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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 20 Nov 2010 16:26:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/20/t1290270312ctmcofsih5hcuma.htm/, Retrieved Sat, 27 Apr 2024 12:26:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98253, Retrieved Sat, 27 Apr 2024 12:26:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS7 - popularitei...] [2010-11-20 16:26:09] [934c3727858e074bf543f25f5906ed72] [Current]
-   P       [Multiple Regression] [WS7 - Determinist...] [2010-11-20 18:14:50] [8ef49741e164ec6343c90c7935194465]
-    D        [Multiple Regression] [ws 7] [2010-11-23 20:49:21] [8214fe6d084e5ad7598b249a26cc9f06]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	1
12	12	8	13	5	1
15	10	12	16	6	1
12	9	7	12	6	1
10	10	10	11	5	1
12	12	7	12	3	1
15	13	16	18	8	1
9	12	11	11	4	1
12	12	14	14	4	1
11	6	6	9	4	1
11	5	16	14	6	1
11	12	11	12	6	1
15	11	16	11	5	1
7	14	12	12	4	1
11	14	7	13	6	1
11	12	13	11	4	1
10	12	11	12	6	1
14	11	15	16	6	1
10	11	7	9	4	2
6	7	9	11	4	2
11	9	7	13	2	2
15	11	14	15	7	2
11	11	15	10	5	2
12	12	7	11	4	2
14	12	15	13	6	2
15	11	17	16	6	2
9	11	15	15	7	2
13	8	14	14	5	2
13	9	14	14	6	2
16	12	8	14	4	2
13	10	8	8	4	2
12	10	14	13	7	2
14	12	14	15	7	2
11	8	8	13	4	3
9	12	11	11	4	3
16	11	16	15	6	3
12	12	10	15	6	3
10	7	8	9	5	3
13	11	14	13	6	3
16	11	16	16	7	3
14	12	13	13	6	3
15	9	5	11	3	3
5	15	8	12	3	3
8	11	10	12	4	3
11	11	8	12	6	3
16	11	13	14	7	3
17	11	15	14	5	3
9	15	6	8	4	3
9	11	12	13	5	3
13	12	16	16	6	3
10	12	5	13	6	3
6	9	15	11	6	4
12	12	12	14	5	4
8	12	8	13	4	4
14	13	13	13	5	4
12	11	14	13	5	4
11	9	12	12	4	4
16	9	16	16	6	4
8	11	10	15	2	4
15	11	15	15	8	4
7	12	8	12	3	4
16	12	16	14	6	4
14	9	19	12	6	4
16	11	14	15	6	4
9	9	6	12	5	4
14	12	13	13	5	4
11	12	15	12	6	4
13	12	7	12	5	4
15	12	13	13	6	5
5	14	4	5	2	5
15	11	14	13	5	5
13	12	13	13	5	5
11	11	11	14	5	5
11	6	14	17	6	5
12	10	12	13	6	5
12	12	15	13	6	5
12	13	14	12	5	5
12	8	13	13	5	5
14	12	8	14	4	5
6	12	6	11	2	5
7	12	7	12	4	5
14	6	13	12	6	5
14	11	13	16	6	5
10	10	11	12	5	5
13	12	5	12	3	5
12	13	12	12	6	5
9	11	8	10	4	6
12	7	11	15	5	6
16	11	14	15	8	6
10	11	9	12	4	6
14	11	10	16	6	6
10	11	13	15	6	6
16	12	16	16	7	6
15	10	16	13	6	6
12	11	11	12	5	6
10	12	8	11	4	6
8	7	4	13	6	6
8	13	7	10	3	6
11	8	14	15	5	6
13	12	11	13	6	6
16	11	17	16	7	6
16	12	15	15	7	6
14	14	17	18	6	6
11	10	5	13	3	6
4	10	4	10	2	6
14	13	10	16	8	6
9	10	11	13	3	7
14	11	15	15	8	7
8	10	10	14	3	7
8	7	9	15	4	7
11	10	12	14	5	7
12	8	15	13	7	7
11	12	7	13	6	7
14	12	13	15	6	7
15	12	12	16	7	7
16	11	14	14	6	7
16	12	14	14	6	7
11	12	8	16	6	7
14	12	15	14	6	7
14	11	12	12	4	7
12	12	12	13	4	7
14	11	16	12	5	7
8	11	9	12	4	7
13	13	15	14	6	7
16	12	15	14	6	7
12	12	6	14	5	7
16	12	14	16	8	7
12	12	15	13	6	7
11	8	10	14	5	7
4	8	6	4	4	7
16	12	14	16	8	7
15	11	12	13	6	7
10	12	8	16	4	7
13	13	11	15	6	7
15	12	13	14	6	7
12	12	9	13	4	7
14	11	15	14	6	7
7	12	13	12	3	8
19	12	15	15	6	8
12	10	14	14	5	8
12	11	16	13	4	8
13	12	14	14	6	8
15	12	14	16	4	8
8	10	10	6	4	8
12	12	10	13	4	8
10	13	4	13	6	8
8	12	8	14	5	8
10	15	15	15	6	8
15	11	16	14	6	8
16	12	12	15	8	9
13	11	12	13	7	10
16	12	15	16	7	10
9	11	9	12	4	14
14	10	12	15	6	14
14	11	14	12	6	14
12	11	11	14	2	14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.305904887836174 + 0.094633084474476FindingFriends[t] + 0.243797535254599KnowingPeople[t] + 0.34915593440117Liked[t] + 0.627017042711136Celebrity[t] -0.00119728221808973Date[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.305904887836174 +  0.094633084474476FindingFriends[t] +  0.243797535254599KnowingPeople[t] +  0.34915593440117Liked[t] +  0.627017042711136Celebrity[t] -0.00119728221808973Date[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.305904887836174 +  0.094633084474476FindingFriends[t] +  0.243797535254599KnowingPeople[t] +  0.34915593440117Liked[t] +  0.627017042711136Celebrity[t] -0.00119728221808973Date[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.305904887836174 + 0.094633084474476FindingFriends[t] + 0.243797535254599KnowingPeople[t] + 0.34915593440117Liked[t] + 0.627017042711136Celebrity[t] -0.00119728221808973Date[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3059048878361741.4350880.21320.8314910.415745
FindingFriends0.0946330844744760.0963830.98180.3277590.163879
KnowingPeople0.2437975352545990.0615913.95830.0001165.8e-05
Liked0.349155934401170.0977093.57340.0004740.000237
Celebrity0.6270170427111360.1565944.00419.8e-054.9e-05
Date-0.001197282218089730.062964-0.0190.9848540.492427

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.305904887836174 & 1.435088 & 0.2132 & 0.831491 & 0.415745 \tabularnewline
FindingFriends & 0.094633084474476 & 0.096383 & 0.9818 & 0.327759 & 0.163879 \tabularnewline
KnowingPeople & 0.243797535254599 & 0.061591 & 3.9583 & 0.000116 & 5.8e-05 \tabularnewline
Liked & 0.34915593440117 & 0.097709 & 3.5734 & 0.000474 & 0.000237 \tabularnewline
Celebrity & 0.627017042711136 & 0.156594 & 4.0041 & 9.8e-05 & 4.9e-05 \tabularnewline
Date & -0.00119728221808973 & 0.062964 & -0.019 & 0.984854 & 0.492427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.305904887836174[/C][C]1.435088[/C][C]0.2132[/C][C]0.831491[/C][C]0.415745[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.094633084474476[/C][C]0.096383[/C][C]0.9818[/C][C]0.327759[/C][C]0.163879[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.243797535254599[/C][C]0.061591[/C][C]3.9583[/C][C]0.000116[/C][C]5.8e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.34915593440117[/C][C]0.097709[/C][C]3.5734[/C][C]0.000474[/C][C]0.000237[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.627017042711136[/C][C]0.156594[/C][C]4.0041[/C][C]9.8e-05[/C][C]4.9e-05[/C][/ROW]
[ROW][C]Date[/C][C]-0.00119728221808973[/C][C]0.062964[/C][C]-0.019[/C][C]0.984854[/C][C]0.492427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3059048878361741.4350880.21320.8314910.415745
FindingFriends0.0946330844744760.0963830.98180.3277590.163879
KnowingPeople0.2437975352545990.0615913.95830.0001165.8e-05
Liked0.349155934401170.0977093.57340.0004740.000237
Celebrity0.6270170427111360.1565944.00419.8e-054.9e-05
Date-0.001197282218089730.062964-0.0190.9848540.492427






Multiple Linear Regression - Regression Statistics
Multiple R0.706541857390106
R-squared0.499201396244261
Adjusted R-squared0.482508109452403
F-TEST (value)29.9043203695357
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11251892472136
Sum Squared Residuals669.410431095885

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.706541857390106 \tabularnewline
R-squared & 0.499201396244261 \tabularnewline
Adjusted R-squared & 0.482508109452403 \tabularnewline
F-TEST (value) & 29.9043203695357 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.11251892472136 \tabularnewline
Sum Squared Residuals & 669.410431095885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.706541857390106[/C][/ROW]
[ROW][C]R-squared[/C][C]0.499201396244261[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.482508109452403[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.9043203695357[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.11251892472136[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.410431095885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.706541857390106
R-squared0.499201396244261
Adjusted R-squared0.482508109452403
F-TEST (value)29.9043203695357
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11251892472136
Sum Squared Residuals669.410431095885






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.36818147269921.63181852730078
21211.06479726211950.935202737880534
31513.52520608010351.47479391989646
41210.81496158175141.1850384182486
51010.6648142948774-0.664814294877374
6129.217809707041432.78219029295857
71516.73664142877-1.73664142876999
8910.4708609563698-1.47086095636979
91212.2497213653371-0.249721365337096
10117.98576290444763.01423709555239
111113.3289189299472-2.32891892994723
121112.0740509761932-1.07405097619323
131512.22223259087942.77776740912056
14711.2530805949745-4.25308059497451
151111.6372829385250-0.637282938524961
161110.9584560268790.0415439731210098
171012.0740509761932-2.07405097619323
181414.3512317703418-0.351231770341828
19108.70152857985651.29847142014350
2069.50890318127013-3.50890318127013
21118.654852063089952.34514793691005
221514.38409806117910.615901938820894
231111.6280818390056-0.628081839005588
24129.494473533133312.50552646686669
251413.39719976939470.602800230605293
261514.83762955863290.162370441367064
27914.6278955964337-5.62789559643371
281312.49700878793220.502991212067762
291313.2186589151178-0.218658915117850
301610.78573887159145.21426112840858
31138.501537096235454.49846290376455
321213.5911531079023-1.59115310790229
331414.4787311456536-0.478731145653582
341110.05685331707430.943146682925747
35910.4684663919336-1.46846639193361
361614.24347880675911.75652119324092
371212.8753266797060-0.875326679705963
38109.192613537706230.807386462293765
391313.0575718674475-0.0575718674475427
401615.21965178387140.780348216128617
411412.90840741666741.09159258333258
42158.094764884271466.90523511572854
4359.74311193128328-4.74311193128328
44810.4791917066057-2.47919170660571
451111.2456307215188-0.245630721518783
461613.78994730930522.21005269069475
471713.02350829439223.97649170560783
4898.485910165880540.514089834119459
49911.9429597542272-2.94295975422721
501314.6872678256347-1.68726782563472
511010.9580271346306-0.958027134630632
52612.4125940827328-6.41259408273276
531212.3855514908848-0.385551490884766
54810.4341883727541-2.43418837275407
551412.37482617621271.62517382378733
561212.4293575425183-0.429357542518318
571110.77632332594790.223676674052136
581614.40217128999321.59782871000679
59810.2714281421689-2.27142814216886
601515.2525180747087-0.252518074708661
6179.45801539564176-2.45801539564176
621613.98775867461432.01224132538570
631413.73694015815230.263059841847675
641613.75468645403182.24531354596821
6599.9405551571314-0.940555157131408
661412.28019309173821.71980690826181
671113.0456492705574-2.04564927055736
681310.46825194580942.53174805419057
691512.90601285223122.09398714776876
7055.59978555783491-0.59978555783491
711512.42816026030022.57183973969977
721312.27899580952010.721004190479895
731112.0459235889376-1.04592358893760
741113.9786356182437-2.97863561824366
751212.4729491480277-0.47294914802769
761213.3936079227404-1.39360792274044
771212.268270494848-0.26827049484801
781211.90046347162220.0995365283777986
791410.78214702493713.21785297506285
8067.99305006580217-1.99305006580217
8179.84003762088021-2.84003762088021
821411.98905841098322.01094158901678
