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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 computationFri, 20 Nov 2009 08:54:20 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587326867upo1di34d03l93.htm/, Retrieved Fri, 29 Mar 2024 08:36:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58288, Retrieved Fri, 29 Mar 2024 08:36:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7 Multiple Reg...] [2009-11-20 13:24:16] [b103a1dc147def8132c7f643ad8c8f84]
-   PD        [Multiple Regression] [Workshop 7] [2009-11-20 15:54:20] [0bdf648420800d03e6dbfbd39fe2311c] [Current]
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Dataseries X:
33	62
39	64
45	62
46	64
45	64
45	69
49	69
50	65
54	56
59	58
58	53
56	62
48	55
50	60
52	59
53	58
55	53
43	57
42	57
38	53
41	54
41	53
39	57
34	57
27	55
15	49
14	50
31	49
41	54
43	58
46	58
42	52
45	56
45	52
40	59
35	53
36	52
38	53
39	51
32	50
24	56
21	52
12	46
29	48
36	46
31	48
28	48
30	49
38	53
27	48
40	51
40	48
44	50
47	55
45	52
42	53
38	52
46	55
37	53
41	53
40	56
33	54
34	52
36	55
36	54
38	59
42	56
35	56
25	51
24	53
22	52
27	51
17	46
30	49
30	46
34	55
37	57
36	53
33	52
33	53
33	50
37	54
40	53
35	50
37	51
43	52
42	47
33	51
39	49
40	53
37	52
44	45
42	53
43	51
40	48
30	48
30	48
31	48
18	40
24	43
22	40
26	39
28	39
23	36
17	41
12	39
9	40
19	39
21	46
18	40
18	37
15	37
24	44
18	41
19	40
30	36
33	38
35	43
36	42
47	45
46	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Spaar[t] = + 50.6298826025243 + 0.224827909425653Alg_E[t] + 0.85574414100791M1[t] + 0.479212425823561M2[t] -1.78112277147162M3[t] -0.431389132537066M4[t] + 0.271033806029273M5[t] + 2.11538884570027M6[t] + 0.824847139715868M7[t] -1.74790201229214M8[t] -1.58340930016167M9[t] -0.766227288399419M10[t] -0.207113175532512M11[t] -0.11952713024534t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Spaar[t] =  +  50.6298826025243 +  0.224827909425653Alg_E[t] +  0.85574414100791M1[t] +  0.479212425823561M2[t] -1.78112277147162M3[t] -0.431389132537066M4[t] +  0.271033806029273M5[t] +  2.11538884570027M6[t] +  0.824847139715868M7[t] -1.74790201229214M8[t] -1.58340930016167M9[t] -0.766227288399419M10[t] -0.207113175532512M11[t] -0.11952713024534t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Spaar[t] =  +  50.6298826025243 +  0.224827909425653Alg_E[t] +  0.85574414100791M1[t] +  0.479212425823561M2[t] -1.78112277147162M3[t] -0.431389132537066M4[t] +  0.271033806029273M5[t] +  2.11538884570027M6[t] +  0.824847139715868M7[t] -1.74790201229214M8[t] -1.58340930016167M9[t] -0.766227288399419M10[t] -0.207113175532512M11[t] -0.11952713024534t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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
Spaar[t] = + 50.6298826025243 + 0.224827909425653Alg_E[t] + 0.85574414100791M1[t] + 0.479212425823561M2[t] -1.78112277147162M3[t] -0.431389132537066M4[t] + 0.271033806029273M5[t] + 2.11538884570027M6[t] + 0.824847139715868M7[t] -1.74790201229214M8[t] -1.58340930016167M9[t] -0.766227288399419M10[t] -0.207113175532512M11[t] -0.11952713024534t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)50.62988260252432.22846422.719600
Alg_E0.2248279094256530.038415.853400
M10.855744141007911.6480740.51920.6046660.302333
M20.4792124258235611.6958350.28260.7780430.389022
M3-1.781122771471621.691833-1.05280.2948150.147407
M4-0.4313891325370661.687817-0.25560.7987580.399379
M50.2710338060292731.6851060.16080.8725220.436261
M62.115388845700271.6849151.25550.2120370.106018
M70.8248471397158681.6847640.48960.6254250.312712
M8-1.747902012292141.684281-1.03780.3017150.150858
M9-1.583409300161671.684061-0.94020.3492160.174608
M10-0.7662272883994191.684891-0.45480.6502010.3251
M11-0.2071131755325121.683906-0.1230.9023410.451171
t-0.119527130245340.011623-10.284100

\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) & 50.6298826025243 & 2.228464 & 22.7196 & 0 & 0 \tabularnewline
Alg_E & 0.224827909425653 & 0.03841 & 5.8534 & 0 & 0 \tabularnewline
M1 & 0.85574414100791 & 1.648074 & 0.5192 & 0.604666 & 0.302333 \tabularnewline
M2 & 0.479212425823561 & 1.695835 & 0.2826 & 0.778043 & 0.389022 \tabularnewline
M3 & -1.78112277147162 & 1.691833 & -1.0528 & 0.294815 & 0.147407 \tabularnewline
M4 & -0.431389132537066 & 1.687817 & -0.2556 & 0.798758 & 0.399379 \tabularnewline
M5 & 0.271033806029273 & 1.685106 & 0.1608 & 0.872522 & 0.436261 \tabularnewline
M6 & 2.11538884570027 & 1.684915 & 1.2555 & 0.212037 & 0.106018 \tabularnewline
M7 & 0.824847139715868 & 1.684764 & 0.4896 & 0.625425 & 0.312712 \tabularnewline
M8 & -1.74790201229214 & 1.684281 & -1.0378 & 0.301715 & 0.150858 \tabularnewline
M9 & -1.58340930016167 & 1.684061 & -0.9402 & 0.349216 & 0.174608 \tabularnewline
M10 & -0.766227288399419 & 1.684891 & -0.4548 & 0.650201 & 0.3251 \tabularnewline
M11 & -0.207113175532512 & 1.683906 & -0.123 & 0.902341 & 0.451171 \tabularnewline
t & -0.11952713024534 & 0.011623 & -10.2841 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&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]50.6298826025243[/C][C]2.228464[/C][C]22.7196[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Alg_E[/C][C]0.224827909425653[/C][C]0.03841[/C][C]5.8534[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.85574414100791[/C][C]1.648074[/C][C]0.5192[/C][C]0.604666[/C][C]0.302333[/C][/ROW]
[ROW][C]M2[/C][C]0.479212425823561[/C][C]1.695835[/C][C]0.2826[/C][C]0.778043[/C][C]0.389022[/C][/ROW]
[ROW][C]M3[/C][C]-1.78112277147162[/C][C]1.691833[/C][C]-1.0528[/C][C]0.294815[/C][C]0.147407[/C][/ROW]
[ROW][C]M4[/C][C]-0.431389132537066[/C][C]1.687817[/C][C]-0.2556[/C][C]0.798758[/C][C]0.399379[/C][/ROW]
[ROW][C]M5[/C][C]0.271033806029273[/C][C]1.685106[/C][C]0.1608[/C][C]0.872522[/C][C]0.436261[/C][/ROW]
[ROW][C]M6[/C][C]2.11538884570027[/C][C]1.684915[/C][C]1.2555[/C][C]0.212037[/C][C]0.106018[/C][/ROW]
[ROW][C]M7[/C][C]0.824847139715868[/C][C]1.684764[/C][C]0.4896[/C][C]0.625425[/C][C]0.312712[/C][/ROW]
[ROW][C]M8[/C][C]-1.74790201229214[/C][C]1.684281[/C][C]-1.0378[/C][C]0.301715[/C][C]0.150858[/C][/ROW]
[ROW][C]M9[/C][C]-1.58340930016167[/C][C]1.684061[/C][C]-0.9402[/C][C]0.349216[/C][C]0.174608[/C][/ROW]
[ROW][C]M10[/C][C]-0.766227288399419[/C][C]1.684891[/C][C]-0.4548[/C][C]0.650201[/C][C]0.3251[/C][/ROW]
[ROW][C]M11[/C][C]-0.207113175532512[/C][C]1.683906[/C][C]-0.123[/C][C]0.902341[/C][C]0.451171[/C][/ROW]
[ROW][C]t[/C][C]-0.11952713024534[/C][C]0.011623[/C][C]-10.2841[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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)50.62988260252432.22846422.719600
Alg_E0.2248279094256530.038415.853400
M10.855744141007911.6480740.51920.6046660.302333
M20.4792124258235611.6958350.28260.7780430.389022
M3-1.781122771471621.691833-1.05280.2948150.147407
M4-0.4313891325370661.687817-0.25560.7987580.399379
M50.2710338060292731.6851060.16080.8725220.436261
M62.115388845700271.6849151.25550.2120370.106018
M70.8248471397158681.6847640.48960.6254250.312712
M8-1.747902012292141.684281-1.03780.3017150.150858
M9-1.583409300161671.684061-0.94020.3492160.174608
M10-0.7662272883994191.684891-0.45480.6502010.3251
M11-0.2071131755325121.683906-0.1230.9023410.451171
t-0.119527130245340.011623-10.284100







Multiple Linear Regression - Regression Statistics
Multiple R0.857292104916475
R-squared0.73494975315212
Adjusted R-squared0.702747386712659
F-TEST (value)22.8228492006564
F-TEST (DF numerator)13
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.76483670875441
Sum Squared Residuals1516.61751246357

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.857292104916475 \tabularnewline
R-squared & 0.73494975315212 \tabularnewline
Adjusted R-squared & 0.702747386712659 \tabularnewline
