Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 21:18:19 +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/23/t12905470015owjiityj9125kq.htm/, Retrieved Thu, 28 Mar 2024 16:48:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99661, Retrieved Thu, 28 Mar 2024 16:48:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
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]
F   PD    [Multiple Regression] [] [2010-11-23 21:18:19] [6fde1c772c7be11768d9b6a0344566b2] [Current]
-   P       [Multiple Regression] [] [2010-11-23 21:27:31] [2db311435ed525bc1ed0ddec922afb8f]
Feedback Forum
2010-11-27 17:31:29 [00c625c7d009d84797af914265b614f9] [reply
Nog steed zeer lage adjusted R² waarde. De P-values zijn zeer hoog, dus de kans dat we ons vergissen bij het verwerpen van de nulhypothese is zeer groot.

Post a new message
Dataseries X:
11	14	11	12	12
11	11	7	8	11
11	6	17	8	14
11	12	10	8	12
11	8	12	9	21
11	10	12	7	12
11	10	11	4	22
11	11	11	11	11
11	16	12	7	10
11	11	13	7	13
11	13	14	12	10
11	12	16	10	8
11	8	11	10	15
11	12	10	8	14
11	11	11	8	10
11	4	15	4	14
11	9	9	9	14
11	8	11	8	11
11	8	17	7	10
11	14	17	11	13
11	15	11	9	7
11	16	18	11	14
11	9	14	13	12
11	14	10	8	14
11	11	11	8	11
11	8	15	9	9
11	9	15	6	11
11	9	13	9	15
11	9	16	9	14
11	9	13	6	13
11	10	9	6	9
11	16	18	16	15
11	11	18	5	10
11	8	12	7	11
11	9	17	9	13
11	16	9	6	8
11	11	9	6	20
11	16	12	5	12
11	12	18	12	10
11	12	12	7	10
11	14	18	10	9
11	9	14	9	14
11	10	15	8	8
11	9	16	5	14
11	10	10	8	11
11	12	11	8	13
11	14	14	10	9
11	14	9	6	11
11	10	12	8	15
11	14	17	7	11
11	16	5	4	10
11	9	12	8	14
11	10	12	8	18
11	6	6	4	14
11	8	24	20	11
11	13	12	8	12
11	10	12	8	13
11	8	14	6	9
11	7	7	4	10
11	15	13	8	15
11	9	12	9	20
11	10	13	6	12
11	12	14	7	12
11	13	8	9	14
11	10	11	5	13
11	11	9	5	11
11	8	11	8	17
11	9	13	8	12
11	13	10	6	13
11	11	11	8	14
11	8	12	7	13
11	9	9	7	15
11	9	15	9	13
11	15	18	11	10
11	9	15	6	11
11	10	12	8	19
11	14	13	6	13
11	12	14	9	17
11	12	10	8	13
11	11	13	6	9
11	14	13	10	11
11	6	11	8	10
11	12	13	8	9
11	8	16	10	12
11	14	8	5	12
11	11	16	7	13
11	10	11	5	13
11	14	9	8	12
11	12	16	14	15
11	10	12	7	22
11	14	14	8	13
11	5	8	6	15
11	11	9	5	13
11	10	15	6	15
11	9	11	10	10
11	10	21	12	11
11	16	14	9	16
11	13	18	12	11
11	9	12	7	11
11	10	13	8	10
11	10	15	10	10
11	7	12	6	16
11	9	19	10	12
11	8	15	10	11
11	14	11	10	16
11	14	11	5	19
11	8	10	7	11
11	9	13	10	16
11	14	15	11	15
11	14	12	6	24
11	8	12	7	14
11	8	16	12	15
11	8	9	11	11
11	7	18	11	15
11	6	8	11	12
11	8	13	5	10
11	6	17	8	14
11	11	9	6	13
11	14	15	9	9
11	11	8	4	15
11	11	7	4	15
11	11	12	7	14
11	14	14	11	11
11	8	6	6	8
11	20	8	7	11
11	11	17	8	11
11	8	10	4	8
11	11	11	8	10
11	10	14	9	11
11	14	11	8	13
11	11	13	11	11
11	9	12	8	20
11	9	11	5	10
11	8	9	4	15
11	10	12	8	12
11	13	20	10	14
11	13	12	6	23
11	12	13	9	14
11	8	12	9	16
11	13	12	13	11
11	14	9	9	12
11	12	15	10	10
11	14	24	20	14
11	15	7	5	12
11	13	17	11	12
11	16	11	6	11
11	9	17	9	12
11	9	11	7	13
11	9	12	9	11
11	8	14	10	19
11	7	11	9	12
11	16	16	8	17
11	11	21	7	9
11	9	14	6	12
11	11	20	13	19
11	9	13	6	18
11	14	11	8	15
11	13	15	10	14
11	16	19	16	11
11				9
11				18
11				16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=99661&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=99661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 17.7769114656762 -0.329756031349403Month[t] -0.0754019001049189Doubts[t] -0.0115990201581907Expectations[t] -0.0388365785511978Criticism[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  17.7769114656762 -0.329756031349403Month[t] -0.0754019001049189Doubts[t] -0.0115990201581907Expectations[t] -0.0388365785511978Criticism[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  17.7769114656762 -0.329756031349403Month[t] -0.0754019001049189Doubts[t] -0.0115990201581907Expectations[t] -0.0388365785511978Criticism[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 17.7769114656762 -0.329756031349403Month[t] -0.0754019001049189Doubts[t] -0.0115990201581907Expectations[t] -0.0388365785511978Criticism[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.77691146567627.0123822.53510.0122210.00611
Month-0.3297560313494030.625842-0.52690.5990070.299503
Doubts-0.07540190010491890.090844-0.830.4077920.203896
Expectations-0.01159902015819070.088215-0.13150.8955590.44778
Criticism-0.03883657855119780.108942-0.35650.7219530.360977

\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) & 17.7769114656762 & 7.012382 & 2.5351 & 0.012221 & 0.00611 \tabularnewline
Month & -0.329756031349403 & 0.625842 & -0.5269 & 0.599007 & 0.299503 \tabularnewline
Doubts & -0.0754019001049189 & 0.090844 & -0.83 & 0.407792 & 0.203896 \tabularnewline
Expectations & -0.0115990201581907 & 0.088215 & -0.1315 & 0.895559 & 0.44778 \tabularnewline
Criticism & -0.0388365785511978 & 0.108942 & -0.3565 & 0.721953 & 0.360977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&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]17.7769114656762[/C][C]7.012382[/C][C]2.5351[/C][C]0.012221[/C][C]0.00611[/C][/ROW]
[ROW][C]Month[/C][C]-0.329756031349403[/C][C]0.625842[/C][C]-0.5269[/C][C]0.599007[/C][C]0.299503[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.0754019001049189[/C][C]0.090844[/C][C]-0.83[/C][C]0.407792[/C][C]0.203896[/C][/ROW]
[ROW][C]Expectations[/C][C]-0.0115990201581907[/C][C]0.088215[/C][C]-0.1315[/C][C]0.895559[/C][C]0.44778[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.0388365785511978[/C][C]0.108942[/C][C]-0.3565[/C][C]0.721953[/C][C]0.360977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.77691146567627.0123822.53510.0122210.00611
Month-0.3297560313494030.625842-0.52690.5990070.299503
Doubts-0.07540190010491890.090844-0.830.4077920.203896
Expectations-0.01159902015819070.088215-0.13150.8955590.44778
Criticism-0.03883657855119780.108942-0.35650.7219530.360977







Multiple Linear Regression - Regression Statistics
Multiple R0.094583923349095
R-squared0.00894611855610749
Adjusted R-squared-0.0163036618628452
F-TEST (value)0.354304806127837
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.84074436535968
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16922646287394
Sum Squared Residuals1576.90743055793

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.094583923349095 \tabularnewline
R-squared & 0.00894611855610749 \tabularnewline
Adjusted R-squared & -0.0163036618628452 \tabularnewline
F-TEST (value) & 0.354304806127837 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.84074436535968 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16922646287394 \tabularnewline
Sum Squared Residuals & 1576.90743055793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.094583923349095[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00894611855610749[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0163036618628452[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.354304806127837[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0.84074436535968[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16922646287394[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1576.90743055793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.094583923349095
R-squared0.00894611855610749
Adjusted R-squared-0.0163036618628452
F-TEST (value)0.354304806127837
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.84074436535968
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16922646287394
Sum Squared Residuals1576.90743055793







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.5003403550095-0.500340355009515
21112.9282884501618-1.92828845016178
31413.18930774910450.81069225089553
41212.8180894895823-0.818089489582292
52113.05766247113447.94233752886561
61212.9845318280269-0.984531828026946
72213.11264058383878.88735941616127
81112.7653826338754-1.76538263387543
91012.5321204273974-2.53212042739743
101312.89753090776380.102469092236164
111012.5409451946398-2.54094519463982
12812.6708222115308-4.67082221153075
131513.03042491274141.96957508725862
141412.81808948958231.18191051041771
151012.8818923695290-2.88189236952902
161413.51865590383550.481344096164519
171413.01705763150400.982942368495959
181113.1080980698438-2.10809806984378
191013.0773405274458-3.07734052744583
201312.46958281261150.530417187388474
21712.5414481905581-5.54144819055815
221412.30717999224351.69282000775650
231212.8037162165083-0.803716216508297
241412.66728568937251.33271431062755
251112.8818923695290-1.88189236952902
26913.0228654106598-4.02286541065982
271113.0639732462085-2.06397324620849
281512.97066155087132.02933844912872
291412.93586449039671.06413550960329
301313.0871712865249-0.087171286524872
31913.0581654670527-4.05816546705272
321512.11299709948752.88700290051249
331012.9172089640753-2.91720896407528
341113.1353356282368-2.13533562823678
351312.92426547023850.0757345297614842
36812.6057540664232-4.6057540664232
372012.98276356694787.0172364330522
381212.6097935844998-0.609793584499828
391012.5699510141120-2.56995101411198
401012.8337280278171-2.83372802781711
41912.4968203710045-3.49682037100453
421412.95906253071311.04093746928691
43812.9108981890012-4.91089818900118
441413.09121080460150.908789195398502
451112.9688932897921-1.96889328979213
461312.80649046942410.193509530575899
47912.5432164516373-3.54321645163730
481112.7565578666330-1.75655786663304
491512.94569524947572.05430475052425
501112.6249291268163-1.62492912681632
511012.7298233041584-2.72982330415836
521413.02109714958070.978902850419333
531812.94569524947575.05430475052425
541413.47224328504940.527756714950641
551112.4912718651729-1.49127186517292
561212.719489549161-0.719489549160992
571312.94569524947570.0543047505242517
58913.1509741664716-4.1509741664716
591013.3852423647863-3.38524236478625
601512.55708672879302.44291327120704
612012.98226057102957.01773942897053
621213.0117693864200-1.01176938641995
631212.8105299875007-0.810529987500727
641412.72704905124261.27295094875744
651313.0738040052875-0.0738040052875324
661113.021600145499-2.02160014549900
671713.10809806984383.89190193015622
681213.0094981294225-1.00949812942248
691312.82036074657980.179639253420231
701412.88189236952901.11810763047098
711313.1353356282368-0.135335628236784
721513.09473078860641.90526921139356
731312.94746351055490.0525364894451028
741012.3825818923484-2.38258189234842
751113.0639732462085-2.06397324620849
761912.94569524947576.05430475052425
771312.71016178600030.289838213999722
781712.73285683039834.26714316960167
791312.81808948958230.181910510417708
80912.9363674863150-3.93636748631503
811112.5548154717955-1.55481547179549
821013.2589018700536-3.25890187005361
83912.7832924291077-3.78329242910772
841212.9724298119504-0.972429811950427
851212.8069934653424-0.806993465342429
861312.86273384728930.137266152710736
871313.0738040052875-0.0738040052875324
881212.6788847095306-0.678884709530645
891512.51547589732602.48452410267404
902212.98453182802699.01546817197305
911312.62088960873970.379110391260309
921513.44677398773551.5532260122645
931313.021600145499-0.0216001454989950
