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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 31 Oct 2012 11:13:36 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/31/t1351696441cm48e48jzc1rknu.htm/, Retrieved Mon, 29 Apr 2024 07:50:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185444, Retrieved Mon, 29 Apr 2024 07:50:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   P   [Multiple Regression] [month] [2012-10-31 11:32:46] [2f324ead08cc3849e52bae5d3f3d905a]
-   PD      [Multiple Regression] [x7] [2012-10-31 15:13:36] [e357aba3893873b930815b56a53f1005] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	32
39	32	16	11	18	11	51
30	35	19	15	11	14	42
31	33	15	6	12	12	41
34	37	14	13	16	21	46
35	29	13	10	18	12	47
39	31	19	12	14	22	37
34	36	15	14	14	11	49
36	35	14	12	15	10	45
37	38	15	6	15	13	47
38	31	16	10	17	10	49
36	34	16	12	19	8	33
38	35	16	12	10	15	42
39	38	16	11	16	14	33
33	37	17	15	18	10	53
32	33	15	12	14	14	36
36	32	15	10	14	14	45
38	38	20	12	17	11	54
39	38	18	11	14	10	41
32	32	16	12	16	13	36
32	33	16	11	18	7	41
31	31	16	12	11	14	44
39	38	19	13	14	12	33
37	39	16	11	12	14	37
39	32	17	9	17	11	52
41	32	17	13	9	9	47
36	35	16	10	16	11	43
33	37	15	14	14	15	44
33	33	16	12	15	14	45
34	33	14	10	11	13	44
31	28	15	12	16	9	49
27	32	12	8	13	15	33
37	31	14	10	17	10	43
34	37	16	12	15	11	54
34	30	14	12	14	13	42
32	33	7	7	16	8	44
29	31	10	6	9	20	37
36	33	14	12	15	12	43
29	31	16	10	17	10	46
35	33	16	10	13	10	42
37	32	16	10	15	9	45
34	33	14	12	16	14	44
38	32	20	15	16	8	33
35	33	14	10	12	14	31
38	28	14	10	12	11	42
37	35	11	12	11	13	40
38	39	14	13	15	9	43
33	34	15	11	15	11	46
36	38	16	11	17	15	42
38	32	14	12	13	11	45
32	38	16	14	16	10	44
32	30	14	10	14	14	40
32	33	12	12	11	18	37
34	38	16	13	12	14	46
32	32	9	5	12	11	36
37	32	14	6	15	12	47
39	34	16	12	16	13	45
29	34	16	12	15	9	42
37	36	15	11	12	10	43
35	34	16	10	12	15	43
30	28	12	7	8	20	32
38	34	16	12	13	12	45
34	35	16	14	11	12	45
31	35	14	11	14	14	31
34	31	16	12	15	13	33
35	37	17	13	10	11	49
36	35	18	14	11	17	42
30	27	18	11	12	12	41
39	40	12	12	15	13	38
35	37	16	12	15	14	42
38	36	10	8	14	13	44
31	38	14	11	16	15	33
34	39	18	14	15	13	48
38	41	18	14	15	10	40
34	27	16	12	13	11	50
39	30	17	9	12	19	49
37	37	16	13	17	13	43
34	31	16	11	13	17	44
28	31	13	12	15	13	47
37	27	16	12	13	9	33
33	36	16	12	15	11	46
37	38	20	12	16	10	0
35	37	16	12	15	9	45
37	33	15	12	16	12	43
32	34	15	11	15	12	44
33	31	16	10	14	13	47
38	39	14	9	15	13	45
33	34	16	12	14	12	42
29	32	16	12	13	15	33
33	33	15	12	7	22	43
31	36	12	9	17	13	46
36	32	17	15	13	15	33
35	41	16	12	15	13	46
32	28	15	12	14	15	48
29	30	13	12	13	10	47
39	36	16	10	16	11	47
37	35	16	13	12	16	43
35	31	16	9	14	11	46
37	34	16	12	17	11	48
32	36	14	10	15	10	46
38	36	16	14	17	10	45
37	35	16	11	12	16	45
36	37	20	15	16	12	52
32	28	15	11	11	11	42
33	39	16	11	15	16	47
40	32	13	12	9	19	41
38	35	17	12	16	11	47
41	39	16	12	15	16	43
36	35	16	11	10	15	33
43	42	12	7	10	24	30
30	34	16	12	15	14	49
31	33	16	14	11	15	44
32	41	17	11	13	11	55
32	33	13	11	14	15	11
37	34	12	10	18	12	47
37	32	18	13	16	10	53
33	40	14	13	14	14	33
34	40	14	8	14	13	44
33	35	13	11	14	9	42
38	36	16	12	14	15	55
33	37	13	11	12	15	33
31	27	16	13	14	14	46
38	39	13	12	15	11	54
37	38	16	14	15	8	47
33	31	15	13	15	11	45
31	33	16	15	13	11	47
39	32	15	10	17	8	55
44	39	17	11	17	10	44
33	36	15	9	19	11	53
35	33	12	11	15	13	44
32	33	16	10	13	11	42
28	32	10	11	9	20	40
40	37	16	8	15	10	46
27	30	12	11	15	15	40
37	38	14	12	15	12	46
32	29	15	12	16	14	53
28	22	13	9	11	23	33
34	35	15	11	14	14	42
30	35	11	10	11	16	35
35	34	12	8	15	11	40
31	35	8	9	13	12	41
32	34	16	8	15	10	33
30	34	15	9	16	14	51
30	35	17	15	14	12	53
31	23	16	11	15	12	46
40	31	10	8	16	11	55
32	27	18	13	16	12	47
36	36	13	12	11	13	38
32	31	16	12	12	11	46
35	32	13	9	9	19	46
38	39	10	7	16	12	53
42	37	15	13	13	17	47
34	38	16	9	16	9	41
35	39	16	6	12	12	44
35	34	14	8	9	19	43
33	31	10	8	13	18	51
36	32	17	15	13	15	33
32	37	13	6	14	14	43
33	36	15	9	19	11	53
34	32	16	11	13	9	51
32	35	12	8	12	18	50
34	36	13	8	13	16	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.55992301497745 -0.047496183329279Connected[t] + 0.0332067483233368Separate[t] + 0.529019027360035Learning[t] -0.0399388089690363Happiness[t] -0.0231956678629946Depression[t] -0.000948402888831708Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.55992301497745 -0.047496183329279Connected[t] +  0.0332067483233368Separate[t] +  0.529019027360035Learning[t] -0.0399388089690363Happiness[t] -0.0231956678629946Depression[t] -0.000948402888831708Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.55992301497745 -0.047496183329279Connected[t] +  0.0332067483233368Separate[t] +  0.529019027360035Learning[t] -0.0399388089690363Happiness[t] -0.0231956678629946Depression[t] -0.000948402888831708Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.55992301497745 -0.047496183329279Connected[t] + 0.0332067483233368Separate[t] + 0.529019027360035Learning[t] -0.0399388089690363Happiness[t] -0.0231956678629946Depression[t] -0.000948402888831708Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.559923014977452.5056781.81980.0707130.035357
Connected-0.0474961833292790.046821-1.01440.3119640.155982
Separate0.03320674832333680.0437940.75830.4494510.224725
Learning0.5290190273600350.0667337.927300
Happiness-0.03993880896903630.074846-0.53360.5943730.297186
Depression-0.02319566786299460.055013-0.42160.6738730.336937
Belonging_Final-0.0009484028888317080.020366-0.04660.9629180.481459

\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) & 4.55992301497745 & 2.505678 & 1.8198 & 0.070713 & 0.035357 \tabularnewline
Connected & -0.047496183329279 & 0.046821 & -1.0144 & 0.311964 & 0.155982 \tabularnewline
Separate & 0.0332067483233368 & 0.043794 & 0.7583 & 0.449451 & 0.224725 \tabularnewline
Learning & 0.529019027360035 & 0.066733 & 7.9273 & 0 & 0 \tabularnewline
Happiness & -0.0399388089690363 & 0.074846 & -0.5336 & 0.594373 & 0.297186 \tabularnewline
Depression & -0.0231956678629946 & 0.055013 & -0.4216 & 0.673873 & 0.336937 \tabularnewline
Belonging_Final & -0.000948402888831708 & 0.020366 & -0.0466 & 0.962918 & 0.481459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&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]4.55992301497745[/C][C]2.505678[/C][C]1.8198[/C][C]0.070713[/C][C]0.035357[/C][/ROW]
[ROW][C]Connected[/C][C]-0.047496183329279[/C][C]0.046821[/C][C]-1.0144[/C][C]0.311964[/C][C]0.155982[/C][/ROW]
[ROW][C]Separate[/C][C]0.0332067483233368[/C][C]0.043794[/C][C]0.7583[/C][C]0.449451[/C][C]0.224725[/C][/ROW]
[ROW][C]Learning[/C][C]0.529019027360035[/C][C]0.066733[/C][C]7.9273[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0399388089690363[/C][C]0.074846[/C][C]-0.5336[/C][C]0.594373[/C][C]0.297186[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0231956678629946[/C][C]0.055013[/C][C]-0.4216[/C][C]0.673873[/C][C]0.336937[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.000948402888831708[/C][C]0.020366[/C][C]-0.0466[/C][C]0.962918[/C][C]0.481459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.559923014977452.5056781.81980.0707130.035357
Connected-0.0474961833292790.046821-1.01440.3119640.155982
Separate0.03320674832333680.0437940.75830.4494510.224725
Learning0.5290190273600350.0667337.927300
Happiness-0.03993880896903630.074846-0.53360.5943730.297186
Depression-0.02319566786299460.055013-0.42160.6738730.336937
Belonging_Final-0.0009484028888317080.020366-0.04660.9629180.481459






Multiple Linear Regression - Regression Statistics
Multiple R0.551801768015629
R-squared0.304485191185174
Adjusted R-squared0.277562037295568
F-TEST (value)11.3094176274319
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.8421963954296e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82038224683142
Sum Squared Residuals513.637686309749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.551801768015629 \tabularnewline
R-squared & 0.304485191185174 \tabularnewline
Adjusted R-squared & 0.277562037295568 \tabularnewline
F-TEST (value) & 11.3094176274319 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.8421963954296e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.82038224683142 \tabularnewline
Sum Squared Residuals & 513.637686309749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.551801768015629[/C][/ROW]
[ROW][C]R-squared[/C][C]0.304485191185174[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.277562037295568[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.3094176274319[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.8421963954296e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.82038224683142[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]513.637686309749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.551801768015629
R-squared0.304485191185174
Adjusted R-squared0.277562037295568
F-TEST (value)11.3094176274319
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.8421963954296e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82038224683142
Sum Squared Residuals513.637686309749






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.883843058079212.11615694192079
21111.2120727939769-0.212072793976896
31513.54473605618431.45526394381572
4611.322151196414-5.32215119641397
51310.41021235127222.58978764872781
6109.695978143936230.30402185606377
71212.6838036575605-0.683803657560514
81411.21501371821042.78498628178959
91210.54484604631781.45515395368223
10611.0545053259519-5.05450532595189
111011.2913935115925-1.29139351159253
121211.46769428723030.532305712769674
131211.5944526485760.405547351424017
141111.438675150265-0.438675150264977
151512.21340152501462.78659847498538
161211.15312807386480.846871926135211
171010.9214009662248-0.92140096622485
181213.6119791769889-1.61197917698888
191112.6617862712644-1.66178627126444
201211.59225840282640.407741597173591
211111.6800195259455-0.680019525945482
221211.77545899170390.224541008296116
231313.1520011860091-0.152001186009144
241111.7228358895677-0.72283588956769
25911.7800822274171-2.78008222741714
261312.0557336826810.944266317318984
271011.5416464299835-1.54164642998347
281411.20767599285522.79232400714479
291211.5861764829270.413823517072977
301010.6645415515056-0.664541551505645
311211.05836199939950.941638000600529
3289.78993350988028-1.78993350988028
331010.2865420575347-0.286542057534732
341211.73255867048060.267441329519411
351210.44700168540621.55299831459381
3676.972685021113750.0273149788862498
3768.64367962518416-2.64367962518416
381210.43393801972281.56606198027723
391011.7217043702225-1.72170437022254
401011.666689614325-1.66668961432501
411011.4789633406015-1.47896334060154
421210.44165183879751.55834816120253
431513.54218096027231.45781903972766
441010.5662401288991-0.566240128899148
451010.3168724091065-0.316872409106461
46129.00520302763972.9947969723603
471310.60777314659322.39222685340679
481111.1590028045905-0.159002804590477
491111.5094935974213-0.509493597421298
501210.40691538476431.59308461523572
511411.85349867324482.14650132675524
521010.5206951899794-0.520695189979416
53129.592156344350982.40784365564902
541311.82358206523271.17641793476729
