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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 11:28:51 -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/t1323793752oghi9xrcraomlex.htm/, Retrieved Fri, 03 May 2024 00:31:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154501, Retrieved Fri, 03 May 2024 00:31:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Multiple Regression] [2011-12-12 20:14:09] [f2faabc3a2466a29562900bc59f67898]
- R P     [Multiple Regression] [Multiple Regression] [2011-12-13 16:22:12] [74be16979710d4c4e7c6647856088456]
-   P       [Multiple Regression] [Multiple Regression] [2011-12-13 16:24:33] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [Multiple Regression] [2011-12-13 16:26:06] [74be16979710d4c4e7c6647856088456]
-   P             [Multiple Regression] [Multiple Regression] [2011-12-13 16:28:51] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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 time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 23.8484305208199 + 0.997099833184553Gender[t] -0.0425700858219636Connected[t] -0.0103927525321674Separate[t] -0.763635979283115Happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  23.8484305208199 +  0.997099833184553Gender[t] -0.0425700858219636Connected[t] -0.0103927525321674Separate[t] -0.763635979283115Happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  23.8484305208199 +  0.997099833184553Gender[t] -0.0425700858219636Connected[t] -0.0103927525321674Separate[t] -0.763635979283115Happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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
Depression[t] = + 23.8484305208199 + 0.997099833184553Gender[t] -0.0425700858219636Connected[t] -0.0103927525321674Separate[t] -0.763635979283115Happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.84843052081992.6459039.013300
Gender0.9970998331845530.4470972.23020.0271550.013577
Connected-0.04257008582196360.066748-0.63780.5245480.262274
Separate-0.01039275253216740.065039-0.15980.873250.436625
Happiness-0.7636359792831150.091703-8.327300

\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) & 23.8484305208199 & 2.645903 & 9.0133 & 0 & 0 \tabularnewline
Gender & 0.997099833184553 & 0.447097 & 2.2302 & 0.027155 & 0.013577 \tabularnewline
Connected & -0.0425700858219636 & 0.066748 & -0.6378 & 0.524548 & 0.262274 \tabularnewline
Separate & -0.0103927525321674 & 0.065039 & -0.1598 & 0.87325 & 0.436625 \tabularnewline
Happiness & -0.763635979283115 & 0.091703 & -8.3273 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&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]23.8484305208199[/C][C]2.645903[/C][C]9.0133[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]0.997099833184553[/C][C]0.447097[/C][C]2.2302[/C][C]0.027155[/C][C]0.013577[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0425700858219636[/C][C]0.066748[/C][C]-0.6378[/C][C]0.524548[/C][C]0.262274[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0103927525321674[/C][C]0.065039[/C][C]-0.1598[/C][C]0.87325[/C][C]0.436625[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.763635979283115[/C][C]0.091703[/C][C]-8.3273[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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)23.84843052081992.6459039.013300
Gender0.9970998331845530.4470972.23020.0271550.013577
Connected-0.04257008582196360.066748-0.63780.5245480.262274
Separate-0.01039275253216740.065039-0.15980.873250.436625
Happiness-0.7636359792831150.091703-8.327300






Multiple Linear Regression - Regression Statistics
Multiple R0.56575104472159
R-squared0.320074244603571
Adjusted R-squared0.302751295421496
F-TEST (value)18.4768910443249
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.84208204245806e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.64378948941538
Sum Squared Residuals1097.37078970189

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.56575104472159 \tabularnewline
R-squared & 0.320074244603571 \tabularnewline
Adjusted R-squared & 0.302751295421496 \tabularnewline
F-TEST (value) & 18.4768910443249 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 1.84208204245806e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.64378948941538 \tabularnewline
Sum Squared Residuals & 1097.37078970189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.56575104472159[/C][/ROW]
[ROW][C]R-squared[/C][C]0.320074244603571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.302751295421496[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.4768910443249[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]1.84208204245806e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.64378948941538[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1097.37078970189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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 R0.56575104472159
R-squared0.320074244603571
Adjusted R-squared0.302751295421496
F-TEST (value)18.4768910443249
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.84208204245806e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.64378948941538
Sum Squared Residuals1097.37078970189






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.0114283623026-1.01142836230257
21110.1043811320070.895618867993023
31415.80178550179-1.80178550178995
41214.0192651085647-2.01926510856466
52111.79253975702229.20746024297781
61210.30583973289131.69416026710866
72213.16931780167168.8306821983284
81113.3302044681206-2.33020446812059
91012.4918210697257-2.49182106972571
101312.41807272630720.581927273692754
11109.923880116459670.0761198835403283
1289.44766990512542-1.44766990512542
131515.2277609613128-0.227760961312805
141411.56929657538022.4307034246198
151010.3078378842779-0.307837884277923
161412.44942306417651.55057693582354
171412.28953547342081.71046452657922
181110.84823068191910.151769318080947
191012.0994687007619-2.09946870076188
201311.9296436913271.07035630867305
2179.394879147044-2.394879147044
221415.8007864260967-1.80078642609666
231213.0965685339464-1.09656853394643
241414.6985879116244-0.698587911624422
25119.870917278105541.12908272189446
26916.8919647739111-7.89196477391108
271110.7310852572580.268914742741956
281513.36238180141041.63761819858962
291412.64031683225591.35968316774406
301314.6551908303819-1.65519083038188
31912.0137847872776-3.01378478727759
321513.43630222510161.56369777489844
331010.9635500354662-0.963550035466187
341112.5561757363053-1.5561757363053
351312.3954611501290.604538849870962
36810.9221511056102-2.92215110561023
372016.41609872312233.58390127687774
381211.51550674160550.484493258394506
391011.3041107220419-1.3041107220419
401013.0853487859937-3.08534878599369
41911.4833294083157-2.4833294083157
421411.83411076715092.16588923284914
43810.6771233432106-2.67712334321062
441413.84898476527680.151015234723199
451114.7703381036563-3.7703381036563
461315.5037949010362-2.50379490103621
47912.3651098879531-3.36510988795311
481112.6299240797238-1.62992407972377
491510.9333708535634.06662914643702
501112.96803128106-1.96803128105996
511011.8672871761339-1.86728717613395
521412.4806013217731.51939867822704
531814.74033100202583.25966899797419
541414.8366909216225-0.83669092162248
551113.9870877752749-2.98708777527486
561212.4804292415003-0.48042924150025
571311.61086758550891.38913241449113
58912.8002044230116-3.80020442301162
591013.7326663360364-3.73266633603637
601514.83569184592920.164308154070814
612017.16834287417992.83165712582008
621212.9472457759956-0.947245775995628
631215.6315051585021-3.6315051585021
641413.46830747811860.531692521881356
651312.61853225149830.381467748501692
661115.3346857137144-4.33468571371436
671715.54636498685821.45363501314183
681214.1241917095796-2.12419170957962
691312.3121470495990.687852950401016
701411.51650581729882.48349418270121
711312.16282429164820.837175708351821
721511.90985726195593.09014273804409
731312.5353902312410.464609768759031
741011.3472245497042-1.34722454970423
751113.1902753870087-2.19027538700865
761914.706982512774.29301748723
771310.90119352027322.09880647972682
781714.14580421006452.85419578993546
791311.87685293324551.12314706675446
80913.0625651295428-4.06256512954276
811111.6120387414749-0.612038741474883
821010.6573369138396-0.657336913839579
83912.5136056504833-3.51360565048334
841210.70930067650041.29069932349958
851212.6724941655457-0.672494165545733
861313.4247383166034-0.424738316603386
871311.36801005476861.63198994523144
881213.3935600590069-1.39356005900688
891514.34826188664220.651738113357812
902218.74940466652093.25059533347915
911311.16700678773711.83299321226287
921514.05027128588840.949728714111557
931312.47203464035470.527965359645329
941513.49848666002191.50151333997815
951014.3690473917065-4.36904739170652
961111.5900820804445-0.590082080444539
971614.74015892175311.25984107824691
981113.3395981449595-2.33959814495946
99119.935271944685131.06472805531487
1001011.6546088272968-1.65460882729685
1011010.8690161869834-0.869016186983388
1021613.74305908856852.25694091143146
1031211.70739958537830.292600414621738
1041114.7922947646866-3.79229476468664
1051612.57796031706293.42203968293707
1061915.93743502654853.0625649734515
1071111.6430449187987-0.64304491879867
1081611.24029979730274.75970020269733
1091515.3129011329567-0.312901132956732
1102415.93926109766248.06073890233763
1111412.75763433718971.24236566281034
1121515.7800009210323-0.780000921032323
1131114.1270168563868-3.12701685638679
1141512.44942306417652.55057693582354
1151210.16873579858661.83126420141343
1161010.7196934290326-0.719693429032583
1171413.33120354381390.66879645618612
1181313.2886334579919-0.288633457991916
119913.3831673064747-4.38316730647472
1201513.15992412483271.84007587516727
1211514.88965375997660.110346240023389
1221413.5514494983760.448550501624017
1231112.3651098879531-1.36510988795311
124812.4180727263072-4.41807272630725
1251112.6611023373203-1.66110233732027
1261114.2527289624661-3.25272896246609
12789.87091727810554-1.87091727810554
1281010.5824174144551-0.582417414455103
129119.554594657526981.44540534247302
1301312.5551766606120.44482333938799
1311113.2130590434596-2.21305904345958
1322016.44827605641213.55172394358794
1331012.3007552213735-2.30075522137352
1341511.92981577159973.07018422840033
1351211.42097289312270.579027106877307
1361411.96082194892352.03917805107654
1372315.02493162316757.9750683768325
1381412.34349738746821.6565026125318
1391615.801785501790.198214498210049
1401112.5447839080798-1.54478390807984
1411213.2348436242172-1.23484362421721
1421012.6724941655457-2.67249416554573
1431410.9968985247223.00310147527801
1441213.5108775639406-1.51087756394061
1451211.8322846960370.167715303963014