941512.98857134610362.01142865389643
951012.9550230126365-2.95502301263646
961112.6859577538472-1.68595775384724
971612.43124922997873.56875077002134
981112.4945491140071-1.49454911400706
991113.0599337281319-2.05993372813187
1001012.9340962293176-2.93409622931756
1011012.8332250318988-2.83322503189878
1021613.24957410689292.7504258931071
1031212.8622308513709-0.862230851370937
1041112.9840288321086-1.98402883210862
1051612.57801351211193.42198648788813
1061912.77219640486796.22780359513214
1071113.1585336685532-2.15853366855317
1081612.93182497232013.06817502767992
1091512.49278085292792.50721914707209
1102412.721760806158511.2782391938415
1111413.13533562823680.864664371763216
1121512.89475665484802.10524334515197
1131113.0147863745066-2.01478637450657
1141512.98579709318782.01420290681223
1151213.1771891948746-1.17718919487459
1161013.201409765181-3.20140976518099
1171413.18930774910450.81069225089553
1181312.98276356694780.0172364330522028
119912.5704540100303-3.5704540100303
1201513.07203574420841.92796425579162
1211513.08363476436661.91636523563343
1221412.90912992792201.09087007207797
1231112.5043798730861-1.50437987308610
124813.2437663277371-5.24376632773712
1251112.2769089076105-1.27690890761052
1261112.8122982485799-1.81229824857988
127813.2750434042068-5.27504340420676
1281012.8818923695290-2.88189236952902
1291112.8836606306082-1.88366063060817
1301312.65568666921430.344313330785737
1311112.7421845935590-1.74218459355904
1322013.02109714958076.97890285041933
1331013.1492059053925-3.14920590539245
1341513.28664242436491.71335757563505
1351212.9456952494757-0.945695249475748
1361412.54902423079311.45097576920693
1372312.797162706263410.2028372937366
1381412.74445585055651.25554414944348
1391613.05766247113442.94233752886561
1401112.525306656405-1.52530665640500
1411212.6400481309794-0.640048130979447
1421012.6824212316889-2.68242123168894
1431412.03886046454341.96113953545659
1441212.7431905853957-0.743190585395701
1451212.5449847127164-0.544984712716445
1461112.5825560261068-1.58255602610682
1471212.9242654702385-0.924265470238516
1481313.0715327482901-0.0715327482900557
1491112.9822605710295-1.98226057102947
1501912.99562785226686.00437214773319
1511213.1446633913975-1.14466339139750
1521712.44688776821354.55311223178653
153912.8047387464983-3.80473874649831
1541213.0755722663667-1.07557226636668
1551912.58331829534936.41668170465069
1561813.08717128652494.91282871347513
1571512.65568666921432.34431333078574
1581412.60701933158401.39298066841598
1591112.1013980793293-1.10139807932932
1601112.6443303842269-1.64433038422688
1611211.08180541665380.91819458334616
162812.5909729167308-4.59097291673082

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.5003403550095 & -0.500340355009515 \tabularnewline
2 & 11 & 12.9282884501618 & -1.92828845016178 \tabularnewline
3 & 14 & 13.1893077491045 & 0.81069225089553 \tabularnewline
4 & 12 & 12.8180894895823 & -0.818089489582292 \tabularnewline
5 & 21 & 13.0576624711344 & 7.94233752886561 \tabularnewline
6 & 12 & 12.9845318280269 & -0.984531828026946 \tabularnewline
7 & 22 & 13.1126405838387 & 8.88735941616127 \tabularnewline
8 & 11 & 12.7653826338754 & -1.76538263387543 \tabularnewline
9 & 10 & 12.5321204273974 & -2.53212042739743 \tabularnewline
10 & 13 & 12.8975309077638 & 0.102469092236164 \tabularnewline
11 & 10 & 12.5409451946398 & -2.54094519463982 \tabularnewline
12 & 8 & 12.6708222115308 & -4.67082221153075 \tabularnewline
13 & 15 & 13.0304249127414 & 1.96957508725862 \tabularnewline
14 & 14 & 12.8180894895823 & 1.18191051041771 \tabularnewline
15 & 10 & 12.8818923695290 & -2.88189236952902 \tabularnewline
16 & 14 & 13.5186559038355 & 0.481344096164519 \tabularnewline
17 & 14 & 13.0170576315040 & 0.982942368495959 \tabularnewline
18 & 11 & 13.1080980698438 & -2.10809806984378 \tabularnewline
19 & 10 & 13.0773405274458 & -3.07734052744583 \tabularnewline
20 & 13 & 12.4695828126115 & 0.530417187388474 \tabularnewline
21 & 7 & 12.5414481905581 & -5.54144819055815 \tabularnewline
22 & 14 & 12.3071799922435 & 1.69282000775650 \tabularnewline
23 & 12 & 12.8037162165083 & -0.803716216508297 \tabularnewline
24 & 14 & 12.6672856893725 & 1.33271431062755 \tabularnewline
25 & 11 & 12.8818923695290 & -1.88189236952902 \tabularnewline
26 & 9 & 13.0228654106598 & -4.02286541065982 \tabularnewline
27 & 11 & 13.0639732462085 & -2.06397324620849 \tabularnewline
28 & 15 & 12.9706615508713 & 2.02933844912872 \tabularnewline
29 & 14 & 12.9358644903967 & 1.06413550960329 \tabularnewline
30 & 13 & 13.0871712865249 & -0.087171286524872 \tabularnewline
31 & 9 & 13.0581654670527 & -4.05816546705272 \tabularnewline
32 & 15 & 12.1129970994875 & 2.88700290051249 \tabularnewline
33 & 10 & 12.9172089640753 & -2.91720896407528 \tabularnewline
34 & 11 & 13.1353356282368 & -2.13533562823678 \tabularnewline
35 & 13 & 12.9242654702385 & 0.0757345297614842 \tabularnewline
36 & 8 & 12.6057540664232 & -4.6057540664232 \tabularnewline
37 & 20 & 12.9827635669478 & 7.0172364330522 \tabularnewline
38 & 12 & 12.6097935844998 & -0.609793584499828 \tabularnewline
39 & 10 & 12.5699510141120 & -2.56995101411198 \tabularnewline
40 & 10 & 12.8337280278171 & -2.83372802781711 \tabularnewline
41 & 9 & 12.4968203710045 & -3.49682037100453 \tabularnewline
42 & 14 & 12.9590625307131 & 1.04093746928691 \tabularnewline
43 & 8 & 12.9108981890012 & -4.91089818900118 \tabularnewline
44 & 14 & 13.0912108046015 & 0.908789195398502 \tabularnewline
45 & 11 & 12.9688932897921 & -1.96889328979213 \tabularnewline
46 & 13 & 12.8064904694241 & 0.193509530575899 \tabularnewline
47 & 9 & 12.5432164516373 & -3.54321645163730 \tabularnewline
48 & 11 & 12.7565578666330 & -1.75655786663304 \tabularnewline
49 & 15 & 12.9456952494757 & 2.05430475052425 \tabularnewline
50 & 11 & 12.6249291268163 & -1.62492912681632 \tabularnewline
51 & 10 & 12.7298233041584 & -2.72982330415836 \tabularnewline
52 & 14 & 13.0210971495807 & 0.978902850419333 \tabularnewline
53 & 18 & 12.9456952494757 & 5.05430475052425 \tabularnewline
54 & 14 & 13.4722432850494 & 0.527756714950641 \tabularnewline
55 & 11 & 12.4912718651729 & -1.49127186517292 \tabularnewline
56 & 12 & 12.719489549161 & -0.719489549160992 \tabularnewline
57 & 13 & 12.9456952494757 & 0.0543047505242517 \tabularnewline
58 & 9 & 13.1509741664716 & -4.1509741664716 \tabularnewline
59 & 10 & 13.3852423647863 & -3.38524236478625 \tabularnewline
60 & 15 & 12.5570867287930 & 2.44291327120704 \tabularnewline
61 & 20 & 12.9822605710295 & 7.01773942897053 \tabularnewline
62 & 12 & 13.0117693864200 & -1.01176938641995 \tabularnewline
63 & 12 & 12.8105299875007 & -0.810529987500727 \tabularnewline
64 & 14 & 12.7270490512426 & 1.27295094875744 \tabularnewline
65 & 13 & 13.0738040052875 & -0.0738040052875324 \tabularnewline
66 & 11 & 13.021600145499 & -2.02160014549900 \tabularnewline
67 & 17 & 13.1080980698438 & 3.89190193015622 \tabularnewline
68 & 12 & 13.0094981294225 & -1.00949812942248 \tabularnewline
69 & 13 & 12.8203607465798 & 0.179639253420231 \tabularnewline
70 & 14 & 12.8818923695290 & 1.11810763047098 \tabularnewline
71 & 13 & 13.1353356282368 & -0.135335628236784 \tabularnewline
72 & 15 & 13.0947307886064 & 1.90526921139356 \tabularnewline
73 & 13 & 12.9474635105549 & 0.0525364894451028 \tabularnewline
74 & 10 & 12.3825818923484 & -2.38258189234842 \tabularnewline
75 & 11 & 13.0639732462085 & -2.06397324620849 \tabularnewline
76 & 19 & 12.9456952494757 & 6.05430475052425 \tabularnewline
77 & 13 & 12.7101617860003 & 0.289838213999722 \tabularnewline
78 & 17 & 12.7328568303983 & 4.26714316960167 \tabularnewline
79 & 13 & 12.8180894895823 & 0.181910510417708 \tabularnewline
80 & 9 & 12.9363674863150 & -3.93636748631503 \tabularnewline
81 & 11 & 12.5548154717955 & -1.55481547179549 \tabularnewline
82 & 10 & 13.2589018700536 & -3.25890187005361 \tabularnewline
83 & 9 & 12.7832924291077 & -3.78329242910772 \tabularnewline
84 & 12 & 12.9724298119504 & -0.972429811950427 \tabularnewline
85 & 12 & 12.8069934653424 & -0.806993465342429 \tabularnewline
86 & 13 & 12.8627338472893 & 0.137266152710736 \tabularnewline
87 & 13 & 13.0738040052875 & -0.0738040052875324 \tabularnewline
88 & 12 & 12.6788847095306 & -0.678884709530645 \tabularnewline
89 & 15 & 12.5154758973260 & 2.48452410267404 \tabularnewline
90 & 22 & 12.9845318280269 & 9.01546817197305 \tabularnewline
91 & 13 & 12.6208896087397 & 0.379110391260309 \tabularnewline
92 & 15 & 13.4467739877355 & 1.5532260122645 \tabularnewline
93 & 13 & 13.021600145499 & -0.0216001454989950 \tabularnewline
94 & 15 & 12.9885713461036 & 2.01142865389643 \tabularnewline
95 & 10 & 12.9550230126365 & -2.95502301263646 \tabularnewline
96 & 11 & 12.6859577538472 & -1.68595775384724 \tabularnewline
97 & 16 & 12.4312492299787 & 3.56875077002134 \tabularnewline
98 & 11 & 12.4945491140071 & -1.49454911400706 \tabularnewline
99 & 11 & 13.0599337281319 & -2.05993372813187 \tabularnewline
100 & 10 & 12.9340962293176 & -2.93409622931756 \tabularnewline
101 & 10 & 12.8332250318988 & -2.83322503189878 \tabularnewline
102 & 16 & 13.2495741068929 & 2.7504258931071 \tabularnewline
103 & 12 & 12.8622308513709 & -0.862230851370937 \tabularnewline
104 & 11 & 12.9840288321086 & -1.98402883210862 \tabularnewline
105 & 16 & 12.5780135121119 & 3.42198648788813 \tabularnewline
106 & 19 & 12.7721964048679 & 6.22780359513214 \tabularnewline
107 & 11 & 13.1585336685532 & -2.15853366855317 \tabularnewline
108 & 16 & 12.9318249723201 & 3.06817502767992 \tabularnewline
109 & 15 & 12.4927808529279 & 2.50721914707209 \tabularnewline
110 & 24 & 12.7217608061585 & 11.2782391938415 \tabularnewline
111 & 14 & 13.1353356282368 & 0.864664371763216 \tabularnewline
112 & 15 & 12.8947566548480 & 2.10524334515197 \tabularnewline
113 & 11 & 13.0147863745066 & -2.01478637450657 \tabularnewline
114 & 15 & 12.9857970931878 & 2.01420290681223 \tabularnewline
115 & 12 & 13.1771891948746 & -1.17718919487459 \tabularnewline
116 & 10 & 13.201409765181 & -3.20140976518099 \tabularnewline
117 & 14 & 13.1893077491045 & 0.81069225089553 \tabularnewline
118 & 13 & 12.9827635669478 & 0.0172364330522028 \tabularnewline
119 & 9 & 12.5704540100303 & -3.5704540100303 \tabularnewline
120 & 15 & 13.0720357442084 & 1.92796425579162 \tabularnewline
121 & 15 & 13.0836347643666 & 1.91636523563343 \tabularnewline
122 & 14 & 12.9091299279220 & 1.09087007207797 \tabularnewline
123 & 11 & 12.5043798730861 & -1.50437987308610 \tabularnewline
124 & 8 & 13.2437663277371 & -5.24376632773712 \tabularnewline
125 & 11 & 12.2769089076105 & -1.27690890761052 \tabularnewline
126 & 11 & 12.8122982485799 & -1.81229824857988 \tabularnewline
127 & 8 & 13.2750434042068 & -5.27504340420676 \tabularnewline
128 & 10 & 12.8818923695290 & -2.88189236952902 \tabularnewline
129 & 11 & 12.8836606306082 & -1.88366063060817 \tabularnewline
130 & 13 & 12.6556866692143 & 0.344313330785737 \tabularnewline
