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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 09:34:20 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t13521261451juzj1ot5xw5wq7.htm/, Retrieved Sun, 05 Feb 2023 23:21:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186082, Retrieved Sun, 05 Feb 2023 23:21:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [wS7] [2012-11-04 21:09:58] [6c4cddab89f59a22f4fb07e80256756e]
- R PD      [Multiple Regression] [WS7] [2012-11-05 14:34:20] [e63fd0294c01e59e996b77216f3d6a82] [Current]
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Dataseries X:
13	41	38	7	2
16	39	32	5	2
19	30	35	5	2
15	31	33	5	1
14	34	37	8	2
13	35	29	6	2
19	39	31	5	2
15	34	36	6	2
14	36	35	5	2
15	37	38	4	2
16	38	31	6	1
16	36	34	5	2
16	38	35	5	1
16	39	38	6	2
17	33	37	7	2
15	32	33	6	1
15	36	32	7	1
20	38	38	6	2
18	39	38	8	1
16	32	32	7	2
16	32	33	5	1
16	31	31	5	2
19	39	38	7	2
16	37	39	7	2
17	39	32	5	1
17	41	32	4	2
16	36	35	10	1
15	33	37	6	2
16	33	33	5	2
14	34	33	5	1
15	31	28	5	2
12	27	32	5	1
14	37	31	6	2
16	34	37	5	2
14	34	30	5	1
7	32	33	5	1
10	29	31	5	1
14	36	33	5	1
16	29	31	5	2
16	35	33	5	1
16	37	32	5	1
14	34	33	7	2
20	38	32	5	1
14	35	33	6	1
14	38	28	7	2
11	37	35	7	2
14	38	39	5	2
15	33	34	5	2
16	36	38	4	2
14	38	32	5	1
16	32	38	4	2
14	32	30	5	1
12	32	33	5	1
16	34	38	7	2
9	32	32	5	1
14	37	32	5	2
16	39	34	6	2
16	29	34	4	2
15	37	36	6	1
16	35	34	6	2
12	30	28	5	1
16	38	34	7	1
16	34	35	6	2
14	31	35	8	2
16	34	31	7	2
17	35	37	5	1
18	36	35	6	2
18	30	27	6	1
12	39	40	5	2
16	35	37	5	1
10	38	36	5	1
14	31	38	5	2
18	34	39	4	2
18	38	41	6	1
16	34	27	6	1
17	39	30	6	2
16	37	37	6	2
16	34	31	7	2
13	28	31	5	1
16	37	27	7	1
16	33	36	6	1
20	37	38	5	1
16	35	37	5	2
15	37	33	4	1
15	32	34	8	2
16	33	31	8	2
14	38	39	5	1
16	33	34	5	2
16	29	32	6	2
15	33	33	4	2
12	31	36	5	2
17	36	32	5	2
16	35	41	5	2
15	32	28	5	2
13	29	30	6	2
16	39	36	6	2
16	37	35	5	2
16	35	31	6	2
16	37	34	5	1
14	32	36	7	1
16	38	36	5	2
16	37	35	6	1
20	36	37	6	2
15	32	28	6	1
16	33	39	4	2
13	40	32	5	1
17	38	35	5	2
16	41	39	7	1
16	36	35	6	1
12	43	42	9	2
16	30	34	6	2
16	31	33	6	2
17	32	41	5	2
13	32	33	6	1
12	37	34	5	2
18	37	32	8	1
14	33	40	7	2
14	34	40	5	2
13	33	35	7	2
16	38	36	6	2
13	33	37	6	2
16	31	27	9	2
13	38	39	7	2
16	37	38	6	2
15	33	31	5	2
16	31	33	5	2
15	39	32	6	1
17	44	39	6	2
15	33	36	7	2
12	35	33	5	2
16	32	33	5	1
10	28	32	5	1
16	40	37	6	2
12	27	30	4	1
14	37	38	5	1
15	32	29	7	2
13	28	22	5	1
15	34	35	7	1
11	30	35	7	2
12	35	34	6	2
8	31	35	5	1
16	32	34	8	2
15	30	34	5	1
17	30	35	5	2
16	31	23	5	1
10	40	31	6	2
18	32	27	4	2
13	36	36	5	1
16	32	31	5	1
13	35	32	7	1
10	38	39	6	2
15	42	37	7	2
16	34	38	10	1
16	35	39	6	2
14	35	34	8	2
10	33	31	4	2
17	36	32	5	2
13	32	37	6	2
15	33	36	7	2
16	34	32	7	2
12	32	35	6	2
13	34	36	6	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Perceived[t] = + 9.59342851434387 + 0.143519178982832Conected[t] -0.017337663001525Seperate[t] + 0.0252129423170627Age[t] + 0.513200298956685Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Perceived[t] =  +  9.59342851434387 +  0.143519178982832Conected[t] -0.017337663001525Seperate[t] +  0.0252129423170627Age[t] +  0.513200298956685Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Perceived[t] =  +  9.59342851434387 +  0.143519178982832Conected[t] -0.017337663001525Seperate[t] +  0.0252129423170627Age[t] +  0.513200298956685Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Perceived[t] = + 9.59342851434387 + 0.143519178982832Conected[t] -0.017337663001525Seperate[t] + 0.0252129423170627Age[t] + 0.513200298956685Gender[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.593428514343872.1877074.38522.1e-051.1e-05
Conected0.1435191789828320.0561292.55690.0115080.005754
Seperate-0.0173376630015250.05447-0.31830.7506840.375342
Age0.02521294231706270.1528050.1650.8691560.434578
Gender0.5132002989566850.3712791.38230.1688580.084429

\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) & 9.59342851434387 & 2.187707 & 4.3852 & 2.1e-05 & 1.1e-05 \tabularnewline
Conected & 0.143519178982832 & 0.056129 & 2.5569 & 0.011508 & 0.005754 \tabularnewline
Seperate & -0.017337663001525 & 0.05447 & -0.3183 & 0.750684 & 0.375342 \tabularnewline
Age & 0.0252129423170627 & 0.152805 & 0.165 & 0.869156 & 0.434578 \tabularnewline
Gender & 0.513200298956685 & 0.371279 & 1.3823 & 0.168858 & 0.084429 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&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]9.59342851434387[/C][C]2.187707[/C][C]4.3852[/C][C]2.1e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]Conected[/C][C]0.143519178982832[/C][C]0.056129[/C][C]2.5569[/C][C]0.011508[/C][C]0.005754[/C][/ROW]
[ROW][C]Seperate[/C][C]-0.017337663001525[/C][C]0.05447[/C][C]-0.3183[/C][C]0.750684[/C][C]0.375342[/C][/ROW]
[ROW][C]Age[/C][C]0.0252129423170627[/C][C]0.152805[/C][C]0.165[/C][C]0.869156[/C][C]0.434578[/C][/ROW]
[ROW][C]Gender[/C][C]0.513200298956685[/C][C]0.371279[/C][C]1.3823[/C][C]0.168858[/C][C]0.084429[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.593428514343872.1877074.38522.1e-051.1e-05
Conected0.1435191789828320.0561292.55690.0115080.005754
Seperate-0.0173376630015250.05447-0.31830.7506840.375342
Age0.02521294231706270.1528050.1650.8691560.434578
Gender0.5132002989566850.3712791.38230.1688580.084429







Multiple Linear Regression - Regression Statistics
Multiple R0.239791881622158
R-squared0.0575001464918949
Adjusted R-squared0.0334874113706694
F-TEST (value)2.39456880699395
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.0527872879156295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21816119154834
Sum Squared Residuals772.477534255507

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.239791881622158 \tabularnewline
R-squared & 0.0575001464918949 \tabularnewline
Adjusted R-squared & 0.0334874113706694 \tabularnewline
F-TEST (value) & 2.39456880699395 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.0527872879156295 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21816119154834 \tabularnewline
Sum Squared Residuals & 772.477534255507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.239791881622158[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0575001464918949[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0334874113706694[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.39456880699395[/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]0.0527872879156295[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21816119154834[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]772.477534255507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.239791881622158
R-squared0.0575001464918949
Adjusted R-squared0.0334874113706694
F-TEST (value)2.39456880699395
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.0527872879156295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21816119154834
Sum Squared Residuals772.477534255507







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0217748527149-3.02177485271485
21615.78833658812420.211663411875811
31914.44465098827414.55534901172587
41514.10964519430330.890354805696676
51415.0596912051536-1.05969120515359
61315.2914858035145-2.2914858035145
71915.80567425112573.19432574887429
81515.026602983521-0.0266029835209918
91415.3057660621711-1.30576606217112
101515.3720593098323-0.372059309832312
111615.17416771550330.825832284496741
121615.32310372517260.676896274827357
131615.07960412118010.920395878819904
141615.70952355243210.290476447567899
151714.89095908385372.1090409161463
161514.27837731560320.721622684396782
171514.89500463685310.104995363146867
182015.56600437344934.43399562655073
191815.24674913810952.75325086189046
201614.83412821987851.16587178012151
211614.25316437328621.74683562671384
221614.65752081926311.34247918073694
231915.73473649474923.26526350525084
241615.4303604737820.569639526218025
251715.27513628916751.7248637108325
261716.05016200377280.949837996227211
271614.91863047479971.08136952520025
281514.86574614153660.134253858463365
291614.90988385122571.09011614877433
301414.5402027312518-0.540202731251819
311514.70953380826760.290466191732366
321213.5529061413735-1.55290614137352
331415.5438488354771-1.54384883547711
341614.98405237820241.0159476217976
351414.5922157202564-0.592215720256394
36714.2531643732862-7.25316437328616
371013.8572821623407-3.85728216234071
381414.8272410892175-0.827241089217482
391614.37048246129741.6295175387026
401614.68372191023471.31627808976535
411614.98809793120181.01190206879816
421415.1038289148426-1.10382891484263
432015.13161711018474.86838288981533
441414.7089348525517-0.708934852551713
451415.7645939457816-1.76459394578158
461115.4997111257881-4.49971112578807
471415.5234537681307-1.52345376813068
481514.89254618822410.107453811775853
491615.22854013084950.77145986915052
501415.1316171101847-1.13161711018467
511614.65446341491821.34553658508185
521414.3051773622907-0.30517736229073
531214.2531643732862-2.25316437328616
541615.0171405998350.982859400164996
55914.2705020362877-5.27050203628768
561415.5012982301585-1.50129823015852
571615.77887420443820.2211257955618
581614.29325652997581.70674347002424
591514.94396022151280.0560397784871984
601615.20479748850690.795202511493126
611214.0528143303281-2.05281433032812
621615.14736766881570.852632331184254
631615.04394064652250.956059353477483
641414.6638089942081-0.663808994208147
651615.13850424084570.861495759154321
661714.61437125822852.38562874177145
671815.33097900448822.66902099551182
681814.09536493564673.90463506435329
691215.649635284112-3.64963528411199
701614.61437125822861.38562874177145
711015.0622664581786-5.06226645817857
721414.5361571782524-0.536157178252384
731814.92416410988233.07583589011771
741815.0007910854882.99920891451199
751614.6694416515781.33055834842197
761715.84822485644431.1517751435557
771615.4398228574680.560177142532038
781615.13850424084570.861495759154321
791313.7137629833579-0.713762983357878
801615.12521213084360.874787869156411
811614.36988350558151.63011649441853
822014.88407195319275.11592804680731
831615.12757155718520.872428442814764
841514.94554732588330.0544526741167487
851514.82466583619250.175334163807496
861615.02019800417990.97980199582009
871415.010253469174-1.010253469174
881614.89254618822411.10745381177585
891614.37835774061291.62164225938707
901514.88467090890860.11532909109139
911214.5708325042554-2.57083250425543
921715.35777905117571.64222094882431
931615.05822090517910.941779094820864
941514.85305298725050.146947012749535
951314.413033066616-1.41303306661598
961615.74419887843520.255801121564849
971615.44928524115390.550714758846051
981615.25681047751140.743189522488552
991614.95342260519881.04657739480121
1001414.2515772689157-0.251577268915706
1011615.57546675713530.424533242864744
1021614.96129788451431.03870211548567
1032015.29630367848514.70369632151487
1041514.36506563061080.634934369389157