831413.85884757096030.141152429039728
841011.2529786356608-1.25297863566079
85138.725425507659884.27457449234012
861212.4076924670499-0.407692467049949
8799.2896929206399-0.289692920639904
881212.0153499032228-0.0153499032227770
891615.00632597501790.993674024982118
901010.2318023246968-0.231802324696840
911413.12625768297840.873742317021613
921013.5084943543410-3.50849435434101
931615.31069302169160.68930697830841
941513.4469420068281.55305799317201
951211.34641443791720.653585562082827
96109.733481939515550.266518060484451
97810.2374723303494-2.23747233034939
9888.60814451162312-0.608144511623122
991112.8413755934610-1.84137559346105
1001312.41722049950400.582779500496046
1011615.45985747247170.540142527528288
1021614.71773955203581.28226044796418
1031415.8150515519863-1.81505155198634
104118.8841179908942.11588200910600
10546.96583560972476-2.96583560972476
1061414.5695579373496-0.56955793734961
107910.3457059202035-1.34570592020351
1081415.2489262280544-1.24892622805439
109810.4510643193501-2.45106431935008
110810.8995405077844-2.89954050778435
1111112.1926934752815-1.19269347528154
1121213.6396980631175-1.63969806311749
1131111.4408330762675-0.44083307626747
1141413.60193015659740.3980698434026
1151514.33430559845510.665694401544895
1161613.40193867297642.59806132702365
1171613.49657175745082.50342824254917
1181112.7320984147256-1.73209841472558
1191413.74036929270540.259630707294573
1201410.96199764824253.03800235175745
1211211.40578666711820.594213332881809
1221412.56420483197211.43579516802792
123810.2306050424788-2.23060504247875
1241313.8350023771799-0.835002377179903
1251613.74036929270542.25963070729457
1261210.91917443270291.08082556729709
1271615.44891771167540.551082288324562
1281213.3912133583043-1.39121335830426
1291111.5158322358234-0.515832235823396
13046.42206570808217-2.42206570808217
1311615.44891771167540.551082288324562
1321512.56518766806602.43481233193401
1331011.4780643293033-1.47806432930331
1341313.2089681705627-0.208968170562678
1351513.25277422219621.74722577780377
1361210.67439406135441.32560593864560
1371413.64573620823100.354263791769049
138710.6722139430424-3.6722139430424
1391914.08832794488854.91167205511149
1401212.6790912635727-0.679091263572652
1411212.2851464414440-0.28514644144402
1421313.4953744752327-0.495374475232739
1431512.93965225861282.06034774138719
14488.28363660463377-0.283636604633769
1451210.91699431439091.08300568560910
1461010.8028762727601-0.80287627276006
147811.4055722209940-3.40557222099401
1481014.3722271983119-4.37222719831194
1491513.88833646126751.11166353873254
1501614.60977214232891.39022785767111
1511313.1886128641229-0.188612864122853
1521615.06210635756460.937893642435368
153910.2222240669521-1.22222406695212
1541413.16048547686720.839514523132779
1551412.69524582864741.30475417135261
1561210.15409692084141.84590307915861

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3681814726992 & 1.63181852730078 \tabularnewline
2 & 12 & 11.0647972621195 & 0.935202737880534 \tabularnewline
3 & 15 & 13.5252060801035 & 1.47479391989646 \tabularnewline
4 & 12 & 10.8149615817514 & 1.1850384182486 \tabularnewline
5 & 10 & 10.6648142948774 & -0.664814294877374 \tabularnewline
6 & 12 & 9.21780970704143 & 2.78219029295857 \tabularnewline
7 & 15 & 16.73664142877 & -1.73664142876999 \tabularnewline
8 & 9 & 10.4708609563698 & -1.47086095636979 \tabularnewline
9 & 12 & 12.2497213653371 & -0.249721365337096 \tabularnewline
10 & 11 & 7.9857629044476 & 3.01423709555239 \tabularnewline
11 & 11 & 13.3289189299472 & -2.32891892994723 \tabularnewline
12 & 11 & 12.0740509761932 & -1.07405097619323 \tabularnewline
13 & 15 & 12.2222325908794 & 2.77776740912056 \tabularnewline
14 & 7 & 11.2530805949745 & -4.25308059497451 \tabularnewline
15 & 11 & 11.6372829385250 & -0.637282938524961 \tabularnewline
16 & 11 & 10.958456026879 & 0.0415439731210098 \tabularnewline
17 & 10 & 12.0740509761932 & -2.07405097619323 \tabularnewline
18 & 14 & 14.3512317703418 & -0.351231770341828 \tabularnewline
19 & 10 & 8.7015285798565 & 1.29847142014350 \tabularnewline
20 & 6 & 9.50890318127013 & -3.50890318127013 \tabularnewline
21 & 11 & 8.65485206308995 & 2.34514793691005 \tabularnewline
22 & 15 & 14.3840980611791 & 0.615901938820894 \tabularnewline
23 & 11 & 11.6280818390056 & -0.628081839005588 \tabularnewline
24 & 12 & 9.49447353313331 & 2.50552646686669 \tabularnewline
25 & 14 & 13.3971997693947 & 0.602800230605293 \tabularnewline
26 & 15 & 14.8376295586329 & 0.162370441367064 \tabularnewline
27 & 9 & 14.6278955964337 & -5.62789559643371 \tabularnewline
28 & 13 & 12.4970087879322 & 0.502991212067762 \tabularnewline
29 & 13 & 13.2186589151178 & -0.218658915117850 \tabularnewline
30 & 16 & 10.7857388715914 & 5.21426112840858 \tabularnewline
31 & 13 & 8.50153709623545 & 4.49846290376455 \tabularnewline
32 & 12 & 13.5911531079023 & -1.59115310790229 \tabularnewline
33 & 14 & 14.4787311456536 & -0.478731145653582 \tabularnewline
34 & 11 & 10.0568533170743 & 0.943146682925747 \tabularnewline
35 & 9 & 10.4684663919336 & -1.46846639193361 \tabularnewline
36 & 16 & 14.2434788067591 & 1.75652119324092 \tabularnewline
37 & 12 & 12.8753266797060 & -0.875326679705963 \tabularnewline
38 & 10 & 9.19261353770623 & 0.807386462293765 \tabularnewline
39 & 13 & 13.0575718674475 & -0.0575718674475427 \tabularnewline
40 & 16 & 15.2196517838714 & 0.780348216128617 \tabularnewline
41 & 14 & 12.9084074166674 & 1.09159258333258 \tabularnewline
42 & 15 & 8.09476488427146 & 6.90523511572854 \tabularnewline
43 & 5 & 9.74311193128328 & -4.74311193128328 \tabularnewline
44 & 8 & 10.4791917066057 & -2.47919170660571 \tabularnewline
45 & 11 & 11.2456307215188 & -0.245630721518783 \tabularnewline
46 & 16 & 13.7899473093052 & 2.21005269069475 \tabularnewline
47 & 17 & 13.0235082943922 & 3.97649170560783 \tabularnewline
48 & 9 & 8.48591016588054 & 0.514089834119459 \tabularnewline
49 & 9 & 11.9429597542272 & -2.94295975422721 \tabularnewline
50 & 13 & 14.6872678256347 & -1.68726782563472 \tabularnewline
51 & 10 & 10.9580271346306 & -0.958027134630632 \tabularnewline
52 & 6 & 12.4125940827328 & -6.41259408273276 \tabularnewline
53 & 12 & 12.3855514908848 & -0.385551490884766 \tabularnewline
54 & 8 & 10.4341883727541 & -2.43418837275407 \tabularnewline
55 & 14 & 12.3748261762127 & 1.62517382378733 \tabularnewline
56 & 12 & 12.4293575425183 & -0.429357542518318 \tabularnewline
57 & 11 & 10.7763233259479 & 0.223676674052136 \tabularnewline
58 & 16 & 14.4021712899932 & 1.59782871000679 \tabularnewline
59 & 8 & 10.2714281421689 & -2.27142814216886 \tabularnewline
60 & 15 & 15.2525180747087 & -0.252518074708661 \tabularnewline
61 & 7 & 9.45801539564176 & -2.45801539564176 \tabularnewline
62 & 16 & 13.9877586746143 & 2.01224132538570 \tabularnewline
63 & 14 & 13.7369401581523 & 0.263059841847675 \tabularnewline
64 & 16 & 13.7546864540318 & 2.24531354596821 \tabularnewline
65 & 9 & 9.9405551571314 & -0.940555157131408 \tabularnewline
66 & 14 & 12.2801930917382 & 1.71980690826181 \tabularnewline
67 & 11 & 13.0456492705574 & -2.04564927055736 \tabularnewline
68 & 13 & 10.4682519458094 & 2.53174805419057 \tabularnewline
69 & 15 & 12.9060128522312 & 2.09398714776876 \tabularnewline
70 & 5 & 5.59978555783491 & -0.59978555783491 \tabularnewline
71 & 15 & 12.4281602603002 & 2.57183973969977 \tabularnewline
72 & 13 & 12.2789958095201 & 0.721004190479895 \tabularnewline
73 & 11 & 12.0459235889376 & -1.04592358893760 \tabularnewline
74 & 11 & 13.9786356182437 & -2.97863561824366 \tabularnewline
75 & 12 & 12.4729491480277 & -0.47294914802769 \tabularnewline
76 & 12 & 13.3936079227404 & -1.39360792274044 \tabularnewline
77 & 12 & 12.268270494848 & -0.26827049484801 \tabularnewline
78 & 12 & 11.9004634716222 & 0.0995365283777986 \tabularnewline
79 & 14 & 10.7821470249371 & 3.21785297506285 \tabularnewline
80 & 6 & 7.99305006580217 & -1.99305006580217 \tabularnewline
81 & 7 & 9.84003762088021 & -2.84003762088021 \tabularnewline
82 & 14 & 11.9890584109832 & 2.01094158901678 \tabularnewline
83 & 14 & 13.8588475709603 & 0.141152429039728 \tabularnewline
84 & 10 & 11.2529786356608 & -1.25297863566079 \tabularnewline
85 & 13 & 8.72542550765988 & 4.27457449234012 \tabularnewline
86 & 12 & 12.4076924670499 & -0.407692467049949 \tabularnewline
87 & 9 & 9.2896929206399 & -0.289692920639904 \tabularnewline
88 & 12 & 12.0153499032228 & -0.0153499032227770 \tabularnewline
89 & 16 & 15.0063259750179 & 0.993674024982118 \tabularnewline
90 & 10 & 10.2318023246968 & -0.231802324696840 \tabularnewline
91 & 14 & 13.1262576829784 & 0.873742317021613 \tabularnewline
92 & 10 & 13.5084943543410 & -3.50849435434101 \tabularnewline
93 & 16 & 15.3106930216916 & 0.68930697830841 \tabularnewline
94 & 15 & 13.446942006828 & 1.55305799317201 \tabularnewline
95 & 12 & 11.3464144379172 & 0.653585562082827 \tabularnewline
96 & 10 & 9.73348193951555 & 0.266518060484451 \tabularnewline
97 & 8 & 10.2374723303494 & -2.23747233034939 \tabularnewline
98 & 8 & 8.60814451162312 & -0.608144511623122 \tabularnewline
99 & 11 & 12.8413755934610 & -1.84137559346105 \tabularnewline
100 & 13 & 12.4172204995040 & 0.582779500496046 \tabularnewline
101 & 16 & 15.4598574724717 & 0.540142527528288 \tabularnewline
102 & 16 & 14.7177395520358 & 1.28226044796418 \tabularnewline
103 & 14 & 15.8150515519863 & -1.81505155198634 \tabularnewline
104 & 11 & 8.884117990894 & 2.11588200910600 \tabularnewline
105 & 4 & 6.96583560972476 & -2.96583560972476 \tabularnewline
106 & 14 & 14.5695579373496 & -0.56955793734961 \tabularnewline
107 & 9 & 10.3457059202035 & -1.34570592020351 \tabularnewline
108 & 14 & 15.2489262280544 & -1.24892622805439 \tabularnewline
109 & 8 & 10.4510643193501 & -2.45106431935008 \tabularnewline
110 & 8 & 10.8995405077844 & -2.89954050778435 \tabularnewline
111 & 11 & 12.1926934752815 & -1.19269347528154 \tabularnewline
112 & 12 & 13.6396980631175 & -1.63969806311749 \tabularnewline
113 & 11 & 11.4408330762675 & -0.44083307626747 \tabularnewline
114 & 14 & 13.6019301565974 & 0.3980698434026 \tabularnewline
115 & 15 & 14.3343055984551 & 0.665694401544895 \tabularnewline
116 & 16 & 13.4019386729764 & 2.59806132702365 \tabularnewline
117 & 16 & 13.4965717574508 & 2.50342824254917 \tabularnewline
118 & 11 & 12.7320984147256 & -1.73209841472558 \tabularnewline
119 & 14 & 13.7403692927054 & 0.259630707294573 \tabularnewline
120 & 14 & 10.9619976482425 & 3.03800235175745 \tabularnewline
121 & 12 & 11.4057866671182 & 0.594213332881809 \tabularnewline
122 & 14 & 12.5642048319721 & 1.43579516802792 \tabularnewline