5558.0952717829083-3.0952717829083
56610.3494414765148-4.34944147651483
571211.31766299016860.682337009831356
581211.92819151254890.0718084874510565
591111.1812888713566-0.181288871356633
601011.6229084294136-1.62290842941358
6179.59928207501813-2.59928207501813
621211.5081712682680.491828731731974
631411.81124036784662.18875963215345
641110.74276074092490.257239259075135
651211.506843305480.493156694519955
661312.41851757380070.581482426199315
671412.66115292525961.33884707474041
681112.7574639718833-1.75746397188333
69129.447404999859382.55259500014062
701211.62685631822830.373143681771693
7188.33828452681129-0.338284526811291
721110.73741089431610.262589105683856
731412.81630930345431.18369069654567
741412.76991229348351.23008770651647
751211.48416241674060.51583758325938
76911.7306426413782-2.73064264137818
771311.47423359860581.52576640139416
781111.483505820189-0.483505820188991
791210.1914856829321.80851431706803
801211.40418805158890.595811948411089
811211.75443532859720.245564671402815
821213.7738235931471-1.7738235931471
831211.73998944887680.260010551123215
841210.87552205478451.12447794521551
851111.1852001258344-0.185200125834424
861011.5810006573347-1.58100065733472
87910.5130936693636-1.51309366936357
881211.70855858461190.29144141538812
891211.81101725266190.18898274733814
901211.1929993901930.807000609807015
9199.60708263215154-0.607082632151542
921512.00756299671692.99243700328306
931211.77908536782930.220914632170678
941210.95251782971911.04748217028087
951210.26024737280641.73975262719359
961011.4285710167636-1.42857101676364
971311.53792714321541.46207285678464
98911.533348029291-2.53334802929098
991211.41626267491770.58373732508234
1001010.7670891250694-0.767089125069389
1011411.46122086476452.53877913523546
1021111.5360303374377-0.5360303374377
1031513.69240474220781.3075952577922
1041111.1708073454112-0.170807345411206
1051111.7371288313634-0.737128831363391
106129.76088749527322.2391125047268
1071211.97187947912960.0281205208703795
1081211.36095297628450.639047023715514
1091111.697980641234-0.697980641234027
11079.27596268465184-2.27596268465184
1111211.75807816968290.24192183031713
1121411.81867682048762.18132317951244
1131112.5683262728418-1.56832627284177
1141110.09560442350250.904395576497483
115109.238000491534320.76199950846568
1161312.46627969537890.533720304621072
1171310.81190531010532.18809468989467
118810.7771723628619-2.77717236286189
1191110.22429525444410.775704745555905
1201211.45557492346840.544425076531637
1211110.23994798785040.76005201214964
1221311.52091876572581.47908123427424
1231210.021930351731.97806964826996
1241411.69950269262692.30049730737311
1251311.06033096250931.9396690374907
1261511.8287366653353.17126333466503
1271010.7887859676196-0.788785967619555
1281111.8058314400077-0.805831440007747
129911.0590222451392-2.05902224513918
130119.399252077580151.60074792241985
1311011.7859824964499-1.78598249644985
132118.721537348170282.27846265182972
133811.4783644614786-3.47836446147856
134119.637003575073711.36299642492629
1351210.54963036934371.45036963065632
1361210.92430061352321.07569938647677
13799.83370114571178-0.833701145711782
1381111.1188587865199-0.118858786519914
139109.272831321799830.727168678200169
14089.4826437731848-1.4826437731848
14197.645492692571361.35450730742864
142811.7710429206976-3.7710429206976
143911.1872235275761-2.18722352757614
1441512.40284047850592.59715952149406
1451111.3945442991894-0.39454429918937
14688.03333970454682-0.0333397045468161
1471312.49702595201560.502974047984359
1481210.14584081979011.85415918020995
1491211.75571419721690.244285802783111
15099.99362639747544-0.993626397475436
15178.37268719570674-1.37268719570674
1521310.77091260746822.22908739253175
153911.7845471831157-2.78454718311569
154611.8575807717304-5.85758077173042
155810.5919041301486-2.59190413014864
15688.32705335127324-0.327053351273243
1571512.00756299671692.99243700328306
158610.2212781922162-4.22127819221624
159911.0590222451392-2.05902224513918
1601111.6956390911945-0.695639091194462
16189.60630179447381-1.60630179447381
162810.0837813418109-2.0837813418109

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.88384305807921 & 2.11615694192079 \tabularnewline
2 & 11 & 11.2120727939769 & -0.212072793976896 \tabularnewline
3 & 15 & 13.5447360561843 & 1.45526394381572 \tabularnewline
4 & 6 & 11.322151196414 & -5.32215119641397 \tabularnewline
5 & 13 & 10.4102123512722 & 2.58978764872781 \tabularnewline
6 & 10 & 9.69597814393623 & 0.30402185606377 \tabularnewline
7 & 12 & 12.6838036575605 & -0.683803657560514 \tabularnewline
8 & 14 & 11.2150137182104 & 2.78498628178959 \tabularnewline
9 & 12 & 10.5448460463178 & 1.45515395368223 \tabularnewline
10 & 6 & 11.0545053259519 & -5.05450532595189 \tabularnewline
11 & 10 & 11.2913935115925 & -1.29139351159253 \tabularnewline
12 & 12 & 11.4676942872303 & 0.532305712769674 \tabularnewline
13 & 12 & 11.594452648576 & 0.405547351424017 \tabularnewline
14 & 11 & 11.438675150265 & -0.438675150264977 \tabularnewline
15 & 15 & 12.2134015250146 & 2.78659847498538 \tabularnewline
16 & 12 & 11.1531280738648 & 0.846871926135211 \tabularnewline
17 & 10 & 10.9214009662248 & -0.92140096622485 \tabularnewline
18 & 12 & 13.6119791769889 & -1.61197917698888 \tabularnewline
19 & 11 & 12.6617862712644 & -1.66178627126444 \tabularnewline
20 & 12 & 11.5922584028264 & 0.407741597173591 \tabularnewline
21 & 11 & 11.6800195259455 & -0.680019525945482 \tabularnewline
22 & 12 & 11.7754589917039 & 0.224541008296116 \tabularnewline
23 & 13 & 13.1520011860091 & -0.152001186009144 \tabularnewline
24 & 11 & 11.7228358895677 & -0.72283588956769 \tabularnewline
25 & 9 & 11.7800822274171 & -2.78008222741714 \tabularnewline
26 & 13 & 12.055733682681 & 0.944266317318984 \tabularnewline
27 & 10 & 11.5416464299835 & -1.54164642998347 \tabularnewline
28 & 14 & 11.2076759928552 & 2.79232400714479 \tabularnewline
29 & 12 & 11.586176482927 & 0.413823517072977 \tabularnewline
30 & 10 & 10.6645415515056 & -0.664541551505645 \tabularnewline
31 & 12 & 11.0583619993995 & 0.941638000600529 \tabularnewline
32 & 8 & 9.78993350988028 & -1.78993350988028 \tabularnewline
33 & 10 & 10.2865420575347 & -0.286542057534732 \tabularnewline
34 & 12 & 11.7325586704806 & 0.267441329519411 \tabularnewline
35 & 12 & 10.4470016854062 & 1.55299831459381 \tabularnewline
36 & 7 & 6.97268502111375 & 0.0273149788862498 \tabularnewline
37 & 6 & 8.64367962518416 & -2.64367962518416 \tabularnewline
38 & 12 & 10.4339380197228 & 1.56606198027723 \tabularnewline
39 & 10 & 11.7217043702225 & -1.72170437022254 \tabularnewline
40 & 10 & 11.666689614325 & -1.66668961432501 \tabularnewline
41 & 10 & 11.4789633406015 & -1.47896334060154 \tabularnewline
42 & 12 & 10.4416518387975 & 1.55834816120253 \tabularnewline
43 & 15 & 13.5421809602723 & 1.45781903972766 \tabularnewline
44 & 10 & 10.5662401288991 & -0.566240128899148 \tabularnewline
45 & 10 & 10.3168724091065 & -0.316872409106461 \tabularnewline
46 & 12 & 9.0052030276397 & 2.9947969723603 \tabularnewline
47 & 13 & 10.6077731465932 & 2.39222685340679 \tabularnewline
48 & 11 & 11.1590028045905 & -0.159002804590477 \tabularnewline
49 & 11 & 11.5094935974213 & -0.509493597421298 \tabularnewline
50 & 12 & 10.4069153847643 & 1.59308461523572 \tabularnewline
51 & 14 & 11.8534986732448 & 2.14650132675524 \tabularnewline
52 & 10 & 10.5206951899794 & -0.520695189979416 \tabularnewline
53 & 12 & 9.59215634435098 & 2.40784365564902 \tabularnewline
54 & 13 & 11.8235820652327 & 1.17641793476729 \tabularnewline
55 & 5 & 8.0952717829083 & -3.0952717829083 \tabularnewline
56 & 6 & 10.3494414765148 & -4.34944147651483 \tabularnewline
57 & 12 & 11.3176629901686 & 0.682337009831356 \tabularnewline
58 & 12 & 11.9281915125489 & 0.0718084874510565 \tabularnewline
59 & 11 & 11.1812888713566 & -0.181288871356633 \tabularnewline
60 & 10 & 11.6229084294136 & -1.62290842941358 \tabularnewline
61 & 7 & 9.59928207501813 & -2.59928207501813 \tabularnewline
62 & 12 & 11.508171268268 & 0.491828731731974 \tabularnewline
63 & 14 & 11.8112403678466 & 2.18875963215345 \tabularnewline
64 & 11 & 10.7427607409249 & 0.257239259075135 \tabularnewline
65 & 12 & 11.50684330548 & 0.493156694519955 \tabularnewline
66 & 13 & 12.4185175738007 & 0.581482426199315 \tabularnewline
67 & 14 & 12.6611529252596 & 1.33884707474041 \tabularnewline
68 & 11 & 12.7574639718833 & -1.75746397188333 \tabularnewline
69 & 12 & 9.44740499985938 & 2.55259500014062 \tabularnewline
70 & 12 & 11.6268563182283 & 0.373143681771693 \tabularnewline
71 & 8 & 8.33828452681129 & -0.338284526811291 \tabularnewline
72 & 11 & 10.7374108943161 & 0.262589105683856 \tabularnewline
73 & 14 & 12.8163093034543 & 1.18369069654567 \tabularnewline
74 & 14 & 12.7699122934835 & 1.23008770651647 \tabularnewline
75 & 12 & 11.4841624167406 & 0.51583758325938 \tabularnewline
76 & 9 & 11.7306426413782 & -2.73064264137818 \tabularnewline
77 & 13 & 11.4742335986058 & 1.52576640139416 \tabularnewline
78 & 11 & 11.483505820189 & -0.483505820188991 \tabularnewline
79 & 12 & 10.191485682932 & 1.80851431706803 \tabularnewline
80 & 12 & 11.4041880515889 & 0.595811948411089 \tabularnewline
81 & 12 & 11.7544353285972 & 0.245564671402815 \tabularnewline
82 & 12 & 13.7738235931471 & -1.7738235931471 \tabularnewline
83 & 12 & 11.7399894488768 & 0.260010551123215 \tabularnewline
84 & 12 & 10.8755220547845 & 1.12447794521551 \tabularnewline
85 & 11 & 11.1852001258344 & -0.185200125834424 \tabularnewline
86 & 10 & 11.5810006573347 & -1.58100065733472 \tabularnewline
87 & 9 & 10.5130936693636 & -1.51309366936357 \tabularnewline
88 & 12 & 11.7085585846119 & 0.29144141538812 \tabularnewline
89 & 12 & 11.8110172526619 & 0.18898274733814 \tabularnewline
90 & 12 & 11.192999390193 & 0.807000609807015 \tabularnewline
91 & 9 & 9.60708263215154 & -0.607082632151542 \tabularnewline
92 & 15 & 12.0075629967169 & 2.99243700328306 \tabularnewline
93 & 12 & 11.7790853678293 & 0.220914632170678 \tabularnewline
94 & 12 & 10.9525178297191 & 1.04748217028087 \tabularnewline
95 & 12 & 10.2602473728064 & 1.73975262719359 \tabularnewline
96 & 10 & 11.4285710167636 & -1.42857101676364 \tabularnewline
97 & 13 & 11.5379271432154 & 1.46207285678464 \tabularnewline
98 & 9 & 11.533348029291 & -2.53334802929098 \tabularnewline
99 & 12 & 11.4162626749177 & 0.58373732508234 \tabularnewline
100 & 10 & 10.7670891250694 & -0.767089125069389 \tabularnewline
101 & 14 & 11.4612208647645 & 2.53877913523546 \tabularnewline
102 & 11 & 11.5360303374377 & -0.5360303374377 \tabularnewline
103 & 15 & 13.6924047422078 & 1.3075952577922 \tabularnewline
104 & 11 & 11.1708073454112 & -0.170807345411206 \tabularnewline
105 & 11 & 11.7371288313634 & -0.737128831363391 \tabularnewline
106 & 12 & 9.7608874952732 & 2.2391125047268 \tabularnewline
107 & 12 & 11.9718794791296 & 0.0281205208703795 \tabularnewline
108 & 12 & 11.3609529762845 & 0.639047023715514 \tabularnewline
109 & 11 & 11.697980641234 & -0.697980641234027 \tabularnewline
110 & 7 & 9.27596268465184 & -2.27596268465184 \tabularnewline