1051614.78064493089951.21935506910054
1061315.4186554681503-2.41865546815033
1071715.59280442013681.40719557986322
1081615.49123689075660.508763109243383
1091614.81777870553151.18222129446851
1101216.2898884433085-4.28988844330852
1111614.48720159359271.51279840640729
1121614.64805843557711.35194156442293
1131714.62766336823062.37233663176936
1141314.2783773156032-1.27837731560322
1151215.4666229041555-3.46662290415548
1161815.0637367581532.93626324184697
1171414.8389460948491-0.838946094849123
1181414.9320393891978-0.932039389197829
1191314.9256344098567-1.92563440985675
1201615.60067969945230.399320300547681
1211314.8657461415366-1.86574614153663
1221614.82772324053741.17227675946259
1231315.5738796527648-2.57387965276481
1241615.42248519446640.577514805533563
1251514.94455917722870.0554408227712777
1261614.622845493261.37715450673999
1271515.3003492314846-0.300349231484565
1281716.40978178434470.590218215655266
1291514.90829674685520.0917032531447773
1301215.1969222091913-3.19692220919134
1311614.25316437328621.74683562671384
1321013.6964253203564-3.69642532035635
1331615.87038039441650.129619605583543
1341213.5623685250595-1.56236852505951
1351414.8840719531927-0.884071953192689
1361514.88614120888310.113858791116934
1371313.8698019503716-0.869801950371603
1381514.55595328988290.444046710117106
1391114.4950768729083-3.49507687290825
1401215.2047974885069-3.20479748850687
141814.0749698683003-6.07496986830027
1421614.82466583619251.1753341638075
1431513.9487883523191.05121164768103
1441714.44465098827412.55534901172587
1451614.28302182431861.71697817568143
1461015.9744063724256-5.97440637242561
1471814.84517770793493.15482229206507
1481314.7752281002129-1.77522810021291
1491614.28783969928921.71216030071079
1501314.7514854578703-1.7514854578703
1511015.5486667104477-5.54866671044774
1521516.1826316946992-1.18263169469918
1531614.57957912782951.42042087217049
1541615.11810917349920.881890826500751
1551415.255223373141-1.255223373141
1561014.9193462349117-4.91934623491166
1571715.35777905117571.64222094882431
1581314.7222269625538-1.7222269625538
1591514.90829674685520.0917032531447773
1601615.12116657784420.878833422155846
1611214.7569022885569-2.75690228855685
1621315.026602983521-2.02660298352099

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0217748527149 & -3.02177485271485 \tabularnewline
2 & 16 & 15.7883365881242 & 0.211663411875811 \tabularnewline
3 & 19 & 14.4446509882741 & 4.55534901172587 \tabularnewline
4 & 15 & 14.1096451943033 & 0.890354805696676 \tabularnewline
5 & 14 & 15.0596912051536 & -1.05969120515359 \tabularnewline
6 & 13 & 15.2914858035145 & -2.2914858035145 \tabularnewline
7 & 19 & 15.8056742511257 & 3.19432574887429 \tabularnewline
8 & 15 & 15.026602983521 & -0.0266029835209918 \tabularnewline
9 & 14 & 15.3057660621711 & -1.30576606217112 \tabularnewline
10 & 15 & 15.3720593098323 & -0.372059309832312 \tabularnewline
11 & 16 & 15.1741677155033 & 0.825832284496741 \tabularnewline
12 & 16 & 15.3231037251726 & 0.676896274827357 \tabularnewline
13 & 16 & 15.0796041211801 & 0.920395878819904 \tabularnewline
14 & 16 & 15.7095235524321 & 0.290476447567899 \tabularnewline
15 & 17 & 14.8909590838537 & 2.1090409161463 \tabularnewline
16 & 15 & 14.2783773156032 & 0.721622684396782 \tabularnewline
17 & 15 & 14.8950046368531 & 0.104995363146867 \tabularnewline
18 & 20 & 15.5660043734493 & 4.43399562655073 \tabularnewline
19 & 18 & 15.2467491381095 & 2.75325086189046 \tabularnewline
20 & 16 & 14.8341282198785 & 1.16587178012151 \tabularnewline
21 & 16 & 14.2531643732862 & 1.74683562671384 \tabularnewline
22 & 16 & 14.6575208192631 & 1.34247918073694 \tabularnewline
23 & 19 & 15.7347364947492 & 3.26526350525084 \tabularnewline
24 & 16 & 15.430360473782 & 0.569639526218025 \tabularnewline
25 & 17 & 15.2751362891675 & 1.7248637108325 \tabularnewline
26 & 17 & 16.0501620037728 & 0.949837996227211 \tabularnewline
27 & 16 & 14.9186304747997 & 1.08136952520025 \tabularnewline
28 & 15 & 14.8657461415366 & 0.134253858463365 \tabularnewline
29 & 16 & 14.9098838512257 & 1.09011614877433 \tabularnewline
30 & 14 & 14.5402027312518 & -0.540202731251819 \tabularnewline
31 & 15 & 14.7095338082676 & 0.290466191732366 \tabularnewline
32 & 12 & 13.5529061413735 & -1.55290614137352 \tabularnewline
33 & 14 & 15.5438488354771 & -1.54384883547711 \tabularnewline
34 & 16 & 14.9840523782024 & 1.0159476217976 \tabularnewline
35 & 14 & 14.5922157202564 & -0.592215720256394 \tabularnewline
36 & 7 & 14.2531643732862 & -7.25316437328616 \tabularnewline
37 & 10 & 13.8572821623407 & -3.85728216234071 \tabularnewline
38 & 14 & 14.8272410892175 & -0.827241089217482 \tabularnewline
39 & 16 & 14.3704824612974 & 1.6295175387026 \tabularnewline
40 & 16 & 14.6837219102347 & 1.31627808976535 \tabularnewline
41 & 16 & 14.9880979312018 & 1.01190206879816 \tabularnewline
42 & 14 & 15.1038289148426 & -1.10382891484263 \tabularnewline
43 & 20 & 15.1316171101847 & 4.86838288981533 \tabularnewline
44 & 14 & 14.7089348525517 & -0.708934852551713 \tabularnewline
45 & 14 & 15.7645939457816 & -1.76459394578158 \tabularnewline
46 & 11 & 15.4997111257881 & -4.49971112578807 \tabularnewline
47 & 14 & 15.5234537681307 & -1.52345376813068 \tabularnewline
48 & 15 & 14.8925461882241 & 0.107453811775853 \tabularnewline
49 & 16 & 15.2285401308495 & 0.77145986915052 \tabularnewline
50 & 14 & 15.1316171101847 & -1.13161711018467 \tabularnewline
51 & 16 & 14.6544634149182 & 1.34553658508185 \tabularnewline
52 & 14 & 14.3051773622907 & -0.30517736229073 \tabularnewline
53 & 12 & 14.2531643732862 & -2.25316437328616 \tabularnewline
54 & 16 & 15.017140599835 & 0.982859400164996 \tabularnewline
55 & 9 & 14.2705020362877 & -5.27050203628768 \tabularnewline
56 & 14 & 15.5012982301585 & -1.50129823015852 \tabularnewline
57 & 16 & 15.7788742044382 & 0.2211257955618 \tabularnewline
58 & 16 & 14.2932565299758 & 1.70674347002424 \tabularnewline
59 & 15 & 14.9439602215128 & 0.0560397784871984 \tabularnewline
60 & 16 & 15.2047974885069 & 0.795202511493126 \tabularnewline
61 & 12 & 14.0528143303281 & -2.05281433032812 \tabularnewline
62 & 16 & 15.1473676688157 & 0.852632331184254 \tabularnewline
63 & 16 & 15.0439406465225 & 0.956059353477483 \tabularnewline
64 & 14 & 14.6638089942081 & -0.663808994208147 \tabularnewline
65 & 16 & 15.1385042408457 & 0.861495759154321 \tabularnewline
66 & 17 & 14.6143712582285 & 2.38562874177145 \tabularnewline
67 & 18 & 15.3309790044882 & 2.66902099551182 \tabularnewline
68 & 18 & 14.0953649356467 & 3.90463506435329 \tabularnewline
69 & 12 & 15.649635284112 & -3.64963528411199 \tabularnewline
70 & 16 & 14.6143712582286 & 1.38562874177145 \tabularnewline
71 & 10 & 15.0622664581786 & -5.06226645817857 \tabularnewline
72 & 14 & 14.5361571782524 & -0.536157178252384 \tabularnewline
73 & 18 & 14.9241641098823 & 3.07583589011771 \tabularnewline
74 & 18 & 15.000791085488 & 2.99920891451199 \tabularnewline
75 & 16 & 14.669441651578 & 1.33055834842197 \tabularnewline
76 & 17 & 15.8482248564443 & 1.1517751435557 \tabularnewline
77 & 16 & 15.439822857468 & 0.560177142532038 \tabularnewline
78 & 16 & 15.1385042408457 & 0.861495759154321 \tabularnewline
79 & 13 & 13.7137629833579 & -0.713762983357878 \tabularnewline
80 & 16 & 15.1252121308436 & 0.874787869156411 \tabularnewline
81 & 16 & 14.3698835055815 & 1.63011649441853 \tabularnewline
82 & 20 & 14.8840719531927 & 5.11592804680731 \tabularnewline
83 & 16 & 15.1275715571852 & 0.872428442814764 \tabularnewline
84 & 15 & 14.9455473258833 & 0.0544526741167487 \tabularnewline
85 & 15 & 14.8246658361925 & 0.175334163807496 \tabularnewline
86 & 16 & 15.0201980041799 & 0.97980199582009 \tabularnewline
87 & 14 & 15.010253469174 & -1.010253469174 \tabularnewline
88 & 16 & 14.8925461882241 & 1.10745381177585 \tabularnewline
89 & 16 & 14.3783577406129 & 1.62164225938707 \tabularnewline
90 & 15 & 14.8846709089086 & 0.11532909109139 \tabularnewline
91 & 12 & 14.5708325042554 & -2.57083250425543 \tabularnewline
92 & 17 & 15.3577790511757 & 1.64222094882431 \tabularnewline
93 & 16 & 15.0582209051791 & 0.941779094820864 \tabularnewline
94 & 15 & 14.8530529872505 & 0.146947012749535 \tabularnewline
95 & 13 & 14.413033066616 & -1.41303306661598 \tabularnewline
96 & 16 & 15.7441988784352 & 0.255801121564849 \tabularnewline
97 & 16 & 15.4492852411539 & 0.550714758846051 \tabularnewline
98 & 16 & 15.2568104775114 & 0.743189522488552 \tabularnewline
99 & 16 & 14.9534226051988 & 1.04657739480121 \tabularnewline
100 & 14 & 14.2515772689157 & -0.251577268915706 \tabularnewline
101 & 16 & 15.5754667571353 & 0.424533242864744 \tabularnewline
102 & 16 & 14.9612978845143 & 1.03870211548567 \tabularnewline
103 & 20 & 15.2963036784851 & 4.70369632151487 \tabularnewline
104 & 15 & 14.3650656306108 & 0.634934369389157 \tabularnewline
105 & 16 & 14.7806449308995 & 1.21935506910054 \tabularnewline
106 & 13 & 15.4186554681503 & -2.41865546815033 \tabularnewline
107 & 17 & 15.5928044201368 & 1.40719557986322 \tabularnewline
108 & 16 & 15.4912368907566 & 0.508763109243383 \tabularnewline
109 & 16 & 14.8177787055315 & 1.18222129446851 \tabularnewline
110 & 12 & 16.2898884433085 & -4.28988844330852 \tabularnewline
111 & 16 & 14.4872015935927 & 1.51279840640729 \tabularnewline
112 & 16 & 14.6480584355771 & 1.35194156442293 \tabularnewline
113 & 17 & 14.6276633682306 & 2.37233663176936 \tabularnewline
114 & 13 & 14.2783773156032 & -1.27837731560322 \tabularnewline
115 & 12 & 15.4666229041555 & -3.46662290415548 \tabularnewline
116 & 18 & 15.063736758153 & 2.93626324184697 \tabularnewline
117 & 14 & 14.8389460948491 & -0.838946094849123 \tabularnewline
118 & 14 & 14.9320393891978 & -0.932039389197829 \tabularnewline
119 & 13 & 14.9256344098567 & -1.92563440985675 \tabularnewline
120 & 16 & 15.6006796994523 & 0.399320300547681 \tabularnewline
121 & 13 & 14.8657461415366 & -1.86574614153663 \tabularnewline
122 & 16 & 14.8277232405374 & 1.17227675946259 \tabularnewline
123 & 13 & 15.5738796527648 & -2.57387965276481 \tabularnewline
124 & 16 & 15.4224851944664 & 0.577514805533563 \tabularnewline
125 & 15 & 14.9445591772287 & 0.0554408227712777 \tabularnewline
126 & 16 & 14.62284549326 & 1.37715450673999 \tabularnewline
127 & 15 & 15.3003492314846 & -0.300349231484565 \tabularnewline
128 & 17 & 16.4097817843447 & 0.590218215655266 \tabularnewline
129 & 15 & 14.9082967468552 & 0.0917032531447773 \tabularnewline
130 & 12 & 15.1969222091913 & -3.19692220919134 \tabularnewline
131 & 16 & 14.2531643732862 & 1.74683562671384 \tabularnewline
132 & 10 & 13.6964253203564 & -3.69642532035635 \tabularnewline
133 & 16 & 15.8703803944165 & 0.129619605583543 \tabularnewline
134 & 12 & 13.5623685250595 & -1.56236852505951 \tabularnewline
135 & 14 & 14.8840719531927 & -0.884071953192689 \tabularnewline
136 & 15 & 14.8861412088831 & 0.113858791116934 \tabularnewline
