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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 computationTue, 13 Dec 2011 18:02:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323817411jz2gsenrryraunq.htm/, Retrieved Fri, 03 May 2024 02:41:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154772, Retrieved Fri, 03 May 2024 02:41:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2011-12-05 14:37:08] [d623f9be707a26b8ffaece1fc4d5a7ee]
- R     [(Partial) Autocorrelation Function] [] [2011-12-05 17:26:34] [74be16979710d4c4e7c6647856088456]
- RMPD      [Multiple Regression] [WS 10 - MR happiness] [2011-12-13 23:02:59] [e1c4030d3eb0ab0fcc7a7b48aeaac474] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	12	30	33	13	16
1	8	8	32	35	11	15
2	14	12	30	35	12	13
2	14	11	33	25	13	14
1	13	11	36	39	12	17
1	16	13	37	37	12	13
1	14	11	31	31	13	12
1	13	10	36	28	12	9
2	15	7	40	38	15	25
1	13	10	31	32	11	13
2	16	12	24	32	13	10
1	20	15	46	46	12	13
1	17	12	40	40	12	9
1	15	15	27	33	12	14
2	16	12	32	25	15	26
1	16	10	41	37	13	12
1	12	10	28	33	13	11
2	9	8	34	33	12	19
2	15	11	31	35	11	12
2	17	14	38	39	11	9
1	12	12	37	36	13	15
1	10	11	34	37	10	15
2	11	6	33	43	12	23
2	16	12	38	27	13	20
1	16	14	27	31	10	0
2	15	11	36	33	12	15
1	13	8	37	35	13	8
2	14	12	35	36	12	12
1	19	15	44	39	11	11
1	16	13	41	31	11	18
2	17	14	29	34	14	19
1	10	12	31	29	12	13
1	15	7	32	37	14	22
1	14	11	35	30	12	12
1	14	7	36	32	12	15
2	16	12	28	31	13	16
1	17	12	34	34	15	16
1	15	12	36	30	12	13
2	17	13	33	33	16	11
2	14	15	35	37	10	16
1	10	9	34	33	13	14
2	14	9	38	28	12	11
2	16	11	35	32	12	20
2	18	14	40	40	16	16
1	15	12	35	39	12	12
1	16	15	32	28	16	17
1	16	12	33	33	13	11
1	10	6	31	36	10	12
2	8	5	32	35	14	14
1	17	13	35	34	13	13
1	14	11	32	35	12	14
1	12	11	26	30	13	19
2	10	6	38	35	16	17
1	14	12	45	37	12	11
1	12	10	36	40	12	12
1	16	6	37	34	13	12
1	16	12	33	37	13	14
1	15	14	35	38	11	15
2	11	6	32	27	14	18
1	16	11	32	27	16	16
2	8	6	32	27	16	16
1	17	14	33	39	14	19
1	16	12	37	37	14	17
1	15	12	40	32	14	15
2	8	8	35	27	14	13
1	13	10	30	35	10	16
1	14	11	36	40	13	17
1	13	7	34	32	14	16
1	16	12	34	36	17	13
2	12	9	37	35	12	15
1	19	13	34	31	12	16
1	19	14	37	34	12	10
1	12	6	43	36	15	19
1	14	12	39	40	10	11
2	15	6	29	33	13	17
1	13	14	41	38	12	19
2	16	12	32	33	13	15
2	10	10	34	35	14	15
1	15	10	34	30	12	17
1	16	12	35	31	13	13
1	15	11	41	42	14	17
2	11	10	32	33	10	12
2	9	7	39	35	12	27
1	16	12	33	33	13	12
1	12	12	30	31	10	15
2	14	12	32	36	13	18
1	14	10	41	32	13	19
1	13	10	24	43	12	21
2	15	12	35	33	12	13
2	17	12	39	34	15	16
2	14	12	32	36	12	13
2	9	9	28	33	16	20
2	11	11	31	32	15	17
1	9	10	36	36	10	10
2	7	5	39	39	13	18
1	13	10	33	30	0	11
2	15	10	36	34	10	18
1	12	12	31	34	12	14
2	15	11	33	36	14	11
2	14	9	33	31	12	14
1	15	15	33	27	13	12
2	9	9	39	28	14	22
1	16	12	35	37	11	12
1	16	16	37	36	11	12
1	14	10	29	31	12	15
2	14	14	34	31	9	13
2	13	10	35	31	13	13
1	14	11	36	34	13	16
2	16	12	29	36	12	12
1	16	14	35	30	14	16
1	13	10	35	37	12	15
2	12	9	36	29	10	19
2	16	12	38	37	11	15
1	16	11	36	38	14	13
1	16	12	37	38	12	9
2	10	7	32	33	13	14
2	14	16	34	34	13	14
2	12	11	29	32	9	12
2	12	12	38	36	13	17
1	12	9	34	30	11	11
1	12	9	33	34	12	17
1	19	15	42	42	13	15
2	14	10	32	24	12	15
1	13	11	31	29	12	11
1	17	14	34	32	11	14
2	16	12	39	31	12	14
1	15	12	38	37	12	14
1	12	12	36	34	13	14
1	8	11	32	35	14	13
1	10	9	37	34	13	14
1	16	11	36	33	12	10
2	10	6	34	31	15	17
2	16	12	34	32	13	11
1	10	12	34	37	14	13
1	18	14	38	39	12	14
1	12	8	33	31	11	14
2	16	15	5	0	12	18
2	10	9	28	30	11	18
2	15	9	33	30	14	18
1	17	11	41	43	13	14
2	16	12	30	31	12	12
2	14	10	31	33	14	16
2	12	11	34	31	13	17
2	11	10	33	38	11	13
2	15	12	37	32	16	16
1	7	11	34	38	13	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 8.92896819210371 + 0.281927432551934Gender[t] + 0.10495263537541Learning[t] -0.084261337937447Software[t] + 0.0295474273972427Connected[t] -0.0146378787307945Separate[t] + 0.146659551236603Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  8.92896819210371 +  0.281927432551934Gender[t] +  0.10495263537541Learning[t] -0.084261337937447Software[t] +  0.0295474273972427Connected[t] -0.0146378787307945Separate[t] +  0.146659551236603Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  8.92896819210371 +  0.281927432551934Gender[t] +  0.10495263537541Learning[t] -0.084261337937447Software[t] +  0.0295474273972427Connected[t] -0.0146378787307945Separate[t] +  0.146659551236603Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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
Happiness[t] = + 8.92896819210371 + 0.281927432551934Gender[t] + 0.10495263537541Learning[t] -0.084261337937447Software[t] + 0.0295474273972427Connected[t] -0.0146378787307945Separate[t] + 0.146659551236603Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.928968192103711.7961654.97112e-061e-06
Gender0.2819274325519340.324880.86780.3870060.193503
Learning0.104952635375410.0715241.46740.1445340.072267
Software-0.0842613379374470.081987-1.02770.3058590.152929
Connected0.02954742739724270.039840.74160.4595520.229776
Separate-0.01463787873079450.03762-0.38910.6977960.348898
Depression0.1466595512366030.044853.270.0013560.000678

\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) & 8.92896819210371 & 1.796165 & 4.9711 & 2e-06 & 1e-06 \tabularnewline
Gender & 0.281927432551934 & 0.32488 & 0.8678 & 0.387006 & 0.193503 \tabularnewline
Learning & 0.10495263537541 & 0.071524 & 1.4674 & 0.144534 & 0.072267 \tabularnewline
Software & -0.084261337937447 & 0.081987 & -1.0277 & 0.305859 & 0.152929 \tabularnewline
Connected & 0.0295474273972427 & 0.03984 & 0.7416 & 0.459552 & 0.229776 \tabularnewline
Separate & -0.0146378787307945 & 0.03762 & -0.3891 & 0.697796 & 0.348898 \tabularnewline
Depression & 0.146659551236603 & 0.04485 & 3.27 & 0.001356 & 0.000678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&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]8.92896819210371[/C][C]1.796165[/C][C]4.9711[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Gender[/C][C]0.281927432551934[/C][C]0.32488[/C][C]0.8678[/C][C]0.387006[/C][C]0.193503[/C][/ROW]
[ROW][C]Learning[/C][C]0.10495263537541[/C][C]0.071524[/C][C]1.4674[/C][C]0.144534[/C][C]0.072267[/C][/ROW]
[ROW][C]Software[/C][C]-0.084261337937447[/C][C]0.081987[/C][C]-1.0277[/C][C]0.305859[/C][C]0.152929[/C][/ROW]
[ROW][C]Connected[/C][C]0.0295474273972427[/C][C]0.03984[/C][C]0.7416[/C][C]0.459552[/C][C]0.229776[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0146378787307945[/C][C]0.03762[/C][C]-0.3891[/C][C]0.697796[/C][C]0.348898[/C][/ROW]
[ROW][C]Depression[/C][C]0.146659551236603[/C][C]0.04485[/C][C]3.27[/C][C]0.001356[/C][C]0.000678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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)8.928968192103711.7961654.97112e-061e-06
Gender0.2819274325519340.324880.86780.3870060.193503
Learning0.104952635375410.0715241.46740.1445340.072267
Software-0.0842613379374470.081987-1.02770.3058590.152929
Connected0.02954742739724270.039840.74160.4595520.229776
Separate-0.01463787873079450.03762-0.38910.6977960.348898
Depression0.1466595512366030.044853.270.0013560.000678






Multiple Linear Regression - Regression Statistics
Multiple R0.340633917669501
R-squared0.116031465866872
Adjusted R-squared0.0778745507244353
F-TEST (value)3.04090268916486
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.00796341131540002
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.79797374988175
Sum Squared Residuals449.346635131673

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.340633917669501 \tabularnewline
R-squared & 0.116031465866872 \tabularnewline
Adjusted R-squared & 0.0778745507244353 \tabularnewline
F-TEST (value) & 3.04090268916486 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0.00796341131540002 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.79797374988175 \tabularnewline
Sum Squared Residuals & 449.346635131673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.340633917669501[/C][/ROW]
[ROW][C]R-squared[/C][C]0.116031465866872[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0778745507244353[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.04090268916486[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]0.00796341131540002[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.79797374988175[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]449.346635131673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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 R0.340633917669501
R-squared0.116031465866872
Adjusted R-squared0.0778745507244353
F-TEST (value)3.04090268916486
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.00796341131540002
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.79797374988175
Sum Squared Residuals449.346635131673






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.59599690542530.404003094574732
21112.0095111938424-1.00951119384236
31212.2316951296293-0.231695129629277
41312.6976370883030.302362911697
51212.6344476540461-0.634447654046072
61212.2529678642098-0.252967864209826
71311.97546842609861.02453157390139
81211.70644924812940.29355075187057
91514.76942970731120.230570292688793
101112.0867988011665-1.08679880116645
111311.86825081847921.13174918152079
121212.6384416678346-0.638441667834607
131211.90027227857560.0997277214243858
141211.88922934514690.110770654853123
151514.55364820855840.446351791441627
161312.47728203637450.522717963625466
171311.58524690239531.41475309760469
181213.0714000789722-1.07140007897219
191112.3037969791028-1.30379697910277
201111.969220059189-0.969220059188964
211312.22537564184960.774624358150366
221011.9964515481137-1.99645154811374
231213.8605400158391-1.86054001583914
241313.8216997080606-0.821699708060619
251010.0544853586089-0.0544853586088744
261212.9207885272604-0.920788527260386
271311.65539464904941.3446053509506
281212.2181348366481-0.218134836648093
291112.2835402263071-1.28354022630707
301113.1922826023666-2.19228260236658
311413.24307811863380.756921881366213
321211.64733185535770.352668144642289
331413.82578208060230.174217919397684
341212.1082960144184-0.108296014418372
351212.8855916898136-0.885591689813624
361312.88103571421860.118964285781399
371512.83743184523312.16256815476685
381212.3051942904902-0.305194290490182