1461111.5994757572834-0.599475757283412
1471211.98160745398780.0183925460122098
1481314.5388724011415-1.53887240114145
1491113.997480527807-2.99748052780703
1501916.15028545565832.84971454434169
1511211.601473908670.39852609133
1521713.74288700829583.25711299170418
153910.7850471713055-1.78504717130547
1541214.7837280832683-2.78372808326835
1551917.12659978377851.87340021622147
1561814.18837429588653.8116257041135
1571514.05027128588840.949728714111557
1581413.40495188723230.595048112767654
159119.554594657526981.44540534247302
160914.1354114575324-5.13541145753237
1611814.95300935086293.04699064913709
1621614.09384044740371.9061595525963

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.0114283623026 & -1.01142836230257 \tabularnewline
2 & 11 & 10.104381132007 & 0.895618867993023 \tabularnewline
3 & 14 & 15.80178550179 & -1.80178550178995 \tabularnewline
4 & 12 & 14.0192651085647 & -2.01926510856466 \tabularnewline
5 & 21 & 11.7925397570222 & 9.20746024297781 \tabularnewline
6 & 12 & 10.3058397328913 & 1.69416026710866 \tabularnewline
7 & 22 & 13.1693178016716 & 8.8306821983284 \tabularnewline
8 & 11 & 13.3302044681206 & -2.33020446812059 \tabularnewline
9 & 10 & 12.4918210697257 & -2.49182106972571 \tabularnewline
10 & 13 & 12.4180727263072 & 0.581927273692754 \tabularnewline
11 & 10 & 9.92388011645967 & 0.0761198835403283 \tabularnewline
12 & 8 & 9.44766990512542 & -1.44766990512542 \tabularnewline
13 & 15 & 15.2277609613128 & -0.227760961312805 \tabularnewline
14 & 14 & 11.5692965753802 & 2.4307034246198 \tabularnewline
15 & 10 & 10.3078378842779 & -0.307837884277923 \tabularnewline
16 & 14 & 12.4494230641765 & 1.55057693582354 \tabularnewline
17 & 14 & 12.2895354734208 & 1.71046452657922 \tabularnewline
18 & 11 & 10.8482306819191 & 0.151769318080947 \tabularnewline
19 & 10 & 12.0994687007619 & -2.09946870076188 \tabularnewline
20 & 13 & 11.929643691327 & 1.07035630867305 \tabularnewline
21 & 7 & 9.394879147044 & -2.394879147044 \tabularnewline
22 & 14 & 15.8007864260967 & -1.80078642609666 \tabularnewline
23 & 12 & 13.0965685339464 & -1.09656853394643 \tabularnewline
24 & 14 & 14.6985879116244 & -0.698587911624422 \tabularnewline
25 & 11 & 9.87091727810554 & 1.12908272189446 \tabularnewline
26 & 9 & 16.8919647739111 & -7.89196477391108 \tabularnewline
27 & 11 & 10.731085257258 & 0.268914742741956 \tabularnewline
28 & 15 & 13.3623818014104 & 1.63761819858962 \tabularnewline
29 & 14 & 12.6403168322559 & 1.35968316774406 \tabularnewline
30 & 13 & 14.6551908303819 & -1.65519083038188 \tabularnewline
31 & 9 & 12.0137847872776 & -3.01378478727759 \tabularnewline
32 & 15 & 13.4363022251016 & 1.56369777489844 \tabularnewline
33 & 10 & 10.9635500354662 & -0.963550035466187 \tabularnewline
34 & 11 & 12.5561757363053 & -1.5561757363053 \tabularnewline
35 & 13 & 12.395461150129 & 0.604538849870962 \tabularnewline
36 & 8 & 10.9221511056102 & -2.92215110561023 \tabularnewline
37 & 20 & 16.4160987231223 & 3.58390127687774 \tabularnewline
38 & 12 & 11.5155067416055 & 0.484493258394506 \tabularnewline
39 & 10 & 11.3041107220419 & -1.3041107220419 \tabularnewline
40 & 10 & 13.0853487859937 & -3.08534878599369 \tabularnewline
41 & 9 & 11.4833294083157 & -2.4833294083157 \tabularnewline
42 & 14 & 11.8341107671509 & 2.16588923284914 \tabularnewline
43 & 8 & 10.6771233432106 & -2.67712334321062 \tabularnewline
44 & 14 & 13.8489847652768 & 0.151015234723199 \tabularnewline
45 & 11 & 14.7703381036563 & -3.7703381036563 \tabularnewline
46 & 13 & 15.5037949010362 & -2.50379490103621 \tabularnewline
47 & 9 & 12.3651098879531 & -3.36510988795311 \tabularnewline
48 & 11 & 12.6299240797238 & -1.62992407972377 \tabularnewline
49 & 15 & 10.933370853563 & 4.06662914643702 \tabularnewline
50 & 11 & 12.96803128106 & -1.96803128105996 \tabularnewline
51 & 10 & 11.8672871761339 & -1.86728717613395 \tabularnewline
52 & 14 & 12.480601321773 & 1.51939867822704 \tabularnewline
53 & 18 & 14.7403310020258 & 3.25966899797419 \tabularnewline
54 & 14 & 14.8366909216225 & -0.83669092162248 \tabularnewline
55 & 11 & 13.9870877752749 & -2.98708777527486 \tabularnewline
56 & 12 & 12.4804292415003 & -0.48042924150025 \tabularnewline
57 & 13 & 11.6108675855089 & 1.38913241449113 \tabularnewline
58 & 9 & 12.8002044230116 & -3.80020442301162 \tabularnewline
59 & 10 & 13.7326663360364 & -3.73266633603637 \tabularnewline
60 & 15 & 14.8356918459292 & 0.164308154070814 \tabularnewline
61 & 20 & 17.1683428741799 & 2.83165712582008 \tabularnewline
62 & 12 & 12.9472457759956 & -0.947245775995628 \tabularnewline
63 & 12 & 15.6315051585021 & -3.6315051585021 \tabularnewline
64 & 14 & 13.4683074781186 & 0.531692521881356 \tabularnewline
65 & 13 & 12.6185322514983 & 0.381467748501692 \tabularnewline
66 & 11 & 15.3346857137144 & -4.33468571371436 \tabularnewline
67 & 17 & 15.5463649868582 & 1.45363501314183 \tabularnewline
68 & 12 & 14.1241917095796 & -2.12419170957962 \tabularnewline
69 & 13 & 12.312147049599 & 0.687852950401016 \tabularnewline
70 & 14 & 11.5165058172988 & 2.48349418270121 \tabularnewline
71 & 13 & 12.1628242916482 & 0.837175708351821 \tabularnewline
72 & 15 & 11.9098572619559 & 3.09014273804409 \tabularnewline
73 & 13 & 12.535390231241 & 0.464609768759031 \tabularnewline
74 & 10 & 11.3472245497042 & -1.34722454970423 \tabularnewline
75 & 11 & 13.1902753870087 & -2.19027538700865 \tabularnewline
76 & 19 & 14.70698251277 & 4.29301748723 \tabularnewline
77 & 13 & 10.9011935202732 & 2.09880647972682 \tabularnewline
78 & 17 & 14.1458042100645 & 2.85419578993546 \tabularnewline
79 & 13 & 11.8768529332455 & 1.12314706675446 \tabularnewline
80 & 9 & 13.0625651295428 & -4.06256512954276 \tabularnewline
81 & 11 & 11.6120387414749 & -0.612038741474883 \tabularnewline
82 & 10 & 10.6573369138396 & -0.657336913839579 \tabularnewline
83 & 9 & 12.5136056504833 & -3.51360565048334 \tabularnewline
84 & 12 & 10.7093006765004 & 1.29069932349958 \tabularnewline
85 & 12 & 12.6724941655457 & -0.672494165545733 \tabularnewline
86 & 13 & 13.4247383166034 & -0.424738316603386 \tabularnewline
87 & 13 & 11.3680100547686 & 1.63198994523144 \tabularnewline
88 & 12 & 13.3935600590069 & -1.39356005900688 \tabularnewline
89 & 15 & 14.3482618866422 & 0.651738113357812 \tabularnewline
90 & 22 & 18.7494046665209 & 3.25059533347915 \tabularnewline
91 & 13 & 11.1670067877371 & 1.83299321226287 \tabularnewline
92 & 15 & 14.0502712858884 & 0.949728714111557 \tabularnewline
93 & 13 & 12.4720346403547 & 0.527965359645329 \tabularnewline
94 & 15 & 13.4984866600219 & 1.50151333997815 \tabularnewline
95 & 10 & 14.3690473917065 & -4.36904739170652 \tabularnewline
96 & 11 & 11.5900820804445 & -0.590082080444539 \tabularnewline
97 & 16 & 14.7401589217531 & 1.25984107824691 \tabularnewline
98 & 11 & 13.3395981449595 & -2.33959814495946 \tabularnewline
99 & 11 & 9.93527194468513 & 1.06472805531487 \tabularnewline
100 & 10 & 11.6546088272968 & -1.65460882729685 \tabularnewline
101 & 10 & 10.8690161869834 & -0.869016186983388 \tabularnewline
102 & 16 & 13.7430590885685 & 2.25694091143146 \tabularnewline
103 & 12 & 11.7073995853783 & 0.292600414621738 \tabularnewline
104 & 11 & 14.7922947646866 & -3.79229476468664 \tabularnewline
105 & 16 & 12.5779603170629 & 3.42203968293707 \tabularnewline
106 & 19 & 15.9374350265485 & 3.0625649734515 \tabularnewline
107 & 11 & 11.6430449187987 & -0.64304491879867 \tabularnewline
108 & 16 & 11.2402997973027 & 4.75970020269733 \tabularnewline
109 & 15 & 15.3129011329567 & -0.312901132956732 \tabularnewline
110 & 24 & 15.9392610976624 & 8.06073890233763 \tabularnewline
111 & 14 & 12.7576343371897 & 1.24236566281034 \tabularnewline
112 & 15 & 15.7800009210323 & -0.780000921032323 \tabularnewline
113 & 11 & 14.1270168563868 & -3.12701685638679 \tabularnewline
114 & 15 & 12.4494230641765 & 2.55057693582354 \tabularnewline
115 & 12 & 10.1687357985866 & 1.83126420141343 \tabularnewline
116 & 10 & 10.7196934290326 & -0.719693429032583 \tabularnewline
117 & 14 & 13.3312035438139 & 0.66879645618612 \tabularnewline
118 & 13 & 13.2886334579919 & -0.288633457991916 \tabularnewline
119 & 9 & 13.3831673064747 & -4.38316730647472 \tabularnewline
120 & 15 & 13.1599241248327 & 1.84007587516727 \tabularnewline
121 & 15 & 14.8896537599766 & 0.110346240023389 \tabularnewline
122 & 14 & 13.551449498376 & 0.448550501624017 \tabularnewline
123 & 11 & 12.3651098879531 & -1.36510988795311 \tabularnewline
124 & 8 & 12.4180727263072 & -4.41807272630725 \tabularnewline
125 & 11 & 12.6611023373203 & -1.66110233732027 \tabularnewline
126 & 11 & 14.2527289624661 & -3.25272896246609 \tabularnewline
127 & 8 & 9.87091727810554 & -1.87091727810554 \tabularnewline
128 & 10 & 10.5824174144551 & -0.582417414455103 \tabularnewline
129 & 11 & 9.55459465752698 & 1.44540534247302 \tabularnewline
130 & 13 & 12.555176660612 & 0.44482333938799 \tabularnewline
131 & 11 & 13.2130590434596 & -2.21305904345958 \tabularnewline
132 & 20 & 16.4482760564121 & 3.55172394358794 \tabularnewline
133 & 10 & 12.3007552213735 & -2.30075522137352 \tabularnewline
134 & 15 & 11.9298157715997 & 3.07018422840033 \tabularnewline
135 & 12 & 11.4209728931227 & 0.579027106877307 \tabularnewline
136 & 14 & 11.9608219489235 & 2.03917805107654 \tabularnewline
137 & 23 & 15.0249316231675 & 7.9750683768325 \tabularnewline
138 & 14 & 12.3434973874682 & 1.6565026125318 \tabularnewline
139 & 16 & 15.80178550179 & 0.198214498210049 \tabularnewline
140 & 11 & 12.5447839080798 & -1.54478390807984 \tabularnewline
141 & 12 & 13.2348436242172 & -1.23484362421721 \tabularnewline
142 & 10 & 12.6724941655457 & -2.67249416554573 \tabularnewline
143 & 14 & 10.996898524722 & 3.00310147527801 \tabularnewline
144 & 12 & 13.5108775639406 & -1.51087756394061 \tabularnewline
145 & 12 & 11.832284696037 & 0.167715303963014 \tabularnewline
146 & 11 & 11.5994757572834 & -0.599475757283412 \tabularnewline
147 & 12 & 11.9816074539878 & 0.0183925460122098 \tabularnewline
148 & 13 & 14.5388724011415 & -1.53887240114145 \tabularnewline
149 & 11 & 13.997480527807 & -2.99748052780703 \tabularnewline
150 & 19 & 16.1502854556583 & 2.84971454434169 \tabularnewline
151 & 12 & 11.60147390867 & 0.39852609133 \tabularnewline
152 & 17 & 13.7428870082958 & 3.25711299170418 \tabularnewline
153 & 9 & 10.7850471713055 & -1.78504717130547 \tabularnewline
154 & 12 & 14.7837280832683 & -2.78372808326835 \tabularnewline
155 & 19 & 17.1265997837785 & 1.87340021622147 \tabularnewline
156 & 18 & 14.1883742958865 & 3.8116257041135 \tabularnewline
157 & 15 & 14.0502712858884 & 0.949728714111557 \tabularnewline
158 & 14 & 13.4049518872323 & 0.595048112767654 \tabularnewline
159 & 11 & 9.55459465752698 & 1.44540534247302 \tabularnewline
160 & 9 & 14.1354114575324 & -5.13541145753237 \tabularnewline
