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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 05:22:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321957420m53lw1uq6s6dej6.htm/, Retrieved Thu, 25 Apr 2024 13:05:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146112, Retrieved Thu, 25 Apr 2024 13:05:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-11-17 14:10:13] [14511500b645ce5186c706473940fe45]
-   PD    [Multiple Regression] [workshop 7 mini t...] [2011-11-22 10:22:08] [8a7469f165590e0a048f07fe1c69d604] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	12	14	12
39	32	11	18	11
30	35	15	11	14
31	33	6	12	12
34	37	13	16	21
35	29	10	18	12
39	31	12	14	22
34	36	14	14	11
36	35	12	15	10
37	38	6	15	13
38	31	10	17	10
36	34	12	19	8
38	35	12	10	15
39	38	11	16	14
33	37	15	18	10
32	33	12	14	14
36	32	10	14	14
38	38	12	17	11
39	38	11	14	10
32	32	12	16	13
32	33	11	18	7
31	31	12	11	14
39	38	13	14	12
37	39	11	12	14
39	32	9	17	11
41	32	13	9	9
36	35	10	16	11
33	37	14	14	15
33	33	12	15	14
34	33	10	11	13
31	28	12	16	9
27	32	8	13	15
37	31	10	17	10
34	37	12	15	11
34	30	12	14	13
32	33	7	16	8
29	31	6	9	20
36	33	12	15	12
29	31	10	17	10
35	33	10	13	10
37	32	10	15	9
34	33	12	16	14
38	32	15	16	8
35	33	10	12	14
38	28	10	12	11
37	35	12	11	13
38	39	13	15	9
33	34	11	15	11
36	38	11	17	15
38	32	12	13	11
32	38	14	16	10
32	30	10	14	14
32	33	12	11	18
34	38	13	12	14
32	32	5	12	11
37	32	6	15	12
39	34	12	16	13
29	34	12	15	9
37	36	11	12	10
35	34	10	12	15
30	28	7	8	20
38	34	12	13	12
34	35	14	11	12
31	35	11	14	14
34	31	12	15	13
35	37	13	10	11
36	35	14	11	17
30	27	11	12	12
39	40	12	15	13
35	37	12	15	14
38	36	8	14	13
31	38	11	16	15
34	39	14	15	13
38	41	14	15	10
34	27	12	13	11
39	30	9	12	19
37	37	13	17	13
34	31	11	13	17
28	31	12	15	13
37	27	12	13	9
33	36	12	15	11
37	38	12	16	10
35	37	12	15	9
37	33	12	16	12
32	34	11	15	12
33	31	10	14	13
38	39	9	15	13
33	34	12	14	12
29	32	12	13	15
33	33	12	7	22
31	36	9	17	13
36	32	15	13	15
35	41	12	15	13
32	28	12	14	15
29	30	12	13	10
39	36	10	16	11
37	35	13	12	16
35	31	9	14	11
37	34	12	17	11
32	36	10	15	10
38	36	14	17	10
37	35	11	12	16
36	37	15	16	12
32	28	11	11	11
33	39	11	15	16
40	32	12	9	19
38	35	12	16	11
41	39	12	15	16
36	35	11	10	15
43	42	7	10	24
30	34	12	15	14
31	33	14	11	15
32	41	11	13	11
32	33	11	14	15
37	34	10	18	12
37	32	13	16	10
33	40	13	14	14
34	40	8	14	13
33	35	11	14	9
38	36	12	14	15
33	37	11	12	15
31	27	13	14	14
38	39	12	15	11
37	38	14	15	8
33	31	13	15	11
31	33	15	13	11
39	32	10	17	8
44	39	11	17	10
33	36	9	19	11
35	33	11	15	13
32	33	10	13	11
28	32	11	9	20
40	37	8	15	10
27	30	11	15	15
37	38	12	15	12
32	29	12	16	14
28	22	9	11	23
34	35	11	14	14
30	35	10	11	16
35	34	8	15	11
31	35	9	13	12
32	34	8	15	10
30	34	9	16	14
30	35	15	14	12
31	23	11	15	12
40	31	8	16	11
32	27	13	16	12
36	36	12	11	13
32	31	12	12	11
35	32	9	9	19
38	39	7	16	12
42	37	13	13	17
34	38	9	16	9
35	39	6	12	12
35	34	8	9	19
33	31	8	13	18
36	32	15	13	15
32	37	6	14	14
33	36	9	19	11
34	32	11	13	9
32	35	8	12	18
34	36	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.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=146112&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.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=146112&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.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] = + 25.3447561551434 -0.0454400451710284connected[t] + 0.0250330389066207separate[t] -0.139283873401028software[t] -0.724176490936219happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
depression
[t] =  +  25.3447561551434 -0.0454400451710284connected[t] +  0.0250330389066207separate[t] -0.139283873401028software[t] -0.724176490936219happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]depression
[t] =  +  25.3447561551434 -0.0454400451710284connected[t] +  0.0250330389066207separate[t] -0.139283873401028software[t] -0.724176490936219happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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] = + 25.3447561551434 -0.0454400451710284connected[t] + 0.0250330389066207separate[t] -0.139283873401028software[t] -0.724176490936219happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.34475615514342.7965979.062700
connected-0.04544004517102840.067353-0.67470.5008870.250444
separate0.02503303890662070.0641180.39040.6967560.348378
software-0.1392838734010280.098685-1.41140.1601050.080052
happiness-0.7241764909362190.09162-7.904100

\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) & 25.3447561551434 & 2.796597 & 9.0627 & 0 & 0 \tabularnewline
connected & -0.0454400451710284 & 0.067353 & -0.6747 & 0.500887 & 0.250444 \tabularnewline
separate & 0.0250330389066207 & 0.064118 & 0.3904 & 0.696756 & 0.348378 \tabularnewline
software & -0.139283873401028 & 0.098685 & -1.4114 & 0.160105 & 0.080052 \tabularnewline
happiness & -0.724176490936219 & 0.09162 & -7.9041 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&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]25.3447561551434[/C][C]2.796597[/C][C]9.0627[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]connected[/C][C]-0.0454400451710284[/C][C]0.067353[/C][C]-0.6747[/C][C]0.500887[/C][C]0.250444[/C][/ROW]
[ROW][C]separate[/C][C]0.0250330389066207[/C][C]0.064118[/C][C]0.3904[/C][C]0.696756[/C][C]0.348378[/C][/ROW]
[ROW][C]software[/C][C]-0.139283873401028[/C][C]0.098685[/C][C]-1.4114[/C][C]0.160105[/C][C]0.080052[/C][/ROW]
[ROW][C]happiness[/C][C]-0.724176490936219[/C][C]0.09162[/C][C]-7.9041[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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)25.34475615514342.7965979.062700
connected-0.04544004517102840.067353-0.67470.5008870.250444
separate0.02503303890662070.0641180.39040.6967560.348378
software-0.1392838734010280.098685-1.41140.1601050.080052
happiness-0.7241764909362190.09162-7.904100






Multiple Linear Regression - Regression Statistics
Multiple R0.554367749190105
R-squared0.307323601342103
Adjusted R-squared0.28967579500687
F-TEST (value)17.4142664252013
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value7.61624097123104e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66846383081789
Sum Squared Residuals1117.94977697217

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.554367749190105 \tabularnewline
R-squared & 0.307323601342103 \tabularnewline
Adjusted R-squared & 0.28967579500687 \tabularnewline
F-TEST (value) & 17.4142664252013 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 7.61624097123104e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.66846383081789 \tabularnewline
Sum Squared Residuals & 1117.94977697217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.554367749190105[/C][/ROW]
[ROW][C]R-squared[/C][C]0.307323601342103[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.28967579500687[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.4142664252013[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]7.61624097123104e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.66846383081789[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1117.94977697217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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 R0.554367749190105
R-squared0.307323601342103
Adjusted R-squared0.28967579500687
F-TEST (value)17.4142664252013
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value7.61624097123104e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66846383081789
Sum Squared Residuals1117.94977697217






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.6230924276635-0.623092427663517
2119.806352194221931.19364780577807
31414.8025116604305-0.802511660430473
41215.2363839071192-3.23638390711923
52111.32850284968069.67149715031944
61210.05229713158721.94770286841279
72212.53874124565929.46125875434084
81112.6125389192453-1.61253891924535
91012.0510170458625-2.05101704586251
101312.91637935781750.083620642182491
111010.6902195648236-0.690219564823586
1289.12927804321101-1.12927804321101
131515.5810194102016-0.58101941020155
141411.40490340953412.59509659046591
15109.647022166177090.352977833822907
161412.90688763966961.0931123603304
171412.97866216688091.02133783311908
181110.58688309036790.413116909632125
191012.8532563914065-2.85325639140653
201311.43350161889051.56649838110946
21710.1494655493258-3.14946554932575
221415.074791079836-1.07479107983605
231212.5746886446045-0.574688644604477
241414.4175225025276-0.417522502527649
251110.80909643196020.190903568039795
26915.9544927755038-6.95449277550379
271111.6054083017283-0.605408301728345
281512.6830120033232.316987996677
291412.13727110356241.86272889643765
301315.2671047689383-2.26710476893826
31911.3788095084351-2.37880950843509
321514.39036681115850.609633188841548
331010.7356596099946-0.735659609994614
341112.1919632140178-1.19196321401781
351312.74090843260770.259091567392319
36812.1549540248023-4.1549540248023
372017.44972739245672.55027260754329
381212.0009509680493-0.000950968049267134
391011.0991799713628-1.09917997136284
401013.7733117418948-3.77331174189479
41912.2090456307737-3.20904563077367
421411.36765456745512.6323454325449
43810.7430097276613-2.74300972766129
441414.497488232831-0.497488232831009
451114.2360029027848-3.23600290278482
461314.9022829644364-1.90228296443636
47911.9209852377459-2.92098523774591
481112.30158801587-1.30158801587
491510.8170470541114.18295294588904
501113.333390820673-2.33339082067303
511011.3051321055282-1.30513210552821
521413.11035626975180.889643730248206
531815.07941711247832.92058288752174
541414.2502418523321-0.250241852332058
551115.3051946964426-4.30519469644261
561212.7661811243778-0.766181124377785
571311.16548738050661.83451261949342
58912.3440643231531-3.34406432315309
591014.3424233858078-4.34242338580779
601514.52252127173760.47747872826237
612017.9140808481012.08591915189899
621213.3834568984863-1.38345689848627
631214.7600353531474-2.76003535314739
641413.14167763605490.858322363945102
651312.04176498057810.958235019421918
661115.6281217501268-4.62812175012685
671714.66915526280532.33084473719467
681214.4352063518454-2.4352063518454
691312.03986210488250.960137895117473
701412.14652316884681.85347683115322
711313.2664819789674-0.266481978967403
721511.76842377090233.23157622909768
731311.9634615450291.03653845497101
741011.8317674421581-1.83176744215812
751113.389985806824-2.38998580682404
761914.37991280882814.62008719117194
771310.46800622323132.53199377676875
781713.62940183585153.37059816414845
791312.31440525160430.685594748395747
80913.253665671311-4.25366567131095
811112.2123702202822-1.21237022028221
821011.3564996264751-1.35649962647512
83912.1465231688468-3.14652316884678
841211.2313344319420.768665568057981
851212.347028061041-0.347028061041029
861313.0899492634874-0.0899492634873859
871312.478120731350.521879268649982
881212.8864806334052-0.886480633405193
891513.74235122721231.25764877278772
902217.93068303105214.06931696894789
911311.27274894895491.72725105104508
921513.0064192908121.993580709188
931312.24665532447330.753344675526739