131 & 11 & 12.7421845935590 & -1.74218459355904 \tabularnewline
132 & 20 & 13.0210971495807 & 6.97890285041933 \tabularnewline
133 & 10 & 13.1492059053925 & -3.14920590539245 \tabularnewline
134 & 15 & 13.2866424243649 & 1.71335757563505 \tabularnewline
135 & 12 & 12.9456952494757 & -0.945695249475748 \tabularnewline
136 & 14 & 12.5490242307931 & 1.45097576920693 \tabularnewline
137 & 23 & 12.7971627062634 & 10.2028372937366 \tabularnewline
138 & 14 & 12.7444558505565 & 1.25554414944348 \tabularnewline
139 & 16 & 13.0576624711344 & 2.94233752886561 \tabularnewline
140 & 11 & 12.525306656405 & -1.52530665640500 \tabularnewline
141 & 12 & 12.6400481309794 & -0.640048130979447 \tabularnewline
142 & 10 & 12.6824212316889 & -2.68242123168894 \tabularnewline
143 & 14 & 12.0388604645434 & 1.96113953545659 \tabularnewline
144 & 12 & 12.7431905853957 & -0.743190585395701 \tabularnewline
145 & 12 & 12.5449847127164 & -0.544984712716445 \tabularnewline
146 & 11 & 12.5825560261068 & -1.58255602610682 \tabularnewline
147 & 12 & 12.9242654702385 & -0.924265470238516 \tabularnewline
148 & 13 & 13.0715327482901 & -0.0715327482900557 \tabularnewline
149 & 11 & 12.9822605710295 & -1.98226057102947 \tabularnewline
150 & 19 & 12.9956278522668 & 6.00437214773319 \tabularnewline
151 & 12 & 13.1446633913975 & -1.14466339139750 \tabularnewline
152 & 17 & 12.4468877682135 & 4.55311223178653 \tabularnewline
153 & 9 & 12.8047387464983 & -3.80473874649831 \tabularnewline
154 & 12 & 13.0755722663667 & -1.07557226636668 \tabularnewline
155 & 19 & 12.5833182953493 & 6.41668170465069 \tabularnewline
156 & 18 & 13.0871712865249 & 4.91282871347513 \tabularnewline
157 & 15 & 12.6556866692143 & 2.34431333078574 \tabularnewline
158 & 14 & 12.6070193315840 & 1.39298066841598 \tabularnewline
159 & 11 & 12.1013980793293 & -1.10139807932932 \tabularnewline
160 & 11 & 12.6443303842269 & -1.64433038422688 \tabularnewline
161 & 12 & 11.0818054166538 & 0.91819458334616 \tabularnewline
162 & 8 & 12.5909729167308 & -4.59097291673082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]12.5003403550095[/C][C]-0.500340355009515[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.9282884501618[/C][C]-1.92828845016178[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.1893077491045[/C][C]0.81069225089553[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.8180894895823[/C][C]-0.818089489582292[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]13.0576624711344[/C][C]7.94233752886561[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.9845318280269[/C][C]-0.984531828026946[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.1126405838387[/C][C]8.88735941616127[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.7653826338754[/C][C]-1.76538263387543[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.5321204273974[/C][C]-2.53212042739743[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.8975309077638[/C][C]0.102469092236164[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.5409451946398[/C][C]-2.54094519463982[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.6708222115308[/C][C]-4.67082221153075[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.0304249127414[/C][C]1.96957508725862[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.8180894895823[/C][C]1.18191051041771[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.8818923695290[/C][C]-2.88189236952902[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.5186559038355[/C][C]0.481344096164519[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.0170576315040[/C][C]0.982942368495959[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]13.1080980698438[/C][C]-2.10809806984378[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]13.0773405274458[/C][C]-3.07734052744583[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.4695828126115[/C][C]0.530417187388474[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.5414481905581[/C][C]-5.54144819055815[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.3071799922435[/C][C]1.69282000775650[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.8037162165083[/C][C]-0.803716216508297[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]12.6672856893725[/C][C]1.33271431062755[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.8818923695290[/C][C]-1.88189236952902[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]13.0228654106598[/C][C]-4.02286541065982[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]13.0639732462085[/C][C]-2.06397324620849[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.9706615508713[/C][C]2.02933844912872[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9358644903967[/C][C]1.06413550960329[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]13.0871712865249[/C][C]-0.087171286524872[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]13.0581654670527[/C][C]-4.05816546705272[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.1129970994875[/C][C]2.88700290051249[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.9172089640753[/C][C]-2.91720896407528[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]13.1353356282368[/C][C]-2.13533562823678[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.9242654702385[/C][C]0.0757345297614842[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.6057540664232[/C][C]-4.6057540664232[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]12.9827635669478[/C][C]7.0172364330522[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.6097935844998[/C][C]-0.609793584499828[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.5699510141120[/C][C]-2.56995101411198[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8337280278171[/C][C]-2.83372802781711[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.4968203710045[/C][C]-3.49682037100453[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.9590625307131[/C][C]1.04093746928691[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.9108981890012[/C][C]-4.91089818900118[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.0912108046015[/C][C]0.908789195398502[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.9688932897921[/C][C]-1.96889328979213[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.8064904694241[/C][C]0.193509530575899[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5432164516373[/C][C]-3.54321645163730[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.7565578666330[/C][C]-1.75655786663304[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.9456952494757[/C][C]2.05430475052425[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.6249291268163[/C][C]-1.62492912681632[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.7298233041584[/C][C]-2.72982330415836[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.0210971495807[/C][C]0.978902850419333[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.9456952494757[/C][C]5.05430475052425[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.4722432850494[/C][C]0.527756714950641[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.4912718651729[/C][C]-1.49127186517292[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.719489549161[/C][C]-0.719489549160992[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.9456952494757[/C][C]0.0543047505242517[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.1509741664716[/C][C]-4.1509741664716[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.3852423647863[/C][C]-3.38524236478625[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.5570867287930[/C][C]2.44291327120704[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]12.9822605710295[/C][C]7.01773942897053[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0117693864200[/C][C]-1.01176938641995[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.8105299875007[/C][C]-0.810529987500727[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.7270490512426[/C][C]1.27295094875744[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.0738040052875[/C][C]-0.0738040052875324[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]13.021600145499[/C][C]-2.02160014549900[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]13.1080980698438[/C][C]3.89190193015622[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.0094981294225[/C][C]-1.00949812942248[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.8203607465798[/C][C]0.179639253420231[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.8818923695290[/C][C]1.11810763047098[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.1353356282368[/C][C]-0.135335628236784[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0947307886064[/C][C]1.90526921139356[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.9474635105549[/C][C]0.0525364894451028[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.3825818923484[/C][C]-2.38258189234842[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.0639732462085[/C][C]-2.06397324620849[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.9456952494757[/C][C]6.05430475052425[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.7101617860003[/C][C]0.289838213999722[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.7328568303983[/C][C]4.26714316960167[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.8180894895823[/C][C]0.181910510417708[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.9363674863150[/C][C]-3.93636748631503[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.5548154717955[/C][C]-1.55481547179549[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.2589018700536[/C][C]-3.25890187005361[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.7832924291077[/C][C]-3.78329242910772[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9724298119504[/C][C]-0.972429811950427[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.8069934653424[/C][C]-0.806993465342429[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.8627338472893[/C][C]0.137266152710736[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.0738040052875[/C][C]-0.0738040052875324[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.6788847095306[/C][C]-0.678884709530645[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.5154758973260[/C][C]2.48452410267404[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]12.9845318280269[/C][C]9.01546817197305[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6208896087397[/C][C]0.379110391260309[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.4467739877355[/C][C]1.5532260122645[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.021600145499[/C][C]-0.0216001454989950[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9885713461036[/C][C]2.01142865389643[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]12.9550230126365[/C][C]-2.95502301263646[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.6859577538472[/C][C]-1.68595775384724[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