F-TEST (value) & 22.8228492006564 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.76483670875441 \tabularnewline
Sum Squared Residuals & 1516.61751246357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.857292104916475[/C][/ROW]
[ROW][C]R-squared[/C][C]0.73494975315212[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.702747386712659[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.8228492006564[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.76483670875441[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1516.61751246357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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 R0.857292104916475
R-squared0.73494975315212
Adjusted R-squared0.702747386712659
F-TEST (value)22.8228492006564
F-TEST (DF numerator)13
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.76483670875441
Sum Squared Residuals1516.61751246357







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16258.78542062433343.21457937566656
26459.63832923545774.36167076454234
36258.6074343644713.39256563552894
46460.0624687825863.93753121741406
56460.42053668148133.57946331851871
66962.1453645909076.85463540909306
76961.63460739237987.36539260762019
86559.16715901955215.83284098044788
95660.1114362391399-4.11143623913986
105861.933230667785-3.93323066778503
115362.1479897409809-9.14798974098094
126261.78591996741680.214080032583189
135560.7235137027742-5.72351370277416
146060.6771106761958-0.677110676195773
155958.74690416750660.253095832493444
165860.2019385856214-2.20193858562142
175361.2344902127937-8.23449021279373
185760.2613832091115-3.26138320911155
195758.6264864634562-1.62648646345616
205355.0348985435002-2.0348985435002
215455.7543478536623-1.75434785366229
225356.4520027351792-3.4520027351792
235756.44193389894950.55806610105054
245755.40538039710841.59461960289163
255554.56780204189140.43219795810863
264951.3738082833538-2.37380828335385
275048.76911804638771.23088195361233
284953.821399015313-4.82139901531298
295456.6525739178905-2.65257391789051
305858.8270576461675-0.827057646167467
315858.0914725382147-0.0914725382146861
325254.4998846182587-2.49988461825873
335655.21933392842080.780666071579181
345255.9169888099377-3.91698880993773
355955.2324362454313.76756375456897
365354.19588274359-1.19588274358994
375255.1569276637782-3.15692766377816
385355.1105246371998-2.11052463719978
395152.9554902190849-1.95549021908491
405052.6119013617946-2.61190136179455
415651.39617389471034.60382610528967
425252.446518075859-0.446518075859024
434649.0129980547984-3.01299805479841
444850.1427962327812-2.14279623278116
454651.7615571806459-5.76155718064586
464851.3350725150345-3.33507251503451
474851.1001757693791-3.10017576937912
484951.6374176335176-2.63741763351759
495354.1722579196854-1.17225791968539
504851.2030920705735-3.20309207057352
515151.7459925655665-0.74599256556648
524852.9761990742557-4.9761990742557
535054.4584065202793-4.45840652027931
545556.8577181579819-1.85771815798191
555254.9979935029009-2.99799350290087
565351.63123349237061.36876650762943
575250.77688743655311.22311256344691
585553.27316559347521.72683440652478
595351.68930139126591.31069860873409
605352.67619907425570.323800925744307
615653.18758817559262.81241182440739
625451.11773396418342.88226603581665
635248.96269954606853.03730045393152
645550.6425618736094.357438126391
655451.225457681932.77454231807000
665953.3999414102075.60005858979304
675652.88918421167983.11081578832017
685648.62311256344697.37688743655308
695146.41979905107554.58020094892448
705346.89262602316686.10737397683322
715246.8825571869375.11744281306296
725148.09428277935252.90571722064753
734646.5822206958585-0.582220695858516
744949.0089246729623-0.00892467296231159
754646.6290623454218-0.62906234542179
765548.75858049181366.24141950818638
775750.01596002841166.98403997158843
785351.51596002841161.48403997158843
795249.43140746390492.56859253609512
805346.73913118165156.26086881834847
815046.78409676353673.21590323646334
825448.38106328275625.61893671724382
835349.49513399365473.50486600634529
845048.45858049181361.54141950818638
855149.64445332142751.35554667857251
865250.49736193255171.50263806744828
874747.8926716955855-0.892671695585544
885147.09942701944393.90057298055612
894949.0312902843188-0.0312902843187983
905350.98094610317012.01905389682990
915248.89639353866343.10360646133659
924547.7779126223896-2.77791262238963
935347.37322238542355.62677761457654
945148.2957051763662.70429482363398
954848.0608084307106-0.0608084307106281
964845.90011538174132.09988461825873
974846.63633239250381.36366760749616
984846.36510145649981.63489854350020
994041.0624763064258-1.06247630642579
1004343.6416502716689-0.641650271668926
1014043.7748902611386-3.77489026113862
1023946.3990298082669-7.39902980826689
1033945.4386167908885-6.43861679088845
1043641.6222009615068-5.62220096150684
1054140.31819908683810.681800913161947
1063939.8917144212267-0.8917144212267
1074039.65681767557130.343182324428691
1083941.992682815115-2.99268281511501
1094643.17855564472892.82144435527112
1104042.0080130710222-2.00801307102224
1113739.6281507434817-2.62815074348171
1123740.1838735238940-3.18387352389397
1134442.79022051704581.20977948295416
1144143.1660809699176-2.16608096991758
1154041.9808400431135-1.98084004311349
1163641.7616707645423-5.76167076454233
1173842.4811200747044-4.48112007470442
1184343.6284307750726-0.628430775072634
1194244.2928456671198-2.29284566711985
1204546.8535387160892-1.85353871608921
1214647.3649278174261-1.36492781742612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 62 & 58.7854206243334 & 3.21457937566656 \tabularnewline
2 & 64 & 59.6383292354577 & 4.36167076454234 \tabularnewline
3 & 62 & 58.607434364471 & 3.39256563552894 \tabularnewline
4 & 64 & 60.062468782586 & 3.93753121741406 \tabularnewline
5 & 64 & 60.4205366814813 & 3.57946331851871 \tabularnewline
6 & 69 & 62.145364590907 & 6.85463540909306 \tabularnewline
7 & 69 & 61.6346073923798 & 7.36539260762019 \tabularnewline
8 & 65 & 59.1671590195521 & 5.83284098044788 \tabularnewline
9 & 56 & 60.1114362391399 & -4.11143623913986 \tabularnewline
10 & 58 & 61.933230667785 & -3.93323066778503 \tabularnewline
11 & 53 & 62.1479897409809 & -9.14798974098094 \tabularnewline
12 & 62 & 61.7859199674168 & 0.214080032583189 \tabularnewline
13 & 55 & 60.7235137027742 & -5.72351370277416 \tabularnewline
14 & 60 & 60.6771106761958 & -0.677110676195773 \tabularnewline
15 & 59 & 58.7469041675066 & 0.253095832493444 \tabularnewline
16 & 58 & 60.2019385856214 & -2.20193858562142 \tabularnewline
17 & 53 & 61.2344902127937 & -8.23449021279373 \tabularnewline
18 & 57 & 60.2613832091115 & -3.26138320911155 \tabularnewline
19 & 57 & 58.6264864634562 & -1.62648646345616 \tabularnewline
20 & 53 & 55.0348985435002 & -2.0348985435002 \tabularnewline
21 & 54 & 55.7543478536623 & -1.75434785366229 \tabularnewline
22 & 53 & 56.4520027351792 & -3.4520027351792 \tabularnewline
23 & 57 & 56.4419338989495 & 0.55806610105054 \tabularnewline
24 & 57 & 55.4053803971084 & 1.59461960289163 \tabularnewline
25 & 55 & 54.5678020418914 & 0.43219795810863 \tabularnewline
26 & 49 & 51.3738082833538 & -2.37380828335385 \tabularnewline
27 & 50 & 48.7691180463877 & 1.23088195361233 \tabularnewline
28 & 49 & 53.821399015313 & -4.82139901531298 \tabularnewline
29 & 54 & 56.6525739178905 & -2.65257391789051 \tabularnewline
30 & 58 & 58.8270576461675 & -0.827057646167467 \tabularnewline
31 & 58 & 58.0914725382147 & -0.0914725382146861 \tabularnewline
32 & 52 & 54.4998846182587 & -2.49988461825873 \tabularnewline
33 & 56 & 55.2193339284208 & 0.780666071579181 \tabularnewline