111 & 12 & 11.7580781696829 & 0.24192183031713 \tabularnewline
112 & 14 & 11.8186768204876 & 2.18132317951244 \tabularnewline
113 & 11 & 12.5683262728418 & -1.56832627284177 \tabularnewline
114 & 11 & 10.0956044235025 & 0.904395576497483 \tabularnewline
115 & 10 & 9.23800049153432 & 0.76199950846568 \tabularnewline
116 & 13 & 12.4662796953789 & 0.533720304621072 \tabularnewline
117 & 13 & 10.8119053101053 & 2.18809468989467 \tabularnewline
118 & 8 & 10.7771723628619 & -2.77717236286189 \tabularnewline
119 & 11 & 10.2242952544441 & 0.775704745555905 \tabularnewline
120 & 12 & 11.4555749234684 & 0.544425076531637 \tabularnewline
121 & 11 & 10.2399479878504 & 0.76005201214964 \tabularnewline
122 & 13 & 11.5209187657258 & 1.47908123427424 \tabularnewline
123 & 12 & 10.02193035173 & 1.97806964826996 \tabularnewline
124 & 14 & 11.6995026926269 & 2.30049730737311 \tabularnewline
125 & 13 & 11.0603309625093 & 1.9396690374907 \tabularnewline
126 & 15 & 11.828736665335 & 3.17126333466503 \tabularnewline
127 & 10 & 10.7887859676196 & -0.788785967619555 \tabularnewline
128 & 11 & 11.8058314400077 & -0.805831440007747 \tabularnewline
129 & 9 & 11.0590222451392 & -2.05902224513918 \tabularnewline
130 & 11 & 9.39925207758015 & 1.60074792241985 \tabularnewline
131 & 10 & 11.7859824964499 & -1.78598249644985 \tabularnewline
132 & 11 & 8.72153734817028 & 2.27846265182972 \tabularnewline
133 & 8 & 11.4783644614786 & -3.47836446147856 \tabularnewline
134 & 11 & 9.63700357507371 & 1.36299642492629 \tabularnewline
135 & 12 & 10.5496303693437 & 1.45036963065632 \tabularnewline
136 & 12 & 10.9243006135232 & 1.07569938647677 \tabularnewline
137 & 9 & 9.83370114571178 & -0.833701145711782 \tabularnewline
138 & 11 & 11.1188587865199 & -0.118858786519914 \tabularnewline
139 & 10 & 9.27283132179983 & 0.727168678200169 \tabularnewline
140 & 8 & 9.4826437731848 & -1.4826437731848 \tabularnewline
141 & 9 & 7.64549269257136 & 1.35450730742864 \tabularnewline
142 & 8 & 11.7710429206976 & -3.7710429206976 \tabularnewline
143 & 9 & 11.1872235275761 & -2.18722352757614 \tabularnewline
144 & 15 & 12.4028404785059 & 2.59715952149406 \tabularnewline
145 & 11 & 11.3945442991894 & -0.39454429918937 \tabularnewline
146 & 8 & 8.03333970454682 & -0.0333397045468161 \tabularnewline
147 & 13 & 12.4970259520156 & 0.502974047984359 \tabularnewline
148 & 12 & 10.1458408197901 & 1.85415918020995 \tabularnewline
149 & 12 & 11.7557141972169 & 0.244285802783111 \tabularnewline
150 & 9 & 9.99362639747544 & -0.993626397475436 \tabularnewline
151 & 7 & 8.37268719570674 & -1.37268719570674 \tabularnewline
152 & 13 & 10.7709126074682 & 2.22908739253175 \tabularnewline
153 & 9 & 11.7845471831157 & -2.78454718311569 \tabularnewline
154 & 6 & 11.8575807717304 & -5.85758077173042 \tabularnewline
155 & 8 & 10.5919041301486 & -2.59190413014864 \tabularnewline
156 & 8 & 8.32705335127324 & -0.327053351273243 \tabularnewline
157 & 15 & 12.0075629967169 & 2.99243700328306 \tabularnewline
158 & 6 & 10.2212781922162 & -4.22127819221624 \tabularnewline
159 & 9 & 11.0590222451392 & -2.05902224513918 \tabularnewline
160 & 11 & 11.6956390911945 & -0.695639091194462 \tabularnewline
161 & 8 & 9.60630179447381 & -1.60630179447381 \tabularnewline
162 & 8 & 10.0837813418109 & -2.0837813418109 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]9.88384305807921[/C][C]2.11615694192079[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.2120727939769[/C][C]-0.212072793976896[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5447360561843[/C][C]1.45526394381572[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.322151196414[/C][C]-5.32215119641397[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.4102123512722[/C][C]2.58978764872781[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.69597814393623[/C][C]0.30402185606377[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.6838036575605[/C][C]-0.683803657560514[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.2150137182104[/C][C]2.78498628178959[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.5448460463178[/C][C]1.45515395368223[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0545053259519[/C][C]-5.05450532595189[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.2913935115925[/C][C]-1.29139351159253[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.4676942872303[/C][C]0.532305712769674[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.594452648576[/C][C]0.405547351424017[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.438675150265[/C][C]-0.438675150264977[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.2134015250146[/C][C]2.78659847498538[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.1531280738648[/C][C]0.846871926135211[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.9214009662248[/C][C]-0.92140096622485[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6119791769889[/C][C]-1.61197917698888[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.6617862712644[/C][C]-1.66178627126444[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5922584028264[/C][C]0.407741597173591[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6800195259455[/C][C]-0.680019525945482[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.7754589917039[/C][C]0.224541008296116[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1520011860091[/C][C]-0.152001186009144[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.7228358895677[/C][C]-0.72283588956769[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.7800822274171[/C][C]-2.78008222741714[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.055733682681[/C][C]0.944266317318984[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.5416464299835[/C][C]-1.54164642998347[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.2076759928552[/C][C]2.79232400714479[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.586176482927[/C][C]0.413823517072977[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.6645415515056[/C][C]-0.664541551505645[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0583619993995[/C][C]0.941638000600529[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.78993350988028[/C][C]-1.78993350988028[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.2865420575347[/C][C]-0.286542057534732[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7325586704806[/C][C]0.267441329519411[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.4470016854062[/C][C]1.55299831459381[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.97268502111375[/C][C]0.0273149788862498[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.64367962518416[/C][C]-2.64367962518416[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.4339380197228[/C][C]1.56606198027723[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.7217043702225[/C][C]-1.72170437022254[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.666689614325[/C][C]-1.66668961432501[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4789633406015[/C][C]-1.47896334060154[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.4416518387975[/C][C]1.55834816120253[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.5421809602723[/C][C]1.45781903972766[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5662401288991[/C][C]-0.566240128899148[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.3168724091065[/C][C]-0.316872409106461[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.0052030276397[/C][C]2.9947969723603[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.6077731465932[/C][C]2.39222685340679[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1590028045905[/C][C]-0.159002804590477[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.5094935974213[/C][C]-0.509493597421298[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.4069153847643[/C][C]1.59308461523572[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8534986732448[/C][C]2.14650132675524[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5206951899794[/C][C]-0.520695189979416[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.59215634435098[/C][C]2.40784365564902[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.8235820652327[/C][C]1.17641793476729[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.0952717829083[/C][C]-3.0952717829083[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.3494414765148[/C][C]-4.34944147651483[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3176629901686[/C][C]0.682337009831356[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9281915125489[/C][C]0.0718084874510565[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.1812888713566[/C][C]-0.181288871356633[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.6229084294136[/C][C]-1.62290842941358[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.59928207501813[/C][C]-2.59928207501813[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.508171268268[/C][C]0.491828731731974[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.8112403678466[/C][C]2.18875963215345[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7427607409249[/C][C]0.257239259075135[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.50684330548[/C][C]0.493156694519955[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.4185175738007[/C][C]0.581482426199315[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.6611529252596[/C][C]1.33884707474041[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.7574639718833[/C][C]-1.75746397188333[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.44740499985938[/C][C]2.55259500014062[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6268563182283[/C][C]0.373143681771693[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.33828452681129[/C][C]-0.338284526811291[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7374108943161[/C][C]0.262589105683856[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8163093034543[/C][C]1.18369069654567[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.7699122934835[/C][C]1.23008770651647[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4841624167406[/C][C]0.51583758325938[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7306426413782[/C][C]-2.73064264137818[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.4742335986058[/C][C]1.52576640139416[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.483505820189[/C][C]-0.483505820188991[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.191485682932[/C][C]1.80851431706803[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.4041880515889[/C][C]0.595811948411089[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7544353285972[/C][C]0.245564671402815[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.7738235931471[/C][C]-1.7738235931471[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7399894488768[/C][C]0.260010551123215[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.8755220547845[/C][C]1.12447794521551[