137 & 13 & 13.8698019503716 & -0.869801950371603 \tabularnewline
138 & 15 & 14.5559532898829 & 0.444046710117106 \tabularnewline
139 & 11 & 14.4950768729083 & -3.49507687290825 \tabularnewline
140 & 12 & 15.2047974885069 & -3.20479748850687 \tabularnewline
141 & 8 & 14.0749698683003 & -6.07496986830027 \tabularnewline
142 & 16 & 14.8246658361925 & 1.1753341638075 \tabularnewline
143 & 15 & 13.948788352319 & 1.05121164768103 \tabularnewline
144 & 17 & 14.4446509882741 & 2.55534901172587 \tabularnewline
145 & 16 & 14.2830218243186 & 1.71697817568143 \tabularnewline
146 & 10 & 15.9744063724256 & -5.97440637242561 \tabularnewline
147 & 18 & 14.8451777079349 & 3.15482229206507 \tabularnewline
148 & 13 & 14.7752281002129 & -1.77522810021291 \tabularnewline
149 & 16 & 14.2878396992892 & 1.71216030071079 \tabularnewline
150 & 13 & 14.7514854578703 & -1.7514854578703 \tabularnewline
151 & 10 & 15.5486667104477 & -5.54866671044774 \tabularnewline
152 & 15 & 16.1826316946992 & -1.18263169469918 \tabularnewline
153 & 16 & 14.5795791278295 & 1.42042087217049 \tabularnewline
154 & 16 & 15.1181091734992 & 0.881890826500751 \tabularnewline
155 & 14 & 15.255223373141 & -1.255223373141 \tabularnewline
156 & 10 & 14.9193462349117 & -4.91934623491166 \tabularnewline
157 & 17 & 15.3577790511757 & 1.64222094882431 \tabularnewline
158 & 13 & 14.7222269625538 & -1.7222269625538 \tabularnewline
159 & 15 & 14.9082967468552 & 0.0917032531447773 \tabularnewline
160 & 16 & 15.1211665778442 & 0.878833422155846 \tabularnewline
161 & 12 & 14.7569022885569 & -2.75690228855685 \tabularnewline
162 & 13 & 15.026602983521 & -2.02660298352099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.0217748527149[/C][C]-3.02177485271485[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.7883365881242[/C][C]0.211663411875811[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.4446509882741[/C][C]4.55534901172587[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.1096451943033[/C][C]0.890354805696676[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.0596912051536[/C][C]-1.05969120515359[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2914858035145[/C][C]-2.2914858035145[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.8056742511257[/C][C]3.19432574887429[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.026602983521[/C][C]-0.0266029835209918[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.3057660621711[/C][C]-1.30576606217112[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.3720593098323[/C][C]-0.372059309832312[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1741677155033[/C][C]0.825832284496741[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.3231037251726[/C][C]0.676896274827357[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.0796041211801[/C][C]0.920395878819904[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.7095235524321[/C][C]0.290476447567899[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]14.8909590838537[/C][C]2.1090409161463[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.2783773156032[/C][C]0.721622684396782[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8950046368531[/C][C]0.104995363146867[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.5660043734493[/C][C]4.43399562655073[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.2467491381095[/C][C]2.75325086189046[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.8341282198785[/C][C]1.16587178012151[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]14.2531643732862[/C][C]1.74683562671384[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.6575208192631[/C][C]1.34247918073694[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.7347364947492[/C][C]3.26526350525084[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.430360473782[/C][C]0.569639526218025[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.2751362891675[/C][C]1.7248637108325[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.0501620037728[/C][C]0.949837996227211[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9186304747997[/C][C]1.08136952520025[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.8657461415366[/C][C]0.134253858463365[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.9098838512257[/C][C]1.09011614877433[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5402027312518[/C][C]-0.540202731251819[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]14.7095338082676[/C][C]0.290466191732366[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5529061413735[/C][C]-1.55290614137352[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.5438488354771[/C][C]-1.54384883547711[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]14.9840523782024[/C][C]1.0159476217976[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.5922157202564[/C][C]-0.592215720256394[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]14.2531643732862[/C][C]-7.25316437328616[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.8572821623407[/C][C]-3.85728216234071[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.8272410892175[/C][C]-0.827241089217482[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3704824612974[/C][C]1.6295175387026[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6837219102347[/C][C]1.31627808976535[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9880979312018[/C][C]1.01190206879816[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.1038289148426[/C][C]-1.10382891484263[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]15.1316171101847[/C][C]4.86838288981533[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.7089348525517[/C][C]-0.708934852551713[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.7645939457816[/C][C]-1.76459394578158[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4997111257881[/C][C]-4.49971112578807[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.5234537681307[/C][C]-1.52345376813068[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8925461882241[/C][C]0.107453811775853[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2285401308495[/C][C]0.77145986915052[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.1316171101847[/C][C]-1.13161711018467[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.6544634149182[/C][C]1.34553658508185[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.3051773622907[/C][C]-0.30517736229073[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.2531643732862[/C][C]-2.25316437328616[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.017140599835[/C][C]0.982859400164996[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.2705020362877[/C][C]-5.27050203628768[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.5012982301585[/C][C]-1.50129823015852[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.7788742044382[/C][C]0.2211257955618[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.2932565299758[/C][C]1.70674347002424[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.9439602215128[/C][C]0.0560397784871984[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]15.2047974885069[/C][C]0.795202511493126[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]14.0528143303281[/C][C]-2.05281433032812[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.1473676688157[/C][C]0.852632331184254[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]15.0439406465225[/C][C]0.956059353477483[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6638089942081[/C][C]-0.663808994208147[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1385042408457[/C][C]0.861495759154321[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]14.6143712582285[/C][C]2.38562874177145[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.3309790044882[/C][C]2.66902099551182[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.0953649356467[/C][C]3.90463506435329[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.649635284112[/C][C]-3.64963528411199[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]14.6143712582286[/C][C]1.38562874177145[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.0622664581786[/C][C]-5.06226645817857[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.5361571782524[/C][C]-0.536157178252384[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]14.9241641098823[/C][C]3.07583589011771[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]15.000791085488[/C][C]2.99920891451199[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]14.669441651578[/C][C]1.33055834842197[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]15.8482248564443[/C][C]1.1517751435557[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.439822857468[/C][C]0.560177142532038[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]15.1385042408457[/C][C]0.861495759154321[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.7137629833579[/C][C]-0.713762983357878[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.1252121308436[/C][C]0.874787869156411[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]14.3698835055815[/C][C]1.63011649441853[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]14.8840719531927[/C][C]5.11592804680731[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.1275715571852[/C][C]0.872428442814764[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.9455473258833[/C][C]0.0544526741167487[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.8246658361925[/C][C]0.175334163807496[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]15.0201980041799[/C][C]0.97980199582009[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.010253469174[/C][C]-1.010253469174[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8925461882241[/C][C]1.10745381177585[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.3783577406129[/C][C]1.62164225938707[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.8846709089086[/C][C]0.11532909109139[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]14.5708325042554[/C][C]-2.57083250425543[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]15.3577790511757[/C][C]1.64222094882431[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0582209051791[/C][C]0.941779094820864[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.8530529872505[/C][C]0.146947012749535[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.413033066616[/C][C]-1.41303306661598[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7441988784352[/C][C]0.255801121564849[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.4492852411539[/C][C]0.550714758846051[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.2568104775114[/C][C]0.743189522488552[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]14.9534226051988[/C][C]1.04657739480121[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