391612.28689063499823.71310936500183
401012.5373511490514-2.53735114905137
411312.07686618767520.923133812324794
421212.5300046112619-0.530004611261914
431213.7441293701524-1.74412937015237
441613.14524652928432.85475347071571
451211.99724640327920.0027535967208147
461612.65508716487233.34491283512772
471311.98427190500831.01572809499173
481011.9037751806302-1.90377518063023
491412.397563088971.60243691102997
501312.34273928098310.657260719016865
511212.2397834410459-0.239783441045879
521312.65908075574860.340919244251408
531613.14047023987672.85952976012333
541212.0703842481012-0.0703842481011873
551211.86582072169430.134179278305704
561312.74005131472770.259948685272265
571312.36569904379490.634300956205094
581112.2833402598449-1.2833402598449
591413.33190089195160.668099108048413
601612.86011084411633.13988915588374
611612.72372388335223.27627611664785
621413.00615100201680.99384899798315
631412.92386740709371.07613259290631
641412.68742734509081.31257265490923
651412.20386483595921.79613516404083
661012.4533163912866-2.45331639128664
671312.72476241069070.275237589309313
681412.86820375088031.13179624911967
691712.26322479868634.73677520131366
701212.7747249669447-0.774724966944703
711213.0069894142389-1.00698941423891
721212.0874994148812-0.0874994148811833
731513.49486643880421.50513356119581
741011.8491860475253-1.84918604752535
751313.4285823276401-0.428582327640129
761212.8373577584239-0.837357758423945
771312.82329011510940.176709884890621
781412.39191607606471.60808392393529
791213.001260316517-1.001260316517
801312.36596161973760.634038380262446
811412.94817642559071.05182357440928
821012.0270709603974-2.02707096039741
831214.4473992063271-2.44739920632709
841312.13093145624490.869068543755123
851012.0917330437229-2.09173304372291
861313.009449861876-0.00944986187598533
871313.3671830179339-0.367183017933911
881212.8922265532398-0.892226553239842
891212.5136606594525-0.51366065945249
901513.26709641477131.7329035852287
911212.276152105693-0.276152105692968
921612.95651372788793.04348627211211
931512.66119782997652.33880217002347
941011.316195228018-1.31619522801804
951313.0275291353986-0.0275291353985576
96011.8818503109493-11.8818503109493
971013.4303906401768-3.43039064017685
981211.93070728369120.0692927163088371
991412.20159440392991.79840559607014
1001212.7783324917931-0.778332491793127
1011311.86102207944191.13897792055811
1021413.64804392538470.351956074615256
1031112.1314747961162-1.13147479611618
1041111.8681621778917-0.868162177891676
1051212.4406135629514-0.440613562951379
106912.2399136782665-3.23991367826653
1071312.50155382203820.498446177961848
1081312.66593013183890.334069868161149
1091212.2507555430155-0.250755543015457
1101412.65205547630331.34794452369673
1111212.4251182195747-0.425118219574658
1121013.4196430168786-3.41964301687864
1131112.9420231645697-1.94202316456966
1141412.37730523395671.62269476604332
1151211.73595311847010.264046881529935
1161312.46822144130750.531778558692451
1171312.17413691743590.825863082564142
118911.9737578543744-2.97375785437444
1191312.8301696042720.169830395727983
1201111.8907064409086-0.890706440908599
1211212.6825648060078-0.6825648060078
1221312.76716994026660.232830059733389
1231212.9136484288106-0.913648428810603
1241211.75313199694820.246868003051818
1251112.4048658243466-1.40486582434664
1261212.9127383131151-0.912738313115063
1271212.4084835454057-0.408483545405709
1281312.07844442067740.921555579322624
1291411.46340807755682.53659192244319
1301312.15087059113610.849129408863861
1311212.0105159739008-0.0105159739008451
1321513.08083204521091.91916795478912
1331312.31038464368820.689615356311755
1341411.61887110770312.38112889229692
1351212.5255430181955-0.525543018195457
1361112.3707611264278-1.37076112642782
1371212.6957542133975-0.695754213397514
1381112.8120608969825-1.81206089698248
1391413.48456121084570.515438789154254
1401312.70346516390090.296534836099062
1411212.3534923640667-0.353492364066672
1421412.89901964407281.10098035592719
1431312.86943062627450.130569373725534
1441112.1300885453773-1.13008854537728
1451613.02737204668762.97262795331242
1461311.66695576325671.33304423674329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.5959969054253 & 0.404003094574732 \tabularnewline
2 & 11 & 12.0095111938424 & -1.00951119384236 \tabularnewline
3 & 12 & 12.2316951296293 & -0.231695129629277 \tabularnewline
4 & 13 & 12.697637088303 & 0.302362911697 \tabularnewline
5 & 12 & 12.6344476540461 & -0.634447654046072 \tabularnewline
6 & 12 & 12.2529678642098 & -0.252967864209826 \tabularnewline
7 & 13 & 11.9754684260986 & 1.02453157390139 \tabularnewline
8 & 12 & 11.7064492481294 & 0.29355075187057 \tabularnewline
9 & 15 & 14.7694297073112 & 0.230570292688793 \tabularnewline
10 & 11 & 12.0867988011665 & -1.08679880116645 \tabularnewline
11 & 13 & 11.8682508184792 & 1.13174918152079 \tabularnewline
12 & 12 & 12.6384416678346 & -0.638441667834607 \tabularnewline
13 & 12 & 11.9002722785756 & 0.0997277214243858 \tabularnewline
14 & 12 & 11.8892293451469 & 0.110770654853123 \tabularnewline
15 & 15 & 14.5536482085584 & 0.446351791441627 \tabularnewline
16 & 13 & 12.4772820363745 & 0.522717963625466 \tabularnewline
17 & 13 & 11.5852469023953 & 1.41475309760469 \tabularnewline
18 & 12 & 13.0714000789722 & -1.07140007897219 \tabularnewline
19 & 11 & 12.3037969791028 & -1.30379697910277 \tabularnewline
20 & 11 & 11.969220059189 & -0.969220059188964 \tabularnewline
21 & 13 & 12.2253756418496 & 0.774624358150366 \tabularnewline
22 & 10 & 11.9964515481137 & -1.99645154811374 \tabularnewline
23 & 12 & 13.8605400158391 & -1.86054001583914 \tabularnewline
24 & 13 & 13.8216997080606 & -0.821699708060619 \tabularnewline
25 & 10 & 10.0544853586089 & -0.0544853586088744 \tabularnewline
26 & 12 & 12.9207885272604 & -0.920788527260386 \tabularnewline
27 & 13 & 11.6553946490494 & 1.3446053509506 \tabularnewline
28 & 12 & 12.2181348366481 & -0.218134836648093 \tabularnewline
29 & 11 & 12.2835402263071 & -1.28354022630707 \tabularnewline
30 & 11 & 13.1922826023666 & -2.19228260236658 \tabularnewline
31 & 14 & 13.2430781186338 & 0.756921881366213 \tabularnewline
32 & 12 & 11.6473318553577 & 0.352668144642289 \tabularnewline
33 & 14 & 13.8257820806023 & 0.174217919397684 \tabularnewline
34 & 12 & 12.1082960144184 & -0.108296014418372 \tabularnewline
35 & 12 & 12.8855916898136 & -0.885591689813624 \tabularnewline
36 & 13 & 12.8810357142186 & 0.118964285781399 \tabularnewline
37 & 15 & 12.8374318452331 & 2.16256815476685 \tabularnewline
38 & 12 & 12.3051942904902 & -0.305194290490182 \tabularnewline
39 & 16 & 12.2868906349982 & 3.71310936500183 \tabularnewline
40 & 10 & 12.5373511490514 & -2.53735114905137 \tabularnewline
41 & 13 & 12.0768661876752 & 0.923133812324794 \tabularnewline
42 & 12 & 12.5300046112619 & -0.530004611261914 \tabularnewline
43 & 12 & 13.7441293701524 & -1.74412937015237 \tabularnewline
44 & 16 & 13.1452465292843 & 2.85475347071571 \tabularnewline
45 & 12 & 11.9972464032792 & 0.0027535967208147 \tabularnewline
46 & 16 & 12.6550871648723 & 3.34491283512772 \tabularnewline
47 & 13 & 11.9842719050083 & 1.01572809499173 \tabularnewline
48 & 10 & 11.9037751806302 & -1.90377518063023 \tabularnewline
49 & 14 & 12.39756308897 & 1.60243691102997 \tabularnewline
50 & 13 & 12.3427392809831 & 0.657260719016865 \tabularnewline
51 & 12 & 12.2397834410459 & -0.239783441045879 \tabularnewline
52 & 13 & 12.6590807557486 & 0.340919244251408 \tabularnewline
53 & 16 & 13.1404702398767 & 2.85952976012333 \tabularnewline
54 & 12 & 12.0703842481012 & -0.0703842481011873 \tabularnewline
55 & 12 & 11.8658207216943 & 0.134179278305704 \tabularnewline
56 & 13 & 12.7400513147277 & 0.259948685272265 \tabularnewline
57 & 13 & 12.3656990437949 & 0.634300956205094 \tabularnewline
58 & 11 & 12.2833402598449 & -1.2833402598449 \tabularnewline
59 & 14 & 13.3319008919516 & 0.668099108048413 \tabularnewline
60 & 16 & 12.8601108441163 & 3.13988915588374 \tabularnewline
61 & 16 & 12.7237238833522 & 3.27627611664785 \tabularnewline
62 & 14 & 13.0061510020168 & 0.99384899798315 \tabularnewline
63 & 14 & 12.9238674070937 & 1.07613259290631 \tabularnewline
64 & 14 & 12.6874273450908 & 1.31257265490923 \tabularnewline
65 & 14 & 12.2038648359592 & 1.79613516404083 \tabularnewline
66 & 10 & 12.4533163912866 & -2.45331639128664 \tabularnewline
67 & 13 & 12.7247624106907 & 0.275237589309313 \tabularnewline
68 & 14 & 12.8682037508803 & 1.13179624911967 \tabularnewline
69 & 17 & 12.2632247986863 & 4.73677520131366 \tabularnewline
70 & 12 & 12.7747249669447 & -0.774724966944703 \tabularnewline
71 & 12 & 13.0069894142389 & -1.00698941423891 \tabularnewline
72 & 12 & 12.0874994148812 & -0.0874994148811833 \tabularnewline
73 & 15 & 13.4948664388042 & 1.50513356119581 \tabularnewline
74 & 10 & 11.8491860475253 & -1.84918604752535 \tabularnewline
75 & 13 & 13.4285823276401 & -0.428582327640129 \tabularnewline
76 & 12 & 12.8373577584239 & -0.837357758423945 \tabularnewline
77 & 13 & 12.8232901151094 & 0.176709884890621 \tabularnewline
78 & 14 & 12.3919160760647 & 1.60808392393529 \tabularnewline
79 & 12 & 13.001260316517 & -1.001260316517 \tabularnewline
80 & 13 & 12.3659616197376 & 0.634038380262446 \tabularnewline
81 & 14 & 12.9481764255907 & 1.05182357440928 \tabularnewline
82 & 10 & 12.0270709603974 & -2.02707096039741 \tabularnewline
83 & 12 & 14.4473992063271 & -2.44739920632709 \tabularnewline
84 & 13 & 12.1309314562449 & 0.869068543755123 \tabularnewline
85 & 10 & 12.0917330437229 & -2.09173304372291 \tabularnewline
86 & 13 & 13.009449861876 & -0.00944986187598533 \tabularnewline
87 & 13 & 13.3671830179339 & -0.367183017933911 \tabularnewline
88 & 12 & 12.8922265532398 & -0.892226553239842 \tabularnewline
89 & 12 & 12.5136606594525 & -0.51366065945249 \tabularnewline
90 & 15 & 13.2670964147713 & 1.7329035852287 \tabularnewline
91 & 12 & 12.276152105693 & -0.276152105692968 \tabularnewline
92 & 16 & 12.9565137278879 & 3.04348627211211 \tabularnewline
93 & 15 & 12.6611978299765 & 2.33880217002347 \tabularnewline
94 & 10 & 11.316195228018 & -1.31619522801804 \tabularnewline
95 & 13 & 13.0275291353986 & -0.0275291353985576 \tabularnewline
96 & 0 & 11.8818503109493 & -11.8818503109493 \tabularnewline
