & -1.38754227664143 \tabularnewline
4 & 12 & 14.5642313919387 & -2.56423139193869 \tabularnewline
5 & 21 & 11.5749232728879 & 9.42507672711211 \tabularnewline
6 & 12 & 9.89192165943761 & 2.10807834056239 \tabularnewline
7 & 22 & 12.6679551990701 & 9.33204480092991 \tabularnewline
8 & 11 & 13.0177985785414 & -2.01779857854142 \tabularnewline
9 & 10 & 12.16705842592 & -2.16705842592003 \tabularnewline
10 & 13 & 12.182168565977 & 0.817831434022999 \tabularnewline
11 & 10 & 10.5204366565145 & -0.520436656514534 \tabularnewline
12 & 8 & 9.21749870264953 & -1.21749870264953 \tabularnewline
13 & 15 & 15.7300230062105 & -0.730023006210466 \tabularnewline
14 & 14 & 11.3526981173434 & 2.64730188265658 \tabularnewline
15 & 10 & 10.159477235153 & -0.159477235153001 \tabularnewline
16 & 14 & 13.0513874103909 & 0.948612589609106 \tabularnewline
17 & 14 & 12.8353218187773 & 1.16467818122274 \tabularnewline
18 & 11 & 10.6693245844292 & 0.330675415570794 \tabularnewline
19 & 10 & 12.8168431269848 & -2.81684312698476 \tabularnewline
20 & 13 & 11.5659726967617 & 1.43402730323826 \tabularnewline
21 & 7 & 10.1230973911082 & -3.12309739110821 \tabularnewline
22 & 14 & 15.2537644887837 & -1.25376448878374 \tabularnewline
23 & 12 & 12.8168431269848 & -0.816843126984761 \tabularnewline
24 & 14 & 14.3996557844268 & -0.399655784426821 \tabularnewline
25 & 11 & 10.4930073885959 & 0.506992611404111 \tabularnewline
26 & 9 & 16.2521894833483 & -7.25218948334834 \tabularnewline
27 & 11 & 11.4349859210994 & -0.434985921099355 \tabularnewline
28 & 15 & 13.0877672544357 & 1.91223274556432 \tabularnewline
29 & 14 & 12.2706159336638 & 1.72938406633623 \tabularnewline
30 & 13 & 15.15020698104 & -2.15020698103999 \tabularnewline
31 & 9 & 11.529592852717 & -2.52959285271696 \tabularnewline
32 & 15 & 14.005685070756 & 0.994314929243971 \tabularnewline
33 & 10 & 10.569135628421 & -0.569135628420989 \tabularnewline
34 & 11 & 12.3069957777086 & -1.30699577770856 \tabularnewline
35 & 13 & 12.8901803546146 & 0.109819645385447 \tabularnewline
36 & 8 & 11.5872424007496 & -3.58724240074956 \tabularnewline
37 & 20 & 16.815307442238 & 3.18469255776201 \tabularnewline
38 & 12 & 12.1245190179444 & -0.124519017944405 \tabularnewline
39 & 10 & 10.9587274036726 & -0.958727403672629 \tabularnewline
40 & 10 & 13.6373629994922 & -3.6373629994922 \tabularnewline
41 & 9 & 12.0545503420501 & -3.05455034205014 \tabularnewline
42 & 14 & 11.4898444569366 & 2.51015554306336 \tabularnewline
43 & 8 & 11.273778865323 & -3.27377886532301 \tabularnewline
44 & 14 & 14.3694355043129 & -0.36943550431287 \tabularnewline
45 & 11 & 14.1169900686545 & -3.11699006865445 \tabularnewline
46 & 13 & 15.0466494732962 & -2.04664947329625 \tabularnewline
47 & 9 & 12.1547392980584 & -3.15473929805836 \tabularnewline
48 & 11 & 12.2918856376516 & -1.29188563765158 \tabularnewline
49 & 15 & 10.7667225282421 & 4.23327747175788 \tabularnewline
50 & 11 & 13.469996379785 & -2.46999637978502 \tabularnewline
51 & 10 & 11.6935909206886 & -1.69359092068861 \tabularnewline
52 & 14 & 12.9875782984275 & 1.01242170157254 \tabularnewline
53 & 18 & 15.2476049248529 & 2.7523950751471 \tabularnewline
54 & 14 & 14.5244829961584 & -0.524482996158376 \tabularnewline
55 & 11 & 14.4942627160444 & -3.49426271604442 \tabularnewline
56 & 12 & 12.0545503420501 & -0.0545503420501392 \tabularnewline
57 & 13 & 11.2676193013922 & 1.73238069860782 \tabularnewline
58 & 9 & 12.4866815252774 & -3.4866815252774 \tabularnewline
59 & 10 & 14.3358466724634 & -4.33584667246339 \tabularnewline
60 & 15 & 14.3907052083007 & 0.609294791699321 \tabularnewline
61 & 20 & 17.4348718631888 & 2.56512813681123 \tabularnewline
62 & 12 & 13.5125357877606 & -1.51253578776064 \tabularnewline
63 & 12 & 15.1927463890156 & -3.19274638901561 \tabularnewline
64 & 14 & 13.142625790273 & 0.857374209727029 \tabularnewline
65 & 13 & 12.1793775537817 & 0.820622446218306 \tabularnewline
66 & 11 & 15.9186593299054 & -4.91865932990545 \tabularnewline
67 & 17 & 15.0953484452027 & 1.9046515547973 \tabularnewline
68 & 12 & 14.4853121399183 & -2.48531213991828 \tabularnewline
69 & 13 & 12.1273100301397 & 0.872689969860288 \tabularnewline
70 & 14 & 12.2582968058021 & 1.7417031941979 \tabularnewline
71 & 13 & 12.8230026909156 & 0.176997309084405 \tabularnewline
72 & 15 & 11.7422898925951 & 3.25771010740494 \tabularnewline
73 & 13 & 12.3495351856842 & 0.650464814315823 \tabularnewline
74 & 10 & 12.197278706034 & -2.19727870603398 \tabularnewline
75 & 11 & 13.5584437474718 & -2.55844374747179 \tabularnewline
76 & 19 & 14.1108305047236 & 4.88916949527638 \tabularnewline
77 & 13 & 10.6967538523479 & 2.30324614765215 \tabularnewline
78 & 17 & 13.643522563423 & 3.35647743657697 \tabularnewline
79 & 13 & 12.4715713852204 & 0.528428614779576 \tabularnewline
80 & 9 & 13.4123468317524 & -4.41234683175243 \tabularnewline
81 & 11 & 12.3344250456272 & -1.3344250456272 \tabularnewline
82 & 10 & 11.4500960611563 & -1.45009606115633 \tabularnewline
83 & 9 & 12.2582968058021 & -3.2582968058021 \tabularnewline
84 & 12 & 11.3437475412173 & 0.656252458782721 \tabularnewline
85 & 12 & 12.340584609558 & -0.340584609558035 \tabularnewline
86 & 13 & 12.9601490305088 & 0.0398509694911812 \tabularnewline
87 & 13 & 12.1547392980584 & 0.845260701941643 \tabularnewline
88 & 12 & 13.0239581424722 & -1.02395814247225 \tabularnewline
89 & 15 & 13.9082871269431 & 1.09171287305688 \tabularnewline
90 & 22 & 18.1271959722291 & 3.87280402777087 \tabularnewline
91 & 13 & 10.9676779797988 & 2.03232202020123 \tabularnewline
92 & 15 & 13.5673943235979 & 1.43260567640207 \tabularnewline
93 & 13 & 12.3433756217533 & 0.656624378246658 \tabularnewline
94 & 15 & 12.9450388904518 & 2.05496110954816 \tabularnewline
95 & 10 & 13.8657477189675 & -3.8657477189675 \tabularnewline
96 & 11 & 11.3101587093678 & -0.310158709367801 \tabularnewline
97 & 16 & 14.3145769684756 & 1.68542303152442 \tabularnewline
98 & 11 & 12.8627510866959 & -1.86275108669591 \tabularnewline
99 & 11 & 10.6329447403844 & 0.36705525961558 \tabularnewline
100 & 10 & 12.3831240175337 & -2.38312401753366 \tabularnewline
101 & 10 & 10.6267851764536 & -0.626785176453586 \tabularnewline
102 & 16 & 14.3145769684756 & 1.68542303152442 \tabularnewline
103 & 12 & 11.477525329075 & 0.522474670925024 \tabularnewline
104 & 11 & 15.1412564049139 & -4.14125640491385 \tabularnewline
105 & 16 & 12.3982341575906 & 3.60176584240937 \tabularnewline
106 & 19 & 16.3008884552548 & 2.69911154474521 \tabularnewline
107 & 11 & 11.3375879772864 & -0.337587977286445 \tabularnewline
108 & 16 & 12.008642382339 & 3.99135761766101 \tabularnewline
109 & 15 & 15.8274209500234 & -0.827420950023374 \tabularnewline
110 & 24 & 15.6354160745929 & 8.36458392540714 \tabularnewline
111 & 14 & 12.4379825533709 & 1.56201744662906 \tabularnewline
112 & 15 & 15.2963038967594 & -0.296303896759359 \tabularnewline
113 & 11 & 13.953617547114 & -2.95361754711405 \tabularnewline
114 & 15 & 13.0513874103909 & 1.94861258960911 \tabularnewline
115 & 12 & 9.90087223556375 & 2.09912776443625 \tabularnewline
116 & 10 & 11.3224778372295 & -1.32247783722947 \tabularnewline
117 & 14 & 13.1515763663991 & 0.848423633600888 \tabularnewline
118 & 13 & 13.1028773944927 & -0.102877394492657 \tabularnewline
119 & 9 & 13.0452278464601 & -4.04522784646006 \tabularnewline
120 & 15 & 12.8230026909156 & 2.17699730908441 \tabularnewline
121 & 15 & 14.551912264077 & 0.44808773592298 \tabularnewline
122 & 14 & 12.9724681583705 & 1.02753184162951 \tabularnewline
123 & 11 & 12.1547392980584 & -1.15473929805836 \tabularnewline
124 & 8 & 12.182168565977 & -4.182168565977 \tabularnewline
125 & 11 & 12.2280765256881 & -1.22807652568815 \tabularnewline
126 & 11 & 13.832158887118 & -2.83215888711802 \tabularnewline
127 & 8 & 10.4930073885959 & -2.49300738859589 \tabularnewline
128 & 10 & 10.3984004569783 & -0.398400456978286 \tabularnewline
129 & 11 & 9.40613502634452 & 1.59386497365548 \tabularnewline
130 & 13 & 12.1732179898509 & 0.826782010149141 \tabularnewline
131 & 11 & 13.7834599152116 & -2.78345991521156 \tabularnewline
132 & 20 & 16.8852761181323 & 3.11472388186775 \tabularnewline
133 & 10 & 12.0148019462698 & -2.01480194626983 \tabularnewline
134 & 15 & 12.4990006531391 & 2.50099934686093 \tabularnewline
135 & 12 & 12.182168565977 & -0.182168565977001 \tabularnewline
136 & 14 & 11.5021635847983 & 2.49783641520169 \tabularnewline
137 & 23 & 15.2084340686128 & 7.79156593138719 \tabularnewline
138 & 14 & 12.9965288745536 & 1.00347112544639 \tabularnewline
139 & 16 & 15.3875422766414 & 0.612457723358565 \tabularnewline
140 & 11 & 12.1944876938387 & -1.19448769383867 \tabularnewline
141 & 12 & 13.8746982950936 & -1.87469829509364 \tabularnewline
142 & 10 & 12.340584609558 & -2.34058460955803 \tabularnewline
143 & 14 & 11.7059100485503 & 2.29408995144972 \tabularnewline
144 & 12 & 13.1913247621794 & -1.19132476217943 \tabularnewline
145 & 12 & 12.1553168375986 & -0.155316837598576 \tabularnewline
146 & 11 & 11.1551112175223 & -0.155111217522294 \tabularnewline
147 & 12 & 11.4596241768227 & 0.540375823177307 \tabularnewline
148 & 13 & 15.1166181491905 & -2.11661814919051 \tabularnewline
149 & 11 & 14.4729930120566 & -3.47299301205661 \tabularnewline
150 & 19 & 16.5443833147871 & 2.45561668521293 \tabularnewline
