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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 computationSun, 27 Nov 2011 12:03:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/27/t1322413424v8pj55711ryc5m5.htm/, Retrieved Fri, 29 Mar 2024 07:04:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147563, Retrieved Fri, 29 Mar 2024 07:04:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R PD      [Multiple Regression] [] [2010-12-02 18:02:49] [94f4aa1c01e87d8321fffb341ed4df07]
- R           [Multiple Regression] [] [2011-11-25 00:47:59] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 17:03:35] [5f9ad3d6882448a3cbf5628cc61fe2a1] [Current]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=147563&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=147563&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
PersonalStandards[t] = + 7.46043060983697 + 0.328154021465218ConcernoverMistakes[t] -0.3627366723898Doubtsaboutactions[t] + 0.186560236681879ParentalExpectations[t] + 0.0233844134026156ParentalCriticism[t] + 0.401270321441297`Organization `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PersonalStandards[t] =  +  7.46043060983697 +  0.328154021465218ConcernoverMistakes[t] -0.3627366723898Doubtsaboutactions[t] +  0.186560236681879ParentalExpectations[t] +  0.0233844134026156ParentalCriticism[t] +  0.401270321441297`Organization
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PersonalStandards[t] =  +  7.46043060983697 +  0.328154021465218ConcernoverMistakes[t] -0.3627366723898Doubtsaboutactions[t] +  0.186560236681879ParentalExpectations[t] +  0.0233844134026156ParentalCriticism[t] +  0.401270321441297`Organization
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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
PersonalStandards[t] = + 7.46043060983697 + 0.328154021465218ConcernoverMistakes[t] -0.3627366723898Doubtsaboutactions[t] + 0.186560236681879ParentalExpectations[t] + 0.0233844134026156ParentalCriticism[t] + 0.401270321441297`Organization `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.460430609836972.2481093.31850.0011310.000565
ConcernoverMistakes0.3281540214652180.0555445.90800
Doubtsaboutactions-0.36273667238980.107118-3.38639e-040.00045
ParentalExpectations0.1865602366818790.101141.84460.0670320.033516
ParentalCriticism0.02338441340261560.1286210.18180.8559730.427987
`Organization `0.4012703214412970.0717735.590800

\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) & 7.46043060983697 & 2.248109 & 3.3185 & 0.001131 & 0.000565 \tabularnewline
ConcernoverMistakes & 0.328154021465218 & 0.055544 & 5.908 & 0 & 0 \tabularnewline
Doubtsaboutactions & -0.3627366723898 & 0.107118 & -3.3863 & 9e-04 & 0.00045 \tabularnewline
ParentalExpectations & 0.186560236681879 & 0.10114 & 1.8446 & 0.067032 & 0.033516 \tabularnewline
ParentalCriticism & 0.0233844134026156 & 0.128621 & 0.1818 & 0.855973 & 0.427987 \tabularnewline
`Organization
` & 0.401270321441297 & 0.071773 & 5.5908 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&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]7.46043060983697[/C][C]2.248109[/C][C]3.3185[/C][C]0.001131[/C][C]0.000565[/C][/ROW]
[ROW][C]ConcernoverMistakes[/C][C]0.328154021465218[/C][C]0.055544[/C][C]5.908[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]-0.3627366723898[/C][C]0.107118[/C][C]-3.3863[/C][C]9e-04[/C][C]0.00045[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.186560236681879[/C][C]0.10114[/C][C]1.8446[/C][C]0.067032[/C][C]0.033516[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.0233844134026156[/C][C]0.128621[/C][C]0.1818[/C][C]0.855973[/C][C]0.427987[/C][/ROW]
[ROW][C]`Organization
`[/C][C]0.401270321441297[/C][C]0.071773[/C][C]5.5908[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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)7.460430609836972.2481093.31850.0011310.000565
ConcernoverMistakes0.3281540214652180.0555445.90800
Doubtsaboutactions-0.36273667238980.107118-3.38639e-040.00045
ParentalExpectations0.1865602366818790.101141.84460.0670320.033516
ParentalCriticism0.02338441340261560.1286210.18180.8559730.427987
`Organization `0.4012703214412970.0717735.590800







Multiple Linear Regression - Regression Statistics
Multiple R0.605858798778077
R-squared0.367064884056814
Adjusted R-squared0.346380729941024
F-TEST (value)17.7461878306445
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value7.54951656745106e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40927289715423
Sum Squared Residuals1778.34067815237

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.605858798778077 \tabularnewline
R-squared & 0.367064884056814 \tabularnewline
Adjusted R-squared & 0.346380729941024 \tabularnewline
F-TEST (value) & 17.7461878306445 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 7.54951656745106e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.40927289715423 \tabularnewline
Sum Squared Residuals & 1778.34067815237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.605858798778077[/C][/ROW]
[ROW][C]R-squared[/C][C]0.367064884056814[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.346380729941024[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.7461878306445[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]7.54951656745106e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.40927289715423[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1778.34067815237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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 R0.605858798778077
R-squared0.367064884056814
Adjusted R-squared0.346380729941024
F-TEST (value)17.7461878306445
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value7.54951656745106e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40927289715423
Sum Squared Residuals1778.34067815237







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.02361763335080.976382366649191
22522.39639210732352.60360789267647
33024.25298630725225.74701369274783
41920.2962579947228-1.29625799472284
52220.53862828528321.46137171471678
62223.1227812851809-1.12278128518093
72522.57360260838692.42639739161312
82319.45685945818933.54314054181066
91718.7937770436835-1.79377704368349
102121.669677585173-0.669677585173036
111922.8153403514384-3.8153403514384
121923.3938848919955-4.39388489199547
131523.2200335336539-8.22003353365394
141617.0860954231925-1.08609542319246
152319.45982251795763.54017748204243
162724.07484238205532.92515761794465
172221.00194328868470.998056711315263
181416.535329320714-2.535329320714
192224.0873202920243-2.08732029202431
202324.0090508616482-1.00905086164818
212321.56883817794231.4311618220577
222124.4545998179461-3.45459981794611
231922.4295524471459-3.42955244714589
241823.8367869289911-5.83678692899107
252023.1800605324662-3.18006053246622
262322.44835255960490.55164744039509
272523.32807873286811.67192126713187
281923.3532655211774-4.35326552117744
292423.8398299312470.160170068753002
302221.56922587366740.430774126332574
312525.1649483334544-0.164948333454416
322623.22321697753742.77678302246258
332922.70020388487516.29979611512492
343225.19655953738236.8034404626177
352521.58307941772023.4169205822798
362924.40994950651354.59005049348653
372824.94670560416253.0532943958375
381717.1062381095522-0.106238109552165
392826.07516352086011.92483647913992
402922.94307220494056.05692779505949
412627.52862185641-1.52862185640998
422523.46670945788321.53329054211676
431419.5469105942641-5.54691059426405
442522.10552217031042.89447782968955
452621.67803938243294.32196061756713
462020.3008968098917-0.300896809891658
471821.4213727878477-3.42137278784772
483224.66103622987577.33896377012431
492525.0045460489836-0.00454604898359148
502521.69155135775733.30844864224273
512320.84547496130682.15452503869318
522122.1588588066973-1.15885880669729
532024.0575114630031-4.05751146300312
541516.5269290283781-1.52692902837805
553026.7173902089883.28260979101198
562425.3394958960663-1.33949589606632
572624.31254918449231.68745081550774
582421.71725246129782.28274753870219
592221.49415047143090.505849528569085
601415.6462203516491-1.64622035164913
612422.2553595200761.74464047992401
622422.92037560857981.07962439142021
632423.35362015671940.646379843280559
642419.98505558381324.01494441618681
651918.51290616425470.487093835745272
663126.82086767642944.17913232357061
672226.601037521453-4.60103752145304
682721.46976210052055.5302378994795
691917.73590694522341.26409305477656
702522.30440358960752.69559641039245
712024.9772106374541-4.97721063745406
722121.502677383273-0.502677383272961
732727.4823128306108-0.482312830610752
742324.3803773473337-1.38037734733371
752525.6982731831007-0.698273183100738
762022.2330812773097-2.23308127730969
772119.24546706874021.75453293125985
782222.4516157406899-0.45161574068992
792323.0320339448358-0.0320339448358368
802524.05217644356390.947823556436075
812523.42308557988541.57691442011462
821723.8630492300674-6.86304923006741
831921.4391304477228-2.43913044772283
842523.97091329555031.02908670444972
851922.3733623294629-3.37336232946291
862023.1407040596382-3.14070405963825
872622.52387072181343.47612927818658
882320.80738427751352.19261572248651
892724.43619578844072.56380421155931
901720.8970807780462-3.8970807780462
911723.3435280898338-6.3435280898338
921920.191563551002-1.191563551002
931719.7459731472925-2.74597314729248
942222.0165688176436-0.0165688176435829
952123.5510323826098-2.55103238260975
963228.63724483998533.36275516001471
972124.6834795872242-3.6834795872242
982124.3266944626333-3.32669446263333
991821.259817450436-3.259817450436
1001821.2889468496438-3.28894684964376
1012322.83953081416060.160469185839441
1021920.6492902959521-1.64929029595212
1032020.9576947616757-0.957694761675737
1042122.3255043730554-1.32550437305537
1052023.8525057494042-3.85250574940422
1061718.7741072251434-1.77410722514342
1071820.3006604066273-2.30066040662729
1081920.749679106004-1.74967910600404
1092222.0682765809344-0.0682765809344118
1101518.766441632154-3.76644163215395
1111418.8510893511519-4.8510893511519
1121826.6429142940773-8.64291429407725
1132421.41144878787982.58855121212023
1143523.562032711943511.4379672880565
1152919.21632460271889.78367539728123
1162121.9816944326307-0.981694432630689
1172520.49532081432834.50467918567166
1182018.49243029639511.50756970360488
1192223.2253187184531-1.22531871845307
1201316.8359413686559-3.83594136865588
1212623.21420021813262.78579978186736
1221716.88722239112380.112777608876169