123 & 8 & 10.2306050424788 & -2.23060504247875 \tabularnewline
124 & 13 & 13.8350023771799 & -0.835002377179903 \tabularnewline
125 & 16 & 13.7403692927054 & 2.25963070729457 \tabularnewline
126 & 12 & 10.9191744327029 & 1.08082556729709 \tabularnewline
127 & 16 & 15.4489177116754 & 0.551082288324562 \tabularnewline
128 & 12 & 13.3912133583043 & -1.39121335830426 \tabularnewline
129 & 11 & 11.5158322358234 & -0.515832235823396 \tabularnewline
130 & 4 & 6.42206570808217 & -2.42206570808217 \tabularnewline
131 & 16 & 15.4489177116754 & 0.551082288324562 \tabularnewline
132 & 15 & 12.5651876680660 & 2.43481233193401 \tabularnewline
133 & 10 & 11.4780643293033 & -1.47806432930331 \tabularnewline
134 & 13 & 13.2089681705627 & -0.208968170562678 \tabularnewline
135 & 15 & 13.2527742221962 & 1.74722577780377 \tabularnewline
136 & 12 & 10.6743940613544 & 1.32560593864560 \tabularnewline
137 & 14 & 13.6457362082310 & 0.354263791769049 \tabularnewline
138 & 7 & 10.6722139430424 & -3.6722139430424 \tabularnewline
139 & 19 & 14.0883279448885 & 4.91167205511149 \tabularnewline
140 & 12 & 12.6790912635727 & -0.679091263572652 \tabularnewline
141 & 12 & 12.2851464414440 & -0.28514644144402 \tabularnewline
142 & 13 & 13.4953744752327 & -0.495374475232739 \tabularnewline
143 & 15 & 12.9396522586128 & 2.06034774138719 \tabularnewline
144 & 8 & 8.28363660463377 & -0.283636604633769 \tabularnewline
145 & 12 & 10.9169943143909 & 1.08300568560910 \tabularnewline
146 & 10 & 10.8028762727601 & -0.80287627276006 \tabularnewline
147 & 8 & 11.4055722209940 & -3.40557222099401 \tabularnewline
148 & 10 & 14.3722271983119 & -4.37222719831194 \tabularnewline
149 & 15 & 13.8883364612675 & 1.11166353873254 \tabularnewline
150 & 16 & 14.6097721423289 & 1.39022785767111 \tabularnewline
151 & 13 & 13.1886128641229 & -0.188612864122853 \tabularnewline
152 & 16 & 15.0621063575646 & 0.937893642435368 \tabularnewline
153 & 9 & 10.2222240669521 & -1.22222406695212 \tabularnewline
154 & 14 & 13.1604854768672 & 0.839514523132779 \tabularnewline
155 & 14 & 12.6952458286474 & 1.30475417135261 \tabularnewline
156 & 12 & 10.1540969208414 & 1.84590307915861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&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]11.3681814726992[/C][C]1.63181852730078[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0647972621195[/C][C]0.935202737880534[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5252060801035[/C][C]1.47479391989646[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8149615817514[/C][C]1.1850384182486[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.6648142948774[/C][C]-0.664814294877374[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.21780970704143[/C][C]2.78219029295857[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.73664142877[/C][C]-1.73664142876999[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.4708609563698[/C][C]-1.47086095636979[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.2497213653371[/C][C]-0.249721365337096[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.9857629044476[/C][C]3.01423709555239[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3289189299472[/C][C]-2.32891892994723[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.0740509761932[/C][C]-1.07405097619323[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2222325908794[/C][C]2.77776740912056[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.2530805949745[/C][C]-4.25308059497451[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.6372829385250[/C][C]-0.637282938524961[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.958456026879[/C][C]0.0415439731210098[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.0740509761932[/C][C]-2.07405097619323[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3512317703418[/C][C]-0.351231770341828[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.7015285798565[/C][C]1.29847142014350[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.50890318127013[/C][C]-3.50890318127013[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.65485206308995[/C][C]2.34514793691005[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3840980611791[/C][C]0.615901938820894[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6280818390056[/C][C]-0.628081839005588[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.49447353313331[/C][C]2.50552646686669[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.3971997693947[/C][C]0.602800230605293[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.8376295586329[/C][C]0.162370441367064[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.6278955964337[/C][C]-5.62789559643371[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4970087879322[/C][C]0.502991212067762[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2186589151178[/C][C]-0.218658915117850[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.7857388715914[/C][C]5.21426112840858[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.50153709623545[/C][C]4.49846290376455[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5911531079023[/C][C]-1.59115310790229[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4787311456536[/C][C]-0.478731145653582[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0568533170743[/C][C]0.943146682925747[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4684663919336[/C][C]-1.46846639193361[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2434788067591[/C][C]1.75652119324092[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.8753266797060[/C][C]-0.875326679705963[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.19261353770623[/C][C]0.807386462293765[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.0575718674475[/C][C]-0.0575718674475427[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2196517838714[/C][C]0.780348216128617[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9084074166674[/C][C]1.09159258333258[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.09476488427146[/C][C]6.90523511572854[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.74311193128328[/C][C]-4.74311193128328[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.4791917066057[/C][C]-2.47919170660571[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2456307215188[/C][C]-0.245630721518783[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7899473093052[/C][C]2.21005269069475[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.0235082943922[/C][C]3.97649170560783[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.48591016588054[/C][C]0.514089834119459[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.9429597542272[/C][C]-2.94295975422721[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6872678256347[/C][C]-1.68726782563472[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9580271346306[/C][C]-0.958027134630632[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4125940827328[/C][C]-6.41259408273276[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.3855514908848[/C][C]-0.385551490884766[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4341883727541[/C][C]-2.43418837275407[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3748261762127[/C][C]1.62517382378733[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4293575425183[/C][C]-0.429357542518318[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7763233259479[/C][C]0.223676674052136[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4021712899932[/C][C]1.59782871000679[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2714281421689[/C][C]-2.27142814216886[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.2525180747087[/C][C]-0.252518074708661[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.45801539564176[/C][C]-2.45801539564176[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.9877586746143[/C][C]2.01224132538570[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.7369401581523[/C][C]0.263059841847675[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7546864540318[/C][C]2.24531354596821[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.9405551571314[/C][C]-0.940555157131408[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2801930917382[/C][C]1.71980690826181[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.0456492705574[/C][C]-2.04564927055736[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4682519458094[/C][C]2.53174805419057[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9060128522312[/C][C]2.09398714776876[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.59978555783491[/C][C]-0.59978555783491[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4281602603002[/C][C]2.57183973969977[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.2789958095201[/C][C]0.721004190479895[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.0459235889376[/C][C]-1.04592358893760[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9786356182437[/C][C]-2.97863561824366[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4729491480277[/C][C]-0.47294914802769[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.3936079227404[/C][C]-1.39360792274044[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.268270494848[/C][C]-0.26827049484801[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9004634716222[/C][C]0.0995365283777986[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7821470249371[/C][C]3.21785297506285[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.99305006580217[/C][C]-1.99305006580217[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.84003762088021[/C][C]-2.84003762088021[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.9890584109832[/C][C]2.01094158901678[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8588475709603[/C][C]0.141152429039728[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2529786356608[/C][C]-1.25297863566079[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.72542550765988[/C][C]4.27457449234012[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4076924670499[/C][C]-0.407692467049949[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.2896929206399[/C][C]-0.289692920639904[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0153499032228[/C][C]-0.0153499032227770[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0063259750179[/C][C]0.993674024982118[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2318023246968[/C][C]-0.231802324696840[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1262576829784[