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1852001258344[/C][C]-0.185200125834424[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5810006573347[/C][C]-1.58100065733472[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5130936693636[/C][C]-1.51309366936357[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.7085585846119[/C][C]0.29144141538812[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8110172526619[/C][C]0.18898274733814[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.192999390193[/C][C]0.807000609807015[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.60708263215154[/C][C]-0.607082632151542[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.0075629967169[/C][C]2.99243700328306[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.7790853678293[/C][C]0.220914632170678[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9525178297191[/C][C]1.04748217028087[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.2602473728064[/C][C]1.73975262719359[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4285710167636[/C][C]-1.42857101676364[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5379271432154[/C][C]1.46207285678464[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.533348029291[/C][C]-2.53334802929098[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4162626749177[/C][C]0.58373732508234[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7670891250694[/C][C]-0.767089125069389[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.4612208647645[/C][C]2.53877913523546[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.5360303374377[/C][C]-0.5360303374377[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.6924047422078[/C][C]1.3075952577922[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.1708073454112[/C][C]-0.170807345411206[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.7371288313634[/C][C]-0.737128831363391[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.7608874952732[/C][C]2.2391125047268[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.9718794791296[/C][C]0.0281205208703795[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.3609529762845[/C][C]0.639047023715514[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.697980641234[/C][C]-0.697980641234027[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.27596268465184[/C][C]-2.27596268465184[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.7580781696829[/C][C]0.24192183031713[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.8186768204876[/C][C]2.18132317951244[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5683262728418[/C][C]-1.56832627284177[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.0956044235025[/C][C]0.904395576497483[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.23800049153432[/C][C]0.76199950846568[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4662796953789[/C][C]0.533720304621072[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.8119053101053[/C][C]2.18809468989467[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7771723628619[/C][C]-2.77717236286189[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.2242952544441[/C][C]0.775704745555905[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4555749234684[/C][C]0.544425076531637[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.2399479878504[/C][C]0.76005201214964[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.5209187657258[/C][C]1.47908123427424[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.02193035173[/C][C]1.97806964826996[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.6995026926269[/C][C]2.30049730737311[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.0603309625093[/C][C]1.9396690374907[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.828736665335[/C][C]3.17126333466503[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.7887859676196[/C][C]-0.788785967619555[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8058314400077[/C][C]-0.805831440007747[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.0590222451392[/C][C]-2.05902224513918[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.39925207758015[/C][C]1.60074792241985[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.7859824964499[/C][C]-1.78598249644985[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.72153734817028[/C][C]2.27846265182972[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.4783644614786[/C][C]-3.47836446147856[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.63700357507371[/C][C]1.36299642492629[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.5496303693437[/C][C]1.45036963065632[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.9243006135232[/C][C]1.07569938647677[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.83370114571178[/C][C]-0.833701145711782[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1188587865199[/C][C]-0.118858786519914[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.27283132179983[/C][C]0.727168678200169[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.4826437731848[/C][C]-1.4826437731848[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.64549269257136[/C][C]1.35450730742864[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.7710429206976[/C][C]-3.7710429206976[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.1872235275761[/C][C]-2.18722352757614[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.4028404785059[/C][C]2.59715952149406[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.3945442991894[/C][C]-0.39454429918937[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.03333970454682[/C][C]-0.0333397045468161[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.4970259520156[/C][C]0.502974047984359[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.1458408197901[/C][C]1.85415918020995[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.7557141972169[/C][C]0.244285802783111[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.99362639747544[/C][C]-0.993626397475436[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.37268719570674[/C][C]-1.37268719570674[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.7709126074682[/C][C]2.22908739253175[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.7845471831157[/C][C]-2.78454718311569[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.8575807717304[/C][C]-5.85758077173042[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.5919041301486[/C][C]-2.59190413014864[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.32705335127324[/C][C]-0.327053351273243[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.0075629967169[/C][C]2.99243700328306[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2212781922162[/C][C]-4.22127819221624[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.0590222451392[/C][C]-2.05902224513918[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.6956390911945[/C][C]-0.695639091194462[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.60630179447381[/C][C]-1.60630179447381[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.0837813418109[/C][C]-2.0837813418109[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.883843058079212.11615694192079
21111.2120727939769-0.212072793976896
31513.54473605618431.45526394381572
4611.322151196414-5.32215119641397
51310.41021235127222.58978764872781
6109.695978143936230.30402185606377
71212.6838036575605-0.683803657560514
81411.21501371821042.78498628178959
91210.54484604631781.45515395368223
10611.0545053259519-5.05450532595189
111011.2913935115925-1.29139351159253
121211.46769428723030.532305712769674
131211.5944526485760.405547351424017
141111.438675150265-0.438675150264977
151512.21340152501462.78659847498538
161211.15312807386480.846871926135211
171010.9214009662248-0.92140096622485
181213.6119791769889-1.61197917698888
191112.6617862712644-1.66178627126444
201211.59225840282640.407741597173591
211111.6800195259455-0.680019525945482
221211.77545899170390.224541008296116
231313.1520011860091-0.152001186009144
241111.7228358895677-0.72283588956769
25911.7800822274171-2.78008222741714
261312.0557336826810.944266317318984
271011.5416464299835-1.54164642998347
281411.20767599285522.79232400714479
291211.5861764829270.413823517072977
301010.6645415515056-0.664541551505645
311211.05836199939950.941638000600529
3289.78993350988028-1.78993350988028
331010.2865420575347-0.286542057534732
341211.73255867048060.267441329519411
351210.44700168540621.55299831459381
3676.972685021113750.0273149788862498
3768.64367962518416-2.64367962518416
381210.43393801972281.56606198027723
391011.7217043702225-1.72170437022254
401011.666689614325-1.66668961432501
411011.4789633406015-1.47896334060154
421210.44165183879751.55834816120253
431513.54218096027231.45781903972766
441010.5662401288991-0.566240128899148
451010.3168724091065-0.316872409106461
46129.00520302763972.9947969723603
471310.60777314659322.39222685340679
481111.1590028045905-0.159002804590477
491111.5094935974213-0.509493597421298
501210.40691538476431.59308461523572
511411.85349867324482.14650132675524
521010.5206951899794-0.520695189979416
53129.592156344350982.40784365564902
541311.82358206523271.17641793476729
5558.0952717829083-3.0952717829083
56610.3494414765148-4.34944147651483
571211.31766299016860.682337009831356
581211.92819151254890.0718084874510565
591111.1812888713566-0.181288871356633
601011.6229084294136-1.62290842941358
6179.59928207501813-2.59928207501813
621211.5081712682680.491828731731974
631411.81124036784662.18875963215345
641110.74276074092490.257239259075135
651211.506843305480.493156694519955
661312.41851757380070.581482426199315
671412.66115292525961.33884707474041
681112.7574639718833-1.75746397188333
69129.447404999859382.55259500014062
701211.62685631822830.373143681771693
7188.33828452681129-0.338284526811291
721110.73741089431610.262589105683856
731412.81630930345431.18369069654567
741412.76991229348351.23008770651647
751211.48416241674060.51583758325938
76911.7306426413782-2.73064264137818
771311.47423359860581.52576640139416
781111.483505820189-0.483505820188991
791210.1914856829321.80851431706803
801211.40418805158890.595811948411089
811211.75443532859720.245564671402815
821213.7738235931471-1.7738235931471
831211.73998944887680.260010551123215
841210.87552205478451.12447794521551
851111.1852001258344-0.185200125834424
861011.5810006573347-1.58100065733472
87910.5130936693636-1.51309366936357
881211.70855858461190.29144141538812
891211.81101725266190.18898274733814
901211.1929993901930.807000609807015
9199.60708263215154-0.607082632151542
921512.00756299671692.99243700328306
931211.77908536782930.220914632170678
941210.95251782971911.04748217028087
951210.26024737280641.73975262719359
961011.4285710167636-1.42857101676364
971311.53792714321541.46207285678464
98911.533348029291-2.53334802929098
991211.41626267491770.58373732508234
1001010.7670891250694-0.767089125069389
1011411.46122086476452.53877913523546
1021111.5360303374377-0.5360303374377