1232520.09471363697434.90528636302571
1242020.7722734892362-0.77227348923623
1251918.12855439318590.871445606814121
1262122.6212243668162-1.62122436681623
1272221.07047528771720.929524712282782
1282422.70567391104881.29432608895116
1292122.9951676639565-1.99516766395646
1302625.4821958514860.517804148514022
1312420.59509309602053.40490690397949
1321620.298739799443-4.29873979944297
1332322.37653195122490.623468048775057
1341820.8107987510593-2.81079875105926
1351622.3793138772619-6.37931387726185
1362624.06985436623751.93014563376249
1371919.0560620045677-0.0560620045676756
1382116.86661990375814.13338009624188
1392122.2168258710245-1.21682587102449
1402218.52601737704043.47398262295963
1412319.73402419127493.26597580872508
1422924.81240594107884.18759405892122
1432119.21565087437541.78434912562462
1442119.96394641313691.03605358686309
1452321.89278796226841.1072120377316
1462722.9960672725994.00393272740102
1472525.3746950753507-0.37469507535065
1482121.000140913778-0.000140913778045968
1491017.1119616413152-7.11196164131521
1502022.6490195793518-2.64901957935177
1512622.57492372824553.42507627175453
1522423.64748326134840.352516738651611
1532931.6584752151628-2.65847521516283
1541918.98258268182110.0174173181788714
1552422.09401617955111.90598382044895
1561920.7666852307848-1.76668523078483
1572423.44015542271860.559844577281407
1582221.82443709696720.175562903032761
1591723.7736200572454-6.77362005724542

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.0236176333508 & 0.976382366649191 \tabularnewline
2 & 25 & 22.3963921073235 & 2.60360789267647 \tabularnewline
3 & 30 & 24.2529863072522 & 5.74701369274783 \tabularnewline
4 & 19 & 20.2962579947228 & -1.29625799472284 \tabularnewline
5 & 22 & 20.5386282852832 & 1.46137171471678 \tabularnewline
6 & 22 & 23.1227812851809 & -1.12278128518093 \tabularnewline
7 & 25 & 22.5736026083869 & 2.42639739161312 \tabularnewline
8 & 23 & 19.4568594581893 & 3.54314054181066 \tabularnewline
9 & 17 & 18.7937770436835 & -1.79377704368349 \tabularnewline
10 & 21 & 21.669677585173 & -0.669677585173036 \tabularnewline
11 & 19 & 22.8153403514384 & -3.8153403514384 \tabularnewline
12 & 19 & 23.3938848919955 & -4.39388489199547 \tabularnewline
13 & 15 & 23.2200335336539 & -8.22003353365394 \tabularnewline
14 & 16 & 17.0860954231925 & -1.08609542319246 \tabularnewline
15 & 23 & 19.4598225179576 & 3.54017748204243 \tabularnewline
16 & 27 & 24.0748423820553 & 2.92515761794465 \tabularnewline
17 & 22 & 21.0019432886847 & 0.998056711315263 \tabularnewline
18 & 14 & 16.535329320714 & -2.535329320714 \tabularnewline
19 & 22 & 24.0873202920243 & -2.08732029202431 \tabularnewline
20 & 23 & 24.0090508616482 & -1.00905086164818 \tabularnewline
21 & 23 & 21.5688381779423 & 1.4311618220577 \tabularnewline
22 & 21 & 24.4545998179461 & -3.45459981794611 \tabularnewline
23 & 19 & 22.4295524471459 & -3.42955244714589 \tabularnewline
24 & 18 & 23.8367869289911 & -5.83678692899107 \tabularnewline
25 & 20 & 23.1800605324662 & -3.18006053246622 \tabularnewline
26 & 23 & 22.4483525596049 & 0.55164744039509 \tabularnewline
27 & 25 & 23.3280787328681 & 1.67192126713187 \tabularnewline
28 & 19 & 23.3532655211774 & -4.35326552117744 \tabularnewline
29 & 24 & 23.839829931247 & 0.160170068753002 \tabularnewline
30 & 22 & 21.5692258736674 & 0.430774126332574 \tabularnewline
31 & 25 & 25.1649483334544 & -0.164948333454416 \tabularnewline
32 & 26 & 23.2232169775374 & 2.77678302246258 \tabularnewline
33 & 29 & 22.7002038848751 & 6.29979611512492 \tabularnewline
34 & 32 & 25.1965595373823 & 6.8034404626177 \tabularnewline
35 & 25 & 21.5830794177202 & 3.4169205822798 \tabularnewline
36 & 29 & 24.4099495065135 & 4.59005049348653 \tabularnewline
37 & 28 & 24.9467056041625 & 3.0532943958375 \tabularnewline
38 & 17 & 17.1062381095522 & -0.106238109552165 \tabularnewline
39 & 28 & 26.0751635208601 & 1.92483647913992 \tabularnewline
40 & 29 & 22.9430722049405 & 6.05692779505949 \tabularnewline
41 & 26 & 27.52862185641 & -1.52862185640998 \tabularnewline
42 & 25 & 23.4667094578832 & 1.53329054211676 \tabularnewline
43 & 14 & 19.5469105942641 & -5.54691059426405 \tabularnewline
44 & 25 & 22.1055221703104 & 2.89447782968955 \tabularnewline
45 & 26 & 21.6780393824329 & 4.32196061756713 \tabularnewline
46 & 20 & 20.3008968098917 & -0.300896809891658 \tabularnewline
47 & 18 & 21.4213727878477 & -3.42137278784772 \tabularnewline
48 & 32 & 24.6610362298757 & 7.33896377012431 \tabularnewline
49 & 25 & 25.0045460489836 & -0.00454604898359148 \tabularnewline
50 & 25 & 21.6915513577573 & 3.30844864224273 \tabularnewline
51 & 23 & 20.8454749613068 & 2.15452503869318 \tabularnewline
52 & 21 & 22.1588588066973 & -1.15885880669729 \tabularnewline
53 & 20 & 24.0575114630031 & -4.05751146300312 \tabularnewline
54 & 15 & 16.5269290283781 & -1.52692902837805 \tabularnewline
55 & 30 & 26.717390208988 & 3.28260979101198 \tabularnewline
56 & 24 & 25.3394958960663 & -1.33949589606632 \tabularnewline
57 & 26 & 24.3125491844923 & 1.68745081550774 \tabularnewline
58 & 24 & 21.7172524612978 & 2.28274753870219 \tabularnewline
59 & 22 & 21.4941504714309 & 0.505849528569085 \tabularnewline
60 & 14 & 15.6462203516491 & -1.64622035164913 \tabularnewline
61 & 24 & 22.255359520076 & 1.74464047992401 \tabularnewline
62 & 24 & 22.9203756085798 & 1.07962439142021 \tabularnewline
63 & 24 & 23.3536201567194 & 0.646379843280559 \tabularnewline
64 & 24 & 19.9850555838132 & 4.01494441618681 \tabularnewline
65 & 19 & 18.5129061642547 & 0.487093835745272 \tabularnewline
66 & 31 & 26.8208676764294 & 4.17913232357061 \tabularnewline
67 & 22 & 26.601037521453 & -4.60103752145304 \tabularnewline
68 & 27 & 21.4697621005205 & 5.5302378994795 \tabularnewline
69 & 19 & 17.7359069452234 & 1.26409305477656 \tabularnewline
70 & 25 & 22.3044035896075 & 2.69559641039245 \tabularnewline
71 & 20 & 24.9772106374541 & -4.97721063745406 \tabularnewline
72 & 21 & 21.502677383273 & -0.502677383272961 \tabularnewline
73 & 27 & 27.4823128306108 & -0.482312830610752 \tabularnewline
74 & 23 & 24.3803773473337 & -1.38037734733371 \tabularnewline
75 & 25 & 25.6982731831007 & -0.698273183100738 \tabularnewline
76 & 20 & 22.2330812773097 & -2.23308127730969 \tabularnewline
77 & 21 & 19.2454670687402 & 1.75453293125985 \tabularnewline
78 & 22 & 22.4516157406899 & -0.45161574068992 \tabularnewline
79 & 23 & 23.0320339448358 & -0.0320339448358368 \tabularnewline
80 & 25 & 24.0521764435639 & 0.947823556436075 \tabularnewline
81 & 25 & 23.4230855798854 & 1.57691442011462 \tabularnewline
82 & 17 & 23.8630492300674 & -6.86304923006741 \tabularnewline
83 & 19 & 21.4391304477228 & -2.43913044772283 \tabularnewline
84 & 25 & 23.9709132955503 & 1.02908670444972 \tabularnewline
85 & 19 & 22.3733623294629 & -3.37336232946291 \tabularnewline
86 & 20 & 23.1407040596382 & -3.14070405963825 \tabularnewline
87 & 26 & 22.5238707218134 & 3.47612927818658 \tabularnewline
88 & 23 & 20.8073842775135 & 2.19261572248651 \tabularnewline
89 & 27 & 24.4361957884407 & 2.56380421155931 \tabularnewline
90 & 17 & 20.8970807780462 & -3.8970807780462 \tabularnewline
91 & 17 & 23.3435280898338 & -6.3435280898338 \tabularnewline
92 & 19 & 20.191563551002 & -1.191563551002 \tabularnewline
93 & 17 & 19.7459731472925 & -2.74597314729248 \tabularnewline
94 & 22 & 22.0165688176436 & -0.0165688176435829 \tabularnewline
95 & 21 & 23.5510323826098 & -2.55103238260975 \tabularnewline
96 & 32 & 28.6372448399853 & 3.36275516001471 \tabularnewline
97 & 21 & 24.6834795872242 & -3.6834795872242 \tabularnewline
98 & 21 & 24.3266944626333 & -3.32669446263333 \tabularnewline
99 & 18 & 21.259817450436 & -3.259817450436 \tabularnewline
100 & 18 & 21.2889468496438 & -3.28894684964376 \tabularnewline
101 & 23 & 22.8395308141606 & 0.160469185839441 \tabularnewline
102 & 19 & 20.6492902959521 & -1.64929029595212 \tabularnewline
103 & 20 & 20.9576947616757 & -0.957694761675737 \tabularnewline
104 & 21 & 22.3255043730554 & -1.32550437305537 \tabularnewline
105 & 20 & 23.8525057494042 & -3.85250574940422 \tabularnewline
106 & 17 & 18.7741072251434 & -1.77410722514342 \tabularnewline
107 & 18 & 20.3006604066273 & -2.30066040662729 \tabularnewline
108 & 19 & 20.749679106004 & -1.74967910600404 \tabularnewline
109 & 22 & 22.0682765809344 & -0.0682765809344118 \tabularnewline
110 & 15 & 18.766441632154 & -3.76644163215395 \tabularnewline
111 & 14 & 18.8510893511519 & -4.8510893511519 \tabularnewline
112 & 18 & 26.6429142940773 & -8.64291429407725 \tabularnewline
113 & 24 & 21.4114487878798 & 2.58855121212023 \tabularnewline
114 & 35 & 23.5620327119435 & 11.4379672880565 \tabularnewline
115 & 29 & 19.2163246027188 & 9.78367539728123 \tabularnewline
116 & 21 & 21.9816944326307 & -0.981694432630689 \tabularnewline
117 & 25 & 20.4953208143283 & 4.50467918567166 \tabularnewline
118 & 20 & 18.4924302963951 & 1.50756970360488 \tabularnewline
119 & 22 & 23.2253187184531 & -1.22531871845307 \tabularnewline
120 & 13 & 16.8359413686559 & -3.83594136865588 \tabularnewline
121 & 26 & 23.2142002181326 & 2.78579978186736 \tabularnewline
122 & 17 & 16.8872223911238 & 0.112777608876169 \tabularnewline
123 & 25 & 20.0947136369743 & 4.90528636302571 \tabularnewline
124 & 20 & 20.7722734892362 & -0.77227348923623 \tabularnewline
125 & 19 & 18.1285543931859 & 0.871445606814121 \tabularnewline
126 & 21 & 22.6212243668162 & -1.62122436681623 \tabularnewline
127 & 22 & 21.0704752877172 & 0.929524712282782 \tabularnewline
128 & 24 & 22.7056739110488 & 1.29432608895116 \tabularnewline
129 & 21 & 22.9951676639565 & -1.99516766395646 \tabularnewline
130 & 26 & 25.482195851486 & 0.517804148514022 \tabularnewline
131 & 24 & 20.5950930960205 & 3.40490690397949 \tabularnewline
132 & 16 & 20.298739799443 & -4.29873979944297 \tabularnewline
133 & 23 & 22.3765319512249 & 0.623468048775057 \tabularnewline
134 & 18 & 20.8107987510593 & -2.81079875105926 \tabularnewline
135 & 16 & 22.3793138772619 & -6.37931387726185 \tabularnewline
136 & 26 & 24.0698543662375 & 1.93014563376249 \tabularnewline
137 & 19 & 19.0560620045677 & -0.0560620045676756 \tabularnewline
138 & 21 & 16.8666199037581 & 4.13338009624188 \tabularnewline
139 & 21 & 22.2168258710245 & -1.21682587102449 \tabularnewline
140 & 22 & 18.5260173770404 & 3.47398262295963 \tabularnewline
141 & 23 & 19.7340241912749 & 3.26597580872508 \tabularnewline
142 & 29 & 24.8124059410788 & 4.18759405892122 \tabularnewline
143 & 21 & 19.2156508743754 & 1.78434912562462 \tabularnewline