151 & 12 & 11.4226667932377 & 0.577333206762313 \tabularnewline
152 & 17 & 13.3815490120983 & 3.61845098790174 \tabularnewline
153 & 9 & 11.5961929768757 & -2.5961929768757 \tabularnewline
154 & 12 & 14.4970537282397 & -2.49705372823973 \tabularnewline
155 & 19 & 16.5869227227627 & 2.41307727723731 \tabularnewline
156 & 18 & 13.6922215353295 & 4.30777846467051 \tabularnewline
157 & 15 & 13.5673943235979 & 1.43260567640207 \tabularnewline
158 & 14 & 13.1364662263421 & 0.863533773657864 \tabularnewline
159 & 11 & 9.40613502634452 & 1.59386497365548 \tabularnewline
160 & 9 & 13.6647922674108 & -4.66479226741084 \tabularnewline
161 & 18 & 14.5580718280079 & 3.44192817199215 \tabularnewline
162 & 16 & 13.7498710833621 & 2.25012891663791 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]12.7194451831719[/C][C]-0.719445183171919[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.76093488377522[/C][C]1.23906511622478[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.3875422766414[/C][C]-1.38754227664143[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.5642313919387[/C][C]-2.56423139193869[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.5749232728879[/C][C]9.42507672711211[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.89192165943761[/C][C]2.10807834056239[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.6679551990701[/C][C]9.33204480092991[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.0177985785414[/C][C]-2.01779857854142[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.16705842592[/C][C]-2.16705842592003[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.182168565977[/C][C]0.817831434022999[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.5204366565145[/C][C]-0.520436656514534[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.21749870264953[/C][C]-1.21749870264953[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.7300230062105[/C][C]-0.730023006210466[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.3526981173434[/C][C]2.64730188265658[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.159477235153[/C][C]-0.159477235153001[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.0513874103909[/C][C]0.948612589609106[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.8353218187773[/C][C]1.16467818122274[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.6693245844292[/C][C]0.330675415570794[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.8168431269848[/C][C]-2.81684312698476[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.5659726967617[/C][C]1.43402730323826[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.1230973911082[/C][C]-3.12309739110821[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.2537644887837[/C][C]-1.25376448878374[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.8168431269848[/C][C]-0.816843126984761[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.3996557844268[/C][C]-0.399655784426821[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.4930073885959[/C][C]0.506992611404111[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.2521894833483[/C][C]-7.25218948334834[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.4349859210994[/C][C]-0.434985921099355[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.0877672544357[/C][C]1.91223274556432[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.2706159336638[/C][C]1.72938406633623[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.15020698104[/C][C]-2.15020698103999[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.529592852717[/C][C]-2.52959285271696[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.005685070756[/C][C]0.994314929243971[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.569135628421[/C][C]-0.569135628420989[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.3069957777086[/C][C]-1.30699577770856[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.8901803546146[/C][C]0.109819645385447[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]11.5872424007496[/C][C]-3.58724240074956[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]16.815307442238[/C][C]3.18469255776201[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.1245190179444[/C][C]-0.124519017944405[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.9587274036726[/C][C]-0.958727403672629[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.6373629994922[/C][C]-3.6373629994922[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.0545503420501[/C][C]-3.05455034205014[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.4898444569366[/C][C]2.51015554306336[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.273778865323[/C][C]-3.27377886532301[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3694355043129[/C][C]-0.36943550431287[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.1169900686545[/C][C]-3.11699006865445[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.0466494732962[/C][C]-2.04664947329625[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.1547392980584[/C][C]-3.15473929805836[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.2918856376516[/C][C]-1.29188563765158[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.7667225282421[/C][C]4.23327747175788[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.469996379785[/C][C]-2.46999637978502[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.6935909206886[/C][C]-1.69359092068861[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.9875782984275[/C][C]1.01242170157254[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.2476049248529[/C][C]2.7523950751471[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.5244829961584[/C][C]-0.524482996158376[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]14.4942627160444[/C][C]-3.49426271604442[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.0545503420501[/C][C]-0.0545503420501392[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2676193013922[/C][C]1.73238069860782[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.4866815252774[/C][C]-3.4866815252774[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.3358466724634[/C][C]-4.33584667246339[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.3907052083007[/C][C]0.609294791699321[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.4348718631888[/C][C]2.56512813681123[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.5125357877606[/C][C]-1.51253578776064[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]15.1927463890156[/C][C]-3.19274638901561[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.142625790273[/C][C]0.857374209727029[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.1793775537817[/C][C]0.820622446218306[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.9186593299054[/C][C]-4.91865932990545[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.0953484452027[/C][C]1.9046515547973[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.4853121399183[/C][C]-2.48531213991828[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.1273100301397[/C][C]0.872689969860288[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.2582968058021[/C][C]1.7417031941979[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.8230026909156[/C][C]0.176997309084405[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.7422898925951[/C][C]3.25771010740494[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.3495351856842[/C][C]0.650464814315823[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.197278706034[/C][C]-2.19727870603398[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.5584437474718[/C][C]-2.55844374747179[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.1108305047236[/C][C]4.88916949527638[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.6967538523479[/C][C]2.30324614765215[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.643522563423[/C][C]3.35647743657697[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.4715713852204[/C][C]0.528428614779576[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.4123468317524[/C][C]-4.41234683175243[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.3344250456272[/C][C]-1.3344250456272[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]11.4500960611563[/C][C]-1.45009606115633[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.2582968058021[/C][C]-3.2582968058021[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.3437475412173[/C][C]0.656252458782721[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.340584609558[/C][C]-0.340584609558035[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.9601490305088[/C][C]0.0398509694911812[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.1547392980584[/C][C]0.845260701941643[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.0239581424722[/C][C]-1.02395814247225[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]13.9082871269431[/C][C]1.09171287305688[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]18.1271959722291[/C][C]3.87280402777087[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]10.9676779797988[/C][C]2.03232202020123[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5673943235979[/C][C]1.43260567640207[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.3433756217533[/C][C]0.656624378246658[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