97 & 10 & 13.4303906401768 & -3.43039064017685 \tabularnewline
98 & 12 & 11.9307072836912 & 0.0692927163088371 \tabularnewline
99 & 14 & 12.2015944039299 & 1.79840559607014 \tabularnewline
100 & 12 & 12.7783324917931 & -0.778332491793127 \tabularnewline
101 & 13 & 11.8610220794419 & 1.13897792055811 \tabularnewline
102 & 14 & 13.6480439253847 & 0.351956074615256 \tabularnewline
103 & 11 & 12.1314747961162 & -1.13147479611618 \tabularnewline
104 & 11 & 11.8681621778917 & -0.868162177891676 \tabularnewline
105 & 12 & 12.4406135629514 & -0.440613562951379 \tabularnewline
106 & 9 & 12.2399136782665 & -3.23991367826653 \tabularnewline
107 & 13 & 12.5015538220382 & 0.498446177961848 \tabularnewline
108 & 13 & 12.6659301318389 & 0.334069868161149 \tabularnewline
109 & 12 & 12.2507555430155 & -0.250755543015457 \tabularnewline
110 & 14 & 12.6520554763033 & 1.34794452369673 \tabularnewline
111 & 12 & 12.4251182195747 & -0.425118219574658 \tabularnewline
112 & 10 & 13.4196430168786 & -3.41964301687864 \tabularnewline
113 & 11 & 12.9420231645697 & -1.94202316456966 \tabularnewline
114 & 14 & 12.3773052339567 & 1.62269476604332 \tabularnewline
115 & 12 & 11.7359531184701 & 0.264046881529935 \tabularnewline
116 & 13 & 12.4682214413075 & 0.531778558692451 \tabularnewline
117 & 13 & 12.1741369174359 & 0.825863082564142 \tabularnewline
118 & 9 & 11.9737578543744 & -2.97375785437444 \tabularnewline
119 & 13 & 12.830169604272 & 0.169830395727983 \tabularnewline
120 & 11 & 11.8907064409086 & -0.890706440908599 \tabularnewline
121 & 12 & 12.6825648060078 & -0.6825648060078 \tabularnewline
122 & 13 & 12.7671699402666 & 0.232830059733389 \tabularnewline
123 & 12 & 12.9136484288106 & -0.913648428810603 \tabularnewline
124 & 12 & 11.7531319969482 & 0.246868003051818 \tabularnewline
125 & 11 & 12.4048658243466 & -1.40486582434664 \tabularnewline
126 & 12 & 12.9127383131151 & -0.912738313115063 \tabularnewline
127 & 12 & 12.4084835454057 & -0.408483545405709 \tabularnewline
128 & 13 & 12.0784444206774 & 0.921555579322624 \tabularnewline
129 & 14 & 11.4634080775568 & 2.53659192244319 \tabularnewline
130 & 13 & 12.1508705911361 & 0.849129408863861 \tabularnewline
131 & 12 & 12.0105159739008 & -0.0105159739008451 \tabularnewline
132 & 15 & 13.0808320452109 & 1.91916795478912 \tabularnewline
133 & 13 & 12.3103846436882 & 0.689615356311755 \tabularnewline
134 & 14 & 11.6188711077031 & 2.38112889229692 \tabularnewline
135 & 12 & 12.5255430181955 & -0.525543018195457 \tabularnewline
136 & 11 & 12.3707611264278 & -1.37076112642782 \tabularnewline
137 & 12 & 12.6957542133975 & -0.695754213397514 \tabularnewline
138 & 11 & 12.8120608969825 & -1.81206089698248 \tabularnewline
139 & 14 & 13.4845612108457 & 0.515438789154254 \tabularnewline
140 & 13 & 12.7034651639009 & 0.296534836099062 \tabularnewline
141 & 12 & 12.3534923640667 & -0.353492364066672 \tabularnewline
142 & 14 & 12.8990196440728 & 1.10098035592719 \tabularnewline
143 & 13 & 12.8694306262745 & 0.130569373725534 \tabularnewline
144 & 11 & 12.1300885453773 & -1.13008854537728 \tabularnewline
145 & 16 & 13.0273720466876 & 2.97262795331242 \tabularnewline
146 & 13 & 11.6669557632567 & 1.33304423674329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]12.5959969054253[/C][C]0.404003094574732[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.0095111938424[/C][C]-1.00951119384236[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]12.2316951296293[/C][C]-0.231695129629277[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]12.697637088303[/C][C]0.302362911697[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]12.6344476540461[/C][C]-0.634447654046072[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.2529678642098[/C][C]-0.252967864209826[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]11.9754684260986[/C][C]1.02453157390139[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]11.7064492481294[/C][C]0.29355075187057[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.7694297073112[/C][C]0.230570292688793[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]12.0867988011665[/C][C]-1.08679880116645[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]11.8682508184792[/C][C]1.13174918152079[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.6384416678346[/C][C]-0.638441667834607[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.9002722785756[/C][C]0.0997277214243858[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.8892293451469[/C][C]0.110770654853123[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]14.5536482085584[/C][C]0.446351791441627[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]12.4772820363745[/C][C]0.522717963625466[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]11.5852469023953[/C][C]1.41475309760469[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.0714000789722[/C][C]-1.07140007897219[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.3037969791028[/C][C]-1.30379697910277[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]11.969220059189[/C][C]-0.969220059188964[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]12.2253756418496[/C][C]0.774624358150366[/C][/ROW]
[ROW][C]22[/C][C]10[/C][C]11.9964515481137[/C][C]-1.99645154811374[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.8605400158391[/C][C]-1.86054001583914[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]13.8216997080606[/C][C]-0.821699708060619[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]10.0544853586089[/C][C]-0.0544853586088744[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.9207885272604[/C][C]-0.920788527260386[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]11.6553946490494[/C][C]1.3446053509506[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]12.2181348366481[/C][C]-0.218134836648093[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]12.2835402263071[/C][C]-1.28354022630707[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.1922826023666[/C][C]-2.19228260236658[/C][/ROW]
[ROW][C]31[/C][C]14[/C][C]13.2430781186338[/C][C]0.756921881366213[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.6473318553577[/C][C]0.352668144642289[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.8257820806023[/C][C]0.174217919397684[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]12.1082960144184[/C][C]-0.108296014418372[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]12.8855916898136[/C][C]-0.885591689813624[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]12.8810357142186[/C][C]0.118964285781399[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]12.8374318452331[/C][C]2.16256815476685[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3051942904902[/C][C]-0.305194290490182[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]12.2868906349982[/C][C]3.71310936500183[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.5373511490514[/C][C]-2.53735114905137[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]12.0768661876752[/C][C]0.923133812324794[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]12.5300046112619[/C][C]-0.530004611261914[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]13.7441293701524[/C][C]-1.74412937015237[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]13.1452465292843[/C][C]2.85475347071571[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]11.9972464032792[/C][C]0.0027535967208147[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]12.6550871648723[/C][C]3.34491283512772[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]11.9842719050083[/C][C]1.01572809499173[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]11.9037751806302[/C][C]-1.90377518063023[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]12.39756308897[/C][C]1.60243691102997[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]12.3427392809831[/C][C]0.657260719016865[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]12.2397834410459[/C][C]-0.239783441045879[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]12.6590807557486[/C][C]0.340919244251408[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]13.1404702398767[/C][C]2.85952976012333[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.0703842481012[/C][C]-0.0703842481011873[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.8658207216943[/C][C]0.134179278305704[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.7400513147277[/C][C]0.259948685272265[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.3656990437949[/C][C]0.634300956205094[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]12.2833402598449[/C][C]-1.2833402598449[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]13.3319008919516[/C][C]0.668099108048413[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]12.8601108441163[/C][C]3.13988915588374[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]12.7237238833522[/C][C]3.27627611664785[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]13.0061510020168[/C][C]0.99384899798315[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.9238674070937[/C][C]1.07613259290631[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.6874273450908[/C][C]1.31257265490923[/C][/ROW]
[ROW][C]65[/C][C]14[/C][C]12.2038648359592[/C][C]1.79613516404083[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]12.4533163912866[/C][C]-2.45331639128664[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]12.7247624106907[/C][C]0.275237589309313[/C][/ROW]
[ROW][C]68[/C][C]14[/C][C]12.8682037508803[/C][C]1.13179624911967[/C][/ROW]