.4312492299787[/C][C]3.56875077002134[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.4945491140071[/C][C]-1.49454911400706[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.0599337281319[/C][C]-2.05993372813187[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.9340962293176[/C][C]-2.93409622931756[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.8332250318988[/C][C]-2.83322503189878[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.2495741068929[/C][C]2.7504258931071[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.8622308513709[/C][C]-0.862230851370937[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]12.9840288321086[/C][C]-1.98402883210862[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.5780135121119[/C][C]3.42198648788813[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.7721964048679[/C][C]6.22780359513214[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]13.1585336685532[/C][C]-2.15853366855317[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.9318249723201[/C][C]3.06817502767992[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]12.4927808529279[/C][C]2.50721914707209[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]12.7217608061585[/C][C]11.2782391938415[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.1353356282368[/C][C]0.864664371763216[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.8947566548480[/C][C]2.10524334515197[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.0147863745066[/C][C]-2.01478637450657[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]12.9857970931878[/C][C]2.01420290681223[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.1771891948746[/C][C]-1.17718919487459[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.201409765181[/C][C]-3.20140976518099[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1893077491045[/C][C]0.81069225089553[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]12.9827635669478[/C][C]0.0172364330522028[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]12.5704540100303[/C][C]-3.5704540100303[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.0720357442084[/C][C]1.92796425579162[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.0836347643666[/C][C]1.91636523563343[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.9091299279220[/C][C]1.09087007207797[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.5043798730861[/C][C]-1.50437987308610[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.2437663277371[/C][C]-5.24376632773712[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.2769089076105[/C][C]-1.27690890761052[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.8122982485799[/C][C]-1.81229824857988[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.2750434042068[/C][C]-5.27504340420676[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]12.8818923695290[/C][C]-2.88189236952902[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]12.8836606306082[/C][C]-1.88366063060817[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6556866692143[/C][C]0.344313330785737[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.7421845935590[/C][C]-1.74218459355904[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.0210971495807[/C][C]6.97890285041933[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.1492059053925[/C][C]-3.14920590539245[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.2866424243649[/C][C]1.71335757563505[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.9456952494757[/C][C]-0.945695249475748[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.5490242307931[/C][C]1.45097576920693[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]12.7971627062634[/C][C]10.2028372937366[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.7444558505565[/C][C]1.25554414944348[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.0576624711344[/C][C]2.94233752886561[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.525306656405[/C][C]-1.52530665640500[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.6400481309794[/C][C]-0.640048130979447[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.6824212316889[/C][C]-2.68242123168894[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.0388604645434[/C][C]1.96113953545659[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.7431905853957[/C][C]-0.743190585395701[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.5449847127164[/C][C]-0.544984712716445[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.5825560261068[/C][C]-1.58255602610682[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.9242654702385[/C][C]-0.924265470238516[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.0715327482901[/C][C]-0.0715327482900557[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]12.9822605710295[/C][C]-1.98226057102947[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]12.9956278522668[/C][C]6.00437214773319[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.1446633913975[/C][C]-1.14466339139750[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.4468877682135[/C][C]4.55311223178653[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.8047387464983[/C][C]-3.80473874649831[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.0755722663667[/C][C]-1.07557226636668[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]12.5833182953493[/C][C]6.41668170465069[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.0871712865249[/C][C]4.91282871347513[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.6556866692143[/C][C]2.34431333078574[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]12.6070193315840[/C][C]1.39298066841598[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.1013980793293[/C][C]-1.10139807932932[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]12.6443303842269[/C][C]-1.64433038422688[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]11.0818054166538[/C][C]0.91819458334616[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]12.5909729167308[/C][C]-4.59097291673082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.5003403550095-0.500340355009515
21112.9282884501618-1.92828845016178
31413.18930774910450.81069225089553
41212.8180894895823-0.818089489582292
52113.05766247113447.94233752886561
61212.9845318280269-0.984531828026946
72213.11264058383878.88735941616127
81112.7653826338754-1.76538263387543
91012.5321204273974-2.53212042739743
101312.89753090776380.102469092236164
111012.5409451946398-2.54094519463982
12812.6708222115308-4.67082221153075
131513.03042491274141.96957508725862
141412.81808948958231.18191051041771
151012.8818923695290-2.88189236952902
161413.51865590383550.481344096164519
171413.01705763150400.982942368495959
181113.1080980698438-2.10809806984378
191013.0773405274458-3.07734052744583
201312.46958281261150.530417187388474
21712.5414481905581-5.54144819055815
221412.30717999224351.69282000775650
231212.8037162165083-0.803716216508297
241412.66728568937251.33271431062755
251112.8818923695290-1.88189236952902
26913.0228654106598-4.02286541065982
271113.0639732462085-2.06397324620849
281512.97066155087132.02933844912872
291412.93586449039671.06413550960329
301313.0871712865249-0.087171286524872
31913.0581654670527-4.05816546705272
321512.11299709948752.88700290051249
331012.9172089640753-2.91720896407528
341113.1353356282368-2.13533562823678
351312.92426547023850.0757345297614842
36812.6057540664232-4.6057540664232
372012.98276356694787.0172364330522
381212.6097935844998-0.609793584499828
391012.5699510141120-2.56995101411198
401012.8337280278171-2.83372802781711
41912.4968203710045-3.49682037100453
421412.95906253071311.04093746928691
43812.9108981890012-4.91089818900118
441413.09121080460150.908789195398502
451112.9688932897921-1.96889328979213
461312.80649046942410.193509530575899
47912.5432164516373-3.54321645163730
481112.7565578666330-1.75655786663304
491512.94569524947572.05430475052425
501112.6249291268163-1.62492912681632
511012.7298233041584-2.72982330415836
521413.02109714958070.978902850419333
531812.94569524947575.05430475052425
541413.47224328504940.527756714950641
551112.4912718651729-1.49127186517292
561212.719489549161-0.719489549160992
571312.94569524947570.0543047505242517
58913.1509741664716-4.1509741664716
591013.3852423647863-3.38524236478625
601512.55708672879302.44291327120704
612012.98226057102957.01773942897053
621213.0117693864200-1.01176938641995
631212.8105299875007-0.810529987500727
641412.72704905124261.27295094875744
651313.0738040052875-0.0738040052875324
661113.021600145499-2.02160014549900
671713.10809806984383.89190193015622
681213.0094981294225-1.00949812942248
691312.82036074657980.179639253420231
701412.88189236952901.11810763047098
711313.1353356282368-0.135335628236784
721513.09473078860641.90526921139356
731312.94746351055490.0525364894451028
741012.3825818923484-2.38258189234842
751113.0639732462085-2.06397324620849
761912.94569524947576.05430475052425
771312.71016178600030.289838213999722
781712.73285683039834.26714316960167
791312.81808948958230.181910510417708
80912.9363674863150-3.93636748631503
811112.5548154717955-1.55481547179549
821013.2589018700536-3.25890187005361
83912.7832924291077-3.78329242910772
841212.9724298119504-0.972429811950427
851212.8069934653424-0.806993465342429
861312.86273384728930.137266152710736
871313.0738040052875-0.0738040052875324
881212.6788847095306-0.678884709530645
891512.51547589732602.48452410267404
902212.98453182802699.01546817197305
911312.62088960873970.379110391260309
921513.44677398773551.5532260122645
931313.021600145499-0.0216001454989950
941512.98857134610362.01142865389643
951012.9550230126365-2.95502301263646
961112.6859577538472-1.68595775384724
971612.43124922997873.56875077002134
981112.4945491140071-1.49454911400706
991113.0599337281319-2.05993372813187
1001012.9340962293176-2.93409622931756
1011012.8332250318988-2.83322503189878
1021613.24957410689292.7504258931071
1031212.8622308513709-0.862230851370937
1041112.9840288321086-1.98402883210862
1051612.57801351211193.42198648788813
1061912.77219640486796.22780359513214
1071113.1585336685532-2.15853366855317
1081612.93182497232013.06817502767992
1091512.49278085292792.50721914707209
1102412.721760806158511.2782391938415
1111413.13533562823680.864664371763216
1121512.89475665484802.10524334515197
1131113.0147863745066-2.01478637450657
1141512.98579709318782.01420290681223
1151213.1771891948746-1.17718919487459
1161013.201409765181-3.20140976518099
1171413.18930774910450.81069225089553
1181312.98276356694780.0172364330522028
119912.5704540100303-3.5704540100303
1201513.07203574420841.92796425579162
1211513.08363476436661.91636523563343
1221412.90912992792201.09087007207797
1231112.5043798730861-1.50437987308610
124813.2437663277371-5.24376632773712
1251112.2769089076105-1.27690890761052
1261112.8122982485799-1.81229824857988