161 & 18 & 14.9530093508629 & 3.04699064913709 \tabularnewline
162 & 16 & 14.0938404474037 & 1.9061595525963 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]13.0114283623026[/C][C]-1.01142836230257[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.104381132007[/C][C]0.895618867993023[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.80178550179[/C][C]-1.80178550178995[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.0192651085647[/C][C]-2.01926510856466[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.7925397570222[/C][C]9.20746024297781[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3058397328913[/C][C]1.69416026710866[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.1693178016716[/C][C]8.8306821983284[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.3302044681206[/C][C]-2.33020446812059[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.4918210697257[/C][C]-2.49182106972571[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.4180727263072[/C][C]0.581927273692754[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]9.92388011645967[/C][C]0.0761198835403283[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.44766990512542[/C][C]-1.44766990512542[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.2277609613128[/C][C]-0.227760961312805[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.5692965753802[/C][C]2.4307034246198[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.3078378842779[/C][C]-0.307837884277923[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.4494230641765[/C][C]1.55057693582354[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.2895354734208[/C][C]1.71046452657922[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.8482306819191[/C][C]0.151769318080947[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.0994687007619[/C][C]-2.09946870076188[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.929643691327[/C][C]1.07035630867305[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]9.394879147044[/C][C]-2.394879147044[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.8007864260967[/C][C]-1.80078642609666[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.0965685339464[/C][C]-1.09656853394643[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.6985879116244[/C][C]-0.698587911624422[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.87091727810554[/C][C]1.12908272189446[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.8919647739111[/C][C]-7.89196477391108[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.731085257258[/C][C]0.268914742741956[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.3623818014104[/C][C]1.63761819858962[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.6403168322559[/C][C]1.35968316774406[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]14.6551908303819[/C][C]-1.65519083038188[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.0137847872776[/C][C]-3.01378478727759[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]13.4363022251016[/C][C]1.56369777489844[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.9635500354662[/C][C]-0.963550035466187[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.5561757363053[/C][C]-1.5561757363053[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.395461150129[/C][C]0.604538849870962[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]10.9221511056102[/C][C]-2.92215110561023[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]16.4160987231223[/C][C]3.58390127687774[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.5155067416055[/C][C]0.484493258394506[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.3041107220419[/C][C]-1.3041107220419[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.0853487859937[/C][C]-3.08534878599369[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.4833294083157[/C][C]-2.4833294083157[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.8341107671509[/C][C]2.16588923284914[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]10.6771233432106[/C][C]-2.67712334321062[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.8489847652768[/C][C]0.151015234723199[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.7703381036563[/C][C]-3.7703381036563[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.5037949010362[/C][C]-2.50379490103621[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.3651098879531[/C][C]-3.36510988795311[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.6299240797238[/C][C]-1.62992407972377[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.933370853563[/C][C]4.06662914643702[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.96803128106[/C][C]-1.96803128105996[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.8672871761339[/C][C]-1.86728717613395[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.480601321773[/C][C]1.51939867822704[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.7403310020258[/C][C]3.25966899797419[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.8366909216225[/C][C]-0.83669092162248[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]13.9870877752749[/C][C]-2.98708777527486[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4804292415003[/C][C]-0.48042924150025[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.6108675855089[/C][C]1.38913241449113[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.8002044230116[/C][C]-3.80020442301162[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.7326663360364[/C][C]-3.73266633603637[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.8356918459292[/C][C]0.164308154070814[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.1683428741799[/C][C]2.83165712582008[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9472457759956[/C][C]-0.947245775995628[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]15.6315051585021[/C][C]-3.6315051585021[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.4683074781186[/C][C]0.531692521881356[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.6185322514983[/C][C]0.381467748501692[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.3346857137144[/C][C]-4.33468571371436[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.5463649868582[/C][C]1.45363501314183[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.1241917095796[/C][C]-2.12419170957962[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.312147049599[/C][C]0.687852950401016[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]11.5165058172988[/C][C]2.48349418270121[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.1628242916482[/C][C]0.837175708351821[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.9098572619559[/C][C]3.09014273804409[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.535390231241[/C][C]0.464609768759031[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.3472245497042[/C][C]-1.34722454970423[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.1902753870087[/C][C]-2.19027538700865[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.70698251277[/C][C]4.29301748723[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.9011935202732[/C][C]2.09880647972682[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.1458042100645[/C][C]2.85419578993546[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.8768529332455[/C][C]1.12314706675446[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.0625651295428[/C][C]-4.06256512954276[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.6120387414749[/C][C]-0.612038741474883[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.6573369138396[/C][C]-0.657336913839579[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.5136056504833[/C][C]-3.51360565048334[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.7093006765004[/C][C]1.29069932349958[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.6724941655457[/C][C]-0.672494165545733[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.4247383166034[/C][C]-0.424738316603386[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]11.3680100547686[/C][C]1.63198994523144[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.3935600590069[/C][C]-1.39356005900688[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.3482618866422[/C][C]0.651738113357812[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]18.7494046665209[/C][C]3.25059533347915[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.1670067877371[/C][C]1.83299321226287[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.0502712858884[/C][C]0.949728714111557[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.4720346403547[/C][C]0.527965359645329[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4984866600219[/C][C]1.50151333997815[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]14.3690473917065[/C][C]-4.36904739170652[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.5900820804445[/C][C]-0.590082080444539[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.7401589217531[/C][C]1.25984107824691[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]13.3395981449595[/C][C]-2.33959814495946[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]9.93527194468513[/C][C]1.06472805531487[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.6546088272968[/C][C]-1.65460882729685[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.8690161869834[/C][C]-0.869016186983388[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.7430590885685[/C][C]2.25694091143146[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]11.7073995853783[/C][C]0.292600414621738[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.7922947646866[/C][C]-3.79229476468664[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.5779603170629[/C][C]3.42203968293707[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]15.9374350265485[/C][C]3.0625649734515[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.6430449187987[/C][C]-0.64304491879867[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.2402997973027[/C][C]4.75970020269733[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.3129011329567[/C][C]-0.312901132956732[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]15.9392610976624[/C][C]8.06073890233763[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.7576343371897[/C][C]1.24236566281034[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]15.7800009210323[/C][C]-0.780000921032323[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]14.1270168563868[/C][C]-3.12701685638679[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]12.4494230641765[/C][C]2.55057693582354[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.1687