9450388904518[/C][C]2.05496110954816[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.8657477189675[/C][C]-3.8657477189675[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.3101587093678[/C][C]-0.310158709367801[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.3145769684756[/C][C]1.68542303152442[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.8627510866959[/C][C]-1.86275108669591[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.6329447403844[/C][C]0.36705525961558[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.3831240175337[/C][C]-2.38312401753366[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.6267851764536[/C][C]-0.626785176453586[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.3145769684756[/C][C]1.68542303152442[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]11.477525329075[/C][C]0.522474670925024[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]15.1412564049139[/C][C]-4.14125640491385[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.3982341575906[/C][C]3.60176584240937[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.3008884552548[/C][C]2.69911154474521[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.3375879772864[/C][C]-0.337587977286445[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.008642382339[/C][C]3.99135761766101[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.8274209500234[/C][C]-0.827420950023374[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]15.6354160745929[/C][C]8.36458392540714[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.4379825533709[/C][C]1.56201744662906[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]15.2963038967594[/C][C]-0.296303896759359[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.953617547114[/C][C]-2.95361754711405[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.0513874103909[/C][C]1.94861258960911[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]9.90087223556375[/C][C]2.09912776443625[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]11.3224778372295[/C][C]-1.32247783722947[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1515763663991[/C][C]0.848423633600888[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.1028773944927[/C][C]-0.102877394492657[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.0452278464601[/C][C]-4.04522784646006[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.8230026909156[/C][C]2.17699730908441[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.551912264077[/C][C]0.44808773592298[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.9724681583705[/C][C]1.02753184162951[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.1547392980584[/C][C]-1.15473929805836[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.182168565977[/C][C]-4.182168565977[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.2280765256881[/C][C]-1.22807652568815[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.832158887118[/C][C]-2.83215888711802[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.4930073885959[/C][C]-2.49300738859589[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.3984004569783[/C][C]-0.398400456978286[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.40613502634452[/C][C]1.59386497365548[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.1732179898509[/C][C]0.826782010149141[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.7834599152116[/C][C]-2.78345991521156[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]16.8852761181323[/C][C]3.11472388186775[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.0148019462698[/C][C]-2.01480194626983[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.4990006531391[/C][C]2.50099934686093[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.182168565977[/C][C]-0.182168565977001[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.5021635847983[/C][C]2.49783641520169[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.2084340686128[/C][C]7.79156593138719[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.9965288745536[/C][C]1.00347112544639[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.3875422766414[/C][C]0.612457723358565[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.1944876938387[/C][C]-1.19448769383867[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.8746982950936[/C][C]-1.87469829509364[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.340584609558[/C][C]-2.34058460955803[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.7059100485503[/C][C]2.29408995144972[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.1913247621794[/C][C]-1.19132476217943[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.1553168375986[/C][C]-0.155316837598576[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.1551112175223[/C][C]-0.155111217522294[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.4596241768227[/C][C]0.540375823177307[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1166181491905[/C][C]-2.11661814919051[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]14.4729930120566[/C][C]-3.47299301205661[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.5443833147871[/C][C]2.45561668521293[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.4226667932377[/C][C]0.577333206762313[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.3815490120983[/C][C]3.61845098790174[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.5961929768757[/C][C]-2.5961929768757[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.4970537282397[/C][C]-2.49705372823973[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.5869227227627[/C][C]2.41307727723731[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.6922215353295[/C][C]4.30777846467051[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.5673943235979[/C][C]1.43260567640207[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.1364662263421[/C][C]0.863533773657864[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.40613502634452[/C][C]1.59386497365548[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]13.6647922674108[/C][C]-4.66479226741084[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.5580718280079[/C][C]3.44192817199215[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]13.7498710833621[/C][C]2.25012891663791[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.7194451831719-0.719445183171919
2119.760934883775221.23906511622478
31415.3875422766414-1.38754227664143
41214.5642313919387-2.56423139193869
52111.57492327288799.42507672711211
6129.891921659437612.10807834056239
72212.66795519907019.33204480092991
81113.0177985785414-2.01779857854142
91012.16705842592-2.16705842592003
101312.1821685659770.817831434022999
111010.5204366565145-0.520436656514534
1289.21749870264953-1.21749870264953
131515.7300230062105-0.730023006210466
141411.35269811734342.64730188265658
151010.159477235153-0.159477235153001
161413.05138741039090.948612589609106
171412.83532181877731.16467818122274
181110.66932458442920.330675415570794
191012.8168431269848-2.81684312698476
201311.56597269676171.43402730323826
21710.1230973911082-3.12309739110821
221415.2537644887837-1.25376448878374
231212.8168431269848-0.816843126984761
241414.3996557844268-0.399655784426821
251110.49300738859590.506992611404111
26916.2521894833483-7.25218948334834
271111.4349859210994-0.434985921099355
281513.08776725443571.91223274556432
291412.27061593366381.72938406633623
301315.15020698104-2.15020698103999
31911.529592852717-2.52959285271696
321514.0056850707560.994314929243971
331010.569135628421-0.569135628420989
341112.3069957777086-1.30699577770856
351312.89018035461460.109819645385447
36811.5872424007496-3.58724240074956
372016.8153074422383.18469255776201
381212.1245190179444-0.124519017944405
391010.9587274036726-0.958727403672629
401013.6373629994922-3.6373629994922
41912.0545503420501-3.05455034205014
421411.48984445693662.51015554306336
43811.273778865323-3.27377886532301
441414.3694355043129-0.36943550431287
451114.1169900686545-3.11699006865445
461315.0466494732962-2.04664947329625
47912.1547392980584-3.15473929805836
481112.2918856376516-1.29188563765158
491510.76672252824214.23327747175788
501113.469996379785-2.46999637978502
511011.6935909206886-1.69359092068861
521412.98757829842751.01242170157254
531815.24760492485292.7523950751471
541414.5244829961584-0.524482996158376
551114.4942627160444-3.49426271604442
561212.0545503420501-0.0545503420501392
571311.26761930139221.73238069860782
58912.4866815252774-3.4866815252774
591014.3358466724634-4.33584667246339
601514.39070520830070.609294791699321
612017.43487186318882.56512813681123
621213.5125357877606-1.51253578776064
631215.1927463890156-3.19274638901561
641413.1426257902730.857374209727029
651312.17937755378170.820622446218306
661115.9186593299054-4.91865932990545
671715.09534844520271.9046515547973
681214.4853121399183-2.48531213991828
691312.12731003013970.872689969860288
701412.25829680580211.7417031941979
711312.82300269091560.176997309084405
721511.74228989259513.25771010740494
731312.34953518568420.650464814315823
741012.197278706034-2.19727870603398
751113.5584437474718-2.55844374747179
761914.11083050472364.88916949527638
771310.69675385234792.30324614765215
781713.6435225634233.35647743657697
791312.47157138522040.528428614779576