941512.78172244513652.2182775548635
951013.692285149399-3.69228514939904
961111.4941212051219-0.49412120512188
971614.03882260009911.96117739990089
981113.1383530465464-2.13835304654636
991110.53219097991240.46780902008758
1001012.5363780122553-2.5363780122553
1011010.2582492657526-0.258249265752578
1021614.31739034690121.68260965309883
1031210.95905501253641.04094498746355
1041115.0935357913462-4.09353579134618
1051612.42675321040313.5732467895969
1061916.13921669407582.86078330592415
1071111.2359604645842-0.235960464584232
1081611.92394897563384.07605102436615
1091515.8111833739446-0.811183373944633
1102416.22546982369797.77453017630211
1111412.29862427798211.70137572201794
1121514.84628941084720.15371058915277
1131113.9706123152598-2.97061231525981
1141513.04617151307061.95382848692937
1151210.08658223577831.91341776422174
1161011.0670175196344-1.06701751963437
1171412.89739499344391.10260500655611
1181313.548374315278-0.548374315277999
119913.0507975457128-4.05079754571284
1201512.70934648536332.29065351463671
1211514.54921660539850.450783394601479
1221412.66284557799991.33715442200012
1231112.0602691111469-1.06026911114693
124811.8021083706093-3.80210837060929
1251111.9479211523481-0.947921152348084
1261113.2586525555738-2.25865255557376
127810.6698125585592-2.66981255855918
1281010.4785597316494-0.478559731649353
129119.733515876740421.26648412325958
1301312.18567488662130.814325113378677
1311113.9096318774079-2.90963187740788
1322016.82378110952923.17621889047078
1331012.4764584365957-2.47645843659575
1341512.47409613126972.52590386873031
1351212.0806761174113-0.0806761174113421
1361411.35840250217072.64159749782932
1372315.40366548539267.59633451460737
1381413.00535750054180.994642499458187
1391615.49893102743560.501068972564388
1401112.628559545731-1.62855954573103
1411214.1444218737932-2.14442187379317
1421012.7648796812441-2.76487968124411
1431411.99229940724892.00770059275108
1441212.6299821876218-0.629982187621815
1451212.1171046782392-0.11710467823923
1461111.6020837122198-0.602083712219804
1471211.16905255095640.83094744904359
1481314.972756048514-1.97275604851401
1491114.3051745437288-3.3051745437288
1501916.78426854013412.21573145986593
1511212.0325119872159-0.0325119872158539
1521713.1375119611213.86248803887901
153911.9106713821913-2.91067138219129
1541215.2048219598748-3.20482195987484
1551916.97361849134832.02638150865166
1561814.09269350122573.90730649877434
1571513.0064192908121.993580709188
1581413.84272303570220.15727696429775
159119.733515876740421.26648412325958
160913.6544348747582-4.65443487475817
1611814.96244219295943.03755780704061
1621614.17241865058771.82758134941226

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.6230924276635 & -0.623092427663517 \tabularnewline
2 & 11 & 9.80635219422193 & 1.19364780577807 \tabularnewline
3 & 14 & 14.8025116604305 & -0.802511660430473 \tabularnewline
4 & 12 & 15.2363839071192 & -3.23638390711923 \tabularnewline
5 & 21 & 11.3285028496806 & 9.67149715031944 \tabularnewline
6 & 12 & 10.0522971315872 & 1.94770286841279 \tabularnewline
7 & 22 & 12.5387412456592 & 9.46125875434084 \tabularnewline
8 & 11 & 12.6125389192453 & -1.61253891924535 \tabularnewline
9 & 10 & 12.0510170458625 & -2.05101704586251 \tabularnewline
10 & 13 & 12.9163793578175 & 0.083620642182491 \tabularnewline
11 & 10 & 10.6902195648236 & -0.690219564823586 \tabularnewline
12 & 8 & 9.12927804321101 & -1.12927804321101 \tabularnewline
13 & 15 & 15.5810194102016 & -0.58101941020155 \tabularnewline
14 & 14 & 11.4049034095341 & 2.59509659046591 \tabularnewline
15 & 10 & 9.64702216617709 & 0.352977833822907 \tabularnewline
16 & 14 & 12.9068876396696 & 1.0931123603304 \tabularnewline
17 & 14 & 12.9786621668809 & 1.02133783311908 \tabularnewline
18 & 11 & 10.5868830903679 & 0.413116909632125 \tabularnewline
19 & 10 & 12.8532563914065 & -2.85325639140653 \tabularnewline
20 & 13 & 11.4335016188905 & 1.56649838110946 \tabularnewline
21 & 7 & 10.1494655493258 & -3.14946554932575 \tabularnewline
22 & 14 & 15.074791079836 & -1.07479107983605 \tabularnewline
23 & 12 & 12.5746886446045 & -0.574688644604477 \tabularnewline
24 & 14 & 14.4175225025276 & -0.417522502527649 \tabularnewline
25 & 11 & 10.8090964319602 & 0.190903568039795 \tabularnewline
26 & 9 & 15.9544927755038 & -6.95449277550379 \tabularnewline
27 & 11 & 11.6054083017283 & -0.605408301728345 \tabularnewline
28 & 15 & 12.683012003323 & 2.316987996677 \tabularnewline
29 & 14 & 12.1372711035624 & 1.86272889643765 \tabularnewline
30 & 13 & 15.2671047689383 & -2.26710476893826 \tabularnewline
31 & 9 & 11.3788095084351 & -2.37880950843509 \tabularnewline
32 & 15 & 14.3903668111585 & 0.609633188841548 \tabularnewline
33 & 10 & 10.7356596099946 & -0.735659609994614 \tabularnewline
34 & 11 & 12.1919632140178 & -1.19196321401781 \tabularnewline
35 & 13 & 12.7409084326077 & 0.259091567392319 \tabularnewline
36 & 8 & 12.1549540248023 & -4.1549540248023 \tabularnewline
37 & 20 & 17.4497273924567 & 2.55027260754329 \tabularnewline
38 & 12 & 12.0009509680493 & -0.000950968049267134 \tabularnewline
39 & 10 & 11.0991799713628 & -1.09917997136284 \tabularnewline
40 & 10 & 13.7733117418948 & -3.77331174189479 \tabularnewline
41 & 9 & 12.2090456307737 & -3.20904563077367 \tabularnewline
42 & 14 & 11.3676545674551 & 2.6323454325449 \tabularnewline
43 & 8 & 10.7430097276613 & -2.74300972766129 \tabularnewline
44 & 14 & 14.497488232831 & -0.497488232831009 \tabularnewline
45 & 11 & 14.2360029027848 & -3.23600290278482 \tabularnewline
46 & 13 & 14.9022829644364 & -1.90228296443636 \tabularnewline
47 & 9 & 11.9209852377459 & -2.92098523774591 \tabularnewline
48 & 11 & 12.30158801587 & -1.30158801587 \tabularnewline
49 & 15 & 10.817047054111 & 4.18295294588904 \tabularnewline
50 & 11 & 13.333390820673 & -2.33339082067303 \tabularnewline
51 & 10 & 11.3051321055282 & -1.30513210552821 \tabularnewline
52 & 14 & 13.1103562697518 & 0.889643730248206 \tabularnewline
53 & 18 & 15.0794171124783 & 2.92058288752174 \tabularnewline
54 & 14 & 14.2502418523321 & -0.250241852332058 \tabularnewline
55 & 11 & 15.3051946964426 & -4.30519469644261 \tabularnewline
56 & 12 & 12.7661811243778 & -0.766181124377785 \tabularnewline
57 & 13 & 11.1654873805066 & 1.83451261949342 \tabularnewline
58 & 9 & 12.3440643231531 & -3.34406432315309 \tabularnewline
59 & 10 & 14.3424233858078 & -4.34242338580779 \tabularnewline
60 & 15 & 14.5225212717376 & 0.47747872826237 \tabularnewline
61 & 20 & 17.914080848101 & 2.08591915189899 \tabularnewline
62 & 12 & 13.3834568984863 & -1.38345689848627 \tabularnewline
63 & 12 & 14.7600353531474 & -2.76003535314739 \tabularnewline
64 & 14 & 13.1416776360549 & 0.858322363945102 \tabularnewline
65 & 13 & 12.0417649805781 & 0.958235019421918 \tabularnewline
66 & 11 & 15.6281217501268 & -4.62812175012685 \tabularnewline
67 & 17 & 14.6691552628053 & 2.33084473719467 \tabularnewline
68 & 12 & 14.4352063518454 & -2.4352063518454 \tabularnewline
69 & 13 & 12.0398621048825 & 0.960137895117473 \tabularnewline
70 & 14 & 12.1465231688468 & 1.85347683115322 \tabularnewline
71 & 13 & 13.2664819789674 & -0.266481978967403 \tabularnewline
72 & 15 & 11.7684237709023 & 3.23157622909768 \tabularnewline
73 & 13 & 11.963461545029 & 1.03653845497101 \tabularnewline
74 & 10 & 11.8317674421581 & -1.83176744215812 \tabularnewline
75 & 11 & 13.389985806824 & -2.38998580682404 \tabularnewline
76 & 19 & 14.3799128088281 & 4.62008719117194 \tabularnewline
77 & 13 & 10.4680062232313 & 2.53199377676875 \tabularnewline
78 & 17 & 13.6294018358515 & 3.37059816414845 \tabularnewline
79 & 13 & 12.3144052516043 & 0.685594748395747 \tabularnewline
80 & 9 & 13.253665671311 & -4.25366567131095 \tabularnewline
81 & 11 & 12.2123702202822 & -1.21237022028221 \tabularnewline
82 & 10 & 11.3564996264751 & -1.35649962647512 \tabularnewline
83 & 9 & 12.1465231688468 & -3.14652316884678 \tabularnewline
84 & 12 & 11.231334431942 & 0.768665568057981 \tabularnewline
85 & 12 & 12.347028061041 & -0.347028061041029 \tabularnewline
86 & 13 & 13.0899492634874 & -0.0899492634873859 \tabularnewline
87 & 13 & 12.47812073135 & 0.521879268649982 \tabularnewline
88 & 12 & 12.8864806334052 & -0.886480633405193 \tabularnewline
89 & 15 & 13.7423512272123 & 1.25764877278772 \tabularnewline
90 & 22 & 17.9306830310521 & 4.06931696894789 \tabularnewline
91 & 13 & 11.2727489489549 & 1.72725105104508 \tabularnewline
92 & 15 & 13.006419290812 & 1.993580709188 \tabularnewline
93 & 13 & 12.2466553244733 & 0.753344675526739 \tabularnewline
94 & 15 & 12.7817224451365 & 2.2182775548635 \tabularnewline
95 & 10 & 13.692285149399 & -3.69228514939904 \tabularnewline
96 & 11 & 11.4941212051219 & -0.49412120512188 \tabularnewline
97 & 16 & 14.0388226000991 & 1.96117739990089 \tabularnewline
98 & 11 & 13.1383530465464 & -2.13835304654636 \tabularnewline
99 & 11 & 10.5321909799124 & 0.46780902008758 \tabularnewline
100 & 10 & 12.5363780122553 & -2.5363780122553 \tabularnewline
101 & 10 & 10.2582492657526 & -0.258249265752578 \tabularnewline
102 & 16 & 14.3173903469012 & 1.68260965309883 \tabularnewline
103 & 12 & 10.9590550125364 & 1.04094498746355 \tabularnewline
104 & 11 & 15.0935357913462 & -4.09353579134618 \tabularnewline
105 & 16 & 12.4267532104031 & 3.5732467895969 \tabularnewline
106 & 19 & 16.1392166940758 & 2.86078330592415 \tabularnewline
107 & 11 & 11.2359604645842 & -0.235960464584232 \tabularnewline
108 & 16 & 11.9239489756338 & 4.07605102436615 \tabularnewline
109 & 15 & 15.8111833739446 & -0.811183373944633 \tabularnewline
110 & 24 & 16.2254698236979 & 7.77453017630211 \tabularnewline
111 & 14 & 12.2986242779821 & 1.70137572201794 \tabularnewline
112 & 15 & 14.8462894108472 & 0.15371058915277 \tabularnewline
113 & 11 & 13.9706123152598 & -2.97061231525981 \tabularnewline
114 & 15 & 13.0461715130706 & 1.95382848692937 \tabularnewline
115 & 12 & 10.0865822357783 & 1.91341776422174 \tabularnewline
116 & 10 & 11.0670175196344 & -1.06701751963437 \tabularnewline
117 & 14 & 12.8973949934439 & 1.10260500655611 \tabularnewline
118 & 13 & 13.548374315278 & -0.548374315277999 \tabularnewline
119 & 9 & 13.0507975457128 & -4.05079754571284 \tabularnewline
120 & 15 & 12.7093464853633 & 2.29065351463671 \tabularnewline
121 & 15 & 14.5492166053985 & 0.450783394601479 \tabularnewline
122 & 14 & 12.6628455779999 & 1.33715442200012 \tabularnewline
123 & 11 & 12.0602691111469 & -1.06026911114693 \tabularnewline
124 & 8 & 11.8021083706093 & -3.80210837060929 \tabularnewline
125 & 11 & 11.9479211523481 & -0.947921152348084 \tabularnewline
126 & 11 & 13.2586525555738 & -2.25865255557376 \tabularnewline
127 & 8 & 10.6698125585592 & -2.66981255855918 \tabularnewline
128 & 10 & 10.4785597316494 & -0.478559731649353 \tabularnewline
129 & 11 & 9.73351587674042 & 1.26648412325958 \tabularnewline
130 & 13 & 12.1856748866213 & 0.814325113378677 \tabularnewline
131 & 11 & 13.9096318774079 & -2.90963187740788 \tabularnewline
132 & 20 & 16.8237811095292 & 3.17621889047078 \tabularnewline