357985866[/C][C]1.83126420141343[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.7196934290326[/C][C]-0.719693429032583[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.3312035438139[/C][C]0.66879645618612[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.2886334579919[/C][C]-0.288633457991916[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.3831673064747[/C][C]-4.38316730647472[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.1599241248327[/C][C]1.84007587516727[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.8896537599766[/C][C]0.110346240023389[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.551449498376[/C][C]0.448550501624017[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.3651098879531[/C][C]-1.36510988795311[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.4180727263072[/C][C]-4.41807272630725[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.6611023373203[/C][C]-1.66110233732027[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]14.2527289624661[/C][C]-3.25272896246609[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.87091727810554[/C][C]-1.87091727810554[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.5824174144551[/C][C]-0.582417414455103[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.55459465752698[/C][C]1.44540534247302[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.555176660612[/C][C]0.44482333938799[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.2130590434596[/C][C]-2.21305904345958[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]16.4482760564121[/C][C]3.55172394358794[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.3007552213735[/C][C]-2.30075522137352[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]11.9298157715997[/C][C]3.07018422840033[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.4209728931227[/C][C]0.579027106877307[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.9608219489235[/C][C]2.03917805107654[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.0249316231675[/C][C]7.9750683768325[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.3434973874682[/C][C]1.6565026125318[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.80178550179[/C][C]0.198214498210049[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.5447839080798[/C][C]-1.54478390807984[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.2348436242172[/C][C]-1.23484362421721[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.6724941655457[/C][C]-2.67249416554573[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.996898524722[/C][C]3.00310147527801[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]13.5108775639406[/C][C]-1.51087756394061[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.832284696037[/C][C]0.167715303963014[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.5994757572834[/C][C]-0.599475757283412[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.9816074539878[/C][C]0.0183925460122098[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.5388724011415[/C][C]-1.53887240114145[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.997480527807[/C][C]-2.99748052780703[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.1502854556583[/C][C]2.84971454434169[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.60147390867[/C][C]0.39852609133[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.7428870082958[/C][C]3.25711299170418[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.7850471713055[/C][C]-1.78504717130547[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.7837280832683[/C][C]-2.78372808326835[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]17.1265997837785[/C][C]1.87340021622147[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.1883742958865[/C][C]3.8116257041135[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]14.0502712858884[/C][C]0.949728714111557[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4049518872323[/C][C]0.595048112767654[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.55459465752698[/C][C]1.44540534247302[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]14.1354114575324[/C][C]-5.13541145753237[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.9530093508629[/C][C]3.04699064913709[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.0938404474037[/C][C]1.9061595525963[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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
11213.0114283623026-1.01142836230257
21110.1043811320070.895618867993023
31415.80178550179-1.80178550178995
41214.0192651085647-2.01926510856466
52111.79253975702229.20746024297781
61210.30583973289131.69416026710866
72213.16931780167168.8306821983284
81113.3302044681206-2.33020446812059
91012.4918210697257-2.49182106972571
101312.41807272630720.581927273692754
11109.923880116459670.0761198835403283
1289.44766990512542-1.44766990512542
131515.2277609613128-0.227760961312805
141411.56929657538022.4307034246198
151010.3078378842779-0.307837884277923
161412.44942306417651.55057693582354
171412.28953547342081.71046452657922
181110.84823068191910.151769318080947
191012.0994687007619-2.09946870076188
201311.9296436913271.07035630867305
2179.394879147044-2.394879147044
221415.8007864260967-1.80078642609666
231213.0965685339464-1.09656853394643
241414.6985879116244-0.698587911624422
25119.870917278105541.12908272189446
26916.8919647739111-7.89196477391108
271110.7310852572580.268914742741956
281513.36238180141041.63761819858962
291412.64031683225591.35968316774406
301314.6551908303819-1.65519083038188
31912.0137847872776-3.01378478727759
321513.43630222510161.56369777489844
331010.9635500354662-0.963550035466187
341112.5561757363053-1.5561757363053
351312.3954611501290.604538849870962
36810.9221511056102-2.92215110561023
372016.41609872312233.58390127687774
381211.51550674160550.484493258394506
391011.3041107220419-1.3041107220419
401013.0853487859937-3.08534878599369
41911.4833294083157-2.4833294083157
421411.83411076715092.16588923284914
43810.6771233432106-2.67712334321062
441413.84898476527680.151015234723199
451114.7703381036563-3.7703381036563
461315.5037949010362-2.50379490103621
47912.3651098879531-3.36510988795311
481112.6299240797238-1.62992407972377
491510.9333708535634.06662914643702
501112.96803128106-1.96803128105996
511011.8672871761339-1.86728717613395
521412.4806013217731.51939867822704
531814.74033100202583.25966899797419
541414.8366909216225-0.83669092162248
551113.9870877752749-2.98708777527486
561212.4804292415003-0.48042924150025
571311.61086758550891.38913241449113
58912.8002044230116-3.80020442301162
591013.7326663360364-3.73266633603637
601514.83569184592920.164308154070814
612017.16834287417992.83165712582008
621212.9472457759956-0.947245775995628
631215.6315051585021-3.6315051585021
641413.46830747811860.531692521881356
651312.61853225149830.381467748501692
661115.3346857137144-4.33468571371436
671715.54636498685821.45363501314183
681214.1241917095796-2.12419170957962
691312.3121470495990.687852950401016
701411.51650581729882.48349418270121
711312.16282429164820.837175708351821
721511.90985726195593.09014273804409
731312.5353902312410.464609768759031
741011.3472245497042-1.34722454970423
751113.1902753870087-2.19027538700865
761914.706982512774.29301748723
771310.90119352027322.09880647972682
781714.14580421006452.85419578993546
791311.87685293324551.12314706675446
80913.0625651295428-4.06256512954276
811111.6120387414749-0.612038741474883
821010.6573369138396-0.657336913839579
83912.5136056504833-3.51360565048334
841210.70930067650041.29069932349958
851212.6724941655457-0.672494165545733
861313.4247383166034-0.424738316603386
871311.36801005476861.63198994523144
881213.3935600590069-1.39356005900688
891514.34826188664220.651738113357812
902218.74940466652093.25059533347915
911311.16700678773711.83299321226287
921514.05027128588840.949728714111557
931312.47203464035470.527965359645329
941513.49848666002191.50151333997815
951014.3690473917065-4.36904739170652
961111.5900820804445-0.590082080444539
971614.74015892175311.25984107824691
981113.3395981449595-2.33959814495946
99119.935271944685131.06472805531487
1001011.6546088272968-1.65460882729685
1011010.8690161869834-0.869016186983388
1021613.74305908856852.25694091143146
1031211.70739958537830.292600414621738
1041114.7922947646866-3.79229476468664
1051612.57796031706293.42203968293707
1061915.93743502654853.0625649734515
1071111.6430449187987-0.64304491879867
1081611.24029979730274.75970020269733
1091515.3129011329567-0.312901132956732
1102415.93926109766248.06073890233763
1111412.75763433718971.24236566281034
1121515.7800009210323-0.780000921032323
1131114.1270168563868-3.12701685638679
1141512.44942306417652.55057693582354
1151210.16873579858661.83126420141343
1161010.7196934290326-0.719693429032583
1171413.33120354381390.66879645618612
1181313.2886334579919-0.288633457991916
119913.3831673064747-4.38316730647472
1201513.15992412483271.84007587516727
1211514.88965375997660.110346240023389
1221413.5514494983760.448550501624017
1231112.3651098879531-1.36510988795311
124812.4180727263072-4.41807272630725
1251112.6611023373203-1.66110233732027
1261114.2527289624661-3.25272896246609
12789.87091727810554-1.87091727810554
1281010.5824174144551-0.582417414455103
129119.554594657526981.44540534247302
1301312.5551766606120.44482333938799
1311113.2130590434596-2.21305904345958
1322016.44827605641213.55172394358794
1331012.3007552213735-2.30075522137352
1341511.92981577159973.07018422840033
1351211.42097289312270.579027106877307
1361411.96082194892352.03917805107654
1372315.02493162316757.9750683768325
1381412.34349738746821.6565026125318
1391615.801785501790.198214498210049
1401112.5447839080798-1.54478390807984
1411213.2348436242172-1.23484362421721
1421012.6724941655457-2.67249416554573
1431410.9968985247223.00310147527801
1441213.5108775639406-1.51087756394061
1451211.8322846960370.167715303963014
1461111.5994757572834-0.599475757283412
1471211.98160745398780.0183925460122098
1481314.5388724011415-1.53887240114145
1491113.997480527807-2.99748052780703
1501916.15028545565832.84971454434169
1511211.601473908670.39852609133
1521713.74288700829583.25711299170418
153910.7850471713055-1.78504717130547
1541214.7837280832683-2.78372808326835
1551917.12659978377851.87340021622147
1561814.18837429588653.8116257041135
1571514.05027128588840.949728714111557
1581413.40495188723230.595048112767654
159119.554594657526981.44540534247302
160914.1354114575324-5.13541145753237
1611814.95300935086293.04699064913709
1621614.09384044740371.9061595525963






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9998304110361230.0003391779277542290.000169588963877114