144 & 21 & 19.9639464131369 & 1.03605358686309 \tabularnewline
145 & 23 & 21.8927879622684 & 1.1072120377316 \tabularnewline
146 & 27 & 22.996067272599 & 4.00393272740102 \tabularnewline
147 & 25 & 25.3746950753507 & -0.37469507535065 \tabularnewline
148 & 21 & 21.000140913778 & -0.000140913778045968 \tabularnewline
149 & 10 & 17.1119616413152 & -7.11196164131521 \tabularnewline
150 & 20 & 22.6490195793518 & -2.64901957935177 \tabularnewline
151 & 26 & 22.5749237282455 & 3.42507627175453 \tabularnewline
152 & 24 & 23.6474832613484 & 0.352516738651611 \tabularnewline
153 & 29 & 31.6584752151628 & -2.65847521516283 \tabularnewline
154 & 19 & 18.9825826818211 & 0.0174173181788714 \tabularnewline
155 & 24 & 22.0940161795511 & 1.90598382044895 \tabularnewline
156 & 19 & 20.7666852307848 & -1.76668523078483 \tabularnewline
157 & 24 & 23.4401554227186 & 0.559844577281407 \tabularnewline
158 & 22 & 21.8244370969672 & 0.175562903032761 \tabularnewline
159 & 17 & 23.7736200572454 & -6.77362005724542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&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]24[/C][C]23.0236176333508[/C][C]0.976382366649191[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]22.3963921073235[/C][C]2.60360789267647[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]24.2529863072522[/C][C]5.74701369274783[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]20.2962579947228[/C][C]-1.29625799472284[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]20.5386282852832[/C][C]1.46137171471678[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]23.1227812851809[/C][C]-1.12278128518093[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.5736026083869[/C][C]2.42639739161312[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]19.4568594581893[/C][C]3.54314054181066[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]18.7937770436835[/C][C]-1.79377704368349[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]21.669677585173[/C][C]-0.669677585173036[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.8153403514384[/C][C]-3.8153403514384[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.3938848919955[/C][C]-4.39388489199547[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]23.2200335336539[/C][C]-8.22003353365394[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]17.0860954231925[/C][C]-1.08609542319246[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]19.4598225179576[/C][C]3.54017748204243[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]24.0748423820553[/C][C]2.92515761794465[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]21.0019432886847[/C][C]0.998056711315263[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]16.535329320714[/C][C]-2.535329320714[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]24.0873202920243[/C][C]-2.08732029202431[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]24.0090508616482[/C][C]-1.00905086164818[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.5688381779423[/C][C]1.4311618220577[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]24.4545998179461[/C][C]-3.45459981794611[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.4295524471459[/C][C]-3.42955244714589[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]23.8367869289911[/C][C]-5.83678692899107[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]23.1800605324662[/C][C]-3.18006053246622[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.4483525596049[/C][C]0.55164744039509[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.3280787328681[/C][C]1.67192126713187[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]23.3532655211774[/C][C]-4.35326552117744[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.839829931247[/C][C]0.160170068753002[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.5692258736674[/C][C]0.430774126332574[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]25.1649483334544[/C][C]-0.164948333454416[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.2232169775374[/C][C]2.77678302246258[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]22.7002038848751[/C][C]6.29979611512492[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]25.1965595373823[/C][C]6.8034404626177[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]21.5830794177202[/C][C]3.4169205822798[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]24.4099495065135[/C][C]4.59005049348653[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]24.9467056041625[/C][C]3.0532943958375[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]17.1062381095522[/C][C]-0.106238109552165[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]26.0751635208601[/C][C]1.92483647913992[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]22.9430722049405[/C][C]6.05692779505949[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]27.52862185641[/C][C]-1.52862185640998[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]23.4667094578832[/C][C]1.53329054211676[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]19.5469105942641[/C][C]-5.54691059426405[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]22.1055221703104[/C][C]2.89447782968955[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.6780393824329[/C][C]4.32196061756713[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]20.3008968098917[/C][C]-0.300896809891658[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]21.4213727878477[/C][C]-3.42137278784772[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]24.6610362298757[/C][C]7.33896377012431[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]25.0045460489836[/C][C]-0.00454604898359148[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]21.6915513577573[/C][C]3.30844864224273[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]20.8454749613068[/C][C]2.15452503869318[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.1588588066973[/C][C]-1.15885880669729[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]24.0575114630031[/C][C]-4.05751146300312[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]16.5269290283781[/C][C]-1.52692902837805[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]26.717390208988[/C][C]3.28260979101198[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]25.3394958960663[/C][C]-1.33949589606632[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]24.3125491844923[/C][C]1.68745081550774[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]21.7172524612978[/C][C]2.28274753870219[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]21.4941504714309[/C][C]0.505849528569085[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]15.6462203516491[/C][C]-1.64622035164913[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]22.255359520076[/C][C]1.74464047992401[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.9203756085798[/C][C]1.07962439142021[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]23.3536201567194[/C][C]0.646379843280559[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]19.9850555838132[/C][C]4.01494441618681[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]18.5129061642547[/C][C]0.487093835745272[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]26.8208676764294[/C][C]4.17913232357061[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]26.601037521453[/C][C]-4.60103752145304[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]21.4697621005205[/C][C]5.5302378994795[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]17.7359069452234[/C][C]1.26409305477656[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]22.3044035896075[/C][C]2.69559641039245[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]24.9772106374541[/C][C]-4.97721063745406[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]21.502677383273[/C][C]-0.502677383272961[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]27.4823128306108[/C][C]-0.482312830610752[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]24.3803773473337[/C][C]-1.38037734733371[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.6982731831007[/C][C]-0.698273183100738[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]22.2330812773097[/C][C]-2.23308127730969[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]19.2454670687402[/C][C]1.75453293125985[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]22.4516157406899[/C][C]-0.45161574068992[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]23.0320339448358[/C][C]-0.0320339448358368[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]24.0521764435639[/C][C]0.947823556436075[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.4230855798854[/C][C]1.57691442011462[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]23.8630492300674[/C][C]-6.86304923006741[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]21.4391304477228[/C][C]-2.43913044772283[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]23.9709132955503[/C][C]1.02908670444972[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]22.3733623294629[/C][C]-3.37336232946291[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]23.1407040596382[/C][C]-3.14070405963825[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]22.5238707218134[/C][C]3.47612927818658[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]20.8073842775135[/C][C]2.19261572248651[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]24.4361957884407[/C][C]2.56380421155931[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]20.8970807780462[/C][C]-3.8970807780462[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]23.3435280898338[/C][C]-6.3435280898338[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]20.191563551002[/C][C]-1.191563551002[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]19.7459731472925[/C][C]-2.74597314729248[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]22.0165688176436[/C][C]-0.0165688176435829[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]23.5510323826098[/C][C]-2.55103238260975[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]28.6372448399853[/C][C]3.36275516001471[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]24.6834795872242[/C][C]-3.6834795872242[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]24.3266944626333[/C][C]-3.32669446263333[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.259817450436[/C][C]-3.259817450436[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]21.2889468496438[/C][C]-3.28894684964376[