133 & 10 & 12.4764584365957 & -2.47645843659575 \tabularnewline
134 & 15 & 12.4740961312697 & 2.52590386873031 \tabularnewline
135 & 12 & 12.0806761174113 & -0.0806761174113421 \tabularnewline
136 & 14 & 11.3584025021707 & 2.64159749782932 \tabularnewline
137 & 23 & 15.4036654853926 & 7.59633451460737 \tabularnewline
138 & 14 & 13.0053575005418 & 0.994642499458187 \tabularnewline
139 & 16 & 15.4989310274356 & 0.501068972564388 \tabularnewline
140 & 11 & 12.628559545731 & -1.62855954573103 \tabularnewline
141 & 12 & 14.1444218737932 & -2.14442187379317 \tabularnewline
142 & 10 & 12.7648796812441 & -2.76487968124411 \tabularnewline
143 & 14 & 11.9922994072489 & 2.00770059275108 \tabularnewline
144 & 12 & 12.6299821876218 & -0.629982187621815 \tabularnewline
145 & 12 & 12.1171046782392 & -0.11710467823923 \tabularnewline
146 & 11 & 11.6020837122198 & -0.602083712219804 \tabularnewline
147 & 12 & 11.1690525509564 & 0.83094744904359 \tabularnewline
148 & 13 & 14.972756048514 & -1.97275604851401 \tabularnewline
149 & 11 & 14.3051745437288 & -3.3051745437288 \tabularnewline
150 & 19 & 16.7842685401341 & 2.21573145986593 \tabularnewline
151 & 12 & 12.0325119872159 & -0.0325119872158539 \tabularnewline
152 & 17 & 13.137511961121 & 3.86248803887901 \tabularnewline
153 & 9 & 11.9106713821913 & -2.91067138219129 \tabularnewline
154 & 12 & 15.2048219598748 & -3.20482195987484 \tabularnewline
155 & 19 & 16.9736184913483 & 2.02638150865166 \tabularnewline
156 & 18 & 14.0926935012257 & 3.90730649877434 \tabularnewline
157 & 15 & 13.006419290812 & 1.993580709188 \tabularnewline
158 & 14 & 13.8427230357022 & 0.15727696429775 \tabularnewline
159 & 11 & 9.73351587674042 & 1.26648412325958 \tabularnewline
160 & 9 & 13.6544348747582 & -4.65443487475817 \tabularnewline
161 & 18 & 14.9624421929594 & 3.03755780704061 \tabularnewline
162 & 16 & 14.1724186505877 & 1.82758134941226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&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]12.6230924276635[/C][C]-0.623092427663517[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.80635219422193[/C][C]1.19364780577807[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.8025116604305[/C][C]-0.802511660430473[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]15.2363839071192[/C][C]-3.23638390711923[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.3285028496806[/C][C]9.67149715031944[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.0522971315872[/C][C]1.94770286841279[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.5387412456592[/C][C]9.46125875434084[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.6125389192453[/C][C]-1.61253891924535[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.0510170458625[/C][C]-2.05101704586251[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.9163793578175[/C][C]0.083620642182491[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.6902195648236[/C][C]-0.690219564823586[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.12927804321101[/C][C]-1.12927804321101[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.5810194102016[/C][C]-0.58101941020155[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.4049034095341[/C][C]2.59509659046591[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.64702216617709[/C][C]0.352977833822907[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9068876396696[/C][C]1.0931123603304[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.9786621668809[/C][C]1.02133783311908[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.5868830903679[/C][C]0.413116909632125[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.8532563914065[/C][C]-2.85325639140653[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.4335016188905[/C][C]1.56649838110946[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.1494655493258[/C][C]-3.14946554932575[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.074791079836[/C][C]-1.07479107983605[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.5746886446045[/C][C]-0.574688644604477[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.4175225025276[/C][C]-0.417522502527649[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.8090964319602[/C][C]0.190903568039795[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.9544927755038[/C][C]-6.95449277550379[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.6054083017283[/C][C]-0.605408301728345[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.683012003323[/C][C]2.316987996677[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.1372711035624[/C][C]1.86272889643765[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.2671047689383[/C][C]-2.26710476893826[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.3788095084351[/C][C]-2.37880950843509[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.3903668111585[/C][C]0.609633188841548[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.7356596099946[/C][C]-0.735659609994614[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.1919632140178[/C][C]-1.19196321401781[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.7409084326077[/C][C]0.259091567392319[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.1549540248023[/C][C]-4.1549540248023[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.4497273924567[/C][C]2.55027260754329[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.0009509680493[/C][C]-0.000950968049267134[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.0991799713628[/C][C]-1.09917997136284[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.7733117418948[/C][C]-3.77331174189479[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.2090456307737[/C][C]-3.20904563077367[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.3676545674551[/C][C]2.6323454325449[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]10.7430097276613[/C][C]-2.74300972766129[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.497488232831[/C][C]-0.497488232831009[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.2360029027848[/C][C]-3.23600290278482[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.9022829644364[/C][C]-1.90228296443636[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]11.9209852377459[/C][C]-2.92098523774591[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.30158801587[/C][C]-1.30158801587[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.817047054111[/C][C]4.18295294588904[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.333390820673[/C][C]-2.33339082067303[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3051321055282[/C][C]-1.30513210552821[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.1103562697518[/C][C]0.889643730248206[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.0794171124783[/C][C]2.92058288752174[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.2502418523321[/C][C]-0.250241852332058[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.3051946964426[/C][C]-4.30519469644261[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.7661811243778[/C][C]-0.766181124377785[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.1654873805066[/C][C]1.83451261949342[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.3440643231531[/C][C]-3.34406432315309[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.3424233858078[/C][C]-4.34242338580779[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5225212717376[/C][C]0.47747872826237[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.914080848101[/C][C]2.08591915189899[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.3834568984863[/C][C]-1.38345689848627[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.7600353531474[/C][C]-2.76003535314739[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.1416776360549[/C][C]0.858322363945102[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.0417649805781[/C][C]0.958235019421918[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.6281217501268[/C][C]-4.62812175012685[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.6691552628053[/C][C]2.33084473719467[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.4352063518454[/C][C]-2.4352063518454[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.0398621048825[/C][C]0.960137895117473[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.1465231688468[/C][C]1.85347683115322[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.2664819789674[/C][C]-0.266481978967403[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.7684237709023[/C][C]3.23157622909768[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.963461545029[/C][C]1.03653845497101[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.8317674421581[/C][C]-1.83176744215812[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.389985806824[/C][C]-2.38998580682404[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.3799128088281[/C][C]4.62008719117194[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.4680062232313[/C][C]2.53199377676875[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.6294018358515[/C][C]3.37059816414845[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.3144052516043[/C][C]0.685594748395747[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.253665671311[/C][C]-4.25366567131095[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.2123702202822[/C][C]-1.21237022028221[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]11.3564996264751[/C][C]-1.35649962647512[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.1465231688468[/C][C]-3.14652316884678[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.231334431942[/C][C]0.768665568057981[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.347028061041[/C][C]-0.347028061041029[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.0899492634874[/C][C]-0.0899492634873859[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.47812073135[/C][C]0.521879268649982[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8864806334052[/C][C]-0.886480633405193[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]13.7423512272123[/C][C]1.25764877278772[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]17.9306830310521[/C][C]4.06931696894789[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.2727489489549[/C][C]1.72725105104508[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.006419290812[/C][C]1.993580709188[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.2466553244733[/C][C]0.753344675526739[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.7817224451365[/C][C]2.2182775548635[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.692285149399[/C][C]-3.69228514939904[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.4941212051219[/C][C]-0.49412120512188[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0388226000991[/C][C]1.96117739990089[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]13.1383530465464[/C][C]-2.13835304654636[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.5321