34 & 52 & 55.9169888099377 & -3.91698880993773 \tabularnewline
35 & 59 & 55.232436245431 & 3.76756375456897 \tabularnewline
36 & 53 & 54.19588274359 & -1.19588274358994 \tabularnewline
37 & 52 & 55.1569276637782 & -3.15692766377816 \tabularnewline
38 & 53 & 55.1105246371998 & -2.11052463719978 \tabularnewline
39 & 51 & 52.9554902190849 & -1.95549021908491 \tabularnewline
40 & 50 & 52.6119013617946 & -2.61190136179455 \tabularnewline
41 & 56 & 51.3961738947103 & 4.60382610528967 \tabularnewline
42 & 52 & 52.446518075859 & -0.446518075859024 \tabularnewline
43 & 46 & 49.0129980547984 & -3.01299805479841 \tabularnewline
44 & 48 & 50.1427962327812 & -2.14279623278116 \tabularnewline
45 & 46 & 51.7615571806459 & -5.76155718064586 \tabularnewline
46 & 48 & 51.3350725150345 & -3.33507251503451 \tabularnewline
47 & 48 & 51.1001757693791 & -3.10017576937912 \tabularnewline
48 & 49 & 51.6374176335176 & -2.63741763351759 \tabularnewline
49 & 53 & 54.1722579196854 & -1.17225791968539 \tabularnewline
50 & 48 & 51.2030920705735 & -3.20309207057352 \tabularnewline
51 & 51 & 51.7459925655665 & -0.74599256556648 \tabularnewline
52 & 48 & 52.9761990742557 & -4.9761990742557 \tabularnewline
53 & 50 & 54.4584065202793 & -4.45840652027931 \tabularnewline
54 & 55 & 56.8577181579819 & -1.85771815798191 \tabularnewline
55 & 52 & 54.9979935029009 & -2.99799350290087 \tabularnewline
56 & 53 & 51.6312334923706 & 1.36876650762943 \tabularnewline
57 & 52 & 50.7768874365531 & 1.22311256344691 \tabularnewline
58 & 55 & 53.2731655934752 & 1.72683440652478 \tabularnewline
59 & 53 & 51.6893013912659 & 1.31069860873409 \tabularnewline
60 & 53 & 52.6761990742557 & 0.323800925744307 \tabularnewline
61 & 56 & 53.1875881755926 & 2.81241182440739 \tabularnewline
62 & 54 & 51.1177339641834 & 2.88226603581665 \tabularnewline
63 & 52 & 48.9626995460685 & 3.03730045393152 \tabularnewline
64 & 55 & 50.642561873609 & 4.357438126391 \tabularnewline
65 & 54 & 51.22545768193 & 2.77454231807000 \tabularnewline
66 & 59 & 53.399941410207 & 5.60005858979304 \tabularnewline
67 & 56 & 52.8891842116798 & 3.11081578832017 \tabularnewline
68 & 56 & 48.6231125634469 & 7.37688743655308 \tabularnewline
69 & 51 & 46.4197990510755 & 4.58020094892448 \tabularnewline
70 & 53 & 46.8926260231668 & 6.10737397683322 \tabularnewline
71 & 52 & 46.882557186937 & 5.11744281306296 \tabularnewline
72 & 51 & 48.0942827793525 & 2.90571722064753 \tabularnewline
73 & 46 & 46.5822206958585 & -0.582220695858516 \tabularnewline
74 & 49 & 49.0089246729623 & -0.00892467296231159 \tabularnewline
75 & 46 & 46.6290623454218 & -0.62906234542179 \tabularnewline
76 & 55 & 48.7585804918136 & 6.24141950818638 \tabularnewline
77 & 57 & 50.0159600284116 & 6.98403997158843 \tabularnewline
78 & 53 & 51.5159600284116 & 1.48403997158843 \tabularnewline
79 & 52 & 49.4314074639049 & 2.56859253609512 \tabularnewline
80 & 53 & 46.7391311816515 & 6.26086881834847 \tabularnewline
81 & 50 & 46.7840967635367 & 3.21590323646334 \tabularnewline
82 & 54 & 48.3810632827562 & 5.61893671724382 \tabularnewline
83 & 53 & 49.4951339936547 & 3.50486600634529 \tabularnewline
84 & 50 & 48.4585804918136 & 1.54141950818638 \tabularnewline
85 & 51 & 49.6444533214275 & 1.35554667857251 \tabularnewline
86 & 52 & 50.4973619325517 & 1.50263806744828 \tabularnewline
87 & 47 & 47.8926716955855 & -0.892671695585544 \tabularnewline
88 & 51 & 47.0994270194439 & 3.90057298055612 \tabularnewline
89 & 49 & 49.0312902843188 & -0.0312902843187983 \tabularnewline
90 & 53 & 50.9809461031701 & 2.01905389682990 \tabularnewline
91 & 52 & 48.8963935386634 & 3.10360646133659 \tabularnewline
92 & 45 & 47.7779126223896 & -2.77791262238963 \tabularnewline
93 & 53 & 47.3732223854235 & 5.62677761457654 \tabularnewline
94 & 51 & 48.295705176366 & 2.70429482363398 \tabularnewline
95 & 48 & 48.0608084307106 & -0.0608084307106281 \tabularnewline
96 & 48 & 45.9001153817413 & 2.09988461825873 \tabularnewline
97 & 48 & 46.6363323925038 & 1.36366760749616 \tabularnewline
98 & 48 & 46.3651014564998 & 1.63489854350020 \tabularnewline
99 & 40 & 41.0624763064258 & -1.06247630642579 \tabularnewline
100 & 43 & 43.6416502716689 & -0.641650271668926 \tabularnewline
101 & 40 & 43.7748902611386 & -3.77489026113862 \tabularnewline
102 & 39 & 46.3990298082669 & -7.39902980826689 \tabularnewline
103 & 39 & 45.4386167908885 & -6.43861679088845 \tabularnewline
104 & 36 & 41.6222009615068 & -5.62220096150684 \tabularnewline
105 & 41 & 40.3181990868381 & 0.681800913161947 \tabularnewline
106 & 39 & 39.8917144212267 & -0.8917144212267 \tabularnewline
107 & 40 & 39.6568176755713 & 0.343182324428691 \tabularnewline
108 & 39 & 41.992682815115 & -2.99268281511501 \tabularnewline
109 & 46 & 43.1785556447289 & 2.82144435527112 \tabularnewline
110 & 40 & 42.0080130710222 & -2.00801307102224 \tabularnewline
111 & 37 & 39.6281507434817 & -2.62815074348171 \tabularnewline
112 & 37 & 40.1838735238940 & -3.18387352389397 \tabularnewline
113 & 44 & 42.7902205170458 & 1.20977948295416 \tabularnewline
114 & 41 & 43.1660809699176 & -2.16608096991758 \tabularnewline
115 & 40 & 41.9808400431135 & -1.98084004311349 \tabularnewline
116 & 36 & 41.7616707645423 & -5.76167076454233 \tabularnewline
117 & 38 & 42.4811200747044 & -4.48112007470442 \tabularnewline
118 & 43 & 43.6284307750726 & -0.628430775072634 \tabularnewline
119 & 42 & 44.2928456671198 & -2.29284566711985 \tabularnewline
120 & 45 & 46.8535387160892 & -1.85353871608921 \tabularnewline
121 & 46 & 47.3649278174261 & -1.36492781742612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&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]62[/C][C]58.7854206243334[/C][C]3.21457937566656[/C][/ROW]
[ROW][C]2[/C][C]64[/C][C]59.6383292354577[/C][C]4.36167076454234[/C][/ROW]
[ROW][C]3[/C][C]62[/C][C]58.607434364471[/C][C]3.39256563552894[/C][/ROW]
[ROW][C]4[/C][C]64[/C][C]60.062468782586[/C][C]3.93753121741406[/C][/ROW]
[ROW][C]5[/C][C]64[/C][C]60.4205366814813[/C][C]3.57946331851871[/C][/ROW]
[ROW][C]6[/C][C]69[/C][C]62.145364590907[/C][C]6.85463540909306[/C][/ROW]
[ROW][C]7[/C][C]69[/C][C]61.6346073923798[/C][C]7.36539260762019[/C][/ROW]
[ROW][C]8[/C][C]65[/C][C]59.1671590195521[/C][C]5.83284098044788[/C][/ROW]
[ROW][C]9[/C][C]56[/C][C]60.1114362391399[/C][C]-4.11143623913986[/C][/ROW]
[ROW][C]10[/C][C]58[/C][C]61.933230667785[/C][C]-3.93323066778503[/C][/ROW]
[ROW][C]11[/C][C]53[/C][C]62.1479897409809[/C][C]-9.14798974098094[/C][/ROW]
[ROW][C]12[/C][C]62[/C][C]61.7859199674168[/C][C]0.214080032583189[/C][/ROW]
[ROW][C]13[/C][C]55[/C][C]60.7235137027742[/C][C]-5.72351370277416[/C][/ROW]
[ROW][C]14[/C][C]60[/C][C]60.6771106761958[/C][C]-0.677110676195773[/C][/ROW]
[ROW][C]15[/C][C]59[/C][C]58.7469041675066[/C][C]0.253095832493444[/C][/ROW]
[ROW][C]16[/C][C]58[/C][C]60.2019385856214[/C][C]-2.20193858562142[/C][/ROW]
[ROW][C]17[/C][C]53[/C][C]61.2344902127937[/C][C]-8.23449021279373[/C][/ROW]
[ROW][C]18[/C][C]57[/C][C]60.2613832091115[/C][C]-3.26138320911155[/C][/ROW]
[ROW][C]19[/C][C]57[/C][C]58.6264864634562[/C][C]-1.62648646345616[/C][/ROW]
[ROW][C]20[/C][C]53[/C][C]55.0348985435002[/C][C]-2.0348985435002[/C][/ROW]
[ROW][C]21[/C][C]54[/C][C]55.7543478536623[/C][C]-1.75434785366229[/C][/ROW]
[ROW][C]22[/C][C]53[/C][C]56.4520027351792[/C][C]-3.4520027351792[/C][/ROW]
[ROW][C]23[/C][C]57[/C][C]56.4419338989495[/C][C]0.55806610105054[/C][/ROW]
[ROW][C]24[/C][C]57[/C][C]55.4053803971084[/C][C]1.59461960289163[/C][/ROW]
[ROW][C]25[/C][C]55[/C][C]54.5678020418914[/C][C]0.43219795810863[/C][/ROW]
[ROW][C]26[/C][C]49[/C][C]51.3738082833538[/C][C]-2.37380828335385[/C][/ROW]
[ROW][C]27[/C][C]50[/C][C]48.7691180463877[/C][C]1.23088195361233[/C][/ROW]