/C][C]0.873742317021613[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5084943543410[/C][C]-3.50849435434101[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3106930216916[/C][C]0.68930697830841[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.446942006828[/C][C]1.55305799317201[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.3464144379172[/C][C]0.653585562082827[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.73348193951555[/C][C]0.266518060484451[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2374723303494[/C][C]-2.23747233034939[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.60814451162312[/C][C]-0.608144511623122[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.8413755934610[/C][C]-1.84137559346105[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.4172204995040[/C][C]0.582779500496046[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4598574724717[/C][C]0.540142527528288[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7177395520358[/C][C]1.28226044796418[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.8150515519863[/C][C]-1.81505155198634[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.884117990894[/C][C]2.11588200910600[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.96583560972476[/C][C]-2.96583560972476[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5695579373496[/C][C]-0.56955793734961[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3457059202035[/C][C]-1.34570592020351[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2489262280544[/C][C]-1.24892622805439[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4510643193501[/C][C]-2.45106431935008[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.8995405077844[/C][C]-2.89954050778435[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1926934752815[/C][C]-1.19269347528154[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6396980631175[/C][C]-1.63969806311749[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4408330762675[/C][C]-0.44083307626747[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.6019301565974[/C][C]0.3980698434026[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.3343055984551[/C][C]0.665694401544895[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4019386729764[/C][C]2.59806132702365[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4965717574508[/C][C]2.50342824254917[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7320984147256[/C][C]-1.73209841472558[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7403692927054[/C][C]0.259630707294573[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9619976482425[/C][C]3.03800235175745[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.4057866671182[/C][C]0.594213332881809[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5642048319721[/C][C]1.43579516802792[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.2306050424788[/C][C]-2.23060504247875[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.8350023771799[/C][C]-0.835002377179903[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.7403692927054[/C][C]2.25963070729457[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.9191744327029[/C][C]1.08082556729709[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.4489177116754[/C][C]0.551082288324562[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.3912133583043[/C][C]-1.39121335830426[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.5158322358234[/C][C]-0.515832235823396[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.42206570808217[/C][C]-2.42206570808217[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.4489177116754[/C][C]0.551082288324562[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5651876680660[/C][C]2.43481233193401[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4780643293033[/C][C]-1.47806432930331[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.2089681705627[/C][C]-0.208968170562678[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.2527742221962[/C][C]1.74722577780377[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.6743940613544[/C][C]1.32560593864560[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6457362082310[/C][C]0.354263791769049[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.6722139430424[/C][C]-3.6722139430424[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0883279448885[/C][C]4.91167205511149[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.6790912635727[/C][C]-0.679091263572652[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2851464414440[/C][C]-0.28514644144402[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.4953744752327[/C][C]-0.495374475232739[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.9396522586128[/C][C]2.06034774138719[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.28363660463377[/C][C]-0.283636604633769[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9169943143909[/C][C]1.08300568560910[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.8028762727601[/C][C]-0.80287627276006[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4055722209940[/C][C]-3.40557222099401[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3722271983119[/C][C]-4.37222719831194[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8883364612675[/C][C]1.11166353873254[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.6097721423289[/C][C]1.39022785767111[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1886128641229[/C][C]-0.188612864122853[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0621063575646[/C][C]0.937893642435368[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.2222240669521[/C][C]-1.22222406695212[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.1604854768672[/C][C]0.839514523132779[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6952458286474[/C][C]1.30475417135261[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1540969208414[/C][C]1.84590307915861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.36818147269921.63181852730078
21211.06479726211950.935202737880534
31513.52520608010351.47479391989646
41210.81496158175141.1850384182486
51010.6648142948774-0.664814294877374
6129.217809707041432.78219029295857
71516.73664142877-1.73664142876999
8910.4708609563698-1.47086095636979
91212.2497213653371-0.249721365337096
10117.98576290444763.01423709555239
111113.3289189299472-2.32891892994723
121112.0740509761932-1.07405097619323
131512.22223259087942.77776740912056
14711.2530805949745-4.25308059497451
151111.6372829385250-0.637282938524961
161110.9584560268790.0415439731210098
171012.0740509761932-2.07405097619323
181414.3512317703418-0.351231770341828
19108.70152857985651.29847142014350
2069.50890318127013-3.50890318127013
21118.654852063089952.34514793691005
221514.38409806117910.615901938820894
231111.6280818390056-0.628081839005588
24129.494473533133312.50552646686669
251413.39719976939470.602800230605293
261514.83762955863290.162370441367064
27914.6278955964337-5.62789559643371
281312.49700878793220.502991212067762
291313.2186589151178-0.218658915117850
301610.78573887159145.21426112840858
31138.501537096235454.49846290376455
321213.5911531079023-1.59115310790229
331414.4787311456536-0.478731145653582
341110.05685331707430.943146682925747
35910.4684663919336-1.46846639193361
361614.24347880675911.75652119324092
371212.8753266797060-0.875326679705963
38109.192613537706230.807386462293765
391313.0575718674475-0.0575718674475427
401615.21965178387140.780348216128617
411412.90840741666741.09159258333258
42158.094764884271466.90523511572854
4359.74311193128328-4.74311193128328
44810.4791917066057-2.47919170660571
451111.2456307215188-0.245630721518783
461613.78994730930522.21005269069475
471713.02350829439223.97649170560783
4898.485910165880540.514089834119459
49911.9429597542272-2.94295975422721
501314.6872678256347-1.68726782563472
511010.9580271346306-0.958027134630632
52612.4125940827328-6.41259408273276
531212.3855514908848-0.385551490884766
54810.4341883727541-2.43418837275407
551412.37482617621271.62517382378733
561212.4293575425183-0.429357542518318
571110.77632332594790.223676674052136
581614.40217128999321.59782871000679
59810.2714281421689-2.27142814216886
601515.2525180747087-0.252518074708661
6179.45801539564176-2.45801539564176
621613.98775867461432.01224132538570
631413.73694015815230.263059841847675
641613.75468645403182.24531354596821
6599.9405551571314-0.940555157131408
661412.28019309173821.71980690826181
671113.0456492705574-2.04564927055736
681310.46825194580942.53174805419057
691512.90601285223122.09398714776876
7055.59978555783491-0.59978555783491
711512.42816026030022.57183973969977
721312.27899580952010.721004190479895
731112.0459235889376-1.04592358893760
741113.9786356182437-2.97863561824366
751212.4729491480277-0.47294914802769
761213.3936079227404-1.39360792274044
771212.268270494848-0.26827049484801
781211.90046347162220.0995365283777986
791410.78214702493713.21785297506285
8067.99305006580217-1.99305006580217
8179.84003762088021-2.84003762088021
821411.98905841098322.01094158901678
831413.85884757096030.141152429039728
841011.2529786356608-1.25297863566079
85138.725425507659884.27457449234012
861212.4076924670499-0.407692467049949
8799.2896929206399-0.289692920639904
881212.0153499032228-0.0153499032227770
891615.00632597501790.993674024982118
901010.2318023246968-0.231802324696840
911413.12625768297840.873742317021613
921013.5084943543410-3.50849435434101
931615.31069302169160.68930697830841
941513.4469420068281.55305799317201
951211.34641443791720.653585562082827
96109.733481939515550.266518060484451
97810.2374723303494-2.23747233034939
9888.60814451162312-0.608144511623122
991112.8413755934610-1.84137559346105
1001312.41722049950400.582779500496046
1011615.45985747247170.540142527528288
1021614.71773955203581.28226044796418
1031415.8150515519863-1.81505155198634
104118.8841179908942.11588200910600
10546.96583560972476-2.96583560972476
1061414.5695579373496-0.56955793734961
107910.3457059202035-1.34570592020351
1081415.2489262280544-1.24892622805439
109810.4510643193501-2.45106431935008
110810.8995405077844-2.89954050778435
1111112.1926934752815-1.19269347528154
1121213.6396980631175-1.63969806311749
1131111.4408330762675-0.44083307626747
1141413.60193015659740.3980698434026
1151514.33430559845510.665694401544895
1161613.40193867297642.59806132702365
1171613.49657175745082.50342824254917
1181112.7320984147256-1.73209841472558
1191413.74036929270540.259630707294573
1201410.96199764824253.03800235175745