80913.4123468317524-4.41234683175243
811112.3344250456272-1.3344250456272
821011.4500960611563-1.45009606115633
83912.2582968058021-3.2582968058021
841211.34374754121730.656252458782721
851212.340584609558-0.340584609558035
861312.96014903050880.0398509694911812
871312.15473929805840.845260701941643
881213.0239581424722-1.02395814247225
891513.90828712694311.09171287305688
902218.12719597222913.87280402777087
911310.96767797979882.03232202020123
921513.56739432359791.43260567640207
931312.34337562175330.656624378246658
941512.94503889045182.05496110954816
951013.8657477189675-3.8657477189675
961111.3101587093678-0.310158709367801
971614.31457696847561.68542303152442
981112.8627510866959-1.86275108669591
991110.63294474038440.36705525961558
1001012.3831240175337-2.38312401753366
1011010.6267851764536-0.626785176453586
1021614.31457696847561.68542303152442
1031211.4775253290750.522474670925024
1041115.1412564049139-4.14125640491385
1051612.39823415759063.60176584240937
1061916.30088845525482.69911154474521
1071111.3375879772864-0.337587977286445
1081612.0086423823393.99135761766101
1091515.8274209500234-0.827420950023374
1102415.63541607459298.36458392540714
1111412.43798255337091.56201744662906
1121515.2963038967594-0.296303896759359
1131113.953617547114-2.95361754711405
1141513.05138741039091.94861258960911
115129.900872235563752.09912776443625
1161011.3224778372295-1.32247783722947
1171413.15157636639910.848423633600888
1181313.1028773944927-0.102877394492657
119913.0452278464601-4.04522784646006
1201512.82300269091562.17699730908441
1211514.5519122640770.44808773592298
1221412.97246815837051.02753184162951
1231112.1547392980584-1.15473929805836
124812.182168565977-4.182168565977
1251112.2280765256881-1.22807652568815
1261113.832158887118-2.83215888711802
127810.4930073885959-2.49300738859589
1281010.3984004569783-0.398400456978286
129119.406135026344521.59386497365548
1301312.17321798985090.826782010149141
1311113.7834599152116-2.78345991521156
1322016.88527611813233.11472388186775
1331012.0148019462698-2.01480194626983
1341512.49900065313912.50099934686093
1351212.182168565977-0.182168565977001
1361411.50216358479832.49783641520169
1372315.20843406861287.79156593138719
1381412.99652887455361.00347112544639
1391615.38754227664140.612457723358565
1401112.1944876938387-1.19448769383867
1411213.8746982950936-1.87469829509364
1421012.340584609558-2.34058460955803
1431411.70591004855032.29408995144972
1441213.1913247621794-1.19132476217943
1451212.1553168375986-0.155316837598576
1461111.1551112175223-0.155111217522294
1471211.45962417682270.540375823177307
1481315.1166181491905-2.11661814919051
1491114.4729930120566-3.47299301205661
1501916.54438331478712.45561668521293
1511211.42266679323770.577333206762313
1521713.38154901209833.61845098790174
153911.5961929768757-2.5961929768757
1541214.4970537282397-2.49705372823973
1551916.58692272276272.41307727723731
1561813.69222153532954.30777846467051
1571513.56739432359791.43260567640207
1581413.13646622634210.863533773657864
159119.406135026344521.59386497365548
160913.6647922674108-4.66479226741084
1611814.55807182800793.44192817199215
1621613.74987108336212.25012891663791






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9997515101378120.0004969797243757560.000248489862187878
80.9996870595497890.0006258809004221060.000312940450211053
90.999705272058050.0005894558838999080.000294727941949954
100.9992901705149860.001419658970028280.000709829485014142
110.9992064017414660.001587196517067510.000793598258533755
120.9989694702581180.002061059483763080.00103052974188154
130.998179791979870.003640416040260550.00182020802013028
140.9970424362108430.005915127578314430.00295756378915721
150.9947465513688340.01050689726233180.00525344863116592
160.9913638966767370.0172722066465270.0086361033232635
170.9859897947526930.02802041049461410.0140102052473071
180.978609370411750.04278125917650060.0213906295882503
190.9803101945554440.03937961088911280.0196898054445564
200.9711285748804770.05774285023904570.0288714251195229
210.9758664719990380.04826705600192370.0241335280009618
220.9663002276662470.06739954466750590.0336997723337529
230.9543753302509260.09124933949814790.0456246697490739
240.936547018794510.1269059624109790.0634529812054896
250.918876824415450.16224635116910.0811231755845498
260.9823507257013490.03529854859730260.0176492742986513
270.9753614407152160.04927711856956890.0246385592847844
280.9706036711455420.05879265770891590.0293963288544579
290.9626939312238560.07461213755228850.0373060687761442
300.9528818408167760.09423631836644750.0471181591832238
310.9518604660109950.09627906797801040.0481395339890052
320.9402275963757820.1195448072484360.0597724036242178
330.9239968306151470.1520063387697060.0760031693848528
340.9093500007554390.1812999984891230.0906499992445614
350.8851941389394370.2296117221211250.114805861060563
360.9063789428858950.1872421142282090.0936210571141047
370.9320728427217740.1358543145564530.0679271572782263
380.9125909434290290.1748181131419410.0874090565709705
390.8951515783512060.2096968432975880.104848421648794
400.9051616412508980.1896767174982050.0948383587491025
410.90623893790170.1875221241965990.0937610620982996
420.9010343189130510.1979313621738980.0989656810869492
430.9065498242104110.1869003515791790.0934501757895894
440.8841111860815830.2317776278368340.115888813918417
450.8774599276059680.2450801447880630.122540072394032
460.8598404825921030.2803190348157950.140159517407897
470.8693343465586760.2613313068826490.130665653441324
480.8473705115807850.3052589768384310.152629488419215
490.8766091921145340.2467816157709320.123390807885466
500.8652298077149490.2695403845701030.134770192285051
510.8550583047511280.2898833904977440.144941695248872
520.8327169608467980.3345660783064040.167283039153202
530.8448310129643840.3103379740712310.155168987035616
540.814990958606840.3700180827863210.18500904139316
550.8275947228208340.3448105543583310.172405277179166
560.7960990856850290.4078018286299420.203900914314971
570.7776654348230470.4446691303539070.222334565176953
580.8027431501041180.3945136997917630.197256849895881
590.8408714372010980.3182571255978050.159128562798902
600.8172452289303220.3655095421393570.182754771069678
610.8337377395910540.3325245208178930.166262260408946
620.811843844304770.376312311390460.18815615569523
630.8196236714039850.3607526571920310.180376328596015
640.791186070712540.4176278585749190.20881392928746
650.7599565403108940.4800869193782110.240043459689106
660.8301516402172640.3396967195654720.169848359782736
670.8248845011431320.3502309977137360.175115498856868
680.8208554780997840.3582890438004320.179144521900216
690.7937876779126960.4124246441746080.206212322087304
700.7748569313772950.450286137245410.225143068622705
710.7404742187361710.5190515625276580.259525781263829
720.7577385389108850.484522922178230.242261461089115
730.7225685482876070.5548629034247850.277431451712393
740.7113971536381570.5772056927236870.288602846361843
750.7103134991010020.5793730017979960.289686500898998
760.8045597442070770.3908805115858460.195440255792923
770.7971077308511240.4057845382977520.202892269148876
780.8161609641532430.3676780716935140.183839035846757
790.7861042724848070.4277914550303860.213895727515193
800.8497817691801390.3004364616397220.150218230819861
810.8285342078538780.3429315842922440.171465792146122
820.8072397994089430.3855204011821140.192760200591057
830.8228458723678230.3543082552643530.177154127632177
840.7930368279501050.4139263440997910.206963172049895
850.759177873216050.4816442535679010.24082212678395
860.7225790694215630.5548418611568740.277420930578437
870.6870463724249380.6259072551501230.312953627575062
880.6522456065397510.6955087869204980.347754393460249
890.6167213374623160.7665573250753680.383278662537684
900.6646462741354920.6707074517290170.335353725864508
910.6547784659602860.6904430680794270.345221534039714
920.6225588826543360.7548822346913290.377441117345664
930.5821922151350790.8356155697298410.41780778486492
940.5600551804803080.8798896390393830.439944819519692
950.6140804020338080.7718391959323840.385919597966192
960.5697530075418430.8604939849163140.430246992458157
970.5392887902552820.9214224194894360.460711209744718
980.5227886744109850.954422651178030.477211325589015
990.4766488245626840.9532976491253670.523351175437316
1000.4640650828773390.9281301657546790.535934917122661
1010.4199758414253520.8399516828507040.580024158574648
1020.3886386825020580.7772773650041160.611361317497942
1030.3458162963383310.6916325926766610.654183703661669
1040.4494859304673680.8989718609347370.550514069532632
1050.5063271894880560.9873456210238870.493672810511944
1060.4916291022269150.9832582044538290.508370897773085
1070.4447654427109050.889530885421810.555234557289095
1080.5008467697578170.9983064604843660.499153230242183
1090.4748738220547420.9497476441094830.525126177945259
1100.8493943825373710.3012112349252590.150605617462629
1110.8295175952158110.3409648095683790.170482404784189
1120.7997516409132320.4004967181735360.200248359086768
1130.7971662678001840.4056674643996320.202833732199816
1140.7766373715310680.4467252569378640.223362628468932
1150.7742707226124720.4514585547750560.225729277387528
1160.7440030523773840.5119938952452330.255996947622616
1170.7160851725543120.5678296548913770.283914827445688