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.8395308141606[/C][C]0.160469185839441[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]20.6492902959521[/C][C]-1.64929029595212[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.9576947616757[/C][C]-0.957694761675737[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]22.3255043730554[/C][C]-1.32550437305537[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]23.8525057494042[/C][C]-3.85250574940422[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]18.7741072251434[/C][C]-1.77410722514342[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]20.3006604066273[/C][C]-2.30066040662729[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]20.749679106004[/C][C]-1.74967910600404[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]22.0682765809344[/C][C]-0.0682765809344118[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]18.766441632154[/C][C]-3.76644163215395[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]18.8510893511519[/C][C]-4.8510893511519[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]26.6429142940773[/C][C]-8.64291429407725[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]21.4114487878798[/C][C]2.58855121212023[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]23.5620327119435[/C][C]11.4379672880565[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]19.2163246027188[/C][C]9.78367539728123[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]21.9816944326307[/C][C]-0.981694432630689[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]20.4953208143283[/C][C]4.50467918567166[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]18.4924302963951[/C][C]1.50756970360488[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]23.2253187184531[/C][C]-1.22531871845307[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]16.8359413686559[/C][C]-3.83594136865588[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]23.2142002181326[/C][C]2.78579978186736[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]16.8872223911238[/C][C]0.112777608876169[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]20.0947136369743[/C][C]4.90528636302571[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]20.7722734892362[/C][C]-0.77227348923623[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]18.1285543931859[/C][C]0.871445606814121[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]22.6212243668162[/C][C]-1.62122436681623[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]21.0704752877172[/C][C]0.929524712282782[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]22.7056739110488[/C][C]1.29432608895116[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]22.9951676639565[/C][C]-1.99516766395646[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]25.482195851486[/C][C]0.517804148514022[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]20.5950930960205[/C][C]3.40490690397949[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]20.298739799443[/C][C]-4.29873979944297[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]22.3765319512249[/C][C]0.623468048775057[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.8107987510593[/C][C]-2.81079875105926[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]22.3793138772619[/C][C]-6.37931387726185[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]24.0698543662375[/C][C]1.93014563376249[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]19.0560620045677[/C][C]-0.0560620045676756[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]16.8666199037581[/C][C]4.13338009624188[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]22.2168258710245[/C][C]-1.21682587102449[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]18.5260173770404[/C][C]3.47398262295963[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]19.7340241912749[/C][C]3.26597580872508[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]24.8124059410788[/C][C]4.18759405892122[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]19.2156508743754[/C][C]1.78434912562462[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]19.9639464131369[/C][C]1.03605358686309[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]21.8927879622684[/C][C]1.1072120377316[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]22.996067272599[/C][C]4.00393272740102[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]25.3746950753507[/C][C]-0.37469507535065[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]21.000140913778[/C][C]-0.000140913778045968[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]17.1119616413152[/C][C]-7.11196164131521[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]22.6490195793518[/C][C]-2.64901957935177[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]22.5749237282455[/C][C]3.42507627175453[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.6474832613484[/C][C]0.352516738651611[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]31.6584752151628[/C][C]-2.65847521516283[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]18.9825826818211[/C][C]0.0174173181788714[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]22.0940161795511[/C][C]1.90598382044895[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]20.7666852307848[/C][C]-1.76668523078483[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.4401554227186[/C][C]0.559844577281407[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.8244370969672[/C][C]0.175562903032761[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]23.7736200572454[/C][C]-6.77362005724542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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
12423.02361763335080.976382366649191
22522.39639210732352.60360789267647
33024.25298630725225.74701369274783
41920.2962579947228-1.29625799472284
52220.53862828528321.46137171471678
62223.1227812851809-1.12278128518093
72522.57360260838692.42639739161312
82319.45685945818933.54314054181066
91718.7937770436835-1.79377704368349
102121.669677585173-0.669677585173036
111922.8153403514384-3.8153403514384
121923.3938848919955-4.39388489199547
131523.2200335336539-8.22003353365394
141617.0860954231925-1.08609542319246
152319.45982251795763.54017748204243
162724.07484238205532.92515761794465
172221.00194328868470.998056711315263
181416.535329320714-2.535329320714
192224.0873202920243-2.08732029202431
202324.0090508616482-1.00905086164818
212321.56883817794231.4311618220577
222124.4545998179461-3.45459981794611
231922.4295524471459-3.42955244714589
241823.8367869289911-5.83678692899107
252023.1800605324662-3.18006053246622
262322.44835255960490.55164744039509
272523.32807873286811.67192126713187
281923.3532655211774-4.35326552117744
292423.8398299312470.160170068753002
302221.56922587366740.430774126332574
312525.1649483334544-0.164948333454416
322623.22321697753742.77678302246258
332922.70020388487516.29979611512492
343225.19655953738236.8034404626177
352521.58307941772023.4169205822798
362924.40994950651354.59005049348653
372824.94670560416253.0532943958375
381717.1062381095522-0.106238109552165
392826.07516352086011.92483647913992
402922.94307220494056.05692779505949
412627.52862185641-1.52862185640998
422523.46670945788321.53329054211676
431419.5469105942641-5.54691059426405
442522.10552217031042.89447782968955
452621.67803938243294.32196061756713
462020.3008968098917-0.300896809891658
471821.4213727878477-3.42137278784772
483224.66103622987577.33896377012431
492525.0045460489836-0.00454604898359148
502521.69155135775733.30844864224273
512320.84547496130682.15452503869318
522122.1588588066973-1.15885880669729
532024.0575114630031-4.05751146300312
541516.5269290283781-1.52692902837805
553026.7173902089883.28260979101198
562425.3394958960663-1.33949589606632
572624.31254918449231.68745081550774
582421.71725246129782.28274753870219
592221.49415047143090.505849528569085
601415.6462203516491-1.64622035164913
612422.2553595200761.74464047992401
622422.92037560857981.07962439142021
632423.35362015671940.646379843280559
642419.98505558381324.01494441618681
651918.51290616425470.487093835745272
663126.82086767642944.17913232357061
672226.601037521453-4.60103752145304
682721.46976210052055.5302378994795
691917.73590694522341.26409305477656
702522.30440358960752.69559641039245
712024.9772106374541-4.97721063745406
722121.502677383273-0.502677383272961
732727.4823128306108-0.482312830610752
742324.3803773473337-1.38037734733371
752525.6982731831007-0.698273183100738
762022.2330812773097-2.23308127730969
772119.24546706874021.75453293125985
782222.4516157406899-0.45161574068992
792323.0320339448358-0.0320339448358368
802524.05217644356390.947823556436075
812523.42308557988541.57691442011462
821723.8630492300674-6.86304923006741
831921.4391304477228-2.43913044772283
842523.97091329555031.02908670444972
851922.3733623294629-3.37336232946291
862023.1407040596382-3.14070405963825
872622.52387072181343.47612927818658
882320.80738427751352.19261572248651
892724.43619578844072.56380421155931
901720.8970807780462-3.8970807780462
911723.3435280898338-6.3435280898338
921920.191563551002-1.191563551002
931719.7459731472925-2.74597314729248
942222.0165688176436-0.0165688176435829
952123.5510323826098-2.55103238260975
963228.63724483998533.36275516001471
972124.6834795872242-3.6834795872242
982124.3266944626333-3.32669446263333
991821.259817450436-3.259817450436
1001821.2889468496438-3.28894684964376
1012322.83953081416060.160469185839441
1021920.6492902959521-1.64929029595212
1032020.9576947616757-0.957694761675737
1042122.3255043730554-1.32550437305537
1052023.8525057494042-3.85250574940422
1061718.7741072251434-1.77410722514342
1071820.3006604066273-2.30066040662729
1081920.749679106004-1.74967910600404
1092222.0682765809344-0.0682765809344118
1101518.766441632154-3.76644163215395
1111418.8510893511519-4.8510893511519
1121826.6429142940773-8.64291429407725
1132421.41144878787982.58855121212023
1143523.562032711943511.4379672880565
1152919.21632460271889.78367539728123
1162121.9816944326307-0.981694432630689
1172520.49532081432834.50467918567166
1182018.49243029639511.50756970360488
1192223.2253187184531-1.22531871845307
1201316.8359413686559-3.83594136865588
1212623.21420021813262.78579978186736
1221716.88722239112380.112777608876169
1232520.09471363697434.90528636302571
1242020.7722734892362-0.77227348923623
1251918.12855439318590.871445606814121
1262122.6212243668162-1.62122436681623
1272221.07047528771720.929524712282782