127813.2750434042068-5.27504340420676
1281012.8818923695290-2.88189236952902
1291112.8836606306082-1.88366063060817
1301312.65568666921430.344313330785737
1311112.7421845935590-1.74218459355904
1322013.02109714958076.97890285041933
1331013.1492059053925-3.14920590539245
1341513.28664242436491.71335757563505
1351212.9456952494757-0.945695249475748
1361412.54902423079311.45097576920693
1372312.797162706263410.2028372937366
1381412.74445585055651.25554414944348
1391613.05766247113442.94233752886561
1401112.525306656405-1.52530665640500
1411212.6400481309794-0.640048130979447
1421012.6824212316889-2.68242123168894
1431412.03886046454341.96113953545659
1441212.7431905853957-0.743190585395701
1451212.5449847127164-0.544984712716445
1461112.5825560261068-1.58255602610682
1471212.9242654702385-0.924265470238516
1481313.0715327482901-0.0715327482900557
1491112.9822605710295-1.98226057102947
1501912.99562785226686.00437214773319
1511213.1446633913975-1.14466339139750
1521712.44688776821354.55311223178653
153912.8047387464983-3.80473874649831
1541213.0755722663667-1.07557226636668
1551912.58331829534936.41668170465069
1561813.08717128652494.91282871347513
1571512.65568666921432.34431333078574
1581412.60701933158401.39298066841598
1591112.1013980793293-1.10139807932932
1601112.6443303842269-1.64433038422688
1611211.08180541665380.91819458334616
162812.5909729167308-4.59097291673082







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9767709882208870.04645802355822520.0232290117791126
90.9552930771120680.0894138457758650.0447069228879325
100.9213490510336380.1573018979327230.0786509489663615
110.8724498804780840.2551002390438310.127550119521916
120.8489561747228540.3020876505542930.151043825277146
130.782010867777030.435978264445940.21798913222297
140.7051176957330520.5897646085338970.294882304266948
150.7251405601440840.5497188797118320.274859439855916
160.7816084313663980.4367831372672040.218391568633602
170.7183690376560080.5632619246879830.281630962343992
180.7306458319664590.5387083360670820.269354168033541
190.7069143393997650.5861713212004710.293085660600235
200.7338273346399860.5323453307200290.266172665360014
210.7704250436505540.4591499126988920.229574956349446
220.814900650216180.370198699567640.18509934978382
230.764612633760610.4707747324787790.235387366239390
240.7262652717807420.5474694564385160.273734728219258
250.6872548455391510.6254903089216980.312745154460849
260.713458972386270.5730820552274590.286541027613730
270.6830350764647920.6339298470704160.316964923535208
280.6513987484138570.6972025031722860.348601251586143
290.6036099779247880.7927800441504230.396390022075212
300.5434822550964050.913035489807190.456517744903595
310.593564277844260.8128714443114790.406435722155739
320.6275092186070250.744981562785950.372490781392975
330.6000183892649950.799963221470010.399981610735005
340.5651417676597920.8697164646804150.434858232340208
350.5074184765764630.9851630468470730.492581523423537
360.5093965345107990.9812069309784020.490603465489201
370.7409116752946230.5181766494107550.259088324705377
380.6987713255408650.6024573489182710.301228674459136
390.6699906476513490.6600187046973030.330009352348651
400.6473552973218190.7052894053563630.352644702678181
410.6296195146740160.7407609706519670.370380485325984
420.5842380649067380.8315238701865240.415761935093262
430.6338891989362920.7322216021274160.366110801063708
440.5948852497113110.8102295005773790.405114750288689
450.5624123405633190.8751753188733610.437587659436681
460.5131253382261960.9737493235476090.486874661773804
470.5031614727985580.9936770544028850.496838527201442
480.4606506852273590.9213013704547190.539349314772641
490.4353647933756150.870729586751230.564635206624385
500.3942961668659630.7885923337319260.605703833134037
510.3694912236605370.7389824473210730.630508776339463
520.3274195604915260.6548391209830520.672580439508474
530.4080905776687700.8161811553375390.59190942233123
540.3637088115983720.7274176231967440.636291188401628
550.3276662649858170.6553325299716330.672333735014183
560.2867443402822790.5734886805645570.713255659717722
570.2465456758117990.4930913516235980.753454324188201
580.2753671941297630.5507343882595250.724632805870237
590.2922722540261370.5845445080522740.707727745973863
600.2968500309393470.5937000618786930.703149969060654
610.4756505025477410.9513010050954830.524349497452259
620.4320239823153150.864047964630630.567976017684685
630.3891464945945760.7782929891891520.610853505405424
640.3521706014689720.7043412029379430.647829398531028
650.3097890052578800.6195780105157610.69021099474212
660.2842833865723420.5685667731446840.715716613427658
670.3003937720243540.6007875440487070.699606227975646
680.2647169514481250.5294339028962490.735283048551875
690.2301085515849190.4602171031698390.76989144841508
700.2006308770377230.4012617540754450.799369122962277
710.1695958775216590.3391917550433180.830404122478341
720.1498220518115500.2996441036230990.85017794818845
730.1243368389810390.2486736779620790.87566316101896
740.1131002877764150.226200575552830.886899712223585
750.1000108065361540.2000216130723080.899989193463846
760.1684259489133470.3368518978266950.831574051086653
770.1461617930285010.2923235860570030.853838206971499
780.1743302539702350.3486605079404690.825669746029765
790.1462856249378460.2925712498756920.853714375062154
800.1604232360027860.3208464720055720.839576763997214
810.1403004222708370.2806008445416740.859699577729163
820.1484922288139780.2969844576279550.851507771186022
830.1605831236920930.3211662473841850.839416876307908
840.1365765795557830.2731531591115650.863423420444217
850.1156561144239680.2313122288479360.884343885576032
860.09707615095558980.1941523019111800.90292384904441
870.0791650749982580.1583301499965160.920834925001742
880.06463384188090120.1292676837618020.935366158119099
890.05938537018695560.1187707403739110.940614629813044
900.2281106528412010.4562213056824030.771889347158799
910.1986157861647200.3972315723294410.80138421383528
920.1785729277862350.357145855572470.821427072213765
930.1500833652285000.3001667304569990.8499166347715
940.1351464318059030.2702928636118060.864853568194097
950.1327063703846510.2654127407693020.867293629615349
960.1169419805826460.2338839611652910.883058019417354
970.1228289645505170.2456579291010350.877171035449483
980.1079052572977770.2158105145955550.892094742702223
990.09616660969751740.1923332193950350.903833390302483
1000.0943676818923920.1887353637847840.905632318107608
1010.09179625129315930.1835925025863190.90820374870684
1020.08631426141276540.1726285228255310.913685738587235
1030.07184473383130270.1436894676626050.928155266168697
1040.06292825709548870.1258565141909770.937071742904511
1050.06366080652215480.1273216130443100.936339193477845
1060.1093369321650750.2186738643301510.890663067834925
1070.09714929705332960.1942985941066590.90285070294667
1080.09432752052962470.1886550410592490.905672479470375
1090.0846180884043150.169236176808630.915381911595685
1100.4805204089054040.9610408178108080.519479591094596
1110.4346538495856680.8693076991713360.565346150414332
1120.4035576234856070.8071152469712130.596442376514393
1130.3693644146382130.7387288292764270.630635585361787
1140.3363288836176930.6726577672353860.663671116382307
1150.294868614882990.589737229765980.70513138511701
1160.2963878032818360.5927756065636730.703612196718164
1170.2547227614368810.5094455228737620.745277238563119
1180.2151793815245370.4303587630490750.784820618475463
1190.2310603746872980.4621207493745960.768939625312702
1200.2087800688017040.4175601376034090.791219931198295
1210.1918967323657590.3837934647315170.808103267634241
1220.1613736390381500.3227472780763010.83862636096185
1230.1381189556223990.2762379112447980.8618810443776
1240.1684658826041350.3369317652082710.831534117395865
1250.1400871714319360.2801743428638730.859912828568064
1260.1259304292310520.2518608584621040.874069570768948
1270.1815727143524590.3631454287049190.81842728564754
1280.1775350862729630.3550701725459260.822464913727037
1290.1614018698052160.3228037396104330.838598130194784
1300.1283912561380910.2567825122761820.871608743861909
1310.1108983742834080.2217967485668160.889101625716592
1320.2116301120250590.4232602240501180.788369887974941
1330.2223211162331670.4446422324663350.777678883766832
1340.1840265582056490.3680531164112970.815973441794351
1350.1517975122174290.3035950244348580.848202487782571
1360.1192756382231070.2385512764462130.880724361776893
1370.5524331754607550.895133649078490.447566824539245
1380.4914550358155260.9829100716310520.508544964184474
1390.473950596603190.947901193206380.52604940339681
1400.4155268126567270.8310536253134530.584473187343273
1410.3453421432492600.6906842864985190.65465785675074
1420.3362558706936630.6725117413873260.663744129306337
1430.2726112007702820.5452224015405640.727388799229718
1440.2109786125397190.4219572250794370.789021387460281
1450.1654211423919520.3308422847839040.834578857608048
1460.1313652334203380.2627304668406770.868634766579662
1470.09978364061143750.1995672812228750.900216359388562
1480.06605700404926950.1321140080985390.93394299595073
1490.05133074842322340.1026614968464470.948669251576777
1500.09465330881310930.1893066176262190.90534669118689
1510.05806349249027240.1161269849805450.941936507509728
1520.0535595176741360.1071190353482720.946440482325864
1530.1663279160225670.3326558320451340.833672083977433
1540.210789761155010.421579522310020.78921023884499

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.976770988220887 & 0.0464580235582252 & 0.0232290117791126 \tabularnewline
9 & 0.955293077112068 & 0.089413845775865 & 0.0447069228879325 \tabularnewline
10 & 0.921349051033638 & 0.157301897932723 & 0.0786509489663615 \tabularnewline
11 & 0.872449880478084 & 0.255100239043831 & 0.127550119521916 \tabularnewline
12 & 0.848956174722854 & 0.302087650554293 & 0.151043825277146 \tabularnewline
13 & 0.78201086777703 & 0.43597826444594 & 0.21798913222297 \tabularnewline
14 & 0.705117695733052 & 0.589764608533897 & 0.294882304266948 \tabularnewline
15 & 0.725140560144084 & 0.549718879711832 & 0.274859439855916 \tabularnewline
16 & 0.781608431366398 & 0.436783137267204 & 0.218391568633602 \tabularnewline
17 & 0.718369037656008 & 0.563261924687983 & 0.281630962343992 \tabularnewline
18 & 0.730645831966459 & 0.538708336067082 & 0.269354168033541 \tabularnewline
19 & 0.706914339399765 & 0.586171321200471 & 0.293085660600235 \tabularnewline
20 & 0.733827334639986 & 0.532345330720029 & 0.266172665360014 \tabularnewline
21 & 0.770425043650554 & 0.459149912698892 & 0.229574956349446 \tabularnewline
22 & 0.81490065021618 & 0.37019869956764 & 0.18509934978382 \tabularnewline
23 & 0.76461263376061 & 0.470774732478779 & 0.235387366239390 \tabularnewline
24 & 0.726265271780742 & 0.547469456438516 & 0.273734728219258 \tabularnewline
25 & 0.687254845539151 & 0.625490308921698 & 0.312745154460849 \tabularnewline