909799124[/C][C]0.46780902008758[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.5363780122553[/C][C]-2.5363780122553[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.2582492657526[/C][C]-0.258249265752578[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.3173903469012[/C][C]1.68260965309883[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.9590550125364[/C][C]1.04094498746355[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]15.0935357913462[/C][C]-4.09353579134618[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.4267532104031[/C][C]3.5732467895969[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.1392166940758[/C][C]2.86078330592415[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.2359604645842[/C][C]-0.235960464584232[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.9239489756338[/C][C]4.07605102436615[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.8111833739446[/C][C]-0.811183373944633[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]16.2254698236979[/C][C]7.77453017630211[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.2986242779821[/C][C]1.70137572201794[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]14.8462894108472[/C][C]0.15371058915277[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.9706123152598[/C][C]-2.97061231525981[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.0461715130706[/C][C]1.95382848692937[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.0865822357783[/C][C]1.91341776422174[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]11.0670175196344[/C][C]-1.06701751963437[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.8973949934439[/C][C]1.10260500655611[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.548374315278[/C][C]-0.548374315277999[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.0507975457128[/C][C]-4.05079754571284[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.7093464853633[/C][C]2.29065351463671[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.5492166053985[/C][C]0.450783394601479[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.6628455779999[/C][C]1.33715442200012[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.0602691111469[/C][C]-1.06026911114693[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.8021083706093[/C][C]-3.80210837060929[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]11.9479211523481[/C][C]-0.947921152348084[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.2586525555738[/C][C]-2.25865255557376[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.6698125585592[/C][C]-2.66981255855918[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.4785597316494[/C][C]-0.478559731649353[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.73351587674042[/C][C]1.26648412325958[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.1856748866213[/C][C]0.814325113378677[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.9096318774079[/C][C]-2.90963187740788[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]16.8237811095292[/C][C]3.17621889047078[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.4764584365957[/C][C]-2.47645843659575[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.4740961312697[/C][C]2.52590386873031[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.0806761174113[/C][C]-0.0806761174113421[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.3584025021707[/C][C]2.64159749782932[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.4036654853926[/C][C]7.59633451460737[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.0053575005418[/C][C]0.994642499458187[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.4989310274356[/C][C]0.501068972564388[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.628559545731[/C][C]-1.62855954573103[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.1444218737932[/C][C]-2.14442187379317[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.7648796812441[/C][C]-2.76487968124411[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.9922994072489[/C][C]2.00770059275108[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.6299821876218[/C][C]-0.629982187621815[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.1171046782392[/C][C]-0.11710467823923[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.6020837122198[/C][C]-0.602083712219804[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.1690525509564[/C][C]0.83094744904359[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.972756048514[/C][C]-1.97275604851401[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]14.3051745437288[/C][C]-3.3051745437288[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.7842685401341[/C][C]2.21573145986593[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.0325119872159[/C][C]-0.0325119872158539[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.137511961121[/C][C]3.86248803887901[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.9106713821913[/C][C]-2.91067138219129[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.2048219598748[/C][C]-3.20482195987484[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.9736184913483[/C][C]2.02638150865166[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.0926935012257[/C][C]3.90730649877434[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.006419290812[/C][C]1.993580709188[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8427230357022[/C][C]0.15727696429775[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.73351587674042[/C][C]1.26648412325958[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]13.6544348747582[/C][C]-4.65443487475817[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.9624421929594[/C][C]3.03755780704061[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.1724186505877[/C][C]1.82758134941226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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
11212.6230924276635-0.623092427663517
2119.806352194221931.19364780577807
31414.8025116604305-0.802511660430473
41215.2363839071192-3.23638390711923
52111.32850284968069.67149715031944
61210.05229713158721.94770286841279
72212.53874124565929.46125875434084
81112.6125389192453-1.61253891924535
91012.0510170458625-2.05101704586251
101312.91637935781750.083620642182491
111010.6902195648236-0.690219564823586
1289.12927804321101-1.12927804321101
131515.5810194102016-0.58101941020155
141411.40490340953412.59509659046591
15109.647022166177090.352977833822907
161412.90688763966961.0931123603304
171412.97866216688091.02133783311908
181110.58688309036790.413116909632125
191012.8532563914065-2.85325639140653
201311.43350161889051.56649838110946
21710.1494655493258-3.14946554932575
221415.074791079836-1.07479107983605
231212.5746886446045-0.574688644604477
241414.4175225025276-0.417522502527649
251110.80909643196020.190903568039795
26915.9544927755038-6.95449277550379
271111.6054083017283-0.605408301728345
281512.6830120033232.316987996677
291412.13727110356241.86272889643765
301315.2671047689383-2.26710476893826
31911.3788095084351-2.37880950843509
321514.39036681115850.609633188841548
331010.7356596099946-0.735659609994614
341112.1919632140178-1.19196321401781
351312.74090843260770.259091567392319
36812.1549540248023-4.1549540248023
372017.44972739245672.55027260754329
381212.0009509680493-0.000950968049267134
391011.0991799713628-1.09917997136284
401013.7733117418948-3.77331174189479
41912.2090456307737-3.20904563077367
421411.36765456745512.6323454325449
43810.7430097276613-2.74300972766129
441414.497488232831-0.497488232831009
451114.2360029027848-3.23600290278482
461314.9022829644364-1.90228296443636
47911.9209852377459-2.92098523774591
481112.30158801587-1.30158801587
491510.8170470541114.18295294588904
501113.333390820673-2.33339082067303
511011.3051321055282-1.30513210552821
521413.11035626975180.889643730248206
531815.07941711247832.92058288752174
541414.2502418523321-0.250241852332058
551115.3051946964426-4.30519469644261
561212.7661811243778-0.766181124377785
571311.16548738050661.83451261949342
58912.3440643231531-3.34406432315309
591014.3424233858078-4.34242338580779
601514.52252127173760.47747872826237
612017.9140808481012.08591915189899
621213.3834568984863-1.38345689848627
631214.7600353531474-2.76003535314739
641413.14167763605490.858322363945102
651312.04176498057810.958235019421918
661115.6281217501268-4.62812175012685
671714.66915526280532.33084473719467
681214.4352063518454-2.4352063518454
691312.03986210488250.960137895117473
701412.14652316884681.85347683115322
711313.2664819789674-0.266481978967403
721511.76842377090233.23157622909768
731311.9634615450291.03653845497101
741011.8317674421581-1.83176744215812
751113.389985806824-2.38998580682404
761914.37991280882814.62008719117194
771310.46800622323132.53199377676875
781713.62940183585153.37059816414845
791312.31440525160430.685594748395747
80913.253665671311-4.25366567131095
811112.2123702202822-1.21237022028221
821011.3564996264751-1.35649962647512
83912.1465231688468-3.14652316884678
841211.2313344319420.768665568057981
851212.347028061041-0.347028061041029
861313.0899492634874-0.0899492634873859
871312.478120731350.521879268649982
881212.8864806334052-0.886480633405193
891513.74235122721231.25764877278772
902217.93068303105214.06931696894789
911311.27274894895491.72725105104508
921513.0064192908121.993580709188
931312.24665532447330.753344675526739
941512.78172244513652.2182775548635
951013.692285149399-3.69228514939904
961111.4941212051219-0.49412120512188
971614.03882260009911.96117739990089
981113.1383530465464-2.13835304654636
991110.53219097991240.46780902008758
1001012.5363780122553-2.5363780122553
1011010.2582492657526-0.258249265752578
1021614.31739034690121.68260965309883
1031210.95905501253641.04094498746355
1041115.0935357913462-4.09353579134618
1051612.42675321040313.5732467895969
1061916.13921669407582.86078330592415
1071111.2359604645842-0.235960464584232
1081611.92394897563384.07605102436615
1091515.8111833739446-0.811183373944633
1102416.22546982369797.77453017630211
1111412.29862427798211.70137572201794
1121514.84628941084720.15371058915277
1131113.9706123152598-2.97061231525981
1141513.04617151307061.95382848692937
1151210.08658223577831.91341776422174
1161011.0670175196344-1.06701751963437
1171412.89739499344391.10260500655611
1181313.548374315278-0.548374315277999
119913.0507975457128-4.05079754571284
1201512.70934648536332.29065351463671
1211514.54921660539850.450783394601479
1221412.66284557799991.33715442200012
1231112.0602691111469-1.06026911114693
124811.8021083706093-3.80210837060929
1251111.9479211523481-0.947921152348084
1261113.2586525555738-2.25865255557376
127810.6698125585592-2.66981255855918
1281010.4785597316494-0.478559731649353
129119.733515876740421.26648412325958
1301312.18567488662130.814325113378677
1311113.9096318774079-2.90963187740788
1322016.82378110952923.17621889047078
1331012.4764584365957-2.47645843659575
1341512.47409613126972.52590386873031