[ROW][C]69[/C][C]17[/C][C]12.2632247986863[/C][C]4.73677520131366[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]12.7747249669447[/C][C]-0.774724966944703[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]13.0069894142389[/C][C]-1.00698941423891[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]12.0874994148812[/C][C]-0.0874994148811833[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.4948664388042[/C][C]1.50513356119581[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.8491860475253[/C][C]-1.84918604752535[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.4285823276401[/C][C]-0.428582327640129[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.8373577584239[/C][C]-0.837357758423945[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.8232901151094[/C][C]0.176709884890621[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]12.3919160760647[/C][C]1.60808392393529[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]13.001260316517[/C][C]-1.001260316517[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.3659616197376[/C][C]0.634038380262446[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]12.9481764255907[/C][C]1.05182357440928[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.0270709603974[/C][C]-2.02707096039741[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]14.4473992063271[/C][C]-2.44739920632709[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]12.1309314562449[/C][C]0.869068543755123[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]12.0917330437229[/C][C]-2.09173304372291[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.009449861876[/C][C]-0.00944986187598533[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]13.3671830179339[/C][C]-0.367183017933911[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8922265532398[/C][C]-0.892226553239842[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.5136606594525[/C][C]-0.51366065945249[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.2670964147713[/C][C]1.7329035852287[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]12.276152105693[/C][C]-0.276152105692968[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]12.9565137278879[/C][C]3.04348627211211[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]12.6611978299765[/C][C]2.33880217002347[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]11.316195228018[/C][C]-1.31619522801804[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.0275291353986[/C][C]-0.0275291353985576[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]11.8818503109493[/C][C]-11.8818503109493[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]13.4303906401768[/C][C]-3.43039064017685[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]11.9307072836912[/C][C]0.0692927163088371[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]12.2015944039299[/C][C]1.79840559607014[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]12.7783324917931[/C][C]-0.778332491793127[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]11.8610220794419[/C][C]1.13897792055811[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]13.6480439253847[/C][C]0.351956074615256[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]12.1314747961162[/C][C]-1.13147479611618[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.8681621778917[/C][C]-0.868162177891676[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]12.4406135629514[/C][C]-0.440613562951379[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]12.2399136782665[/C][C]-3.23991367826653[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]12.5015538220382[/C][C]0.498446177961848[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]12.6659301318389[/C][C]0.334069868161149[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.2507555430155[/C][C]-0.250755543015457[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]12.6520554763033[/C][C]1.34794452369673[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]12.4251182195747[/C][C]-0.425118219574658[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]13.4196430168786[/C][C]-3.41964301687864[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.9420231645697[/C][C]-1.94202316456966[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.3773052339567[/C][C]1.62269476604332[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.7359531184701[/C][C]0.264046881529935[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4682214413075[/C][C]0.531778558692451[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]12.1741369174359[/C][C]0.825863082564142[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]11.9737578543744[/C][C]-2.97375785437444[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]12.830169604272[/C][C]0.169830395727983[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]11.8907064409086[/C][C]-0.890706440908599[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.6825648060078[/C][C]-0.6825648060078[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]12.7671699402666[/C][C]0.232830059733389[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]12.9136484288106[/C][C]-0.913648428810603[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]11.7531319969482[/C][C]0.246868003051818[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.4048658243466[/C][C]-1.40486582434664[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]12.9127383131151[/C][C]-0.912738313115063[/C][/ROW]
[ROW][C]127[/C][C]12[/C][C]12.4084835454057[/C][C]-0.408483545405709[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]12.0784444206774[/C][C]0.921555579322624[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]11.4634080775568[/C][C]2.53659192244319[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.1508705911361[/C][C]0.849129408863861[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]12.0105159739008[/C][C]-0.0105159739008451[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.0808320452109[/C][C]1.91916795478912[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]12.3103846436882[/C][C]0.689615356311755[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]11.6188711077031[/C][C]2.38112889229692[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.5255430181955[/C][C]-0.525543018195457[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]12.3707611264278[/C][C]-1.37076112642782[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]12.6957542133975[/C][C]-0.695754213397514[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]12.8120608969825[/C][C]-1.81206089698248[/C][/ROW]
[ROW][C]139[/C][C]14[/C][C]13.4845612108457[/C][C]0.515438789154254[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.7034651639009[/C][C]0.296534836099062[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.3534923640667[/C][C]-0.353492364066672[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.8990196440728[/C][C]1.10098035592719[/C][/ROW]
[ROW][C]143[/C][C]13[/C][C]12.8694306262745[/C][C]0.130569373725534[/C][/ROW]
[ROW][C]144[/C][C]11[/C][C]12.1300885453773[/C][C]-1.13008854537728[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]13.0273720466876[/C][C]2.97262795331242[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]11.6669557632567[/C][C]1.33304423674329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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
11312.59599690542530.404003094574732
21112.0095111938424-1.00951119384236
31212.2316951296293-0.231695129629277
41312.6976370883030.302362911697
51212.6344476540461-0.634447654046072
61212.2529678642098-0.252967864209826
71311.97546842609861.02453157390139
81211.70644924812940.29355075187057
91514.76942970731120.230570292688793
101112.0867988011665-1.08679880116645
111311.86825081847921.13174918152079
121212.6384416678346-0.638441667834607
131211.90027227857560.0997277214243858
141211.88922934514690.110770654853123
151514.55364820855840.446351791441627
161312.47728203637450.522717963625466
171311.58524690239531.41475309760469
181213.0714000789722-1.07140007897219
191112.3037969791028-1.30379697910277
201111.969220059189-0.969220059188964
211312.22537564184960.774624358150366
221011.9964515481137-1.99645154811374
231213.8605400158391-1.86054001583914
241313.8216997080606-0.821699708060619
251010.0544853586089-0.0544853586088744
261212.9207885272604-0.920788527260386
271311.65539464904941.3446053509506
281212.2181348366481-0.218134836648093
291112.2835402263071-1.28354022630707
301113.1922826023666-2.19228260236658
311413.24307811863380.756921881366213
321211.64733185535770.352668144642289
331413.82578208060230.174217919397684
341212.1082960144184-0.108296014418372
351212.8855916898136-0.885591689813624
361312.88103571421860.118964285781399
371512.83743184523312.16256815476685
381212.3051942904902-0.305194290490182
391612.28689063499823.71310936500183
401012.5373511490514-2.53735114905137
411312.07686618767520.923133812324794
421212.5300046112619-0.530004611261914
431213.7441293701524-1.74412937015237
441613.14524652928432.85475347071571
451211.99724640327920.0027535967208147
461612.65508716487233.34491283512772
471311.98427190500831.01572809499173
481011.9037751806302-1.90377518063023
491412.397563088971.60243691102997
501312.34273928098310.657260719016865
511212.2397834410459-0.239783441045879
521312.65908075574860.340919244251408
531613.14047023987672.85952976012333
541212.0703842481012-0.0703842481011873
551211.86582072169430.134179278305704
561312.74005131472770.259948685272265
571312.36569904379490.634300956205094
581112.2833402598449-1.2833402598449
591413.33190089195160.668099108048413
601612.86011084411633.13988915588374
611612.72372388335223.27627611664785
621413.00615100201680.99384899798315
631412.92386740709371.07613259290631
641412.68742734509081.31257265490923
651412.20386483595921.79613516404083
661012.4533163912866-2.45331639128664
671312.72476241069070.275237589309313
681412.86820375088031.13179624911967
691712.26322479868634.73677520131366
701212.7747249669447-0.774724966944703
711213.0069894142389-1.00698941423891
721212.0874994148812-0.0874994148811833
731513.49486643880421.50513356119581
741011.8491860475253-1.84918604752535
751313.4285823276401-0.428582327640129
761212.8373577584239-0.837357758423945
771312.82329011510940.176709884890621
781412.39191607606471.60808392393529
791213.001260316517-1.001260316517
801312.36596161973760.634038380262446
811412.94817642559071.05182357440928
821012.0270709603974-2.02707096039741
831214.4473992063271-2.44739920632709
841312.13093145624490.869068543755123
851012.0917330437229-2.09173304372291
861313.009449861876-0.00944986187598533
871313.3671830179339-0.367183017933911
881212.8922265532398-0.892226553239842
891212.5136606594525-0.51366065945249
901513.26709641477131.7329035852287
911212.276152105693-0.276152105692968
921612.95651372788793.04348627211211
931512.66119782997652.33880217002347