[ROW][C]28[/C][C]49[/C][C]53.821399015313[/C][C]-4.82139901531298[/C][/ROW]
[ROW][C]29[/C][C]54[/C][C]56.6525739178905[/C][C]-2.65257391789051[/C][/ROW]
[ROW][C]30[/C][C]58[/C][C]58.8270576461675[/C][C]-0.827057646167467[/C][/ROW]
[ROW][C]31[/C][C]58[/C][C]58.0914725382147[/C][C]-0.0914725382146861[/C][/ROW]
[ROW][C]32[/C][C]52[/C][C]54.4998846182587[/C][C]-2.49988461825873[/C][/ROW]
[ROW][C]33[/C][C]56[/C][C]55.2193339284208[/C][C]0.780666071579181[/C][/ROW]
[ROW][C]34[/C][C]52[/C][C]55.9169888099377[/C][C]-3.91698880993773[/C][/ROW]
[ROW][C]35[/C][C]59[/C][C]55.232436245431[/C][C]3.76756375456897[/C][/ROW]
[ROW][C]36[/C][C]53[/C][C]54.19588274359[/C][C]-1.19588274358994[/C][/ROW]
[ROW][C]37[/C][C]52[/C][C]55.1569276637782[/C][C]-3.15692766377816[/C][/ROW]
[ROW][C]38[/C][C]53[/C][C]55.1105246371998[/C][C]-2.11052463719978[/C][/ROW]
[ROW][C]39[/C][C]51[/C][C]52.9554902190849[/C][C]-1.95549021908491[/C][/ROW]
[ROW][C]40[/C][C]50[/C][C]52.6119013617946[/C][C]-2.61190136179455[/C][/ROW]
[ROW][C]41[/C][C]56[/C][C]51.3961738947103[/C][C]4.60382610528967[/C][/ROW]
[ROW][C]42[/C][C]52[/C][C]52.446518075859[/C][C]-0.446518075859024[/C][/ROW]
[ROW][C]43[/C][C]46[/C][C]49.0129980547984[/C][C]-3.01299805479841[/C][/ROW]
[ROW][C]44[/C][C]48[/C][C]50.1427962327812[/C][C]-2.14279623278116[/C][/ROW]
[ROW][C]45[/C][C]46[/C][C]51.7615571806459[/C][C]-5.76155718064586[/C][/ROW]
[ROW][C]46[/C][C]48[/C][C]51.3350725150345[/C][C]-3.33507251503451[/C][/ROW]
[ROW][C]47[/C][C]48[/C][C]51.1001757693791[/C][C]-3.10017576937912[/C][/ROW]
[ROW][C]48[/C][C]49[/C][C]51.6374176335176[/C][C]-2.63741763351759[/C][/ROW]
[ROW][C]49[/C][C]53[/C][C]54.1722579196854[/C][C]-1.17225791968539[/C][/ROW]
[ROW][C]50[/C][C]48[/C][C]51.2030920705735[/C][C]-3.20309207057352[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]51.7459925655665[/C][C]-0.74599256556648[/C][/ROW]
[ROW][C]52[/C][C]48[/C][C]52.9761990742557[/C][C]-4.9761990742557[/C][/ROW]
[ROW][C]53[/C][C]50[/C][C]54.4584065202793[/C][C]-4.45840652027931[/C][/ROW]
[ROW][C]54[/C][C]55[/C][C]56.8577181579819[/C][C]-1.85771815798191[/C][/ROW]
[ROW][C]55[/C][C]52[/C][C]54.9979935029009[/C][C]-2.99799350290087[/C][/ROW]
[ROW][C]56[/C][C]53[/C][C]51.6312334923706[/C][C]1.36876650762943[/C][/ROW]
[ROW][C]57[/C][C]52[/C][C]50.7768874365531[/C][C]1.22311256344691[/C][/ROW]
[ROW][C]58[/C][C]55[/C][C]53.2731655934752[/C][C]1.72683440652478[/C][/ROW]
[ROW][C]59[/C][C]53[/C][C]51.6893013912659[/C][C]1.31069860873409[/C][/ROW]
[ROW][C]60[/C][C]53[/C][C]52.6761990742557[/C][C]0.323800925744307[/C][/ROW]
[ROW][C]61[/C][C]56[/C][C]53.1875881755926[/C][C]2.81241182440739[/C][/ROW]
[ROW][C]62[/C][C]54[/C][C]51.1177339641834[/C][C]2.88226603581665[/C][/ROW]
[ROW][C]63[/C][C]52[/C][C]48.9626995460685[/C][C]3.03730045393152[/C][/ROW]
[ROW][C]64[/C][C]55[/C][C]50.642561873609[/C][C]4.357438126391[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]51.22545768193[/C][C]2.77454231807000[/C][/ROW]
[ROW][C]66[/C][C]59[/C][C]53.399941410207[/C][C]5.60005858979304[/C][/ROW]
[ROW][C]67[/C][C]56[/C][C]52.8891842116798[/C][C]3.11081578832017[/C][/ROW]
[ROW][C]68[/C][C]56[/C][C]48.6231125634469[/C][C]7.37688743655308[/C][/ROW]
[ROW][C]69[/C][C]51[/C][C]46.4197990510755[/C][C]4.58020094892448[/C][/ROW]
[ROW][C]70[/C][C]53[/C][C]46.8926260231668[/C][C]6.10737397683322[/C][/ROW]
[ROW][C]71[/C][C]52[/C][C]46.882557186937[/C][C]5.11744281306296[/C][/ROW]
[ROW][C]72[/C][C]51[/C][C]48.0942827793525[/C][C]2.90571722064753[/C][/ROW]
[ROW][C]73[/C][C]46[/C][C]46.5822206958585[/C][C]-0.582220695858516[/C][/ROW]
[ROW][C]74[/C][C]49[/C][C]49.0089246729623[/C][C]-0.00892467296231159[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]46.6290623454218[/C][C]-0.62906234542179[/C][/ROW]
[ROW][C]76[/C][C]55[/C][C]48.7585804918136[/C][C]6.24141950818638[/C][/ROW]
[ROW][C]77[/C][C]57[/C][C]50.0159600284116[/C][C]6.98403997158843[/C][/ROW]
[ROW][C]78[/C][C]53[/C][C]51.5159600284116[/C][C]1.48403997158843[/C][/ROW]
[ROW][C]79[/C][C]52[/C][C]49.4314074639049[/C][C]2.56859253609512[/C][/ROW]
[ROW][C]80[/C][C]53[/C][C]46.7391311816515[/C][C]6.26086881834847[/C][/ROW]
[ROW][C]81[/C][C]50[/C][C]46.7840967635367[/C][C]3.21590323646334[/C][/ROW]
[ROW][C]82[/C][C]54[/C][C]48.3810632827562[/C][C]5.61893671724382[/C][/ROW]
[ROW][C]83[/C][C]53[/C][C]49.4951339936547[/C][C]3.50486600634529[/C][/ROW]
[ROW][C]84[/C][C]50[/C][C]48.4585804918136[/C][C]1.54141950818638[/C][/ROW]
[ROW][C]85[/C][C]51[/C][C]49.6444533214275[/C][C]1.35554667857251[/C][/ROW]
[ROW][C]86[/C][C]52[/C][C]50.4973619325517[/C][C]1.50263806744828[/C][/ROW]
[ROW][C]87[/C][C]47[/C][C]47.8926716955855[/C][C]-0.892671695585544[/C][/ROW]
[ROW][C]88[/C][C]51[/C][C]47.0994270194439[/C][C]3.90057298055612[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]49.0312902843188[/C][C]-0.0312902843187983[/C][/ROW]
[ROW][C]90[/C][C]53[/C][C]50.9809461031701[/C][C]2.01905389682990[/C][/ROW]
[ROW][C]91[/C][C]52[/C][C]48.8963935386634[/C][C]3.10360646133659[/C][/ROW]
[ROW][C]92[/C][C]45[/C][C]47.7779126223896[/C][C]-2.77791262238963[/C][/ROW]
[ROW][C]93[/C][C]53[/C][C]47.3732223854235[/C][C]5.62677761457654[/C][/ROW]
[ROW][C]94[/C][C]51[/C][C]48.295705176366[/C][C]2.70429482363398[/C][/ROW]
[ROW][C]95[/C][C]48[/C][C]48.0608084307106[/C][C]-0.0608084307106281[/C][/ROW]
[ROW][C]96[/C][C]48[/C][C]45.9001153817413[/C][C]2.09988461825873[/C][/ROW]
[ROW][C]97[/C][C]48[/C][C]46.6363323925038[/C][C]1.36366760749616[/C][/ROW]
[ROW][C]98[/C][C]48[/C][C]46.3651014564998[/C][C]1.63489854350020[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]41.0624763064258[/C][C]-1.06247630642579[/C][/ROW]
[ROW][C]100[/C][C]43[/C][C]43.6416502716689[/C][C]-0.641650271668926[/C][/ROW]
[ROW][C]101[/C][C]40[/C][C]43.7748902611386[/C][C]-3.77489026113862[/C][/ROW]
[ROW][C]102[/C][C]39[/C][C]46.3990298082669[/C][C]-7.39902980826689[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]45.4386167908885[/C][C]-6.43861679088845[/C][/ROW]
[ROW][C]104[/C][C]36[/C][C]41.6222009615068[/C][C]-5.62220096150684[/C][/ROW]
[ROW][C]105[/C][C]41[/C][C]40.3181990868381[/C][C]0.681800913161947[/C][/ROW]
[ROW][C]106[/C][C]39[/C][C]39.8917144212267[/C][C]-0.8917144212267[/C][/ROW]
[ROW][C]107[/C][C]40[/C][C]39.6568176755713[/C][C]0.343182324428691[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]41.992682815115[/C][C]-2.99268281511501[/C][/ROW]
[ROW][C]109[/C][C]46[/C][C]43.1785556447289[/C][C]2.82144435527112[/C][/ROW]
[ROW][C]110[/C][C]40[/C][C]42.0080130710222[/C][C]-2.00801307102224[/C][/ROW]
[ROW][C]111[/C][C]37[/C][C]39.6281507434817[/C][C]-2.62815074348171[/C][/ROW]
[ROW][C]112[/C][C]37[/C][C]40.1838735238940[/C][C]-3.18387352389397[/C][/ROW]
[ROW][C]113[/C][C]44[/C][C]42.7902205170458[/C][C]1.20977948295416[/C][/ROW]
[ROW][C]114[/C][C]41[/C][C]43.1660809699176[/C][C]-2.16608096991758[/C][/ROW]
[ROW][C]115[/C][C]40[/C][C]41.9808400431135[/C][C]-1.98084004311349[/C][/ROW]
[ROW][C]116[/C][C]36[/C][C]41.7616707645423[/C][C]-5.76167076454233[/C][/ROW]
[ROW][C]117[/C][C]38[/C][C]42.4811200747044[/C][C]-4.48112007470442[/C][/ROW]
[ROW][C]118[/C][C]43[/C][C]43.6284307750726[/C][C]-0.628430775072634[/C][/ROW]
[ROW][C]119[/C][C]42[/C][C]44.2928456671198[/C][C]-2.29284566711985[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]46.8535387160892[/C][C]-1.85353871608921[/C][/ROW]
[ROW][C]121[/C][C]46[/C][C]47.3649278174261[/C][C]-1.36492781742612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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
16258.78542062433343.21457937566656
26459.63832923545774.36167076454234
36258.6074343644713.39256563552894
46460.0624687825863.93753121741406
56460.42053668148133.57946331851871