1351212.0806761174113-0.0806761174113421
1361411.35840250217072.64159749782932
1372315.40366548539267.59633451460737
1381413.00535750054180.994642499458187
1391615.49893102743560.501068972564388
1401112.628559545731-1.62855954573103
1411214.1444218737932-2.14442187379317
1421012.7648796812441-2.76487968124411
1431411.99229940724892.00770059275108
1441212.6299821876218-0.629982187621815
1451212.1171046782392-0.11710467823923
1461111.6020837122198-0.602083712219804
1471211.16905255095640.83094744904359
1481314.972756048514-1.97275604851401
1491114.3051745437288-3.3051745437288
1501916.78426854013412.21573145986593
1511212.0325119872159-0.0325119872158539
1521713.1375119611213.86248803887901
153911.9106713821913-2.91067138219129
1541215.2048219598748-3.20482195987484
1551916.97361849134832.02638150865166
1561814.09269350122573.90730649877434
1571513.0064192908121.993580709188
1581413.84272303570220.15727696429775
159119.733515876740421.26648412325958
160913.6544348747582-4.65443487475817
1611814.96244219295943.03755780704061
1621614.17241865058771.82758134941226






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9998161273039070.0003677453921862830.000183872696093141
90.9998060763937720.0003878472124569570.000193923606228478
100.9995883869544310.0008232260911387880.000411613045569394
110.9994869352906950.001026129418609920.000513064709304961
120.9993862404631630.001227519073674480.00061375953683724
130.9988899150898790.002220169820241470.00111008491012074
140.9981253077589770.003749384482046830.00187469224102342
150.9967506095801120.006498780839775230.00324939041988762
160.9944072509683220.01118549806335630.00559274903167814
170.9905289080260410.01894218394791760.00947109197395881
180.9850583978424190.02988320431516250.0149416021575812
190.9858614202151190.02827715956976280.0141385797848814
200.9787133679638720.0425732640722550.0212866320361275
210.9811913413533780.03761731729324320.0188086586466216
220.9734954725876170.05300905482476520.0265045274123826
230.9645374751269450.07092504974611070.0354625248730553
240.9497396836279950.100520632744010.0502603163720051
250.9326565373329830.1346869253340340.0673434626670169
260.9881531015994010.02369379680119780.0118468984005989
270.9829711668509890.03405766629802220.0170288331490111
280.9794138894015660.0411722211968670.0205861105984335
290.9734723950014070.05305520999718580.0265276049985929
300.9657987104382830.06840257912343440.0342012895617172
310.9653322765822690.06933544683546220.0346677234177311
320.9568849508845680.08623009823086430.0431150491154322
330.9440486605649040.1119026788701910.0559513394350955
340.9320453781340650.1359092437318710.0679546218659353
350.9121498268889020.1757003462221960.0878501731110978
360.9277184127109470.1445631745781050.0722815872890526
370.9507595259686860.09848094806262740.0492404740313137
380.9353242554964470.1293514890071060.0646757445035531
390.9214041666260360.1571916667479270.0785958333739637
400.9302904209471320.1394191581057360.0697095790528681
410.9319213929687390.1361572140625220.068078607031261
420.928069536287810.1438609274243790.0719304637121896
430.9299583012837740.1400833974324510.0700416987162255
440.9117658006202160.1764683987595670.0882341993797836
450.9076273413566380.1847453172867240.0923726586433621
460.8920082544846670.2159834910306650.107991745515333
470.8974388007068470.2051223985863060.102561199293153
480.8787085993858330.2425828012283350.121291400614167
490.9026877433447430.1946245133105130.0973122566552567
500.8916175121598540.2167649756802910.108382487840146
510.8797888465680750.2404223068638490.120211153431925
520.8593139480795450.2813721038409110.140686051920455
530.8728740098173210.2542519803653580.127125990182679
540.8458194835005160.3083610329989680.154180516499484
550.8732981974328880.2534036051342230.126701802567112
560.8510091111784460.2979817776431090.148990888821554
570.8366694937145820.3266610125708360.163330506285418
580.8552447878777010.2895104242445990.144755212122299
590.886850439184440.226299121631120.11314956081556
600.8677740926788180.2644518146423630.132225907321182
610.8768333346157810.2463333307684390.123166665384219
620.8575123508357360.2849752983285270.142487649164264
630.8555835687378950.2888328625242090.144416431262105
640.8306070464455760.3387859071088490.169392953554424
650.8037805029998560.3924389940002870.196219497000144
660.8564211163109960.2871577673780080.143578883689004
670.8562706128632540.2874587742734930.143729387136746
680.8526270350752760.2947459298494480.147372964924724
690.8288590104524010.3422819790951980.171140989547599
700.8135479328875930.3729041342248130.186452067112407
710.7833673863529890.4332652272940230.216632613647011
720.7991468464933540.4017063070132920.200853153506646
730.7716825263470570.4566349473058860.228317473652943
740.7531669032586410.4936661934827180.246833096741359
750.7491348942085850.5017302115828290.250865105791415
760.8253898914985220.3492202170029560.174610108501478
770.8247406995438790.3505186009122430.175259300456122
780.8426226653638220.3147546692723560.157377334636178
790.816157663189370.3676846736212610.18384233681063
800.8708469985976670.2583060028046660.129153001402333
810.8501202747922250.299759450415550.149879725207775
820.8291138390292680.3417723219414640.170886160970732
830.8399448432052020.3201103135895960.160055156794798
840.8124241883352360.3751516233295290.187575811664764
850.7801588382263870.4396823235472250.219841161773613
860.7455013174884670.5089973650230650.254498682511533
870.7084005888284350.583198822343130.291599411171565
880.6722937654823840.6554124690352330.327706234517616
890.6401379156988180.7197241686023630.359862084301182
900.6951161277914010.6097677444171990.3048838722086
910.6781619405754950.6436761188490110.321838059424505
920.6566059699486160.6867880601027680.343394030051384
930.6202389855113990.7595220289772030.379761014488601
940.6019989967381980.7960020065236040.398001003261802
950.6466650644444450.7066698711111090.353334935555555
960.6040911385265740.7918177229468520.395908861473426
970.5795013097845430.8409973804309130.420498690215457
980.574884261596270.850231476807460.42511573840373
990.5296400571221990.9407198857556020.470359942877801
1000.5209120292104520.9581759415790960.479087970789548
1010.4741440823049320.9482881646098640.525855917695068
1020.4420223152740370.8840446305480730.557977684725963
1030.4090672291943970.8181344583887940.590932770805603
1040.522463302752630.955073394494740.47753669724737
1050.5913296176640060.8173407646719890.408670382335994
1060.5783795837904310.8432408324191380.421620416209569
1070.5301141905620630.9397716188758750.469885809437937
1080.6137463362384670.7725073275230660.386253663761533
1090.5844632368529650.8310735262940710.415536763147036
1100.8746701903151080.2506596193697850.125329809684892
1110.8616740264365750.2766519471268510.138325973563425
1120.8318135950936460.3363728098127080.168186404906354
1130.8259130395293360.3481739209413270.174086960470664
1140.8086725461317480.3826549077365040.191327453868252
1150.8023570579475760.3952858841048480.197642942052424
1160.7681593984278060.4636812031443880.231840601572194
1170.7611054500535130.4777890998929730.238894549946487
1180.7214310983789960.5571378032420080.278568901621004
1190.7736777505620750.452644498875850.226322249437925
1200.7826885487260810.4346229025478380.217311451273919
1210.7446566161165170.5106867677669650.255343383883483
1220.705396222817290.5892075543654190.29460377718271
1230.6624385550170390.6751228899659230.337561444982961
1240.6658701203398020.6682597593203950.334129879660198
1250.6237349207265830.7525301585468340.376265079273417
1260.6182672792329970.7634654415340060.381732720767003
1270.6253187059819170.7493625880361660.374681294018083
1280.5780606159371460.8438787681257090.421939384062854
1290.5579282607339310.8841434785321380.442071739266069
1300.5026182390228410.9947635219543180.497381760977159
1310.5445646287105420.9108707425789160.455435371289458
1320.5189350617037740.9621298765924520.481064938296226
1330.493746363968520.987492727937040.50625363603148
1340.4668811126309880.9337622252619770.533118887369012
1350.4093107860702580.8186215721405170.590689213929742
1360.384596704073350.7691934081466990.61540329592665
1370.6362500322502520.7274999354994950.363749967749748
1380.5797192156818380.8405615686363240.420280784318162
1390.5166143643156190.9667712713687630.483385635684381
1400.4726497173960960.9452994347921920.527350282603904
1410.4299285323056590.8598570646113190.570071467694341
1420.4271007940408160.8542015880816320.572899205959184
1430.4126414136623690.8252828273247370.587358586337631
1440.3397148089874830.6794296179749660.660285191012517
1450.2729698322077660.5459396644155320.727030167792234
1460.3708889142196560.7417778284393120.629111085780344
1470.296469395843880.5929387916877590.70353060415612
1480.2273360233617510.4546720467235020.772663976638249
1490.2537020530667980.5074041061335960.746297946933202
1500.1810218566664050.362043713332810.818978143333595
1510.1265815302247960.2531630604495920.873418469775204
1520.1584893814572910.3169787629145830.841510618542709
1530.1097942928318090.2195885856636180.890205707168191
1540.09988602838196930.1997720567639390.900113971618031

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.999816127303907 & 0.000367745392186283 & 0.000183872696093141 \tabularnewline
9 & 0.999806076393772 & 0.000387847212456957 & 0.000193923606228478 \tabularnewline
10 & 0.999588386954431 & 0.000823226091138788 & 0.000411613045569394 \tabularnewline
11 & 0.999486935290695 & 0.00102612941860992 & 0.000513064709304961 \tabularnewline
12 & 0.999386240463163 & 0.00122751907367448 & 0.00061375953683724 \tabularnewline
13 & 0.998889915089879 & 0.00222016982024147 & 0.00111008491012074 \tabularnewline
14 & 0.998125307758977 & 0.00374938448204683 & 0.00187469224102342 \tabularnewline
15 & 0.996750609580112 & 0.00649878083977523 & 0.00324939041988762 \tabularnewline
16 & 0.994407250968322 & 0.0111854980633563 & 0.00559274903167814 \tabularnewline
17 & 0.990528908026041 & 0.0189421839479176 & 0.00947109197395881 \tabularnewline
18 & 0.985058397842419 & 0.0298832043151625 & 0.0149416021575812 \tabularnewline
19 & 0.985861420215119 & 0.0282771595697628 & 0.0141385797848814 \tabularnewline
20 & 0.978713367963872 & 0.042573264072255 & 0.0212866320361275 \tabularnewline
21 & 0.981191341353378 & 0.0376173172932432 & 0.0188086586466216 \tabularnewline
22 & 0.973495472587617 & 0.0530090548247652 & 0.0265045274123826 \tabularnewline
23 & 0.964537475126945 & 0.0709250497461107 & 0.0354625248730553 \tabularnewline
24 & 0.949739683627995 & 0.10052063274401 & 0.0502603163720051 \tabularnewline
25 & 0.932656537332983 & 0.134686925334034 & 0.0673434626670169 \tabularnewline
26 & 0.988153101599401 & 0.0236937968011978 & 0.0118468984005989 \tabularnewline
27 & 0.982971166850989 & 0.0340576662980222 & 0.0170288331490111 \tabularnewline
28 & 0.979413889401566 & 0.041172221196867 & 0.0205861105984335 \tabularnewline