941011.316195228018-1.31619522801804
951313.0275291353986-0.0275291353985576
96011.8818503109493-11.8818503109493
971013.4303906401768-3.43039064017685
981211.93070728369120.0692927163088371
991412.20159440392991.79840559607014
1001212.7783324917931-0.778332491793127
1011311.86102207944191.13897792055811
1021413.64804392538470.351956074615256
1031112.1314747961162-1.13147479611618
1041111.8681621778917-0.868162177891676
1051212.4406135629514-0.440613562951379
106912.2399136782665-3.23991367826653
1071312.50155382203820.498446177961848
1081312.66593013183890.334069868161149
1091212.2507555430155-0.250755543015457
1101412.65205547630331.34794452369673
1111212.4251182195747-0.425118219574658
1121013.4196430168786-3.41964301687864
1131112.9420231645697-1.94202316456966
1141412.37730523395671.62269476604332
1151211.73595311847010.264046881529935
1161312.46822144130750.531778558692451
1171312.17413691743590.825863082564142
118911.9737578543744-2.97375785437444
1191312.8301696042720.169830395727983
1201111.8907064409086-0.890706440908599
1211212.6825648060078-0.6825648060078
1221312.76716994026660.232830059733389
1231212.9136484288106-0.913648428810603
1241211.75313199694820.246868003051818
1251112.4048658243466-1.40486582434664
1261212.9127383131151-0.912738313115063
1271212.4084835454057-0.408483545405709
1281312.07844442067740.921555579322624
1291411.46340807755682.53659192244319
1301312.15087059113610.849129408863861
1311212.0105159739008-0.0105159739008451
1321513.08083204521091.91916795478912
1331312.31038464368820.689615356311755
1341411.61887110770312.38112889229692
1351212.5255430181955-0.525543018195457
1361112.3707611264278-1.37076112642782
1371212.6957542133975-0.695754213397514
1381112.8120608969825-1.81206089698248
1391413.48456121084570.515438789154254
1401312.70346516390090.296534836099062
1411212.3534923640667-0.353492364066672
1421412.89901964407281.10098035592719
1431312.86943062627450.130569373725534
1441112.1300885453773-1.13008854537728
1451613.02737204668762.97262795331242
1461311.66695576325671.33304423674329






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1171484086443230.2342968172886450.882851591355677
110.04816978498109910.09633956996219830.951830215018901
120.01701187167953260.03402374335906510.982988128320467
130.005981861887477440.01196372377495490.994018138112523
140.001821806955335580.003643613910671170.998178193044664
150.0005533766276715340.001106753255343070.999446623372328
160.0002095196360407720.0004190392720815430.999790480363959
170.0003774664389240640.0007549328778481280.999622533561076
180.0001260781208005440.0002521562416010890.999873921879199
190.0004715590057558490.0009431180115116980.999528440994244
200.0001807026940749870.0003614053881499740.999819297305925
210.0003201831238380780.0006403662476761550.999679816876162
220.0003364781471241390.0006729562942482780.999663521852876
230.0001939863408376540.0003879726816753070.999806013659162
240.0001459589354310670.0002919178708621330.999854041064569
259.78459387449536e-050.0001956918774899070.999902154061255
264.80215506586865e-059.6043101317373e-050.999951978449341
274.6652351419873e-059.3304702839746e-050.99995334764858
282.47323529576514e-054.94647059153029e-050.999975267647042
291.83706168358527e-053.67412336717055e-050.999981629383164
305.30487873369653e-050.0001060975746739310.999946951212663
313.49692707760457e-056.99385415520914e-050.999965030729224
322.2029659755412e-054.40593195108239e-050.999977970340245
331.06694070403683e-052.13388140807366e-050.99998933059296
344.77232278151794e-069.54464556303587e-060.999995227677218
354.24195082547549e-068.48390165095098e-060.999995758049175
361.85642661119599e-063.71285322239199e-060.999998143573389
374.84474568034126e-069.68949136068253e-060.99999515525432
382.29286676812662e-064.58573353625323e-060.999997707133232
390.0001240152108213420.0002480304216426850.999875984789179
400.0001448957809539090.0002897915619078190.999855104219046
410.0001416336993260990.0002832673986521980.999858366300674
428.32221942573059e-050.0001664443885146120.999916777805743
439.55562117586802e-050.000191112423517360.999904443788241
440.0009171814723820730.001834362944764150.999082818527618
450.0005479933669156250.001095986733831250.999452006633084
460.001934938583121710.003869877166243420.998065061416878
470.001293054272748710.002586108545497410.998706945727251
480.001293862657100530.002587725314201070.998706137342899
490.002484534713298240.004969069426596470.997515465286702
500.001635903951422020.003271807902844030.998364096048578
510.00106242107279310.002124842145586210.998937578927207
520.000663421492022810.001326842984045620.999336578507977
530.002961253171660930.005922506343321870.997038746828339
540.002000368386098950.00400073677219790.997999631613901
550.001337932339565430.002675864679130860.998662067660435
560.0008798619457469950.001759723891493990.999120138054253
570.0005806342205250290.001161268441050060.999419365779475
580.0004470871492676960.0008941742985353920.999552912850732
590.0002858613248904760.0005717226497809520.999714138675109
600.0006071604441761260.001214320888352250.999392839555824
610.001832223049231440.003664446098462880.998167776950769
620.001466850524128020.002933701048256040.998533149475872
630.0011602074155080.002320414831016010.998839792584492
640.0009631908734282020.00192638174685640.999036809126572
650.0009775393151684410.001955078630336880.999022460684832
660.001669505401214750.003339010802429490.998330494598785
670.001166667498689910.002333334997379810.99883333250131
680.0009024275131611020.00180485502632220.999097572486839
690.01068845360882710.02137690721765430.989311546391173
700.008071625361109210.01614325072221840.991928374638891
710.00750494373945710.01500988747891420.992495056260543
720.005506908280043370.01101381656008670.994493091719957
730.00556801944820680.01113603889641360.994431980551793
740.005300574882468770.01060114976493750.994699425117531
750.004208045392786450.00841609078557290.995791954607214
760.003243293997355580.006486587994711160.996756706002644
770.0022256704762660.0044513409525320.997774329523734
780.002149958562056160.004299917124112330.997850041437944
790.00179363477860180.003587269557203590.998206365221398
800.001309730938260060.002619461876520130.99869026906174
810.001097921157336170.002195842314672340.998902078842664
820.00130270542302280.00260541084604560.998697294576977
830.001930710120005750.003861420240011490.998069289879994
840.001534981235396170.003069962470792340.998465018764604
850.001867774696661040.003735549393322080.998132225303339
860.00128383659610.00256767319220.9987161634039
870.0008633057930977290.001726611586195460.999136694206902
880.0008366609518485880.001673321903697180.999163339048151
890.0005678854450882410.001135770890176480.999432114554912
900.0005735845341067470.001147169068213490.999426415465893
910.0003744507152452120.0007489014304904250.999625549284755
920.0006759908085352490.00135198161707050.999324009191465
930.0007886622932556180.001577324586511240.999211337706744
940.0005890128219420660.001178025643884130.999410987178058
950.0003786413937084250.000757282787416850.999621358606292
960.9809906055130910.03801878897381850.0190093944869092
970.9915021556366240.01699568872675190.00849784436337595
980.9878385136589280.02432297268214470.0121614863410724
990.9904860727152050.01902785456959020.00951392728479511
1000.9866838723085580.02663225538288470.0133161276914424
1010.9833809283941450.03323814321170930.0166190716058546
1020.9765160271733670.04696794565326580.0234839728266329
1030.9717246652898910.05655066942021720.0282753347101086
1040.9658488968323570.06830220633528630.0341511031676431
1050.9544708345482290.09105833090354180.0455291654517709
1060.9828166461356560.03436670772868760.0171833538643438
1070.9766461024962010.04670779500759790.023353897503799
1080.9669314961471780.06613700770564490.0330685038528225
1090.954114730835470.09177053832906050.0458852691645302
1100.9458273266824580.1083453466350840.0541726733175421
1110.9286402096016110.1427195807967780.071359790398389
1120.9780205169555590.04395896608888110.0219794830444406
1130.9818060687489690.03638786250206110.0181939312510305
1140.9846760638979360.03064787220412740.0153239361020637
1150.9784511354961580.04309772900768480.0215488645038424
1160.970223995125520.05955200974896030.0297760048744802
1170.9566461801022610.08670763979547730.0433538198977387
1180.984289540856780.031420918286440.01571045914322
1190.9799437115175220.04011257696495530.0200562884824776
1200.972681884064430.05463623187114070.0273181159355703
1210.9605390808152470.07892183836950610.0394609191847531
1220.9404480536780440.1191038926439130.0595519463219565
1230.9295890175097110.1408219649805780.0704109824902889
1240.8967345921148480.2065308157703050.103265407885152
1250.8878429336276750.2243141327446490.112157066372325
1260.9285554574826970.1428890850346070.0714445425173035
1270.9096427655467480.1807144689065040.0903572344532519
1280.8751003734870840.2497992530258320.124899626512916
1290.8862680484293390.2274639031413210.113731951570661
1300.8323623648012540.3352752703974920.167637635198746
1310.759114424348330.4817711513033390.24088557565167
1320.7713127032779890.4573745934440220.228687296722011
1330.6656602199933510.6686795600132980.334339780006649
1340.6865496790978110.6269006418043780.313450320902189
1350.7287464591586530.5425070816826930.271253540841347
1360.565286870254380.8694262594912410.43471312974562

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.117148408644323 & 0.234296817288645 & 0.882851591355677 \tabularnewline
11 & 0.0481697849810991 & 0.0963395699621983 & 0.951830215018901 \tabularnewline
12 & 0.0170118716795326 & 0.0340237433590651 & 0.982988128320467 \tabularnewline
13 & 0.00598186188747744 & 0.0119637237749549 & 0.994018138112523 \tabularnewline
14 & 0.00182180695533558 & 0.00364361391067117 & 0.998178193044664 \tabularnewline
15 & 0.000553376627671534 & 0.00110675325534307 & 0.999446623372328 \tabularnewline
16 & 0.000209519636040772 & 0.000419039272081543 & 0.999790480363959 \tabularnewline
17 & 0.000377466438924064 & 0.000754932877848128 & 0.999622533561076 \tabularnewline
18 & 0.000126078120800544 & 0.000252156241601089 & 0.999873921879199 \tabularnewline