66962.1453645909076.85463540909306
76961.63460739237987.36539260762019
86559.16715901955215.83284098044788
95660.1114362391399-4.11143623913986
105861.933230667785-3.93323066778503
115362.1479897409809-9.14798974098094
126261.78591996741680.214080032583189
135560.7235137027742-5.72351370277416
146060.6771106761958-0.677110676195773
155958.74690416750660.253095832493444
165860.2019385856214-2.20193858562142
175361.2344902127937-8.23449021279373
185760.2613832091115-3.26138320911155
195758.6264864634562-1.62648646345616
205355.0348985435002-2.0348985435002
215455.7543478536623-1.75434785366229
225356.4520027351792-3.4520027351792
235756.44193389894950.55806610105054
245755.40538039710841.59461960289163
255554.56780204189140.43219795810863
264951.3738082833538-2.37380828335385
275048.76911804638771.23088195361233
284953.821399015313-4.82139901531298
295456.6525739178905-2.65257391789051
305858.8270576461675-0.827057646167467
315858.0914725382147-0.0914725382146861
325254.4998846182587-2.49988461825873
335655.21933392842080.780666071579181
345255.9169888099377-3.91698880993773
355955.2324362454313.76756375456897
365354.19588274359-1.19588274358994
375255.1569276637782-3.15692766377816
385355.1105246371998-2.11052463719978
395152.9554902190849-1.95549021908491
405052.6119013617946-2.61190136179455
415651.39617389471034.60382610528967
425252.446518075859-0.446518075859024
434649.0129980547984-3.01299805479841
444850.1427962327812-2.14279623278116
454651.7615571806459-5.76155718064586
464851.3350725150345-3.33507251503451
474851.1001757693791-3.10017576937912
484951.6374176335176-2.63741763351759
495354.1722579196854-1.17225791968539
504851.2030920705735-3.20309207057352
515151.7459925655665-0.74599256556648
524852.9761990742557-4.9761990742557
535054.4584065202793-4.45840652027931
545556.8577181579819-1.85771815798191
555254.9979935029009-2.99799350290087
565351.63123349237061.36876650762943
575250.77688743655311.22311256344691
585553.27316559347521.72683440652478
595351.68930139126591.31069860873409
605352.67619907425570.323800925744307
615653.18758817559262.81241182440739
625451.11773396418342.88226603581665
635248.96269954606853.03730045393152
645550.6425618736094.357438126391
655451.225457681932.77454231807000
665953.3999414102075.60005858979304
675652.88918421167983.11081578832017
685648.62311256344697.37688743655308
695146.41979905107554.58020094892448
705346.89262602316686.10737397683322
715246.8825571869375.11744281306296
725148.09428277935252.90571722064753
734646.5822206958585-0.582220695858516
744949.0089246729623-0.00892467296231159
754646.6290623454218-0.62906234542179
765548.75858049181366.24141950818638
775750.01596002841166.98403997158843
785351.51596002841161.48403997158843
795249.43140746390492.56859253609512
805346.73913118165156.26086881834847
815046.78409676353673.21590323646334
825448.38106328275625.61893671724382
835349.49513399365473.50486600634529
845048.45858049181361.54141950818638
855149.64445332142751.35554667857251
865250.49736193255171.50263806744828
874747.8926716955855-0.892671695585544
885147.09942701944393.90057298055612
894949.0312902843188-0.0312902843187983
905350.98094610317012.01905389682990
915248.89639353866343.10360646133659
924547.7779126223896-2.77791262238963
935347.37322238542355.62677761457654
945148.2957051763662.70429482363398
954848.0608084307106-0.0608084307106281
964845.90011538174132.09988461825873
974846.63633239250381.36366760749616
984846.36510145649981.63489854350020
994041.0624763064258-1.06247630642579
1004343.6416502716689-0.641650271668926
1014043.7748902611386-3.77489026113862
1023946.3990298082669-7.39902980826689
1033945.4386167908885-6.43861679088845
1043641.6222009615068-5.62220096150684
1054140.31819908683810.681800913161947
1063939.8917144212267-0.8917144212267
1074039.65681767557130.343182324428691
1083941.992682815115-2.99268281511501
1094643.17855564472892.82144435527112
1104042.0080130710222-2.00801307102224
1113739.6281507434817-2.62815074348171
1123740.1838735238940-3.18387352389397
1134442.79022051704581.20977948295416
1144143.1660809699176-2.16608096991758
1154041.9808400431135-1.98084004311349
1163641.7616707645423-5.76167076454233
1173842.4811200747044-4.48112007470442
1184343.6284307750726-0.628430775072634
1194244.2928456671198-2.29284566711985
1204546.8535387160892-1.85353871608921
1214647.3649278174261-1.36492781742612







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.3252348810478650.6504697620957290.674765118952135
180.3225219208337650.6450438416675310.677478079166235
190.1956578950246130.3913157900492250.804342104975387
200.1137101679673670.2274203359347340.886289832032633
210.3977901523412360.7955803046824710.602209847658764
220.3584686911358620.7169373822717250.641531308864138
230.6528763026910130.6942473946179730.347123697308987
240.5621589989722550.875682002055490.437841001027745
250.5555034862123170.8889930275753650.444496513787683
260.6127901802234780.7744196395530440.387209819776522
270.5518923492509340.8962153014981310.448107650749066
280.5166778236216950.966644352756610.483322176378305
290.535800385509080.928399228981840.46419961449092
300.5075708575145450.984858284970910.492429142485455
310.4650793883106120.9301587766212240.534920611689388
320.3999654044301360.7999308088602720.600034595569864
330.5531318114613840.8937363770772320.446868188538616
340.559930391398970.8801392172020610.440069608601031
350.7692043572226670.4615912855546650.230795642777333
360.7155477883046590.5689044233906830.284452211695341
370.6883071917746710.6233856164506570.311692808225329
380.6500712061873090.6998575876253820.349928793812691
390.595394311969730.809211376060540.40460568803027
400.55476414289170.89047171421660.4452358571083
410.642581473897410.714837052205180.35741852610259
420.585655127028010.828689745943980.41434487297199
430.6125359142738950.774928171452210.387464085726105
440.5615706234837430.8768587530325130.438429376516257
450.6104866802493020.7790266395013960.389513319750698
460.6324772763978160.7350454472043680.367522723602184
470.6307209034878610.7385581930242770.369279096512139
480.6134977051519230.7730045896961530.386502294848077
490.6461262208664460.7077475582671080.353873779133554
500.6553517570965990.6892964858068020.344648242903401
510.6291246215099070.7417507569801850.370875378490093
520.747156985334340.5056860293313180.252843014665659
530.8398243788260560.3203512423478890.160175621173944
540.8541381104914530.2917237790170940.145861889508547
550.8928285002527390.2143429994945220.107171499747261
560.9017794832470960.1964410335058080.0982205167529041
570.9396674828873940.1206650342252130.0603325171126064
580.9762785557272440.0474428885455130.0237214442727565
590.9841834434203480.03163311315930490.0158165565796525
600.9883471825235730.02330563495285340.0116528174764267
610.991683951198630.01663209760274190.00831604880137097
620.9915464868599490.01690702628010240.0084535131400512
630.9892131556454870.02157368870902640.0107868443545132
640.9917470846856360.01650583062872830.00825291531436416
650.992040807330850.0159183853383010.0079591926691505
660.9924703324043720.01505933519125650.00752966759562824
670.990107535552390.01978492889522010.00989246444761007
680.9950295954523340.009940809095331780.00497040454766589
690.9943690165354920.01126196692901580.00563098346450792
700.9946975278853140.01060494422937260.00530247211468631
710.9934870505775880.01302589884482370.00651294942241186
720.9902758460725760.01944830785484740.0097241539274237
730.9931480736621420.01370385267571630.00685192633785813
740.99353678119990.01292643760019950.00646321880009973
750.9929418088857150.01411638222856940.00705819111428468