26 & 0.71345897238627 & 0.573082055227459 & 0.286541027613730 \tabularnewline
27 & 0.683035076464792 & 0.633929847070416 & 0.316964923535208 \tabularnewline
28 & 0.651398748413857 & 0.697202503172286 & 0.348601251586143 \tabularnewline
29 & 0.603609977924788 & 0.792780044150423 & 0.396390022075212 \tabularnewline
30 & 0.543482255096405 & 0.91303548980719 & 0.456517744903595 \tabularnewline
31 & 0.59356427784426 & 0.812871444311479 & 0.406435722155739 \tabularnewline
32 & 0.627509218607025 & 0.74498156278595 & 0.372490781392975 \tabularnewline
33 & 0.600018389264995 & 0.79996322147001 & 0.399981610735005 \tabularnewline
34 & 0.565141767659792 & 0.869716464680415 & 0.434858232340208 \tabularnewline
35 & 0.507418476576463 & 0.985163046847073 & 0.492581523423537 \tabularnewline
36 & 0.509396534510799 & 0.981206930978402 & 0.490603465489201 \tabularnewline
37 & 0.740911675294623 & 0.518176649410755 & 0.259088324705377 \tabularnewline
38 & 0.698771325540865 & 0.602457348918271 & 0.301228674459136 \tabularnewline
39 & 0.669990647651349 & 0.660018704697303 & 0.330009352348651 \tabularnewline
40 & 0.647355297321819 & 0.705289405356363 & 0.352644702678181 \tabularnewline
41 & 0.629619514674016 & 0.740760970651967 & 0.370380485325984 \tabularnewline
42 & 0.584238064906738 & 0.831523870186524 & 0.415761935093262 \tabularnewline
43 & 0.633889198936292 & 0.732221602127416 & 0.366110801063708 \tabularnewline
44 & 0.594885249711311 & 0.810229500577379 & 0.405114750288689 \tabularnewline
45 & 0.562412340563319 & 0.875175318873361 & 0.437587659436681 \tabularnewline
46 & 0.513125338226196 & 0.973749323547609 & 0.486874661773804 \tabularnewline
47 & 0.503161472798558 & 0.993677054402885 & 0.496838527201442 \tabularnewline
48 & 0.460650685227359 & 0.921301370454719 & 0.539349314772641 \tabularnewline
49 & 0.435364793375615 & 0.87072958675123 & 0.564635206624385 \tabularnewline
50 & 0.394296166865963 & 0.788592333731926 & 0.605703833134037 \tabularnewline
51 & 0.369491223660537 & 0.738982447321073 & 0.630508776339463 \tabularnewline
52 & 0.327419560491526 & 0.654839120983052 & 0.672580439508474 \tabularnewline
53 & 0.408090577668770 & 0.816181155337539 & 0.59190942233123 \tabularnewline
54 & 0.363708811598372 & 0.727417623196744 & 0.636291188401628 \tabularnewline
55 & 0.327666264985817 & 0.655332529971633 & 0.672333735014183 \tabularnewline
56 & 0.286744340282279 & 0.573488680564557 & 0.713255659717722 \tabularnewline
57 & 0.246545675811799 & 0.493091351623598 & 0.753454324188201 \tabularnewline
58 & 0.275367194129763 & 0.550734388259525 & 0.724632805870237 \tabularnewline
59 & 0.292272254026137 & 0.584544508052274 & 0.707727745973863 \tabularnewline
60 & 0.296850030939347 & 0.593700061878693 & 0.703149969060654 \tabularnewline
61 & 0.475650502547741 & 0.951301005095483 & 0.524349497452259 \tabularnewline
62 & 0.432023982315315 & 0.86404796463063 & 0.567976017684685 \tabularnewline
63 & 0.389146494594576 & 0.778292989189152 & 0.610853505405424 \tabularnewline
64 & 0.352170601468972 & 0.704341202937943 & 0.647829398531028 \tabularnewline
65 & 0.309789005257880 & 0.619578010515761 & 0.69021099474212 \tabularnewline
66 & 0.284283386572342 & 0.568566773144684 & 0.715716613427658 \tabularnewline
67 & 0.300393772024354 & 0.600787544048707 & 0.699606227975646 \tabularnewline
68 & 0.264716951448125 & 0.529433902896249 & 0.735283048551875 \tabularnewline
69 & 0.230108551584919 & 0.460217103169839 & 0.76989144841508 \tabularnewline
70 & 0.200630877037723 & 0.401261754075445 & 0.799369122962277 \tabularnewline
71 & 0.169595877521659 & 0.339191755043318 & 0.830404122478341 \tabularnewline
72 & 0.149822051811550 & 0.299644103623099 & 0.85017794818845 \tabularnewline
73 & 0.124336838981039 & 0.248673677962079 & 0.87566316101896 \tabularnewline
74 & 0.113100287776415 & 0.22620057555283 & 0.886899712223585 \tabularnewline
75 & 0.100010806536154 & 0.200021613072308 & 0.899989193463846 \tabularnewline
76 & 0.168425948913347 & 0.336851897826695 & 0.831574051086653 \tabularnewline
77 & 0.146161793028501 & 0.292323586057003 & 0.853838206971499 \tabularnewline
78 & 0.174330253970235 & 0.348660507940469 & 0.825669746029765 \tabularnewline
79 & 0.146285624937846 & 0.292571249875692 & 0.853714375062154 \tabularnewline
80 & 0.160423236002786 & 0.320846472005572 & 0.839576763997214 \tabularnewline
81 & 0.140300422270837 & 0.280600844541674 & 0.859699577729163 \tabularnewline
82 & 0.148492228813978 & 0.296984457627955 & 0.851507771186022 \tabularnewline
83 & 0.160583123692093 & 0.321166247384185 & 0.839416876307908 \tabularnewline
84 & 0.136576579555783 & 0.273153159111565 & 0.863423420444217 \tabularnewline
85 & 0.115656114423968 & 0.231312228847936 & 0.884343885576032 \tabularnewline
86 & 0.0970761509555898 & 0.194152301911180 & 0.90292384904441 \tabularnewline
87 & 0.079165074998258 & 0.158330149996516 & 0.920834925001742 \tabularnewline
88 & 0.0646338418809012 & 0.129267683761802 & 0.935366158119099 \tabularnewline
89 & 0.0593853701869556 & 0.118770740373911 & 0.940614629813044 \tabularnewline
90 & 0.228110652841201 & 0.456221305682403 & 0.771889347158799 \tabularnewline
91 & 0.198615786164720 & 0.397231572329441 & 0.80138421383528 \tabularnewline
92 & 0.178572927786235 & 0.35714585557247 & 0.821427072213765 \tabularnewline
93 & 0.150083365228500 & 0.300166730456999 & 0.8499166347715 \tabularnewline
94 & 0.135146431805903 & 0.270292863611806 & 0.864853568194097 \tabularnewline
95 & 0.132706370384651 & 0.265412740769302 & 0.867293629615349 \tabularnewline
96 & 0.116941980582646 & 0.233883961165291 & 0.883058019417354 \tabularnewline
97 & 0.122828964550517 & 0.245657929101035 & 0.877171035449483 \tabularnewline
98 & 0.107905257297777 & 0.215810514595555 & 0.892094742702223 \tabularnewline
99 & 0.0961666096975174 & 0.192333219395035 & 0.903833390302483 \tabularnewline
100 & 0.094367681892392 & 0.188735363784784 & 0.905632318107608 \tabularnewline
101 & 0.0917962512931593 & 0.183592502586319 & 0.90820374870684 \tabularnewline
102 & 0.0863142614127654 & 0.172628522825531 & 0.913685738587235 \tabularnewline
103 & 0.0718447338313027 & 0.143689467662605 & 0.928155266168697 \tabularnewline
104 & 0.0629282570954887 & 0.125856514190977 & 0.937071742904511 \tabularnewline
105 & 0.0636608065221548 & 0.127321613044310 & 0.936339193477845 \tabularnewline
106 & 0.109336932165075 & 0.218673864330151 & 0.890663067834925 \tabularnewline
107 & 0.0971492970533296 & 0.194298594106659 & 0.90285070294667 \tabularnewline
108 & 0.0943275205296247 & 0.188655041059249 & 0.905672479470375 \tabularnewline
109 & 0.084618088404315 & 0.16923617680863 & 0.915381911595685 \tabularnewline
110 & 0.480520408905404 & 0.961040817810808 & 0.519479591094596 \tabularnewline
111 & 0.434653849585668 & 0.869307699171336 & 0.565346150414332 \tabularnewline
112 & 0.403557623485607 & 0.807115246971213 & 0.596442376514393 \tabularnewline
113 & 0.369364414638213 & 0.738728829276427 & 0.630635585361787 \tabularnewline
114 & 0.336328883617693 & 0.672657767235386 & 0.663671116382307 \tabularnewline
115 & 0.29486861488299 & 0.58973722976598 & 0.70513138511701 \tabularnewline
116 & 0.296387803281836 & 0.592775606563673 & 0.703612196718164 \tabularnewline
117 & 0.254722761436881 & 0.509445522873762 & 0.745277238563119 \tabularnewline
118 & 0.215179381524537 & 0.430358763049075 & 0.784820618475463 \tabularnewline
119 & 0.231060374687298 & 0.462120749374596 & 0.768939625312702 \tabularnewline
120 & 0.208780068801704 & 0.417560137603409 & 0.791219931198295 \tabularnewline
121 & 0.191896732365759 & 0.383793464731517 & 0.808103267634241 \tabularnewline
122 & 0.161373639038150 & 0.322747278076301 & 0.83862636096185 \tabularnewline
123 & 0.138118955622399 & 0.276237911244798 & 0.8618810443776 \tabularnewline
124 & 0.168465882604135 & 0.336931765208271 & 0.831534117395865 \tabularnewline
125 & 0.140087171431936 & 0.280174342863873 & 0.859912828568064 \tabularnewline
126 & 0.125930429231052 & 0.251860858462104 & 0.874069570768948 \tabularnewline
127 & 0.181572714352459 & 0.363145428704919 & 0.81842728564754 \tabularnewline
128 & 0.177535086272963 & 0.355070172545926 & 0.822464913727037 \tabularnewline
129 & 0.161401869805216 & 0.322803739610433 & 0.838598130194784 \tabularnewline
130 & 0.128391256138091 & 0.256782512276182 & 0.871608743861909 \tabularnewline
131 & 0.110898374283408 & 0.221796748566816 & 0.889101625716592 \tabularnewline
132 & 0.211630112025059 & 0.423260224050118 & 0.788369887974941 \tabularnewline
133 & 0.222321116233167 & 0.444642232466335 & 0.777678883766832 \tabularnewline
134 & 0.184026558205649 & 0.368053116411297 & 0.815973441794351 \tabularnewline
135 & 0.151797512217429 & 0.303595024434858 & 0.848202487782571 \tabularnewline
136 & 0.119275638223107 & 0.238551276446213 & 0.880724361776893 \tabularnewline
137 & 0.552433175460755 & 0.89513364907849 & 0.447566824539245 \tabularnewline
138 & 0.491455035815526 & 0.982910071631052 & 0.508544964184474 \tabularnewline
139 & 0.47395059660319 & 0.94790119320638 & 0.52604940339681 \tabularnewline
140 & 0.415526812656727 & 0.831053625313453 & 0.584473187343273 \tabularnewline
141 & 0.345342143249260 & 0.690684286498519 & 0.65465785675074 \tabularnewline
142 & 0.336255870693663 & 0.672511741387326 & 0.663744129306337 \tabularnewline
143 & 0.272611200770282 & 0.545222401540564 & 0.727388799229718 \tabularnewline
144 & 0.210978612539719 & 0.421957225079437 & 0.789021387460281 \tabularnewline
145 & 0.165421142391952 & 0.330842284783904 & 0.834578857608048 \tabularnewline
146 & 0.131365233420338 & 0.262730466840677 & 0.868634766579662 \tabularnewline
147 & 0.0997836406114375 & 0.199567281222875 & 0.900216359388562 \tabularnewline
148 & 0.0660570040492695 & 0.132114008098539 & 0.93394299595073 \tabularnewline
149 & 0.0513307484232234 & 0.102661496846447 & 0.948669251576777 \tabularnewline
150 & 0.0946533088131093 & 0.189306617626219 & 0.90534669118689 \tabularnewline
151 & 0.0580634924902724 & 0.116126984980545 & 0.941936507509728 \tabularnewline
152 & 0.053559517674136 & 0.107119035348272 & 0.946440482325864 \tabularnewline
153 & 0.166327916022567 & 0.332655832045134 & 0.833672083977433 \tabularnewline
154 & 0.21078976115501 & 0.42157952231002 & 0.78921023884499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.976770988220887[/C][C]0.0464580235582252[/C][C]0.0232290117791126[/C][/ROW]
[ROW][C]9[/C][C]0.955293077112068[/C][C]0.089413845775865[/C][C]0.0447069228879325[/C][/ROW]
[ROW][C]10[/C][C]0.921349051033638[/C][C]0.157301897932723[/C][C]0.0786509489663615[/C][/ROW]
[ROW][C]11[/C][C]0.872449880478084[/C][C]0.255100239043831[/C][C]0.127550119521916[/C][/ROW]
[ROW][C]12[/C][C]0.848956174722854[/C][C]0.302087650554293[/C][C]0.151043825277146[/C][/ROW]
[ROW][C]13[/C][C]0.78201086777703[/C][C]0.43597826444594[/C][C]0.21798913222297[/C][/ROW]
[ROW][C]14[/C][C]0.705117695733052[/C][C]0.589764608533897[/C][C]0.294882304266948[/C][/ROW]