1211211.40578666711820.594213332881809
1221412.56420483197211.43579516802792
123810.2306050424788-2.23060504247875
1241313.8350023771799-0.835002377179903
1251613.74036929270542.25963070729457
1261210.91917443270291.08082556729709
1271615.44891771167540.551082288324562
1281213.3912133583043-1.39121335830426
1291111.5158322358234-0.515832235823396
13046.42206570808217-2.42206570808217
1311615.44891771167540.551082288324562
1321512.56518766806602.43481233193401
1331011.4780643293033-1.47806432930331
1341313.2089681705627-0.208968170562678
1351513.25277422219621.74722577780377
1361210.67439406135441.32560593864560
1371413.64573620823100.354263791769049
138710.6722139430424-3.6722139430424
1391914.08832794488854.91167205511149
1401212.6790912635727-0.679091263572652
1411212.2851464414440-0.28514644144402
1421313.4953744752327-0.495374475232739
1431512.93965225861282.06034774138719
14488.28363660463377-0.283636604633769
1451210.91699431439091.08300568560910
1461010.8028762727601-0.80287627276006
147811.4055722209940-3.40557222099401
1481014.3722271983119-4.37222719831194
1491513.88833646126751.11166353873254
1501614.60977214232891.39022785767111
1511313.1886128641229-0.188612864122853
1521615.06210635756460.937893642435368
153910.2222240669521-1.22222406695212
1541413.16048547686720.839514523132779
1551412.69524582864741.30475417135261
1561210.15409692084141.84590307915861






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1097323263420690.2194646526841370.890267673657931
100.04783346061747790.09566692123495580.952166539382522
110.07256329705488770.1451265941097750.927436702945112
120.03790860604819140.07581721209638290.962091393951809
130.4402168488493220.8804336976986440.559783151150678
140.7602912191048020.4794175617903950.239708780895198
150.6790903837862120.6418192324275760.320909616213788
160.589628206579950.8207435868400990.410371793420050
170.5297430233366890.940513953326620.47025697666331
180.4422181255603020.8844362511206040.557781874439698
190.3606956713455170.7213913426910330.639304328654483
200.5476487928621490.9047024142757020.452351207137851
210.5089890058391230.9820219883217550.491010994160877
220.5513269796458050.897346040708390.448673020354195
230.4848080209580340.9696160419160680.515191979041966
240.4724393460955650.944878692191130.527560653904435
250.4285154894072160.8570309788144330.571484510592784
260.3650298407677910.7300596815355820.634970159232209
270.6332280842098670.7335438315802660.366771915790133
280.5763878959184730.8472242081630540.423612104081527
290.5151430663324310.9697138673351380.484856933667569
300.675753256286070.6484934874278620.324246743713931
310.7856144386220790.4287711227558420.214385561377921
320.7470040565092130.5059918869815740.252995943490787
330.6991556691883810.6016886616232390.300844330811619
340.668006570219060.663986859561880.33199342978094
350.6741277603951170.6517444792097670.325872239604883
360.6818794403440560.6362411193118890.318120559655944
370.6488953604462430.7022092791075150.351104639553757
380.599646387680870.800707224638260.40035361231913
390.5456774647669080.9086450704661850.454322535233092
400.5168993035186230.9662013929627540.483100696481377
410.476959800621120.953919601242240.52304019937888
420.7564668140056420.4870663719887160.243533185994358
430.9512084630217760.09758307395644830.0487915369782241
440.962390920518310.07521815896338140.0376090794816907
450.9511668016155970.09766639676880570.0488331983844028
460.9569971036914280.08600579261714430.0430028963085722
470.979388886253030.04122222749393950.0206111137469698
480.972757252239960.05448549552008080.0272427477600404
490.9804060113576630.0391879772846740.019593988642337
500.9772716076768810.04545678464623770.0227283923231189
510.9725246929242620.05495061415147690.0274753070757384
520.9972831623599020.005433675280195480.00271683764009774
530.9960840088038520.007831982392296630.00391599119614831
540.9966829885117850.006634022976429270.00331701148821464
550.9967068694267150.006586261146570420.00329313057328521
560.9953428405433430.009314318913314280.00465715945665714
570.9935068298823610.01298634023527720.00649317011763862
580.9929499949147360.01410001017052830.00705000508526415
590.9945024838018050.01099503239638970.00549751619819487
600.9927735691703120.01445286165937650.00722643082968827
610.9935141732017350.01297165359653050.00648582679826523
620.994323469641150.01135306071770120.00567653035885061
630.9924526977370180.01509460452596320.0075473022629816
640.9931701124782320.01365977504353660.0068298875217683
650.991321188244740.01735762351052110.00867881175526056
660.9907255015667320.01854899686653660.00927449843326828
670.990334306044440.01933138791111830.00966569395555914
680.9921381357893970.01572372842120550.00786186421060273
690.9924502821635570.01509943567288530.00754971783644266
700.9898321114738350.02033577705233090.0101678885261655
710.9915548686973490.01689026260530250.00844513130265125
720.9888733654720850.022253269055830.011126634527915
730.9860188432745340.02796231345093290.0139811567254665
740.989770688684290.02045862263142180.0102293113157109
750.98616400447990.02767199104020.0138359955201
760.9835767086890350.03284658262193080.0164232913109654
770.9782570615429190.04348587691416290.0217429384570814
780.9713884755314270.05722304893714620.0286115244685731
790.9824994858335960.03500102833280890.0175005141664045
800.981556018804750.03688796239050080.0184439811952504
810.9843825627560680.03123487448786450.0156174372439322
820.9856252078053580.02874958438928470.0143747921946423
830.9807462553530350.03850748929392930.0192537446469647
840.9760745474561430.0478509050877150.0239254525438575
850.9941302435197450.01173951296051020.00586975648025509
860.9918568108641980.01628637827160420.0081431891358021
870.98891400538420.02217198923159760.0110859946157988
880.9854914061056740.02901718778865150.0145085938943257
890.981877312644270.03624537471146090.0181226873557305
900.9760561606269330.04788767874613450.0239438393730673
910.971388476426040.05722304714791860.0286115235739593
920.9830930987920920.03381380241581660.0169069012079083
930.9781495557687490.0437008884625030.0218504442312515
940.9754471440525530.04910571189489420.0245528559474471
950.9692633660094640.06147326798107280.0307366339905364
960.9613620106903540.07727597861929160.0386379893096458
970.9577663170428260.08446736591434780.0422336829571739
980.9459512498353620.1080975003292750.0540487501646376
990.9409753311686360.1180493376627270.0590246688313636
1000.9275760952011480.1448478095977040.0724239047988522
1010.9100462302509820.1799075394980370.0899537697490184
1020.895866448766480.2082671024670410.104133551233521
1030.9017506489929080.1964987020141840.0982493510070922
1040.935921715432250.1281565691355000.0640782845677502
1050.9331265841172260.1337468317655480.066873415882774
1060.9153664254646350.1692671490707300.0846335745353651
1070.8969917378655320.2060165242689350.103008262134468
1080.8914406004548910.2171187990902180.108559399545109
1090.8872633734899420.2254732530201160.112736626510058
1100.905040622554820.1899187548903590.0949593774451794
1110.8935553062907960.2128893874184080.106444693709204
1120.9216010189367640.1567979621264720.0783989810632361
1130.8999937227507850.2000125544984300.100006277249215
1140.874653575561550.25069284887690.12534642443845
1150.8453737805769120.3092524388461760.154626219423088
1160.8474932927998290.3050134144003420.152506707200171
1170.8578164366444540.2843671267110930.142183563355546
1180.8462740961236270.3074518077527470.153725903876373
1190.809705471634230.3805890567315390.190294528365770
1200.8666100745443220.2667798509113560.133389925455678
1210.8417271809346220.3165456381307550.158272819065378
1220.8205004462972950.358999107405410.179499553702705
1230.8071592769864290.3856814460271420.192840723013571
1240.7660779865829950.4678440268340110.233922013417005
1250.7700790675493060.4598418649013870.229920932450694
1260.7566403013228390.4867193973543220.243359698677161
1270.7042207694195210.5915584611609580.295779230580479
1280.6722192358585870.6555615282828270.327780764141413
1290.6784505578145050.643098884370990.321549442185495
1300.670834675243280.6583306495134390.329165324756719
1310.6112189101353290.7775621797293410.388781089864671
1320.5966224540262470.8067550919475050.403377545973753
1330.5696212234316520.8607575531366960.430378776568348
1340.4948391983063560.9896783966127120.505160801693644
1350.4660444206766280.9320888413532550.533955579323372
1360.4625990109927220.9251980219854450.537400989007278
1370.3869269689138450.773853937827690.613073031086155
1380.4475486005800400.8950972011600810.55245139941996
1390.8020358211947480.3959283576105030.197964178805251
1400.8274186807669610.3451626384660780.172581319233039
1410.7905529607395020.4188940785209950.209447039260498
1420.7112983837208510.5774032325582970.288701616279149
1430.6572941897015650.685411620596870.342705810298435
1440.5450682873593230.9098634252813540.454931712640677
1450.5368457204685420.9263085590629170.463154279531458
1460.6693256941842390.6613486116315220.330674305815761
1470.5716048637825270.8567902724349470.428395136217473

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.109732326342069 & 0.219464652684137 & 0.890267673657931 \tabularnewline
10 & 0.0478334606174779 & 0.0956669212349558 & 0.952166539382522 \tabularnewline
11 & 0.0725632970548877 & 0.145126594109775 & 0.927436702945112 \tabularnewline
12 & 0.0379086060481914 & 0.0758172120963829 & 0.962091393951809 \tabularnewline
13 & 0.440216848849322 & 0.880433697698644 & 0.559783151150678 \tabularnewline
14 & 0.760291219104802 & 0.479417561790395 & 0.239708780895198 \tabularnewline
15 & 0.679090383786212 & 0.641819232427576 & 0.320909616213788 \tabularnewline
16 & 0.58962820657995 & 0.820743586840099 & 0.410371793420050 \tabularnewline
17 & 0.529743023336689 & 0.94051395332662 & 0.47025697666331 \tabularnewline
18 & 0.442218125560302 & 0.884436251120604 & 0.557781874439698 \tabularnewline
19 & 0.360695671345517 & 0.721391342691033 & 0.639304328654483 \tabularnewline
20 & 0.547648792862149 & 0.904702414275702 & 0.452351207137851 \tabularnewline
21 & 0.508989005839123 & 0.982021988321755 & 0.491010994160877 \tabularnewline
22 & 0.551326979645805 & 0.89734604070839 & 0.448673020354195 \tabularnewline
23 & 0.484808020958034 & 0.969616041916068 & 0.515191979041966 \tabularnewline
24 & 0.472439346095565 & 0.94487869219113 & 0.527560653904435 \tabularnewline
25 & 0.428515489407216 & 0.857030978814433 & 0.571484510592784 \tabularnewline