2515772689157[/C][C]-0.251577268915706[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5754667571353[/C][C]0.424533242864744[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9612978845143[/C][C]1.03870211548567[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]15.2963036784851[/C][C]4.70369632151487[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.3650656306108[/C][C]0.634934369389157[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.7806449308995[/C][C]1.21935506910054[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4186554681503[/C][C]-2.41865546815033[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.5928044201368[/C][C]1.40719557986322[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.4912368907566[/C][C]0.508763109243383[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.8177787055315[/C][C]1.18222129446851[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]16.2898884433085[/C][C]-4.28988844330852[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.4872015935927[/C][C]1.51279840640729[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]14.6480584355771[/C][C]1.35194156442293[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6276633682306[/C][C]2.37233663176936[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.2783773156032[/C][C]-1.27837731560322[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.4666229041555[/C][C]-3.46662290415548[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]15.063736758153[/C][C]2.93626324184697[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.8389460948491[/C][C]-0.838946094849123[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.9320393891978[/C][C]-0.932039389197829[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9256344098567[/C][C]-1.92563440985675[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6006796994523[/C][C]0.399320300547681[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.8657461415366[/C][C]-1.86574614153663[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.8277232405374[/C][C]1.17227675946259[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.5738796527648[/C][C]-2.57387965276481[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.4224851944664[/C][C]0.577514805533563[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.9445591772287[/C][C]0.0554408227712777[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.62284549326[/C][C]1.37715450673999[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3003492314846[/C][C]-0.300349231484565[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.4097817843447[/C][C]0.590218215655266[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.9082967468552[/C][C]0.0917032531447773[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.1969222091913[/C][C]-3.19692220919134[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2531643732862[/C][C]1.74683562671384[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.6964253203564[/C][C]-3.69642532035635[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]15.8703803944165[/C][C]0.129619605583543[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.5623685250595[/C][C]-1.56236852505951[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]14.8840719531927[/C][C]-0.884071953192689[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.8861412088831[/C][C]0.113858791116934[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.8698019503716[/C][C]-0.869801950371603[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5559532898829[/C][C]0.444046710117106[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]14.4950768729083[/C][C]-3.49507687290825[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]15.2047974885069[/C][C]-3.20479748850687[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]14.0749698683003[/C][C]-6.07496986830027[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]14.8246658361925[/C][C]1.1753341638075[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.948788352319[/C][C]1.05121164768103[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]14.4446509882741[/C][C]2.55534901172587[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.2830218243186[/C][C]1.71697817568143[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]15.9744063724256[/C][C]-5.97440637242561[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]14.8451777079349[/C][C]3.15482229206507[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.7752281002129[/C][C]-1.77522810021291[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.2878396992892[/C][C]1.71216030071079[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]14.7514854578703[/C][C]-1.7514854578703[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]15.5486667104477[/C][C]-5.54866671044774[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1826316946992[/C][C]-1.18263169469918[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.5795791278295[/C][C]1.42042087217049[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]15.1181091734992[/C][C]0.881890826500751[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]15.255223373141[/C][C]-1.255223373141[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]14.9193462349117[/C][C]-4.91934623491166[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]15.3577790511757[/C][C]1.64222094882431[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]14.7222269625538[/C][C]-1.7222269625538[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.9082967468552[/C][C]0.0917032531447773[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1211665778442[/C][C]0.878833422155846[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.7569022885569[/C][C]-2.75690228855685[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]15.026602983521[/C][C]-2.02660298352099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0217748527149-3.02177485271485
21615.78833658812420.211663411875811
31914.44465098827414.55534901172587
41514.10964519430330.890354805696676
51415.0596912051536-1.05969120515359
61315.2914858035145-2.2914858035145
71915.80567425112573.19432574887429
81515.026602983521-0.0266029835209918
91415.3057660621711-1.30576606217112
101515.3720593098323-0.372059309832312
111615.17416771550330.825832284496741
121615.32310372517260.676896274827357
131615.07960412118010.920395878819904
141615.70952355243210.290476447567899
151714.89095908385372.1090409161463
161514.27837731560320.721622684396782
171514.89500463685310.104995363146867
182015.56600437344934.43399562655073
191815.24674913810952.75325086189046
201614.83412821987851.16587178012151
211614.25316437328621.74683562671384
221614.65752081926311.34247918073694
231915.73473649474923.26526350525084
241615.4303604737820.569639526218025
251715.27513628916751.7248637108325
261716.05016200377280.949837996227211
271614.91863047479971.08136952520025
281514.86574614153660.134253858463365
291614.90988385122571.09011614877433
301414.5402027312518-0.540202731251819
311514.70953380826760.290466191732366
321213.5529061413735-1.55290614137352
331415.5438488354771-1.54384883547711
341614.98405237820241.0159476217976
351414.5922157202564-0.592215720256394
36714.2531643732862-7.25316437328616
371013.8572821623407-3.85728216234071
381414.8272410892175-0.827241089217482
391614.37048246129741.6295175387026
401614.68372191023471.31627808976535
411614.98809793120181.01190206879816
421415.1038289148426-1.10382891484263
432015.13161711018474.86838288981533
441414.7089348525517-0.708934852551713
451415.7645939457816-1.76459394578158
461115.4997111257881-4.49971112578807
471415.5234537681307-1.52345376813068
481514.89254618822410.107453811775853
491615.22854013084950.77145986915052
501415.1316171101847-1.13161711018467
511614.65446341491821.34553658508185
521414.3051773622907-0.30517736229073
531214.2531643732862-2.25316437328616
541615.0171405998350.982859400164996
55914.2705020362877-5.27050203628768
561415.5012982301585-1.50129823015852
571615.77887420443820.2211257955618
581614.29325652997581.70674347002424
591514.94396022151280.0560397784871984
601615.20479748850690.795202511493126
611214.0528143303281-2.05281433032812
621615.14736766881570.852632331184254
631615.04394064652250.956059353477483
641414.6638089942081-0.663808994208147
651615.13850424084570.861495759154321
661714.61437125822852.38562874177145
671815.33097900448822.66902099551182
681814.09536493564673.90463506435329
691215.649635284112-3.64963528411199
701614.61437125822861.38562874177145
711015.0622664581786-5.06226645817857
721414.5361571782524-0.536157178252384
731814.92416410988233.07583589011771
741815.0007910854882.99920891451199
751614.6694416515781.33055834842197
761715.84822485644431.1517751435557
771615.4398228574680.560177142532038
781615.13850424084570.861495759154321
791313.7137629833579-0.713762983357878
801615.12521213084360.874787869156411
811614.36988350558151.63011649441853
822014.88407195319275.11592804680731
831615.12757155718520.872428442814764
841514.94554732588330.0544526741167487
851514.82466583619250.175334163807496
861615.02019800417990.97980199582009
871415.010253469174-1.010253469174
881614.89254618822411.10745381177585
891614.37835774061291.62164225938707
901514.88467090890860.11532909109139
911214.5708325042554-2.57083250425543
921715.35777905117571.64222094882431
931615.05822090517910.941779094820864
941514.85305298725050.146947012749535
951314.413033066616-1.41303306661598
961615.74419887843520.255801121564849
971615.44928524115390.550714758846051
981615.25681047751140.743189522488552
991614.95342260519881.04657739480121
1001414.2515772689157-0.251577268915706
1011615.57546675713530.424533242864744
1021614.96129788451431.03870211548567
1032015.29630367848514.70369632151487
1041514.36506563061080.634934369389157
1051614.78064493089951.21935506910054
1061315.4186554681503-2.41865546815033
1071715.59280442013681.40719557986322
1081615.49123689075660.508763109243383
1091614.81777870553151.18222129446851
1101216.2898884433085-4.28988844330852
1111614.48720159359271.51279840640729
1121614.64805843557711.35194156442293
1131714.62766336823062.37233663176936
1141314.2783773156032-1.27837731560322
1151215.4666229041555-3.46662290415548
1161815.0637367581532.93626324184697
1171414.8389460948491-0.838946094849123
1181414.9320393891978-0.932039389197829
1191314.9256344098567-1.92563440985675
1201615.60067969945230.399320300547681
1211314.8657461415366-1.86574614153663
1221614.82772324053741.17227675946259
1231315.5738796527648-2.57387965276481
1241615.42248519446640.577514805533563
1251514.94455917722870.0554408227712777
1261614.622845493261.37715450673999
1271515.3003492314846-0.300349231484565
1281716.40978178434470.590218215655266
1291514.90829674685520.0917032531447773
1301215.1969222091913-3.19692220919134
1311614.25316437328621.74683562671384
1321013.6964253203564-3.69642532035635