19 & 0.000471559005755849 & 0.000943118011511698 & 0.999528440994244 \tabularnewline
20 & 0.000180702694074987 & 0.000361405388149974 & 0.999819297305925 \tabularnewline
21 & 0.000320183123838078 & 0.000640366247676155 & 0.999679816876162 \tabularnewline
22 & 0.000336478147124139 & 0.000672956294248278 & 0.999663521852876 \tabularnewline
23 & 0.000193986340837654 & 0.000387972681675307 & 0.999806013659162 \tabularnewline
24 & 0.000145958935431067 & 0.000291917870862133 & 0.999854041064569 \tabularnewline
25 & 9.78459387449536e-05 & 0.000195691877489907 & 0.999902154061255 \tabularnewline
26 & 4.80215506586865e-05 & 9.6043101317373e-05 & 0.999951978449341 \tabularnewline
27 & 4.6652351419873e-05 & 9.3304702839746e-05 & 0.99995334764858 \tabularnewline
28 & 2.47323529576514e-05 & 4.94647059153029e-05 & 0.999975267647042 \tabularnewline
29 & 1.83706168358527e-05 & 3.67412336717055e-05 & 0.999981629383164 \tabularnewline
30 & 5.30487873369653e-05 & 0.000106097574673931 & 0.999946951212663 \tabularnewline
31 & 3.49692707760457e-05 & 6.99385415520914e-05 & 0.999965030729224 \tabularnewline
32 & 2.2029659755412e-05 & 4.40593195108239e-05 & 0.999977970340245 \tabularnewline
33 & 1.06694070403683e-05 & 2.13388140807366e-05 & 0.99998933059296 \tabularnewline
34 & 4.77232278151794e-06 & 9.54464556303587e-06 & 0.999995227677218 \tabularnewline
35 & 4.24195082547549e-06 & 8.48390165095098e-06 & 0.999995758049175 \tabularnewline
36 & 1.85642661119599e-06 & 3.71285322239199e-06 & 0.999998143573389 \tabularnewline
37 & 4.84474568034126e-06 & 9.68949136068253e-06 & 0.99999515525432 \tabularnewline
38 & 2.29286676812662e-06 & 4.58573353625323e-06 & 0.999997707133232 \tabularnewline
39 & 0.000124015210821342 & 0.000248030421642685 & 0.999875984789179 \tabularnewline
40 & 0.000144895780953909 & 0.000289791561907819 & 0.999855104219046 \tabularnewline
41 & 0.000141633699326099 & 0.000283267398652198 & 0.999858366300674 \tabularnewline
42 & 8.32221942573059e-05 & 0.000166444388514612 & 0.999916777805743 \tabularnewline
43 & 9.55562117586802e-05 & 0.00019111242351736 & 0.999904443788241 \tabularnewline
44 & 0.000917181472382073 & 0.00183436294476415 & 0.999082818527618 \tabularnewline
45 & 0.000547993366915625 & 0.00109598673383125 & 0.999452006633084 \tabularnewline
46 & 0.00193493858312171 & 0.00386987716624342 & 0.998065061416878 \tabularnewline
47 & 0.00129305427274871 & 0.00258610854549741 & 0.998706945727251 \tabularnewline
48 & 0.00129386265710053 & 0.00258772531420107 & 0.998706137342899 \tabularnewline
49 & 0.00248453471329824 & 0.00496906942659647 & 0.997515465286702 \tabularnewline
50 & 0.00163590395142202 & 0.00327180790284403 & 0.998364096048578 \tabularnewline
51 & 0.0010624210727931 & 0.00212484214558621 & 0.998937578927207 \tabularnewline
52 & 0.00066342149202281 & 0.00132684298404562 & 0.999336578507977 \tabularnewline
53 & 0.00296125317166093 & 0.00592250634332187 & 0.997038746828339 \tabularnewline
54 & 0.00200036838609895 & 0.0040007367721979 & 0.997999631613901 \tabularnewline
55 & 0.00133793233956543 & 0.00267586467913086 & 0.998662067660435 \tabularnewline
56 & 0.000879861945746995 & 0.00175972389149399 & 0.999120138054253 \tabularnewline
57 & 0.000580634220525029 & 0.00116126844105006 & 0.999419365779475 \tabularnewline
58 & 0.000447087149267696 & 0.000894174298535392 & 0.999552912850732 \tabularnewline
59 & 0.000285861324890476 & 0.000571722649780952 & 0.999714138675109 \tabularnewline
60 & 0.000607160444176126 & 0.00121432088835225 & 0.999392839555824 \tabularnewline
61 & 0.00183222304923144 & 0.00366444609846288 & 0.998167776950769 \tabularnewline
62 & 0.00146685052412802 & 0.00293370104825604 & 0.998533149475872 \tabularnewline
63 & 0.001160207415508 & 0.00232041483101601 & 0.998839792584492 \tabularnewline
64 & 0.000963190873428202 & 0.0019263817468564 & 0.999036809126572 \tabularnewline
65 & 0.000977539315168441 & 0.00195507863033688 & 0.999022460684832 \tabularnewline
66 & 0.00166950540121475 & 0.00333901080242949 & 0.998330494598785 \tabularnewline
67 & 0.00116666749868991 & 0.00233333499737981 & 0.99883333250131 \tabularnewline
68 & 0.000902427513161102 & 0.0018048550263222 & 0.999097572486839 \tabularnewline
69 & 0.0106884536088271 & 0.0213769072176543 & 0.989311546391173 \tabularnewline
70 & 0.00807162536110921 & 0.0161432507222184 & 0.991928374638891 \tabularnewline
71 & 0.0075049437394571 & 0.0150098874789142 & 0.992495056260543 \tabularnewline
72 & 0.00550690828004337 & 0.0110138165600867 & 0.994493091719957 \tabularnewline
73 & 0.0055680194482068 & 0.0111360388964136 & 0.994431980551793 \tabularnewline
74 & 0.00530057488246877 & 0.0106011497649375 & 0.994699425117531 \tabularnewline
75 & 0.00420804539278645 & 0.0084160907855729 & 0.995791954607214 \tabularnewline
76 & 0.00324329399735558 & 0.00648658799471116 & 0.996756706002644 \tabularnewline
77 & 0.002225670476266 & 0.004451340952532 & 0.997774329523734 \tabularnewline
78 & 0.00214995856205616 & 0.00429991712411233 & 0.997850041437944 \tabularnewline
79 & 0.0017936347786018 & 0.00358726955720359 & 0.998206365221398 \tabularnewline
80 & 0.00130973093826006 & 0.00261946187652013 & 0.99869026906174 \tabularnewline
81 & 0.00109792115733617 & 0.00219584231467234 & 0.998902078842664 \tabularnewline
82 & 0.0013027054230228 & 0.0026054108460456 & 0.998697294576977 \tabularnewline
83 & 0.00193071012000575 & 0.00386142024001149 & 0.998069289879994 \tabularnewline
84 & 0.00153498123539617 & 0.00306996247079234 & 0.998465018764604 \tabularnewline
85 & 0.00186777469666104 & 0.00373554939332208 & 0.998132225303339 \tabularnewline
86 & 0.0012838365961 & 0.0025676731922 & 0.9987161634039 \tabularnewline
87 & 0.000863305793097729 & 0.00172661158619546 & 0.999136694206902 \tabularnewline
88 & 0.000836660951848588 & 0.00167332190369718 & 0.999163339048151 \tabularnewline
89 & 0.000567885445088241 & 0.00113577089017648 & 0.999432114554912 \tabularnewline
90 & 0.000573584534106747 & 0.00114716906821349 & 0.999426415465893 \tabularnewline
91 & 0.000374450715245212 & 0.000748901430490425 & 0.999625549284755 \tabularnewline
92 & 0.000675990808535249 & 0.0013519816170705 & 0.999324009191465 \tabularnewline
93 & 0.000788662293255618 & 0.00157732458651124 & 0.999211337706744 \tabularnewline
94 & 0.000589012821942066 & 0.00117802564388413 & 0.999410987178058 \tabularnewline
95 & 0.000378641393708425 & 0.00075728278741685 & 0.999621358606292 \tabularnewline
96 & 0.980990605513091 & 0.0380187889738185 & 0.0190093944869092 \tabularnewline
97 & 0.991502155636624 & 0.0169956887267519 & 0.00849784436337595 \tabularnewline
98 & 0.987838513658928 & 0.0243229726821447 & 0.0121614863410724 \tabularnewline
99 & 0.990486072715205 & 0.0190278545695902 & 0.00951392728479511 \tabularnewline
100 & 0.986683872308558 & 0.0266322553828847 & 0.0133161276914424 \tabularnewline
101 & 0.983380928394145 & 0.0332381432117093 & 0.0166190716058546 \tabularnewline
102 & 0.976516027173367 & 0.0469679456532658 & 0.0234839728266329 \tabularnewline
103 & 0.971724665289891 & 0.0565506694202172 & 0.0282753347101086 \tabularnewline
104 & 0.965848896832357 & 0.0683022063352863 & 0.0341511031676431 \tabularnewline
105 & 0.954470834548229 & 0.0910583309035418 & 0.0455291654517709 \tabularnewline
106 & 0.982816646135656 & 0.0343667077286876 & 0.0171833538643438 \tabularnewline
107 & 0.976646102496201 & 0.0467077950075979 & 0.023353897503799 \tabularnewline
108 & 0.966931496147178 & 0.0661370077056449 & 0.0330685038528225 \tabularnewline
109 & 0.95411473083547 & 0.0917705383290605 & 0.0458852691645302 \tabularnewline
110 & 0.945827326682458 & 0.108345346635084 & 0.0541726733175421 \tabularnewline
111 & 0.928640209601611 & 0.142719580796778 & 0.071359790398389 \tabularnewline
112 & 0.978020516955559 & 0.0439589660888811 & 0.0219794830444406 \tabularnewline
113 & 0.981806068748969 & 0.0363878625020611 & 0.0181939312510305 \tabularnewline
114 & 0.984676063897936 & 0.0306478722041274 & 0.0153239361020637 \tabularnewline
115 & 0.978451135496158 & 0.0430977290076848 & 0.0215488645038424 \tabularnewline
116 & 0.97022399512552 & 0.0595520097489603 & 0.0297760048744802 \tabularnewline
117 & 0.956646180102261 & 0.0867076397954773 & 0.0433538198977387 \tabularnewline
118 & 0.98428954085678 & 0.03142091828644 & 0.01571045914322 \tabularnewline
119 & 0.979943711517522 & 0.0401125769649553 & 0.0200562884824776 \tabularnewline
120 & 0.97268188406443 & 0.0546362318711407 & 0.0273181159355703 \tabularnewline
121 & 0.960539080815247 & 0.0789218383695061 & 0.0394609191847531 \tabularnewline
122 & 0.940448053678044 & 0.119103892643913 & 0.0595519463219565 \tabularnewline
123 & 0.929589017509711 & 0.140821964980578 & 0.0704109824902889 \tabularnewline
124 & 0.896734592114848 & 0.206530815770305 & 0.103265407885152 \tabularnewline
125 & 0.887842933627675 & 0.224314132744649 & 0.112157066372325 \tabularnewline
126 & 0.928555457482697 & 0.142889085034607 & 0.0714445425173035 \tabularnewline
127 & 0.909642765546748 & 0.180714468906504 & 0.0903572344532519 \tabularnewline
128 & 0.875100373487084 & 0.249799253025832 & 0.124899626512916 \tabularnewline
129 & 0.886268048429339 & 0.227463903141321 & 0.113731951570661 \tabularnewline
130 & 0.832362364801254 & 0.335275270397492 & 0.167637635198746 \tabularnewline
131 & 0.75911442434833 & 0.481771151303339 & 0.24088557565167 \tabularnewline
132 & 0.771312703277989 & 0.457374593444022 & 0.228687296722011 \tabularnewline
133 & 0.665660219993351 & 0.668679560013298 & 0.334339780006649 \tabularnewline
134 & 0.686549679097811 & 0.626900641804378 & 0.313450320902189 \tabularnewline
135 & 0.728746459158653 & 0.542507081682693 & 0.271253540841347 \tabularnewline
136 & 0.56528687025438 & 0.869426259491241 & 0.43471312974562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&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]0.117148408644323[/C][C]0.234296817288645[/C][C]0.882851591355677[/C][/ROW]
[ROW][C]11[/C][C]0.0481697849810991[/C][C]0.0963395699621983[/C][C]0.951830215018901[/C][/ROW]
[ROW][C]12[/C][C]0.0170118716795326[/C][C]0.0340237433590651[/C][C]0.982988128320467[/C][/ROW]
[ROW][C]13[/C][C]0.00598186188747744[/C][C]0.0119637237749549[/C][C]0.994018138112523[/C][/ROW]