760.99238592556120.01522814887760150.00761407443880074
770.9938893160451190.01222136790976280.00611068395488138
780.9903342727445250.01933145451094980.0096657272554749
790.9850207049961840.02995859000763220.0149792950038161
800.9960043131617330.007991373676534520.00399568683826726
810.993691725009060.01261654998187870.00630827499093935
820.9920400936888910.01591981262221740.00795990631110871
830.9876071773411220.0247856453177560.012392822658878
840.9807217623490270.03855647530194670.0192782376509734
850.9774091624453730.04518167510925440.0225908375546272
860.9669676721911840.06606465561763170.0330323278088158
870.9595791533930470.08084169321390680.0404208466069534
880.9489690455700970.1020619088598070.0510309544299035
890.932709138224120.1345817235517610.0672908617758806
900.9262140528278280.1475718943443430.0737859471721716
910.9366939852540290.1266120294919430.0633060147459715
920.9176621788590190.1646756422819630.0823378211409813
930.9557980753040360.08840384939192760.0442019246959638
940.9470025607627150.1059948784745700.0529974392372848
950.9190311877953380.1619376244093250.0809688122046623
960.9236113573316910.1527772853366180.0763886426683088
970.8801416384599260.2397167230801490.119858361540074
980.9095210902872380.1809578194255240.090478909712762
990.884406539455860.2311869210882790.115593460544140
1000.94642419098920.1071516180216000.0535758090108002
1010.930956339874710.1380873202505790.0690436601252893
1020.9013474316485540.1973051367028930.0986525683514464
1030.8869216108369340.2261567783261310.113078389163066
1040.8875897963205150.2248204073589700.112410203679485

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.325234881047865 & 0.650469762095729 & 0.674765118952135 \tabularnewline
18 & 0.322521920833765 & 0.645043841667531 & 0.677478079166235 \tabularnewline
19 & 0.195657895024613 & 0.391315790049225 & 0.804342104975387 \tabularnewline
20 & 0.113710167967367 & 0.227420335934734 & 0.886289832032633 \tabularnewline
21 & 0.397790152341236 & 0.795580304682471 & 0.602209847658764 \tabularnewline
22 & 0.358468691135862 & 0.716937382271725 & 0.641531308864138 \tabularnewline
23 & 0.652876302691013 & 0.694247394617973 & 0.347123697308987 \tabularnewline
24 & 0.562158998972255 & 0.87568200205549 & 0.437841001027745 \tabularnewline
25 & 0.555503486212317 & 0.888993027575365 & 0.444496513787683 \tabularnewline
26 & 0.612790180223478 & 0.774419639553044 & 0.387209819776522 \tabularnewline
27 & 0.551892349250934 & 0.896215301498131 & 0.448107650749066 \tabularnewline
28 & 0.516677823621695 & 0.96664435275661 & 0.483322176378305 \tabularnewline
29 & 0.53580038550908 & 0.92839922898184 & 0.46419961449092 \tabularnewline
30 & 0.507570857514545 & 0.98485828497091 & 0.492429142485455 \tabularnewline
31 & 0.465079388310612 & 0.930158776621224 & 0.534920611689388 \tabularnewline
32 & 0.399965404430136 & 0.799930808860272 & 0.600034595569864 \tabularnewline
33 & 0.553131811461384 & 0.893736377077232 & 0.446868188538616 \tabularnewline
34 & 0.55993039139897 & 0.880139217202061 & 0.440069608601031 \tabularnewline
35 & 0.769204357222667 & 0.461591285554665 & 0.230795642777333 \tabularnewline
36 & 0.715547788304659 & 0.568904423390683 & 0.284452211695341 \tabularnewline
37 & 0.688307191774671 & 0.623385616450657 & 0.311692808225329 \tabularnewline
38 & 0.650071206187309 & 0.699857587625382 & 0.349928793812691 \tabularnewline
39 & 0.59539431196973 & 0.80921137606054 & 0.40460568803027 \tabularnewline
40 & 0.5547641428917 & 0.8904717142166 & 0.4452358571083 \tabularnewline
41 & 0.64258147389741 & 0.71483705220518 & 0.35741852610259 \tabularnewline
42 & 0.58565512702801 & 0.82868974594398 & 0.41434487297199 \tabularnewline
43 & 0.612535914273895 & 0.77492817145221 & 0.387464085726105 \tabularnewline
44 & 0.561570623483743 & 0.876858753032513 & 0.438429376516257 \tabularnewline
45 & 0.610486680249302 & 0.779026639501396 & 0.389513319750698 \tabularnewline
46 & 0.632477276397816 & 0.735045447204368 & 0.367522723602184 \tabularnewline
47 & 0.630720903487861 & 0.738558193024277 & 0.369279096512139 \tabularnewline
48 & 0.613497705151923 & 0.773004589696153 & 0.386502294848077 \tabularnewline
49 & 0.646126220866446 & 0.707747558267108 & 0.353873779133554 \tabularnewline
50 & 0.655351757096599 & 0.689296485806802 & 0.344648242903401 \tabularnewline
51 & 0.629124621509907 & 0.741750756980185 & 0.370875378490093 \tabularnewline
52 & 0.74715698533434 & 0.505686029331318 & 0.252843014665659 \tabularnewline
53 & 0.839824378826056 & 0.320351242347889 & 0.160175621173944 \tabularnewline
54 & 0.854138110491453 & 0.291723779017094 & 0.145861889508547 \tabularnewline
55 & 0.892828500252739 & 0.214342999494522 & 0.107171499747261 \tabularnewline
56 & 0.901779483247096 & 0.196441033505808 & 0.0982205167529041 \tabularnewline
57 & 0.939667482887394 & 0.120665034225213 & 0.0603325171126064 \tabularnewline
58 & 0.976278555727244 & 0.047442888545513 & 0.0237214442727565 \tabularnewline
59 & 0.984183443420348 & 0.0316331131593049 & 0.0158165565796525 \tabularnewline
60 & 0.988347182523573 & 0.0233056349528534 & 0.0116528174764267 \tabularnewline
61 & 0.99168395119863 & 0.0166320976027419 & 0.00831604880137097 \tabularnewline
62 & 0.991546486859949 & 0.0169070262801024 & 0.0084535131400512 \tabularnewline
63 & 0.989213155645487 & 0.0215736887090264 & 0.0107868443545132 \tabularnewline
64 & 0.991747084685636 & 0.0165058306287283 & 0.00825291531436416 \tabularnewline
65 & 0.99204080733085 & 0.015918385338301 & 0.0079591926691505 \tabularnewline
66 & 0.992470332404372 & 0.0150593351912565 & 0.00752966759562824 \tabularnewline
67 & 0.99010753555239 & 0.0197849288952201 & 0.00989246444761007 \tabularnewline
68 & 0.995029595452334 & 0.00994080909533178 & 0.00497040454766589 \tabularnewline
69 & 0.994369016535492 & 0.0112619669290158 & 0.00563098346450792 \tabularnewline
70 & 0.994697527885314 & 0.0106049442293726 & 0.00530247211468631 \tabularnewline
71 & 0.993487050577588 & 0.0130258988448237 & 0.00651294942241186 \tabularnewline
72 & 0.990275846072576 & 0.0194483078548474 & 0.0097241539274237 \tabularnewline
73 & 0.993148073662142 & 0.0137038526757163 & 0.00685192633785813 \tabularnewline
74 & 0.9935367811999 & 0.0129264376001995 & 0.00646321880009973 \tabularnewline
75 & 0.992941808885715 & 0.0141163822285694 & 0.00705819111428468 \tabularnewline
76 & 0.9923859255612 & 0.0152281488776015 & 0.00761407443880074 \tabularnewline
77 & 0.993889316045119 & 0.0122213679097628 & 0.00611068395488138 \tabularnewline
78 & 0.990334272744525 & 0.0193314545109498 & 0.0096657272554749 \tabularnewline
79 & 0.985020704996184 & 0.0299585900076322 & 0.0149792950038161 \tabularnewline
80 & 0.996004313161733 & 0.00799137367653452 & 0.00399568683826726 \tabularnewline
81 & 0.99369172500906 & 0.0126165499818787 & 0.00630827499093935 \tabularnewline
82 & 0.992040093688891 & 0.0159198126222174 & 0.00795990631110871 \tabularnewline
83 & 0.987607177341122 & 0.024785645317756 & 0.012392822658878 \tabularnewline
84 & 0.980721762349027 & 0.0385564753019467 & 0.0192782376509734 \tabularnewline
85 & 0.977409162445373 & 0.0451816751092544 & 0.0225908375546272 \tabularnewline
86 & 0.966967672191184 & 0.0660646556176317 & 0.0330323278088158 \tabularnewline
87 & 0.959579153393047 & 0.0808416932139068 & 0.0404208466069534 \tabularnewline
88 & 0.948969045570097 & 0.102061908859807 & 0.0510309544299035 \tabularnewline
89 & 0.93270913822412 & 0.134581723551761 & 0.0672908617758806 \tabularnewline
90 & 0.926214052827828 & 0.147571894344343 & 0.0737859471721716 \tabularnewline
91 & 0.936693985254029 & 0.126612029491943 & 0.0633060147459715 \tabularnewline
92 & 0.917662178859019 & 0.164675642281963 & 0.0823378211409813 \tabularnewline