1282422.70567391104881.29432608895116
1292122.9951676639565-1.99516766395646
1302625.4821958514860.517804148514022
1312420.59509309602053.40490690397949
1321620.298739799443-4.29873979944297
1332322.37653195122490.623468048775057
1341820.8107987510593-2.81079875105926
1351622.3793138772619-6.37931387726185
1362624.06985436623751.93014563376249
1371919.0560620045677-0.0560620045676756
1382116.86661990375814.13338009624188
1392122.2168258710245-1.21682587102449
1402218.52601737704043.47398262295963
1412319.73402419127493.26597580872508
1422924.81240594107884.18759405892122
1432119.21565087437541.78434912562462
1442119.96394641313691.03605358686309
1452321.89278796226841.1072120377316
1462722.9960672725994.00393272740102
1472525.3746950753507-0.37469507535065
1482121.000140913778-0.000140913778045968
1491017.1119616413152-7.11196164131521
1502022.6490195793518-2.64901957935177
1512622.57492372824553.42507627175453
1522423.64748326134840.352516738651611
1532931.6584752151628-2.65847521516283
1541918.98258268182110.0174173181788714
1552422.09401617955111.90598382044895
1561920.7666852307848-1.76668523078483
1572423.44015542271860.559844577281407
1582221.82443709696720.175562903032761
1591723.7736200572454-6.77362005724542







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3031671748716910.6063343497433810.696832825128309
100.1832719061613780.3665438123227550.816728093838622
110.3389657526651670.6779315053303340.661034247334833
120.2896088181375320.5792176362750640.710391181862468
130.8660476345142660.2679047309714680.133952365485734
140.8049343753023460.3901312493953070.195065624697654
150.7599021021059230.4801957957881550.240097897894077
160.697170058283160.605659883433680.30282994171684
170.6192827515251910.7614344969496170.380717248474809
180.5910386873954530.8179226252090950.408961312604547
190.5240253891279140.9519492217441710.475974610872086
200.5182040233470260.9635919533059470.481795976652974
210.4978243196694550.9956486393389110.502175680330545
220.4308793698654340.8617587397308670.569120630134566
230.3858946773320640.7717893546641280.614105322667936
240.434994510379910.8699890207598210.56500548962009
250.4477210430820560.8954420861641110.552278956917944
260.3796057155477920.7592114310955840.620394284452208
270.3292817477595950.6585634955191890.670718252240405
280.3452913229534790.6905826459069570.654708677046521
290.2916634513073030.5833269026146070.708336548692697
300.2385028724348370.4770057448696730.761497127565163
310.1919525543454280.3839051086908570.808047445654572
320.2121741136405950.4243482272811890.787825886359405
330.3620847025213470.7241694050426950.637915297478653
340.5966832722808640.8066334554382720.403316727719136
350.5738366538928480.8523266922143040.426163346107152
360.6339974668983340.7320050662033310.366002533101666
370.6250464751355140.7499070497289720.374953524864486
380.5812071018714420.8375857962571170.418792898128558
390.549265039489190.901469921021620.45073496051081
400.6340149807260250.731970038547950.365985019273975
410.6026968069944010.7946063860111990.397303193005599
420.5574937840280790.8850124319438430.442506215971921
430.6440455231616670.7119089536766660.355954476838333
440.6160009972083780.7679980055832440.383999002791622
450.6392042044274110.7215915911451780.360795795572589
460.5897039664504020.8205920670991960.410296033549598
470.5804433493207140.8391133013585710.419556650679286
480.6916894681453150.616621063709370.308310531854685
490.6504936064723960.6990127870552090.349506393527605
500.6396856720783830.7206286558432350.360314327921617
510.6011762085280550.797647582943890.398823791471945
520.5602233061220130.8795533877559740.439776693877987
530.5965480101363390.8069039797273220.403451989863661
540.563409560023510.8731808799529810.43659043997649
550.61489342470360.7702131505927990.3851065752964
560.5818554444291550.836289111141690.418144555570845
570.5421330768352510.9157338463294980.457866923164749
580.5110416534641810.9779166930716390.488958346535819
590.4639671439414380.9279342878828750.536032856058562
600.4227622766070280.8455245532140560.577237723392972
610.3898561495725370.7797122991450740.610143850427463
620.3500535873542720.7001071747085440.649946412645728
630.3092759614041220.6185519228082450.690724038595878
640.3422148706163030.6844297412326060.657785129383697
650.3007893406112010.6015786812224020.699210659388799
660.3146221898098260.6292443796196510.685377810190174
670.3767222998931580.7534445997863150.623277700106842
680.453735810270280.907471620540560.54626418972972
690.4151735985958460.8303471971916920.584826401404154
700.3989624309001420.7979248618002850.601037569099858
710.4658051335482980.9316102670965960.534194866451702
720.4205472185147670.8410944370295350.579452781485233
730.3794200836156610.7588401672313220.620579916384339
740.3439879474214350.6879758948428690.656012052578565
750.3119416427304130.6238832854608260.688058357269587
760.2883223227826730.5766446455653460.711677677217327
770.2657600541081590.5315201082163180.734239945891841
780.2299636642113290.4599273284226580.770036335788671
790.1976096247202450.3952192494404910.802390375279755
800.1708897029030080.3417794058060160.829110297096992
810.1499277119968980.2998554239937960.850072288003102
820.24395879877380.48791759754760.7560412012262
830.2257696281323710.4515392562647410.774230371867629
840.1966305245031690.3932610490063390.803369475496831
850.1986355252872070.3972710505744140.801364474712793
860.1941090879224530.3882181758449060.805890912077547
870.2015667567121540.4031335134243090.798433243287846
880.1884416088080550.376883217616110.811558391191945
890.1762940990469140.3525881980938270.823705900953086
900.181308594520280.3626171890405610.81869140547972
910.2585526515285150.5171053030570290.741447348471486
920.2242277450197680.4484554900395350.775772254980232
930.207472771899060.414945543798120.79252722810094
940.1766470053708130.3532940107416250.823352994629187
950.1618605312911050.323721062582210.838139468708895
960.1655959476425470.3311918952850940.834404052357453
970.1673739261214720.3347478522429440.832626073878528
980.1634576383143220.3269152766286440.836542361685678
990.1563495215565740.3126990431131480.843650478443426
1000.1511986693387110.3023973386774220.848801330661289
1010.1248112791346940.2496225582693880.875188720865306
1020.1046907682744890.2093815365489770.895309231725511
1030.08483471781537450.1696694356307490.915165282184625
1040.06916769519316890.1383353903863380.930832304806831
1050.07399200919051750.1479840183810350.926007990809482
1060.06100521051733480.122010421034670.938994789482665
1070.05390697383997740.1078139476799550.946093026160023
1080.04722096954841710.09444193909683420.952779030451583
1090.03639850899195870.07279701798391740.963601491008041
1100.03589925779670310.07179851559340620.964100742203297
1110.0452125173350230.0904250346700460.954787482664977
1120.1809556831836490.3619113663672980.819044316816351
1130.1650544506177970.3301089012355940.834945549382203
1140.5973072309482380.8053855381035240.402692769051762
1150.8745101486783280.2509797026433430.125489851321672
1160.8449794584935530.3100410830128940.155020541506447
1170.8946327136175350.2107345727649290.105367286382465
1180.8727944380000390.2544111239999210.127205561999961
1190.8476895587960550.304620882407890.152310441203945
1200.8779717084798720.2440565830402570.122028291520128
1210.8576716573844740.2846566852310520.142328342615526
1220.8219494008906320.3561011982187360.178050599109368
1230.8504007252332240.2991985495335530.149599274766776
1240.8127759558628560.3744480882742870.187224044137144
1250.777274978352170.4454500432956610.22272502164783
1260.7328483037800980.5343033924398040.267151696219902
1270.6990319788557970.6019360422884070.300968021144203
1280.6596255452167970.6807489095664070.340374454783203
1290.6039978115214540.7920043769570920.396002188478546
1300.5426180058472160.9147639883055680.457381994152784
1310.5413969713934630.9172060572130730.458603028606537
1320.5415073068328980.9169853863342040.458492693167102
1330.4920184220027190.9840368440054390.507981577997281
1340.4442985071129760.8885970142259520.555701492887024
1350.5968431137570330.8063137724859340.403156886242967
1360.5602255371199120.8795489257601760.439774462880088
1370.4880603972503470.9761207945006940.511939602749653
1380.5004657470716310.9990685058567380.499534252928369
1390.4286773557970480.8573547115940960.571322644202952
1400.3923947448995880.7847894897991770.607605255100412
1410.3578991219123230.7157982438246470.642100878087677
1420.4175660577931260.8351321155862520.582433942206874
1430.5709504908183460.8580990183633070.429049509181654
1440.4976619352808620.9953238705617240.502338064719138
1450.416814948413820.8336298968276410.58318505158618
1460.4119343683938630.8238687367877260.588065631606137
1470.3604609236692450.7209218473384910.639539076330754
1480.2510712087408650.5021424174817310.748928791259135
1490.4565674196640470.9131348393280940.543432580335953
1500.3855213410646060.7710426821292120.614478658935394

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.303167174871691 & 0.606334349743381 & 0.696832825128309 \tabularnewline
10 & 0.183271906161378 & 0.366543812322755 & 0.816728093838622 \tabularnewline
11 & 0.338965752665167 & 0.677931505330334 & 0.661034247334833 \tabularnewline
12 & 0.289608818137532 & 0.579217636275064 & 0.710391181862468 \tabularnewline
13 & 0.866047634514266 & 0.267904730971468 & 0.133952365485734 \tabularnewline
14 & 0.804934375302346 & 0.390131249395307 & 0.195065624697654 \tabularnewline
15 & 0.759902102105923 & 0.480195795788155 & 0.240097897894077 \tabularnewline
16 & 0.69717005828316 & 0.60565988343368 & 0.30282994171684 \tabularnewline
17 & 0.619282751525191 & 0.761434496949617 & 0.380717248474809 \tabularnewline
18 & 0.591038687395453 & 0.817922625209095 & 0.408961312604547 \tabularnewline
19 & 0.524025389127914 & 0.951949221744171 & 0.475974610872086 \tabularnewline
20 & 0.518204023347026 & 0.963591953305947 & 0.481795976652974 \tabularnewline
21 & 0.497824319669455 & 0.995648639338911 & 0.502175680330545 \tabularnewline
22 & 0.430879369865434 & 0.861758739730867 & 0.569120630134566 \tabularnewline
23 & 0.385894677332064 & 0.771789354664128 & 0.614105322667936 \tabularnewline
24 & 0.43499451037991 & 0.869989020759821 & 0.56500548962009 \tabularnewline
25 & 0.447721043082056 & 0.895442086164111 & 0.552278956917944 \tabularnewline
26 & 0.379605715547792 & 0.759211431095584 & 0.620394284452208 \tabularnewline