1031513.69240474220781.3075952577922
1041111.1708073454112-0.170807345411206
1051111.7371288313634-0.737128831363391
106129.76088749527322.2391125047268
1071211.97187947912960.0281205208703795
1081211.36095297628450.639047023715514
1091111.697980641234-0.697980641234027
11079.27596268465184-2.27596268465184
1111211.75807816968290.24192183031713
1121411.81867682048762.18132317951244
1131112.5683262728418-1.56832627284177
1141110.09560442350250.904395576497483
115109.238000491534320.76199950846568
1161312.46627969537890.533720304621072
1171310.81190531010532.18809468989467
118810.7771723628619-2.77717236286189
1191110.22429525444410.775704745555905
1201211.45557492346840.544425076531637
1211110.23994798785040.76005201214964
1221311.52091876572581.47908123427424
1231210.021930351731.97806964826996
1241411.69950269262692.30049730737311
1251311.06033096250931.9396690374907
1261511.8287366653353.17126333466503
1271010.7887859676196-0.788785967619555
1281111.8058314400077-0.805831440007747
129911.0590222451392-2.05902224513918
130119.399252077580151.60074792241985
1311011.7859824964499-1.78598249644985
132118.721537348170282.27846265182972
133811.4783644614786-3.47836446147856
134119.637003575073711.36299642492629
1351210.54963036934371.45036963065632
1361210.92430061352321.07569938647677
13799.83370114571178-0.833701145711782
1381111.1188587865199-0.118858786519914
139109.272831321799830.727168678200169
14089.4826437731848-1.4826437731848
14197.645492692571361.35450730742864
142811.7710429206976-3.7710429206976
143911.1872235275761-2.18722352757614
1441512.40284047850592.59715952149406
1451111.3945442991894-0.39454429918937
14688.03333970454682-0.0333397045468161
1471312.49702595201560.502974047984359
1481210.14584081979011.85415918020995
1491211.75571419721690.244285802783111
15099.99362639747544-0.993626397475436
15178.37268719570674-1.37268719570674
1521310.77091260746822.22908739253175
153911.7845471831157-2.78454718311569
154611.8575807717304-5.85758077173042
155810.5919041301486-2.59190413014864
15688.32705335127324-0.327053351273243
1571512.00756299671692.99243700328306
158610.2212781922162-4.22127819221624
159911.0590222451392-2.05902224513918
1601111.6956390911945-0.695639091194462
16189.60630179447381-1.60630179447381
162810.0837813418109-2.0837813418109






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9998514244770430.0002971510459148640.000148575522957432
110.9995847523122880.0008304953754242230.000415247687712112
120.9995082546316130.0009834907367745780.000491745368387289
130.999067052566540.00186589486691920.000932947433459601
140.9983206259252370.003358748149525840.00167937407476292
150.9980160670595990.003967865880802890.00198393294040144
160.9966271290892280.006745741821544370.00337287091077219
170.9939418304141570.01211633917168530.00605816958584267
180.9925595804137280.01488083917254470.00744041958627234
190.9888419816497230.02231603670055430.0111580183502771
200.9819088842023250.03618223159535070.0180911157976753
210.9734032231928330.05319355361433450.0265967768071673
220.9623495014067280.07530099718654460.0376504985932723
230.9464922559142210.1070154881715590.0535077440857793
240.9277640679186320.1444718641627350.0722359320813676
250.9249256925239750.150148614952050.0750743074760248
260.9432744410981960.1134511178036080.0567255589018042
270.9329122635026370.1341754729947270.0670877364973635
280.9396189109731640.1207621780536730.0603810890268364
290.9188414503004350.162317099399130.0811585496995648
300.898292753897370.203414492205260.10170724610263
310.8796245828358730.2407508343282540.120375417164127
320.897169231969240.2056615360615190.10283076803076
330.8680482385305240.2639035229389520.131951761469476
340.8337787806967710.3324424386064590.166221219303229
350.8242422272016850.3515155455966310.175757772798315
360.7879935934533020.4240128130933950.212006406546698
370.8259965262043150.3480069475913690.174003473795685
380.8173564124695740.3652871750608510.182643587530426
390.8067182126379050.386563574724190.193281787362095
400.7878571511769460.4242856976461080.212142848823054
410.7631497430820590.4737005138358830.236850256917941
420.7480326273755570.5039347452488860.251967372624443
430.747977478813140.5040450423737210.25202252118686
440.7054389256640440.5891221486719120.294561074335956
450.6611798129367410.6776403741265180.338820187063259
460.7287155443430960.5425689113138080.271284455656904
470.7348324740276950.530335051944610.265167525972305
480.6908340710480110.6183318579039770.309165928951989
490.6530452437777910.6939095124444190.346954756222209
500.6389974058703450.722005188259310.361002594129655
510.6445322858072410.7109354283855180.355467714192759
520.5979238159667980.8041523680664050.402076184033202
530.6260448904137870.7479102191724260.373955109586213
540.5908366638208030.8183266723583940.409163336179197
550.6844008447132340.6311983105735320.315599155286766
560.8444847252368730.3110305495262530.155515274763126
570.8173297774608190.3653404450783630.182670222539181
580.7834283066493070.4331433867013850.216571693350693
590.74705992254310.5058801549138010.2529400774569
600.7356413720702920.5287172558594160.264358627929708
610.753698900506260.492602198987480.24630109949374
620.7175736425777510.5648527148444990.282426357422249
630.7327480383860950.534503923227810.267251961613905
640.6925283281531880.6149433436936240.307471671846812
650.6577035896025150.6845928207949690.342296410397485
660.6162816992774260.7674366014451490.383718300722574
670.5961819035518550.807636192896290.403818096448145
680.5791536517384970.8416926965230050.420846348261503
690.5971044244711310.8057911510577380.402895575528869
700.5533156934833340.8933686130333320.446684306516666
710.5132683362786160.9734633274427680.486731663721384
720.4700333801587650.9400667603175310.529966619841235
730.4421622481006450.884324496201290.557837751899355
740.4179353049074690.8358706098149380.582064695092531
750.3924509771304940.7849019542609880.607549022869506
760.4404883653207470.8809767306414930.559511634679253
770.426218264824580.8524365296491610.57378173517542
780.385500643081460.771001286162920.61449935691854
790.3910248831547740.7820497663095480.608975116845226
800.3679238463667080.7358476927334160.632076153633292
810.327336592654010.6546731853080190.67266340734599
820.3229214269096970.6458428538193940.677078573090303
830.2848734328719790.5697468657439580.715126567128021
840.2596060890911730.5192121781823450.740393910908827
850.2238730903930030.4477461807860070.776126909606996
860.2159950075811930.4319900151623870.784004992418807
870.2159875948727690.4319751897455380.784012405127231
880.1839810568373570.3679621136747140.816018943162643
890.1561584720590150.312316944118030.843841527940985
900.1354669854499220.2709339708998430.864533014550078
910.1160262475055860.2320524950111720.883973752494414
920.1604832073464570.3209664146929140.839516792653543
930.1401827576286930.2803655152573860.859817242371307
940.1248910123936880.2497820247873770.875108987606312
950.1209546788314030.2419093576628070.879045321168597
960.1127624241819130.2255248483638270.887237575818087
970.1039963255922940.2079926511845870.896003674407706
980.1286002770573230.2572005541146450.871399722942677
990.1071992093494480.2143984186988950.892800790650553
1000.09061175046184210.1812235009236840.909388249538158
1010.1101361174726890.2202722349453790.889863882527311
1020.09105073758238530.1821014751647710.908949262417615
1030.08730006804969330.1746001360993870.912699931950307
1040.07365389204341710.1473077840868340.926346107956583
1050.06228989728302330.1245797945660470.937710102716977
1060.06363986283095330.1272797256619070.936360137169047
1070.05011052334001930.1002210466800390.949889476659981
1080.04414704124297110.08829408248594220.955852958757029
1090.03528453826327230.07056907652654460.964715461736728
1100.03680186994461590.07360373988923180.963198130055384
1110.02897914327536550.05795828655073090.971020856724635
1120.03251126146042690.06502252292085370.967488738539573
1130.02930892770013420.05861785540026840.970691072299866
1140.02321712519683680.04643425039367350.976782874803163
1150.01821738159467960.03643476318935930.98178261840532
1160.01378970855891680.02757941711783350.986210291441083
1170.0216542811500810.04330856230016190.978345718849919
1180.02486545483764020.04973090967528050.97513454516236
1190.01906996693508890.03813993387017790.980930033064911
1200.01487461440214920.02974922880429840.985125385597851
1210.01235182196452270.02470364392904540.987648178035477
1220.0103459041178880.0206918082357760.989654095882112
1230.01231056033954040.02462112067908090.98768943966046
1240.01932789220894460.03865578441788910.980672107791055
1250.02035044429990470.04070088859980940.979649555700095
1260.04885802009730460.09771604019460920.951141979902695
1270.0373474715302820.0746949430605640.962652528469718
1280.02863465723370940.05726931446741880.971365342766291
1290.02402418833653780.04804837667307550.975975811663462
1300.0226070744368040.0452141488736080.977392925563196
1310.01832525672508690.03665051345017380.981674743274913
1320.02079950843098250.0415990168619650.979200491569017
1330.03284367086299510.06568734172599030.967156329137005
1340.03575727304942770.07151454609885530.964242726950572
1350.03983613550725050.07967227101450090.960163864492749
1360.0338065458537060.0676130917074120.966193454146294
1370.02929563903795240.05859127807590480.970704360962048
1380.02129629726141620.04259259452283250.978703702738584
1390.02077920513243280.04155841026486550.979220794867567
1400.01490155533779420.02980311067558850.985098444662206
1410.04762296039798430.09524592079596860.952377039602016
1420.05098790191205720.1019758038241140.949012098087943
1430.03752332297255060.07504664594510120.962476677027449
1440.354997124566370.709994249132740.64500287543363
1450.3969204847297760.7938409694595520.603079515270224
1460.7087480987426940.5825038025146120.291251901257306
1470.7074647827271410.5850704345457180.292535217272859
1480.8675893726376280.2648212547247430.132410627362372
1490.8676406361125750.264718727774850.132359363887425
1500.7872015163176370.4255969673647250.212798483682363
1510.6706876025442380.6586247949115250.329312397455762
1520.6745723851160340.6508552297679320.325427614883966

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.999851424477043 & 0.000297151045914864 & 0.000148575522957432 \tabularnewline
11 & 0.999584752312288 & 0.000830495375424223 & 0.000415247687712112 \tabularnewline
12 & 0.999508254631613 & 0.000983490736774578 & 0.000491745368387289 \tabularnewline
13 & 0.99906705256654 & 0.0018658948669192 & 0.000932947433459601 \tabularnewline
14 & 0.998320625925237 & 0.00335874814952584 & 0.00167937407476292 \tabularnewline
15 & 0.998016067059599 & 0.00396786588080289 & 0.00198393294040144 \tabularnewline
16 & 0.996627129089228 & 0.00674574182154437 & 0.00337287091077219 \tabularnewline
17 & 0.993941830414157 & 0.0121163391716853 & 0.00605816958584267 \tabularnewline
18 & 0.992559580413728 & 0.0148808391725447 & 0.00744041958627234 \tabularnewline