[ROW][C]14[/C][C]0.00182180695533558[/C][C]0.00364361391067117[/C][C]0.998178193044664[/C][/ROW]
[ROW][C]15[/C][C]0.000553376627671534[/C][C]0.00110675325534307[/C][C]0.999446623372328[/C][/ROW]
[ROW][C]16[/C][C]0.000209519636040772[/C][C]0.000419039272081543[/C][C]0.999790480363959[/C][/ROW]
[ROW][C]17[/C][C]0.000377466438924064[/C][C]0.000754932877848128[/C][C]0.999622533561076[/C][/ROW]
[ROW][C]18[/C][C]0.000126078120800544[/C][C]0.000252156241601089[/C][C]0.999873921879199[/C][/ROW]
[ROW][C]19[/C][C]0.000471559005755849[/C][C]0.000943118011511698[/C][C]0.999528440994244[/C][/ROW]
[ROW][C]20[/C][C]0.000180702694074987[/C][C]0.000361405388149974[/C][C]0.999819297305925[/C][/ROW]
[ROW][C]21[/C][C]0.000320183123838078[/C][C]0.000640366247676155[/C][C]0.999679816876162[/C][/ROW]
[ROW][C]22[/C][C]0.000336478147124139[/C][C]0.000672956294248278[/C][C]0.999663521852876[/C][/ROW]
[ROW][C]23[/C][C]0.000193986340837654[/C][C]0.000387972681675307[/C][C]0.999806013659162[/C][/ROW]
[ROW][C]24[/C][C]0.000145958935431067[/C][C]0.000291917870862133[/C][C]0.999854041064569[/C][/ROW]
[ROW][C]25[/C][C]9.78459387449536e-05[/C][C]0.000195691877489907[/C][C]0.999902154061255[/C][/ROW]
[ROW][C]26[/C][C]4.80215506586865e-05[/C][C]9.6043101317373e-05[/C][C]0.999951978449341[/C][/ROW]
[ROW][C]27[/C][C]4.6652351419873e-05[/C][C]9.3304702839746e-05[/C][C]0.99995334764858[/C][/ROW]
[ROW][C]28[/C][C]2.47323529576514e-05[/C][C]4.94647059153029e-05[/C][C]0.999975267647042[/C][/ROW]
[ROW][C]29[/C][C]1.83706168358527e-05[/C][C]3.67412336717055e-05[/C][C]0.999981629383164[/C][/ROW]
[ROW][C]30[/C][C]5.30487873369653e-05[/C][C]0.000106097574673931[/C][C]0.999946951212663[/C][/ROW]
[ROW][C]31[/C][C]3.49692707760457e-05[/C][C]6.99385415520914e-05[/C][C]0.999965030729224[/C][/ROW]
[ROW][C]32[/C][C]2.2029659755412e-05[/C][C]4.40593195108239e-05[/C][C]0.999977970340245[/C][/ROW]
[ROW][C]33[/C][C]1.06694070403683e-05[/C][C]2.13388140807366e-05[/C][C]0.99998933059296[/C][/ROW]
[ROW][C]34[/C][C]4.77232278151794e-06[/C][C]9.54464556303587e-06[/C][C]0.999995227677218[/C][/ROW]
[ROW][C]35[/C][C]4.24195082547549e-06[/C][C]8.48390165095098e-06[/C][C]0.999995758049175[/C][/ROW]
[ROW][C]36[/C][C]1.85642661119599e-06[/C][C]3.71285322239199e-06[/C][C]0.999998143573389[/C][/ROW]
[ROW][C]37[/C][C]4.84474568034126e-06[/C][C]9.68949136068253e-06[/C][C]0.99999515525432[/C][/ROW]
[ROW][C]38[/C][C]2.29286676812662e-06[/C][C]4.58573353625323e-06[/C][C]0.999997707133232[/C][/ROW]
[ROW][C]39[/C][C]0.000124015210821342[/C][C]0.000248030421642685[/C][C]0.999875984789179[/C][/ROW]
[ROW][C]40[/C][C]0.000144895780953909[/C][C]0.000289791561907819[/C][C]0.999855104219046[/C][/ROW]
[ROW][C]41[/C][C]0.000141633699326099[/C][C]0.000283267398652198[/C][C]0.999858366300674[/C][/ROW]
[ROW][C]42[/C][C]8.32221942573059e-05[/C][C]0.000166444388514612[/C][C]0.999916777805743[/C][/ROW]
[ROW][C]43[/C][C]9.55562117586802e-05[/C][C]0.00019111242351736[/C][C]0.999904443788241[/C][/ROW]
[ROW][C]44[/C][C]0.000917181472382073[/C][C]0.00183436294476415[/C][C]0.999082818527618[/C][/ROW]
[ROW][C]45[/C][C]0.000547993366915625[/C][C]0.00109598673383125[/C][C]0.999452006633084[/C][/ROW]
[ROW][C]46[/C][C]0.00193493858312171[/C][C]0.00386987716624342[/C][C]0.998065061416878[/C][/ROW]
[ROW][C]47[/C][C]0.00129305427274871[/C][C]0.00258610854549741[/C][C]0.998706945727251[/C][/ROW]
[ROW][C]48[/C][C]0.00129386265710053[/C][C]0.00258772531420107[/C][C]0.998706137342899[/C][/ROW]
[ROW][C]49[/C][C]0.00248453471329824[/C][C]0.00496906942659647[/C][C]0.997515465286702[/C][/ROW]
[ROW][C]50[/C][C]0.00163590395142202[/C][C]0.00327180790284403[/C][C]0.998364096048578[/C][/ROW]
[ROW][C]51[/C][C]0.0010624210727931[/C][C]0.00212484214558621[/C][C]0.998937578927207[/C][/ROW]
[ROW][C]52[/C][C]0.00066342149202281[/C][C]0.00132684298404562[/C][C]0.999336578507977[/C][/ROW]
[ROW][C]53[/C][C]0.00296125317166093[/C][C]0.00592250634332187[/C][C]0.997038746828339[/C][/ROW]
[ROW][C]54[/C][C]0.00200036838609895[/C][C]0.0040007367721979[/C][C]0.997999631613901[/C][/ROW]
[ROW][C]55[/C][C]0.00133793233956543[/C][C]0.00267586467913086[/C][C]0.998662067660435[/C][/ROW]
[ROW][C]56[/C][C]0.000879861945746995[/C][C]0.00175972389149399[/C][C]0.999120138054253[/C][/ROW]
[ROW][C]57[/C][C]0.000580634220525029[/C][C]0.00116126844105006[/C][C]0.999419365779475[/C][/ROW]
[ROW][C]58[/C][C]0.000447087149267696[/C][C]0.000894174298535392[/C][C]0.999552912850732[/C][/ROW]
[ROW][C]59[/C][C]0.000285861324890476[/C][C]0.000571722649780952[/C][C]0.999714138675109[/C][/ROW]
[ROW][C]60[/C][C]0.000607160444176126[/C][C]0.00121432088835225[/C][C]0.999392839555824[/C][/ROW]
[ROW][C]61[/C][C]0.00183222304923144[/C][C]0.00366444609846288[/C][C]0.998167776950769[/C][/ROW]
[ROW][C]62[/C][C]0.00146685052412802[/C][C]0.00293370104825604[/C][C]0.998533149475872[/C][/ROW]
[ROW][C]63[/C][C]0.001160207415508[/C][C]0.00232041483101601[/C][C]0.998839792584492[/C][/ROW]
[ROW][C]64[/C][C]0.000963190873428202[/C][C]0.0019263817468564[/C][C]0.999036809126572[/C][/ROW]
[ROW][C]65[/C][C]0.000977539315168441[/C][C]0.00195507863033688[/C][C]0.999022460684832[/C][/ROW]
[ROW][C]66[/C][C]0.00166950540121475[/C][C]0.00333901080242949[/C][C]0.998330494598785[/C][/ROW]
[ROW][C]67[/C][C]0.00116666749868991[/C][C]0.00233333499737981[/C][C]0.99883333250131[/C][/ROW]
[ROW][C]68[/C][C]0.000902427513161102[/C][C]0.0018048550263222[/C][C]0.999097572486839[/C][/ROW]
[ROW][C]69[/C][C]0.0106884536088271[/C][C]0.0213769072176543[/C][C]0.989311546391173[/C][/ROW]
[ROW][C]70[/C][C]0.00807162536110921[/C][C]0.0161432507222184[/C][C]0.991928374638891[/C][/ROW]
[ROW][C]71[/C][C]0.0075049437394571[/C][C]0.0150098874789142[/C][C]0.992495056260543[/C][/ROW]
[ROW][C]72[/C][C]0.00550690828004337[/C][C]0.0110138165600867[/C][C]0.994493091719957[/C][/ROW]
[ROW][C]73[/C][C]0.0055680194482068[/C][C]0.0111360388964136[/C][C]0.994431980551793[/C][/ROW]
[ROW][C]74[/C][C]0.00530057488246877[/C][C]0.0106011497649375[/C][C]0.994699425117531[/C][/ROW]
[ROW][C]75[/C][C]0.00420804539278645[/C][C]0.0084160907855729[/C][C]0.995791954607214[/C][/ROW]
[ROW][C]76[/C][C]0.00324329399735558[/C][C]0.00648658799471116[/C][C]0.996756706002644[/C][/ROW]
[ROW][C]77[/C][C]0.002225670476266[/C][C]0.004451340952532[/C][C]0.997774329523734[/C][/ROW]
[ROW][C]78[/C][C]0.00214995856205616[/C][C]0.00429991712411233[/C][C]0.997850041437944[/C][/ROW]
[ROW][C]79[/C][C]0.0017936347786018[/C][C]0.00358726955720359[/C][C]0.998206365221398[/C][/ROW]
[ROW][C]80[/C][C]0.00130973093826006[/C][C]0.00261946187652013[/C][C]0.99869026906174[/C][/ROW]
[ROW][C]81[/C][C]0.00109792115733617[/C][C]0.00219584231467234[/C][C]0.998902078842664[/C][/ROW]
[ROW][C]82[/C][C]0.0013027054230228[/C][C]0.0026054108460456[/C][C]0.998697294576977[/C][/ROW]
[ROW][C]83[/C][C]0.00193071012000575[/C][C]0.00386142024001149[/C][C]0.998069289879994[/C][/ROW]
[ROW][C]84[/C][C]0.00153498123539617[/C][C]0.00306996247079234[/C][C]0.998465018764604[/C][/ROW]
[ROW][C]85[/C][C]0.00186777469666104[/C][C]0.00373554939332208[/C][C]0.998132225303339[/C][/ROW]
[ROW][C]86[/C][C]0.0012838365961[/C][C]0.0025676731922[/C][C]0.9987161634039[/C][/ROW]
[ROW][C]87[/C][C]0.000863305793097729[/C][C]0.00172661158619546[/C][C]0.999136694206902[/C][/ROW]
[ROW][C]88[/C][C]0.000836660951848588[/C][C]0.00167332190369718[/C][C]0.999163339048151[/C][/ROW]
[ROW][C]89[/C][C]0.000567885445088241[/C][C]0.00113577089017648[/C][C]0.999432114554912[/C][/ROW]
[ROW][C]90[/C][C]0.000573584534106747[/C][C]0.00114716906821349[/C][C]0.999426415465893[/C][/ROW]
[ROW][C]91[/C][C]0.000374450715245212[/C][C]0.000748901430490425[/C][C]0.999625549284755[/C][/ROW]
[ROW][C]92[/C][C]0.000675990808535249[/C][C]0.0013519816170705[/C][C]0.999324009191465[/C][/ROW]
[ROW][C]93[/C][C]0.000788662293255618[/C][C]0.00157732458651124[/C][C]0.999211337706744[/C][/ROW]
[ROW][C]94[/C][C]0.000589012821942066[/C][C]0.00117802564388413[/C][C]0.999410987178058[/C][/ROW]
[ROW][C]95[/C][C]0.000378641393708425[/C][C]0.00075728278741685[/C][C]0.999621358606292[/C][/ROW]
[ROW][C]96[/C][C]0.980990605513091[/C][C]0.0380187889738185[/C][C]0.0190093944869092[/C][/ROW]
[ROW][C]97[/C][C]0.991502155636624[/C][C]0.0169956887267519[/C][C]0.00849784436337595[/C][/ROW]
[ROW][C]98[/C][C]0.987838513658928[/C][C]0.0243229726821447[/C][C]0.0121614863410724[/C][/ROW]
[ROW][C]99[/C][C]0.990486072715205[/C][C]0.0190278545695902[/C][C]0.00951392728479511[/C][/ROW]
[ROW][C]100[/C][C]0.986683872308558[/C][C]0.0266322553828847[/C][C]0.0133161276914424[/C][/ROW]
[ROW][C]101[/C][C]0.983380928394145[/C][C]0.0332381432117093[/C][C]0.0166190716058546[/C][/ROW]
[ROW][C]102[/C][C]0.976516027173367[/C][C]0.0469679456532658[/C][C]0.0234839728266329[/C][/ROW]
[ROW][C]103[/C][C]0.971724665289891[/C][C]0.0565506694202172[/C][C]0.0282753347101086[/C][/ROW]
[ROW][C]104[/C][C]0.965848896832357[/C][C]0.0683022063352863[/C][C]0.0341511031676431[/C][/ROW]
[ROW][C]105[/C][C]0.954470834548229[/C][C]0.0910583309035418[/C][C]0.0455291654517709[/C][/ROW]
[ROW][C]106[/C][C]0.982816646135656[/C][C]0.0343667077286876[/C][C]0.0171833538643438[/C][/ROW]
[ROW][C]107[/C][C]0.976646102496201[/C][C]0.0467077950075979[/C][C]0.023353897503799[/C][/ROW]
[ROW][C]108[/C][C]0.966931496147178[/C][C]0.0661370077056449[/C][C]0.0330685038528225[/C][/ROW]
[ROW][C]109[/C][C]0.95411473083547[/C][C]0.0917705383290605[/C][C]0.0458852691645302[/C][/ROW]
[ROW][C]110[/C][C]0.945827326682458[/C][C]0.108345346635084[/C][C]0.0541726733175421[/C][/ROW]
[ROW][C]111[/C][C]0.928640209601611[/C][C]0.142719580796778[/C][C]0.071359790398389[/C][/ROW]
[ROW][C]112[/C][C]0.978020516955559[/C][C]0.0439589660888811[/C][C]0.0219794830444406[/C][/ROW]
[ROW][C]113[/C][C]0.981806068748969[/C][C]0.0363878625020611[/C][C]0.0181939312510305[/C][/ROW]
[ROW][C]114[/C][C]0.984676063897936[/C][C]0.0306478722041274[/C][C]0.0153239361020637[/C][/ROW]
[ROW][C]115[/C][C]0.978451135496158[/C][C]0.0430977290076848[/C][C]0.0215488645038424[/C][/ROW]
[ROW][C]116[/C][C]0.97022399512552[/C][C]0.0595520097489603[/C][C]0.0297760048744802[/C][/ROW]
[ROW][C]117[/C][C]0.956646180102261[/C][C]0.0867076397954773[/C][C]0.0433538198977387[/C][/ROW]
[ROW][C]118[/C][C]0.98428954085678[/C][C]0.03142091828644[/C][C]0.01571045914322[/C][/ROW]