29 & 0.973472395001407 & 0.0530552099971858 & 0.0265276049985929 \tabularnewline
30 & 0.965798710438283 & 0.0684025791234344 & 0.0342012895617172 \tabularnewline
31 & 0.965332276582269 & 0.0693354468354622 & 0.0346677234177311 \tabularnewline
32 & 0.956884950884568 & 0.0862300982308643 & 0.0431150491154322 \tabularnewline
33 & 0.944048660564904 & 0.111902678870191 & 0.0559513394350955 \tabularnewline
34 & 0.932045378134065 & 0.135909243731871 & 0.0679546218659353 \tabularnewline
35 & 0.912149826888902 & 0.175700346222196 & 0.0878501731110978 \tabularnewline
36 & 0.927718412710947 & 0.144563174578105 & 0.0722815872890526 \tabularnewline
37 & 0.950759525968686 & 0.0984809480626274 & 0.0492404740313137 \tabularnewline
38 & 0.935324255496447 & 0.129351489007106 & 0.0646757445035531 \tabularnewline
39 & 0.921404166626036 & 0.157191666747927 & 0.0785958333739637 \tabularnewline
40 & 0.930290420947132 & 0.139419158105736 & 0.0697095790528681 \tabularnewline
41 & 0.931921392968739 & 0.136157214062522 & 0.068078607031261 \tabularnewline
42 & 0.92806953628781 & 0.143860927424379 & 0.0719304637121896 \tabularnewline
43 & 0.929958301283774 & 0.140083397432451 & 0.0700416987162255 \tabularnewline
44 & 0.911765800620216 & 0.176468398759567 & 0.0882341993797836 \tabularnewline
45 & 0.907627341356638 & 0.184745317286724 & 0.0923726586433621 \tabularnewline
46 & 0.892008254484667 & 0.215983491030665 & 0.107991745515333 \tabularnewline
47 & 0.897438800706847 & 0.205122398586306 & 0.102561199293153 \tabularnewline
48 & 0.878708599385833 & 0.242582801228335 & 0.121291400614167 \tabularnewline
49 & 0.902687743344743 & 0.194624513310513 & 0.0973122566552567 \tabularnewline
50 & 0.891617512159854 & 0.216764975680291 & 0.108382487840146 \tabularnewline
51 & 0.879788846568075 & 0.240422306863849 & 0.120211153431925 \tabularnewline
52 & 0.859313948079545 & 0.281372103840911 & 0.140686051920455 \tabularnewline
53 & 0.872874009817321 & 0.254251980365358 & 0.127125990182679 \tabularnewline
54 & 0.845819483500516 & 0.308361032998968 & 0.154180516499484 \tabularnewline
55 & 0.873298197432888 & 0.253403605134223 & 0.126701802567112 \tabularnewline
56 & 0.851009111178446 & 0.297981777643109 & 0.148990888821554 \tabularnewline
57 & 0.836669493714582 & 0.326661012570836 & 0.163330506285418 \tabularnewline
58 & 0.855244787877701 & 0.289510424244599 & 0.144755212122299 \tabularnewline
59 & 0.88685043918444 & 0.22629912163112 & 0.11314956081556 \tabularnewline
60 & 0.867774092678818 & 0.264451814642363 & 0.132225907321182 \tabularnewline
61 & 0.876833334615781 & 0.246333330768439 & 0.123166665384219 \tabularnewline
62 & 0.857512350835736 & 0.284975298328527 & 0.142487649164264 \tabularnewline
63 & 0.855583568737895 & 0.288832862524209 & 0.144416431262105 \tabularnewline
64 & 0.830607046445576 & 0.338785907108849 & 0.169392953554424 \tabularnewline
65 & 0.803780502999856 & 0.392438994000287 & 0.196219497000144 \tabularnewline
66 & 0.856421116310996 & 0.287157767378008 & 0.143578883689004 \tabularnewline
67 & 0.856270612863254 & 0.287458774273493 & 0.143729387136746 \tabularnewline
68 & 0.852627035075276 & 0.294745929849448 & 0.147372964924724 \tabularnewline
69 & 0.828859010452401 & 0.342281979095198 & 0.171140989547599 \tabularnewline
70 & 0.813547932887593 & 0.372904134224813 & 0.186452067112407 \tabularnewline
71 & 0.783367386352989 & 0.433265227294023 & 0.216632613647011 \tabularnewline
72 & 0.799146846493354 & 0.401706307013292 & 0.200853153506646 \tabularnewline
73 & 0.771682526347057 & 0.456634947305886 & 0.228317473652943 \tabularnewline
74 & 0.753166903258641 & 0.493666193482718 & 0.246833096741359 \tabularnewline
75 & 0.749134894208585 & 0.501730211582829 & 0.250865105791415 \tabularnewline
76 & 0.825389891498522 & 0.349220217002956 & 0.174610108501478 \tabularnewline
77 & 0.824740699543879 & 0.350518600912243 & 0.175259300456122 \tabularnewline
78 & 0.842622665363822 & 0.314754669272356 & 0.157377334636178 \tabularnewline
79 & 0.81615766318937 & 0.367684673621261 & 0.18384233681063 \tabularnewline
80 & 0.870846998597667 & 0.258306002804666 & 0.129153001402333 \tabularnewline
81 & 0.850120274792225 & 0.29975945041555 & 0.149879725207775 \tabularnewline
82 & 0.829113839029268 & 0.341772321941464 & 0.170886160970732 \tabularnewline
83 & 0.839944843205202 & 0.320110313589596 & 0.160055156794798 \tabularnewline
84 & 0.812424188335236 & 0.375151623329529 & 0.187575811664764 \tabularnewline
85 & 0.780158838226387 & 0.439682323547225 & 0.219841161773613 \tabularnewline
86 & 0.745501317488467 & 0.508997365023065 & 0.254498682511533 \tabularnewline
87 & 0.708400588828435 & 0.58319882234313 & 0.291599411171565 \tabularnewline
88 & 0.672293765482384 & 0.655412469035233 & 0.327706234517616 \tabularnewline
89 & 0.640137915698818 & 0.719724168602363 & 0.359862084301182 \tabularnewline
90 & 0.695116127791401 & 0.609767744417199 & 0.3048838722086 \tabularnewline
91 & 0.678161940575495 & 0.643676118849011 & 0.321838059424505 \tabularnewline
92 & 0.656605969948616 & 0.686788060102768 & 0.343394030051384 \tabularnewline
93 & 0.620238985511399 & 0.759522028977203 & 0.379761014488601 \tabularnewline
94 & 0.601998996738198 & 0.796002006523604 & 0.398001003261802 \tabularnewline
95 & 0.646665064444445 & 0.706669871111109 & 0.353334935555555 \tabularnewline
96 & 0.604091138526574 & 0.791817722946852 & 0.395908861473426 \tabularnewline
97 & 0.579501309784543 & 0.840997380430913 & 0.420498690215457 \tabularnewline
98 & 0.57488426159627 & 0.85023147680746 & 0.42511573840373 \tabularnewline
99 & 0.529640057122199 & 0.940719885755602 & 0.470359942877801 \tabularnewline
100 & 0.520912029210452 & 0.958175941579096 & 0.479087970789548 \tabularnewline
101 & 0.474144082304932 & 0.948288164609864 & 0.525855917695068 \tabularnewline
102 & 0.442022315274037 & 0.884044630548073 & 0.557977684725963 \tabularnewline
103 & 0.409067229194397 & 0.818134458388794 & 0.590932770805603 \tabularnewline
104 & 0.52246330275263 & 0.95507339449474 & 0.47753669724737 \tabularnewline
105 & 0.591329617664006 & 0.817340764671989 & 0.408670382335994 \tabularnewline
106 & 0.578379583790431 & 0.843240832419138 & 0.421620416209569 \tabularnewline
107 & 0.530114190562063 & 0.939771618875875 & 0.469885809437937 \tabularnewline
108 & 0.613746336238467 & 0.772507327523066 & 0.386253663761533 \tabularnewline
109 & 0.584463236852965 & 0.831073526294071 & 0.415536763147036 \tabularnewline
110 & 0.874670190315108 & 0.250659619369785 & 0.125329809684892 \tabularnewline
111 & 0.861674026436575 & 0.276651947126851 & 0.138325973563425 \tabularnewline
112 & 0.831813595093646 & 0.336372809812708 & 0.168186404906354 \tabularnewline
113 & 0.825913039529336 & 0.348173920941327 & 0.174086960470664 \tabularnewline
114 & 0.808672546131748 & 0.382654907736504 & 0.191327453868252 \tabularnewline
115 & 0.802357057947576 & 0.395285884104848 & 0.197642942052424 \tabularnewline
116 & 0.768159398427806 & 0.463681203144388 & 0.231840601572194 \tabularnewline
117 & 0.761105450053513 & 0.477789099892973 & 0.238894549946487 \tabularnewline
118 & 0.721431098378996 & 0.557137803242008 & 0.278568901621004 \tabularnewline
119 & 0.773677750562075 & 0.45264449887585 & 0.226322249437925 \tabularnewline
120 & 0.782688548726081 & 0.434622902547838 & 0.217311451273919 \tabularnewline
121 & 0.744656616116517 & 0.510686767766965 & 0.255343383883483 \tabularnewline
122 & 0.70539622281729 & 0.589207554365419 & 0.29460377718271 \tabularnewline
123 & 0.662438555017039 & 0.675122889965923 & 0.337561444982961 \tabularnewline
124 & 0.665870120339802 & 0.668259759320395 & 0.334129879660198 \tabularnewline
125 & 0.623734920726583 & 0.752530158546834 & 0.376265079273417 \tabularnewline
126 & 0.618267279232997 & 0.763465441534006 & 0.381732720767003 \tabularnewline
127 & 0.625318705981917 & 0.749362588036166 & 0.374681294018083 \tabularnewline
128 & 0.578060615937146 & 0.843878768125709 & 0.421939384062854 \tabularnewline
129 & 0.557928260733931 & 0.884143478532138 & 0.442071739266069 \tabularnewline
130 & 0.502618239022841 & 0.994763521954318 & 0.497381760977159 \tabularnewline
131 & 0.544564628710542 & 0.910870742578916 & 0.455435371289458 \tabularnewline
132 & 0.518935061703774 & 0.962129876592452 & 0.481064938296226 \tabularnewline
133 & 0.49374636396852 & 0.98749272793704 & 0.50625363603148 \tabularnewline
134 & 0.466881112630988 & 0.933762225261977 & 0.533118887369012 \tabularnewline
135 & 0.409310786070258 & 0.818621572140517 & 0.590689213929742 \tabularnewline
136 & 0.38459670407335 & 0.769193408146699 & 0.61540329592665 \tabularnewline
137 & 0.636250032250252 & 0.727499935499495 & 0.363749967749748 \tabularnewline
138 & 0.579719215681838 & 0.840561568636324 & 0.420280784318162 \tabularnewline
139 & 0.516614364315619 & 0.966771271368763 & 0.483385635684381 \tabularnewline
140 & 0.472649717396096 & 0.945299434792192 & 0.527350282603904 \tabularnewline
141 & 0.429928532305659 & 0.859857064611319 & 0.570071467694341 \tabularnewline
142 & 0.427100794040816 & 0.854201588081632 & 0.572899205959184 \tabularnewline
143 & 0.412641413662369 & 0.825282827324737 & 0.587358586337631 \tabularnewline
144 & 0.339714808987483 & 0.679429617974966 & 0.660285191012517 \tabularnewline
145 & 0.272969832207766 & 0.545939664415532 & 0.727030167792234 \tabularnewline
146 & 0.370888914219656 & 0.741777828439312 & 0.629111085780344 \tabularnewline
147 & 0.29646939584388 & 0.592938791687759 & 0.70353060415612 \tabularnewline
148 & 0.227336023361751 & 0.454672046723502 & 0.772663976638249 \tabularnewline
149 & 0.253702053066798 & 0.507404106133596 & 0.746297946933202 \tabularnewline
150 & 0.181021856666405 & 0.36204371333281 & 0.818978143333595 \tabularnewline
151 & 0.126581530224796 & 0.253163060449592 & 0.873418469775204 \tabularnewline
152 & 0.158489381457291 & 0.316978762914583 & 0.841510618542709 \tabularnewline
153 & 0.109794292831809 & 0.219588585663618 & 0.890205707168191 \tabularnewline
154 & 0.0998860283819693 & 0.199772056763939 & 0.900113971618031 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.999816127303907[/C][C]0.000367745392186283[/C][C]0.000183872696093141[/C][/ROW]
[ROW][C]9[/C][C]0.999806076393772[/C][C]0.000387847212456957[/C][C]0.000193923606228478[/C][/ROW]
[ROW][C]10[/C][C]0.999588386954431[/C][C]0.000823226091138788[/C][C]0.000411613045569394[/C][/ROW]
[ROW][C]11[/C][C]0.999486935290695[/C][C]0.00102612941860992[/C][C]0.000513064709304961[/C][/ROW]
[ROW][C]12[/C][C]0.999386240463163[/C][C]0.00122751907367448[/C][C]0.00061375953683724[/C][/ROW]
[ROW][C]13[/C][C]0.998889915089879[/C][C]0.00222016982024147[/C][C]0.00111008491012074[/C][/ROW]
[ROW][C]14[/C][C]0.998125307758977[/C][C]0.00374938448204683[/C][C]0.00187469224102342[/C][/ROW]
[ROW][C]15[/C][C]0.996750609580112[/C][C]0.00649878083977523[/C][C]0.00324939041988762[/C][/ROW]
[ROW][C]16[/C][C]0.994407250968322[/C][C]0.0111854980633563[/C][C]0.00559274903167814[/C][/ROW]
[ROW][C]17[/C][C]0.990528908026041[/C][C]0.0189421839479176[/C][C]0.00947109197395881[/C][/ROW]