19 & 0.988841981649723 & 0.0223160367005543 & 0.0111580183502771 \tabularnewline
20 & 0.981908884202325 & 0.0361822315953507 & 0.0180911157976753 \tabularnewline
21 & 0.973403223192833 & 0.0531935536143345 & 0.0265967768071673 \tabularnewline
22 & 0.962349501406728 & 0.0753009971865446 & 0.0376504985932723 \tabularnewline
23 & 0.946492255914221 & 0.107015488171559 & 0.0535077440857793 \tabularnewline
24 & 0.927764067918632 & 0.144471864162735 & 0.0722359320813676 \tabularnewline
25 & 0.924925692523975 & 0.15014861495205 & 0.0750743074760248 \tabularnewline
26 & 0.943274441098196 & 0.113451117803608 & 0.0567255589018042 \tabularnewline
27 & 0.932912263502637 & 0.134175472994727 & 0.0670877364973635 \tabularnewline
28 & 0.939618910973164 & 0.120762178053673 & 0.0603810890268364 \tabularnewline
29 & 0.918841450300435 & 0.16231709939913 & 0.0811585496995648 \tabularnewline
30 & 0.89829275389737 & 0.20341449220526 & 0.10170724610263 \tabularnewline
31 & 0.879624582835873 & 0.240750834328254 & 0.120375417164127 \tabularnewline
32 & 0.89716923196924 & 0.205661536061519 & 0.10283076803076 \tabularnewline
33 & 0.868048238530524 & 0.263903522938952 & 0.131951761469476 \tabularnewline
34 & 0.833778780696771 & 0.332442438606459 & 0.166221219303229 \tabularnewline
35 & 0.824242227201685 & 0.351515545596631 & 0.175757772798315 \tabularnewline
36 & 0.787993593453302 & 0.424012813093395 & 0.212006406546698 \tabularnewline
37 & 0.825996526204315 & 0.348006947591369 & 0.174003473795685 \tabularnewline
38 & 0.817356412469574 & 0.365287175060851 & 0.182643587530426 \tabularnewline
39 & 0.806718212637905 & 0.38656357472419 & 0.193281787362095 \tabularnewline
40 & 0.787857151176946 & 0.424285697646108 & 0.212142848823054 \tabularnewline
41 & 0.763149743082059 & 0.473700513835883 & 0.236850256917941 \tabularnewline
42 & 0.748032627375557 & 0.503934745248886 & 0.251967372624443 \tabularnewline
43 & 0.74797747881314 & 0.504045042373721 & 0.25202252118686 \tabularnewline
44 & 0.705438925664044 & 0.589122148671912 & 0.294561074335956 \tabularnewline
45 & 0.661179812936741 & 0.677640374126518 & 0.338820187063259 \tabularnewline
46 & 0.728715544343096 & 0.542568911313808 & 0.271284455656904 \tabularnewline
47 & 0.734832474027695 & 0.53033505194461 & 0.265167525972305 \tabularnewline
48 & 0.690834071048011 & 0.618331857903977 & 0.309165928951989 \tabularnewline
49 & 0.653045243777791 & 0.693909512444419 & 0.346954756222209 \tabularnewline
50 & 0.638997405870345 & 0.72200518825931 & 0.361002594129655 \tabularnewline
51 & 0.644532285807241 & 0.710935428385518 & 0.355467714192759 \tabularnewline
52 & 0.597923815966798 & 0.804152368066405 & 0.402076184033202 \tabularnewline
53 & 0.626044890413787 & 0.747910219172426 & 0.373955109586213 \tabularnewline
54 & 0.590836663820803 & 0.818326672358394 & 0.409163336179197 \tabularnewline
55 & 0.684400844713234 & 0.631198310573532 & 0.315599155286766 \tabularnewline
56 & 0.844484725236873 & 0.311030549526253 & 0.155515274763126 \tabularnewline
57 & 0.817329777460819 & 0.365340445078363 & 0.182670222539181 \tabularnewline
58 & 0.783428306649307 & 0.433143386701385 & 0.216571693350693 \tabularnewline
59 & 0.7470599225431 & 0.505880154913801 & 0.2529400774569 \tabularnewline
60 & 0.735641372070292 & 0.528717255859416 & 0.264358627929708 \tabularnewline
61 & 0.75369890050626 & 0.49260219898748 & 0.24630109949374 \tabularnewline
62 & 0.717573642577751 & 0.564852714844499 & 0.282426357422249 \tabularnewline
63 & 0.732748038386095 & 0.53450392322781 & 0.267251961613905 \tabularnewline
64 & 0.692528328153188 & 0.614943343693624 & 0.307471671846812 \tabularnewline
65 & 0.657703589602515 & 0.684592820794969 & 0.342296410397485 \tabularnewline
66 & 0.616281699277426 & 0.767436601445149 & 0.383718300722574 \tabularnewline
67 & 0.596181903551855 & 0.80763619289629 & 0.403818096448145 \tabularnewline
68 & 0.579153651738497 & 0.841692696523005 & 0.420846348261503 \tabularnewline
69 & 0.597104424471131 & 0.805791151057738 & 0.402895575528869 \tabularnewline
70 & 0.553315693483334 & 0.893368613033332 & 0.446684306516666 \tabularnewline
71 & 0.513268336278616 & 0.973463327442768 & 0.486731663721384 \tabularnewline
72 & 0.470033380158765 & 0.940066760317531 & 0.529966619841235 \tabularnewline
73 & 0.442162248100645 & 0.88432449620129 & 0.557837751899355 \tabularnewline
74 & 0.417935304907469 & 0.835870609814938 & 0.582064695092531 \tabularnewline
75 & 0.392450977130494 & 0.784901954260988 & 0.607549022869506 \tabularnewline
76 & 0.440488365320747 & 0.880976730641493 & 0.559511634679253 \tabularnewline
77 & 0.42621826482458 & 0.852436529649161 & 0.57378173517542 \tabularnewline
78 & 0.38550064308146 & 0.77100128616292 & 0.61449935691854 \tabularnewline
79 & 0.391024883154774 & 0.782049766309548 & 0.608975116845226 \tabularnewline
80 & 0.367923846366708 & 0.735847692733416 & 0.632076153633292 \tabularnewline
81 & 0.32733659265401 & 0.654673185308019 & 0.67266340734599 \tabularnewline
82 & 0.322921426909697 & 0.645842853819394 & 0.677078573090303 \tabularnewline
83 & 0.284873432871979 & 0.569746865743958 & 0.715126567128021 \tabularnewline
84 & 0.259606089091173 & 0.519212178182345 & 0.740393910908827 \tabularnewline
85 & 0.223873090393003 & 0.447746180786007 & 0.776126909606996 \tabularnewline
86 & 0.215995007581193 & 0.431990015162387 & 0.784004992418807 \tabularnewline
87 & 0.215987594872769 & 0.431975189745538 & 0.784012405127231 \tabularnewline
88 & 0.183981056837357 & 0.367962113674714 & 0.816018943162643 \tabularnewline
89 & 0.156158472059015 & 0.31231694411803 & 0.843841527940985 \tabularnewline
90 & 0.135466985449922 & 0.270933970899843 & 0.864533014550078 \tabularnewline
91 & 0.116026247505586 & 0.232052495011172 & 0.883973752494414 \tabularnewline
92 & 0.160483207346457 & 0.320966414692914 & 0.839516792653543 \tabularnewline
93 & 0.140182757628693 & 0.280365515257386 & 0.859817242371307 \tabularnewline
94 & 0.124891012393688 & 0.249782024787377 & 0.875108987606312 \tabularnewline
95 & 0.120954678831403 & 0.241909357662807 & 0.879045321168597 \tabularnewline
96 & 0.112762424181913 & 0.225524848363827 & 0.887237575818087 \tabularnewline
97 & 0.103996325592294 & 0.207992651184587 & 0.896003674407706 \tabularnewline
98 & 0.128600277057323 & 0.257200554114645 & 0.871399722942677 \tabularnewline
99 & 0.107199209349448 & 0.214398418698895 & 0.892800790650553 \tabularnewline
100 & 0.0906117504618421 & 0.181223500923684 & 0.909388249538158 \tabularnewline
101 & 0.110136117472689 & 0.220272234945379 & 0.889863882527311 \tabularnewline
102 & 0.0910507375823853 & 0.182101475164771 & 0.908949262417615 \tabularnewline
103 & 0.0873000680496933 & 0.174600136099387 & 0.912699931950307 \tabularnewline
104 & 0.0736538920434171 & 0.147307784086834 & 0.926346107956583 \tabularnewline
105 & 0.0622898972830233 & 0.124579794566047 & 0.937710102716977 \tabularnewline
106 & 0.0636398628309533 & 0.127279725661907 & 0.936360137169047 \tabularnewline
107 & 0.0501105233400193 & 0.100221046680039 & 0.949889476659981 \tabularnewline
108 & 0.0441470412429711 & 0.0882940824859422 & 0.955852958757029 \tabularnewline
109 & 0.0352845382632723 & 0.0705690765265446 & 0.964715461736728 \tabularnewline
110 & 0.0368018699446159 & 0.0736037398892318 & 0.963198130055384 \tabularnewline
111 & 0.0289791432753655 & 0.0579582865507309 & 0.971020856724635 \tabularnewline
112 & 0.0325112614604269 & 0.0650225229208537 & 0.967488738539573 \tabularnewline
113 & 0.0293089277001342 & 0.0586178554002684 & 0.970691072299866 \tabularnewline
114 & 0.0232171251968368 & 0.0464342503936735 & 0.976782874803163 \tabularnewline
115 & 0.0182173815946796 & 0.0364347631893593 & 0.98178261840532 \tabularnewline
116 & 0.0137897085589168 & 0.0275794171178335 & 0.986210291441083 \tabularnewline
117 & 0.021654281150081 & 0.0433085623001619 & 0.978345718849919 \tabularnewline
118 & 0.0248654548376402 & 0.0497309096752805 & 0.97513454516236 \tabularnewline
119 & 0.0190699669350889 & 0.0381399338701779 & 0.980930033064911 \tabularnewline
120 & 0.0148746144021492 & 0.0297492288042984 & 0.985125385597851 \tabularnewline
121 & 0.0123518219645227 & 0.0247036439290454 & 0.987648178035477 \tabularnewline
122 & 0.010345904117888 & 0.020691808235776 & 0.989654095882112 \tabularnewline
123 & 0.0123105603395404 & 0.0246211206790809 & 0.98768943966046 \tabularnewline
124 & 0.0193278922089446 & 0.0386557844178891 & 0.980672107791055 \tabularnewline
125 & 0.0203504442999047 & 0.0407008885998094 & 0.979649555700095 \tabularnewline
126 & 0.0488580200973046 & 0.0977160401946092 & 0.951141979902695 \tabularnewline
127 & 0.037347471530282 & 0.074694943060564 & 0.962652528469718 \tabularnewline
128 & 0.0286346572337094 & 0.0572693144674188 & 0.971365342766291 \tabularnewline
129 & 0.0240241883365378 & 0.0480483766730755 & 0.975975811663462 \tabularnewline
130 & 0.022607074436804 & 0.045214148873608 & 0.977392925563196 \tabularnewline
131 & 0.0183252567250869 & 0.0366505134501738 & 0.981674743274913 \tabularnewline
132 & 0.0207995084309825 & 0.041599016861965 & 0.979200491569017 \tabularnewline
133 & 0.0328436708629951 & 0.0656873417259903 & 0.967156329137005 \tabularnewline
134 & 0.0357572730494277 & 0.0715145460988553 & 0.964242726950572 \tabularnewline
135 & 0.0398361355072505 & 0.0796722710145009 & 0.960163864492749 \tabularnewline
136 & 0.033806545853706 & 0.067613091707412 & 0.966193454146294 \tabularnewline
137 & 0.0292956390379524 & 0.0585912780759048 & 0.970704360962048 \tabularnewline
138 & 0.0212962972614162 & 0.0425925945228325 & 0.978703702738584 \tabularnewline
139 & 0.0207792051324328 & 0.0415584102648655 & 0.979220794867567 \tabularnewline
140 & 0.0149015553377942 & 0.0298031106755885 & 0.985098444662206 \tabularnewline
141 & 0.0476229603979843 & 0.0952459207959686 & 0.952377039602016 \tabularnewline
142 & 0.0509879019120572 & 0.101975803824114 & 0.949012098087943 \tabularnewline
143 & 0.0375233229725506 & 0.0750466459451012 & 0.962476677027449 \tabularnewline
144 & 0.35499712456637 & 0.70999424913274 & 0.64500287543363 \tabularnewline
145 & 0.396920484729776 & 0.793840969459552 & 0.603079515270224 \tabularnewline
146 & 0.708748098742694 & 0.582503802514612 & 0.291251901257306 \tabularnewline
147 & 0.707464782727141 & 0.585070434545718 & 0.292535217272859 \tabularnewline
148 & 0.867589372637628 & 0.264821254724743 & 0.132410627362372 \tabularnewline
149 & 0.867640636112575 & 0.26471872777485 & 0.132359363887425 \tabularnewline
150 & 0.787201516317637 & 0.425596967364725 & 0.212798483682363 \tabularnewline
151 & 0.670687602544238 & 0.658624794911525 & 0.329312397455762 \tabularnewline
152 & 0.674572385116034 & 0.650855229767932 & 0.325427614883966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&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]10[/C][C]0.999851424477043[/C][C]0.000297151045914864[/C][C]0.000148575522957432[/C][/ROW]
[ROW][C]11[/C][C]0.999584752312288[/C][C]0.000830495375424223[/C][C]0.000415247687712112[/C][/ROW]