26 & 0.365029840767791 & 0.730059681535582 & 0.634970159232209 \tabularnewline
27 & 0.633228084209867 & 0.733543831580266 & 0.366771915790133 \tabularnewline
28 & 0.576387895918473 & 0.847224208163054 & 0.423612104081527 \tabularnewline
29 & 0.515143066332431 & 0.969713867335138 & 0.484856933667569 \tabularnewline
30 & 0.67575325628607 & 0.648493487427862 & 0.324246743713931 \tabularnewline
31 & 0.785614438622079 & 0.428771122755842 & 0.214385561377921 \tabularnewline
32 & 0.747004056509213 & 0.505991886981574 & 0.252995943490787 \tabularnewline
33 & 0.699155669188381 & 0.601688661623239 & 0.300844330811619 \tabularnewline
34 & 0.66800657021906 & 0.66398685956188 & 0.33199342978094 \tabularnewline
35 & 0.674127760395117 & 0.651744479209767 & 0.325872239604883 \tabularnewline
36 & 0.681879440344056 & 0.636241119311889 & 0.318120559655944 \tabularnewline
37 & 0.648895360446243 & 0.702209279107515 & 0.351104639553757 \tabularnewline
38 & 0.59964638768087 & 0.80070722463826 & 0.40035361231913 \tabularnewline
39 & 0.545677464766908 & 0.908645070466185 & 0.454322535233092 \tabularnewline
40 & 0.516899303518623 & 0.966201392962754 & 0.483100696481377 \tabularnewline
41 & 0.47695980062112 & 0.95391960124224 & 0.52304019937888 \tabularnewline
42 & 0.756466814005642 & 0.487066371988716 & 0.243533185994358 \tabularnewline
43 & 0.951208463021776 & 0.0975830739564483 & 0.0487915369782241 \tabularnewline
44 & 0.96239092051831 & 0.0752181589633814 & 0.0376090794816907 \tabularnewline
45 & 0.951166801615597 & 0.0976663967688057 & 0.0488331983844028 \tabularnewline
46 & 0.956997103691428 & 0.0860057926171443 & 0.0430028963085722 \tabularnewline
47 & 0.97938888625303 & 0.0412222274939395 & 0.0206111137469698 \tabularnewline
48 & 0.97275725223996 & 0.0544854955200808 & 0.0272427477600404 \tabularnewline
49 & 0.980406011357663 & 0.039187977284674 & 0.019593988642337 \tabularnewline
50 & 0.977271607676881 & 0.0454567846462377 & 0.0227283923231189 \tabularnewline
51 & 0.972524692924262 & 0.0549506141514769 & 0.0274753070757384 \tabularnewline
52 & 0.997283162359902 & 0.00543367528019548 & 0.00271683764009774 \tabularnewline
53 & 0.996084008803852 & 0.00783198239229663 & 0.00391599119614831 \tabularnewline
54 & 0.996682988511785 & 0.00663402297642927 & 0.00331701148821464 \tabularnewline
55 & 0.996706869426715 & 0.00658626114657042 & 0.00329313057328521 \tabularnewline
56 & 0.995342840543343 & 0.00931431891331428 & 0.00465715945665714 \tabularnewline
57 & 0.993506829882361 & 0.0129863402352772 & 0.00649317011763862 \tabularnewline
58 & 0.992949994914736 & 0.0141000101705283 & 0.00705000508526415 \tabularnewline
59 & 0.994502483801805 & 0.0109950323963897 & 0.00549751619819487 \tabularnewline
60 & 0.992773569170312 & 0.0144528616593765 & 0.00722643082968827 \tabularnewline
61 & 0.993514173201735 & 0.0129716535965305 & 0.00648582679826523 \tabularnewline
62 & 0.99432346964115 & 0.0113530607177012 & 0.00567653035885061 \tabularnewline
63 & 0.992452697737018 & 0.0150946045259632 & 0.0075473022629816 \tabularnewline
64 & 0.993170112478232 & 0.0136597750435366 & 0.0068298875217683 \tabularnewline
65 & 0.99132118824474 & 0.0173576235105211 & 0.00867881175526056 \tabularnewline
66 & 0.990725501566732 & 0.0185489968665366 & 0.00927449843326828 \tabularnewline
67 & 0.99033430604444 & 0.0193313879111183 & 0.00966569395555914 \tabularnewline
68 & 0.992138135789397 & 0.0157237284212055 & 0.00786186421060273 \tabularnewline
69 & 0.992450282163557 & 0.0150994356728853 & 0.00754971783644266 \tabularnewline
70 & 0.989832111473835 & 0.0203357770523309 & 0.0101678885261655 \tabularnewline
71 & 0.991554868697349 & 0.0168902626053025 & 0.00844513130265125 \tabularnewline
72 & 0.988873365472085 & 0.02225326905583 & 0.011126634527915 \tabularnewline
73 & 0.986018843274534 & 0.0279623134509329 & 0.0139811567254665 \tabularnewline
74 & 0.98977068868429 & 0.0204586226314218 & 0.0102293113157109 \tabularnewline
75 & 0.9861640044799 & 0.0276719910402 & 0.0138359955201 \tabularnewline
76 & 0.983576708689035 & 0.0328465826219308 & 0.0164232913109654 \tabularnewline
77 & 0.978257061542919 & 0.0434858769141629 & 0.0217429384570814 \tabularnewline
78 & 0.971388475531427 & 0.0572230489371462 & 0.0286115244685731 \tabularnewline
79 & 0.982499485833596 & 0.0350010283328089 & 0.0175005141664045 \tabularnewline
80 & 0.98155601880475 & 0.0368879623905008 & 0.0184439811952504 \tabularnewline
81 & 0.984382562756068 & 0.0312348744878645 & 0.0156174372439322 \tabularnewline
82 & 0.985625207805358 & 0.0287495843892847 & 0.0143747921946423 \tabularnewline
83 & 0.980746255353035 & 0.0385074892939293 & 0.0192537446469647 \tabularnewline
84 & 0.976074547456143 & 0.047850905087715 & 0.0239254525438575 \tabularnewline
85 & 0.994130243519745 & 0.0117395129605102 & 0.00586975648025509 \tabularnewline
86 & 0.991856810864198 & 0.0162863782716042 & 0.0081431891358021 \tabularnewline
87 & 0.9889140053842 & 0.0221719892315976 & 0.0110859946157988 \tabularnewline
88 & 0.985491406105674 & 0.0290171877886515 & 0.0145085938943257 \tabularnewline
89 & 0.98187731264427 & 0.0362453747114609 & 0.0181226873557305 \tabularnewline
90 & 0.976056160626933 & 0.0478876787461345 & 0.0239438393730673 \tabularnewline
91 & 0.97138847642604 & 0.0572230471479186 & 0.0286115235739593 \tabularnewline
92 & 0.983093098792092 & 0.0338138024158166 & 0.0169069012079083 \tabularnewline
93 & 0.978149555768749 & 0.043700888462503 & 0.0218504442312515 \tabularnewline
94 & 0.975447144052553 & 0.0491057118948942 & 0.0245528559474471 \tabularnewline
95 & 0.969263366009464 & 0.0614732679810728 & 0.0307366339905364 \tabularnewline
96 & 0.961362010690354 & 0.0772759786192916 & 0.0386379893096458 \tabularnewline
97 & 0.957766317042826 & 0.0844673659143478 & 0.0422336829571739 \tabularnewline
98 & 0.945951249835362 & 0.108097500329275 & 0.0540487501646376 \tabularnewline
99 & 0.940975331168636 & 0.118049337662727 & 0.0590246688313636 \tabularnewline
100 & 0.927576095201148 & 0.144847809597704 & 0.0724239047988522 \tabularnewline
101 & 0.910046230250982 & 0.179907539498037 & 0.0899537697490184 \tabularnewline
102 & 0.89586644876648 & 0.208267102467041 & 0.104133551233521 \tabularnewline
103 & 0.901750648992908 & 0.196498702014184 & 0.0982493510070922 \tabularnewline
104 & 0.93592171543225 & 0.128156569135500 & 0.0640782845677502 \tabularnewline
105 & 0.933126584117226 & 0.133746831765548 & 0.066873415882774 \tabularnewline
106 & 0.915366425464635 & 0.169267149070730 & 0.0846335745353651 \tabularnewline
107 & 0.896991737865532 & 0.206016524268935 & 0.103008262134468 \tabularnewline
108 & 0.891440600454891 & 0.217118799090218 & 0.108559399545109 \tabularnewline
109 & 0.887263373489942 & 0.225473253020116 & 0.112736626510058 \tabularnewline
110 & 0.90504062255482 & 0.189918754890359 & 0.0949593774451794 \tabularnewline
111 & 0.893555306290796 & 0.212889387418408 & 0.106444693709204 \tabularnewline
112 & 0.921601018936764 & 0.156797962126472 & 0.0783989810632361 \tabularnewline
113 & 0.899993722750785 & 0.200012554498430 & 0.100006277249215 \tabularnewline
114 & 0.87465357556155 & 0.2506928488769 & 0.12534642443845 \tabularnewline
115 & 0.845373780576912 & 0.309252438846176 & 0.154626219423088 \tabularnewline
116 & 0.847493292799829 & 0.305013414400342 & 0.152506707200171 \tabularnewline
117 & 0.857816436644454 & 0.284367126711093 & 0.142183563355546 \tabularnewline
118 & 0.846274096123627 & 0.307451807752747 & 0.153725903876373 \tabularnewline
119 & 0.80970547163423 & 0.380589056731539 & 0.190294528365770 \tabularnewline
120 & 0.866610074544322 & 0.266779850911356 & 0.133389925455678 \tabularnewline
121 & 0.841727180934622 & 0.316545638130755 & 0.158272819065378 \tabularnewline
122 & 0.820500446297295 & 0.35899910740541 & 0.179499553702705 \tabularnewline
123 & 0.807159276986429 & 0.385681446027142 & 0.192840723013571 \tabularnewline
124 & 0.766077986582995 & 0.467844026834011 & 0.233922013417005 \tabularnewline
125 & 0.770079067549306 & 0.459841864901387 & 0.229920932450694 \tabularnewline
126 & 0.756640301322839 & 0.486719397354322 & 0.243359698677161 \tabularnewline
127 & 0.704220769419521 & 0.591558461160958 & 0.295779230580479 \tabularnewline
128 & 0.672219235858587 & 0.655561528282827 & 0.327780764141413 \tabularnewline
129 & 0.678450557814505 & 0.64309888437099 & 0.321549442185495 \tabularnewline
130 & 0.67083467524328 & 0.658330649513439 & 0.329165324756719 \tabularnewline
131 & 0.611218910135329 & 0.777562179729341 & 0.388781089864671 \tabularnewline
132 & 0.596622454026247 & 0.806755091947505 & 0.403377545973753 \tabularnewline
133 & 0.569621223431652 & 0.860757553136696 & 0.430378776568348 \tabularnewline
134 & 0.494839198306356 & 0.989678396612712 & 0.505160801693644 \tabularnewline
135 & 0.466044420676628 & 0.932088841353255 & 0.533955579323372 \tabularnewline
136 & 0.462599010992722 & 0.925198021985445 & 0.537400989007278 \tabularnewline
137 & 0.386926968913845 & 0.77385393782769 & 0.613073031086155 \tabularnewline
138 & 0.447548600580040 & 0.895097201160081 & 0.55245139941996 \tabularnewline
139 & 0.802035821194748 & 0.395928357610503 & 0.197964178805251 \tabularnewline
140 & 0.827418680766961 & 0.345162638466078 & 0.172581319233039 \tabularnewline
141 & 0.790552960739502 & 0.418894078520995 & 0.209447039260498 \tabularnewline
142 & 0.711298383720851 & 0.577403232558297 & 0.288701616279149 \tabularnewline
143 & 0.657294189701565 & 0.68541162059687 & 0.342705810298435 \tabularnewline
144 & 0.545068287359323 & 0.909863425281354 & 0.454931712640677 \tabularnewline
145 & 0.536845720468542 & 0.926308559062917 & 0.463154279531458 \tabularnewline
146 & 0.669325694184239 & 0.661348611631522 & 0.330674305815761 \tabularnewline
147 & 0.571604863782527 & 0.856790272434947 & 0.428395136217473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&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]9[/C][C]0.109732326342069[/C][C]0.219464652684137[/C][C]0.890267673657931[/C][/ROW]
[ROW][C]10[/C][C]0.0478334606174779[/C][C]0.0956669212349558[/C][C]0.952166539382522[/C][/ROW]
[ROW][C]11[/C][C]0.0725632970548877[/C][C]0.145126594109775[/C][C]0.927436702945112[/C][/ROW]
[ROW][C]12[/C][C]0.0379086060481914[/C][C]0.0758172120963829[/C][C]0.962091393951809[/C][/ROW]
[ROW][C]13[/C][C]0.440216848849322[/C][C]0.880433697698644[/C][C]0.559783151150678[/C][/ROW]
[ROW][C]14[/C][C]0.760291219104802[/C][C]0.479417561790395[/C][C]0.239708780895198[/C][/ROW]
[ROW][C]15[/C][C]0.679090383786212[/C][C]0.641819232427576[/C][C]0.320909616213788[/C][/ROW]