1180.6752421602158230.6495156795683530.324757839784177
1190.7370547992397750.525890401520450.262945200760225
1200.7316735793011290.5366528413977420.268326420698871
1210.687188872053490.6256222558930190.31281112794651
1220.6447972815236120.7104054369527750.355202718476388
1230.5970148318731540.8059703362536920.402985168126846
1240.644136817101850.7117263657962990.35586318289815
1250.6139989968023060.7720020063953880.386001003197694
1260.6499790108823490.7000419782353010.350020989117651
1270.6519106459379860.6961787081240290.348089354062015
1280.6028666597520240.7942666804959520.397133340247976
1290.5874338480439640.8251323039120710.412566151956035
1300.5321259708460330.9357480583079340.467874029153967
1310.5730477430441420.8539045139117170.426952256955858
1320.543966276882750.9120674462344990.45603372311725
1330.5071280982214480.9857438035571050.492871901778552
1340.4750206041983990.9500412083967980.524979395801601
1350.4129436933240470.8258873866480930.587056306675953
1360.3784666039764550.756933207952910.621533396023545
1370.6784206023084890.6431587953830230.321579397691511
1380.6215510693549770.7568978612900460.378448930645023
1390.5593682639917640.8812634720164720.440631736008236
1400.507639025498390.9847219490032210.49236097450161
1410.4637756402022930.9275512804045850.536224359797707
1420.4459314386905040.8918628773810090.554068561309496
1430.438547880726380.877095761452760.56145211927362
1440.3690590498779220.7381180997558450.630940950122078
1450.2973626007760940.5947252015521870.702637399223906
1460.2515831307332010.5031662614664010.748416869266799
1470.1893598068732710.3787196137465410.810640193126729
1480.1907659490837580.3815318981675160.809234050916242
1490.2860026886966390.5720053773932780.713997311303361
1500.2130136929291050.426027385858210.786986307070895
1510.1460735465695590.2921470931391180.853926453430441
1520.2340954003961120.4681908007922240.765904599603888
1530.1803319572886770.3606639145773550.819668042711323
1540.1568616957652670.3137233915305350.843138304234733
1550.09036735427790890.1807347085558180.909632645722091

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.999751510137812 & 0.000496979724375756 & 0.000248489862187878 \tabularnewline
8 & 0.999687059549789 & 0.000625880900422106 & 0.000312940450211053 \tabularnewline
9 & 0.99970527205805 & 0.000589455883899908 & 0.000294727941949954 \tabularnewline
10 & 0.999290170514986 & 0.00141965897002828 & 0.000709829485014142 \tabularnewline
11 & 0.999206401741466 & 0.00158719651706751 & 0.000793598258533755 \tabularnewline
12 & 0.998969470258118 & 0.00206105948376308 & 0.00103052974188154 \tabularnewline
13 & 0.99817979197987 & 0.00364041604026055 & 0.00182020802013028 \tabularnewline
14 & 0.997042436210843 & 0.00591512757831443 & 0.00295756378915721 \tabularnewline
15 & 0.994746551368834 & 0.0105068972623318 & 0.00525344863116592 \tabularnewline
16 & 0.991363896676737 & 0.017272206646527 & 0.0086361033232635 \tabularnewline
17 & 0.985989794752693 & 0.0280204104946141 & 0.0140102052473071 \tabularnewline
18 & 0.97860937041175 & 0.0427812591765006 & 0.0213906295882503 \tabularnewline
19 & 0.980310194555444 & 0.0393796108891128 & 0.0196898054445564 \tabularnewline
20 & 0.971128574880477 & 0.0577428502390457 & 0.0288714251195229 \tabularnewline
21 & 0.975866471999038 & 0.0482670560019237 & 0.0241335280009618 \tabularnewline
22 & 0.966300227666247 & 0.0673995446675059 & 0.0336997723337529 \tabularnewline
23 & 0.954375330250926 & 0.0912493394981479 & 0.0456246697490739 \tabularnewline
24 & 0.93654701879451 & 0.126905962410979 & 0.0634529812054896 \tabularnewline
25 & 0.91887682441545 & 0.1622463511691 & 0.0811231755845498 \tabularnewline
26 & 0.982350725701349 & 0.0352985485973026 & 0.0176492742986513 \tabularnewline
27 & 0.975361440715216 & 0.0492771185695689 & 0.0246385592847844 \tabularnewline
28 & 0.970603671145542 & 0.0587926577089159 & 0.0293963288544579 \tabularnewline
29 & 0.962693931223856 & 0.0746121375522885 & 0.0373060687761442 \tabularnewline
30 & 0.952881840816776 & 0.0942363183664475 & 0.0471181591832238 \tabularnewline
31 & 0.951860466010995 & 0.0962790679780104 & 0.0481395339890052 \tabularnewline
32 & 0.940227596375782 & 0.119544807248436 & 0.0597724036242178 \tabularnewline
33 & 0.923996830615147 & 0.152006338769706 & 0.0760031693848528 \tabularnewline
34 & 0.909350000755439 & 0.181299998489123 & 0.0906499992445614 \tabularnewline
35 & 0.885194138939437 & 0.229611722121125 & 0.114805861060563 \tabularnewline
36 & 0.906378942885895 & 0.187242114228209 & 0.0936210571141047 \tabularnewline
37 & 0.932072842721774 & 0.135854314556453 & 0.0679271572782263 \tabularnewline
38 & 0.912590943429029 & 0.174818113141941 & 0.0874090565709705 \tabularnewline
39 & 0.895151578351206 & 0.209696843297588 & 0.104848421648794 \tabularnewline
40 & 0.905161641250898 & 0.189676717498205 & 0.0948383587491025 \tabularnewline
41 & 0.9062389379017 & 0.187522124196599 & 0.0937610620982996 \tabularnewline
42 & 0.901034318913051 & 0.197931362173898 & 0.0989656810869492 \tabularnewline
43 & 0.906549824210411 & 0.186900351579179 & 0.0934501757895894 \tabularnewline
44 & 0.884111186081583 & 0.231777627836834 & 0.115888813918417 \tabularnewline
45 & 0.877459927605968 & 0.245080144788063 & 0.122540072394032 \tabularnewline
46 & 0.859840482592103 & 0.280319034815795 & 0.140159517407897 \tabularnewline
47 & 0.869334346558676 & 0.261331306882649 & 0.130665653441324 \tabularnewline
48 & 0.847370511580785 & 0.305258976838431 & 0.152629488419215 \tabularnewline
49 & 0.876609192114534 & 0.246781615770932 & 0.123390807885466 \tabularnewline
50 & 0.865229807714949 & 0.269540384570103 & 0.134770192285051 \tabularnewline
51 & 0.855058304751128 & 0.289883390497744 & 0.144941695248872 \tabularnewline
52 & 0.832716960846798 & 0.334566078306404 & 0.167283039153202 \tabularnewline
53 & 0.844831012964384 & 0.310337974071231 & 0.155168987035616 \tabularnewline
54 & 0.81499095860684 & 0.370018082786321 & 0.18500904139316 \tabularnewline
55 & 0.827594722820834 & 0.344810554358331 & 0.172405277179166 \tabularnewline
56 & 0.796099085685029 & 0.407801828629942 & 0.203900914314971 \tabularnewline
57 & 0.777665434823047 & 0.444669130353907 & 0.222334565176953 \tabularnewline
58 & 0.802743150104118 & 0.394513699791763 & 0.197256849895881 \tabularnewline
59 & 0.840871437201098 & 0.318257125597805 & 0.159128562798902 \tabularnewline
60 & 0.817245228930322 & 0.365509542139357 & 0.182754771069678 \tabularnewline
61 & 0.833737739591054 & 0.332524520817893 & 0.166262260408946 \tabularnewline
62 & 0.81184384430477 & 0.37631231139046 & 0.18815615569523 \tabularnewline
63 & 0.819623671403985 & 0.360752657192031 & 0.180376328596015 \tabularnewline
64 & 0.79118607071254 & 0.417627858574919 & 0.20881392928746 \tabularnewline
65 & 0.759956540310894 & 0.480086919378211 & 0.240043459689106 \tabularnewline
66 & 0.830151640217264 & 0.339696719565472 & 0.169848359782736 \tabularnewline
67 & 0.824884501143132 & 0.350230997713736 & 0.175115498856868 \tabularnewline
68 & 0.820855478099784 & 0.358289043800432 & 0.179144521900216 \tabularnewline
69 & 0.793787677912696 & 0.412424644174608 & 0.206212322087304 \tabularnewline
70 & 0.774856931377295 & 0.45028613724541 & 0.225143068622705 \tabularnewline
71 & 0.740474218736171 & 0.519051562527658 & 0.259525781263829 \tabularnewline
72 & 0.757738538910885 & 0.48452292217823 & 0.242261461089115 \tabularnewline
73 & 0.722568548287607 & 0.554862903424785 & 0.277431451712393 \tabularnewline
74 & 0.711397153638157 & 0.577205692723687 & 0.288602846361843 \tabularnewline
75 & 0.710313499101002 & 0.579373001797996 & 0.289686500898998 \tabularnewline
76 & 0.804559744207077 & 0.390880511585846 & 0.195440255792923 \tabularnewline
77 & 0.797107730851124 & 0.405784538297752 & 0.202892269148876 \tabularnewline
78 & 0.816160964153243 & 0.367678071693514 & 0.183839035846757 \tabularnewline
79 & 0.786104272484807 & 0.427791455030386 & 0.213895727515193 \tabularnewline
80 & 0.849781769180139 & 0.300436461639722 & 0.150218230819861 \tabularnewline
81 & 0.828534207853878 & 0.342931584292244 & 0.171465792146122 \tabularnewline
82 & 0.807239799408943 & 0.385520401182114 & 0.192760200591057 \tabularnewline
83 & 0.822845872367823 & 0.354308255264353 & 0.177154127632177 \tabularnewline
84 & 0.793036827950105 & 0.413926344099791 & 0.206963172049895 \tabularnewline
85 & 0.75917787321605 & 0.481644253567901 & 0.24082212678395 \tabularnewline
86 & 0.722579069421563 & 0.554841861156874 & 0.277420930578437 \tabularnewline
87 & 0.687046372424938 & 0.625907255150123 & 0.312953627575062 \tabularnewline
88 & 0.652245606539751 & 0.695508786920498 & 0.347754393460249 \tabularnewline
89 & 0.616721337462316 & 0.766557325075368 & 0.383278662537684 \tabularnewline
90 & 0.664646274135492 & 0.670707451729017 & 0.335353725864508 \tabularnewline
91 & 0.654778465960286 & 0.690443068079427 & 0.345221534039714 \tabularnewline
92 & 0.622558882654336 & 0.754882234691329 & 0.377441117345664 \tabularnewline
93 & 0.582192215135079 & 0.835615569729841 & 0.41780778486492 \tabularnewline
94 & 0.560055180480308 & 0.879889639039383 & 0.439944819519692 \tabularnewline
95 & 0.614080402033808 & 0.771839195932384 & 0.385919597966192 \tabularnewline
96 & 0.569753007541843 & 0.860493984916314 & 0.430246992458157 \tabularnewline