[ROW][C]12[/C][C]0.999508254631613[/C][C]0.000983490736774578[/C][C]0.000491745368387289[/C][/ROW]
[ROW][C]13[/C][C]0.99906705256654[/C][C]0.0018658948669192[/C][C]0.000932947433459601[/C][/ROW]
[ROW][C]14[/C][C]0.998320625925237[/C][C]0.00335874814952584[/C][C]0.00167937407476292[/C][/ROW]
[ROW][C]15[/C][C]0.998016067059599[/C][C]0.00396786588080289[/C][C]0.00198393294040144[/C][/ROW]
[ROW][C]16[/C][C]0.996627129089228[/C][C]0.00674574182154437[/C][C]0.00337287091077219[/C][/ROW]
[ROW][C]17[/C][C]0.993941830414157[/C][C]0.0121163391716853[/C][C]0.00605816958584267[/C][/ROW]
[ROW][C]18[/C][C]0.992559580413728[/C][C]0.0148808391725447[/C][C]0.00744041958627234[/C][/ROW]
[ROW][C]19[/C][C]0.988841981649723[/C][C]0.0223160367005543[/C][C]0.0111580183502771[/C][/ROW]
[ROW][C]20[/C][C]0.981908884202325[/C][C]0.0361822315953507[/C][C]0.0180911157976753[/C][/ROW]
[ROW][C]21[/C][C]0.973403223192833[/C][C]0.0531935536143345[/C][C]0.0265967768071673[/C][/ROW]
[ROW][C]22[/C][C]0.962349501406728[/C][C]0.0753009971865446[/C][C]0.0376504985932723[/C][/ROW]
[ROW][C]23[/C][C]0.946492255914221[/C][C]0.107015488171559[/C][C]0.0535077440857793[/C][/ROW]
[ROW][C]24[/C][C]0.927764067918632[/C][C]0.144471864162735[/C][C]0.0722359320813676[/C][/ROW]
[ROW][C]25[/C][C]0.924925692523975[/C][C]0.15014861495205[/C][C]0.0750743074760248[/C][/ROW]
[ROW][C]26[/C][C]0.943274441098196[/C][C]0.113451117803608[/C][C]0.0567255589018042[/C][/ROW]
[ROW][C]27[/C][C]0.932912263502637[/C][C]0.134175472994727[/C][C]0.0670877364973635[/C][/ROW]
[ROW][C]28[/C][C]0.939618910973164[/C][C]0.120762178053673[/C][C]0.0603810890268364[/C][/ROW]
[ROW][C]29[/C][C]0.918841450300435[/C][C]0.16231709939913[/C][C]0.0811585496995648[/C][/ROW]
[ROW][C]30[/C][C]0.89829275389737[/C][C]0.20341449220526[/C][C]0.10170724610263[/C][/ROW]
[ROW][C]31[/C][C]0.879624582835873[/C][C]0.240750834328254[/C][C]0.120375417164127[/C][/ROW]
[ROW][C]32[/C][C]0.89716923196924[/C][C]0.205661536061519[/C][C]0.10283076803076[/C][/ROW]
[ROW][C]33[/C][C]0.868048238530524[/C][C]0.263903522938952[/C][C]0.131951761469476[/C][/ROW]
[ROW][C]34[/C][C]0.833778780696771[/C][C]0.332442438606459[/C][C]0.166221219303229[/C][/ROW]
[ROW][C]35[/C][C]0.824242227201685[/C][C]0.351515545596631[/C][C]0.175757772798315[/C][/ROW]
[ROW][C]36[/C][C]0.787993593453302[/C][C]0.424012813093395[/C][C]0.212006406546698[/C][/ROW]
[ROW][C]37[/C][C]0.825996526204315[/C][C]0.348006947591369[/C][C]0.174003473795685[/C][/ROW]
[ROW][C]38[/C][C]0.817356412469574[/C][C]0.365287175060851[/C][C]0.182643587530426[/C][/ROW]
[ROW][C]39[/C][C]0.806718212637905[/C][C]0.38656357472419[/C][C]0.193281787362095[/C][/ROW]
[ROW][C]40[/C][C]0.787857151176946[/C][C]0.424285697646108[/C][C]0.212142848823054[/C][/ROW]
[ROW][C]41[/C][C]0.763149743082059[/C][C]0.473700513835883[/C][C]0.236850256917941[/C][/ROW]
[ROW][C]42[/C][C]0.748032627375557[/C][C]0.503934745248886[/C][C]0.251967372624443[/C][/ROW]
[ROW][C]43[/C][C]0.74797747881314[/C][C]0.504045042373721[/C][C]0.25202252118686[/C][/ROW]
[ROW][C]44[/C][C]0.705438925664044[/C][C]0.589122148671912[/C][C]0.294561074335956[/C][/ROW]
[ROW][C]45[/C][C]0.661179812936741[/C][C]0.677640374126518[/C][C]0.338820187063259[/C][/ROW]
[ROW][C]46[/C][C]0.728715544343096[/C][C]0.542568911313808[/C][C]0.271284455656904[/C][/ROW]
[ROW][C]47[/C][C]0.734832474027695[/C][C]0.53033505194461[/C][C]0.265167525972305[/C][/ROW]
[ROW][C]48[/C][C]0.690834071048011[/C][C]0.618331857903977[/C][C]0.309165928951989[/C][/ROW]
[ROW][C]49[/C][C]0.653045243777791[/C][C]0.693909512444419[/C][C]0.346954756222209[/C][/ROW]
[ROW][C]50[/C][C]0.638997405870345[/C][C]0.72200518825931[/C][C]0.361002594129655[/C][/ROW]
[ROW][C]51[/C][C]0.644532285807241[/C][C]0.710935428385518[/C][C]0.355467714192759[/C][/ROW]
[ROW][C]52[/C][C]0.597923815966798[/C][C]0.804152368066405[/C][C]0.402076184033202[/C][/ROW]
[ROW][C]53[/C][C]0.626044890413787[/C][C]0.747910219172426[/C][C]0.373955109586213[/C][/ROW]
[ROW][C]54[/C][C]0.590836663820803[/C][C]0.818326672358394[/C][C]0.409163336179197[/C][/ROW]
[ROW][C]55[/C][C]0.684400844713234[/C][C]0.631198310573532[/C][C]0.315599155286766[/C][/ROW]
[ROW][C]56[/C][C]0.844484725236873[/C][C]0.311030549526253[/C][C]0.155515274763126[/C][/ROW]
[ROW][C]57[/C][C]0.817329777460819[/C][C]0.365340445078363[/C][C]0.182670222539181[/C][/ROW]
[ROW][C]58[/C][C]0.783428306649307[/C][C]0.433143386701385[/C][C]0.216571693350693[/C][/ROW]
[ROW][C]59[/C][C]0.7470599225431[/C][C]0.505880154913801[/C][C]0.2529400774569[/C][/ROW]
[ROW][C]60[/C][C]0.735641372070292[/C][C]0.528717255859416[/C][C]0.264358627929708[/C][/ROW]
[ROW][C]61[/C][C]0.75369890050626[/C][C]0.49260219898748[/C][C]0.24630109949374[/C][/ROW]
[ROW][C]62[/C][C]0.717573642577751[/C][C]0.564852714844499[/C][C]0.282426357422249[/C][/ROW]
[ROW][C]63[/C][C]0.732748038386095[/C][C]0.53450392322781[/C][C]0.267251961613905[/C][/ROW]
[ROW][C]64[/C][C]0.692528328153188[/C][C]0.614943343693624[/C][C]0.307471671846812[/C][/ROW]
[ROW][C]65[/C][C]0.657703589602515[/C][C]0.684592820794969[/C][C]0.342296410397485[/C][/ROW]
[ROW][C]66[/C][C]0.616281699277426[/C][C]0.767436601445149[/C][C]0.383718300722574[/C][/ROW]
[ROW][C]67[/C][C]0.596181903551855[/C][C]0.80763619289629[/C][C]0.403818096448145[/C][/ROW]
[ROW][C]68[/C][C]0.579153651738497[/C][C]0.841692696523005[/C][C]0.420846348261503[/C][/ROW]
[ROW][C]69[/C][C]0.597104424471131[/C][C]0.805791151057738[/C][C]0.402895575528869[/C][/ROW]
[ROW][C]70[/C][C]0.553315693483334[/C][C]0.893368613033332[/C][C]0.446684306516666[/C][/ROW]
[ROW][C]71[/C][C]0.513268336278616[/C][C]0.973463327442768[/C][C]0.486731663721384[/C][/ROW]
[ROW][C]72[/C][C]0.470033380158765[/C][C]0.940066760317531[/C][C]0.529966619841235[/C][/ROW]
[ROW][C]73[/C][C]0.442162248100645[/C][C]0.88432449620129[/C][C]0.557837751899355[/C][/ROW]
[ROW][C]74[/C][C]0.417935304907469[/C][C]0.835870609814938[/C][C]0.582064695092531[/C][/ROW]
[ROW][C]75[/C][C]0.392450977130494[/C][C]0.784901954260988[/C][C]0.607549022869506[/C][/ROW]
[ROW][C]76[/C][C]0.440488365320747[/C][C]0.880976730641493[/C][C]0.559511634679253[/C][/ROW]
[ROW][C]77[/C][C]0.42621826482458[/C][C]0.852436529649161[/C][C]0.57378173517542[/C][/ROW]
[ROW][C]78[/C][C]0.38550064308146[/C][C]0.77100128616292[/C][C]0.61449935691854[/C][/ROW]
[ROW][C]79[/C][C]0.391024883154774[/C][C]0.782049766309548[/C][C]0.608975116845226[/C][/ROW]
[ROW][C]80[/C][C]0.367923846366708[/C][C]0.735847692733416[/C][C]0.632076153633292[/C][/ROW]
[ROW][C]81[/C][C]0.32733659265401[/C][C]0.654673185308019[/C][C]0.67266340734599[/C][/ROW]
[ROW][C]82[/C][C]0.322921426909697[/C][C]0.645842853819394[/C][C]0.677078573090303[/C][/ROW]
[ROW][C]83[/C][C]0.284873432871979[/C][C]0.569746865743958[/C][C]0.715126567128021[/C][/ROW]
[ROW][C]84[/C][C]0.259606089091173[/C][C]0.519212178182345[/C][C]0.740393910908827[/C][/ROW]
[ROW][C]85[/C][C]0.223873090393003[/C][C]0.447746180786007[/C][C]0.776126909606996[/C][/ROW]
[ROW][C]86[/C][C]0.215995007581193[/C][C]0.431990015162387[/C][C]0.784004992418807[/C][/ROW]
[ROW][C]87[/C][C]0.215987594872769[/C][C]0.431975189745538[/C][C]0.784012405127231[/C][/ROW]
[ROW][C]88[/C][C]0.183981056837357[/C][C]0.367962113674714[/C][C]0.816018943162643[/C][/ROW]
[ROW][C]89[/C][C]0.156158472059015[/C][C]0.31231694411803[/C][C]0.843841527940985[/C][/ROW]
[ROW][C]90[/C][C]0.135466985449922[/C][C]0.270933970899843[/C][C]0.864533014550078[/C][/ROW]
[ROW][C]91[/C][C]0.116026247505586[/C][C]0.232052495011172[/C][C]0.883973752494414[/C][/ROW]
[ROW][C]92[/C][C]0.160483207346457[/C][C]0.320966414692914[/C][C]0.839516792653543[/C][/ROW]
[ROW][C]93[/C][C]0.140182757628693[/C][C]0.280365515257386[/C][C]0.859817242371307[/C][/ROW]
[ROW][C]94[/C][C]0.124891012393688[/C][C]0.249782024787377[/C][C]0.875108987606312[/C][/ROW]
[ROW][C]95[/C][C]0.120954678831403[/C][C]0.241909357662807[/C][C]0.879045321168597[/C][/ROW]
[ROW][C]96[/C][C]0.112762424181913[/C][C]0.225524848363827[/C][C]0.887237575818087[/C][/ROW]
[ROW][C]97[/C][C]0.103996325592294[/C][C]0.207992651184587[/C][C]0.896003674407706[/C][/ROW]
[ROW][C]98[/C][C]0.128600277057323[/C][C]0.257200554114645[/C][C]0.871399722942677[/C][/ROW]
[ROW][C]99[/C][C]0.107199209349448[/C][C]0.214398418698895[/C][C]0.892800790650553[/C][/ROW]
[ROW][C]100[/C][C]0.0906117504618421[/C][C]0.181223500923684[/C][C]0.909388249538158[/C][/ROW]
[ROW][C]101[/C][C]0.110136117472689[/C][C]0.220272234945379[/C][C]0.889863882527311[/C][/ROW]
[ROW][C]102[/C][C]0.0910507375823853[/C][C]0.182101475164771[/C][C]0.908949262417615[/C][/ROW]
[ROW][C]103[/C][C]0.0873000680496933[/C][C]0.174600136099387[/C][C]0.912699931950307[/C][/ROW]
[ROW][C]104[/C][C]0.0736538920434171[/C][C]0.147307784086834[/C][C]0.926346107956583[/C][/ROW]
[ROW][C]105[/C][C]0.0622898972830233[/C][C]0.124579794566047[/C][C]0.937710102716977[/C][/ROW]
[ROW][C]106[/C][C]0.0636398628309533[/C][C]0.127279725661907[/C][C]0.936360137169047[/C][/ROW]
[ROW][C]107[/C][C]0.0501105233400193[/C][C]0.100221046680039[/C][C]0.949889476659981[/C][/ROW]
[ROW][C]108[/C][C]0.0441470412429711[/C][C]0.0882940824859422[/C][C]0.955852958757029[/C][/ROW]
[ROW][C]109[/C][C]0.0352845382632723[/C][C]0.0705690765265446[/C][C]0.964715461736728[/C][/ROW]
[ROW][C]110[/C][C]0.0368018699446159[/C][C]0.0736037398892318[/C][C]0.963198130055384[/C][/ROW]
[ROW][C]111[/C][C]0.0289791432753655[/C][C]0.0579582865507309[/C][C]0.971020856724635[/C][/ROW]
[ROW][C]112[/C][C]0.0325112614604269[/C][C]0.0650225229208537[/C][C]0.967488738539573[/C][/ROW]
[ROW][C]113[/C][C]0.0293089277001342[/C][C]0.0586178554002684[/C][C]0.970691072299866[/C][/ROW]
[ROW][C]114[/C][C]0.0232171251968368[/C][C]0.0464342503936735[/C][C]0.976782874803163[/C][/ROW]
[ROW][C]115[/C][C]0.0182173815946796[/C][C]0.0364347631893593[/C][C]0.98178261840532[/C][/ROW]
[ROW][C]116[/C][C]0.0137897085589168[/C][C]0.0275794171178335[/C][C]0.986210291441083[/C][/ROW]
[ROW][C]117[/C][C]0.021654281150081[/C][C]0.0433085623001619[/C][C]0.978345718849919[/C][/ROW]
[ROW][C]118[/C][C]0.0248654548376402[/C][C]0.0497309096752805[/C][C]0.97513454516236[/C][/ROW]
[ROW][C]119[/C][C]0.0190699669350889[/C][C]0.0381399338701779[/C][C]0.980930033064911[/C][/ROW]
[ROW][C]120[/C][C]0.0148746144021492[/C][C]0.0297492288042984[/C][C]0.985125385597851[/C][/ROW]
[ROW][C]121[/C][C]0.0123518219645227[/C][C]0.0247036439290454[/C][C]0.987648178035477[/C][/ROW]
[ROW][C]122[/C][C]0.010345904117888[/C][C]0.020691808235776[/C][C]0.989654095882112[/C][/ROW]
[ROW][C]123[/C][C]0.0123105603395404[/C][C]0.0246211206790809[/C][C]0.98768943966046[/C][/ROW]
[ROW][C]124[/C][C]0.0193278922089446[/C][C]0.0386557844178891[/C][C]0.980672107791055[/C][/ROW]
[ROW][C]125[/C][C]0.0203504442999047[/C][C]0.0407008885998094[/C][C]0.979649555700095[/C][/ROW]
[ROW][C]126[/C][C]0.0488580200973046[/C][C]0.0977160401946092[/C][C]0.951141979902695[/C][/ROW]
[ROW][C]127[/C][C]0.037347471530282[/C][C]0.074694943060564[/C][C]0.962652528469718[/C][/ROW]
[ROW][C]128[/C][C]0.0286346572337094[/C][C]0.0572693144674188[/C][C]0.971365342766291[/C][/ROW]
[ROW][C]129[/C][C]0.0240241883365378[/C][C]0.0480483766730755[/C][C]0.975975811663462[/C][/ROW]