[ROW][C]15[/C][C]0.725140560144084[/C][C]0.549718879711832[/C][C]0.274859439855916[/C][/ROW]
[ROW][C]16[/C][C]0.781608431366398[/C][C]0.436783137267204[/C][C]0.218391568633602[/C][/ROW]
[ROW][C]17[/C][C]0.718369037656008[/C][C]0.563261924687983[/C][C]0.281630962343992[/C][/ROW]
[ROW][C]18[/C][C]0.730645831966459[/C][C]0.538708336067082[/C][C]0.269354168033541[/C][/ROW]
[ROW][C]19[/C][C]0.706914339399765[/C][C]0.586171321200471[/C][C]0.293085660600235[/C][/ROW]
[ROW][C]20[/C][C]0.733827334639986[/C][C]0.532345330720029[/C][C]0.266172665360014[/C][/ROW]
[ROW][C]21[/C][C]0.770425043650554[/C][C]0.459149912698892[/C][C]0.229574956349446[/C][/ROW]
[ROW][C]22[/C][C]0.81490065021618[/C][C]0.37019869956764[/C][C]0.18509934978382[/C][/ROW]
[ROW][C]23[/C][C]0.76461263376061[/C][C]0.470774732478779[/C][C]0.235387366239390[/C][/ROW]
[ROW][C]24[/C][C]0.726265271780742[/C][C]0.547469456438516[/C][C]0.273734728219258[/C][/ROW]
[ROW][C]25[/C][C]0.687254845539151[/C][C]0.625490308921698[/C][C]0.312745154460849[/C][/ROW]
[ROW][C]26[/C][C]0.71345897238627[/C][C]0.573082055227459[/C][C]0.286541027613730[/C][/ROW]
[ROW][C]27[/C][C]0.683035076464792[/C][C]0.633929847070416[/C][C]0.316964923535208[/C][/ROW]
[ROW][C]28[/C][C]0.651398748413857[/C][C]0.697202503172286[/C][C]0.348601251586143[/C][/ROW]
[ROW][C]29[/C][C]0.603609977924788[/C][C]0.792780044150423[/C][C]0.396390022075212[/C][/ROW]
[ROW][C]30[/C][C]0.543482255096405[/C][C]0.91303548980719[/C][C]0.456517744903595[/C][/ROW]
[ROW][C]31[/C][C]0.59356427784426[/C][C]0.812871444311479[/C][C]0.406435722155739[/C][/ROW]
[ROW][C]32[/C][C]0.627509218607025[/C][C]0.74498156278595[/C][C]0.372490781392975[/C][/ROW]
[ROW][C]33[/C][C]0.600018389264995[/C][C]0.79996322147001[/C][C]0.399981610735005[/C][/ROW]
[ROW][C]34[/C][C]0.565141767659792[/C][C]0.869716464680415[/C][C]0.434858232340208[/C][/ROW]
[ROW][C]35[/C][C]0.507418476576463[/C][C]0.985163046847073[/C][C]0.492581523423537[/C][/ROW]
[ROW][C]36[/C][C]0.509396534510799[/C][C]0.981206930978402[/C][C]0.490603465489201[/C][/ROW]
[ROW][C]37[/C][C]0.740911675294623[/C][C]0.518176649410755[/C][C]0.259088324705377[/C][/ROW]
[ROW][C]38[/C][C]0.698771325540865[/C][C]0.602457348918271[/C][C]0.301228674459136[/C][/ROW]
[ROW][C]39[/C][C]0.669990647651349[/C][C]0.660018704697303[/C][C]0.330009352348651[/C][/ROW]
[ROW][C]40[/C][C]0.647355297321819[/C][C]0.705289405356363[/C][C]0.352644702678181[/C][/ROW]
[ROW][C]41[/C][C]0.629619514674016[/C][C]0.740760970651967[/C][C]0.370380485325984[/C][/ROW]
[ROW][C]42[/C][C]0.584238064906738[/C][C]0.831523870186524[/C][C]0.415761935093262[/C][/ROW]
[ROW][C]43[/C][C]0.633889198936292[/C][C]0.732221602127416[/C][C]0.366110801063708[/C][/ROW]
[ROW][C]44[/C][C]0.594885249711311[/C][C]0.810229500577379[/C][C]0.405114750288689[/C][/ROW]
[ROW][C]45[/C][C]0.562412340563319[/C][C]0.875175318873361[/C][C]0.437587659436681[/C][/ROW]
[ROW][C]46[/C][C]0.513125338226196[/C][C]0.973749323547609[/C][C]0.486874661773804[/C][/ROW]
[ROW][C]47[/C][C]0.503161472798558[/C][C]0.993677054402885[/C][C]0.496838527201442[/C][/ROW]
[ROW][C]48[/C][C]0.460650685227359[/C][C]0.921301370454719[/C][C]0.539349314772641[/C][/ROW]
[ROW][C]49[/C][C]0.435364793375615[/C][C]0.87072958675123[/C][C]0.564635206624385[/C][/ROW]
[ROW][C]50[/C][C]0.394296166865963[/C][C]0.788592333731926[/C][C]0.605703833134037[/C][/ROW]
[ROW][C]51[/C][C]0.369491223660537[/C][C]0.738982447321073[/C][C]0.630508776339463[/C][/ROW]
[ROW][C]52[/C][C]0.327419560491526[/C][C]0.654839120983052[/C][C]0.672580439508474[/C][/ROW]
[ROW][C]53[/C][C]0.408090577668770[/C][C]0.816181155337539[/C][C]0.59190942233123[/C][/ROW]
[ROW][C]54[/C][C]0.363708811598372[/C][C]0.727417623196744[/C][C]0.636291188401628[/C][/ROW]
[ROW][C]55[/C][C]0.327666264985817[/C][C]0.655332529971633[/C][C]0.672333735014183[/C][/ROW]
[ROW][C]56[/C][C]0.286744340282279[/C][C]0.573488680564557[/C][C]0.713255659717722[/C][/ROW]
[ROW][C]57[/C][C]0.246545675811799[/C][C]0.493091351623598[/C][C]0.753454324188201[/C][/ROW]
[ROW][C]58[/C][C]0.275367194129763[/C][C]0.550734388259525[/C][C]0.724632805870237[/C][/ROW]
[ROW][C]59[/C][C]0.292272254026137[/C][C]0.584544508052274[/C][C]0.707727745973863[/C][/ROW]
[ROW][C]60[/C][C]0.296850030939347[/C][C]0.593700061878693[/C][C]0.703149969060654[/C][/ROW]
[ROW][C]61[/C][C]0.475650502547741[/C][C]0.951301005095483[/C][C]0.524349497452259[/C][/ROW]
[ROW][C]62[/C][C]0.432023982315315[/C][C]0.86404796463063[/C][C]0.567976017684685[/C][/ROW]
[ROW][C]63[/C][C]0.389146494594576[/C][C]0.778292989189152[/C][C]0.610853505405424[/C][/ROW]
[ROW][C]64[/C][C]0.352170601468972[/C][C]0.704341202937943[/C][C]0.647829398531028[/C][/ROW]
[ROW][C]65[/C][C]0.309789005257880[/C][C]0.619578010515761[/C][C]0.69021099474212[/C][/ROW]
[ROW][C]66[/C][C]0.284283386572342[/C][C]0.568566773144684[/C][C]0.715716613427658[/C][/ROW]
[ROW][C]67[/C][C]0.300393772024354[/C][C]0.600787544048707[/C][C]0.699606227975646[/C][/ROW]
[ROW][C]68[/C][C]0.264716951448125[/C][C]0.529433902896249[/C][C]0.735283048551875[/C][/ROW]
[ROW][C]69[/C][C]0.230108551584919[/C][C]0.460217103169839[/C][C]0.76989144841508[/C][/ROW]
[ROW][C]70[/C][C]0.200630877037723[/C][C]0.401261754075445[/C][C]0.799369122962277[/C][/ROW]
[ROW][C]71[/C][C]0.169595877521659[/C][C]0.339191755043318[/C][C]0.830404122478341[/C][/ROW]
[ROW][C]72[/C][C]0.149822051811550[/C][C]0.299644103623099[/C][C]0.85017794818845[/C][/ROW]
[ROW][C]73[/C][C]0.124336838981039[/C][C]0.248673677962079[/C][C]0.87566316101896[/C][/ROW]
[ROW][C]74[/C][C]0.113100287776415[/C][C]0.22620057555283[/C][C]0.886899712223585[/C][/ROW]
[ROW][C]75[/C][C]0.100010806536154[/C][C]0.200021613072308[/C][C]0.899989193463846[/C][/ROW]
[ROW][C]76[/C][C]0.168425948913347[/C][C]0.336851897826695[/C][C]0.831574051086653[/C][/ROW]
[ROW][C]77[/C][C]0.146161793028501[/C][C]0.292323586057003[/C][C]0.853838206971499[/C][/ROW]
[ROW][C]78[/C][C]0.174330253970235[/C][C]0.348660507940469[/C][C]0.825669746029765[/C][/ROW]
[ROW][C]79[/C][C]0.146285624937846[/C][C]0.292571249875692[/C][C]0.853714375062154[/C][/ROW]
[ROW][C]80[/C][C]0.160423236002786[/C][C]0.320846472005572[/C][C]0.839576763997214[/C][/ROW]
[ROW][C]81[/C][C]0.140300422270837[/C][C]0.280600844541674[/C][C]0.859699577729163[/C][/ROW]
[ROW][C]82[/C][C]0.148492228813978[/C][C]0.296984457627955[/C][C]0.851507771186022[/C][/ROW]
[ROW][C]83[/C][C]0.160583123692093[/C][C]0.321166247384185[/C][C]0.839416876307908[/C][/ROW]
[ROW][C]84[/C][C]0.136576579555783[/C][C]0.273153159111565[/C][C]0.863423420444217[/C][/ROW]
[ROW][C]85[/C][C]0.115656114423968[/C][C]0.231312228847936[/C][C]0.884343885576032[/C][/ROW]
[ROW][C]86[/C][C]0.0970761509555898[/C][C]0.194152301911180[/C][C]0.90292384904441[/C][/ROW]
[ROW][C]87[/C][C]0.079165074998258[/C][C]0.158330149996516[/C][C]0.920834925001742[/C][/ROW]
[ROW][C]88[/C][C]0.0646338418809012[/C][C]0.129267683761802[/C][C]0.935366158119099[/C][/ROW]
[ROW][C]89[/C][C]0.0593853701869556[/C][C]0.118770740373911[/C][C]0.940614629813044[/C][/ROW]
[ROW][C]90[/C][C]0.228110652841201[/C][C]0.456221305682403[/C][C]0.771889347158799[/C][/ROW]
[ROW][C]91[/C][C]0.198615786164720[/C][C]0.397231572329441[/C][C]0.80138421383528[/C][/ROW]
[ROW][C]92[/C][C]0.178572927786235[/C][C]0.35714585557247[/C][C]0.821427072213765[/C][/ROW]
[ROW][C]93[/C][C]0.150083365228500[/C][C]0.300166730456999[/C][C]0.8499166347715[/C][/ROW]
[ROW][C]94[/C][C]0.135146431805903[/C][C]0.270292863611806[/C][C]0.864853568194097[/C][/ROW]
[ROW][C]95[/C][C]0.132706370384651[/C][C]0.265412740769302[/C][C]0.867293629615349[/C][/ROW]
[ROW][C]96[/C][C]0.116941980582646[/C][C]0.233883961165291[/C][C]0.883058019417354[/C][/ROW]
[ROW][C]97[/C][C]0.122828964550517[/C][C]0.245657929101035[/C][C]0.877171035449483[/C][/ROW]
[ROW][C]98[/C][C]0.107905257297777[/C][C]0.215810514595555[/C][C]0.892094742702223[/C][/ROW]
[ROW][C]99[/C][C]0.0961666096975174[/C][C]0.192333219395035[/C][C]0.903833390302483[/C][/ROW]
[ROW][C]100[/C][C]0.094367681892392[/C][C]0.188735363784784[/C][C]0.905632318107608[/C][/ROW]
[ROW][C]101[/C][C]0.0917962512931593[/C][C]0.183592502586319[/C][C]0.90820374870684[/C][/ROW]
[ROW][C]102[/C][C]0.0863142614127654[/C][C]0.172628522825531[/C][C]0.913685738587235[/C][/ROW]
[ROW][C]103[/C][C]0.0718447338313027[/C][C]0.143689467662605[/C][C]0.928155266168697[/C][/ROW]
[ROW][C]104[/C][C]0.0629282570954887[/C][C]0.125856514190977[/C][C]0.937071742904511[/C][/ROW]
[ROW][C]105[/C][C]0.0636608065221548[/C][C]0.127321613044310[/C][C]0.936339193477845[/C][/ROW]
[ROW][C]106[/C][C]0.109336932165075[/C][C]0.218673864330151[/C][C]0.890663067834925[/C][/ROW]
[ROW][C]107[/C][C]0.0971492970533296[/C][C]0.194298594106659[/C][C]0.90285070294667[/C][/ROW]
[ROW][C]108[/C][C]0.0943275205296247[/C][C]0.188655041059249[/C][C]0.905672479470375[/C][/ROW]
[ROW][C]109[/C][C]0.084618088404315[/C][C]0.16923617680863[/C][C]0.915381911595685[/C][/ROW]
[ROW][C]110[/C][C]0.480520408905404[/C][C]0.961040817810808[/C][C]0.519479591094596[/C][/ROW]
[ROW][C]111[/C][C]0.434653849585668[/C][C]0.869307699171336[/C][C]0.565346150414332[/C][/ROW]
[ROW][C]112[/C][C]0.403557623485607[/C][C]0.807115246971213[/C][C]0.596442376514393[/C][/ROW]
[ROW][C]113[/C][C]0.369364414638213[/C][C]0.738728829276427[/C][C]0.630635585361787[/C][/ROW]
[ROW][C]114[/C][C]0.336328883617693[/C][C]0.672657767235386[/C][C]0.663671116382307[/C][/ROW]
[ROW][C]115[/C][C]0.29486861488299[/C][C]0.58973722976598[/C][C]0.70513138511701[/C][/ROW]
[ROW][C]116[/C][C]0.296387803281836[/C][C]0.592775606563673[/C][C]0.703612196718164[/C][/ROW]
[ROW][C]117[/C][C]0.254722761436881[/C][C]0.509445522873762[/C][C]0.745277238563119[/C][/ROW]
[ROW][C]118[/C][C]0.215179381524537[/C][C]0.430358763049075[/C][C]0.784820618475463[/C][/ROW]
[ROW][C]119[/C][C]0.231060374687298[/C][C]0.462120749374596[/C][C]0.768939625312702[/C][/ROW]
[ROW][C]120[/C][C]0.208780068801704[/C][C]0.417560137603409[/C][C]0.791219931198295[/C][/ROW]
[ROW][C]121[/C][C]0.191896732365759[/C][C]0.383793464731517[/C][C]0.808103267634241[/C][/ROW]
[ROW][C]122[/C][C]0.161373639038150[/C][C]0.322747278076301[/C][C]0.83862636096185[/C][/ROW]
[ROW][C]123[/C][C]0.138118955622399[/C][C]0.276237911244798[/C][C]0.8618810443776[/C][/ROW]
[ROW][C]124[/C][C]0.168465882604135[/C][C]0.336931765208271[/C][C]0.831534117395865[/C][/ROW]
[ROW][C]125[/C][C]0.140087171431936[/C][C]0.280174342863873[/C][C]0.859912828568064[/C][/ROW]
[ROW][C]126[/C][C]0.125930429231052[/C][C]0.251860858462104[/C][C]0.874069570768948[/C][/ROW]
[ROW][C]127[/C][C]0.181572714352459[/C][C]0.363145428704919[/C][C]0.81842728564754[/C][/ROW]
[ROW][C]128[/C][C]0.177535086272963[/C][C]0.355070172545926[/C][C]0.822464913727037[/C][/ROW]
[ROW][C]129[/C][C]0.161401869805216[/C][C]0.322803739610433[/C][C]0.838598130194784[/C][/ROW]
[ROW][C]130[/C][C]0.128391256138091[/C][C]0.256782512276182[/C][C]0.871608743861909[/C][/ROW]
[ROW][C]131[/C][C]0.110898374283408[/C][C]0.221796748566816[/C][C]0.889101625716592[/C][/ROW]