97 & 0.539288790255282 & 0.921422419489436 & 0.460711209744718 \tabularnewline
98 & 0.522788674410985 & 0.95442265117803 & 0.477211325589015 \tabularnewline
99 & 0.476648824562684 & 0.953297649125367 & 0.523351175437316 \tabularnewline
100 & 0.464065082877339 & 0.928130165754679 & 0.535934917122661 \tabularnewline
101 & 0.419975841425352 & 0.839951682850704 & 0.580024158574648 \tabularnewline
102 & 0.388638682502058 & 0.777277365004116 & 0.611361317497942 \tabularnewline
103 & 0.345816296338331 & 0.691632592676661 & 0.654183703661669 \tabularnewline
104 & 0.449485930467368 & 0.898971860934737 & 0.550514069532632 \tabularnewline
105 & 0.506327189488056 & 0.987345621023887 & 0.493672810511944 \tabularnewline
106 & 0.491629102226915 & 0.983258204453829 & 0.508370897773085 \tabularnewline
107 & 0.444765442710905 & 0.88953088542181 & 0.555234557289095 \tabularnewline
108 & 0.500846769757817 & 0.998306460484366 & 0.499153230242183 \tabularnewline
109 & 0.474873822054742 & 0.949747644109483 & 0.525126177945259 \tabularnewline
110 & 0.849394382537371 & 0.301211234925259 & 0.150605617462629 \tabularnewline
111 & 0.829517595215811 & 0.340964809568379 & 0.170482404784189 \tabularnewline
112 & 0.799751640913232 & 0.400496718173536 & 0.200248359086768 \tabularnewline
113 & 0.797166267800184 & 0.405667464399632 & 0.202833732199816 \tabularnewline
114 & 0.776637371531068 & 0.446725256937864 & 0.223362628468932 \tabularnewline
115 & 0.774270722612472 & 0.451458554775056 & 0.225729277387528 \tabularnewline
116 & 0.744003052377384 & 0.511993895245233 & 0.255996947622616 \tabularnewline
117 & 0.716085172554312 & 0.567829654891377 & 0.283914827445688 \tabularnewline
118 & 0.675242160215823 & 0.649515679568353 & 0.324757839784177 \tabularnewline
119 & 0.737054799239775 & 0.52589040152045 & 0.262945200760225 \tabularnewline
120 & 0.731673579301129 & 0.536652841397742 & 0.268326420698871 \tabularnewline
121 & 0.68718887205349 & 0.625622255893019 & 0.31281112794651 \tabularnewline
122 & 0.644797281523612 & 0.710405436952775 & 0.355202718476388 \tabularnewline
123 & 0.597014831873154 & 0.805970336253692 & 0.402985168126846 \tabularnewline
124 & 0.64413681710185 & 0.711726365796299 & 0.35586318289815 \tabularnewline
125 & 0.613998996802306 & 0.772002006395388 & 0.386001003197694 \tabularnewline
126 & 0.649979010882349 & 0.700041978235301 & 0.350020989117651 \tabularnewline
127 & 0.651910645937986 & 0.696178708124029 & 0.348089354062015 \tabularnewline
128 & 0.602866659752024 & 0.794266680495952 & 0.397133340247976 \tabularnewline
129 & 0.587433848043964 & 0.825132303912071 & 0.412566151956035 \tabularnewline
130 & 0.532125970846033 & 0.935748058307934 & 0.467874029153967 \tabularnewline
131 & 0.573047743044142 & 0.853904513911717 & 0.426952256955858 \tabularnewline
132 & 0.54396627688275 & 0.912067446234499 & 0.45603372311725 \tabularnewline
133 & 0.507128098221448 & 0.985743803557105 & 0.492871901778552 \tabularnewline
134 & 0.475020604198399 & 0.950041208396798 & 0.524979395801601 \tabularnewline
135 & 0.412943693324047 & 0.825887386648093 & 0.587056306675953 \tabularnewline
136 & 0.378466603976455 & 0.75693320795291 & 0.621533396023545 \tabularnewline
137 & 0.678420602308489 & 0.643158795383023 & 0.321579397691511 \tabularnewline
138 & 0.621551069354977 & 0.756897861290046 & 0.378448930645023 \tabularnewline
139 & 0.559368263991764 & 0.881263472016472 & 0.440631736008236 \tabularnewline
140 & 0.50763902549839 & 0.984721949003221 & 0.49236097450161 \tabularnewline
141 & 0.463775640202293 & 0.927551280404585 & 0.536224359797707 \tabularnewline
142 & 0.445931438690504 & 0.891862877381009 & 0.554068561309496 \tabularnewline
143 & 0.43854788072638 & 0.87709576145276 & 0.56145211927362 \tabularnewline
144 & 0.369059049877922 & 0.738118099755845 & 0.630940950122078 \tabularnewline
145 & 0.297362600776094 & 0.594725201552187 & 0.702637399223906 \tabularnewline
146 & 0.251583130733201 & 0.503166261466401 & 0.748416869266799 \tabularnewline
147 & 0.189359806873271 & 0.378719613746541 & 0.810640193126729 \tabularnewline
148 & 0.190765949083758 & 0.381531898167516 & 0.809234050916242 \tabularnewline
149 & 0.286002688696639 & 0.572005377393278 & 0.713997311303361 \tabularnewline
150 & 0.213013692929105 & 0.42602738585821 & 0.786986307070895 \tabularnewline
151 & 0.146073546569559 & 0.292147093139118 & 0.853926453430441 \tabularnewline
152 & 0.234095400396112 & 0.468190800792224 & 0.765904599603888 \tabularnewline
153 & 0.180331957288677 & 0.360663914577355 & 0.819668042711323 \tabularnewline
154 & 0.156861695765267 & 0.313723391530535 & 0.843138304234733 \tabularnewline
155 & 0.0903673542779089 & 0.180734708555818 & 0.909632645722091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.999751510137812[/C][C]0.000496979724375756[/C][C]0.000248489862187878[/C][/ROW]
[ROW][C]8[/C][C]0.999687059549789[/C][C]0.000625880900422106[/C][C]0.000312940450211053[/C][/ROW]
[ROW][C]9[/C][C]0.99970527205805[/C][C]0.000589455883899908[/C][C]0.000294727941949954[/C][/ROW]
[ROW][C]10[/C][C]0.999290170514986[/C][C]0.00141965897002828[/C][C]0.000709829485014142[/C][/ROW]
[ROW][C]11[/C][C]0.999206401741466[/C][C]0.00158719651706751[/C][C]0.000793598258533755[/C][/ROW]
[ROW][C]12[/C][C]0.998969470258118[/C][C]0.00206105948376308[/C][C]0.00103052974188154[/C][/ROW]
[ROW][C]13[/C][C]0.99817979197987[/C][C]0.00364041604026055[/C][C]0.00182020802013028[/C][/ROW]
[ROW][C]14[/C][C]0.997042436210843[/C][C]0.00591512757831443[/C][C]0.00295756378915721[/C][/ROW]
[ROW][C]15[/C][C]0.994746551368834[/C][C]0.0105068972623318[/C][C]0.00525344863116592[/C][/ROW]
[ROW][C]16[/C][C]0.991363896676737[/C][C]0.017272206646527[/C][C]0.0086361033232635[/C][/ROW]
[ROW][C]17[/C][C]0.985989794752693[/C][C]0.0280204104946141[/C][C]0.0140102052473071[/C][/ROW]
[ROW][C]18[/C][C]0.97860937041175[/C][C]0.0427812591765006[/C][C]0.0213906295882503[/C][/ROW]
[ROW][C]19[/C][C]0.980310194555444[/C][C]0.0393796108891128[/C][C]0.0196898054445564[/C][/ROW]
[ROW][C]20[/C][C]0.971128574880477[/C][C]0.0577428502390457[/C][C]0.0288714251195229[/C][/ROW]
[ROW][C]21[/C][C]0.975866471999038[/C][C]0.0482670560019237[/C][C]0.0241335280009618[/C][/ROW]
[ROW][C]22[/C][C]0.966300227666247[/C][C]0.0673995446675059[/C][C]0.0336997723337529[/C][/ROW]
[ROW][C]23[/C][C]0.954375330250926[/C][C]0.0912493394981479[/C][C]0.0456246697490739[/C][/ROW]
[ROW][C]24[/C][C]0.93654701879451[/C][C]0.126905962410979[/C][C]0.0634529812054896[/C][/ROW]
[ROW][C]25[/C][C]0.91887682441545[/C][C]0.1622463511691[/C][C]0.0811231755845498[/C][/ROW]
[ROW][C]26[/C][C]0.982350725701349[/C][C]0.0352985485973026[/C][C]0.0176492742986513[/C][/ROW]
[ROW][C]27[/C][C]0.975361440715216[/C][C]0.0492771185695689[/C][C]0.0246385592847844[/C][/ROW]
[ROW][C]28[/C][C]0.970603671145542[/C][C]0.0587926577089159[/C][C]0.0293963288544579[/C][/ROW]
[ROW][C]29[/C][C]0.962693931223856[/C][C]0.0746121375522885[/C][C]0.0373060687761442[/C][/ROW]
[ROW][C]30[/C][C]0.952881840816776[/C][C]0.0942363183664475[/C][C]0.0471181591832238[/C][/ROW]
[ROW][C]31[/C][C]0.951860466010995[/C][C]0.0962790679780104[/C][C]0.0481395339890052[/C][/ROW]
[ROW][C]32[/C][C]0.940227596375782[/C][C]0.119544807248436[/C][C]0.0597724036242178[/C][/ROW]
[ROW][C]33[/C][C]0.923996830615147[/C][C]0.152006338769706[/C][C]0.0760031693848528[/C][/ROW]
[ROW][C]34[/C][C]0.909350000755439[/C][C]0.181299998489123[/C][C]0.0906499992445614[/C][/ROW]
[ROW][C]35[/C][C]0.885194138939437[/C][C]0.229611722121125[/C][C]0.114805861060563[/C][/ROW]
[ROW][C]36[/C][C]0.906378942885895[/C][C]0.187242114228209[/C][C]0.0936210571141047[/C][/ROW]
[ROW][C]37[/C][C]0.932072842721774[/C][C]0.135854314556453[/C][C]0.0679271572782263[/C][/ROW]
[ROW][C]38[/C][C]0.912590943429029[/C][C]0.174818113141941[/C][C]0.0874090565709705[/C][/ROW]
[ROW][C]39[/C][C]0.895151578351206[/C][C]0.209696843297588[/C][C]0.104848421648794[/C][/ROW]
[ROW][C]40[/C][C]0.905161641250898[/C][C]0.189676717498205[/C][C]0.0948383587491025[/C][/ROW]
[ROW][C]41[/C][C]0.9062389379017[/C][C]0.187522124196599[/C][C]0.0937610620982996[/C][/ROW]
[ROW][C]42[/C][C]0.901034318913051[/C][C]0.197931362173898[/C][C]0.0989656810869492[/C][/ROW]
[ROW][C]43[/C][C]0.906549824210411[/C][C]0.186900351579179[/C][C]0.0934501757895894[/C][/ROW]
[ROW][C]44[/C][C]0.884111186081583[/C][C]0.231777627836834[/C][C]0.115888813918417[/C][/ROW]
[ROW][C]45[/C][C]0.877459927605968[/C][C]0.245080144788063[/C][C]0.122540072394032[/C][/ROW]
[ROW][C]46[/C][C]0.859840482592103[/C][C]0.280319034815795[/C][C]0.140159517407897[/C][/ROW]
[ROW][C]47[/C][C]0.869334346558676[/C][C]0.261331306882649[/C][C]0.130665653441324[/C][/ROW]
[ROW][C]48[/C][C]0.847370511580785[/C][C]0.305258976838431[/C][C]0.152629488419215[/C][/ROW]
[ROW][C]49[/C][C]0.876609192114534[/C][C]0.246781615770932[/C][C]0.123390807885466[/C][/ROW]
[ROW][C]50[/C][C]0.865229807714949[/C][C]0.269540384570103[/C][C]0.134770192285051[/C][/ROW]
[ROW][C]51[/C][C]0.855058304751128[/C][C]0.289883390497744[/C][C]0.144941695248872[/C][/ROW]
[ROW][C]52[/C][C]0.832716960846798[/C][C]0.334566078306404[/C][C]0.167283039153202[/C][/ROW]
[ROW][C]53[/C][C]0.844831012964384[/C][C]0.310337974071231[/C][C]0.155168987035616[/C][/ROW]
[ROW][C]54[/C][C]0.81499095860684[/C][C]0.370018082786321[/C][C]0.18500904139316[/C][/ROW]
[ROW][C]55[/C][C]0.827594722820834[/C][C]0.344810554358331[/C][C]0.172405277179166[/C][/ROW]