27 & 0.329281747759595 & 0.658563495519189 & 0.670718252240405 \tabularnewline
28 & 0.345291322953479 & 0.690582645906957 & 0.654708677046521 \tabularnewline
29 & 0.291663451307303 & 0.583326902614607 & 0.708336548692697 \tabularnewline
30 & 0.238502872434837 & 0.477005744869673 & 0.761497127565163 \tabularnewline
31 & 0.191952554345428 & 0.383905108690857 & 0.808047445654572 \tabularnewline
32 & 0.212174113640595 & 0.424348227281189 & 0.787825886359405 \tabularnewline
33 & 0.362084702521347 & 0.724169405042695 & 0.637915297478653 \tabularnewline
34 & 0.596683272280864 & 0.806633455438272 & 0.403316727719136 \tabularnewline
35 & 0.573836653892848 & 0.852326692214304 & 0.426163346107152 \tabularnewline
36 & 0.633997466898334 & 0.732005066203331 & 0.366002533101666 \tabularnewline
37 & 0.625046475135514 & 0.749907049728972 & 0.374953524864486 \tabularnewline
38 & 0.581207101871442 & 0.837585796257117 & 0.418792898128558 \tabularnewline
39 & 0.54926503948919 & 0.90146992102162 & 0.45073496051081 \tabularnewline
40 & 0.634014980726025 & 0.73197003854795 & 0.365985019273975 \tabularnewline
41 & 0.602696806994401 & 0.794606386011199 & 0.397303193005599 \tabularnewline
42 & 0.557493784028079 & 0.885012431943843 & 0.442506215971921 \tabularnewline
43 & 0.644045523161667 & 0.711908953676666 & 0.355954476838333 \tabularnewline
44 & 0.616000997208378 & 0.767998005583244 & 0.383999002791622 \tabularnewline
45 & 0.639204204427411 & 0.721591591145178 & 0.360795795572589 \tabularnewline
46 & 0.589703966450402 & 0.820592067099196 & 0.410296033549598 \tabularnewline
47 & 0.580443349320714 & 0.839113301358571 & 0.419556650679286 \tabularnewline
48 & 0.691689468145315 & 0.61662106370937 & 0.308310531854685 \tabularnewline
49 & 0.650493606472396 & 0.699012787055209 & 0.349506393527605 \tabularnewline
50 & 0.639685672078383 & 0.720628655843235 & 0.360314327921617 \tabularnewline
51 & 0.601176208528055 & 0.79764758294389 & 0.398823791471945 \tabularnewline
52 & 0.560223306122013 & 0.879553387755974 & 0.439776693877987 \tabularnewline
53 & 0.596548010136339 & 0.806903979727322 & 0.403451989863661 \tabularnewline
54 & 0.56340956002351 & 0.873180879952981 & 0.43659043997649 \tabularnewline
55 & 0.6148934247036 & 0.770213150592799 & 0.3851065752964 \tabularnewline
56 & 0.581855444429155 & 0.83628911114169 & 0.418144555570845 \tabularnewline
57 & 0.542133076835251 & 0.915733846329498 & 0.457866923164749 \tabularnewline
58 & 0.511041653464181 & 0.977916693071639 & 0.488958346535819 \tabularnewline
59 & 0.463967143941438 & 0.927934287882875 & 0.536032856058562 \tabularnewline
60 & 0.422762276607028 & 0.845524553214056 & 0.577237723392972 \tabularnewline
61 & 0.389856149572537 & 0.779712299145074 & 0.610143850427463 \tabularnewline
62 & 0.350053587354272 & 0.700107174708544 & 0.649946412645728 \tabularnewline
63 & 0.309275961404122 & 0.618551922808245 & 0.690724038595878 \tabularnewline
64 & 0.342214870616303 & 0.684429741232606 & 0.657785129383697 \tabularnewline
65 & 0.300789340611201 & 0.601578681222402 & 0.699210659388799 \tabularnewline
66 & 0.314622189809826 & 0.629244379619651 & 0.685377810190174 \tabularnewline
67 & 0.376722299893158 & 0.753444599786315 & 0.623277700106842 \tabularnewline
68 & 0.45373581027028 & 0.90747162054056 & 0.54626418972972 \tabularnewline
69 & 0.415173598595846 & 0.830347197191692 & 0.584826401404154 \tabularnewline
70 & 0.398962430900142 & 0.797924861800285 & 0.601037569099858 \tabularnewline
71 & 0.465805133548298 & 0.931610267096596 & 0.534194866451702 \tabularnewline
72 & 0.420547218514767 & 0.841094437029535 & 0.579452781485233 \tabularnewline
73 & 0.379420083615661 & 0.758840167231322 & 0.620579916384339 \tabularnewline
74 & 0.343987947421435 & 0.687975894842869 & 0.656012052578565 \tabularnewline
75 & 0.311941642730413 & 0.623883285460826 & 0.688058357269587 \tabularnewline
76 & 0.288322322782673 & 0.576644645565346 & 0.711677677217327 \tabularnewline
77 & 0.265760054108159 & 0.531520108216318 & 0.734239945891841 \tabularnewline
78 & 0.229963664211329 & 0.459927328422658 & 0.770036335788671 \tabularnewline
79 & 0.197609624720245 & 0.395219249440491 & 0.802390375279755 \tabularnewline
80 & 0.170889702903008 & 0.341779405806016 & 0.829110297096992 \tabularnewline
81 & 0.149927711996898 & 0.299855423993796 & 0.850072288003102 \tabularnewline
82 & 0.2439587987738 & 0.4879175975476 & 0.7560412012262 \tabularnewline
83 & 0.225769628132371 & 0.451539256264741 & 0.774230371867629 \tabularnewline
84 & 0.196630524503169 & 0.393261049006339 & 0.803369475496831 \tabularnewline
85 & 0.198635525287207 & 0.397271050574414 & 0.801364474712793 \tabularnewline
86 & 0.194109087922453 & 0.388218175844906 & 0.805890912077547 \tabularnewline
87 & 0.201566756712154 & 0.403133513424309 & 0.798433243287846 \tabularnewline
88 & 0.188441608808055 & 0.37688321761611 & 0.811558391191945 \tabularnewline
89 & 0.176294099046914 & 0.352588198093827 & 0.823705900953086 \tabularnewline
90 & 0.18130859452028 & 0.362617189040561 & 0.81869140547972 \tabularnewline
91 & 0.258552651528515 & 0.517105303057029 & 0.741447348471486 \tabularnewline
92 & 0.224227745019768 & 0.448455490039535 & 0.775772254980232 \tabularnewline
93 & 0.20747277189906 & 0.41494554379812 & 0.79252722810094 \tabularnewline
94 & 0.176647005370813 & 0.353294010741625 & 0.823352994629187 \tabularnewline
95 & 0.161860531291105 & 0.32372106258221 & 0.838139468708895 \tabularnewline
96 & 0.165595947642547 & 0.331191895285094 & 0.834404052357453 \tabularnewline
97 & 0.167373926121472 & 0.334747852242944 & 0.832626073878528 \tabularnewline
98 & 0.163457638314322 & 0.326915276628644 & 0.836542361685678 \tabularnewline
99 & 0.156349521556574 & 0.312699043113148 & 0.843650478443426 \tabularnewline
100 & 0.151198669338711 & 0.302397338677422 & 0.848801330661289 \tabularnewline
101 & 0.124811279134694 & 0.249622558269388 & 0.875188720865306 \tabularnewline
102 & 0.104690768274489 & 0.209381536548977 & 0.895309231725511 \tabularnewline
103 & 0.0848347178153745 & 0.169669435630749 & 0.915165282184625 \tabularnewline
104 & 0.0691676951931689 & 0.138335390386338 & 0.930832304806831 \tabularnewline
105 & 0.0739920091905175 & 0.147984018381035 & 0.926007990809482 \tabularnewline
106 & 0.0610052105173348 & 0.12201042103467 & 0.938994789482665 \tabularnewline
107 & 0.0539069738399774 & 0.107813947679955 & 0.946093026160023 \tabularnewline
108 & 0.0472209695484171 & 0.0944419390968342 & 0.952779030451583 \tabularnewline
109 & 0.0363985089919587 & 0.0727970179839174 & 0.963601491008041 \tabularnewline
110 & 0.0358992577967031 & 0.0717985155934062 & 0.964100742203297 \tabularnewline
111 & 0.045212517335023 & 0.090425034670046 & 0.954787482664977 \tabularnewline
112 & 0.180955683183649 & 0.361911366367298 & 0.819044316816351 \tabularnewline
113 & 0.165054450617797 & 0.330108901235594 & 0.834945549382203 \tabularnewline
114 & 0.597307230948238 & 0.805385538103524 & 0.402692769051762 \tabularnewline
115 & 0.874510148678328 & 0.250979702643343 & 0.125489851321672 \tabularnewline
116 & 0.844979458493553 & 0.310041083012894 & 0.155020541506447 \tabularnewline
117 & 0.894632713617535 & 0.210734572764929 & 0.105367286382465 \tabularnewline
118 & 0.872794438000039 & 0.254411123999921 & 0.127205561999961 \tabularnewline
119 & 0.847689558796055 & 0.30462088240789 & 0.152310441203945 \tabularnewline
120 & 0.877971708479872 & 0.244056583040257 & 0.122028291520128 \tabularnewline
121 & 0.857671657384474 & 0.284656685231052 & 0.142328342615526 \tabularnewline
122 & 0.821949400890632 & 0.356101198218736 & 0.178050599109368 \tabularnewline
123 & 0.850400725233224 & 0.299198549533553 & 0.149599274766776 \tabularnewline
124 & 0.812775955862856 & 0.374448088274287 & 0.187224044137144 \tabularnewline
125 & 0.77727497835217 & 0.445450043295661 & 0.22272502164783 \tabularnewline
126 & 0.732848303780098 & 0.534303392439804 & 0.267151696219902 \tabularnewline
127 & 0.699031978855797 & 0.601936042288407 & 0.300968021144203 \tabularnewline
128 & 0.659625545216797 & 0.680748909566407 & 0.340374454783203 \tabularnewline
129 & 0.603997811521454 & 0.792004376957092 & 0.396002188478546 \tabularnewline
130 & 0.542618005847216 & 0.914763988305568 & 0.457381994152784 \tabularnewline
131 & 0.541396971393463 & 0.917206057213073 & 0.458603028606537 \tabularnewline
132 & 0.541507306832898 & 0.916985386334204 & 0.458492693167102 \tabularnewline
133 & 0.492018422002719 & 0.984036844005439 & 0.507981577997281 \tabularnewline
134 & 0.444298507112976 & 0.888597014225952 & 0.555701492887024 \tabularnewline
135 & 0.596843113757033 & 0.806313772485934 & 0.403156886242967 \tabularnewline
136 & 0.560225537119912 & 0.879548925760176 & 0.439774462880088 \tabularnewline
137 & 0.488060397250347 & 0.976120794500694 & 0.511939602749653 \tabularnewline
138 & 0.500465747071631 & 0.999068505856738 & 0.499534252928369 \tabularnewline
139 & 0.428677355797048 & 0.857354711594096 & 0.571322644202952 \tabularnewline
140 & 0.392394744899588 & 0.784789489799177 & 0.607605255100412 \tabularnewline
141 & 0.357899121912323 & 0.715798243824647 & 0.642100878087677 \tabularnewline
142 & 0.417566057793126 & 0.835132115586252 & 0.582433942206874 \tabularnewline
143 & 0.570950490818346 & 0.858099018363307 & 0.429049509181654 \tabularnewline
144 & 0.497661935280862 & 0.995323870561724 & 0.502338064719138 \tabularnewline
145 & 0.41681494841382 & 0.833629896827641 & 0.58318505158618 \tabularnewline
146 & 0.411934368393863 & 0.823868736787726 & 0.588065631606137 \tabularnewline
147 & 0.360460923669245 & 0.720921847338491 & 0.639539076330754 \tabularnewline
148 & 0.251071208740865 & 0.502142417481731 & 0.748928791259135 \tabularnewline
149 & 0.456567419664047 & 0.913134839328094 & 0.543432580335953 \tabularnewline
150 & 0.385521341064606 & 0.771042682129212 & 0.614478658935394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.303167174871691[/C][C]0.606334349743381[/C][C]0.696832825128309[/C][/ROW]
[ROW][C]10[/C][C]0.183271906161378[/C][C]0.366543812322755[/C][C]0.816728093838622[/C][/ROW]
[ROW][C]11[/C][C]0.338965752665167[/C][C]0.677931505330334[/C][C]0.661034247334833[/C][/ROW]
[ROW][C]12[/C][C]0.289608818137532[/C][C]0.579217636275064[/C][C]0.710391181862468[/C][/ROW]
[ROW][C]13[/C][C]0.866047634514266[/C][C]0.267904730971468[/C][C]0.133952365485734[/C][/ROW]
[ROW][C]14[/C][C]0.804934375302346[/C][C]0.390131249395307[/C][C]0.195065624697654[/C][/ROW]
[ROW][C]15[/C][C]0.759902102105923[/C][C]0.480195795788155[/C][C]0.240097897894077[/C][/ROW]