[ROW][C]130[/C][C]0.022607074436804[/C][C]0.045214148873608[/C][C]0.977392925563196[/C][/ROW]
[ROW][C]131[/C][C]0.0183252567250869[/C][C]0.0366505134501738[/C][C]0.981674743274913[/C][/ROW]
[ROW][C]132[/C][C]0.0207995084309825[/C][C]0.041599016861965[/C][C]0.979200491569017[/C][/ROW]
[ROW][C]133[/C][C]0.0328436708629951[/C][C]0.0656873417259903[/C][C]0.967156329137005[/C][/ROW]
[ROW][C]134[/C][C]0.0357572730494277[/C][C]0.0715145460988553[/C][C]0.964242726950572[/C][/ROW]
[ROW][C]135[/C][C]0.0398361355072505[/C][C]0.0796722710145009[/C][C]0.960163864492749[/C][/ROW]
[ROW][C]136[/C][C]0.033806545853706[/C][C]0.067613091707412[/C][C]0.966193454146294[/C][/ROW]
[ROW][C]137[/C][C]0.0292956390379524[/C][C]0.0585912780759048[/C][C]0.970704360962048[/C][/ROW]
[ROW][C]138[/C][C]0.0212962972614162[/C][C]0.0425925945228325[/C][C]0.978703702738584[/C][/ROW]
[ROW][C]139[/C][C]0.0207792051324328[/C][C]0.0415584102648655[/C][C]0.979220794867567[/C][/ROW]
[ROW][C]140[/C][C]0.0149015553377942[/C][C]0.0298031106755885[/C][C]0.985098444662206[/C][/ROW]
[ROW][C]141[/C][C]0.0476229603979843[/C][C]0.0952459207959686[/C][C]0.952377039602016[/C][/ROW]
[ROW][C]142[/C][C]0.0509879019120572[/C][C]0.101975803824114[/C][C]0.949012098087943[/C][/ROW]
[ROW][C]143[/C][C]0.0375233229725506[/C][C]0.0750466459451012[/C][C]0.962476677027449[/C][/ROW]
[ROW][C]144[/C][C]0.35499712456637[/C][C]0.70999424913274[/C][C]0.64500287543363[/C][/ROW]
[ROW][C]145[/C][C]0.396920484729776[/C][C]0.793840969459552[/C][C]0.603079515270224[/C][/ROW]
[ROW][C]146[/C][C]0.708748098742694[/C][C]0.582503802514612[/C][C]0.291251901257306[/C][/ROW]
[ROW][C]147[/C][C]0.707464782727141[/C][C]0.585070434545718[/C][C]0.292535217272859[/C][/ROW]
[ROW][C]148[/C][C]0.867589372637628[/C][C]0.264821254724743[/C][C]0.132410627362372[/C][/ROW]
[ROW][C]149[/C][C]0.867640636112575[/C][C]0.26471872777485[/C][C]0.132359363887425[/C][/ROW]
[ROW][C]150[/C][C]0.787201516317637[/C][C]0.425596967364725[/C][C]0.212798483682363[/C][/ROW]
[ROW][C]151[/C][C]0.670687602544238[/C][C]0.658624794911525[/C][C]0.329312397455762[/C][/ROW]
[ROW][C]152[/C][C]0.674572385116034[/C][C]0.650855229767932[/C][C]0.325427614883966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9998514244770430.0002971510459148640.000148575522957432
110.9995847523122880.0008304953754242230.000415247687712112
120.9995082546316130.0009834907367745780.000491745368387289
130.999067052566540.00186589486691920.000932947433459601
140.9983206259252370.003358748149525840.00167937407476292
150.9980160670595990.003967865880802890.00198393294040144
160.9966271290892280.006745741821544370.00337287091077219
170.9939418304141570.01211633917168530.00605816958584267
180.9925595804137280.01488083917254470.00744041958627234
190.9888419816497230.02231603670055430.0111580183502771
200.9819088842023250.03618223159535070.0180911157976753
210.9734032231928330.05319355361433450.0265967768071673
220.9623495014067280.07530099718654460.0376504985932723
230.9464922559142210.1070154881715590.0535077440857793
240.9277640679186320.1444718641627350.0722359320813676
250.9249256925239750.150148614952050.0750743074760248
260.9432744410981960.1134511178036080.0567255589018042
270.9329122635026370.1341754729947270.0670877364973635
280.9396189109731640.1207621780536730.0603810890268364
290.9188414503004350.162317099399130.0811585496995648
300.898292753897370.203414492205260.10170724610263
310.8796245828358730.2407508343282540.120375417164127
320.897169231969240.2056615360615190.10283076803076
330.8680482385305240.2639035229389520.131951761469476
340.8337787806967710.3324424386064590.166221219303229
350.8242422272016850.3515155455966310.175757772798315
360.7879935934533020.4240128130933950.212006406546698
370.8259965262043150.3480069475913690.174003473795685
380.8173564124695740.3652871750608510.182643587530426
390.8067182126379050.386563574724190.193281787362095
400.7878571511769460.4242856976461080.212142848823054
410.7631497430820590.4737005138358830.236850256917941
420.7480326273755570.5039347452488860.251967372624443
430.747977478813140.5040450423737210.25202252118686
440.7054389256640440.5891221486719120.294561074335956
450.6611798129367410.6776403741265180.338820187063259
460.7287155443430960.5425689113138080.271284455656904
470.7348324740276950.530335051944610.265167525972305
480.6908340710480110.6183318579039770.309165928951989
490.6530452437777910.6939095124444190.346954756222209
500.6389974058703450.722005188259310.361002594129655
510.6445322858072410.7109354283855180.355467714192759
520.5979238159667980.8041523680664050.402076184033202
530.6260448904137870.7479102191724260.373955109586213
540.5908366638208030.8183266723583940.409163336179197
550.6844008447132340.6311983105735320.315599155286766
560.8444847252368730.3110305495262530.155515274763126
570.8173297774608190.3653404450783630.182670222539181
580.7834283066493070.4331433867013850.216571693350693
590.74705992254310.5058801549138010.2529400774569
600.7356413720702920.5287172558594160.264358627929708
610.753698900506260.492602198987480.24630109949374
620.7175736425777510.5648527148444990.282426357422249
630.7327480383860950.534503923227810.267251961613905
640.6925283281531880.6149433436936240.307471671846812
650.6577035896025150.6845928207949690.342296410397485
660.6162816992774260.7674366014451490.383718300722574
670.5961819035518550.807636192896290.403818096448145
680.5791536517384970.8416926965230050.420846348261503
690.5971044244711310.8057911510577380.402895575528869
700.5533156934833340.8933686130333320.446684306516666
710.5132683362786160.9734633274427680.486731663721384
720.4700333801587650.9400667603175310.529966619841235
730.4421622481006450.884324496201290.557837751899355
740.4179353049074690.8358706098149380.582064695092531
750.3924509771304940.7849019542609880.607549022869506
760.4404883653207470.8809767306414930.559511634679253
770.426218264824580.8524365296491610.57378173517542
780.385500643081460.771001286162920.61449935691854
790.3910248831547740.7820497663095480.608975116845226
800.3679238463667080.7358476927334160.632076153633292
810.327336592654010.6546731853080190.67266340734599
820.3229214269096970.6458428538193940.677078573090303
830.2848734328719790.5697468657439580.715126567128021
840.2596060890911730.5192121781823450.740393910908827
850.2238730903930030.4477461807860070.776126909606996
860.2159950075811930.4319900151623870.784004992418807
870.2159875948727690.4319751897455380.784012405127231
880.1839810568373570.3679621136747140.816018943162643
890.1561584720590150.312316944118030.843841527940985
900.1354669854499220.2709339708998430.864533014550078
910.1160262475055860.2320524950111720.883973752494414
920.1604832073464570.3209664146929140.839516792653543
930.1401827576286930.2803655152573860.859817242371307
940.1248910123936880.2497820247873770.875108987606312
950.1209546788314030.2419093576628070.879045321168597
960.1127624241819130.2255248483638270.887237575818087
970.1039963255922940.2079926511845870.896003674407706
980.1286002770573230.2572005541146450.871399722942677
990.1071992093494480.2143984186988950.892800790650553
1000.09061175046184210.1812235009236840.909388249538158
1010.1101361174726890.2202722349453790.889863882527311
1020.09105073758238530.1821014751647710.908949262417615
1030.08730006804969330.1746001360993870.912699931950307
1040.07365389204341710.1473077840868340.926346107956583
1050.06228989728302330.1245797945660470.937710102716977
1060.06363986283095330.1272797256619070.936360137169047
1070.05011052334001930.1002210466800390.949889476659981
1080.04414704124297110.08829408248594220.955852958757029
1090.03528453826327230.07056907652654460.964715461736728
1100.03680186994461590.07360373988923180.963198130055384
1110.02897914327536550.05795828655073090.971020856724635
1120.03251126146042690.06502252292085370.967488738539573
1130.02930892770013420.05861785540026840.970691072299866
1140.02321712519683680.04643425039367350.976782874803163
1150.01821738159467960.03643476318935930.98178261840532
1160.01378970855891680.02757941711783350.986210291441083
1170.0216542811500810.04330856230016190.978345718849919
1180.02486545483764020.04973090967528050.97513454516236
1190.01906996693508890.03813993387017790.980930033064911
1200.01487461440214920.02974922880429840.985125385597851
1210.01235182196452270.02470364392904540.987648178035477
1220.0103459041178880.0206918082357760.989654095882112
1230.01231056033954040.02462112067908090.98768943966046
1240.01932789220894460.03865578441788910.980672107791055
1250.02035044429990470.04070088859980940.979649555700095
1260.04885802009730460.09771604019460920.951141979902695
1270.0373474715302820.0746949430605640.962652528469718
1280.02863465723370940.05726931446741880.971365342766291
1290.02402418833653780.04804837667307550.975975811663462
1300.0226070744368040.0452141488736080.977392925563196
1310.01832525672508690.03665051345017380.981674743274913
1320.02079950843098250.0415990168619650.979200491569017
1330.03284367086299510.06568734172599030.967156329137005
1340.03575727304942770.07151454609885530.964242726950572
1350.03983613550725050.07967227101450090.960163864492749
1360.0338065458537060.0676130917074120.966193454146294
1370.02929563903795240.05859127807590480.970704360962048
1380.02129629726141620.04259259452283250.978703702738584
1390.02077920513243280.04155841026486550.979220794867567
1400.01490155533779420.02980311067558850.985098444662206
1410.04762296039798430.09524592079596860.952377039602016
1420.05098790191205720.1019758038241140.949012098087943
1430.03752332297255060.07504664594510120.962476677027449
1440.354997124566370.709994249132740.64500287543363
1450.3969204847297760.7938409694595520.603079515270224
1460.7087480987426940.5825038025146120.291251901257306
1470.7074647827271410.5850704345457180.292535217272859
1480.8675893726376280.2648212547247430.132410627362372
1490.8676406361125750.264718727774850.132359363887425
1500.7872015163176370.4255969673647250.212798483682363
1510.6706876025442380.6586247949115250.329312397455762
1520.6745723851160340.6508552297679320.325427614883966






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level300.20979020979021NOK
10% type I error level480.335664335664336NOK

\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 & 7 & 0.048951048951049 & NOK \tabularnewline
5% type I error level & 30 & 0.20979020979021 & NOK \tabularnewline
10% type I error level & 48 & 0.335664335664336 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185444&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]7[/C][C]0.048951048951049[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.20979020979021[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.335664335664336[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185444&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185444&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level300.20979020979021NOK
10% type I error level480.335664335664336NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}