[ROW][C]56[/C][C]0.796099085685029[/C][C]0.407801828629942[/C][C]0.203900914314971[/C][/ROW]
[ROW][C]57[/C][C]0.777665434823047[/C][C]0.444669130353907[/C][C]0.222334565176953[/C][/ROW]
[ROW][C]58[/C][C]0.802743150104118[/C][C]0.394513699791763[/C][C]0.197256849895881[/C][/ROW]
[ROW][C]59[/C][C]0.840871437201098[/C][C]0.318257125597805[/C][C]0.159128562798902[/C][/ROW]
[ROW][C]60[/C][C]0.817245228930322[/C][C]0.365509542139357[/C][C]0.182754771069678[/C][/ROW]
[ROW][C]61[/C][C]0.833737739591054[/C][C]0.332524520817893[/C][C]0.166262260408946[/C][/ROW]
[ROW][C]62[/C][C]0.81184384430477[/C][C]0.37631231139046[/C][C]0.18815615569523[/C][/ROW]
[ROW][C]63[/C][C]0.819623671403985[/C][C]0.360752657192031[/C][C]0.180376328596015[/C][/ROW]
[ROW][C]64[/C][C]0.79118607071254[/C][C]0.417627858574919[/C][C]0.20881392928746[/C][/ROW]
[ROW][C]65[/C][C]0.759956540310894[/C][C]0.480086919378211[/C][C]0.240043459689106[/C][/ROW]
[ROW][C]66[/C][C]0.830151640217264[/C][C]0.339696719565472[/C][C]0.169848359782736[/C][/ROW]
[ROW][C]67[/C][C]0.824884501143132[/C][C]0.350230997713736[/C][C]0.175115498856868[/C][/ROW]
[ROW][C]68[/C][C]0.820855478099784[/C][C]0.358289043800432[/C][C]0.179144521900216[/C][/ROW]
[ROW][C]69[/C][C]0.793787677912696[/C][C]0.412424644174608[/C][C]0.206212322087304[/C][/ROW]
[ROW][C]70[/C][C]0.774856931377295[/C][C]0.45028613724541[/C][C]0.225143068622705[/C][/ROW]
[ROW][C]71[/C][C]0.740474218736171[/C][C]0.519051562527658[/C][C]0.259525781263829[/C][/ROW]
[ROW][C]72[/C][C]0.757738538910885[/C][C]0.48452292217823[/C][C]0.242261461089115[/C][/ROW]
[ROW][C]73[/C][C]0.722568548287607[/C][C]0.554862903424785[/C][C]0.277431451712393[/C][/ROW]
[ROW][C]74[/C][C]0.711397153638157[/C][C]0.577205692723687[/C][C]0.288602846361843[/C][/ROW]
[ROW][C]75[/C][C]0.710313499101002[/C][C]0.579373001797996[/C][C]0.289686500898998[/C][/ROW]
[ROW][C]76[/C][C]0.804559744207077[/C][C]0.390880511585846[/C][C]0.195440255792923[/C][/ROW]
[ROW][C]77[/C][C]0.797107730851124[/C][C]0.405784538297752[/C][C]0.202892269148876[/C][/ROW]
[ROW][C]78[/C][C]0.816160964153243[/C][C]0.367678071693514[/C][C]0.183839035846757[/C][/ROW]
[ROW][C]79[/C][C]0.786104272484807[/C][C]0.427791455030386[/C][C]0.213895727515193[/C][/ROW]
[ROW][C]80[/C][C]0.849781769180139[/C][C]0.300436461639722[/C][C]0.150218230819861[/C][/ROW]
[ROW][C]81[/C][C]0.828534207853878[/C][C]0.342931584292244[/C][C]0.171465792146122[/C][/ROW]
[ROW][C]82[/C][C]0.807239799408943[/C][C]0.385520401182114[/C][C]0.192760200591057[/C][/ROW]
[ROW][C]83[/C][C]0.822845872367823[/C][C]0.354308255264353[/C][C]0.177154127632177[/C][/ROW]
[ROW][C]84[/C][C]0.793036827950105[/C][C]0.413926344099791[/C][C]0.206963172049895[/C][/ROW]
[ROW][C]85[/C][C]0.75917787321605[/C][C]0.481644253567901[/C][C]0.24082212678395[/C][/ROW]
[ROW][C]86[/C][C]0.722579069421563[/C][C]0.554841861156874[/C][C]0.277420930578437[/C][/ROW]
[ROW][C]87[/C][C]0.687046372424938[/C][C]0.625907255150123[/C][C]0.312953627575062[/C][/ROW]
[ROW][C]88[/C][C]0.652245606539751[/C][C]0.695508786920498[/C][C]0.347754393460249[/C][/ROW]
[ROW][C]89[/C][C]0.616721337462316[/C][C]0.766557325075368[/C][C]0.383278662537684[/C][/ROW]
[ROW][C]90[/C][C]0.664646274135492[/C][C]0.670707451729017[/C][C]0.335353725864508[/C][/ROW]
[ROW][C]91[/C][C]0.654778465960286[/C][C]0.690443068079427[/C][C]0.345221534039714[/C][/ROW]
[ROW][C]92[/C][C]0.622558882654336[/C][C]0.754882234691329[/C][C]0.377441117345664[/C][/ROW]
[ROW][C]93[/C][C]0.582192215135079[/C][C]0.835615569729841[/C][C]0.41780778486492[/C][/ROW]
[ROW][C]94[/C][C]0.560055180480308[/C][C]0.879889639039383[/C][C]0.439944819519692[/C][/ROW]
[ROW][C]95[/C][C]0.614080402033808[/C][C]0.771839195932384[/C][C]0.385919597966192[/C][/ROW]
[ROW][C]96[/C][C]0.569753007541843[/C][C]0.860493984916314[/C][C]0.430246992458157[/C][/ROW]
[ROW][C]97[/C][C]0.539288790255282[/C][C]0.921422419489436[/C][C]0.460711209744718[/C][/ROW]
[ROW][C]98[/C][C]0.522788674410985[/C][C]0.95442265117803[/C][C]0.477211325589015[/C][/ROW]
[ROW][C]99[/C][C]0.476648824562684[/C][C]0.953297649125367[/C][C]0.523351175437316[/C][/ROW]
[ROW][C]100[/C][C]0.464065082877339[/C][C]0.928130165754679[/C][C]0.535934917122661[/C][/ROW]
[ROW][C]101[/C][C]0.419975841425352[/C][C]0.839951682850704[/C][C]0.580024158574648[/C][/ROW]
[ROW][C]102[/C][C]0.388638682502058[/C][C]0.777277365004116[/C][C]0.611361317497942[/C][/ROW]
[ROW][C]103[/C][C]0.345816296338331[/C][C]0.691632592676661[/C][C]0.654183703661669[/C][/ROW]
[ROW][C]104[/C][C]0.449485930467368[/C][C]0.898971860934737[/C][C]0.550514069532632[/C][/ROW]
[ROW][C]105[/C][C]0.506327189488056[/C][C]0.987345621023887[/C][C]0.493672810511944[/C][/ROW]
[ROW][C]106[/C][C]0.491629102226915[/C][C]0.983258204453829[/C][C]0.508370897773085[/C][/ROW]
[ROW][C]107[/C][C]0.444765442710905[/C][C]0.88953088542181[/C][C]0.555234557289095[/C][/ROW]
[ROW][C]108[/C][C]0.500846769757817[/C][C]0.998306460484366[/C][C]0.499153230242183[/C][/ROW]
[ROW][C]109[/C][C]0.474873822054742[/C][C]0.949747644109483[/C][C]0.525126177945259[/C][/ROW]
[ROW][C]110[/C][C]0.849394382537371[/C][C]0.301211234925259[/C][C]0.150605617462629[/C][/ROW]
[ROW][C]111[/C][C]0.829517595215811[/C][C]0.340964809568379[/C][C]0.170482404784189[/C][/ROW]
[ROW][C]112[/C][C]0.799751640913232[/C][C]0.400496718173536[/C][C]0.200248359086768[/C][/ROW]
[ROW][C]113[/C][C]0.797166267800184[/C][C]0.405667464399632[/C][C]0.202833732199816[/C][/ROW]
[ROW][C]114[/C][C]0.776637371531068[/C][C]0.446725256937864[/C][C]0.223362628468932[/C][/ROW]
[ROW][C]115[/C][C]0.774270722612472[/C][C]0.451458554775056[/C][C]0.225729277387528[/C][/ROW]
[ROW][C]116[/C][C]0.744003052377384[/C][C]0.511993895245233[/C][C]0.255996947622616[/C][/ROW]
[ROW][C]117[/C][C]0.716085172554312[/C][C]0.567829654891377[/C][C]0.283914827445688[/C][/ROW]
[ROW][C]118[/C][C]0.675242160215823[/C][C]0.649515679568353[/C][C]0.324757839784177[/C][/ROW]
[ROW][C]119[/C][C]0.737054799239775[/C][C]0.52589040152045[/C][C]0.262945200760225[/C][/ROW]
[ROW][C]120[/C][C]0.731673579301129[/C][C]0.536652841397742[/C][C]0.268326420698871[/C][/ROW]
[ROW][C]121[/C][C]0.68718887205349[/C][C]0.625622255893019[/C][C]0.31281112794651[/C][/ROW]
[ROW][C]122[/C][C]0.644797281523612[/C][C]0.710405436952775[/C][C]0.355202718476388[/C][/ROW]
[ROW][C]123[/C][C]0.597014831873154[/C][C]0.805970336253692[/C][C]0.402985168126846[/C][/ROW]
[ROW][C]124[/C][C]0.64413681710185[/C][C]0.711726365796299[/C][C]0.35586318289815[/C][/ROW]
[ROW][C]125[/C][C]0.613998996802306[/C][C]0.772002006395388[/C][C]0.386001003197694[/C][/ROW]
[ROW][C]126[/C][C]0.649979010882349[/C][C]0.700041978235301[/C][C]0.350020989117651[/C][/ROW]
[ROW][C]127[/C][C]0.651910645937986[/C][C]0.696178708124029[/C][C]0.348089354062015[/C][/ROW]
[ROW][C]128[/C][C]0.602866659752024[/C][C]0.794266680495952[/C][C]0.397133340247976[/C][/ROW]
[ROW][C]129[/C][C]0.587433848043964[/C][C]0.825132303912071[/C][C]0.412566151956035[/C][/ROW]
[ROW][C]130[/C][C]0.532125970846033[/C][C]0.935748058307934[/C][C]0.467874029153967[/C][/ROW]
[ROW][C]131[/C][C]0.573047743044142[/C][C]0.853904513911717[/C][C]0.426952256955858[/C][/ROW]
[ROW][C]132[/C][C]0.54396627688275[/C][C]0.912067446234499[/C][C]0.45603372311725[/C][/ROW]
[ROW][C]133[/C][C]0.507128098221448[/C][C]0.985743803557105[/C][C]0.492871901778552[/C][/ROW]
[ROW][C]134[/C][C]0.475020604198399[/C][C]0.950041208396798[/C][C]0.524979395801601[/C][/ROW]
[ROW][C]135[/C][C]0.412943693324047[/C][C]0.825887386648093[/C][C]0.587056306675953[/C][/ROW]
[ROW][C]136[/C][C]0.378466603976455[/C][C]0.75693320795291[/C][C]0.621533396023545[/C][/ROW]
[ROW][C]137[/C][C]0.678420602308489[/C][C]0.643158795383023[/C][C]0.321579397691511[/C][/ROW]
[ROW][C]138[/C][C]0.621551069354977[/C][C]0.756897861290046[/C][C]0.378448930645023[/C][/ROW]
[ROW][C]139[/C][C]0.559368263991764[/C][C]0.881263472016472[/C][C]0.440631736008236[/C][/ROW]
[ROW][C]140[/C][C]0.50763902549839[/C][C]0.984721949003221[/C][C]0.49236097450161[/C][/ROW]
[ROW][C]141[/C][C]0.463775640202293[/C][C]0.927551280404585[/C][C]0.536224359797707[/C][/ROW]
[ROW][C]142[/C][C]0.445931438690504[/C][C]0.891862877381009[/C][C]0.554068561309496[/C][/ROW]
[ROW][C]143[/C][C]0.43854788072638[/C][C]0.87709576145276[/C][C]0.56145211927362[/C][/ROW]
[ROW][C]144[/C][C]0.369059049877922[/C][C]0.738118099755845[/C][C]0.630940950122078[/C][/ROW]
[ROW][C]145[/C][C]0.297362600776094[/C][C]0.594725201552187[/C][C]0.702637399223906[/C][/ROW]
[ROW][C]146[/C][C]0.251583130733201[/C][C]0.503166261466401[/C][C]0.748416869266799[/C][/ROW]
[ROW][C]147[/C][C]0.189359806873271[/C][C]0.378719613746541[/C][C]0.810640193126729[/C][/ROW]
[ROW][C]148[/C][C]0.190765949083758[/C][C]0.381531898167516[/C][C]0.809234050916242[/C][/ROW]
[ROW][C]149[/C][C]0.286002688696639[/C][C]0.572005377393278[/C][C]0.713997311303361[/C][/ROW]
[ROW][C]150[/C][C]0.213013692929105[/C][C]0.42602738585821[/C][C]0.786986307070895[/C][/ROW]
[ROW][C]151[/C][C]0.146073546569559[/C][C]0.292147093139118[/C][C]0.853926453430441[/C][/ROW]
[ROW][C]152[/C][C]0.234095400396112[/C][C]0.468190800792224[/C][C]0.765904599603888[/C][/ROW]
[ROW][C]153[/C][C]0.180331957288677[/C][C]0.360663914577355[/C][C]0.819668042711323[/C][/ROW]
[ROW][C]154[/C][C]0.156861695765267[/C][C]0.313723391530535[/C][C]0.843138304234733[/C][/ROW]