90.9998456126151770.000308774769645290.000154387384822645
100.9995813018627470.0008373962745068760.000418698137253438
110.9989987987909490.002002402418102550.00100120120905128
120.998732809212870.002534381574259720.00126719078712986
130.9973968127378510.005206374524298550.00260318726214928
140.9958350018441460.008329996311708060.00416499815585403
150.992414717235460.01517056552908030.00758528276454016
160.9909869928219250.01802601435614930.00901300717807463
170.9862643235556340.02747135288873160.0137356764443658
180.9782363816378160.04352723672436880.0217636183621844
190.9696562791059740.06068744178805140.0303437208940257
200.9554239435767650.08915211284646980.0445760564232349
210.9409131220418420.1181737559163170.0590868779581583
220.9405145690008460.1189708619983090.0594854309991544
230.9264005530291960.1471988939416080.0735994469708042
240.9007908513196060.1984182973607880.0992091486803939
250.8703942268542520.2592115462914970.129605773145748
260.9852210016164860.02955799676702870.0147789983835143
270.978410107902670.04317978419465970.0215898920973298
280.9727357476948120.05452850461037670.0272642523051883
290.9633804708315350.07323905833693040.0366195291684652
300.9514980509769670.09700389804606560.0485019490230328
310.9581874543166070.08362509136678580.0418125456833929
320.9517715493871220.09645690122575680.0482284506128784
330.9396511051624580.1206977896750840.060348894837542
340.9292388126512110.1415223746975780.0707611873487889
350.9105954570406780.1788090859186440.089404542959322
360.9173031435752490.1653937128495020.0826968564247509
370.9462588441568480.1074823116863040.0537411558431522
380.9302326293182530.1395347413634930.0697673706817465
390.9185404510101880.1629190979796240.0814595489898119
400.9196306623312680.1607386753374630.0803693376687315
410.9132868460371330.1734263079257350.0867131539628673
420.9034050481524520.1931899036950960.0965949518475479
430.8987776066557620.2024447866884760.101222393344238
440.8763662850468570.2472674299062870.123633714953143
450.8828961799712180.2342076400575630.117103820028782
460.8715661455055460.2568677089889080.128433854494454
470.8851452214187940.2297095571624110.114854778581206
480.8687459892171120.2625080215657770.131254010782888
490.8925289389349030.2149421221301930.107471061065096
500.8769772492204810.2460455015590370.123022750779519
510.8699576748497340.2600846503005320.130042325150266
520.8538896467115280.2922207065769440.146110353288472
530.8749635852141740.2500728295716520.125036414785826
540.849977431099950.3000451378000990.15002256890005
550.8521562648349690.2956874703300620.147843735165031
560.8234027931267780.3531944137464440.176597206873222
570.802056991691690.3958860166166210.19794300830831
580.8358599235835020.3282801528329960.164140076416498
590.8558898114536270.2882203770927470.144110188546373
600.8305742240839830.3388515518320340.169425775916017
610.8489689858618330.3020620282763330.151031014138167
620.8237539321640520.3524921356718950.176246067835948
630.8422446334406020.3155107331187970.157755366559399
640.8138836363427480.3722327273145040.186116363657252
650.7817685919607710.4364628160784590.218231408039229
660.830138450070130.3397230998597390.16986154992987
670.8169077611554490.3661844776891020.183092238844551
680.8072423935236020.3855152129527960.192757606476398
690.7780994626687280.4438010746625450.221900537331272
700.7740396334286390.4519207331427230.225960366571361
710.7430907879206220.5138184241587550.256909212079378
720.7564591028331770.4870817943336460.243540897166823
730.7200798804791480.5598402390417050.279920119520853
740.6928800433886260.6142399132227480.307119956611374
750.6839109293449170.6321781413101670.316089070655083
760.7574805019850890.4850389960298230.242519498014911
770.745449843814490.509100312371020.25455015618551
780.7522201033375470.4955597933249070.247779896662453
790.7207334228132910.5585331543734190.279266577186709
800.7863369268747820.4273261462504360.213663073125218
810.7539763123507120.4920473752985760.246023687649288
820.7201810804018370.5596378391963260.279818919598163
830.7491809827886370.5016380344227270.250819017211363
840.7187316715598790.5625366568802420.281268328440121
850.6811633251131960.6376733497736080.318836674886804
860.6400294201052470.7199411597895060.359970579894753
870.6113711279975180.7772577440049630.388628872002482
880.5784884709981680.8430230580036640.421511529001832
890.536331630444710.927336739110580.46366836955529
900.5625187029758070.8749625940483860.437481297024193
910.5483642706997510.9032714586004980.451635729300249
920.5073283953126830.9853432093746340.492671604687317
930.4646360712977160.9292721425954310.535363928702284
940.4328901303179420.8657802606358840.567109869682058
950.5116067344394780.9767865311210450.488393265560522
960.4672254187260780.9344508374521550.532774581273922
970.4298976435080930.8597952870161850.570102356491907
980.4245130810598170.8490261621196340.575486918940183
990.384567471417250.76913494283450.61543252858275
1000.3581813958194810.7163627916389620.641818604180519
1010.3191880894888370.6383761789776750.680811910511163
1020.3016027572982270.6032055145964530.698397242701773
1030.2622827160114650.5245654320229290.737717283988535
1040.345649424299230.691298848598460.65435057570077
1050.3952586932719050.7905173865438110.604741306728095
1060.3912226672583360.7824453345166720.608777332741664
1070.3479300392980350.695860078596070.652069960701965
1080.4356329281198930.8712658562397860.564367071880107
1090.4026793288906590.8053586577813170.597320671109341
1100.7949731089858510.4100537820282980.205026891014149
1110.7671246274080540.4657507451838930.232875372591946
1120.734433892354840.5311322152903190.26556610764516
1130.7368877443118060.5262245113763890.263112255688194
1140.7264795878112380.5470408243775250.273520412188762
1150.7190949391517270.5618101216965470.280905060848273
1160.6778884303851840.6442231392296330.322111569614816
1170.6446130776667860.7107738446664290.355386922333214
1180.5994963843273880.8010072313452240.400503615672612
1190.6829760123722170.6340479752555670.317023987627783
1200.6713399894264350.657320021147130.328660010573565
1210.6211927482156570.7576145035686850.378807251784343
1220.5753734968492090.8492530063015820.424626503150791
1230.5272744847562470.9454510304875070.472725515243753
1240.5879061221143320.8241877557713370.412093877885668
1250.5673124758564160.8653750482871670.432687524143584
1260.6346413991910420.7307172016179160.365358600808958
1270.6066432050756210.7867135898487590.393356794924379
1280.5594886209395470.8810227581209060.440511379060453
1290.5378782539361040.9242434921277910.462121746063896
1300.4787912277976910.9575824555953820.521208772202309
1310.4974085006604910.9948170013209830.502591499339509
1320.4778057662165880.9556115324331770.522194233783412
1330.4440609344810310.8881218689620630.555939065518969
1340.4257586992605670.8515173985211330.574241300739433
1350.3728127308353190.7456254616706370.627187269164681
1360.3286900308098580.6573800616197170.671309969190142
1370.6553647870902640.6892704258194730.344635212909736
1380.6193033862247420.7613932275505160.380696613775258
1390.5491673762528950.901665247494210.450832623747105
1400.5063408519956750.9873182960086490.493659148004325
1410.4401843380490730.8803686760981460.559815661950927
1420.4466659342565610.8933318685131220.553334065743439
1430.5383698302911110.9232603394177770.461630169708889
1440.4832108623074210.9664217246148430.516789137692579
1450.4148694086351060.8297388172702120.585130591364894
1460.3592073193811260.7184146387622520.640792680618874
1470.2834963356319890.5669926712639770.716503664368011
1480.2195915615068310.4391831230136620.780408438493169
1490.2278772852549080.4557545705098160.772122714745092
1500.2003562968579180.4007125937158370.799643703142082
1510.1341457428875150.2682914857750310.865854257112485
1520.1726704277811310.3453408555622620.827329572218869
1530.1025528197531190.2051056395062380.897447180246881
1540.08105037504689160.1621007500937830.918949624953108

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.999830411036123 & 0.000339177927754229 & 0.000169588963877114 \tabularnewline
9 & 0.999845612615177 & 0.00030877476964529 & 0.000154387384822645 \tabularnewline
10 & 0.999581301862747 & 0.000837396274506876 & 0.000418698137253438 \tabularnewline
11 & 0.998998798790949 & 0.00200240241810255 & 0.00100120120905128 \tabularnewline
12 & 0.99873280921287 & 0.00253438157425972 & 0.00126719078712986 \tabularnewline
13 & 0.997396812737851 & 0.00520637452429855 & 0.00260318726214928 \tabularnewline
14 & 0.995835001844146 & 0.00832999631170806 & 0.00416499815585403 \tabularnewline
15 & 0.99241471723546 & 0.0151705655290803 & 0.00758528276454016 \tabularnewline
16 & 0.990986992821925 & 0.0180260143561493 & 0.00901300717807463 \tabularnewline
17 & 0.986264323555634 & 0.0274713528887316 & 0.0137356764443658 \tabularnewline
18 & 0.978236381637816 & 0.0435272367243688 & 0.0217636183621844 \tabularnewline
19 & 0.969656279105974 & 0.0606874417880514 & 0.0303437208940257 \tabularnewline
20 & 0.955423943576765 & 0.0891521128464698 & 0.0445760564232349 \tabularnewline
21 & 0.940913122041842 & 0.118173755916317 & 0.0590868779581583 \tabularnewline
22 & 0.940514569000846 & 0.118970861998309 & 0.0594854309991544 \tabularnewline
23 & 0.926400553029196 & 0.147198893941608 & 0.0735994469708042 \tabularnewline
24 & 0.900790851319606 & 0.198418297360788 & 0.0992091486803939 \tabularnewline
25 & 0.870394226854252 & 0.259211546291497 & 0.129605773145748 \tabularnewline
26 & 0.985221001616486 & 0.0295579967670287 & 0.0147789983835143 \tabularnewline
27 & 0.97841010790267 & 0.0431797841946597 & 0.0215898920973298 \tabularnewline
28 & 0.972735747694812 & 0.0545285046103767 & 0.0272642523051883 \tabularnewline
29 & 0.963380470831535 & 0.0732390583369304 & 0.0366195291684652 \tabularnewline
30 & 0.951498050976967 & 0.0970038980460656 & 0.0485019490230328 \tabularnewline
31 & 0.958187454316607 & 0.0836250913667858 & 0.0418125456833929 \tabularnewline
32 & 0.951771549387122 & 0.0964569012257568 & 0.0482284506128784 \tabularnewline
33 & 0.939651105162458 & 0.120697789675084 & 0.060348894837542 \tabularnewline
34 & 0.929238812651211 & 0.141522374697578 & 0.0707611873487889 \tabularnewline
35 & 0.910595457040678 & 0.178809085918644 & 0.089404542959322 \tabularnewline
36 & 0.917303143575249 & 0.165393712849502 & 0.0826968564247509 \tabularnewline
37 & 0.946258844156848 & 0.107482311686304 & 0.0537411558431522 \tabularnewline
38 & 0.930232629318253 & 0.139534741363493 & 0.0697673706817465 \tabularnewline
39 & 0.918540451010188 & 0.162919097979624 & 0.0814595489898119 \tabularnewline
40 & 0.919630662331268 & 0.160738675337463 & 0.0803693376687315 \tabularnewline