[ROW][C]132[/C][C]0.211630112025059[/C][C]0.423260224050118[/C][C]0.788369887974941[/C][/ROW]
[ROW][C]133[/C][C]0.222321116233167[/C][C]0.444642232466335[/C][C]0.777678883766832[/C][/ROW]
[ROW][C]134[/C][C]0.184026558205649[/C][C]0.368053116411297[/C][C]0.815973441794351[/C][/ROW]
[ROW][C]135[/C][C]0.151797512217429[/C][C]0.303595024434858[/C][C]0.848202487782571[/C][/ROW]
[ROW][C]136[/C][C]0.119275638223107[/C][C]0.238551276446213[/C][C]0.880724361776893[/C][/ROW]
[ROW][C]137[/C][C]0.552433175460755[/C][C]0.89513364907849[/C][C]0.447566824539245[/C][/ROW]
[ROW][C]138[/C][C]0.491455035815526[/C][C]0.982910071631052[/C][C]0.508544964184474[/C][/ROW]
[ROW][C]139[/C][C]0.47395059660319[/C][C]0.94790119320638[/C][C]0.52604940339681[/C][/ROW]
[ROW][C]140[/C][C]0.415526812656727[/C][C]0.831053625313453[/C][C]0.584473187343273[/C][/ROW]
[ROW][C]141[/C][C]0.345342143249260[/C][C]0.690684286498519[/C][C]0.65465785675074[/C][/ROW]
[ROW][C]142[/C][C]0.336255870693663[/C][C]0.672511741387326[/C][C]0.663744129306337[/C][/ROW]
[ROW][C]143[/C][C]0.272611200770282[/C][C]0.545222401540564[/C][C]0.727388799229718[/C][/ROW]
[ROW][C]144[/C][C]0.210978612539719[/C][C]0.421957225079437[/C][C]0.789021387460281[/C][/ROW]
[ROW][C]145[/C][C]0.165421142391952[/C][C]0.330842284783904[/C][C]0.834578857608048[/C][/ROW]
[ROW][C]146[/C][C]0.131365233420338[/C][C]0.262730466840677[/C][C]0.868634766579662[/C][/ROW]
[ROW][C]147[/C][C]0.0997836406114375[/C][C]0.199567281222875[/C][C]0.900216359388562[/C][/ROW]
[ROW][C]148[/C][C]0.0660570040492695[/C][C]0.132114008098539[/C][C]0.93394299595073[/C][/ROW]
[ROW][C]149[/C][C]0.0513307484232234[/C][C]0.102661496846447[/C][C]0.948669251576777[/C][/ROW]
[ROW][C]150[/C][C]0.0946533088131093[/C][C]0.189306617626219[/C][C]0.90534669118689[/C][/ROW]
[ROW][C]151[/C][C]0.0580634924902724[/C][C]0.116126984980545[/C][C]0.941936507509728[/C][/ROW]
[ROW][C]152[/C][C]0.053559517674136[/C][C]0.107119035348272[/C][C]0.946440482325864[/C][/ROW]
[ROW][C]153[/C][C]0.166327916022567[/C][C]0.332655832045134[/C][C]0.833672083977433[/C][/ROW]
[ROW][C]154[/C][C]0.21078976115501[/C][C]0.42157952231002[/C][C]0.78921023884499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9767709882208870.04645802355822520.0232290117791126
90.9552930771120680.0894138457758650.0447069228879325
100.9213490510336380.1573018979327230.0786509489663615
110.8724498804780840.2551002390438310.127550119521916
120.8489561747228540.3020876505542930.151043825277146
130.782010867777030.435978264445940.21798913222297
140.7051176957330520.5897646085338970.294882304266948
150.7251405601440840.5497188797118320.274859439855916
160.7816084313663980.4367831372672040.218391568633602
170.7183690376560080.5632619246879830.281630962343992
180.7306458319664590.5387083360670820.269354168033541
190.7069143393997650.5861713212004710.293085660600235
200.7338273346399860.5323453307200290.266172665360014
210.7704250436505540.4591499126988920.229574956349446
220.814900650216180.370198699567640.18509934978382
230.764612633760610.4707747324787790.235387366239390
240.7262652717807420.5474694564385160.273734728219258
250.6872548455391510.6254903089216980.312745154460849
260.713458972386270.5730820552274590.286541027613730
270.6830350764647920.6339298470704160.316964923535208
280.6513987484138570.6972025031722860.348601251586143
290.6036099779247880.7927800441504230.396390022075212
300.5434822550964050.913035489807190.456517744903595
310.593564277844260.8128714443114790.406435722155739
320.6275092186070250.744981562785950.372490781392975
330.6000183892649950.799963221470010.399981610735005
340.5651417676597920.8697164646804150.434858232340208
350.5074184765764630.9851630468470730.492581523423537
360.5093965345107990.9812069309784020.490603465489201
370.7409116752946230.5181766494107550.259088324705377
380.6987713255408650.6024573489182710.301228674459136
390.6699906476513490.6600187046973030.330009352348651
400.6473552973218190.7052894053563630.352644702678181
410.6296195146740160.7407609706519670.370380485325984
420.5842380649067380.8315238701865240.415761935093262
430.6338891989362920.7322216021274160.366110801063708
440.5948852497113110.8102295005773790.405114750288689
450.5624123405633190.8751753188733610.437587659436681
460.5131253382261960.9737493235476090.486874661773804
470.5031614727985580.9936770544028850.496838527201442
480.4606506852273590.9213013704547190.539349314772641
490.4353647933756150.870729586751230.564635206624385
500.3942961668659630.7885923337319260.605703833134037
510.3694912236605370.7389824473210730.630508776339463
520.3274195604915260.6548391209830520.672580439508474
530.4080905776687700.8161811553375390.59190942233123
540.3637088115983720.7274176231967440.636291188401628
550.3276662649858170.6553325299716330.672333735014183
560.2867443402822790.5734886805645570.713255659717722
570.2465456758117990.4930913516235980.753454324188201
580.2753671941297630.5507343882595250.724632805870237
590.2922722540261370.5845445080522740.707727745973863
600.2968500309393470.5937000618786930.703149969060654
610.4756505025477410.9513010050954830.524349497452259
620.4320239823153150.864047964630630.567976017684685
630.3891464945945760.7782929891891520.610853505405424
640.3521706014689720.7043412029379430.647829398531028
650.3097890052578800.6195780105157610.69021099474212
660.2842833865723420.5685667731446840.715716613427658
670.3003937720243540.6007875440487070.699606227975646
680.2647169514481250.5294339028962490.735283048551875
690.2301085515849190.4602171031698390.76989144841508
700.2006308770377230.4012617540754450.799369122962277
710.1695958775216590.3391917550433180.830404122478341
720.1498220518115500.2996441036230990.85017794818845
730.1243368389810390.2486736779620790.87566316101896
740.1131002877764150.226200575552830.886899712223585
750.1000108065361540.2000216130723080.899989193463846
760.1684259489133470.3368518978266950.831574051086653
770.1461617930285010.2923235860570030.853838206971499
780.1743302539702350.3486605079404690.825669746029765
790.1462856249378460.2925712498756920.853714375062154
800.1604232360027860.3208464720055720.839576763997214
810.1403004222708370.2806008445416740.859699577729163
820.1484922288139780.2969844576279550.851507771186022
830.1605831236920930.3211662473841850.839416876307908
840.1365765795557830.2731531591115650.863423420444217
850.1156561144239680.2313122288479360.884343885576032
860.09707615095558980.1941523019111800.90292384904441
870.0791650749982580.1583301499965160.920834925001742
880.06463384188090120.1292676837618020.935366158119099
890.05938537018695560.1187707403739110.940614629813044
900.2281106528412010.4562213056824030.771889347158799
910.1986157861647200.3972315723294410.80138421383528
920.1785729277862350.357145855572470.821427072213765
930.1500833652285000.3001667304569990.8499166347715
940.1351464318059030.2702928636118060.864853568194097
950.1327063703846510.2654127407693020.867293629615349
960.1169419805826460.2338839611652910.883058019417354
970.1228289645505170.2456579291010350.877171035449483
980.1079052572977770.2158105145955550.892094742702223
990.09616660969751740.1923332193950350.903833390302483
1000.0943676818923920.1887353637847840.905632318107608
1010.09179625129315930.1835925025863190.90820374870684
1020.08631426141276540.1726285228255310.913685738587235
1030.07184473383130270.1436894676626050.928155266168697
1040.06292825709548870.1258565141909770.937071742904511
1050.06366080652215480.1273216130443100.936339193477845
1060.1093369321650750.2186738643301510.890663067834925
1070.09714929705332960.1942985941066590.90285070294667
1080.09432752052962470.1886550410592490.905672479470375
1090.0846180884043150.169236176808630.915381911595685
1100.4805204089054040.9610408178108080.519479591094596
1110.4346538495856680.8693076991713360.565346150414332
1120.4035576234856070.8071152469712130.596442376514393
1130.3693644146382130.7387288292764270.630635585361787
1140.3363288836176930.6726577672353860.663671116382307
1150.294868614882990.589737229765980.70513138511701
1160.2963878032818360.5927756065636730.703612196718164
1170.2547227614368810.5094455228737620.745277238563119
1180.2151793815245370.4303587630490750.784820618475463
1190.2310603746872980.4621207493745960.768939625312702
1200.2087800688017040.4175601376034090.791219931198295
1210.1918967323657590.3837934647315170.808103267634241
1220.1613736390381500.3227472780763010.83862636096185
1230.1381189556223990.2762379112447980.8618810443776
1240.1684658826041350.3369317652082710.831534117395865
1250.1400871714319360.2801743428638730.859912828568064
1260.1259304292310520.2518608584621040.874069570768948
1270.1815727143524590.3631454287049190.81842728564754
1280.1775350862729630.3550701725459260.822464913727037
1290.1614018698052160.3228037396104330.838598130194784
1300.1283912561380910.2567825122761820.871608743861909
1310.1108983742834080.2217967485668160.889101625716592
1320.2116301120250590.4232602240501180.788369887974941
1330.2223211162331670.4446422324663350.777678883766832
1340.1840265582056490.3680531164112970.815973441794351
1350.1517975122174290.3035950244348580.848202487782571
1360.1192756382231070.2385512764462130.880724361776893
1370.5524331754607550.895133649078490.447566824539245
1380.4914550358155260.9829100716310520.508544964184474
1390.473950596603190.947901193206380.52604940339681
1400.4155268126567270.8310536253134530.584473187343273
1410.3453421432492600.6906842864985190.65465785675074
1420.3362558706936630.6725117413873260.663744129306337
1430.2726112007702820.5452224015405640.727388799229718
1440.2109786125397190.4219572250794370.789021387460281
1450.1654211423919520.3308422847839040.834578857608048
1460.1313652334203380.2627304668406770.868634766579662
1470.09978364061143750.1995672812228750.900216359388562
1480.06605700404926950.1321140080985390.93394299595073
1490.05133074842322340.1026614968464470.948669251576777
1500.09465330881310930.1893066176262190.90534669118689
1510.05806349249027240.1161269849805450.941936507509728
1520.0535595176741360.1071190353482720.946440482325864
1530.1663279160225670.3326558320451340.833672083977433
1540.210789761155010.421579522310020.78921023884499







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00680272108843537OK
10% type I error level20.0136054421768707OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00680272108843537 & OK \tabularnewline
10% type I error level & 2 & 0.0136054421768707 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99661&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00680272108843537[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0136054421768707[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99661&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99661&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00680272108843537OK
10% type I error level20.0136054421768707OK



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