[ROW][C]18[/C][C]0.985058397842419[/C][C]0.0298832043151625[/C][C]0.0149416021575812[/C][/ROW]
[ROW][C]19[/C][C]0.985861420215119[/C][C]0.0282771595697628[/C][C]0.0141385797848814[/C][/ROW]
[ROW][C]20[/C][C]0.978713367963872[/C][C]0.042573264072255[/C][C]0.0212866320361275[/C][/ROW]
[ROW][C]21[/C][C]0.981191341353378[/C][C]0.0376173172932432[/C][C]0.0188086586466216[/C][/ROW]
[ROW][C]22[/C][C]0.973495472587617[/C][C]0.0530090548247652[/C][C]0.0265045274123826[/C][/ROW]
[ROW][C]23[/C][C]0.964537475126945[/C][C]0.0709250497461107[/C][C]0.0354625248730553[/C][/ROW]
[ROW][C]24[/C][C]0.949739683627995[/C][C]0.10052063274401[/C][C]0.0502603163720051[/C][/ROW]
[ROW][C]25[/C][C]0.932656537332983[/C][C]0.134686925334034[/C][C]0.0673434626670169[/C][/ROW]
[ROW][C]26[/C][C]0.988153101599401[/C][C]0.0236937968011978[/C][C]0.0118468984005989[/C][/ROW]
[ROW][C]27[/C][C]0.982971166850989[/C][C]0.0340576662980222[/C][C]0.0170288331490111[/C][/ROW]
[ROW][C]28[/C][C]0.979413889401566[/C][C]0.041172221196867[/C][C]0.0205861105984335[/C][/ROW]
[ROW][C]29[/C][C]0.973472395001407[/C][C]0.0530552099971858[/C][C]0.0265276049985929[/C][/ROW]
[ROW][C]30[/C][C]0.965798710438283[/C][C]0.0684025791234344[/C][C]0.0342012895617172[/C][/ROW]
[ROW][C]31[/C][C]0.965332276582269[/C][C]0.0693354468354622[/C][C]0.0346677234177311[/C][/ROW]
[ROW][C]32[/C][C]0.956884950884568[/C][C]0.0862300982308643[/C][C]0.0431150491154322[/C][/ROW]
[ROW][C]33[/C][C]0.944048660564904[/C][C]0.111902678870191[/C][C]0.0559513394350955[/C][/ROW]
[ROW][C]34[/C][C]0.932045378134065[/C][C]0.135909243731871[/C][C]0.0679546218659353[/C][/ROW]
[ROW][C]35[/C][C]0.912149826888902[/C][C]0.175700346222196[/C][C]0.0878501731110978[/C][/ROW]
[ROW][C]36[/C][C]0.927718412710947[/C][C]0.144563174578105[/C][C]0.0722815872890526[/C][/ROW]
[ROW][C]37[/C][C]0.950759525968686[/C][C]0.0984809480626274[/C][C]0.0492404740313137[/C][/ROW]
[ROW][C]38[/C][C]0.935324255496447[/C][C]0.129351489007106[/C][C]0.0646757445035531[/C][/ROW]
[ROW][C]39[/C][C]0.921404166626036[/C][C]0.157191666747927[/C][C]0.0785958333739637[/C][/ROW]
[ROW][C]40[/C][C]0.930290420947132[/C][C]0.139419158105736[/C][C]0.0697095790528681[/C][/ROW]
[ROW][C]41[/C][C]0.931921392968739[/C][C]0.136157214062522[/C][C]0.068078607031261[/C][/ROW]
[ROW][C]42[/C][C]0.92806953628781[/C][C]0.143860927424379[/C][C]0.0719304637121896[/C][/ROW]
[ROW][C]43[/C][C]0.929958301283774[/C][C]0.140083397432451[/C][C]0.0700416987162255[/C][/ROW]
[ROW][C]44[/C][C]0.911765800620216[/C][C]0.176468398759567[/C][C]0.0882341993797836[/C][/ROW]
[ROW][C]45[/C][C]0.907627341356638[/C][C]0.184745317286724[/C][C]0.0923726586433621[/C][/ROW]
[ROW][C]46[/C][C]0.892008254484667[/C][C]0.215983491030665[/C][C]0.107991745515333[/C][/ROW]
[ROW][C]47[/C][C]0.897438800706847[/C][C]0.205122398586306[/C][C]0.102561199293153[/C][/ROW]
[ROW][C]48[/C][C]0.878708599385833[/C][C]0.242582801228335[/C][C]0.121291400614167[/C][/ROW]
[ROW][C]49[/C][C]0.902687743344743[/C][C]0.194624513310513[/C][C]0.0973122566552567[/C][/ROW]
[ROW][C]50[/C][C]0.891617512159854[/C][C]0.216764975680291[/C][C]0.108382487840146[/C][/ROW]
[ROW][C]51[/C][C]0.879788846568075[/C][C]0.240422306863849[/C][C]0.120211153431925[/C][/ROW]
[ROW][C]52[/C][C]0.859313948079545[/C][C]0.281372103840911[/C][C]0.140686051920455[/C][/ROW]
[ROW][C]53[/C][C]0.872874009817321[/C][C]0.254251980365358[/C][C]0.127125990182679[/C][/ROW]
[ROW][C]54[/C][C]0.845819483500516[/C][C]0.308361032998968[/C][C]0.154180516499484[/C][/ROW]
[ROW][C]55[/C][C]0.873298197432888[/C][C]0.253403605134223[/C][C]0.126701802567112[/C][/ROW]
[ROW][C]56[/C][C]0.851009111178446[/C][C]0.297981777643109[/C][C]0.148990888821554[/C][/ROW]
[ROW][C]57[/C][C]0.836669493714582[/C][C]0.326661012570836[/C][C]0.163330506285418[/C][/ROW]
[ROW][C]58[/C][C]0.855244787877701[/C][C]0.289510424244599[/C][C]0.144755212122299[/C][/ROW]
[ROW][C]59[/C][C]0.88685043918444[/C][C]0.22629912163112[/C][C]0.11314956081556[/C][/ROW]
[ROW][C]60[/C][C]0.867774092678818[/C][C]0.264451814642363[/C][C]0.132225907321182[/C][/ROW]
[ROW][C]61[/C][C]0.876833334615781[/C][C]0.246333330768439[/C][C]0.123166665384219[/C][/ROW]
[ROW][C]62[/C][C]0.857512350835736[/C][C]0.284975298328527[/C][C]0.142487649164264[/C][/ROW]
[ROW][C]63[/C][C]0.855583568737895[/C][C]0.288832862524209[/C][C]0.144416431262105[/C][/ROW]
[ROW][C]64[/C][C]0.830607046445576[/C][C]0.338785907108849[/C][C]0.169392953554424[/C][/ROW]
[ROW][C]65[/C][C]0.803780502999856[/C][C]0.392438994000287[/C][C]0.196219497000144[/C][/ROW]
[ROW][C]66[/C][C]0.856421116310996[/C][C]0.287157767378008[/C][C]0.143578883689004[/C][/ROW]
[ROW][C]67[/C][C]0.856270612863254[/C][C]0.287458774273493[/C][C]0.143729387136746[/C][/ROW]
[ROW][C]68[/C][C]0.852627035075276[/C][C]0.294745929849448[/C][C]0.147372964924724[/C][/ROW]
[ROW][C]69[/C][C]0.828859010452401[/C][C]0.342281979095198[/C][C]0.171140989547599[/C][/ROW]
[ROW][C]70[/C][C]0.813547932887593[/C][C]0.372904134224813[/C][C]0.186452067112407[/C][/ROW]
[ROW][C]71[/C][C]0.783367386352989[/C][C]0.433265227294023[/C][C]0.216632613647011[/C][/ROW]
[ROW][C]72[/C][C]0.799146846493354[/C][C]0.401706307013292[/C][C]0.200853153506646[/C][/ROW]
[ROW][C]73[/C][C]0.771682526347057[/C][C]0.456634947305886[/C][C]0.228317473652943[/C][/ROW]
[ROW][C]74[/C][C]0.753166903258641[/C][C]0.493666193482718[/C][C]0.246833096741359[/C][/ROW]
[ROW][C]75[/C][C]0.749134894208585[/C][C]0.501730211582829[/C][C]0.250865105791415[/C][/ROW]
[ROW][C]76[/C][C]0.825389891498522[/C][C]0.349220217002956[/C][C]0.174610108501478[/C][/ROW]
[ROW][C]77[/C][C]0.824740699543879[/C][C]0.350518600912243[/C][C]0.175259300456122[/C][/ROW]
[ROW][C]78[/C][C]0.842622665363822[/C][C]0.314754669272356[/C][C]0.157377334636178[/C][/ROW]
[ROW][C]79[/C][C]0.81615766318937[/C][C]0.367684673621261[/C][C]0.18384233681063[/C][/ROW]
[ROW][C]80[/C][C]0.870846998597667[/C][C]0.258306002804666[/C][C]0.129153001402333[/C][/ROW]
[ROW][C]81[/C][C]0.850120274792225[/C][C]0.29975945041555[/C][C]0.149879725207775[/C][/ROW]
[ROW][C]82[/C][C]0.829113839029268[/C][C]0.341772321941464[/C][C]0.170886160970732[/C][/ROW]
[ROW][C]83[/C][C]0.839944843205202[/C][C]0.320110313589596[/C][C]0.160055156794798[/C][/ROW]
[ROW][C]84[/C][C]0.812424188335236[/C][C]0.375151623329529[/C][C]0.187575811664764[/C][/ROW]
[ROW][C]85[/C][C]0.780158838226387[/C][C]0.439682323547225[/C][C]0.219841161773613[/C][/ROW]
[ROW][C]86[/C][C]0.745501317488467[/C][C]0.508997365023065[/C][C]0.254498682511533[/C][/ROW]
[ROW][C]87[/C][C]0.708400588828435[/C][C]0.58319882234313[/C][C]0.291599411171565[/C][/ROW]
[ROW][C]88[/C][C]0.672293765482384[/C][C]0.655412469035233[/C][C]0.327706234517616[/C][/ROW]
[ROW][C]89[/C][C]0.640137915698818[/C][C]0.719724168602363[/C][C]0.359862084301182[/C][/ROW]
[ROW][C]90[/C][C]0.695116127791401[/C][C]0.609767744417199[/C][C]0.3048838722086[/C][/ROW]
[ROW][C]91[/C][C]0.678161940575495[/C][C]0.643676118849011[/C][C]0.321838059424505[/C][/ROW]
[ROW][C]92[/C][C]0.656605969948616[/C][C]0.686788060102768[/C][C]0.343394030051384[/C][/ROW]
[ROW][C]93[/C][C]0.620238985511399[/C][C]0.759522028977203[/C][C]0.379761014488601[/C][/ROW]
[ROW][C]94[/C][C]0.601998996738198[/C][C]0.796002006523604[/C][C]0.398001003261802[/C][/ROW]
[ROW][C]95[/C][C]0.646665064444445[/C][C]0.706669871111109[/C][C]0.353334935555555[/C][/ROW]
[ROW][C]96[/C][C]0.604091138526574[/C][C]0.791817722946852[/C][C]0.395908861473426[/C][/ROW]
[ROW][C]97[/C][C]0.579501309784543[/C][C]0.840997380430913[/C][C]0.420498690215457[/C][/ROW]
[ROW][C]98[/C][C]0.57488426159627[/C][C]0.85023147680746[/C][C]0.42511573840373[/C][/ROW]
[ROW][C]99[/C][C]0.529640057122199[/C][C]0.940719885755602[/C][C]0.470359942877801[/C][/ROW]
[ROW][C]100[/C][C]0.520912029210452[/C][C]0.958175941579096[/C][C]0.479087970789548[/C][/ROW]
[ROW][C]101[/C][C]0.474144082304932[/C][C]0.948288164609864[/C][C]0.525855917695068[/C][/ROW]
[ROW][C]102[/C][C]0.442022315274037[/C][C]0.884044630548073[/C][C]0.557977684725963[/C][/ROW]
[ROW][C]103[/C][C]0.409067229194397[/C][C]0.818134458388794[/C][C]0.590932770805603[/C][/ROW]
[ROW][C]104[/C][C]0.52246330275263[/C][C]0.95507339449474[/C][C]0.47753669724737[/C][/ROW]
[ROW][C]105[/C][C]0.591329617664006[/C][C]0.817340764671989[/C][C]0.408670382335994[/C][/ROW]
[ROW][C]106[/C][C]0.578379583790431[/C][C]0.843240832419138[/C][C]0.421620416209569[/C][/ROW]
[ROW][C]107[/C][C]0.530114190562063[/C][C]0.939771618875875[/C][C]0.469885809437937[/C][/ROW]
[ROW][C]108[/C][C]0.613746336238467[/C][C]0.772507327523066[/C][C]0.386253663761533[/C][/ROW]
[ROW][C]109[/C][C]0.584463236852965[/C][C]0.831073526294071[/C][C]0.415536763147036[/C][/ROW]
[ROW][C]110[/C][C]0.874670190315108[/C][C]0.250659619369785[/C][C]0.125329809684892[/C][/ROW]
[ROW][C]111[/C][C]0.861674026436575[/C][C]0.276651947126851[/C][C]0.138325973563425[/C][/ROW]
[ROW][C]112[/C][C]0.831813595093646[/C][C]0.336372809812708[/C][C]0.168186404906354[/C][/ROW]
[ROW][C]113[/C][C]0.825913039529336[/C][C]0.348173920941327[/C][C]0.174086960470664[/C][/ROW]
[ROW][C]114[/C][C]0.808672546131748[/C][C]0.382654907736504[/C][C]0.191327453868252[/C][/ROW]
[ROW][C]115[/C][C]0.802357057947576[/C][C]0.395285884104848[/C][C]0.197642942052424[/C][/ROW]
[ROW][C]116[/C][C]0.768159398427806[/C][C]0.463681203144388[/C][C]0.231840601572194[/C][/ROW]
[ROW][C]117[/C][C]0.761105450053513[/C][C]0.477789099892973[/C][C]0.238894549946487[/C][/ROW]
[ROW][C]118[/C][C]0.721431098378996[/C][C]0.557137803242008[/C][C]0.278568901621004[/C][/ROW]
[ROW][C]119[/C][C]0.773677750562075[/C][C]0.45264449887585[/C][C]0.226322249437925[/C][/ROW]
[ROW][C]120[/C][C]0.782688548726081[/C][C]0.434622902547838[/C][C]0.217311451273919[/C][/ROW]
[ROW][C]121[/C][C]0.744656616116517[/C][C]0.510686767766965[/C][C]0.255343383883483[/C][/ROW]
[ROW][C]122[/C][C]0.70539622281729[/C][C]0.589207554365419[/C][C]0.29460377718271[/C][/ROW]
[ROW][C]123[/C][C]0.662438555017039[/C][C]0.675122889965923[/C][C]0.337561444982961[/C][/ROW]
[ROW][C]124[/C][C]0.665870120339802[/C][C]0.668259759320395[/C][C]0.334129879660198[/C][/ROW]
[ROW][C]125[/C][C]0.623734920726583[/C][C]0.752530158546834[/C][C]0.376265079273417[/C][/ROW]
[ROW][C]126[/C][C]0.618267279232997[/C][C]0.763465441534006[/C][C]0.381732720767003[/C][/ROW]
[ROW][C]127[/C][C]0.625318705981917[/C][C]0.749362588036166[/C][C]0.374681294018083[/C][/ROW]
[ROW][C]128[/C][C]0.578060615937146[/C][C]0.843878768125709[/C][C]0.421939384062854[/C][/ROW]
[ROW][C]129[/C][C]0.557928260733931[/C][C]0.884143478532138[/C][C]0.442071739266069[/C][/ROW]
[ROW][C]130[/C][C]0.502618239022841[/C][C]0.994763521954318[/C][C]0.497381760977159[/C][/ROW]
[ROW][C]131[/C][C]0.544564628710542[/C][C]0.910870742578916[/C][C]0.455435371289458[/C][/ROW]
[ROW][C]132[/C][C]0.518935061703774[/C][C]0.962129876592452[/C][C]0.481064938296226[/C][/ROW]
[ROW][C]133[/C][C]0.49374636396852[/C][C]0.98749272793704[/C][C]0.50625363603148[/C][/ROW]
[ROW][C]134[/C][C]0.466881112630988[/C][C]0.933762225261977[/C][C]0.533118887369012[/C][/ROW]