41 & 0.913286846037133 & 0.173426307925735 & 0.0867131539628673 \tabularnewline
42 & 0.903405048152452 & 0.193189903695096 & 0.0965949518475479 \tabularnewline
43 & 0.898777606655762 & 0.202444786688476 & 0.101222393344238 \tabularnewline
44 & 0.876366285046857 & 0.247267429906287 & 0.123633714953143 \tabularnewline
45 & 0.882896179971218 & 0.234207640057563 & 0.117103820028782 \tabularnewline
46 & 0.871566145505546 & 0.256867708988908 & 0.128433854494454 \tabularnewline
47 & 0.885145221418794 & 0.229709557162411 & 0.114854778581206 \tabularnewline
48 & 0.868745989217112 & 0.262508021565777 & 0.131254010782888 \tabularnewline
49 & 0.892528938934903 & 0.214942122130193 & 0.107471061065096 \tabularnewline
50 & 0.876977249220481 & 0.246045501559037 & 0.123022750779519 \tabularnewline
51 & 0.869957674849734 & 0.260084650300532 & 0.130042325150266 \tabularnewline
52 & 0.853889646711528 & 0.292220706576944 & 0.146110353288472 \tabularnewline
53 & 0.874963585214174 & 0.250072829571652 & 0.125036414785826 \tabularnewline
54 & 0.84997743109995 & 0.300045137800099 & 0.15002256890005 \tabularnewline
55 & 0.852156264834969 & 0.295687470330062 & 0.147843735165031 \tabularnewline
56 & 0.823402793126778 & 0.353194413746444 & 0.176597206873222 \tabularnewline
57 & 0.80205699169169 & 0.395886016616621 & 0.19794300830831 \tabularnewline
58 & 0.835859923583502 & 0.328280152832996 & 0.164140076416498 \tabularnewline
59 & 0.855889811453627 & 0.288220377092747 & 0.144110188546373 \tabularnewline
60 & 0.830574224083983 & 0.338851551832034 & 0.169425775916017 \tabularnewline
61 & 0.848968985861833 & 0.302062028276333 & 0.151031014138167 \tabularnewline
62 & 0.823753932164052 & 0.352492135671895 & 0.176246067835948 \tabularnewline
63 & 0.842244633440602 & 0.315510733118797 & 0.157755366559399 \tabularnewline
64 & 0.813883636342748 & 0.372232727314504 & 0.186116363657252 \tabularnewline
65 & 0.781768591960771 & 0.436462816078459 & 0.218231408039229 \tabularnewline
66 & 0.83013845007013 & 0.339723099859739 & 0.16986154992987 \tabularnewline
67 & 0.816907761155449 & 0.366184477689102 & 0.183092238844551 \tabularnewline
68 & 0.807242393523602 & 0.385515212952796 & 0.192757606476398 \tabularnewline
69 & 0.778099462668728 & 0.443801074662545 & 0.221900537331272 \tabularnewline
70 & 0.774039633428639 & 0.451920733142723 & 0.225960366571361 \tabularnewline
71 & 0.743090787920622 & 0.513818424158755 & 0.256909212079378 \tabularnewline
72 & 0.756459102833177 & 0.487081794333646 & 0.243540897166823 \tabularnewline
73 & 0.720079880479148 & 0.559840239041705 & 0.279920119520853 \tabularnewline
74 & 0.692880043388626 & 0.614239913222748 & 0.307119956611374 \tabularnewline
75 & 0.683910929344917 & 0.632178141310167 & 0.316089070655083 \tabularnewline
76 & 0.757480501985089 & 0.485038996029823 & 0.242519498014911 \tabularnewline
77 & 0.74544984381449 & 0.50910031237102 & 0.25455015618551 \tabularnewline
78 & 0.752220103337547 & 0.495559793324907 & 0.247779896662453 \tabularnewline
79 & 0.720733422813291 & 0.558533154373419 & 0.279266577186709 \tabularnewline
80 & 0.786336926874782 & 0.427326146250436 & 0.213663073125218 \tabularnewline
81 & 0.753976312350712 & 0.492047375298576 & 0.246023687649288 \tabularnewline
82 & 0.720181080401837 & 0.559637839196326 & 0.279818919598163 \tabularnewline
83 & 0.749180982788637 & 0.501638034422727 & 0.250819017211363 \tabularnewline
84 & 0.718731671559879 & 0.562536656880242 & 0.281268328440121 \tabularnewline
85 & 0.681163325113196 & 0.637673349773608 & 0.318836674886804 \tabularnewline
86 & 0.640029420105247 & 0.719941159789506 & 0.359970579894753 \tabularnewline
87 & 0.611371127997518 & 0.777257744004963 & 0.388628872002482 \tabularnewline
88 & 0.578488470998168 & 0.843023058003664 & 0.421511529001832 \tabularnewline
89 & 0.53633163044471 & 0.92733673911058 & 0.46366836955529 \tabularnewline
90 & 0.562518702975807 & 0.874962594048386 & 0.437481297024193 \tabularnewline
91 & 0.548364270699751 & 0.903271458600498 & 0.451635729300249 \tabularnewline
92 & 0.507328395312683 & 0.985343209374634 & 0.492671604687317 \tabularnewline
93 & 0.464636071297716 & 0.929272142595431 & 0.535363928702284 \tabularnewline
94 & 0.432890130317942 & 0.865780260635884 & 0.567109869682058 \tabularnewline
95 & 0.511606734439478 & 0.976786531121045 & 0.488393265560522 \tabularnewline
96 & 0.467225418726078 & 0.934450837452155 & 0.532774581273922 \tabularnewline
97 & 0.429897643508093 & 0.859795287016185 & 0.570102356491907 \tabularnewline
98 & 0.424513081059817 & 0.849026162119634 & 0.575486918940183 \tabularnewline
99 & 0.38456747141725 & 0.7691349428345 & 0.61543252858275 \tabularnewline
100 & 0.358181395819481 & 0.716362791638962 & 0.641818604180519 \tabularnewline
101 & 0.319188089488837 & 0.638376178977675 & 0.680811910511163 \tabularnewline
102 & 0.301602757298227 & 0.603205514596453 & 0.698397242701773 \tabularnewline
103 & 0.262282716011465 & 0.524565432022929 & 0.737717283988535 \tabularnewline
104 & 0.34564942429923 & 0.69129884859846 & 0.65435057570077 \tabularnewline
105 & 0.395258693271905 & 0.790517386543811 & 0.604741306728095 \tabularnewline
106 & 0.391222667258336 & 0.782445334516672 & 0.608777332741664 \tabularnewline
107 & 0.347930039298035 & 0.69586007859607 & 0.652069960701965 \tabularnewline
108 & 0.435632928119893 & 0.871265856239786 & 0.564367071880107 \tabularnewline
109 & 0.402679328890659 & 0.805358657781317 & 0.597320671109341 \tabularnewline
110 & 0.794973108985851 & 0.410053782028298 & 0.205026891014149 \tabularnewline
111 & 0.767124627408054 & 0.465750745183893 & 0.232875372591946 \tabularnewline
112 & 0.73443389235484 & 0.531132215290319 & 0.26556610764516 \tabularnewline
113 & 0.736887744311806 & 0.526224511376389 & 0.263112255688194 \tabularnewline
114 & 0.726479587811238 & 0.547040824377525 & 0.273520412188762 \tabularnewline
115 & 0.719094939151727 & 0.561810121696547 & 0.280905060848273 \tabularnewline
116 & 0.677888430385184 & 0.644223139229633 & 0.322111569614816 \tabularnewline
117 & 0.644613077666786 & 0.710773844666429 & 0.355386922333214 \tabularnewline
118 & 0.599496384327388 & 0.801007231345224 & 0.400503615672612 \tabularnewline
119 & 0.682976012372217 & 0.634047975255567 & 0.317023987627783 \tabularnewline
120 & 0.671339989426435 & 0.65732002114713 & 0.328660010573565 \tabularnewline
121 & 0.621192748215657 & 0.757614503568685 & 0.378807251784343 \tabularnewline
122 & 0.575373496849209 & 0.849253006301582 & 0.424626503150791 \tabularnewline
123 & 0.527274484756247 & 0.945451030487507 & 0.472725515243753 \tabularnewline
124 & 0.587906122114332 & 0.824187755771337 & 0.412093877885668 \tabularnewline
125 & 0.567312475856416 & 0.865375048287167 & 0.432687524143584 \tabularnewline
126 & 0.634641399191042 & 0.730717201617916 & 0.365358600808958 \tabularnewline
127 & 0.606643205075621 & 0.786713589848759 & 0.393356794924379 \tabularnewline
128 & 0.559488620939547 & 0.881022758120906 & 0.440511379060453 \tabularnewline
129 & 0.537878253936104 & 0.924243492127791 & 0.462121746063896 \tabularnewline
130 & 0.478791227797691 & 0.957582455595382 & 0.521208772202309 \tabularnewline
131 & 0.497408500660491 & 0.994817001320983 & 0.502591499339509 \tabularnewline
132 & 0.477805766216588 & 0.955611532433177 & 0.522194233783412 \tabularnewline
133 & 0.444060934481031 & 0.888121868962063 & 0.555939065518969 \tabularnewline
134 & 0.425758699260567 & 0.851517398521133 & 0.574241300739433 \tabularnewline
135 & 0.372812730835319 & 0.745625461670637 & 0.627187269164681 \tabularnewline
136 & 0.328690030809858 & 0.657380061619717 & 0.671309969190142 \tabularnewline
137 & 0.655364787090264 & 0.689270425819473 & 0.344635212909736 \tabularnewline
138 & 0.619303386224742 & 0.761393227550516 & 0.380696613775258 \tabularnewline
139 & 0.549167376252895 & 0.90166524749421 & 0.450832623747105 \tabularnewline
140 & 0.506340851995675 & 0.987318296008649 & 0.493659148004325 \tabularnewline
141 & 0.440184338049073 & 0.880368676098146 & 0.559815661950927 \tabularnewline
142 & 0.446665934256561 & 0.893331868513122 & 0.553334065743439 \tabularnewline
143 & 0.538369830291111 & 0.923260339417777 & 0.461630169708889 \tabularnewline
144 & 0.483210862307421 & 0.966421724614843 & 0.516789137692579 \tabularnewline
145 & 0.414869408635106 & 0.829738817270212 & 0.585130591364894 \tabularnewline
146 & 0.359207319381126 & 0.718414638762252 & 0.640792680618874 \tabularnewline
147 & 0.283496335631989 & 0.566992671263977 & 0.716503664368011 \tabularnewline
148 & 0.219591561506831 & 0.439183123013662 & 0.780408438493169 \tabularnewline
149 & 0.227877285254908 & 0.455754570509816 & 0.772122714745092 \tabularnewline
150 & 0.200356296857918 & 0.400712593715837 & 0.799643703142082 \tabularnewline
151 & 0.134145742887515 & 0.268291485775031 & 0.865854257112485 \tabularnewline
152 & 0.172670427781131 & 0.345340855562262 & 0.827329572218869 \tabularnewline
153 & 0.102552819753119 & 0.205105639506238 & 0.897447180246881 \tabularnewline
154 & 0.0810503750468916 & 0.162100750093783 & 0.918949624953108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.999830411036123[/C][C]0.000339177927754229[/C][C]0.000169588963877114[/C][/ROW]
[ROW][C]9[/C][C]0.999845612615177[/C][C]0.00030877476964529[/C][C]0.000154387384822645[/C][/ROW]
[ROW][C]10[/C][C]0.999581301862747[/C][C]0.000837396274506876[/C][C]0.000418698137253438[/C][/ROW]
[ROW][C]11[/C][C]0.998998798790949[/C][C]0.00200240241810255[/C][C]0.00100120120905128[/C][/ROW]
[ROW][C]12[/C][C]0.99873280921287[/C][C]0.00253438157425972[/C][C]0.00126719078712986[/C][/ROW]
[ROW][C]13[/C][C]0.997396812737851[/C][C]0.00520637452429855[/C][C]0.00260318726214928[/C][/ROW]
[ROW][C]14[/C][C]0.995835001844146[/C][C]0.00832999631170806[/C][C]0.00416499815585403[/C][/ROW]
[ROW][C]15[/C][C]0.99241471723546[/C][C]0.0151705655290803[/C][C]0.00758528276454016[/C][/ROW]
[ROW][C]16[/C][C]0.990986992821925[/C][C]0.0180260143561493[/C][C]0.00901300717807463[/C][/ROW]
[ROW][C]17[/C][C]0.986264323555634[/C][C]0.0274713528887316[/C][C]0.0137356764443658[/C][/ROW]
[ROW][C]18[/C][C]0.978236381637816[/C][C]0.0435272367243688[/C][C]0.0217636183621844[/C][/ROW]
[ROW][C]19[/C][C]0.969656279105974[/C][C]0.0606874417880514[/C][C]0.0303437208940257[/C][/ROW]
[ROW][C]20[/C][C]0.955423943576765[/C][C]0.0891521128464698[/C][C]0.0445760564232349[/C][/ROW]
[ROW][C]21[/C][C]0.940913122041842[/C][C]0.118173755916317[/C][C]0.0590868779581583[/C][/ROW]
[ROW][C]22[/C][C]0.940514569000846[/C][C]0.118970861998309[/C][C]0.0594854309991544[/C][/ROW]
[ROW][C]23[/C][C]0.926400553029196[/C][C]0.147198893941608[/C][C]0.0735994469708042[/C][/ROW]
[ROW][C]24[/C][C]0.900790851319606[/C][C]0.198418297360788[/C][C]0.0992091486803939[/C][/ROW]