[ROW][C]155[/C][C]0.0903673542779089[/C][C]0.180734708555818[/C][C]0.909632645722091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9997515101378120.0004969797243757560.000248489862187878
80.9996870595497890.0006258809004221060.000312940450211053
90.999705272058050.0005894558838999080.000294727941949954
100.9992901705149860.001419658970028280.000709829485014142
110.9992064017414660.001587196517067510.000793598258533755
120.9989694702581180.002061059483763080.00103052974188154
130.998179791979870.003640416040260550.00182020802013028
140.9970424362108430.005915127578314430.00295756378915721
150.9947465513688340.01050689726233180.00525344863116592
160.9913638966767370.0172722066465270.0086361033232635
170.9859897947526930.02802041049461410.0140102052473071
180.978609370411750.04278125917650060.0213906295882503
190.9803101945554440.03937961088911280.0196898054445564
200.9711285748804770.05774285023904570.0288714251195229
210.9758664719990380.04826705600192370.0241335280009618
220.9663002276662470.06739954466750590.0336997723337529
230.9543753302509260.09124933949814790.0456246697490739
240.936547018794510.1269059624109790.0634529812054896
250.918876824415450.16224635116910.0811231755845498
260.9823507257013490.03529854859730260.0176492742986513
270.9753614407152160.04927711856956890.0246385592847844
280.9706036711455420.05879265770891590.0293963288544579
290.9626939312238560.07461213755228850.0373060687761442
300.9528818408167760.09423631836644750.0471181591832238
310.9518604660109950.09627906797801040.0481395339890052
320.9402275963757820.1195448072484360.0597724036242178
330.9239968306151470.1520063387697060.0760031693848528
340.9093500007554390.1812999984891230.0906499992445614
350.8851941389394370.2296117221211250.114805861060563
360.9063789428858950.1872421142282090.0936210571141047
370.9320728427217740.1358543145564530.0679271572782263
380.9125909434290290.1748181131419410.0874090565709705
390.8951515783512060.2096968432975880.104848421648794
400.9051616412508980.1896767174982050.0948383587491025
410.90623893790170.1875221241965990.0937610620982996
420.9010343189130510.1979313621738980.0989656810869492
430.9065498242104110.1869003515791790.0934501757895894
440.8841111860815830.2317776278368340.115888813918417
450.8774599276059680.2450801447880630.122540072394032
460.8598404825921030.2803190348157950.140159517407897
470.8693343465586760.2613313068826490.130665653441324
480.8473705115807850.3052589768384310.152629488419215
490.8766091921145340.2467816157709320.123390807885466
500.8652298077149490.2695403845701030.134770192285051
510.8550583047511280.2898833904977440.144941695248872
520.8327169608467980.3345660783064040.167283039153202
530.8448310129643840.3103379740712310.155168987035616
540.814990958606840.3700180827863210.18500904139316
550.8275947228208340.3448105543583310.172405277179166
560.7960990856850290.4078018286299420.203900914314971
570.7776654348230470.4446691303539070.222334565176953
580.8027431501041180.3945136997917630.197256849895881
590.8408714372010980.3182571255978050.159128562798902
600.8172452289303220.3655095421393570.182754771069678
610.8337377395910540.3325245208178930.166262260408946
620.811843844304770.376312311390460.18815615569523
630.8196236714039850.3607526571920310.180376328596015
640.791186070712540.4176278585749190.20881392928746
650.7599565403108940.4800869193782110.240043459689106
660.8301516402172640.3396967195654720.169848359782736
670.8248845011431320.3502309977137360.175115498856868
680.8208554780997840.3582890438004320.179144521900216
690.7937876779126960.4124246441746080.206212322087304
700.7748569313772950.450286137245410.225143068622705
710.7404742187361710.5190515625276580.259525781263829
720.7577385389108850.484522922178230.242261461089115
730.7225685482876070.5548629034247850.277431451712393
740.7113971536381570.5772056927236870.288602846361843
750.7103134991010020.5793730017979960.289686500898998
760.8045597442070770.3908805115858460.195440255792923
770.7971077308511240.4057845382977520.202892269148876
780.8161609641532430.3676780716935140.183839035846757
790.7861042724848070.4277914550303860.213895727515193
800.8497817691801390.3004364616397220.150218230819861
810.8285342078538780.3429315842922440.171465792146122
820.8072397994089430.3855204011821140.192760200591057
830.8228458723678230.3543082552643530.177154127632177
840.7930368279501050.4139263440997910.206963172049895
850.759177873216050.4816442535679010.24082212678395
860.7225790694215630.5548418611568740.277420930578437
870.6870463724249380.6259072551501230.312953627575062
880.6522456065397510.6955087869204980.347754393460249
890.6167213374623160.7665573250753680.383278662537684
900.6646462741354920.6707074517290170.335353725864508
910.6547784659602860.6904430680794270.345221534039714
920.6225588826543360.7548822346913290.377441117345664
930.5821922151350790.8356155697298410.41780778486492
940.5600551804803080.8798896390393830.439944819519692
950.6140804020338080.7718391959323840.385919597966192
960.5697530075418430.8604939849163140.430246992458157
970.5392887902552820.9214224194894360.460711209744718
980.5227886744109850.954422651178030.477211325589015
990.4766488245626840.9532976491253670.523351175437316
1000.4640650828773390.9281301657546790.535934917122661
1010.4199758414253520.8399516828507040.580024158574648
1020.3886386825020580.7772773650041160.611361317497942
1030.3458162963383310.6916325926766610.654183703661669
1040.4494859304673680.8989718609347370.550514069532632
1050.5063271894880560.9873456210238870.493672810511944
1060.4916291022269150.9832582044538290.508370897773085
1070.4447654427109050.889530885421810.555234557289095
1080.5008467697578170.9983064604843660.499153230242183
1090.4748738220547420.9497476441094830.525126177945259
1100.8493943825373710.3012112349252590.150605617462629
1110.8295175952158110.3409648095683790.170482404784189
1120.7997516409132320.4004967181735360.200248359086768
1130.7971662678001840.4056674643996320.202833732199816
1140.7766373715310680.4467252569378640.223362628468932
1150.7742707226124720.4514585547750560.225729277387528
1160.7440030523773840.5119938952452330.255996947622616
1170.7160851725543120.5678296548913770.283914827445688
1180.6752421602158230.6495156795683530.324757839784177
1190.7370547992397750.525890401520450.262945200760225
1200.7316735793011290.5366528413977420.268326420698871
1210.687188872053490.6256222558930190.31281112794651
1220.6447972815236120.7104054369527750.355202718476388
1230.5970148318731540.8059703362536920.402985168126846
1240.644136817101850.7117263657962990.35586318289815
1250.6139989968023060.7720020063953880.386001003197694
1260.6499790108823490.7000419782353010.350020989117651
1270.6519106459379860.6961787081240290.348089354062015
1280.6028666597520240.7942666804959520.397133340247976
1290.5874338480439640.8251323039120710.412566151956035
1300.5321259708460330.9357480583079340.467874029153967
1310.5730477430441420.8539045139117170.426952256955858
1320.543966276882750.9120674462344990.45603372311725
1330.5071280982214480.9857438035571050.492871901778552
1340.4750206041983990.9500412083967980.524979395801601
1350.4129436933240470.8258873866480930.587056306675953
1360.3784666039764550.756933207952910.621533396023545
1370.6784206023084890.6431587953830230.321579397691511
1380.6215510693549770.7568978612900460.378448930645023
1390.5593682639917640.8812634720164720.440631736008236
1400.507639025498390.9847219490032210.49236097450161
1410.4637756402022930.9275512804045850.536224359797707
1420.4459314386905040.8918628773810090.554068561309496
1430.438547880726380.877095761452760.56145211927362
1440.3690590498779220.7381180997558450.630940950122078
1450.2973626007760940.5947252015521870.702637399223906
1460.2515831307332010.5031662614664010.748416869266799
1470.1893598068732710.3787196137465410.810640193126729
1480.1907659490837580.3815318981675160.809234050916242
1490.2860026886966390.5720053773932780.713997311303361
1500.2130136929291050.426027385858210.786986307070895
1510.1460735465695590.2921470931391180.853926453430441
1520.2340954003961120.4681908007922240.765904599603888
1530.1803319572886770.3606639145773550.819668042711323
1540.1568616957652670.3137233915305350.843138304234733
1550.09036735427790890.1807347085558180.909632645722091






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0536912751677852NOK
5% type I error level160.10738255033557NOK
10% type I error level230.154362416107383NOK

\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 & 8 & 0.0536912751677852 & NOK \tabularnewline
5% type I error level & 16 & 0.10738255033557 & NOK \tabularnewline
10% type I error level & 23 & 0.154362416107383 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190680&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]8[/C][C]0.0536912751677852[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.10738255033557[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.154362416107383[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190680&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190680&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0536912751677852NOK
5% type I error level160.10738255033557NOK
10% type I error level230.154362416107383NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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