1331615.87038039441650.129619605583543
1341213.5623685250595-1.56236852505951
1351414.8840719531927-0.884071953192689
1361514.88614120888310.113858791116934
1371313.8698019503716-0.869801950371603
1381514.55595328988290.444046710117106
1391114.4950768729083-3.49507687290825
1401215.2047974885069-3.20479748850687
141814.0749698683003-6.07496986830027
1421614.82466583619251.1753341638075
1431513.9487883523191.05121164768103
1441714.44465098827412.55534901172587
1451614.28302182431861.71697817568143
1461015.9744063724256-5.97440637242561
1471814.84517770793493.15482229206507
1481314.7752281002129-1.77522810021291
1491614.28783969928921.71216030071079
1501314.7514854578703-1.7514854578703
1511015.5486667104477-5.54866671044774
1521516.1826316946992-1.18263169469918
1531614.57957912782951.42042087217049
1541615.11810917349920.881890826500751
1551415.255223373141-1.255223373141
1561014.9193462349117-4.91934623491166
1571715.35777905117571.64222094882431
1581314.7222269625538-1.7222269625538
1591514.90829674685520.0917032531447773
1601615.12116657784420.878833422155846
1611214.7569022885569-2.75690228855685
1621315.026602983521-2.02660298352099







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5850091058214070.8299817883571850.414990894178593
90.7042159889022720.5915680221954550.295784011097728
100.6632183128299010.6735633743401970.336781687170099
110.6106343404902980.7787313190194040.389365659509702
120.4941084319847880.9882168639695750.505891568015212
130.3931088885259070.7862177770518150.606891111474092
140.330899324856720.661798649713440.66910067514328
150.3379418234138090.6758836468276180.662058176586191
160.2598859334206240.5197718668412480.740114066579376
170.1921158689317620.3842317378635240.807884131068238
180.4492012682414090.8984025364828180.550798731758591
190.491267519177170.9825350383543390.50873248082283
200.4246429479328890.8492858958657780.575357052067111
210.3543810100931290.7087620201862590.645618989906871
220.2894642479247480.5789284958494950.710535752075252
230.3389694493676330.6779388987352660.661030550632367
240.279031427712010.558062855424020.72096857228799
250.236565486225370.473130972450740.76343451377463
260.1903009671380340.3806019342760680.809699032861966
270.1513454229344020.3026908458688040.848654577065598
280.1237211704529020.2474423409058050.876278829547098
290.09374033623560350.1874806724712070.906259663764396
300.08793147525947760.1758629505189550.912068524740522
310.0647349900411050.129469980082210.935265009958895
320.07712993563467830.1542598712693570.922870064365322
330.07191474976508530.1438294995301710.928085250234915
340.05357499569634440.1071499913926890.946425004303656
350.04175525253410990.08351050506821980.95824474746589
360.4893898454189530.9787796908379060.510610154581047
370.5643954325800240.8712091348399530.435604567419976
380.5142201493488740.9715597013022510.485779850651126
390.491323635912530.982647271825060.50867636408747
400.4606042950165450.9212085900330910.539395704983455
410.4194235208871790.8388470417743580.580576479112821
420.3900046450418070.7800092900836140.609995354958193
430.573558827389830.852882345220340.42644117261017
440.5285069211691770.9429861576616460.471493078830823
450.5072979049006180.9854041901987640.492702095099382
460.6860593041301290.6278813917397410.313940695869871
470.6916172933253420.6167654133493150.308382706674658
480.6454674785473750.7090650429052510.354532521452625
490.6006581467209250.7986837065581510.399341853279075
500.568330551166510.8633388976669810.43166944883349
510.5284215796988050.9431568406023890.471578420301195
520.4788012059213080.9576024118426170.521198794078692
530.478716645624840.957433291249680.52128335437516
540.4352647085189110.8705294170378210.564735291481089
550.6372383003935450.7255233992129090.362761699606455
560.6141965850760580.7716068298478840.385803414923942
570.5683015406688210.8633969186623570.431698459331179
580.548470609423960.903058781152080.45152939057604
590.5005981037174880.9988037925650250.499401896282512
600.4579046784104790.9158093568209590.542095321589521
610.4379251081221810.8758502162443630.562074891877819
620.3981509604018970.7963019208037950.601849039598103
630.3596137495757360.7192274991514720.640386250424264
640.3192563613342010.6385127226684020.680743638665799
650.2875651012960360.5751302025920720.712434898703964
660.2848624713022830.5697249426045660.715137528697717
670.2968430037217140.5936860074434290.703156996278286
680.4283996200333790.8567992400667590.571600379966621
690.5312296316597830.9375407366804340.468770368340217
700.5002172046111910.9995655907776180.499782795388809
710.6847523729450420.6304952541099150.315247627054958
720.6462994942331070.7074010115337870.353700505766893
730.6797843620579050.6404312758841910.320215637942095
740.7061183397790450.587763320441910.293881660220955
750.6830707678723260.6338584642553470.316929232127674
760.6529270573962660.6941458852074680.347072942603734
770.6135437594512250.7729124810975490.386456240548775
780.5755801206075170.8488397587849660.424419879392483
790.5351179202646460.9297641594707080.464882079735354
800.4971785208574650.994357041714930.502821479142535
810.4738852894198960.9477705788397930.526114710580104
820.6661858799728390.6676282400543230.333814120027161
830.6326417963966720.7347164072066560.367358203603328
840.5898652438458720.8202695123082550.410134756154128
850.5449248976633470.9101502046733070.455075102336653
860.5072715538892310.9854568922215390.492728446110769
870.4732140289085640.9464280578171290.526785971091436
880.4406138250352930.8812276500705850.559386174964707
890.4193034344319380.8386068688638750.580696565568062
900.3760365542768660.7520731085537310.623963445723134
910.3899537366352640.7799074732705290.610046263364736
920.3748314984439310.7496629968878610.625168501556069
930.3451715514005720.6903431028011440.654828448599428
940.3039804673699370.6079609347398740.696019532630063
950.2792266600821880.5584533201643750.720773339917812
960.245914377033660.491828754067320.75408562296634
970.2168911382037830.4337822764075670.783108861796217
980.1896795449341410.3793590898682820.810320455065859
990.1700448764424420.3400897528848840.829955123557558
1000.142481037683260.2849620753665210.85751896231674
1010.1228651369780130.2457302739560260.877134863021987
1020.1082076695693020.2164153391386030.891792330430698
1030.2265531497115470.4531062994230940.773446850288453
1040.1958460353083210.3916920706166420.804153964691679
1050.1877853450944990.3755706901889990.812214654905501
1060.1820480996736520.3640961993473040.817951900326348
1070.1817430053978210.3634860107956410.818256994602179
1080.1620058018459780.3240116036919550.837994198154022
1090.1502779495017440.3005558990034870.849722050498256
1100.2231476122552610.4462952245105230.776852387744739
1110.2086998431290370.4173996862580730.791300156870963
1120.1932036303017310.3864072606034610.80679636969827
1130.2490086862600120.4980173725200240.750991313739988
1140.2194633394922630.4389266789845260.780536660507737
1150.2443099740805580.4886199481611160.755690025919442
1160.2694785980797330.5389571961594650.730521401920267
1170.2346319420435960.4692638840871920.765368057956404
1180.2057414533771140.4114829067542280.794258546622886
1190.1885957520826380.3771915041652770.811404247917362
1200.1665905123839010.3331810247678030.833409487616099
1210.146117738181820.2922354763636410.853882261818179
1220.1199089147525590.2398178295051180.880091085247441
1230.1129654198766050.2259308397532090.887034580123395
1240.1010406789045730.2020813578091460.898959321095427
1250.08149225963440410.1629845192688080.918507740365596
1260.08066191837657860.1613238367531570.919338081623421
1270.0621596823921090.1243193647842180.937840317607891
1280.06350237574097680.1270047514819540.936497624259023
1290.04967454741673050.09934909483346110.95032545258327
1300.04903607540022250.09807215080044490.950963924599778
1310.0526206766080080.1052413532160160.947379323391992
1320.07075612439537490.141512248790750.929243875604625
1330.06878056292249520.137561125844990.931219437077505
1340.05839244282052690.1167848856410540.941607557179473
1350.04871605266560440.09743210533120880.951283947334396
1360.03489556181451460.06979112362902930.965104438185485
1370.0368647130587670.0737294261175340.963135286941233
1380.02774535089112090.05549070178224180.972254649108879
1390.05390924945048290.1078184989009660.946090750549517
1400.05286838399615810.1057367679923160.947131616003842
1410.2441875476526990.4883750953053970.755812452347302
1420.1919231786662680.3838463573325360.808076821333732
1430.1458774397904690.2917548795809380.854122560209531
1440.1487837589262630.2975675178525270.851216241073737
1450.1102017025603290.2204034051206590.889798297439671
1460.2602916076446370.5205832152892740.739708392355363
1470.3304785595811980.6609571191623960.669521440418802
1480.2536020947915610.5072041895831220.746397905208439
1490.305708746177780.611417492355560.69429125382222
1500.2349591469265720.4699182938531430.765040853073428
1510.4337689234172840.8675378468345680.566231076582716
1520.6556620162019610.6886759675960790.344337983798039
1530.5108810159427330.9782379681145340.489118984057267
1540.3485917667630920.6971835335261830.651408233236908

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.585009105821407 & 0.829981788357185 & 0.414990894178593 \tabularnewline
9 & 0.704215988902272 & 0.591568022195455 & 0.295784011097728 \tabularnewline
10 & 0.663218312829901 & 0.673563374340197 & 0.336781687170099 \tabularnewline
11 & 0.610634340490298 & 0.778731319019404 & 0.389365659509702 \tabularnewline
12 & 0.494108431984788 & 0.988216863969575 & 0.505891568015212 \tabularnewline
13 & 0.393108888525907 & 0.786217777051815 & 0.606891111474092 \tabularnewline
14 & 0.33089932485672 & 0.66179864971344 & 0.66910067514328 \tabularnewline
15 & 0.337941823413809 & 0.675883646827618 & 0.662058176586191 \tabularnewline
16 & 0.259885933420624 & 0.519771866841248 & 0.740114066579376 \tabularnewline
17 & 0.192115868931762 & 0.384231737863524 & 0.807884131068238 \tabularnewline
18 & 0.449201268241409 & 0.898402536482818 & 0.550798731758591 \tabularnewline
19 & 0.49126751917717 & 0.982535038354339 & 0.50873248082283 \tabularnewline
20 & 0.424642947932889 & 0.849285895865778 & 0.575357052067111 \tabularnewline
21 & 0.354381010093129 & 0.708762020186259 & 0.645618989906871 \tabularnewline
22 & 0.289464247924748 & 0.578928495849495 & 0.710535752075252 \tabularnewline
23 & 0.338969449367633 & 0.677938898735266 & 0.661030550632367 \tabularnewline
24 & 0.27903142771201 & 0.55806285542402 & 0.72096857228799 \tabularnewline
25 & 0.23656548622537 & 0.47313097245074 & 0.76343451377463 \tabularnewline
26 & 0.190300967138034 & 0.380601934276068 & 0.809699032861966 \tabularnewline
27 & 0.151345422934402 & 0.302690845868804 & 0.848654577065598 \tabularnewline