[ROW][C]16[/C][C]0.69717005828316[/C][C]0.60565988343368[/C][C]0.30282994171684[/C][/ROW]
[ROW][C]17[/C][C]0.619282751525191[/C][C]0.761434496949617[/C][C]0.380717248474809[/C][/ROW]
[ROW][C]18[/C][C]0.591038687395453[/C][C]0.817922625209095[/C][C]0.408961312604547[/C][/ROW]
[ROW][C]19[/C][C]0.524025389127914[/C][C]0.951949221744171[/C][C]0.475974610872086[/C][/ROW]
[ROW][C]20[/C][C]0.518204023347026[/C][C]0.963591953305947[/C][C]0.481795976652974[/C][/ROW]
[ROW][C]21[/C][C]0.497824319669455[/C][C]0.995648639338911[/C][C]0.502175680330545[/C][/ROW]
[ROW][C]22[/C][C]0.430879369865434[/C][C]0.861758739730867[/C][C]0.569120630134566[/C][/ROW]
[ROW][C]23[/C][C]0.385894677332064[/C][C]0.771789354664128[/C][C]0.614105322667936[/C][/ROW]
[ROW][C]24[/C][C]0.43499451037991[/C][C]0.869989020759821[/C][C]0.56500548962009[/C][/ROW]
[ROW][C]25[/C][C]0.447721043082056[/C][C]0.895442086164111[/C][C]0.552278956917944[/C][/ROW]
[ROW][C]26[/C][C]0.379605715547792[/C][C]0.759211431095584[/C][C]0.620394284452208[/C][/ROW]
[ROW][C]27[/C][C]0.329281747759595[/C][C]0.658563495519189[/C][C]0.670718252240405[/C][/ROW]
[ROW][C]28[/C][C]0.345291322953479[/C][C]0.690582645906957[/C][C]0.654708677046521[/C][/ROW]
[ROW][C]29[/C][C]0.291663451307303[/C][C]0.583326902614607[/C][C]0.708336548692697[/C][/ROW]
[ROW][C]30[/C][C]0.238502872434837[/C][C]0.477005744869673[/C][C]0.761497127565163[/C][/ROW]
[ROW][C]31[/C][C]0.191952554345428[/C][C]0.383905108690857[/C][C]0.808047445654572[/C][/ROW]
[ROW][C]32[/C][C]0.212174113640595[/C][C]0.424348227281189[/C][C]0.787825886359405[/C][/ROW]
[ROW][C]33[/C][C]0.362084702521347[/C][C]0.724169405042695[/C][C]0.637915297478653[/C][/ROW]
[ROW][C]34[/C][C]0.596683272280864[/C][C]0.806633455438272[/C][C]0.403316727719136[/C][/ROW]
[ROW][C]35[/C][C]0.573836653892848[/C][C]0.852326692214304[/C][C]0.426163346107152[/C][/ROW]
[ROW][C]36[/C][C]0.633997466898334[/C][C]0.732005066203331[/C][C]0.366002533101666[/C][/ROW]
[ROW][C]37[/C][C]0.625046475135514[/C][C]0.749907049728972[/C][C]0.374953524864486[/C][/ROW]
[ROW][C]38[/C][C]0.581207101871442[/C][C]0.837585796257117[/C][C]0.418792898128558[/C][/ROW]
[ROW][C]39[/C][C]0.54926503948919[/C][C]0.90146992102162[/C][C]0.45073496051081[/C][/ROW]
[ROW][C]40[/C][C]0.634014980726025[/C][C]0.73197003854795[/C][C]0.365985019273975[/C][/ROW]
[ROW][C]41[/C][C]0.602696806994401[/C][C]0.794606386011199[/C][C]0.397303193005599[/C][/ROW]
[ROW][C]42[/C][C]0.557493784028079[/C][C]0.885012431943843[/C][C]0.442506215971921[/C][/ROW]
[ROW][C]43[/C][C]0.644045523161667[/C][C]0.711908953676666[/C][C]0.355954476838333[/C][/ROW]
[ROW][C]44[/C][C]0.616000997208378[/C][C]0.767998005583244[/C][C]0.383999002791622[/C][/ROW]
[ROW][C]45[/C][C]0.639204204427411[/C][C]0.721591591145178[/C][C]0.360795795572589[/C][/ROW]
[ROW][C]46[/C][C]0.589703966450402[/C][C]0.820592067099196[/C][C]0.410296033549598[/C][/ROW]
[ROW][C]47[/C][C]0.580443349320714[/C][C]0.839113301358571[/C][C]0.419556650679286[/C][/ROW]
[ROW][C]48[/C][C]0.691689468145315[/C][C]0.61662106370937[/C][C]0.308310531854685[/C][/ROW]
[ROW][C]49[/C][C]0.650493606472396[/C][C]0.699012787055209[/C][C]0.349506393527605[/C][/ROW]
[ROW][C]50[/C][C]0.639685672078383[/C][C]0.720628655843235[/C][C]0.360314327921617[/C][/ROW]
[ROW][C]51[/C][C]0.601176208528055[/C][C]0.79764758294389[/C][C]0.398823791471945[/C][/ROW]
[ROW][C]52[/C][C]0.560223306122013[/C][C]0.879553387755974[/C][C]0.439776693877987[/C][/ROW]
[ROW][C]53[/C][C]0.596548010136339[/C][C]0.806903979727322[/C][C]0.403451989863661[/C][/ROW]
[ROW][C]54[/C][C]0.56340956002351[/C][C]0.873180879952981[/C][C]0.43659043997649[/C][/ROW]
[ROW][C]55[/C][C]0.6148934247036[/C][C]0.770213150592799[/C][C]0.3851065752964[/C][/ROW]
[ROW][C]56[/C][C]0.581855444429155[/C][C]0.83628911114169[/C][C]0.418144555570845[/C][/ROW]
[ROW][C]57[/C][C]0.542133076835251[/C][C]0.915733846329498[/C][C]0.457866923164749[/C][/ROW]
[ROW][C]58[/C][C]0.511041653464181[/C][C]0.977916693071639[/C][C]0.488958346535819[/C][/ROW]
[ROW][C]59[/C][C]0.463967143941438[/C][C]0.927934287882875[/C][C]0.536032856058562[/C][/ROW]
[ROW][C]60[/C][C]0.422762276607028[/C][C]0.845524553214056[/C][C]0.577237723392972[/C][/ROW]
[ROW][C]61[/C][C]0.389856149572537[/C][C]0.779712299145074[/C][C]0.610143850427463[/C][/ROW]
[ROW][C]62[/C][C]0.350053587354272[/C][C]0.700107174708544[/C][C]0.649946412645728[/C][/ROW]
[ROW][C]63[/C][C]0.309275961404122[/C][C]0.618551922808245[/C][C]0.690724038595878[/C][/ROW]
[ROW][C]64[/C][C]0.342214870616303[/C][C]0.684429741232606[/C][C]0.657785129383697[/C][/ROW]
[ROW][C]65[/C][C]0.300789340611201[/C][C]0.601578681222402[/C][C]0.699210659388799[/C][/ROW]
[ROW][C]66[/C][C]0.314622189809826[/C][C]0.629244379619651[/C][C]0.685377810190174[/C][/ROW]
[ROW][C]67[/C][C]0.376722299893158[/C][C]0.753444599786315[/C][C]0.623277700106842[/C][/ROW]
[ROW][C]68[/C][C]0.45373581027028[/C][C]0.90747162054056[/C][C]0.54626418972972[/C][/ROW]
[ROW][C]69[/C][C]0.415173598595846[/C][C]0.830347197191692[/C][C]0.584826401404154[/C][/ROW]
[ROW][C]70[/C][C]0.398962430900142[/C][C]0.797924861800285[/C][C]0.601037569099858[/C][/ROW]
[ROW][C]71[/C][C]0.465805133548298[/C][C]0.931610267096596[/C][C]0.534194866451702[/C][/ROW]
[ROW][C]72[/C][C]0.420547218514767[/C][C]0.841094437029535[/C][C]0.579452781485233[/C][/ROW]
[ROW][C]73[/C][C]0.379420083615661[/C][C]0.758840167231322[/C][C]0.620579916384339[/C][/ROW]
[ROW][C]74[/C][C]0.343987947421435[/C][C]0.687975894842869[/C][C]0.656012052578565[/C][/ROW]
[ROW][C]75[/C][C]0.311941642730413[/C][C]0.623883285460826[/C][C]0.688058357269587[/C][/ROW]
[ROW][C]76[/C][C]0.288322322782673[/C][C]0.576644645565346[/C][C]0.711677677217327[/C][/ROW]
[ROW][C]77[/C][C]0.265760054108159[/C][C]0.531520108216318[/C][C]0.734239945891841[/C][/ROW]
[ROW][C]78[/C][C]0.229963664211329[/C][C]0.459927328422658[/C][C]0.770036335788671[/C][/ROW]
[ROW][C]79[/C][C]0.197609624720245[/C][C]0.395219249440491[/C][C]0.802390375279755[/C][/ROW]
[ROW][C]80[/C][C]0.170889702903008[/C][C]0.341779405806016[/C][C]0.829110297096992[/C][/ROW]
[ROW][C]81[/C][C]0.149927711996898[/C][C]0.299855423993796[/C][C]0.850072288003102[/C][/ROW]
[ROW][C]82[/C][C]0.2439587987738[/C][C]0.4879175975476[/C][C]0.7560412012262[/C][/ROW]
[ROW][C]83[/C][C]0.225769628132371[/C][C]0.451539256264741[/C][C]0.774230371867629[/C][/ROW]
[ROW][C]84[/C][C]0.196630524503169[/C][C]0.393261049006339[/C][C]0.803369475496831[/C][/ROW]
[ROW][C]85[/C][C]0.198635525287207[/C][C]0.397271050574414[/C][C]0.801364474712793[/C][/ROW]
[ROW][C]86[/C][C]0.194109087922453[/C][C]0.388218175844906[/C][C]0.805890912077547[/C][/ROW]
[ROW][C]87[/C][C]0.201566756712154[/C][C]0.403133513424309[/C][C]0.798433243287846[/C][/ROW]
[ROW][C]88[/C][C]0.188441608808055[/C][C]0.37688321761611[/C][C]0.811558391191945[/C][/ROW]
[ROW][C]89[/C][C]0.176294099046914[/C][C]0.352588198093827[/C][C]0.823705900953086[/C][/ROW]
[ROW][C]90[/C][C]0.18130859452028[/C][C]0.362617189040561[/C][C]0.81869140547972[/C][/ROW]
[ROW][C]91[/C][C]0.258552651528515[/C][C]0.517105303057029[/C][C]0.741447348471486[/C][/ROW]
[ROW][C]92[/C][C]0.224227745019768[/C][C]0.448455490039535[/C][C]0.775772254980232[/C][/ROW]
[ROW][C]93[/C][C]0.20747277189906[/C][C]0.41494554379812[/C][C]0.79252722810094[/C][/ROW]
[ROW][C]94[/C][C]0.176647005370813[/C][C]0.353294010741625[/C][C]0.823352994629187[/C][/ROW]
[ROW][C]95[/C][C]0.161860531291105[/C][C]0.32372106258221[/C][C]0.838139468708895[/C][/ROW]
[ROW][C]96[/C][C]0.165595947642547[/C][C]0.331191895285094[/C][C]0.834404052357453[/C][/ROW]
[ROW][C]97[/C][C]0.167373926121472[/C][C]0.334747852242944[/C][C]0.832626073878528[/C][/ROW]
[ROW][C]98[/C][C]0.163457638314322[/C][C]0.326915276628644[/C][C]0.836542361685678[/C][/ROW]
[ROW][C]99[/C][C]0.156349521556574[/C][C]0.312699043113148[/C][C]0.843650478443426[/C][/ROW]
[ROW][C]100[/C][C]0.151198669338711[/C][C]0.302397338677422[/C][C]0.848801330661289[/C][/ROW]
[ROW][C]101[/C][C]0.124811279134694[/C][C]0.249622558269388[/C][C]0.875188720865306[/C][/ROW]
[ROW][C]102[/C][C]0.104690768274489[/C][C]0.209381536548977[/C][C]0.895309231725511[/C][/ROW]
[ROW][C]103[/C][C]0.0848347178153745[/C][C]0.169669435630749[/C][C]0.915165282184625[/C][/ROW]
[ROW][C]104[/C][C]0.0691676951931689[/C][C]0.138335390386338[/C][C]0.930832304806831[/C][/ROW]
[ROW][C]105[/C][C]0.0739920091905175[/C][C]0.147984018381035[/C][C]0.926007990809482[/C][/ROW]
[ROW][C]106[/C][C]0.0610052105173348[/C][C]0.12201042103467[/C][C]0.938994789482665[/C][/ROW]
[ROW][C]107[/C][C]0.0539069738399774[/C][C]0.107813947679955[/C][C]0.946093026160023[/C][/ROW]
[ROW][C]108[/C][C]0.0472209695484171[/C][C]0.0944419390968342[/C][C]0.952779030451583[/C][/ROW]
[ROW][C]109[/C][C]0.0363985089919587[/C][C]0.0727970179839174[/C][C]0.963601491008041[/C][/ROW]
[ROW][C]110[/C][C]0.0358992577967031[/C][C]0.0717985155934062[/C][C]0.964100742203297[/C][/ROW]
[ROW][C]111[/C][C]0.045212517335023[/C][C]0.090425034670046[/C][C]0.954787482664977[/C][/ROW]
[ROW][C]112[/C][C]0.180955683183649[/C][C]0.361911366367298[/C][C]0.819044316816351[/C][/ROW]
[ROW][C]113[/C][C]0.165054450617797[/C][C]0.330108901235594[/C][C]0.834945549382203[/C][/ROW]
[ROW][C]114[/C][C]0.597307230948238[/C][C]0.805385538103524[/C][C]0.402692769051762[/C][/ROW]
[ROW][C]115[/C][C]0.874510148678328[/C][C]0.250979702643343[/C][C]0.125489851321672[/C][/ROW]
[ROW][C]116[/C][C]0.844979458493553[/C][C]0.310041083012894[/C][C]0.155020541506447[/C][/ROW]
[ROW][C]117[/C][C]0.894632713617535[/C][C]0.210734572764929[/C][C]0.105367286382465[/C][/ROW]
[ROW][C]118[/C][C]0.872794438000039[/C][C]0.254411123999921[/C][C]0.127205561999961[/C][/ROW]
[ROW][C]119[/C][C]0.847689558796055[/C][C]0.30462088240789[/C][C]0.152310441203945[/C][/ROW]
[ROW][C]120[/C][C]0.877971708479872[/C][C]0.244056583040257[/C][C]0.122028291520128[/C][/ROW]
[ROW][C]121[/C][C]0.857671657384474[/C][C]0.284656685231052[/C][C]0.142328342615526[/C][/ROW]
[ROW][C]122[/C][C]0.821949400890632[/C][C]0.356101198218736[/C][C]0.178050599109368[/C][/ROW]
[ROW][C]123[/C][C]0.850400725233224[/C][C]0.299198549533553[/C][C]0.149599274766776[/C][/ROW]
[ROW][C]124[/C][C]0.812775955862856[/C][C]0.374448088274287[/C][C]0.187224044137144[/C][/ROW]
[ROW][C]125[/C][C]0.77727497835217[/C][C]0.445450043295661[/C][C]0.22272502164783[/C][/ROW]
[ROW][C]126[/C][C]0.732848303780098[/C][C]0.534303392439804[/C][C]0.267151696219902[/C][/ROW]
[ROW][C]127[/C][C]0.699031978855797[/C][C]0.601936042288407[/C][C]0.300968021144203[/C][/ROW]