[ROW][C]25[/C][C]0.870394226854252[/C][C]0.259211546291497[/C][C]0.129605773145748[/C][/ROW]
[ROW][C]26[/C][C]0.985221001616486[/C][C]0.0295579967670287[/C][C]0.0147789983835143[/C][/ROW]
[ROW][C]27[/C][C]0.97841010790267[/C][C]0.0431797841946597[/C][C]0.0215898920973298[/C][/ROW]
[ROW][C]28[/C][C]0.972735747694812[/C][C]0.0545285046103767[/C][C]0.0272642523051883[/C][/ROW]
[ROW][C]29[/C][C]0.963380470831535[/C][C]0.0732390583369304[/C][C]0.0366195291684652[/C][/ROW]
[ROW][C]30[/C][C]0.951498050976967[/C][C]0.0970038980460656[/C][C]0.0485019490230328[/C][/ROW]
[ROW][C]31[/C][C]0.958187454316607[/C][C]0.0836250913667858[/C][C]0.0418125456833929[/C][/ROW]
[ROW][C]32[/C][C]0.951771549387122[/C][C]0.0964569012257568[/C][C]0.0482284506128784[/C][/ROW]
[ROW][C]33[/C][C]0.939651105162458[/C][C]0.120697789675084[/C][C]0.060348894837542[/C][/ROW]
[ROW][C]34[/C][C]0.929238812651211[/C][C]0.141522374697578[/C][C]0.0707611873487889[/C][/ROW]
[ROW][C]35[/C][C]0.910595457040678[/C][C]0.178809085918644[/C][C]0.089404542959322[/C][/ROW]
[ROW][C]36[/C][C]0.917303143575249[/C][C]0.165393712849502[/C][C]0.0826968564247509[/C][/ROW]
[ROW][C]37[/C][C]0.946258844156848[/C][C]0.107482311686304[/C][C]0.0537411558431522[/C][/ROW]
[ROW][C]38[/C][C]0.930232629318253[/C][C]0.139534741363493[/C][C]0.0697673706817465[/C][/ROW]
[ROW][C]39[/C][C]0.918540451010188[/C][C]0.162919097979624[/C][C]0.0814595489898119[/C][/ROW]
[ROW][C]40[/C][C]0.919630662331268[/C][C]0.160738675337463[/C][C]0.0803693376687315[/C][/ROW]
[ROW][C]41[/C][C]0.913286846037133[/C][C]0.173426307925735[/C][C]0.0867131539628673[/C][/ROW]
[ROW][C]42[/C][C]0.903405048152452[/C][C]0.193189903695096[/C][C]0.0965949518475479[/C][/ROW]
[ROW][C]43[/C][C]0.898777606655762[/C][C]0.202444786688476[/C][C]0.101222393344238[/C][/ROW]
[ROW][C]44[/C][C]0.876366285046857[/C][C]0.247267429906287[/C][C]0.123633714953143[/C][/ROW]
[ROW][C]45[/C][C]0.882896179971218[/C][C]0.234207640057563[/C][C]0.117103820028782[/C][/ROW]
[ROW][C]46[/C][C]0.871566145505546[/C][C]0.256867708988908[/C][C]0.128433854494454[/C][/ROW]
[ROW][C]47[/C][C]0.885145221418794[/C][C]0.229709557162411[/C][C]0.114854778581206[/C][/ROW]
[ROW][C]48[/C][C]0.868745989217112[/C][C]0.262508021565777[/C][C]0.131254010782888[/C][/ROW]
[ROW][C]49[/C][C]0.892528938934903[/C][C]0.214942122130193[/C][C]0.107471061065096[/C][/ROW]
[ROW][C]50[/C][C]0.876977249220481[/C][C]0.246045501559037[/C][C]0.123022750779519[/C][/ROW]
[ROW][C]51[/C][C]0.869957674849734[/C][C]0.260084650300532[/C][C]0.130042325150266[/C][/ROW]
[ROW][C]52[/C][C]0.853889646711528[/C][C]0.292220706576944[/C][C]0.146110353288472[/C][/ROW]
[ROW][C]53[/C][C]0.874963585214174[/C][C]0.250072829571652[/C][C]0.125036414785826[/C][/ROW]
[ROW][C]54[/C][C]0.84997743109995[/C][C]0.300045137800099[/C][C]0.15002256890005[/C][/ROW]
[ROW][C]55[/C][C]0.852156264834969[/C][C]0.295687470330062[/C][C]0.147843735165031[/C][/ROW]
[ROW][C]56[/C][C]0.823402793126778[/C][C]0.353194413746444[/C][C]0.176597206873222[/C][/ROW]
[ROW][C]57[/C][C]0.80205699169169[/C][C]0.395886016616621[/C][C]0.19794300830831[/C][/ROW]
[ROW][C]58[/C][C]0.835859923583502[/C][C]0.328280152832996[/C][C]0.164140076416498[/C][/ROW]
[ROW][C]59[/C][C]0.855889811453627[/C][C]0.288220377092747[/C][C]0.144110188546373[/C][/ROW]
[ROW][C]60[/C][C]0.830574224083983[/C][C]0.338851551832034[/C][C]0.169425775916017[/C][/ROW]
[ROW][C]61[/C][C]0.848968985861833[/C][C]0.302062028276333[/C][C]0.151031014138167[/C][/ROW]
[ROW][C]62[/C][C]0.823753932164052[/C][C]0.352492135671895[/C][C]0.176246067835948[/C][/ROW]
[ROW][C]63[/C][C]0.842244633440602[/C][C]0.315510733118797[/C][C]0.157755366559399[/C][/ROW]
[ROW][C]64[/C][C]0.813883636342748[/C][C]0.372232727314504[/C][C]0.186116363657252[/C][/ROW]
[ROW][C]65[/C][C]0.781768591960771[/C][C]0.436462816078459[/C][C]0.218231408039229[/C][/ROW]
[ROW][C]66[/C][C]0.83013845007013[/C][C]0.339723099859739[/C][C]0.16986154992987[/C][/ROW]
[ROW][C]67[/C][C]0.816907761155449[/C][C]0.366184477689102[/C][C]0.183092238844551[/C][/ROW]
[ROW][C]68[/C][C]0.807242393523602[/C][C]0.385515212952796[/C][C]0.192757606476398[/C][/ROW]
[ROW][C]69[/C][C]0.778099462668728[/C][C]0.443801074662545[/C][C]0.221900537331272[/C][/ROW]
[ROW][C]70[/C][C]0.774039633428639[/C][C]0.451920733142723[/C][C]0.225960366571361[/C][/ROW]
[ROW][C]71[/C][C]0.743090787920622[/C][C]0.513818424158755[/C][C]0.256909212079378[/C][/ROW]
[ROW][C]72[/C][C]0.756459102833177[/C][C]0.487081794333646[/C][C]0.243540897166823[/C][/ROW]
[ROW][C]73[/C][C]0.720079880479148[/C][C]0.559840239041705[/C][C]0.279920119520853[/C][/ROW]
[ROW][C]74[/C][C]0.692880043388626[/C][C]0.614239913222748[/C][C]0.307119956611374[/C][/ROW]
[ROW][C]75[/C][C]0.683910929344917[/C][C]0.632178141310167[/C][C]0.316089070655083[/C][/ROW]
[ROW][C]76[/C][C]0.757480501985089[/C][C]0.485038996029823[/C][C]0.242519498014911[/C][/ROW]
[ROW][C]77[/C][C]0.74544984381449[/C][C]0.50910031237102[/C][C]0.25455015618551[/C][/ROW]
[ROW][C]78[/C][C]0.752220103337547[/C][C]0.495559793324907[/C][C]0.247779896662453[/C][/ROW]
[ROW][C]79[/C][C]0.720733422813291[/C][C]0.558533154373419[/C][C]0.279266577186709[/C][/ROW]
[ROW][C]80[/C][C]0.786336926874782[/C][C]0.427326146250436[/C][C]0.213663073125218[/C][/ROW]
[ROW][C]81[/C][C]0.753976312350712[/C][C]0.492047375298576[/C][C]0.246023687649288[/C][/ROW]
[ROW][C]82[/C][C]0.720181080401837[/C][C]0.559637839196326[/C][C]0.279818919598163[/C][/ROW]
[ROW][C]83[/C][C]0.749180982788637[/C][C]0.501638034422727[/C][C]0.250819017211363[/C][/ROW]
[ROW][C]84[/C][C]0.718731671559879[/C][C]0.562536656880242[/C][C]0.281268328440121[/C][/ROW]
[ROW][C]85[/C][C]0.681163325113196[/C][C]0.637673349773608[/C][C]0.318836674886804[/C][/ROW]
[ROW][C]86[/C][C]0.640029420105247[/C][C]0.719941159789506[/C][C]0.359970579894753[/C][/ROW]
[ROW][C]87[/C][C]0.611371127997518[/C][C]0.777257744004963[/C][C]0.388628872002482[/C][/ROW]
[ROW][C]88[/C][C]0.578488470998168[/C][C]0.843023058003664[/C][C]0.421511529001832[/C][/ROW]
[ROW][C]89[/C][C]0.53633163044471[/C][C]0.92733673911058[/C][C]0.46366836955529[/C][/ROW]
[ROW][C]90[/C][C]0.562518702975807[/C][C]0.874962594048386[/C][C]0.437481297024193[/C][/ROW]
[ROW][C]91[/C][C]0.548364270699751[/C][C]0.903271458600498[/C][C]0.451635729300249[/C][/ROW]
[ROW][C]92[/C][C]0.507328395312683[/C][C]0.985343209374634[/C][C]0.492671604687317[/C][/ROW]
[ROW][C]93[/C][C]0.464636071297716[/C][C]0.929272142595431[/C][C]0.535363928702284[/C][/ROW]
[ROW][C]94[/C][C]0.432890130317942[/C][C]0.865780260635884[/C][C]0.567109869682058[/C][/ROW]
[ROW][C]95[/C][C]0.511606734439478[/C][C]0.976786531121045[/C][C]0.488393265560522[/C][/ROW]
[ROW][C]96[/C][C]0.467225418726078[/C][C]0.934450837452155[/C][C]0.532774581273922[/C][/ROW]
[ROW][C]97[/C][C]0.429897643508093[/C][C]0.859795287016185[/C][C]0.570102356491907[/C][/ROW]
[ROW][C]98[/C][C]0.424513081059817[/C][C]0.849026162119634[/C][C]0.575486918940183[/C][/ROW]
[ROW][C]99[/C][C]0.38456747141725[/C][C]0.7691349428345[/C][C]0.61543252858275[/C][/ROW]
[ROW][C]100[/C][C]0.358181395819481[/C][C]0.716362791638962[/C][C]0.641818604180519[/C][/ROW]
[ROW][C]101[/C][C]0.319188089488837[/C][C]0.638376178977675[/C][C]0.680811910511163[/C][/ROW]
[ROW][C]102[/C][C]0.301602757298227[/C][C]0.603205514596453[/C][C]0.698397242701773[/C][/ROW]
[ROW][C]103[/C][C]0.262282716011465[/C][C]0.524565432022929[/C][C]0.737717283988535[/C][/ROW]
[ROW][C]104[/C][C]0.34564942429923[/C][C]0.69129884859846[/C][C]0.65435057570077[/C][/ROW]
[ROW][C]105[/C][C]0.395258693271905[/C][C]0.790517386543811[/C][C]0.604741306728095[/C][/ROW]
[ROW][C]106[/C][C]0.391222667258336[/C][C]0.782445334516672[/C][C]0.608777332741664[/C][/ROW]
[ROW][C]107[/C][C]0.347930039298035[/C][C]0.69586007859607[/C][C]0.652069960701965[/C][/ROW]
[ROW][C]108[/C][C]0.435632928119893[/C][C]0.871265856239786[/C][C]0.564367071880107[/C][/ROW]
[ROW][C]109[/C][C]0.402679328890659[/C][C]0.805358657781317[/C][C]0.597320671109341[/C][/ROW]
[ROW][C]110[/C][C]0.794973108985851[/C][C]0.410053782028298[/C][C]0.205026891014149[/C][/ROW]
[ROW][C]111[/C][C]0.767124627408054[/C][C]0.465750745183893[/C][C]0.232875372591946[/C][/ROW]
[ROW][C]112[/C][C]0.73443389235484[/C][C]0.531132215290319[/C][C]0.26556610764516[/C][/ROW]
[ROW][C]113[/C][C]0.736887744311806[/C][C]0.526224511376389[/C][C]0.263112255688194[/C][/ROW]
[ROW][C]114[/C][C]0.726479587811238[/C][C]0.547040824377525[/C][C]0.273520412188762[/C][/ROW]
[ROW][C]115[/C][C]0.719094939151727[/C][C]0.561810121696547[/C][C]0.280905060848273[/C][/ROW]
[ROW][C]116[/C][C]0.677888430385184[/C][C]0.644223139229633[/C][C]0.322111569614816[/C][/ROW]
[ROW][C]117[/C][C]0.644613077666786[/C][C]0.710773844666429[/C][C]0.355386922333214[/C][/ROW]
[ROW][C]118[/C][C]0.599496384327388[/C][C]0.801007231345224[/C][C]0.400503615672612[/C][/ROW]
[ROW][C]119[/C][C]0.682976012372217[/C][C]0.634047975255567[/C][C]0.317023987627783[/C][/ROW]
[ROW][C]120[/C][C]0.671339989426435[/C][C]0.65732002114713[/C][C]0.328660010573565[/C][/ROW]
[ROW][C]121[/C][C]0.621192748215657[/C][C]0.757614503568685[/C][C]0.378807251784343[/C][/ROW]
[ROW][C]122[/C][C]0.575373496849209[/C][C]0.849253006301582[/C][C]0.424626503150791[/C][/ROW]
[ROW][C]123[/C][C]0.527274484756247[/C][C]0.945451030487507[/C][C]0.472725515243753[/C][/ROW]
[ROW][C]124[/C][C]0.587906122114332[/C][C]0.824187755771337[/C][C]0.412093877885668[/C][/ROW]
[ROW][C]125[/C][C]0.567312475856416[/C][C]0.865375048287167[/C][C]0.432687524143584[/C][/ROW]
[ROW][C]126[/C][C]0.634641399191042[/C][C]0.730717201617916[/C][C]0.365358600808958[/C][/ROW]
[ROW][C]127[/C][C]0.606643205075621[/C][C]0.786713589848759[/C][C]0.393356794924379[/C][/ROW]
[ROW][C]128[/C][C]0.559488620939547[/C][C]0.881022758120906[/C][C]0.440511379060453[/C][/ROW]
[ROW][C]129[/C][C]0.537878253936104[/C][C]0.924243492127791[/C][C]0.462121746063896[/C][/ROW]
[ROW][C]130[/C][C]0.478791227797691[/C][C]0.957582455595382[/C][C]0.521208772202309[/C][/ROW]
[ROW][C]131[/C][C]0.497408500660491[/C][C]0.994817001320983[/C][C]0.502591499339509[/C][/ROW]
[ROW][C]132[/C][C]0.477805766216588[/C][C]0.955611532433177[/C][C]0.522194233783412[/C][/ROW]
[ROW][C]133[/C][C]0.444060934481031[/C][C]0.888121868962063[/C][C]0.555939065518969[/C][/ROW]
[ROW][C]134[/C][C]0.425758699260567[/C][C]0.851517398521133[/C][C]0.574241300739433[/C][/ROW]
[ROW][C]135[/C][C]0.372812730835319[/C][C]0.745625461670637[/C][C]0.627187269164681[/C][/ROW]
[ROW][C]136[/C][C]0.328690030809858[/C][C]0.657380061619717[/C][C]0.671309969190142[/C][/ROW]
[ROW][C]137[/C][C]0.655364787090264[/C][C]0.689270425819473[/C][C]0.344635212909736[/C][/ROW]
[ROW][C]138[/C][C]0.619303386224742[/C][C]0.761393227550516[/C][C]0.380696613775258[/C][/ROW]