28 & 0.123721170452902 & 0.247442340905805 & 0.876278829547098 \tabularnewline
29 & 0.0937403362356035 & 0.187480672471207 & 0.906259663764396 \tabularnewline
30 & 0.0879314752594776 & 0.175862950518955 & 0.912068524740522 \tabularnewline
31 & 0.064734990041105 & 0.12946998008221 & 0.935265009958895 \tabularnewline
32 & 0.0771299356346783 & 0.154259871269357 & 0.922870064365322 \tabularnewline
33 & 0.0719147497650853 & 0.143829499530171 & 0.928085250234915 \tabularnewline
34 & 0.0535749956963444 & 0.107149991392689 & 0.946425004303656 \tabularnewline
35 & 0.0417552525341099 & 0.0835105050682198 & 0.95824474746589 \tabularnewline
36 & 0.489389845418953 & 0.978779690837906 & 0.510610154581047 \tabularnewline
37 & 0.564395432580024 & 0.871209134839953 & 0.435604567419976 \tabularnewline
38 & 0.514220149348874 & 0.971559701302251 & 0.485779850651126 \tabularnewline
39 & 0.49132363591253 & 0.98264727182506 & 0.50867636408747 \tabularnewline
40 & 0.460604295016545 & 0.921208590033091 & 0.539395704983455 \tabularnewline
41 & 0.419423520887179 & 0.838847041774358 & 0.580576479112821 \tabularnewline
42 & 0.390004645041807 & 0.780009290083614 & 0.609995354958193 \tabularnewline
43 & 0.57355882738983 & 0.85288234522034 & 0.42644117261017 \tabularnewline
44 & 0.528506921169177 & 0.942986157661646 & 0.471493078830823 \tabularnewline
45 & 0.507297904900618 & 0.985404190198764 & 0.492702095099382 \tabularnewline
46 & 0.686059304130129 & 0.627881391739741 & 0.313940695869871 \tabularnewline
47 & 0.691617293325342 & 0.616765413349315 & 0.308382706674658 \tabularnewline
48 & 0.645467478547375 & 0.709065042905251 & 0.354532521452625 \tabularnewline
49 & 0.600658146720925 & 0.798683706558151 & 0.399341853279075 \tabularnewline
50 & 0.56833055116651 & 0.863338897666981 & 0.43166944883349 \tabularnewline
51 & 0.528421579698805 & 0.943156840602389 & 0.471578420301195 \tabularnewline
52 & 0.478801205921308 & 0.957602411842617 & 0.521198794078692 \tabularnewline
53 & 0.47871664562484 & 0.95743329124968 & 0.52128335437516 \tabularnewline
54 & 0.435264708518911 & 0.870529417037821 & 0.564735291481089 \tabularnewline
55 & 0.637238300393545 & 0.725523399212909 & 0.362761699606455 \tabularnewline
56 & 0.614196585076058 & 0.771606829847884 & 0.385803414923942 \tabularnewline
57 & 0.568301540668821 & 0.863396918662357 & 0.431698459331179 \tabularnewline
58 & 0.54847060942396 & 0.90305878115208 & 0.45152939057604 \tabularnewline
59 & 0.500598103717488 & 0.998803792565025 & 0.499401896282512 \tabularnewline
60 & 0.457904678410479 & 0.915809356820959 & 0.542095321589521 \tabularnewline
61 & 0.437925108122181 & 0.875850216244363 & 0.562074891877819 \tabularnewline
62 & 0.398150960401897 & 0.796301920803795 & 0.601849039598103 \tabularnewline
63 & 0.359613749575736 & 0.719227499151472 & 0.640386250424264 \tabularnewline
64 & 0.319256361334201 & 0.638512722668402 & 0.680743638665799 \tabularnewline
65 & 0.287565101296036 & 0.575130202592072 & 0.712434898703964 \tabularnewline
66 & 0.284862471302283 & 0.569724942604566 & 0.715137528697717 \tabularnewline
67 & 0.296843003721714 & 0.593686007443429 & 0.703156996278286 \tabularnewline
68 & 0.428399620033379 & 0.856799240066759 & 0.571600379966621 \tabularnewline
69 & 0.531229631659783 & 0.937540736680434 & 0.468770368340217 \tabularnewline
70 & 0.500217204611191 & 0.999565590777618 & 0.499782795388809 \tabularnewline
71 & 0.684752372945042 & 0.630495254109915 & 0.315247627054958 \tabularnewline
72 & 0.646299494233107 & 0.707401011533787 & 0.353700505766893 \tabularnewline
73 & 0.679784362057905 & 0.640431275884191 & 0.320215637942095 \tabularnewline
74 & 0.706118339779045 & 0.58776332044191 & 0.293881660220955 \tabularnewline
75 & 0.683070767872326 & 0.633858464255347 & 0.316929232127674 \tabularnewline
76 & 0.652927057396266 & 0.694145885207468 & 0.347072942603734 \tabularnewline
77 & 0.613543759451225 & 0.772912481097549 & 0.386456240548775 \tabularnewline
78 & 0.575580120607517 & 0.848839758784966 & 0.424419879392483 \tabularnewline
79 & 0.535117920264646 & 0.929764159470708 & 0.464882079735354 \tabularnewline
80 & 0.497178520857465 & 0.99435704171493 & 0.502821479142535 \tabularnewline
81 & 0.473885289419896 & 0.947770578839793 & 0.526114710580104 \tabularnewline
82 & 0.666185879972839 & 0.667628240054323 & 0.333814120027161 \tabularnewline
83 & 0.632641796396672 & 0.734716407206656 & 0.367358203603328 \tabularnewline
84 & 0.589865243845872 & 0.820269512308255 & 0.410134756154128 \tabularnewline
85 & 0.544924897663347 & 0.910150204673307 & 0.455075102336653 \tabularnewline
86 & 0.507271553889231 & 0.985456892221539 & 0.492728446110769 \tabularnewline
87 & 0.473214028908564 & 0.946428057817129 & 0.526785971091436 \tabularnewline
88 & 0.440613825035293 & 0.881227650070585 & 0.559386174964707 \tabularnewline
89 & 0.419303434431938 & 0.838606868863875 & 0.580696565568062 \tabularnewline
90 & 0.376036554276866 & 0.752073108553731 & 0.623963445723134 \tabularnewline
91 & 0.389953736635264 & 0.779907473270529 & 0.610046263364736 \tabularnewline
92 & 0.374831498443931 & 0.749662996887861 & 0.625168501556069 \tabularnewline
93 & 0.345171551400572 & 0.690343102801144 & 0.654828448599428 \tabularnewline
94 & 0.303980467369937 & 0.607960934739874 & 0.696019532630063 \tabularnewline
95 & 0.279226660082188 & 0.558453320164375 & 0.720773339917812 \tabularnewline
96 & 0.24591437703366 & 0.49182875406732 & 0.75408562296634 \tabularnewline
97 & 0.216891138203783 & 0.433782276407567 & 0.783108861796217 \tabularnewline
98 & 0.189679544934141 & 0.379359089868282 & 0.810320455065859 \tabularnewline
99 & 0.170044876442442 & 0.340089752884884 & 0.829955123557558 \tabularnewline
100 & 0.14248103768326 & 0.284962075366521 & 0.85751896231674 \tabularnewline
101 & 0.122865136978013 & 0.245730273956026 & 0.877134863021987 \tabularnewline
102 & 0.108207669569302 & 0.216415339138603 & 0.891792330430698 \tabularnewline
103 & 0.226553149711547 & 0.453106299423094 & 0.773446850288453 \tabularnewline
104 & 0.195846035308321 & 0.391692070616642 & 0.804153964691679 \tabularnewline
105 & 0.187785345094499 & 0.375570690188999 & 0.812214654905501 \tabularnewline
106 & 0.182048099673652 & 0.364096199347304 & 0.817951900326348 \tabularnewline
107 & 0.181743005397821 & 0.363486010795641 & 0.818256994602179 \tabularnewline
108 & 0.162005801845978 & 0.324011603691955 & 0.837994198154022 \tabularnewline
109 & 0.150277949501744 & 0.300555899003487 & 0.849722050498256 \tabularnewline
110 & 0.223147612255261 & 0.446295224510523 & 0.776852387744739 \tabularnewline
111 & 0.208699843129037 & 0.417399686258073 & 0.791300156870963 \tabularnewline
112 & 0.193203630301731 & 0.386407260603461 & 0.80679636969827 \tabularnewline
113 & 0.249008686260012 & 0.498017372520024 & 0.750991313739988 \tabularnewline
114 & 0.219463339492263 & 0.438926678984526 & 0.780536660507737 \tabularnewline
115 & 0.244309974080558 & 0.488619948161116 & 0.755690025919442 \tabularnewline
116 & 0.269478598079733 & 0.538957196159465 & 0.730521401920267 \tabularnewline
117 & 0.234631942043596 & 0.469263884087192 & 0.765368057956404 \tabularnewline
118 & 0.205741453377114 & 0.411482906754228 & 0.794258546622886 \tabularnewline
119 & 0.188595752082638 & 0.377191504165277 & 0.811404247917362 \tabularnewline
120 & 0.166590512383901 & 0.333181024767803 & 0.833409487616099 \tabularnewline
121 & 0.14611773818182 & 0.292235476363641 & 0.853882261818179 \tabularnewline
122 & 0.119908914752559 & 0.239817829505118 & 0.880091085247441 \tabularnewline
123 & 0.112965419876605 & 0.225930839753209 & 0.887034580123395 \tabularnewline
124 & 0.101040678904573 & 0.202081357809146 & 0.898959321095427 \tabularnewline
125 & 0.0814922596344041 & 0.162984519268808 & 0.918507740365596 \tabularnewline
126 & 0.0806619183765786 & 0.161323836753157 & 0.919338081623421 \tabularnewline
127 & 0.062159682392109 & 0.124319364784218 & 0.937840317607891 \tabularnewline
128 & 0.0635023757409768 & 0.127004751481954 & 0.936497624259023 \tabularnewline
129 & 0.0496745474167305 & 0.0993490948334611 & 0.95032545258327 \tabularnewline
130 & 0.0490360754002225 & 0.0980721508004449 & 0.950963924599778 \tabularnewline
131 & 0.052620676608008 & 0.105241353216016 & 0.947379323391992 \tabularnewline
132 & 0.0707561243953749 & 0.14151224879075 & 0.929243875604625 \tabularnewline
133 & 0.0687805629224952 & 0.13756112584499 & 0.931219437077505 \tabularnewline
134 & 0.0583924428205269 & 0.116784885641054 & 0.941607557179473 \tabularnewline
135 & 0.0487160526656044 & 0.0974321053312088 & 0.951283947334396 \tabularnewline
136 & 0.0348955618145146 & 0.0697911236290293 & 0.965104438185485 \tabularnewline
137 & 0.036864713058767 & 0.073729426117534 & 0.963135286941233 \tabularnewline
138 & 0.0277453508911209 & 0.0554907017822418 & 0.972254649108879 \tabularnewline
139 & 0.0539092494504829 & 0.107818498900966 & 0.946090750549517 \tabularnewline
140 & 0.0528683839961581 & 0.105736767992316 & 0.947131616003842 \tabularnewline
141 & 0.244187547652699 & 0.488375095305397 & 0.755812452347302 \tabularnewline
142 & 0.191923178666268 & 0.383846357332536 & 0.808076821333732 \tabularnewline
143 & 0.145877439790469 & 0.291754879580938 & 0.854122560209531 \tabularnewline
144 & 0.148783758926263 & 0.297567517852527 & 0.851216241073737 \tabularnewline
145 & 0.110201702560329 & 0.220403405120659 & 0.889798297439671 \tabularnewline
146 & 0.260291607644637 & 0.520583215289274 & 0.739708392355363 \tabularnewline
147 & 0.330478559581198 & 0.660957119162396 & 0.669521440418802 \tabularnewline
148 & 0.253602094791561 & 0.507204189583122 & 0.746397905208439 \tabularnewline
149 & 0.30570874617778 & 0.61141749235556 & 0.69429125382222 \tabularnewline
150 & 0.234959146926572 & 0.469918293853143 & 0.765040853073428 \tabularnewline
151 & 0.433768923417284 & 0.867537846834568 & 0.566231076582716 \tabularnewline
152 & 0.655662016201961 & 0.688675967596079 & 0.344337983798039 \tabularnewline
153 & 0.510881015942733 & 0.978237968114534 & 0.489118984057267 \tabularnewline
154 & 0.348591766763092 & 0.697183533526183 & 0.651408233236908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&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]0.585009105821407[/C][C]0.829981788357185[/C][C]0.414990894178593[/C][/ROW]
[ROW][C]9[/C][C]0.704215988902272[/C][C]0.591568022195455[/C][C]0.295784011097728[/C][/ROW]
[ROW][C]10[/C][C]0.663218312829901[/C][C]0.673563374340197[/C][C]0.336781687170099[/C][/ROW]
[ROW][C]11[/C][C]0.610634340490298[/C][C]0.778731319019404[/C][C]0.389365659509702[/C][/ROW]
[ROW][C]12[/C][C]0.494108431984788[/C][C]0.988216863969575[/C][C]0.505891568015212[/C][/ROW]
[ROW][C]13[/C][C]0.393108888525907[/C][C]0.786217777051815[/C][C]0.606891111474092[/C][/ROW]
[ROW][C]14[/C][C]0.33089932485672[/C][C]0.66179864971344[/C][C]0.66910067514328[/C][/ROW]