[ROW][C]16[/C][C]0.58962820657995[/C][C]0.820743586840099[/C][C]0.410371793420050[/C][/ROW]
[ROW][C]17[/C][C]0.529743023336689[/C][C]0.94051395332662[/C][C]0.47025697666331[/C][/ROW]
[ROW][C]18[/C][C]0.442218125560302[/C][C]0.884436251120604[/C][C]0.557781874439698[/C][/ROW]
[ROW][C]19[/C][C]0.360695671345517[/C][C]0.721391342691033[/C][C]0.639304328654483[/C][/ROW]
[ROW][C]20[/C][C]0.547648792862149[/C][C]0.904702414275702[/C][C]0.452351207137851[/C][/ROW]
[ROW][C]21[/C][C]0.508989005839123[/C][C]0.982021988321755[/C][C]0.491010994160877[/C][/ROW]
[ROW][C]22[/C][C]0.551326979645805[/C][C]0.89734604070839[/C][C]0.448673020354195[/C][/ROW]
[ROW][C]23[/C][C]0.484808020958034[/C][C]0.969616041916068[/C][C]0.515191979041966[/C][/ROW]
[ROW][C]24[/C][C]0.472439346095565[/C][C]0.94487869219113[/C][C]0.527560653904435[/C][/ROW]
[ROW][C]25[/C][C]0.428515489407216[/C][C]0.857030978814433[/C][C]0.571484510592784[/C][/ROW]
[ROW][C]26[/C][C]0.365029840767791[/C][C]0.730059681535582[/C][C]0.634970159232209[/C][/ROW]
[ROW][C]27[/C][C]0.633228084209867[/C][C]0.733543831580266[/C][C]0.366771915790133[/C][/ROW]
[ROW][C]28[/C][C]0.576387895918473[/C][C]0.847224208163054[/C][C]0.423612104081527[/C][/ROW]
[ROW][C]29[/C][C]0.515143066332431[/C][C]0.969713867335138[/C][C]0.484856933667569[/C][/ROW]
[ROW][C]30[/C][C]0.67575325628607[/C][C]0.648493487427862[/C][C]0.324246743713931[/C][/ROW]
[ROW][C]31[/C][C]0.785614438622079[/C][C]0.428771122755842[/C][C]0.214385561377921[/C][/ROW]
[ROW][C]32[/C][C]0.747004056509213[/C][C]0.505991886981574[/C][C]0.252995943490787[/C][/ROW]
[ROW][C]33[/C][C]0.699155669188381[/C][C]0.601688661623239[/C][C]0.300844330811619[/C][/ROW]
[ROW][C]34[/C][C]0.66800657021906[/C][C]0.66398685956188[/C][C]0.33199342978094[/C][/ROW]
[ROW][C]35[/C][C]0.674127760395117[/C][C]0.651744479209767[/C][C]0.325872239604883[/C][/ROW]
[ROW][C]36[/C][C]0.681879440344056[/C][C]0.636241119311889[/C][C]0.318120559655944[/C][/ROW]
[ROW][C]37[/C][C]0.648895360446243[/C][C]0.702209279107515[/C][C]0.351104639553757[/C][/ROW]
[ROW][C]38[/C][C]0.59964638768087[/C][C]0.80070722463826[/C][C]0.40035361231913[/C][/ROW]
[ROW][C]39[/C][C]0.545677464766908[/C][C]0.908645070466185[/C][C]0.454322535233092[/C][/ROW]
[ROW][C]40[/C][C]0.516899303518623[/C][C]0.966201392962754[/C][C]0.483100696481377[/C][/ROW]
[ROW][C]41[/C][C]0.47695980062112[/C][C]0.95391960124224[/C][C]0.52304019937888[/C][/ROW]
[ROW][C]42[/C][C]0.756466814005642[/C][C]0.487066371988716[/C][C]0.243533185994358[/C][/ROW]
[ROW][C]43[/C][C]0.951208463021776[/C][C]0.0975830739564483[/C][C]0.0487915369782241[/C][/ROW]
[ROW][C]44[/C][C]0.96239092051831[/C][C]0.0752181589633814[/C][C]0.0376090794816907[/C][/ROW]
[ROW][C]45[/C][C]0.951166801615597[/C][C]0.0976663967688057[/C][C]0.0488331983844028[/C][/ROW]
[ROW][C]46[/C][C]0.956997103691428[/C][C]0.0860057926171443[/C][C]0.0430028963085722[/C][/ROW]
[ROW][C]47[/C][C]0.97938888625303[/C][C]0.0412222274939395[/C][C]0.0206111137469698[/C][/ROW]
[ROW][C]48[/C][C]0.97275725223996[/C][C]0.0544854955200808[/C][C]0.0272427477600404[/C][/ROW]
[ROW][C]49[/C][C]0.980406011357663[/C][C]0.039187977284674[/C][C]0.019593988642337[/C][/ROW]
[ROW][C]50[/C][C]0.977271607676881[/C][C]0.0454567846462377[/C][C]0.0227283923231189[/C][/ROW]
[ROW][C]51[/C][C]0.972524692924262[/C][C]0.0549506141514769[/C][C]0.0274753070757384[/C][/ROW]
[ROW][C]52[/C][C]0.997283162359902[/C][C]0.00543367528019548[/C][C]0.00271683764009774[/C][/ROW]
[ROW][C]53[/C][C]0.996084008803852[/C][C]0.00783198239229663[/C][C]0.00391599119614831[/C][/ROW]
[ROW][C]54[/C][C]0.996682988511785[/C][C]0.00663402297642927[/C][C]0.00331701148821464[/C][/ROW]
[ROW][C]55[/C][C]0.996706869426715[/C][C]0.00658626114657042[/C][C]0.00329313057328521[/C][/ROW]
[ROW][C]56[/C][C]0.995342840543343[/C][C]0.00931431891331428[/C][C]0.00465715945665714[/C][/ROW]
[ROW][C]57[/C][C]0.993506829882361[/C][C]0.0129863402352772[/C][C]0.00649317011763862[/C][/ROW]
[ROW][C]58[/C][C]0.992949994914736[/C][C]0.0141000101705283[/C][C]0.00705000508526415[/C][/ROW]
[ROW][C]59[/C][C]0.994502483801805[/C][C]0.0109950323963897[/C][C]0.00549751619819487[/C][/ROW]
[ROW][C]60[/C][C]0.992773569170312[/C][C]0.0144528616593765[/C][C]0.00722643082968827[/C][/ROW]
[ROW][C]61[/C][C]0.993514173201735[/C][C]0.0129716535965305[/C][C]0.00648582679826523[/C][/ROW]
[ROW][C]62[/C][C]0.99432346964115[/C][C]0.0113530607177012[/C][C]0.00567653035885061[/C][/ROW]
[ROW][C]63[/C][C]0.992452697737018[/C][C]0.0150946045259632[/C][C]0.0075473022629816[/C][/ROW]
[ROW][C]64[/C][C]0.993170112478232[/C][C]0.0136597750435366[/C][C]0.0068298875217683[/C][/ROW]
[ROW][C]65[/C][C]0.99132118824474[/C][C]0.0173576235105211[/C][C]0.00867881175526056[/C][/ROW]
[ROW][C]66[/C][C]0.990725501566732[/C][C]0.0185489968665366[/C][C]0.00927449843326828[/C][/ROW]
[ROW][C]67[/C][C]0.99033430604444[/C][C]0.0193313879111183[/C][C]0.00966569395555914[/C][/ROW]
[ROW][C]68[/C][C]0.992138135789397[/C][C]0.0157237284212055[/C][C]0.00786186421060273[/C][/ROW]
[ROW][C]69[/C][C]0.992450282163557[/C][C]0.0150994356728853[/C][C]0.00754971783644266[/C][/ROW]
[ROW][C]70[/C][C]0.989832111473835[/C][C]0.0203357770523309[/C][C]0.0101678885261655[/C][/ROW]
[ROW][C]71[/C][C]0.991554868697349[/C][C]0.0168902626053025[/C][C]0.00844513130265125[/C][/ROW]
[ROW][C]72[/C][C]0.988873365472085[/C][C]0.02225326905583[/C][C]0.011126634527915[/C][/ROW]
[ROW][C]73[/C][C]0.986018843274534[/C][C]0.0279623134509329[/C][C]0.0139811567254665[/C][/ROW]
[ROW][C]74[/C][C]0.98977068868429[/C][C]0.0204586226314218[/C][C]0.0102293113157109[/C][/ROW]
[ROW][C]75[/C][C]0.9861640044799[/C][C]0.0276719910402[/C][C]0.0138359955201[/C][/ROW]
[ROW][C]76[/C][C]0.983576708689035[/C][C]0.0328465826219308[/C][C]0.0164232913109654[/C][/ROW]
[ROW][C]77[/C][C]0.978257061542919[/C][C]0.0434858769141629[/C][C]0.0217429384570814[/C][/ROW]
[ROW][C]78[/C][C]0.971388475531427[/C][C]0.0572230489371462[/C][C]0.0286115244685731[/C][/ROW]
[ROW][C]79[/C][C]0.982499485833596[/C][C]0.0350010283328089[/C][C]0.0175005141664045[/C][/ROW]
[ROW][C]80[/C][C]0.98155601880475[/C][C]0.0368879623905008[/C][C]0.0184439811952504[/C][/ROW]
[ROW][C]81[/C][C]0.984382562756068[/C][C]0.0312348744878645[/C][C]0.0156174372439322[/C][/ROW]
[ROW][C]82[/C][C]0.985625207805358[/C][C]0.0287495843892847[/C][C]0.0143747921946423[/C][/ROW]
[ROW][C]83[/C][C]0.980746255353035[/C][C]0.0385074892939293[/C][C]0.0192537446469647[/C][/ROW]
[ROW][C]84[/C][C]0.976074547456143[/C][C]0.047850905087715[/C][C]0.0239254525438575[/C][/ROW]
[ROW][C]85[/C][C]0.994130243519745[/C][C]0.0117395129605102[/C][C]0.00586975648025509[/C][/ROW]
[ROW][C]86[/C][C]0.991856810864198[/C][C]0.0162863782716042[/C][C]0.0081431891358021[/C][/ROW]
[ROW][C]87[/C][C]0.9889140053842[/C][C]0.0221719892315976[/C][C]0.0110859946157988[/C][/ROW]
[ROW][C]88[/C][C]0.985491406105674[/C][C]0.0290171877886515[/C][C]0.0145085938943257[/C][/ROW]
[ROW][C]89[/C][C]0.98187731264427[/C][C]0.0362453747114609[/C][C]0.0181226873557305[/C][/ROW]
[ROW][C]90[/C][C]0.976056160626933[/C][C]0.0478876787461345[/C][C]0.0239438393730673[/C][/ROW]
[ROW][C]91[/C][C]0.97138847642604[/C][C]0.0572230471479186[/C][C]0.0286115235739593[/C][/ROW]
[ROW][C]92[/C][C]0.983093098792092[/C][C]0.0338138024158166[/C][C]0.0169069012079083[/C][/ROW]
[ROW][C]93[/C][C]0.978149555768749[/C][C]0.043700888462503[/C][C]0.0218504442312515[/C][/ROW]
[ROW][C]94[/C][C]0.975447144052553[/C][C]0.0491057118948942[/C][C]0.0245528559474471[/C][/ROW]
[ROW][C]95[/C][C]0.969263366009464[/C][C]0.0614732679810728[/C][C]0.0307366339905364[/C][/ROW]
[ROW][C]96[/C][C]0.961362010690354[/C][C]0.0772759786192916[/C][C]0.0386379893096458[/C][/ROW]
[ROW][C]97[/C][C]0.957766317042826[/C][C]0.0844673659143478[/C][C]0.0422336829571739[/C][/ROW]
[ROW][C]98[/C][C]0.945951249835362[/C][C]0.108097500329275[/C][C]0.0540487501646376[/C][/ROW]
[ROW][C]99[/C][C]0.940975331168636[/C][C]0.118049337662727[/C][C]0.0590246688313636[/C][/ROW]
[ROW][C]100[/C][C]0.927576095201148[/C][C]0.144847809597704[/C][C]0.0724239047988522[/C][/ROW]
[ROW][C]101[/C][C]0.910046230250982[/C][C]0.179907539498037[/C][C]0.0899537697490184[/C][/ROW]
[ROW][C]102[/C][C]0.89586644876648[/C][C]0.208267102467041[/C][C]0.104133551233521[/C][/ROW]
[ROW][C]103[/C][C]0.901750648992908[/C][C]0.196498702014184[/C][C]0.0982493510070922[/C][/ROW]
[ROW][C]104[/C][C]0.93592171543225[/C][C]0.128156569135500[/C][C]0.0640782845677502[/C][/ROW]
[ROW][C]105[/C][C]0.933126584117226[/C][C]0.133746831765548[/C][C]0.066873415882774[/C][/ROW]
[ROW][C]106[/C][C]0.915366425464635[/C][C]0.169267149070730[/C][C]0.0846335745353651[/C][/ROW]
[ROW][C]107[/C][C]0.896991737865532[/C][C]0.206016524268935[/C][C]0.103008262134468[/C][/ROW]
[ROW][C]108[/C][C]0.891440600454891[/C][C]0.217118799090218[/C][C]0.108559399545109[/C][/ROW]
[ROW][C]109[/C][C]0.887263373489942[/C][C]0.225473253020116[/C][C]0.112736626510058[/C][/ROW]
[ROW][C]110[/C][C]0.90504062255482[/C][C]0.189918754890359[/C][C]0.0949593774451794[/C][/ROW]
[ROW][C]111[/C][C]0.893555306290796[/C][C]0.212889387418408[/C][C]0.106444693709204[/C][/ROW]
[ROW][C]112[/C][C]0.921601018936764[/C][C]0.156797962126472[/C][C]0.0783989810632361[/C][/ROW]
[ROW][C]113[/C][C]0.899993722750785[/C][C]0.200012554498430[/C][C]0.100006277249215[/C][/ROW]
[ROW][C]114[/C][C]0.87465357556155[/C][C]0.2506928488769[/C][C]0.12534642443845[/C][/ROW]
[ROW][C]115[/C][C]0.845373780576912[/C][C]0.309252438846176[/C][C]0.154626219423088[/C][/ROW]
[ROW][C]116[/C][C]0.847493292799829[/C][C]0.305013414400342[/C][C]0.152506707200171[/C][/ROW]
[ROW][C]117[/C][C]0.857816436644454[/C][C]0.284367126711093[/C][C]0.142183563355546[/C][/ROW]
[ROW][C]118[/C][C]0.846274096123627[/C][C]0.307451807752747[/C][C]0.153725903876373[/C][/ROW]
[ROW][C]119[/C][C]0.80970547163423[/C][C]0.380589056731539[/C][C]0.190294528365770[/C][/ROW]
[ROW][C]120[/C][C]0.866610074544322[/C][C]0.266779850911356[/C][C]0.133389925455678[/C][/ROW]
[ROW][C]121[/C][C]0.841727180934622[/C][C]0.316545638130755[/C][C]0.158272819065378[/C][/ROW]
[ROW][C]122[/C][C]0.820500446297295[/C][C]0.35899910740541[/C][C]0.179499553702705[/C][/ROW]
[ROW][C]123[/C][C]0.807159276986429[/C][C]0.385681446027142[/C][C]0.192840723013571[/C][/ROW]
[ROW][C]124[/C][C]0.766077986582995[/C][C]0.467844026834011[/C][C]0.233922013417005[/C][/ROW]
[ROW][C]125[/C][C]0.770079067549306[/C][C]0.459841864901387[/C][C]0.229920932450694[/C][/ROW]
[ROW][C]126[/C][C]0.756640301322839[/C][C]0.486719397354322[/C][C]0.243359698677161[/C][/ROW]
[ROW][C]127[/C][C]0.704220769419521[/C][C]0.591558461160958[/C][C]0.295779230580479[/C][/ROW]