93 & 0.955798075304036 & 0.0884038493919276 & 0.0442019246959638 \tabularnewline
94 & 0.947002560762715 & 0.105994878474570 & 0.0529974392372848 \tabularnewline
95 & 0.919031187795338 & 0.161937624409325 & 0.0809688122046623 \tabularnewline
96 & 0.923611357331691 & 0.152777285336618 & 0.0763886426683088 \tabularnewline
97 & 0.880141638459926 & 0.239716723080149 & 0.119858361540074 \tabularnewline
98 & 0.909521090287238 & 0.180957819425524 & 0.090478909712762 \tabularnewline
99 & 0.88440653945586 & 0.231186921088279 & 0.115593460544140 \tabularnewline
100 & 0.9464241909892 & 0.107151618021600 & 0.0535758090108002 \tabularnewline
101 & 0.93095633987471 & 0.138087320250579 & 0.0690436601252893 \tabularnewline
102 & 0.901347431648554 & 0.197305136702893 & 0.0986525683514464 \tabularnewline
103 & 0.886921610836934 & 0.226156778326131 & 0.113078389163066 \tabularnewline
104 & 0.887589796320515 & 0.224820407358970 & 0.112410203679485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&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]17[/C][C]0.325234881047865[/C][C]0.650469762095729[/C][C]0.674765118952135[/C][/ROW]
[ROW][C]18[/C][C]0.322521920833765[/C][C]0.645043841667531[/C][C]0.677478079166235[/C][/ROW]
[ROW][C]19[/C][C]0.195657895024613[/C][C]0.391315790049225[/C][C]0.804342104975387[/C][/ROW]
[ROW][C]20[/C][C]0.113710167967367[/C][C]0.227420335934734[/C][C]0.886289832032633[/C][/ROW]
[ROW][C]21[/C][C]0.397790152341236[/C][C]0.795580304682471[/C][C]0.602209847658764[/C][/ROW]
[ROW][C]22[/C][C]0.358468691135862[/C][C]0.716937382271725[/C][C]0.641531308864138[/C][/ROW]
[ROW][C]23[/C][C]0.652876302691013[/C][C]0.694247394617973[/C][C]0.347123697308987[/C][/ROW]
[ROW][C]24[/C][C]0.562158998972255[/C][C]0.87568200205549[/C][C]0.437841001027745[/C][/ROW]
[ROW][C]25[/C][C]0.555503486212317[/C][C]0.888993027575365[/C][C]0.444496513787683[/C][/ROW]
[ROW][C]26[/C][C]0.612790180223478[/C][C]0.774419639553044[/C][C]0.387209819776522[/C][/ROW]
[ROW][C]27[/C][C]0.551892349250934[/C][C]0.896215301498131[/C][C]0.448107650749066[/C][/ROW]
[ROW][C]28[/C][C]0.516677823621695[/C][C]0.96664435275661[/C][C]0.483322176378305[/C][/ROW]
[ROW][C]29[/C][C]0.53580038550908[/C][C]0.92839922898184[/C][C]0.46419961449092[/C][/ROW]
[ROW][C]30[/C][C]0.507570857514545[/C][C]0.98485828497091[/C][C]0.492429142485455[/C][/ROW]
[ROW][C]31[/C][C]0.465079388310612[/C][C]0.930158776621224[/C][C]0.534920611689388[/C][/ROW]
[ROW][C]32[/C][C]0.399965404430136[/C][C]0.799930808860272[/C][C]0.600034595569864[/C][/ROW]
[ROW][C]33[/C][C]0.553131811461384[/C][C]0.893736377077232[/C][C]0.446868188538616[/C][/ROW]
[ROW][C]34[/C][C]0.55993039139897[/C][C]0.880139217202061[/C][C]0.440069608601031[/C][/ROW]
[ROW][C]35[/C][C]0.769204357222667[/C][C]0.461591285554665[/C][C]0.230795642777333[/C][/ROW]
[ROW][C]36[/C][C]0.715547788304659[/C][C]0.568904423390683[/C][C]0.284452211695341[/C][/ROW]
[ROW][C]37[/C][C]0.688307191774671[/C][C]0.623385616450657[/C][C]0.311692808225329[/C][/ROW]
[ROW][C]38[/C][C]0.650071206187309[/C][C]0.699857587625382[/C][C]0.349928793812691[/C][/ROW]
[ROW][C]39[/C][C]0.59539431196973[/C][C]0.80921137606054[/C][C]0.40460568803027[/C][/ROW]
[ROW][C]40[/C][C]0.5547641428917[/C][C]0.8904717142166[/C][C]0.4452358571083[/C][/ROW]
[ROW][C]41[/C][C]0.64258147389741[/C][C]0.71483705220518[/C][C]0.35741852610259[/C][/ROW]
[ROW][C]42[/C][C]0.58565512702801[/C][C]0.82868974594398[/C][C]0.41434487297199[/C][/ROW]
[ROW][C]43[/C][C]0.612535914273895[/C][C]0.77492817145221[/C][C]0.387464085726105[/C][/ROW]
[ROW][C]44[/C][C]0.561570623483743[/C][C]0.876858753032513[/C][C]0.438429376516257[/C][/ROW]
[ROW][C]45[/C][C]0.610486680249302[/C][C]0.779026639501396[/C][C]0.389513319750698[/C][/ROW]
[ROW][C]46[/C][C]0.632477276397816[/C][C]0.735045447204368[/C][C]0.367522723602184[/C][/ROW]
[ROW][C]47[/C][C]0.630720903487861[/C][C]0.738558193024277[/C][C]0.369279096512139[/C][/ROW]
[ROW][C]48[/C][C]0.613497705151923[/C][C]0.773004589696153[/C][C]0.386502294848077[/C][/ROW]
[ROW][C]49[/C][C]0.646126220866446[/C][C]0.707747558267108[/C][C]0.353873779133554[/C][/ROW]
[ROW][C]50[/C][C]0.655351757096599[/C][C]0.689296485806802[/C][C]0.344648242903401[/C][/ROW]
[ROW][C]51[/C][C]0.629124621509907[/C][C]0.741750756980185[/C][C]0.370875378490093[/C][/ROW]
[ROW][C]52[/C][C]0.74715698533434[/C][C]0.505686029331318[/C][C]0.252843014665659[/C][/ROW]
[ROW][C]53[/C][C]0.839824378826056[/C][C]0.320351242347889[/C][C]0.160175621173944[/C][/ROW]
[ROW][C]54[/C][C]0.854138110491453[/C][C]0.291723779017094[/C][C]0.145861889508547[/C][/ROW]
[ROW][C]55[/C][C]0.892828500252739[/C][C]0.214342999494522[/C][C]0.107171499747261[/C][/ROW]
[ROW][C]56[/C][C]0.901779483247096[/C][C]0.196441033505808[/C][C]0.0982205167529041[/C][/ROW]
[ROW][C]57[/C][C]0.939667482887394[/C][C]0.120665034225213[/C][C]0.0603325171126064[/C][/ROW]
[ROW][C]58[/C][C]0.976278555727244[/C][C]0.047442888545513[/C][C]0.0237214442727565[/C][/ROW]
[ROW][C]59[/C][C]0.984183443420348[/C][C]0.0316331131593049[/C][C]0.0158165565796525[/C][/ROW]
[ROW][C]60[/C][C]0.988347182523573[/C][C]0.0233056349528534[/C][C]0.0116528174764267[/C][/ROW]
[ROW][C]61[/C][C]0.99168395119863[/C][C]0.0166320976027419[/C][C]0.00831604880137097[/C][/ROW]
[ROW][C]62[/C][C]0.991546486859949[/C][C]0.0169070262801024[/C][C]0.0084535131400512[/C][/ROW]
[ROW][C]63[/C][C]0.989213155645487[/C][C]0.0215736887090264[/C][C]0.0107868443545132[/C][/ROW]
[ROW][C]64[/C][C]0.991747084685636[/C][C]0.0165058306287283[/C][C]0.00825291531436416[/C][/ROW]
[ROW][C]65[/C][C]0.99204080733085[/C][C]0.015918385338301[/C][C]0.0079591926691505[/C][/ROW]
[ROW][C]66[/C][C]0.992470332404372[/C][C]0.0150593351912565[/C][C]0.00752966759562824[/C][/ROW]
[ROW][C]67[/C][C]0.99010753555239[/C][C]0.0197849288952201[/C][C]0.00989246444761007[/C][/ROW]
[ROW][C]68[/C][C]0.995029595452334[/C][C]0.00994080909533178[/C][C]0.00497040454766589[/C][/ROW]
[ROW][C]69[/C][C]0.994369016535492[/C][C]0.0112619669290158[/C][C]0.00563098346450792[/C][/ROW]
[ROW][C]70[/C][C]0.994697527885314[/C][C]0.0106049442293726[/C][C]0.00530247211468631[/C][/ROW]
[ROW][C]71[/C][C]0.993487050577588[/C][C]0.0130258988448237[/C][C]0.00651294942241186[/C][/ROW]
[ROW][C]72[/C][C]0.990275846072576[/C][C]0.0194483078548474[/C][C]0.0097241539274237[/C][/ROW]
[ROW][C]73[/C][C]0.993148073662142[/C][C]0.0137038526757163[/C][C]0.00685192633785813[/C][/ROW]
[ROW][C]74[/C][C]0.9935367811999[/C][C]0.0129264376001995[/C][C]0.00646321880009973[/C][/ROW]
[ROW][C]75[/C][C]0.992941808885715[/C][C]0.0141163822285694[/C][C]0.00705819111428468[/C][/ROW]
[ROW][C]76[/C][C]0.9923859255612[/C][C]0.0152281488776015[/C][C]0.00761407443880074[/C][/ROW]
[ROW][C]77[/C][C]0.993889316045119[/C][C]0.0122213679097628[/C][C]0.00611068395488138[/C][/ROW]
[ROW][C]78[/C][C]0.990334272744525[/C][C]0.0193314545109498[/C][C]0.0096657272554749[/C][/ROW]
[ROW][C]79[/C][C]0.985020704996184[/C][C]0.0299585900076322[/C][C]0.0149792950038161[/C][/ROW]
[ROW][C]80[/C][C]0.996004313161733[/C][C]0.00799137367653452[/C][C]0.00399568683826726[/C][/ROW]
[ROW][C]81[/C][C]0.99369172500906[/C][C]0.0126165499818787[/C][C]0.00630827499093935[/C][/ROW]
[ROW][C]82[/C][C]0.992040093688891[/C][C]0.0159198126222174[/C][C]0.00795990631110871[/C][/ROW]
[ROW][C]83[/C][C]0.987607177341122[/C][C]0.024785645317756[/C][C]0.012392822658878[/C][/ROW]
[ROW][C]84[/C][C]0.980721762349027[/C][C]0.0385564753019467[/C][C]0.0192782376509734[/C][/ROW]
[ROW][C]85[/C][C]0.977409162445373[/C][C]0.0451816751092544[/C][C]0.0225908375546272[/C][/ROW]
[ROW][C]86[/C][C]0.966967672191184[/C][C]0.0660646556176317[/C][C]0.0330323278088158[/C][/ROW]