[ROW][C]15[/C][C]0.337941823413809[/C][C]0.675883646827618[/C][C]0.662058176586191[/C][/ROW]
[ROW][C]16[/C][C]0.259885933420624[/C][C]0.519771866841248[/C][C]0.740114066579376[/C][/ROW]
[ROW][C]17[/C][C]0.192115868931762[/C][C]0.384231737863524[/C][C]0.807884131068238[/C][/ROW]
[ROW][C]18[/C][C]0.449201268241409[/C][C]0.898402536482818[/C][C]0.550798731758591[/C][/ROW]
[ROW][C]19[/C][C]0.49126751917717[/C][C]0.982535038354339[/C][C]0.50873248082283[/C][/ROW]
[ROW][C]20[/C][C]0.424642947932889[/C][C]0.849285895865778[/C][C]0.575357052067111[/C][/ROW]
[ROW][C]21[/C][C]0.354381010093129[/C][C]0.708762020186259[/C][C]0.645618989906871[/C][/ROW]
[ROW][C]22[/C][C]0.289464247924748[/C][C]0.578928495849495[/C][C]0.710535752075252[/C][/ROW]
[ROW][C]23[/C][C]0.338969449367633[/C][C]0.677938898735266[/C][C]0.661030550632367[/C][/ROW]
[ROW][C]24[/C][C]0.27903142771201[/C][C]0.55806285542402[/C][C]0.72096857228799[/C][/ROW]
[ROW][C]25[/C][C]0.23656548622537[/C][C]0.47313097245074[/C][C]0.76343451377463[/C][/ROW]
[ROW][C]26[/C][C]0.190300967138034[/C][C]0.380601934276068[/C][C]0.809699032861966[/C][/ROW]
[ROW][C]27[/C][C]0.151345422934402[/C][C]0.302690845868804[/C][C]0.848654577065598[/C][/ROW]
[ROW][C]28[/C][C]0.123721170452902[/C][C]0.247442340905805[/C][C]0.876278829547098[/C][/ROW]
[ROW][C]29[/C][C]0.0937403362356035[/C][C]0.187480672471207[/C][C]0.906259663764396[/C][/ROW]
[ROW][C]30[/C][C]0.0879314752594776[/C][C]0.175862950518955[/C][C]0.912068524740522[/C][/ROW]
[ROW][C]31[/C][C]0.064734990041105[/C][C]0.12946998008221[/C][C]0.935265009958895[/C][/ROW]
[ROW][C]32[/C][C]0.0771299356346783[/C][C]0.154259871269357[/C][C]0.922870064365322[/C][/ROW]
[ROW][C]33[/C][C]0.0719147497650853[/C][C]0.143829499530171[/C][C]0.928085250234915[/C][/ROW]
[ROW][C]34[/C][C]0.0535749956963444[/C][C]0.107149991392689[/C][C]0.946425004303656[/C][/ROW]
[ROW][C]35[/C][C]0.0417552525341099[/C][C]0.0835105050682198[/C][C]0.95824474746589[/C][/ROW]
[ROW][C]36[/C][C]0.489389845418953[/C][C]0.978779690837906[/C][C]0.510610154581047[/C][/ROW]
[ROW][C]37[/C][C]0.564395432580024[/C][C]0.871209134839953[/C][C]0.435604567419976[/C][/ROW]
[ROW][C]38[/C][C]0.514220149348874[/C][C]0.971559701302251[/C][C]0.485779850651126[/C][/ROW]
[ROW][C]39[/C][C]0.49132363591253[/C][C]0.98264727182506[/C][C]0.50867636408747[/C][/ROW]
[ROW][C]40[/C][C]0.460604295016545[/C][C]0.921208590033091[/C][C]0.539395704983455[/C][/ROW]
[ROW][C]41[/C][C]0.419423520887179[/C][C]0.838847041774358[/C][C]0.580576479112821[/C][/ROW]
[ROW][C]42[/C][C]0.390004645041807[/C][C]0.780009290083614[/C][C]0.609995354958193[/C][/ROW]
[ROW][C]43[/C][C]0.57355882738983[/C][C]0.85288234522034[/C][C]0.42644117261017[/C][/ROW]
[ROW][C]44[/C][C]0.528506921169177[/C][C]0.942986157661646[/C][C]0.471493078830823[/C][/ROW]
[ROW][C]45[/C][C]0.507297904900618[/C][C]0.985404190198764[/C][C]0.492702095099382[/C][/ROW]
[ROW][C]46[/C][C]0.686059304130129[/C][C]0.627881391739741[/C][C]0.313940695869871[/C][/ROW]
[ROW][C]47[/C][C]0.691617293325342[/C][C]0.616765413349315[/C][C]0.308382706674658[/C][/ROW]
[ROW][C]48[/C][C]0.645467478547375[/C][C]0.709065042905251[/C][C]0.354532521452625[/C][/ROW]
[ROW][C]49[/C][C]0.600658146720925[/C][C]0.798683706558151[/C][C]0.399341853279075[/C][/ROW]
[ROW][C]50[/C][C]0.56833055116651[/C][C]0.863338897666981[/C][C]0.43166944883349[/C][/ROW]
[ROW][C]51[/C][C]0.528421579698805[/C][C]0.943156840602389[/C][C]0.471578420301195[/C][/ROW]
[ROW][C]52[/C][C]0.478801205921308[/C][C]0.957602411842617[/C][C]0.521198794078692[/C][/ROW]
[ROW][C]53[/C][C]0.47871664562484[/C][C]0.95743329124968[/C][C]0.52128335437516[/C][/ROW]
[ROW][C]54[/C][C]0.435264708518911[/C][C]0.870529417037821[/C][C]0.564735291481089[/C][/ROW]
[ROW][C]55[/C][C]0.637238300393545[/C][C]0.725523399212909[/C][C]0.362761699606455[/C][/ROW]
[ROW][C]56[/C][C]0.614196585076058[/C][C]0.771606829847884[/C][C]0.385803414923942[/C][/ROW]
[ROW][C]57[/C][C]0.568301540668821[/C][C]0.863396918662357[/C][C]0.431698459331179[/C][/ROW]
[ROW][C]58[/C][C]0.54847060942396[/C][C]0.90305878115208[/C][C]0.45152939057604[/C][/ROW]
[ROW][C]59[/C][C]0.500598103717488[/C][C]0.998803792565025[/C][C]0.499401896282512[/C][/ROW]
[ROW][C]60[/C][C]0.457904678410479[/C][C]0.915809356820959[/C][C]0.542095321589521[/C][/ROW]
[ROW][C]61[/C][C]0.437925108122181[/C][C]0.875850216244363[/C][C]0.562074891877819[/C][/ROW]
[ROW][C]62[/C][C]0.398150960401897[/C][C]0.796301920803795[/C][C]0.601849039598103[/C][/ROW]
[ROW][C]63[/C][C]0.359613749575736[/C][C]0.719227499151472[/C][C]0.640386250424264[/C][/ROW]
[ROW][C]64[/C][C]0.319256361334201[/C][C]0.638512722668402[/C][C]0.680743638665799[/C][/ROW]
[ROW][C]65[/C][C]0.287565101296036[/C][C]0.575130202592072[/C][C]0.712434898703964[/C][/ROW]
[ROW][C]66[/C][C]0.284862471302283[/C][C]0.569724942604566[/C][C]0.715137528697717[/C][/ROW]
[ROW][C]67[/C][C]0.296843003721714[/C][C]0.593686007443429[/C][C]0.703156996278286[/C][/ROW]
[ROW][C]68[/C][C]0.428399620033379[/C][C]0.856799240066759[/C][C]0.571600379966621[/C][/ROW]
[ROW][C]69[/C][C]0.531229631659783[/C][C]0.937540736680434[/C][C]0.468770368340217[/C][/ROW]
[ROW][C]70[/C][C]0.500217204611191[/C][C]0.999565590777618[/C][C]0.499782795388809[/C][/ROW]
[ROW][C]71[/C][C]0.684752372945042[/C][C]0.630495254109915[/C][C]0.315247627054958[/C][/ROW]
[ROW][C]72[/C][C]0.646299494233107[/C][C]0.707401011533787[/C][C]0.353700505766893[/C][/ROW]
[ROW][C]73[/C][C]0.679784362057905[/C][C]0.640431275884191[/C][C]0.320215637942095[/C][/ROW]
[ROW][C]74[/C][C]0.706118339779045[/C][C]0.58776332044191[/C][C]0.293881660220955[/C][/ROW]
[ROW][C]75[/C][C]0.683070767872326[/C][C]0.633858464255347[/C][C]0.316929232127674[/C][/ROW]
[ROW][C]76[/C][C]0.652927057396266[/C][C]0.694145885207468[/C][C]0.347072942603734[/C][/ROW]
[ROW][C]77[/C][C]0.613543759451225[/C][C]0.772912481097549[/C][C]0.386456240548775[/C][/ROW]
[ROW][C]78[/C][C]0.575580120607517[/C][C]0.848839758784966[/C][C]0.424419879392483[/C][/ROW]
[ROW][C]79[/C][C]0.535117920264646[/C][C]0.929764159470708[/C][C]0.464882079735354[/C][/ROW]
[ROW][C]80[/C][C]0.497178520857465[/C][C]0.99435704171493[/C][C]0.502821479142535[/C][/ROW]
[ROW][C]81[/C][C]0.473885289419896[/C][C]0.947770578839793[/C][C]0.526114710580104[/C][/ROW]
[ROW][C]82[/C][C]0.666185879972839[/C][C]0.667628240054323[/C][C]0.333814120027161[/C][/ROW]
[ROW][C]83[/C][C]0.632641796396672[/C][C]0.734716407206656[/C][C]0.367358203603328[/C][/ROW]
[ROW][C]84[/C][C]0.589865243845872[/C][C]0.820269512308255[/C][C]0.410134756154128[/C][/ROW]
[ROW][C]85[/C][C]0.544924897663347[/C][C]0.910150204673307[/C][C]0.455075102336653[/C][/ROW]
[ROW][C]86[/C][C]0.507271553889231[/C][C]0.985456892221539[/C][C]0.492728446110769[/C][/ROW]
[ROW][C]87[/C][C]0.473214028908564[/C][C]0.946428057817129[/C][C]0.526785971091436[/C][/ROW]
[ROW][C]88[/C][C]0.440613825035293[/C][C]0.881227650070585[/C][C]0.559386174964707[/C][/ROW]
[ROW][C]89[/C][C]0.419303434431938[/C][C]0.838606868863875[/C][C]0.580696565568062[/C][/ROW]
[ROW][C]90[/C][C]0.376036554276866[/C][C]0.752073108553731[/C][C]0.623963445723134[/C][/ROW]
[ROW][C]91[/C][C]0.389953736635264[/C][C]0.779907473270529[/C][C]0.610046263364736[/C][/ROW]
[ROW][C]92[/C][C]0.374831498443931[/C][C]0.749662996887861[/C][C]0.625168501556069[/C][/ROW]
[ROW][C]93[/C][C]0.345171551400572[/C][C]0.690343102801144[/C][C]0.654828448599428[/C][/ROW]
[ROW][C]94[/C][C]0.303980467369937[/C][C]0.607960934739874[/C][C]0.696019532630063[/C][/ROW]
[ROW][C]95[/C][C]0.279226660082188[/C][C]0.558453320164375[/C][C]0.720773339917812[/C][/ROW]
[ROW][C]96[/C][C]0.24591437703366[/C][C]0.49182875406732[/C][C]0.75408562296634[/C][/ROW]
[ROW][C]97[/C][C]0.216891138203783[/C][C]0.433782276407567[/C][C]0.783108861796217[/C][/ROW]
[ROW][C]98[/C][C]0.189679544934141[/C][C]0.379359089868282[/C][C]0.810320455065859[/C][/ROW]
[ROW][C]99[/C][C]0.170044876442442[/C][C]0.340089752884884[/C][C]0.829955123557558[/C][/ROW]
[ROW][C]100[/C][C]0.14248103768326[/C][C]0.284962075366521[/C][C]0.85751896231674[/C][/ROW]
[ROW][C]101[/C][C]0.122865136978013[/C][C]0.245730273956026[/C][C]0.877134863021987[/C][/ROW]
[ROW][C]102[/C][C]0.108207669569302[/C][C]0.216415339138603[/C][C]0.891792330430698[/C][/ROW]
[ROW][C]103[/C][C]0.226553149711547[/C][C]0.453106299423094[/C][C]0.773446850288453[/C][/ROW]
[ROW][C]104[/C][C]0.195846035308321[/C][C]0.391692070616642[/C][C]0.804153964691679[/C][/ROW]
[ROW][C]105[/C][C]0.187785345094499[/C][C]0.375570690188999[/C][C]0.812214654905501[/C][/ROW]
[ROW][C]106[/C][C]0.182048099673652[/C][C]0.364096199347304[/C][C]0.817951900326348[/C][/ROW]
[ROW][C]107[/C][C]0.181743005397821[/C][C]0.363486010795641[/C][C]0.818256994602179[/C][/ROW]
[ROW][C]108[/C][C]0.162005801845978[/C][C]0.324011603691955[/C][C]0.837994198154022[/C][/ROW]
[ROW][C]109[/C][C]0.150277949501744[/C][C]0.300555899003487[/C][C]0.849722050498256[/C][/ROW]
[ROW][C]110[/C][C]0.223147612255261[/C][C]0.446295224510523[/C][C]0.776852387744739[/C][/ROW]
[ROW][C]111[/C][C]0.208699843129037[/C][C]0.417399686258073[/C][C]0.791300156870963[/C][/ROW]
[ROW][C]112[/C][C]0.193203630301731[/C][C]0.386407260603461[/C][C]0.80679636969827[/C][/ROW]
[ROW][C]113[/C][C]0.249008686260012[/C][C]0.498017372520024[/C][C]0.750991313739988[/C][/ROW]
[ROW][C]114[/C][C]0.219463339492263[/C][C]0.438926678984526[/C][C]0.780536660507737[/C][/ROW]
[ROW][C]115[/C][C]0.244309974080558[/C][C]0.488619948161116[/C][C]0.755690025919442[/C][/ROW]
[ROW][C]116[/C][C]0.269478598079733[/C][C]0.538957196159465[/C][C]0.730521401920267[/C][/ROW]
[ROW][C]117[/C][C]0.234631942043596[/C][C]0.469263884087192[/C][C]0.765368057956404[/C][/ROW]
[ROW][C]118[/C][C]0.205741453377114[/C][C]0.411482906754228[/C][C]0.794258546622886[/C][/ROW]
[ROW][C]119[/C][C]0.188595752082638[/C][C]0.377191504165277[/C][C]0.811404247917362[/C][/ROW]
[ROW][C]120[/C][C]0.166590512383901[/C][C]0.333181024767803[/C][C]0.833409487616099[/C][/ROW]
[ROW][C]121[/C][C]0.14611773818182[/C][C]0.292235476363641[/C][C]0.853882261818179[/C][/ROW]
[ROW][C]122[/C][C]0.119908914752559[/C][C]0.239817829505118[/C][C]0.880091085247441[/C][/ROW]
[ROW][C]123[/C][C]0.112965419876605[/C][C]0.225930839753209[/C][C]0.887034580123395[/C][/ROW]
[ROW][C]124[/C][C]0.101040678904573[/C][C]0.202081357809146[/C][C]0.898959321095427[/C][/ROW]
[ROW][C]125[/C][C]0.0814922596344041[/C][C]0.162984519268808[/C][C]0.918507740365596[/C][/ROW]
[ROW][C]126[/C][C]0.0806619183765786[/C][C]0.161323836753157[/C][C]0.919338081623421[/C][/ROW]
[ROW][C]127[/C][C]0.062159682392109[/C][C]0.124319364784218[/C][C]0.937840317607891[/C][/ROW]
[ROW][C]128[/C][C]0.0635023757409768[/C][C]0.127004751481954[/C][C]0.936497624259023[/C][/ROW]
[ROW][C]129[/C][C]0.0496745474167305[/C][C]0.0993490948334611[/C][C]0.95032545258327[/C][/ROW]
[ROW][C]130[/C][C]0.0490360754002225[/C][C]0.0980721508004449[/C][C]0.950963924599778[/C][/ROW]