[ROW][C]119[/C][C]0.979943711517522[/C][C]0.0401125769649553[/C][C]0.0200562884824776[/C][/ROW]
[ROW][C]120[/C][C]0.97268188406443[/C][C]0.0546362318711407[/C][C]0.0273181159355703[/C][/ROW]
[ROW][C]121[/C][C]0.960539080815247[/C][C]0.0789218383695061[/C][C]0.0394609191847531[/C][/ROW]
[ROW][C]122[/C][C]0.940448053678044[/C][C]0.119103892643913[/C][C]0.0595519463219565[/C][/ROW]
[ROW][C]123[/C][C]0.929589017509711[/C][C]0.140821964980578[/C][C]0.0704109824902889[/C][/ROW]
[ROW][C]124[/C][C]0.896734592114848[/C][C]0.206530815770305[/C][C]0.103265407885152[/C][/ROW]
[ROW][C]125[/C][C]0.887842933627675[/C][C]0.224314132744649[/C][C]0.112157066372325[/C][/ROW]
[ROW][C]126[/C][C]0.928555457482697[/C][C]0.142889085034607[/C][C]0.0714445425173035[/C][/ROW]
[ROW][C]127[/C][C]0.909642765546748[/C][C]0.180714468906504[/C][C]0.0903572344532519[/C][/ROW]
[ROW][C]128[/C][C]0.875100373487084[/C][C]0.249799253025832[/C][C]0.124899626512916[/C][/ROW]
[ROW][C]129[/C][C]0.886268048429339[/C][C]0.227463903141321[/C][C]0.113731951570661[/C][/ROW]
[ROW][C]130[/C][C]0.832362364801254[/C][C]0.335275270397492[/C][C]0.167637635198746[/C][/ROW]
[ROW][C]131[/C][C]0.75911442434833[/C][C]0.481771151303339[/C][C]0.24088557565167[/C][/ROW]
[ROW][C]132[/C][C]0.771312703277989[/C][C]0.457374593444022[/C][C]0.228687296722011[/C][/ROW]
[ROW][C]133[/C][C]0.665660219993351[/C][C]0.668679560013298[/C][C]0.334339780006649[/C][/ROW]
[ROW][C]134[/C][C]0.686549679097811[/C][C]0.626900641804378[/C][C]0.313450320902189[/C][/ROW]
[ROW][C]135[/C][C]0.728746459158653[/C][C]0.542507081682693[/C][C]0.271253540841347[/C][/ROW]
[ROW][C]136[/C][C]0.56528687025438[/C][C]0.869426259491241[/C][C]0.43471312974562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&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
100.1171484086443230.2342968172886450.882851591355677
110.04816978498109910.09633956996219830.951830215018901
120.01701187167953260.03402374335906510.982988128320467
130.005981861887477440.01196372377495490.994018138112523
140.001821806955335580.003643613910671170.998178193044664
150.0005533766276715340.001106753255343070.999446623372328
160.0002095196360407720.0004190392720815430.999790480363959
170.0003774664389240640.0007549328778481280.999622533561076
180.0001260781208005440.0002521562416010890.999873921879199
190.0004715590057558490.0009431180115116980.999528440994244
200.0001807026940749870.0003614053881499740.999819297305925
210.0003201831238380780.0006403662476761550.999679816876162
220.0003364781471241390.0006729562942482780.999663521852876
230.0001939863408376540.0003879726816753070.999806013659162
240.0001459589354310670.0002919178708621330.999854041064569
259.78459387449536e-050.0001956918774899070.999902154061255
264.80215506586865e-059.6043101317373e-050.999951978449341
274.6652351419873e-059.3304702839746e-050.99995334764858
282.47323529576514e-054.94647059153029e-050.999975267647042
291.83706168358527e-053.67412336717055e-050.999981629383164
305.30487873369653e-050.0001060975746739310.999946951212663
313.49692707760457e-056.99385415520914e-050.999965030729224
322.2029659755412e-054.40593195108239e-050.999977970340245
331.06694070403683e-052.13388140807366e-050.99998933059296
344.77232278151794e-069.54464556303587e-060.999995227677218
354.24195082547549e-068.48390165095098e-060.999995758049175
361.85642661119599e-063.71285322239199e-060.999998143573389
374.84474568034126e-069.68949136068253e-060.99999515525432
382.29286676812662e-064.58573353625323e-060.999997707133232
390.0001240152108213420.0002480304216426850.999875984789179
400.0001448957809539090.0002897915619078190.999855104219046
410.0001416336993260990.0002832673986521980.999858366300674
428.32221942573059e-050.0001664443885146120.999916777805743
439.55562117586802e-050.000191112423517360.999904443788241
440.0009171814723820730.001834362944764150.999082818527618
450.0005479933669156250.001095986733831250.999452006633084
460.001934938583121710.003869877166243420.998065061416878
470.001293054272748710.002586108545497410.998706945727251
480.001293862657100530.002587725314201070.998706137342899
490.002484534713298240.004969069426596470.997515465286702
500.001635903951422020.003271807902844030.998364096048578
510.00106242107279310.002124842145586210.998937578927207
520.000663421492022810.001326842984045620.999336578507977
530.002961253171660930.005922506343321870.997038746828339
540.002000368386098950.00400073677219790.997999631613901
550.001337932339565430.002675864679130860.998662067660435
560.0008798619457469950.001759723891493990.999120138054253
570.0005806342205250290.001161268441050060.999419365779475
580.0004470871492676960.0008941742985353920.999552912850732
590.0002858613248904760.0005717226497809520.999714138675109
600.0006071604441761260.001214320888352250.999392839555824
610.001832223049231440.003664446098462880.998167776950769
620.001466850524128020.002933701048256040.998533149475872
630.0011602074155080.002320414831016010.998839792584492
640.0009631908734282020.00192638174685640.999036809126572
650.0009775393151684410.001955078630336880.999022460684832
660.001669505401214750.003339010802429490.998330494598785
670.001166667498689910.002333334997379810.99883333250131
680.0009024275131611020.00180485502632220.999097572486839
690.01068845360882710.02137690721765430.989311546391173
700.008071625361109210.01614325072221840.991928374638891
710.00750494373945710.01500988747891420.992495056260543
720.005506908280043370.01101381656008670.994493091719957
730.00556801944820680.01113603889641360.994431980551793
740.005300574882468770.01060114976493750.994699425117531
750.004208045392786450.00841609078557290.995791954607214
760.003243293997355580.006486587994711160.996756706002644
770.0022256704762660.0044513409525320.997774329523734
780.002149958562056160.004299917124112330.997850041437944
790.00179363477860180.003587269557203590.998206365221398
800.001309730938260060.002619461876520130.99869026906174
810.001097921157336170.002195842314672340.998902078842664
820.00130270542302280.00260541084604560.998697294576977
830.001930710120005750.003861420240011490.998069289879994
840.001534981235396170.003069962470792340.998465018764604
850.001867774696661040.003735549393322080.998132225303339
860.00128383659610.00256767319220.9987161634039
870.0008633057930977290.001726611586195460.999136694206902
880.0008366609518485880.001673321903697180.999163339048151
890.0005678854450882410.001135770890176480.999432114554912
900.0005735845341067470.001147169068213490.999426415465893
910.0003744507152452120.0007489014304904250.999625549284755
920.0006759908085352490.00135198161707050.999324009191465
930.0007886622932556180.001577324586511240.999211337706744
940.0005890128219420660.001178025643884130.999410987178058
950.0003786413937084250.000757282787416850.999621358606292
960.9809906055130910.03801878897381850.0190093944869092
970.9915021556366240.01699568872675190.00849784436337595
980.9878385136589280.02432297268214470.0121614863410724
990.9904860727152050.01902785456959020.00951392728479511
1000.9866838723085580.02663225538288470.0133161276914424
1010.9833809283941450.03323814321170930.0166190716058546
1020.9765160271733670.04696794565326580.0234839728266329
1030.9717246652898910.05655066942021720.0282753347101086
1040.9658488968323570.06830220633528630.0341511031676431
1050.9544708345482290.09105833090354180.0455291654517709
1060.9828166461356560.03436670772868760.0171833538643438
1070.9766461024962010.04670779500759790.023353897503799
1080.9669314961471780.06613700770564490.0330685038528225
1090.954114730835470.09177053832906050.0458852691645302
1100.9458273266824580.1083453466350840.0541726733175421
1110.9286402096016110.1427195807967780.071359790398389
1120.9780205169555590.04395896608888110.0219794830444406
1130.9818060687489690.03638786250206110.0181939312510305
1140.9846760638979360.03064787220412740.0153239361020637
1150.9784511354961580.04309772900768480.0215488645038424
1160.970223995125520.05955200974896030.0297760048744802
1170.9566461801022610.08670763979547730.0433538198977387
1180.984289540856780.031420918286440.01571045914322
1190.9799437115175220.04011257696495530.0200562884824776
1200.972681884064430.05463623187114070.0273181159355703
1210.9605390808152470.07892183836950610.0394609191847531
1220.9404480536780440.1191038926439130.0595519463219565
1230.9295890175097110.1408219649805780.0704109824902889
1240.8967345921148480.2065308157703050.103265407885152
1250.8878429336276750.2243141327446490.112157066372325
1260.9285554574826970.1428890850346070.0714445425173035
1270.9096427655467480.1807144689065040.0903572344532519
1280.8751003734870840.2497992530258320.124899626512916
1290.8862680484293390.2274639031413210.113731951570661
1300.8323623648012540.3352752703974920.167637635198746
1310.759114424348330.4817711513033390.24088557565167
1320.7713127032779890.4573745934440220.228687296722011
1330.6656602199933510.6686795600132980.334339780006649
1340.6865496790978110.6269006418043780.313450320902189
1350.7287464591586530.5425070816826930.271253540841347
1360.565286870254380.8694262594912410.43471312974562






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level760.598425196850394NOK
5% type I error level990.779527559055118NOK
10% type I error level1090.858267716535433NOK

\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 & 76 & 0.598425196850394 & NOK \tabularnewline
5% type I error level & 99 & 0.779527559055118 & NOK \tabularnewline
10% type I error level & 109 & 0.858267716535433 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154772&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]76[/C][C]0.598425196850394[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]99[/C][C]0.779527559055118[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]109[/C][C]0.858267716535433[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154772&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154772&T=6

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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 level760.598425196850394NOK
5% type I error level990.779527559055118NOK
10% type I error level1090.858267716535433NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}