[ROW][C]128[/C][C]0.659625545216797[/C][C]0.680748909566407[/C][C]0.340374454783203[/C][/ROW]
[ROW][C]129[/C][C]0.603997811521454[/C][C]0.792004376957092[/C][C]0.396002188478546[/C][/ROW]
[ROW][C]130[/C][C]0.542618005847216[/C][C]0.914763988305568[/C][C]0.457381994152784[/C][/ROW]
[ROW][C]131[/C][C]0.541396971393463[/C][C]0.917206057213073[/C][C]0.458603028606537[/C][/ROW]
[ROW][C]132[/C][C]0.541507306832898[/C][C]0.916985386334204[/C][C]0.458492693167102[/C][/ROW]
[ROW][C]133[/C][C]0.492018422002719[/C][C]0.984036844005439[/C][C]0.507981577997281[/C][/ROW]
[ROW][C]134[/C][C]0.444298507112976[/C][C]0.888597014225952[/C][C]0.555701492887024[/C][/ROW]
[ROW][C]135[/C][C]0.596843113757033[/C][C]0.806313772485934[/C][C]0.403156886242967[/C][/ROW]
[ROW][C]136[/C][C]0.560225537119912[/C][C]0.879548925760176[/C][C]0.439774462880088[/C][/ROW]
[ROW][C]137[/C][C]0.488060397250347[/C][C]0.976120794500694[/C][C]0.511939602749653[/C][/ROW]
[ROW][C]138[/C][C]0.500465747071631[/C][C]0.999068505856738[/C][C]0.499534252928369[/C][/ROW]
[ROW][C]139[/C][C]0.428677355797048[/C][C]0.857354711594096[/C][C]0.571322644202952[/C][/ROW]
[ROW][C]140[/C][C]0.392394744899588[/C][C]0.784789489799177[/C][C]0.607605255100412[/C][/ROW]
[ROW][C]141[/C][C]0.357899121912323[/C][C]0.715798243824647[/C][C]0.642100878087677[/C][/ROW]
[ROW][C]142[/C][C]0.417566057793126[/C][C]0.835132115586252[/C][C]0.582433942206874[/C][/ROW]
[ROW][C]143[/C][C]0.570950490818346[/C][C]0.858099018363307[/C][C]0.429049509181654[/C][/ROW]
[ROW][C]144[/C][C]0.497661935280862[/C][C]0.995323870561724[/C][C]0.502338064719138[/C][/ROW]
[ROW][C]145[/C][C]0.41681494841382[/C][C]0.833629896827641[/C][C]0.58318505158618[/C][/ROW]
[ROW][C]146[/C][C]0.411934368393863[/C][C]0.823868736787726[/C][C]0.588065631606137[/C][/ROW]
[ROW][C]147[/C][C]0.360460923669245[/C][C]0.720921847338491[/C][C]0.639539076330754[/C][/ROW]
[ROW][C]148[/C][C]0.251071208740865[/C][C]0.502142417481731[/C][C]0.748928791259135[/C][/ROW]
[ROW][C]149[/C][C]0.456567419664047[/C][C]0.913134839328094[/C][C]0.543432580335953[/C][/ROW]
[ROW][C]150[/C][C]0.385521341064606[/C][C]0.771042682129212[/C][C]0.614478658935394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&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
90.3031671748716910.6063343497433810.696832825128309
100.1832719061613780.3665438123227550.816728093838622
110.3389657526651670.6779315053303340.661034247334833
120.2896088181375320.5792176362750640.710391181862468
130.8660476345142660.2679047309714680.133952365485734
140.8049343753023460.3901312493953070.195065624697654
150.7599021021059230.4801957957881550.240097897894077
160.697170058283160.605659883433680.30282994171684
170.6192827515251910.7614344969496170.380717248474809
180.5910386873954530.8179226252090950.408961312604547
190.5240253891279140.9519492217441710.475974610872086
200.5182040233470260.9635919533059470.481795976652974
210.4978243196694550.9956486393389110.502175680330545
220.4308793698654340.8617587397308670.569120630134566
230.3858946773320640.7717893546641280.614105322667936
240.434994510379910.8699890207598210.56500548962009
250.4477210430820560.8954420861641110.552278956917944
260.3796057155477920.7592114310955840.620394284452208
270.3292817477595950.6585634955191890.670718252240405
280.3452913229534790.6905826459069570.654708677046521
290.2916634513073030.5833269026146070.708336548692697
300.2385028724348370.4770057448696730.761497127565163
310.1919525543454280.3839051086908570.808047445654572
320.2121741136405950.4243482272811890.787825886359405
330.3620847025213470.7241694050426950.637915297478653
340.5966832722808640.8066334554382720.403316727719136
350.5738366538928480.8523266922143040.426163346107152
360.6339974668983340.7320050662033310.366002533101666
370.6250464751355140.7499070497289720.374953524864486
380.5812071018714420.8375857962571170.418792898128558
390.549265039489190.901469921021620.45073496051081
400.6340149807260250.731970038547950.365985019273975
410.6026968069944010.7946063860111990.397303193005599
420.5574937840280790.8850124319438430.442506215971921
430.6440455231616670.7119089536766660.355954476838333
440.6160009972083780.7679980055832440.383999002791622
450.6392042044274110.7215915911451780.360795795572589
460.5897039664504020.8205920670991960.410296033549598
470.5804433493207140.8391133013585710.419556650679286
480.6916894681453150.616621063709370.308310531854685
490.6504936064723960.6990127870552090.349506393527605
500.6396856720783830.7206286558432350.360314327921617
510.6011762085280550.797647582943890.398823791471945
520.5602233061220130.8795533877559740.439776693877987
530.5965480101363390.8069039797273220.403451989863661
540.563409560023510.8731808799529810.43659043997649
550.61489342470360.7702131505927990.3851065752964
560.5818554444291550.836289111141690.418144555570845
570.5421330768352510.9157338463294980.457866923164749
580.5110416534641810.9779166930716390.488958346535819
590.4639671439414380.9279342878828750.536032856058562
600.4227622766070280.8455245532140560.577237723392972
610.3898561495725370.7797122991450740.610143850427463
620.3500535873542720.7001071747085440.649946412645728
630.3092759614041220.6185519228082450.690724038595878
640.3422148706163030.6844297412326060.657785129383697
650.3007893406112010.6015786812224020.699210659388799
660.3146221898098260.6292443796196510.685377810190174
670.3767222998931580.7534445997863150.623277700106842
680.453735810270280.907471620540560.54626418972972
690.4151735985958460.8303471971916920.584826401404154
700.3989624309001420.7979248618002850.601037569099858
710.4658051335482980.9316102670965960.534194866451702
720.4205472185147670.8410944370295350.579452781485233
730.3794200836156610.7588401672313220.620579916384339
740.3439879474214350.6879758948428690.656012052578565
750.3119416427304130.6238832854608260.688058357269587
760.2883223227826730.5766446455653460.711677677217327
770.2657600541081590.5315201082163180.734239945891841
780.2299636642113290.4599273284226580.770036335788671
790.1976096247202450.3952192494404910.802390375279755
800.1708897029030080.3417794058060160.829110297096992
810.1499277119968980.2998554239937960.850072288003102
820.24395879877380.48791759754760.7560412012262
830.2257696281323710.4515392562647410.774230371867629
840.1966305245031690.3932610490063390.803369475496831
850.1986355252872070.3972710505744140.801364474712793
860.1941090879224530.3882181758449060.805890912077547
870.2015667567121540.4031335134243090.798433243287846
880.1884416088080550.376883217616110.811558391191945
890.1762940990469140.3525881980938270.823705900953086
900.181308594520280.3626171890405610.81869140547972
910.2585526515285150.5171053030570290.741447348471486
920.2242277450197680.4484554900395350.775772254980232
930.207472771899060.414945543798120.79252722810094
940.1766470053708130.3532940107416250.823352994629187
950.1618605312911050.323721062582210.838139468708895
960.1655959476425470.3311918952850940.834404052357453
970.1673739261214720.3347478522429440.832626073878528
980.1634576383143220.3269152766286440.836542361685678
990.1563495215565740.3126990431131480.843650478443426
1000.1511986693387110.3023973386774220.848801330661289
1010.1248112791346940.2496225582693880.875188720865306
1020.1046907682744890.2093815365489770.895309231725511
1030.08483471781537450.1696694356307490.915165282184625
1040.06916769519316890.1383353903863380.930832304806831
1050.07399200919051750.1479840183810350.926007990809482
1060.06100521051733480.122010421034670.938994789482665
1070.05390697383997740.1078139476799550.946093026160023
1080.04722096954841710.09444193909683420.952779030451583
1090.03639850899195870.07279701798391740.963601491008041
1100.03589925779670310.07179851559340620.964100742203297
1110.0452125173350230.0904250346700460.954787482664977
1120.1809556831836490.3619113663672980.819044316816351
1130.1650544506177970.3301089012355940.834945549382203
1140.5973072309482380.8053855381035240.402692769051762
1150.8745101486783280.2509797026433430.125489851321672
1160.8449794584935530.3100410830128940.155020541506447
1170.8946327136175350.2107345727649290.105367286382465
1180.8727944380000390.2544111239999210.127205561999961
1190.8476895587960550.304620882407890.152310441203945
1200.8779717084798720.2440565830402570.122028291520128
1210.8576716573844740.2846566852310520.142328342615526
1220.8219494008906320.3561011982187360.178050599109368
1230.8504007252332240.2991985495335530.149599274766776
1240.8127759558628560.3744480882742870.187224044137144
1250.777274978352170.4454500432956610.22272502164783
1260.7328483037800980.5343033924398040.267151696219902
1270.6990319788557970.6019360422884070.300968021144203
1280.6596255452167970.6807489095664070.340374454783203
1290.6039978115214540.7920043769570920.396002188478546
1300.5426180058472160.9147639883055680.457381994152784
1310.5413969713934630.9172060572130730.458603028606537
1320.5415073068328980.9169853863342040.458492693167102
1330.4920184220027190.9840368440054390.507981577997281
1340.4442985071129760.8885970142259520.555701492887024
1350.5968431137570330.8063137724859340.403156886242967
1360.5602255371199120.8795489257601760.439774462880088
1370.4880603972503470.9761207945006940.511939602749653
1380.5004657470716310.9990685058567380.499534252928369
1390.4286773557970480.8573547115940960.571322644202952
1400.3923947448995880.7847894897991770.607605255100412
1410.3578991219123230.7157982438246470.642100878087677
1420.4175660577931260.8351321155862520.582433942206874
1430.5709504908183460.8580990183633070.429049509181654
1440.4976619352808620.9953238705617240.502338064719138
1450.416814948413820.8336298968276410.58318505158618
1460.4119343683938630.8238687367877260.588065631606137
1470.3604609236692450.7209218473384910.639539076330754
1480.2510712087408650.5021424174817310.748928791259135
1490.4565674196640470.9131348393280940.543432580335953
1500.3855213410646060.7710426821292120.614478658935394







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.028169014084507OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.028169014084507 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147563&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.028169014084507[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147563&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147563&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.028169014084507OK



Parameters (Session):
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')
}