[ROW][C]139[/C][C]0.549167376252895[/C][C]0.90166524749421[/C][C]0.450832623747105[/C][/ROW]
[ROW][C]140[/C][C]0.506340851995675[/C][C]0.987318296008649[/C][C]0.493659148004325[/C][/ROW]
[ROW][C]141[/C][C]0.440184338049073[/C][C]0.880368676098146[/C][C]0.559815661950927[/C][/ROW]
[ROW][C]142[/C][C]0.446665934256561[/C][C]0.893331868513122[/C][C]0.553334065743439[/C][/ROW]
[ROW][C]143[/C][C]0.538369830291111[/C][C]0.923260339417777[/C][C]0.461630169708889[/C][/ROW]
[ROW][C]144[/C][C]0.483210862307421[/C][C]0.966421724614843[/C][C]0.516789137692579[/C][/ROW]
[ROW][C]145[/C][C]0.414869408635106[/C][C]0.829738817270212[/C][C]0.585130591364894[/C][/ROW]
[ROW][C]146[/C][C]0.359207319381126[/C][C]0.718414638762252[/C][C]0.640792680618874[/C][/ROW]
[ROW][C]147[/C][C]0.283496335631989[/C][C]0.566992671263977[/C][C]0.716503664368011[/C][/ROW]
[ROW][C]148[/C][C]0.219591561506831[/C][C]0.439183123013662[/C][C]0.780408438493169[/C][/ROW]
[ROW][C]149[/C][C]0.227877285254908[/C][C]0.455754570509816[/C][C]0.772122714745092[/C][/ROW]
[ROW][C]150[/C][C]0.200356296857918[/C][C]0.400712593715837[/C][C]0.799643703142082[/C][/ROW]
[ROW][C]151[/C][C]0.134145742887515[/C][C]0.268291485775031[/C][C]0.865854257112485[/C][/ROW]
[ROW][C]152[/C][C]0.172670427781131[/C][C]0.345340855562262[/C][C]0.827329572218869[/C][/ROW]
[ROW][C]153[/C][C]0.102552819753119[/C][C]0.205105639506238[/C][C]0.897447180246881[/C][/ROW]
[ROW][C]154[/C][C]0.0810503750468916[/C][C]0.162100750093783[/C][C]0.918949624953108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&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
80.9998304110361230.0003391779277542290.000169588963877114
90.9998456126151770.000308774769645290.000154387384822645
100.9995813018627470.0008373962745068760.000418698137253438
110.9989987987909490.002002402418102550.00100120120905128
120.998732809212870.002534381574259720.00126719078712986
130.9973968127378510.005206374524298550.00260318726214928
140.9958350018441460.008329996311708060.00416499815585403
150.992414717235460.01517056552908030.00758528276454016
160.9909869928219250.01802601435614930.00901300717807463
170.9862643235556340.02747135288873160.0137356764443658
180.9782363816378160.04352723672436880.0217636183621844
190.9696562791059740.06068744178805140.0303437208940257
200.9554239435767650.08915211284646980.0445760564232349
210.9409131220418420.1181737559163170.0590868779581583
220.9405145690008460.1189708619983090.0594854309991544
230.9264005530291960.1471988939416080.0735994469708042
240.9007908513196060.1984182973607880.0992091486803939
250.8703942268542520.2592115462914970.129605773145748
260.9852210016164860.02955799676702870.0147789983835143
270.978410107902670.04317978419465970.0215898920973298
280.9727357476948120.05452850461037670.0272642523051883
290.9633804708315350.07323905833693040.0366195291684652
300.9514980509769670.09700389804606560.0485019490230328
310.9581874543166070.08362509136678580.0418125456833929
320.9517715493871220.09645690122575680.0482284506128784
330.9396511051624580.1206977896750840.060348894837542
340.9292388126512110.1415223746975780.0707611873487889
350.9105954570406780.1788090859186440.089404542959322
360.9173031435752490.1653937128495020.0826968564247509
370.9462588441568480.1074823116863040.0537411558431522
380.9302326293182530.1395347413634930.0697673706817465
390.9185404510101880.1629190979796240.0814595489898119
400.9196306623312680.1607386753374630.0803693376687315
410.9132868460371330.1734263079257350.0867131539628673
420.9034050481524520.1931899036950960.0965949518475479
430.8987776066557620.2024447866884760.101222393344238
440.8763662850468570.2472674299062870.123633714953143
450.8828961799712180.2342076400575630.117103820028782
460.8715661455055460.2568677089889080.128433854494454
470.8851452214187940.2297095571624110.114854778581206
480.8687459892171120.2625080215657770.131254010782888
490.8925289389349030.2149421221301930.107471061065096
500.8769772492204810.2460455015590370.123022750779519
510.8699576748497340.2600846503005320.130042325150266
520.8538896467115280.2922207065769440.146110353288472
530.8749635852141740.2500728295716520.125036414785826
540.849977431099950.3000451378000990.15002256890005
550.8521562648349690.2956874703300620.147843735165031
560.8234027931267780.3531944137464440.176597206873222
570.802056991691690.3958860166166210.19794300830831
580.8358599235835020.3282801528329960.164140076416498
590.8558898114536270.2882203770927470.144110188546373
600.8305742240839830.3388515518320340.169425775916017
610.8489689858618330.3020620282763330.151031014138167
620.8237539321640520.3524921356718950.176246067835948
630.8422446334406020.3155107331187970.157755366559399
640.8138836363427480.3722327273145040.186116363657252
650.7817685919607710.4364628160784590.218231408039229
660.830138450070130.3397230998597390.16986154992987
670.8169077611554490.3661844776891020.183092238844551
680.8072423935236020.3855152129527960.192757606476398
690.7780994626687280.4438010746625450.221900537331272
700.7740396334286390.4519207331427230.225960366571361
710.7430907879206220.5138184241587550.256909212079378
720.7564591028331770.4870817943336460.243540897166823
730.7200798804791480.5598402390417050.279920119520853
740.6928800433886260.6142399132227480.307119956611374
750.6839109293449170.6321781413101670.316089070655083
760.7574805019850890.4850389960298230.242519498014911
770.745449843814490.509100312371020.25455015618551
780.7522201033375470.4955597933249070.247779896662453
790.7207334228132910.5585331543734190.279266577186709
800.7863369268747820.4273261462504360.213663073125218
810.7539763123507120.4920473752985760.246023687649288
820.7201810804018370.5596378391963260.279818919598163
830.7491809827886370.5016380344227270.250819017211363
840.7187316715598790.5625366568802420.281268328440121
850.6811633251131960.6376733497736080.318836674886804
860.6400294201052470.7199411597895060.359970579894753
870.6113711279975180.7772577440049630.388628872002482
880.5784884709981680.8430230580036640.421511529001832
890.536331630444710.927336739110580.46366836955529
900.5625187029758070.8749625940483860.437481297024193
910.5483642706997510.9032714586004980.451635729300249
920.5073283953126830.9853432093746340.492671604687317
930.4646360712977160.9292721425954310.535363928702284
940.4328901303179420.8657802606358840.567109869682058
950.5116067344394780.9767865311210450.488393265560522
960.4672254187260780.9344508374521550.532774581273922
970.4298976435080930.8597952870161850.570102356491907
980.4245130810598170.8490261621196340.575486918940183
990.384567471417250.76913494283450.61543252858275
1000.3581813958194810.7163627916389620.641818604180519
1010.3191880894888370.6383761789776750.680811910511163
1020.3016027572982270.6032055145964530.698397242701773
1030.2622827160114650.5245654320229290.737717283988535
1040.345649424299230.691298848598460.65435057570077
1050.3952586932719050.7905173865438110.604741306728095
1060.3912226672583360.7824453345166720.608777332741664
1070.3479300392980350.695860078596070.652069960701965
1080.4356329281198930.8712658562397860.564367071880107
1090.4026793288906590.8053586577813170.597320671109341
1100.7949731089858510.4100537820282980.205026891014149
1110.7671246274080540.4657507451838930.232875372591946
1120.734433892354840.5311322152903190.26556610764516
1130.7368877443118060.5262245113763890.263112255688194
1140.7264795878112380.5470408243775250.273520412188762
1150.7190949391517270.5618101216965470.280905060848273
1160.6778884303851840.6442231392296330.322111569614816
1170.6446130776667860.7107738446664290.355386922333214
1180.5994963843273880.8010072313452240.400503615672612
1190.6829760123722170.6340479752555670.317023987627783
1200.6713399894264350.657320021147130.328660010573565
1210.6211927482156570.7576145035686850.378807251784343
1220.5753734968492090.8492530063015820.424626503150791
1230.5272744847562470.9454510304875070.472725515243753
1240.5879061221143320.8241877557713370.412093877885668
1250.5673124758564160.8653750482871670.432687524143584
1260.6346413991910420.7307172016179160.365358600808958
1270.6066432050756210.7867135898487590.393356794924379
1280.5594886209395470.8810227581209060.440511379060453
1290.5378782539361040.9242434921277910.462121746063896
1300.4787912277976910.9575824555953820.521208772202309
1310.4974085006604910.9948170013209830.502591499339509
1320.4778057662165880.9556115324331770.522194233783412
1330.4440609344810310.8881218689620630.555939065518969
1340.4257586992605670.8515173985211330.574241300739433
1350.3728127308353190.7456254616706370.627187269164681
1360.3286900308098580.6573800616197170.671309969190142
1370.6553647870902640.6892704258194730.344635212909736
1380.6193033862247420.7613932275505160.380696613775258
1390.5491673762528950.901665247494210.450832623747105
1400.5063408519956750.9873182960086490.493659148004325
1410.4401843380490730.8803686760981460.559815661950927
1420.4466659342565610.8933318685131220.553334065743439
1430.5383698302911110.9232603394177770.461630169708889
1440.4832108623074210.9664217246148430.516789137692579
1450.4148694086351060.8297388172702120.585130591364894
1460.3592073193811260.7184146387622520.640792680618874
1470.2834963356319890.5669926712639770.716503664368011
1480.2195915615068310.4391831230136620.780408438493169
1490.2278772852549080.4557545705098160.772122714745092
1500.2003562968579180.4007125937158370.799643703142082
1510.1341457428875150.2682914857750310.865854257112485
1520.1726704277811310.3453408555622620.827329572218869
1530.1025528197531190.2051056395062380.897447180246881
1540.08105037504689160.1621007500937830.918949624953108






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0476190476190476NOK
5% type I error level130.0884353741496599NOK
10% type I error level200.136054421768707NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 7 & 0.0476190476190476 & NOK \tabularnewline
5% type I error level & 13 & 0.0884353741496599 & NOK \tabularnewline
10% type I error level & 20 & 0.136054421768707 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154501&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]7[/C][C]0.0476190476190476[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.0884353741496599[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.136054421768707[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154501&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154501&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0476190476190476NOK
5% type I error level130.0884353741496599NOK
10% type I error level200.136054421768707NOK


Figures (Output of Computation)

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

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