[ROW][C]135[/C][C]0.409310786070258[/C][C]0.818621572140517[/C][C]0.590689213929742[/C][/ROW]
[ROW][C]136[/C][C]0.38459670407335[/C][C]0.769193408146699[/C][C]0.61540329592665[/C][/ROW]
[ROW][C]137[/C][C]0.636250032250252[/C][C]0.727499935499495[/C][C]0.363749967749748[/C][/ROW]
[ROW][C]138[/C][C]0.579719215681838[/C][C]0.840561568636324[/C][C]0.420280784318162[/C][/ROW]
[ROW][C]139[/C][C]0.516614364315619[/C][C]0.966771271368763[/C][C]0.483385635684381[/C][/ROW]
[ROW][C]140[/C][C]0.472649717396096[/C][C]0.945299434792192[/C][C]0.527350282603904[/C][/ROW]
[ROW][C]141[/C][C]0.429928532305659[/C][C]0.859857064611319[/C][C]0.570071467694341[/C][/ROW]
[ROW][C]142[/C][C]0.427100794040816[/C][C]0.854201588081632[/C][C]0.572899205959184[/C][/ROW]
[ROW][C]143[/C][C]0.412641413662369[/C][C]0.825282827324737[/C][C]0.587358586337631[/C][/ROW]
[ROW][C]144[/C][C]0.339714808987483[/C][C]0.679429617974966[/C][C]0.660285191012517[/C][/ROW]
[ROW][C]145[/C][C]0.272969832207766[/C][C]0.545939664415532[/C][C]0.727030167792234[/C][/ROW]
[ROW][C]146[/C][C]0.370888914219656[/C][C]0.741777828439312[/C][C]0.629111085780344[/C][/ROW]
[ROW][C]147[/C][C]0.29646939584388[/C][C]0.592938791687759[/C][C]0.70353060415612[/C][/ROW]
[ROW][C]148[/C][C]0.227336023361751[/C][C]0.454672046723502[/C][C]0.772663976638249[/C][/ROW]
[ROW][C]149[/C][C]0.253702053066798[/C][C]0.507404106133596[/C][C]0.746297946933202[/C][/ROW]
[ROW][C]150[/C][C]0.181021856666405[/C][C]0.36204371333281[/C][C]0.818978143333595[/C][/ROW]
[ROW][C]151[/C][C]0.126581530224796[/C][C]0.253163060449592[/C][C]0.873418469775204[/C][/ROW]
[ROW][C]152[/C][C]0.158489381457291[/C][C]0.316978762914583[/C][C]0.841510618542709[/C][/ROW]
[ROW][C]153[/C][C]0.109794292831809[/C][C]0.219588585663618[/C][C]0.890205707168191[/C][/ROW]
[ROW][C]154[/C][C]0.0998860283819693[/C][C]0.199772056763939[/C][C]0.900113971618031[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9998161273039070.0003677453921862830.000183872696093141
90.9998060763937720.0003878472124569570.000193923606228478
100.9995883869544310.0008232260911387880.000411613045569394
110.9994869352906950.001026129418609920.000513064709304961
120.9993862404631630.001227519073674480.00061375953683724
130.9988899150898790.002220169820241470.00111008491012074
140.9981253077589770.003749384482046830.00187469224102342
150.9967506095801120.006498780839775230.00324939041988762
160.9944072509683220.01118549806335630.00559274903167814
170.9905289080260410.01894218394791760.00947109197395881
180.9850583978424190.02988320431516250.0149416021575812
190.9858614202151190.02827715956976280.0141385797848814
200.9787133679638720.0425732640722550.0212866320361275
210.9811913413533780.03761731729324320.0188086586466216
220.9734954725876170.05300905482476520.0265045274123826
230.9645374751269450.07092504974611070.0354625248730553
240.9497396836279950.100520632744010.0502603163720051
250.9326565373329830.1346869253340340.0673434626670169
260.9881531015994010.02369379680119780.0118468984005989
270.9829711668509890.03405766629802220.0170288331490111
280.9794138894015660.0411722211968670.0205861105984335
290.9734723950014070.05305520999718580.0265276049985929
300.9657987104382830.06840257912343440.0342012895617172
310.9653322765822690.06933544683546220.0346677234177311
320.9568849508845680.08623009823086430.0431150491154322
330.9440486605649040.1119026788701910.0559513394350955
340.9320453781340650.1359092437318710.0679546218659353
350.9121498268889020.1757003462221960.0878501731110978
360.9277184127109470.1445631745781050.0722815872890526
370.9507595259686860.09848094806262740.0492404740313137
380.9353242554964470.1293514890071060.0646757445035531
390.9214041666260360.1571916667479270.0785958333739637
400.9302904209471320.1394191581057360.0697095790528681
410.9319213929687390.1361572140625220.068078607031261
420.928069536287810.1438609274243790.0719304637121896
430.9299583012837740.1400833974324510.0700416987162255
440.9117658006202160.1764683987595670.0882341993797836
450.9076273413566380.1847453172867240.0923726586433621
460.8920082544846670.2159834910306650.107991745515333
470.8974388007068470.2051223985863060.102561199293153
480.8787085993858330.2425828012283350.121291400614167
490.9026877433447430.1946245133105130.0973122566552567
500.8916175121598540.2167649756802910.108382487840146
510.8797888465680750.2404223068638490.120211153431925
520.8593139480795450.2813721038409110.140686051920455
530.8728740098173210.2542519803653580.127125990182679
540.8458194835005160.3083610329989680.154180516499484
550.8732981974328880.2534036051342230.126701802567112
560.8510091111784460.2979817776431090.148990888821554
570.8366694937145820.3266610125708360.163330506285418
580.8552447878777010.2895104242445990.144755212122299
590.886850439184440.226299121631120.11314956081556
600.8677740926788180.2644518146423630.132225907321182
610.8768333346157810.2463333307684390.123166665384219
620.8575123508357360.2849752983285270.142487649164264
630.8555835687378950.2888328625242090.144416431262105
640.8306070464455760.3387859071088490.169392953554424
650.8037805029998560.3924389940002870.196219497000144
660.8564211163109960.2871577673780080.143578883689004
670.8562706128632540.2874587742734930.143729387136746
680.8526270350752760.2947459298494480.147372964924724
690.8288590104524010.3422819790951980.171140989547599
700.8135479328875930.3729041342248130.186452067112407
710.7833673863529890.4332652272940230.216632613647011
720.7991468464933540.4017063070132920.200853153506646
730.7716825263470570.4566349473058860.228317473652943
740.7531669032586410.4936661934827180.246833096741359
750.7491348942085850.5017302115828290.250865105791415
760.8253898914985220.3492202170029560.174610108501478
770.8247406995438790.3505186009122430.175259300456122
780.8426226653638220.3147546692723560.157377334636178
790.816157663189370.3676846736212610.18384233681063
800.8708469985976670.2583060028046660.129153001402333
810.8501202747922250.299759450415550.149879725207775
820.8291138390292680.3417723219414640.170886160970732
830.8399448432052020.3201103135895960.160055156794798
840.8124241883352360.3751516233295290.187575811664764
850.7801588382263870.4396823235472250.219841161773613
860.7455013174884670.5089973650230650.254498682511533
870.7084005888284350.583198822343130.291599411171565
880.6722937654823840.6554124690352330.327706234517616
890.6401379156988180.7197241686023630.359862084301182
900.6951161277914010.6097677444171990.3048838722086
910.6781619405754950.6436761188490110.321838059424505
920.6566059699486160.6867880601027680.343394030051384
930.6202389855113990.7595220289772030.379761014488601
940.6019989967381980.7960020065236040.398001003261802
950.6466650644444450.7066698711111090.353334935555555
960.6040911385265740.7918177229468520.395908861473426
970.5795013097845430.8409973804309130.420498690215457
980.574884261596270.850231476807460.42511573840373
990.5296400571221990.9407198857556020.470359942877801
1000.5209120292104520.9581759415790960.479087970789548
1010.4741440823049320.9482881646098640.525855917695068
1020.4420223152740370.8840446305480730.557977684725963
1030.4090672291943970.8181344583887940.590932770805603
1040.522463302752630.955073394494740.47753669724737
1050.5913296176640060.8173407646719890.408670382335994
1060.5783795837904310.8432408324191380.421620416209569
1070.5301141905620630.9397716188758750.469885809437937
1080.6137463362384670.7725073275230660.386253663761533
1090.5844632368529650.8310735262940710.415536763147036
1100.8746701903151080.2506596193697850.125329809684892
1110.8616740264365750.2766519471268510.138325973563425
1120.8318135950936460.3363728098127080.168186404906354
1130.8259130395293360.3481739209413270.174086960470664
1140.8086725461317480.3826549077365040.191327453868252
1150.8023570579475760.3952858841048480.197642942052424
1160.7681593984278060.4636812031443880.231840601572194
1170.7611054500535130.4777890998929730.238894549946487
1180.7214310983789960.5571378032420080.278568901621004
1190.7736777505620750.452644498875850.226322249437925
1200.7826885487260810.4346229025478380.217311451273919
1210.7446566161165170.5106867677669650.255343383883483
1220.705396222817290.5892075543654190.29460377718271
1230.6624385550170390.6751228899659230.337561444982961
1240.6658701203398020.6682597593203950.334129879660198
1250.6237349207265830.7525301585468340.376265079273417
1260.6182672792329970.7634654415340060.381732720767003
1270.6253187059819170.7493625880361660.374681294018083
1280.5780606159371460.8438787681257090.421939384062854
1290.5579282607339310.8841434785321380.442071739266069
1300.5026182390228410.9947635219543180.497381760977159
1310.5445646287105420.9108707425789160.455435371289458
1320.5189350617037740.9621298765924520.481064938296226
1330.493746363968520.987492727937040.50625363603148
1340.4668811126309880.9337622252619770.533118887369012
1350.4093107860702580.8186215721405170.590689213929742
1360.384596704073350.7691934081466990.61540329592665
1370.6362500322502520.7274999354994950.363749967749748
1380.5797192156818380.8405615686363240.420280784318162
1390.5166143643156190.9667712713687630.483385635684381
1400.4726497173960960.9452994347921920.527350282603904
1410.4299285323056590.8598570646113190.570071467694341
1420.4271007940408160.8542015880816320.572899205959184
1430.4126414136623690.8252828273247370.587358586337631
1440.3397148089874830.6794296179749660.660285191012517
1450.2729698322077660.5459396644155320.727030167792234
1460.3708889142196560.7417778284393120.629111085780344
1470.296469395843880.5929387916877590.70353060415612
1480.2273360233617510.4546720467235020.772663976638249
1490.2537020530667980.5074041061335960.746297946933202
1500.1810218566664050.362043713332810.818978143333595
1510.1265815302247960.2531630604495920.873418469775204
1520.1584893814572910.3169787629145830.841510618542709
1530.1097942928318090.2195885856636180.890205707168191
1540.09988602838196930.1997720567639390.900113971618031






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.054421768707483NOK
5% type I error level170.115646258503401NOK
10% type I error level240.163265306122449NOK

\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 & 0.054421768707483 & NOK \tabularnewline
5% type I error level & 17 & 0.115646258503401 & NOK \tabularnewline
10% type I error level & 24 & 0.163265306122449 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146112&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]0.054421768707483[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.115646258503401[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.163265306122449[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146112&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146112&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 level80.054421768707483NOK
5% type I error level170.115646258503401NOK
10% type I error level240.163265306122449NOK


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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}