[ROW][C]128[/C][C]0.672219235858587[/C][C]0.655561528282827[/C][C]0.327780764141413[/C][/ROW]
[ROW][C]129[/C][C]0.678450557814505[/C][C]0.64309888437099[/C][C]0.321549442185495[/C][/ROW]
[ROW][C]130[/C][C]0.67083467524328[/C][C]0.658330649513439[/C][C]0.329165324756719[/C][/ROW]
[ROW][C]131[/C][C]0.611218910135329[/C][C]0.777562179729341[/C][C]0.388781089864671[/C][/ROW]
[ROW][C]132[/C][C]0.596622454026247[/C][C]0.806755091947505[/C][C]0.403377545973753[/C][/ROW]
[ROW][C]133[/C][C]0.569621223431652[/C][C]0.860757553136696[/C][C]0.430378776568348[/C][/ROW]
[ROW][C]134[/C][C]0.494839198306356[/C][C]0.989678396612712[/C][C]0.505160801693644[/C][/ROW]
[ROW][C]135[/C][C]0.466044420676628[/C][C]0.932088841353255[/C][C]0.533955579323372[/C][/ROW]
[ROW][C]136[/C][C]0.462599010992722[/C][C]0.925198021985445[/C][C]0.537400989007278[/C][/ROW]
[ROW][C]137[/C][C]0.386926968913845[/C][C]0.77385393782769[/C][C]0.613073031086155[/C][/ROW]
[ROW][C]138[/C][C]0.447548600580040[/C][C]0.895097201160081[/C][C]0.55245139941996[/C][/ROW]
[ROW][C]139[/C][C]0.802035821194748[/C][C]0.395928357610503[/C][C]0.197964178805251[/C][/ROW]
[ROW][C]140[/C][C]0.827418680766961[/C][C]0.345162638466078[/C][C]0.172581319233039[/C][/ROW]
[ROW][C]141[/C][C]0.790552960739502[/C][C]0.418894078520995[/C][C]0.209447039260498[/C][/ROW]
[ROW][C]142[/C][C]0.711298383720851[/C][C]0.577403232558297[/C][C]0.288701616279149[/C][/ROW]
[ROW][C]143[/C][C]0.657294189701565[/C][C]0.68541162059687[/C][C]0.342705810298435[/C][/ROW]
[ROW][C]144[/C][C]0.545068287359323[/C][C]0.909863425281354[/C][C]0.454931712640677[/C][/ROW]
[ROW][C]145[/C][C]0.536845720468542[/C][C]0.926308559062917[/C][C]0.463154279531458[/C][/ROW]
[ROW][C]146[/C][C]0.669325694184239[/C][C]0.661348611631522[/C][C]0.330674305815761[/C][/ROW]
[ROW][C]147[/C][C]0.571604863782527[/C][C]0.856790272434947[/C][C]0.428395136217473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1097323263420690.2194646526841370.890267673657931
100.04783346061747790.09566692123495580.952166539382522
110.07256329705488770.1451265941097750.927436702945112
120.03790860604819140.07581721209638290.962091393951809
130.4402168488493220.8804336976986440.559783151150678
140.7602912191048020.4794175617903950.239708780895198
150.6790903837862120.6418192324275760.320909616213788
160.589628206579950.8207435868400990.410371793420050
170.5297430233366890.940513953326620.47025697666331
180.4422181255603020.8844362511206040.557781874439698
190.3606956713455170.7213913426910330.639304328654483
200.5476487928621490.9047024142757020.452351207137851
210.5089890058391230.9820219883217550.491010994160877
220.5513269796458050.897346040708390.448673020354195
230.4848080209580340.9696160419160680.515191979041966
240.4724393460955650.944878692191130.527560653904435
250.4285154894072160.8570309788144330.571484510592784
260.3650298407677910.7300596815355820.634970159232209
270.6332280842098670.7335438315802660.366771915790133
280.5763878959184730.8472242081630540.423612104081527
290.5151430663324310.9697138673351380.484856933667569
300.675753256286070.6484934874278620.324246743713931
310.7856144386220790.4287711227558420.214385561377921
320.7470040565092130.5059918869815740.252995943490787
330.6991556691883810.6016886616232390.300844330811619
340.668006570219060.663986859561880.33199342978094
350.6741277603951170.6517444792097670.325872239604883
360.6818794403440560.6362411193118890.318120559655944
370.6488953604462430.7022092791075150.351104639553757
380.599646387680870.800707224638260.40035361231913
390.5456774647669080.9086450704661850.454322535233092
400.5168993035186230.9662013929627540.483100696481377
410.476959800621120.953919601242240.52304019937888
420.7564668140056420.4870663719887160.243533185994358
430.9512084630217760.09758307395644830.0487915369782241
440.962390920518310.07521815896338140.0376090794816907
450.9511668016155970.09766639676880570.0488331983844028
460.9569971036914280.08600579261714430.0430028963085722
470.979388886253030.04122222749393950.0206111137469698
480.972757252239960.05448549552008080.0272427477600404
490.9804060113576630.0391879772846740.019593988642337
500.9772716076768810.04545678464623770.0227283923231189
510.9725246929242620.05495061415147690.0274753070757384
520.9972831623599020.005433675280195480.00271683764009774
530.9960840088038520.007831982392296630.00391599119614831
540.9966829885117850.006634022976429270.00331701148821464
550.9967068694267150.006586261146570420.00329313057328521
560.9953428405433430.009314318913314280.00465715945665714
570.9935068298823610.01298634023527720.00649317011763862
580.9929499949147360.01410001017052830.00705000508526415
590.9945024838018050.01099503239638970.00549751619819487
600.9927735691703120.01445286165937650.00722643082968827
610.9935141732017350.01297165359653050.00648582679826523
620.994323469641150.01135306071770120.00567653035885061
630.9924526977370180.01509460452596320.0075473022629816
640.9931701124782320.01365977504353660.0068298875217683
650.991321188244740.01735762351052110.00867881175526056
660.9907255015667320.01854899686653660.00927449843326828
670.990334306044440.01933138791111830.00966569395555914
680.9921381357893970.01572372842120550.00786186421060273
690.9924502821635570.01509943567288530.00754971783644266
700.9898321114738350.02033577705233090.0101678885261655
710.9915548686973490.01689026260530250.00844513130265125
720.9888733654720850.022253269055830.011126634527915
730.9860188432745340.02796231345093290.0139811567254665
740.989770688684290.02045862263142180.0102293113157109
750.98616400447990.02767199104020.0138359955201
760.9835767086890350.03284658262193080.0164232913109654
770.9782570615429190.04348587691416290.0217429384570814
780.9713884755314270.05722304893714620.0286115244685731
790.9824994858335960.03500102833280890.0175005141664045
800.981556018804750.03688796239050080.0184439811952504
810.9843825627560680.03123487448786450.0156174372439322
820.9856252078053580.02874958438928470.0143747921946423
830.9807462553530350.03850748929392930.0192537446469647
840.9760745474561430.0478509050877150.0239254525438575
850.9941302435197450.01173951296051020.00586975648025509
860.9918568108641980.01628637827160420.0081431891358021
870.98891400538420.02217198923159760.0110859946157988
880.9854914061056740.02901718778865150.0145085938943257
890.981877312644270.03624537471146090.0181226873557305
900.9760561606269330.04788767874613450.0239438393730673
910.971388476426040.05722304714791860.0286115235739593
920.9830930987920920.03381380241581660.0169069012079083
930.9781495557687490.0437008884625030.0218504442312515
940.9754471440525530.04910571189489420.0245528559474471
950.9692633660094640.06147326798107280.0307366339905364
960.9613620106903540.07727597861929160.0386379893096458
970.9577663170428260.08446736591434780.0422336829571739
980.9459512498353620.1080975003292750.0540487501646376
990.9409753311686360.1180493376627270.0590246688313636
1000.9275760952011480.1448478095977040.0724239047988522
1010.9100462302509820.1799075394980370.0899537697490184
1020.895866448766480.2082671024670410.104133551233521
1030.9017506489929080.1964987020141840.0982493510070922
1040.935921715432250.1281565691355000.0640782845677502
1050.9331265841172260.1337468317655480.066873415882774
1060.9153664254646350.1692671490707300.0846335745353651
1070.8969917378655320.2060165242689350.103008262134468
1080.8914406004548910.2171187990902180.108559399545109
1090.8872633734899420.2254732530201160.112736626510058
1100.905040622554820.1899187548903590.0949593774451794
1110.8935553062907960.2128893874184080.106444693709204
1120.9216010189367640.1567979621264720.0783989810632361
1130.8999937227507850.2000125544984300.100006277249215
1140.874653575561550.25069284887690.12534642443845
1150.8453737805769120.3092524388461760.154626219423088
1160.8474932927998290.3050134144003420.152506707200171
1170.8578164366444540.2843671267110930.142183563355546
1180.8462740961236270.3074518077527470.153725903876373
1190.809705471634230.3805890567315390.190294528365770
1200.8666100745443220.2667798509113560.133389925455678
1210.8417271809346220.3165456381307550.158272819065378
1220.8205004462972950.358999107405410.179499553702705
1230.8071592769864290.3856814460271420.192840723013571
1240.7660779865829950.4678440268340110.233922013417005
1250.7700790675493060.4598418649013870.229920932450694
1260.7566403013228390.4867193973543220.243359698677161
1270.7042207694195210.5915584611609580.295779230580479
1280.6722192358585870.6555615282828270.327780764141413
1290.6784505578145050.643098884370990.321549442185495
1300.670834675243280.6583306495134390.329165324756719
1310.6112189101353290.7775621797293410.388781089864671
1320.5966224540262470.8067550919475050.403377545973753
1330.5696212234316520.8607575531366960.430378776568348
1340.4948391983063560.9896783966127120.505160801693644
1350.4660444206766280.9320888413532550.533955579323372
1360.4625990109927220.9251980219854450.537400989007278
1370.3869269689138450.773853937827690.613073031086155
1380.4475486005800400.8950972011600810.55245139941996
1390.8020358211947480.3959283576105030.197964178805251
1400.8274186807669610.3451626384660780.172581319233039
1410.7905529607395020.4188940785209950.209447039260498
1420.7112983837208510.5774032325582970.288701616279149
1430.6572941897015650.685411620596870.342705810298435
1440.5450682873593230.9098634252813540.454931712640677
1450.5368457204685420.9263085590629170.463154279531458
1460.6693256941842390.6613486116315220.330674305815761
1470.5716048637825270.8567902724349470.428395136217473






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0359712230215827NOK
5% type I error level440.316546762589928NOK
10% type I error level570.410071942446043NOK

\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 & 5 & 0.0359712230215827 & NOK \tabularnewline
5% type I error level & 44 & 0.316546762589928 & NOK \tabularnewline
10% type I error level & 57 & 0.410071942446043 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98253&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]5[/C][C]0.0359712230215827[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.316546762589928[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.410071942446043[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98253&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98253&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0359712230215827NOK
5% type I error level440.316546762589928NOK
10% type I error level570.410071942446043NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}