[ROW][C]87[/C][C]0.959579153393047[/C][C]0.0808416932139068[/C][C]0.0404208466069534[/C][/ROW]
[ROW][C]88[/C][C]0.948969045570097[/C][C]0.102061908859807[/C][C]0.0510309544299035[/C][/ROW]
[ROW][C]89[/C][C]0.93270913822412[/C][C]0.134581723551761[/C][C]0.0672908617758806[/C][/ROW]
[ROW][C]90[/C][C]0.926214052827828[/C][C]0.147571894344343[/C][C]0.0737859471721716[/C][/ROW]
[ROW][C]91[/C][C]0.936693985254029[/C][C]0.126612029491943[/C][C]0.0633060147459715[/C][/ROW]
[ROW][C]92[/C][C]0.917662178859019[/C][C]0.164675642281963[/C][C]0.0823378211409813[/C][/ROW]
[ROW][C]93[/C][C]0.955798075304036[/C][C]0.0884038493919276[/C][C]0.0442019246959638[/C][/ROW]
[ROW][C]94[/C][C]0.947002560762715[/C][C]0.105994878474570[/C][C]0.0529974392372848[/C][/ROW]
[ROW][C]95[/C][C]0.919031187795338[/C][C]0.161937624409325[/C][C]0.0809688122046623[/C][/ROW]
[ROW][C]96[/C][C]0.923611357331691[/C][C]0.152777285336618[/C][C]0.0763886426683088[/C][/ROW]
[ROW][C]97[/C][C]0.880141638459926[/C][C]0.239716723080149[/C][C]0.119858361540074[/C][/ROW]
[ROW][C]98[/C][C]0.909521090287238[/C][C]0.180957819425524[/C][C]0.090478909712762[/C][/ROW]
[ROW][C]99[/C][C]0.88440653945586[/C][C]0.231186921088279[/C][C]0.115593460544140[/C][/ROW]
[ROW][C]100[/C][C]0.9464241909892[/C][C]0.107151618021600[/C][C]0.0535758090108002[/C][/ROW]
[ROW][C]101[/C][C]0.93095633987471[/C][C]0.138087320250579[/C][C]0.0690436601252893[/C][/ROW]
[ROW][C]102[/C][C]0.901347431648554[/C][C]0.197305136702893[/C][C]0.0986525683514464[/C][/ROW]
[ROW][C]103[/C][C]0.886921610836934[/C][C]0.226156778326131[/C][C]0.113078389163066[/C][/ROW]
[ROW][C]104[/C][C]0.887589796320515[/C][C]0.224820407358970[/C][C]0.112410203679485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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
170.3252348810478650.6504697620957290.674765118952135
180.3225219208337650.6450438416675310.677478079166235
190.1956578950246130.3913157900492250.804342104975387
200.1137101679673670.2274203359347340.886289832032633
210.3977901523412360.7955803046824710.602209847658764
220.3584686911358620.7169373822717250.641531308864138
230.6528763026910130.6942473946179730.347123697308987
240.5621589989722550.875682002055490.437841001027745
250.5555034862123170.8889930275753650.444496513787683
260.6127901802234780.7744196395530440.387209819776522
270.5518923492509340.8962153014981310.448107650749066
280.5166778236216950.966644352756610.483322176378305
290.535800385509080.928399228981840.46419961449092
300.5075708575145450.984858284970910.492429142485455
310.4650793883106120.9301587766212240.534920611689388
320.3999654044301360.7999308088602720.600034595569864
330.5531318114613840.8937363770772320.446868188538616
340.559930391398970.8801392172020610.440069608601031
350.7692043572226670.4615912855546650.230795642777333
360.7155477883046590.5689044233906830.284452211695341
370.6883071917746710.6233856164506570.311692808225329
380.6500712061873090.6998575876253820.349928793812691
390.595394311969730.809211376060540.40460568803027
400.55476414289170.89047171421660.4452358571083
410.642581473897410.714837052205180.35741852610259
420.585655127028010.828689745943980.41434487297199
430.6125359142738950.774928171452210.387464085726105
440.5615706234837430.8768587530325130.438429376516257
450.6104866802493020.7790266395013960.389513319750698
460.6324772763978160.7350454472043680.367522723602184
470.6307209034878610.7385581930242770.369279096512139
480.6134977051519230.7730045896961530.386502294848077
490.6461262208664460.7077475582671080.353873779133554
500.6553517570965990.6892964858068020.344648242903401
510.6291246215099070.7417507569801850.370875378490093
520.747156985334340.5056860293313180.252843014665659
530.8398243788260560.3203512423478890.160175621173944
540.8541381104914530.2917237790170940.145861889508547
550.8928285002527390.2143429994945220.107171499747261
560.9017794832470960.1964410335058080.0982205167529041
570.9396674828873940.1206650342252130.0603325171126064
580.9762785557272440.0474428885455130.0237214442727565
590.9841834434203480.03163311315930490.0158165565796525
600.9883471825235730.02330563495285340.0116528174764267
610.991683951198630.01663209760274190.00831604880137097
620.9915464868599490.01690702628010240.0084535131400512
630.9892131556454870.02157368870902640.0107868443545132
640.9917470846856360.01650583062872830.00825291531436416
650.992040807330850.0159183853383010.0079591926691505
660.9924703324043720.01505933519125650.00752966759562824
670.990107535552390.01978492889522010.00989246444761007
680.9950295954523340.009940809095331780.00497040454766589
690.9943690165354920.01126196692901580.00563098346450792
700.9946975278853140.01060494422937260.00530247211468631
710.9934870505775880.01302589884482370.00651294942241186
720.9902758460725760.01944830785484740.0097241539274237
730.9931480736621420.01370385267571630.00685192633785813
740.99353678119990.01292643760019950.00646321880009973
750.9929418088857150.01411638222856940.00705819111428468
760.99238592556120.01522814887760150.00761407443880074
770.9938893160451190.01222136790976280.00611068395488138
780.9903342727445250.01933145451094980.0096657272554749
790.9850207049961840.02995859000763220.0149792950038161
800.9960043131617330.007991373676534520.00399568683826726
810.993691725009060.01261654998187870.00630827499093935
820.9920400936888910.01591981262221740.00795990631110871
830.9876071773411220.0247856453177560.012392822658878
840.9807217623490270.03855647530194670.0192782376509734
850.9774091624453730.04518167510925440.0225908375546272
860.9669676721911840.06606465561763170.0330323278088158
870.9595791533930470.08084169321390680.0404208466069534
880.9489690455700970.1020619088598070.0510309544299035
890.932709138224120.1345817235517610.0672908617758806
900.9262140528278280.1475718943443430.0737859471721716
910.9366939852540290.1266120294919430.0633060147459715
920.9176621788590190.1646756422819630.0823378211409813
930.9557980753040360.08840384939192760.0442019246959638
940.9470025607627150.1059948784745700.0529974392372848
950.9190311877953380.1619376244093250.0809688122046623
960.9236113573316910.1527772853366180.0763886426683088
970.8801416384599260.2397167230801490.119858361540074
980.9095210902872380.1809578194255240.090478909712762
990.884406539455860.2311869210882790.115593460544140
1000.94642419098920.1071516180216000.0535758090108002
1010.930956339874710.1380873202505790.0690436601252893
1020.9013474316485540.1973051367028930.0986525683514464
1030.8869216108369340.2261567783261310.113078389163066
1040.8875897963205150.2248204073589700.112410203679485







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0227272727272727NOK
5% type I error level280.318181818181818NOK
10% type I error level310.352272727272727NOK

\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 & 2 & 0.0227272727272727 & NOK \tabularnewline
5% type I error level & 28 & 0.318181818181818 & NOK \tabularnewline
10% type I error level & 31 & 0.352272727272727 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58288&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]2[/C][C]0.0227272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.318181818181818[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]31[/C][C]0.352272727272727[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58288&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58288&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 level20.0227272727272727NOK
5% type I error level280.318181818181818NOK
10% type I error level310.352272727272727NOK



Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = 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')
}