[ROW][C]131[/C][C]0.052620676608008[/C][C]0.105241353216016[/C][C]0.947379323391992[/C][/ROW]
[ROW][C]132[/C][C]0.0707561243953749[/C][C]0.14151224879075[/C][C]0.929243875604625[/C][/ROW]
[ROW][C]133[/C][C]0.0687805629224952[/C][C]0.13756112584499[/C][C]0.931219437077505[/C][/ROW]
[ROW][C]134[/C][C]0.0583924428205269[/C][C]0.116784885641054[/C][C]0.941607557179473[/C][/ROW]
[ROW][C]135[/C][C]0.0487160526656044[/C][C]0.0974321053312088[/C][C]0.951283947334396[/C][/ROW]
[ROW][C]136[/C][C]0.0348955618145146[/C][C]0.0697911236290293[/C][C]0.965104438185485[/C][/ROW]
[ROW][C]137[/C][C]0.036864713058767[/C][C]0.073729426117534[/C][C]0.963135286941233[/C][/ROW]
[ROW][C]138[/C][C]0.0277453508911209[/C][C]0.0554907017822418[/C][C]0.972254649108879[/C][/ROW]
[ROW][C]139[/C][C]0.0539092494504829[/C][C]0.107818498900966[/C][C]0.946090750549517[/C][/ROW]
[ROW][C]140[/C][C]0.0528683839961581[/C][C]0.105736767992316[/C][C]0.947131616003842[/C][/ROW]
[ROW][C]141[/C][C]0.244187547652699[/C][C]0.488375095305397[/C][C]0.755812452347302[/C][/ROW]
[ROW][C]142[/C][C]0.191923178666268[/C][C]0.383846357332536[/C][C]0.808076821333732[/C][/ROW]
[ROW][C]143[/C][C]0.145877439790469[/C][C]0.291754879580938[/C][C]0.854122560209531[/C][/ROW]
[ROW][C]144[/C][C]0.148783758926263[/C][C]0.297567517852527[/C][C]0.851216241073737[/C][/ROW]
[ROW][C]145[/C][C]0.110201702560329[/C][C]0.220403405120659[/C][C]0.889798297439671[/C][/ROW]
[ROW][C]146[/C][C]0.260291607644637[/C][C]0.520583215289274[/C][C]0.739708392355363[/C][/ROW]
[ROW][C]147[/C][C]0.330478559581198[/C][C]0.660957119162396[/C][C]0.669521440418802[/C][/ROW]
[ROW][C]148[/C][C]0.253602094791561[/C][C]0.507204189583122[/C][C]0.746397905208439[/C][/ROW]
[ROW][C]149[/C][C]0.30570874617778[/C][C]0.61141749235556[/C][C]0.69429125382222[/C][/ROW]
[ROW][C]150[/C][C]0.234959146926572[/C][C]0.469918293853143[/C][C]0.765040853073428[/C][/ROW]
[ROW][C]151[/C][C]0.433768923417284[/C][C]0.867537846834568[/C][C]0.566231076582716[/C][/ROW]
[ROW][C]152[/C][C]0.655662016201961[/C][C]0.688675967596079[/C][C]0.344337983798039[/C][/ROW]
[ROW][C]153[/C][C]0.510881015942733[/C][C]0.978237968114534[/C][C]0.489118984057267[/C][/ROW]
[ROW][C]154[/C][C]0.348591766763092[/C][C]0.697183533526183[/C][C]0.651408233236908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5850091058214070.8299817883571850.414990894178593
90.7042159889022720.5915680221954550.295784011097728
100.6632183128299010.6735633743401970.336781687170099
110.6106343404902980.7787313190194040.389365659509702
120.4941084319847880.9882168639695750.505891568015212
130.3931088885259070.7862177770518150.606891111474092
140.330899324856720.661798649713440.66910067514328
150.3379418234138090.6758836468276180.662058176586191
160.2598859334206240.5197718668412480.740114066579376
170.1921158689317620.3842317378635240.807884131068238
180.4492012682414090.8984025364828180.550798731758591
190.491267519177170.9825350383543390.50873248082283
200.4246429479328890.8492858958657780.575357052067111
210.3543810100931290.7087620201862590.645618989906871
220.2894642479247480.5789284958494950.710535752075252
230.3389694493676330.6779388987352660.661030550632367
240.279031427712010.558062855424020.72096857228799
250.236565486225370.473130972450740.76343451377463
260.1903009671380340.3806019342760680.809699032861966
270.1513454229344020.3026908458688040.848654577065598
280.1237211704529020.2474423409058050.876278829547098
290.09374033623560350.1874806724712070.906259663764396
300.08793147525947760.1758629505189550.912068524740522
310.0647349900411050.129469980082210.935265009958895
320.07712993563467830.1542598712693570.922870064365322
330.07191474976508530.1438294995301710.928085250234915
340.05357499569634440.1071499913926890.946425004303656
350.04175525253410990.08351050506821980.95824474746589
360.4893898454189530.9787796908379060.510610154581047
370.5643954325800240.8712091348399530.435604567419976
380.5142201493488740.9715597013022510.485779850651126
390.491323635912530.982647271825060.50867636408747
400.4606042950165450.9212085900330910.539395704983455
410.4194235208871790.8388470417743580.580576479112821
420.3900046450418070.7800092900836140.609995354958193
430.573558827389830.852882345220340.42644117261017
440.5285069211691770.9429861576616460.471493078830823
450.5072979049006180.9854041901987640.492702095099382
460.6860593041301290.6278813917397410.313940695869871
470.6916172933253420.6167654133493150.308382706674658
480.6454674785473750.7090650429052510.354532521452625
490.6006581467209250.7986837065581510.399341853279075
500.568330551166510.8633388976669810.43166944883349
510.5284215796988050.9431568406023890.471578420301195
520.4788012059213080.9576024118426170.521198794078692
530.478716645624840.957433291249680.52128335437516
540.4352647085189110.8705294170378210.564735291481089
550.6372383003935450.7255233992129090.362761699606455
560.6141965850760580.7716068298478840.385803414923942
570.5683015406688210.8633969186623570.431698459331179
580.548470609423960.903058781152080.45152939057604
590.5005981037174880.9988037925650250.499401896282512
600.4579046784104790.9158093568209590.542095321589521
610.4379251081221810.8758502162443630.562074891877819
620.3981509604018970.7963019208037950.601849039598103
630.3596137495757360.7192274991514720.640386250424264
640.3192563613342010.6385127226684020.680743638665799
650.2875651012960360.5751302025920720.712434898703964
660.2848624713022830.5697249426045660.715137528697717
670.2968430037217140.5936860074434290.703156996278286
680.4283996200333790.8567992400667590.571600379966621
690.5312296316597830.9375407366804340.468770368340217
700.5002172046111910.9995655907776180.499782795388809
710.6847523729450420.6304952541099150.315247627054958
720.6462994942331070.7074010115337870.353700505766893
730.6797843620579050.6404312758841910.320215637942095
740.7061183397790450.587763320441910.293881660220955
750.6830707678723260.6338584642553470.316929232127674
760.6529270573962660.6941458852074680.347072942603734
770.6135437594512250.7729124810975490.386456240548775
780.5755801206075170.8488397587849660.424419879392483
790.5351179202646460.9297641594707080.464882079735354
800.4971785208574650.994357041714930.502821479142535
810.4738852894198960.9477705788397930.526114710580104
820.6661858799728390.6676282400543230.333814120027161
830.6326417963966720.7347164072066560.367358203603328
840.5898652438458720.8202695123082550.410134756154128
850.5449248976633470.9101502046733070.455075102336653
860.5072715538892310.9854568922215390.492728446110769
870.4732140289085640.9464280578171290.526785971091436
880.4406138250352930.8812276500705850.559386174964707
890.4193034344319380.8386068688638750.580696565568062
900.3760365542768660.7520731085537310.623963445723134
910.3899537366352640.7799074732705290.610046263364736
920.3748314984439310.7496629968878610.625168501556069
930.3451715514005720.6903431028011440.654828448599428
940.3039804673699370.6079609347398740.696019532630063
950.2792266600821880.5584533201643750.720773339917812
960.245914377033660.491828754067320.75408562296634
970.2168911382037830.4337822764075670.783108861796217
980.1896795449341410.3793590898682820.810320455065859
990.1700448764424420.3400897528848840.829955123557558
1000.142481037683260.2849620753665210.85751896231674
1010.1228651369780130.2457302739560260.877134863021987
1020.1082076695693020.2164153391386030.891792330430698
1030.2265531497115470.4531062994230940.773446850288453
1040.1958460353083210.3916920706166420.804153964691679
1050.1877853450944990.3755706901889990.812214654905501
1060.1820480996736520.3640961993473040.817951900326348
1070.1817430053978210.3634860107956410.818256994602179
1080.1620058018459780.3240116036919550.837994198154022
1090.1502779495017440.3005558990034870.849722050498256
1100.2231476122552610.4462952245105230.776852387744739
1110.2086998431290370.4173996862580730.791300156870963
1120.1932036303017310.3864072606034610.80679636969827
1130.2490086862600120.4980173725200240.750991313739988
1140.2194633394922630.4389266789845260.780536660507737
1150.2443099740805580.4886199481611160.755690025919442
1160.2694785980797330.5389571961594650.730521401920267
1170.2346319420435960.4692638840871920.765368057956404
1180.2057414533771140.4114829067542280.794258546622886
1190.1885957520826380.3771915041652770.811404247917362
1200.1665905123839010.3331810247678030.833409487616099
1210.146117738181820.2922354763636410.853882261818179
1220.1199089147525590.2398178295051180.880091085247441
1230.1129654198766050.2259308397532090.887034580123395
1240.1010406789045730.2020813578091460.898959321095427
1250.08149225963440410.1629845192688080.918507740365596
1260.08066191837657860.1613238367531570.919338081623421
1270.0621596823921090.1243193647842180.937840317607891
1280.06350237574097680.1270047514819540.936497624259023
1290.04967454741673050.09934909483346110.95032545258327
1300.04903607540022250.09807215080044490.950963924599778
1310.0526206766080080.1052413532160160.947379323391992
1320.07075612439537490.141512248790750.929243875604625
1330.06878056292249520.137561125844990.931219437077505
1340.05839244282052690.1167848856410540.941607557179473
1350.04871605266560440.09743210533120880.951283947334396
1360.03489556181451460.06979112362902930.965104438185485
1370.0368647130587670.0737294261175340.963135286941233
1380.02774535089112090.05549070178224180.972254649108879
1390.05390924945048290.1078184989009660.946090750549517
1400.05286838399615810.1057367679923160.947131616003842
1410.2441875476526990.4883750953053970.755812452347302
1420.1919231786662680.3838463573325360.808076821333732
1430.1458774397904690.2917548795809380.854122560209531
1440.1487837589262630.2975675178525270.851216241073737
1450.1102017025603290.2204034051206590.889798297439671
1460.2602916076446370.5205832152892740.739708392355363
1470.3304785595811980.6609571191623960.669521440418802
1480.2536020947915610.5072041895831220.746397905208439
1490.305708746177780.611417492355560.69429125382222
1500.2349591469265720.4699182938531430.765040853073428
1510.4337689234172840.8675378468345680.566231076582716
1520.6556620162019610.6886759675960790.344337983798039
1530.5108810159427330.9782379681145340.489118984057267
1540.3485917667630920.6971835335261830.651408233236908







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0476190476190476OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0476190476190476 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186082&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0476190476190476[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186082&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186082&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0476190476190476OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}