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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 15:20:34 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t13221662449276d3ys6d7r6lh.htm/, Retrieved Fri, 26 Apr 2024 21:41:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147195, Retrieved Fri, 26 Apr 2024 21:41:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [workshop 7: deter...] [2011-11-24 20:20:34] [d7127d50f40450f0f3837a0965e389eb] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.3497419249505 + 0.108770127871114Connected[t] -0.0196534564877116Separate[t] + 0.534475689426862Software[t] + 0.0608299746430892Happiness[t] -0.0730079569894138Depression[t] -0.00386059736518057t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.3497419249505 +  0.108770127871114Connected[t] -0.0196534564877116Separate[t] +  0.534475689426862Software[t] +  0.0608299746430892Happiness[t] -0.0730079569894138Depression[t] -0.00386059736518057t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.3497419249505 +  0.108770127871114Connected[t] -0.0196534564877116Separate[t] +  0.534475689426862Software[t] +  0.0608299746430892Happiness[t] -0.0730079569894138Depression[t] -0.00386059736518057t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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
Learning[t] = + 6.3497419249505 + 0.108770127871114Connected[t] -0.0196534564877116Separate[t] + 0.534475689426862Software[t] + 0.0608299746430892Happiness[t] -0.0730079569894138Depression[t] -0.00386059736518057t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.34974192495052.4156612.62860.0094370.004719
Connected0.1087701278711140.0468332.32250.0215050.010753
Separate-0.01965345648771160.044373-0.44290.6584460.329223
Software0.5344756894268620.0690557.739800
Happiness0.06082997464308920.0747290.8140.4168870.208444
Depression-0.07300795698941380.055147-1.32390.1874920.093746
t-0.003860597365180570.003171-1.21740.2253170.112659

\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) & 6.3497419249505 & 2.415661 & 2.6286 & 0.009437 & 0.004719 \tabularnewline
Connected & 0.108770127871114 & 0.046833 & 2.3225 & 0.021505 & 0.010753 \tabularnewline
Separate & -0.0196534564877116 & 0.044373 & -0.4429 & 0.658446 & 0.329223 \tabularnewline
Software & 0.534475689426862 & 0.069055 & 7.7398 & 0 & 0 \tabularnewline
Happiness & 0.0608299746430892 & 0.074729 & 0.814 & 0.416887 & 0.208444 \tabularnewline
Depression & -0.0730079569894138 & 0.055147 & -1.3239 & 0.187492 & 0.093746 \tabularnewline
t & -0.00386059736518057 & 0.003171 & -1.2174 & 0.225317 & 0.112659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&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]6.3497419249505[/C][C]2.415661[/C][C]2.6286[/C][C]0.009437[/C][C]0.004719[/C][/ROW]
[ROW][C]Connected[/C][C]0.108770127871114[/C][C]0.046833[/C][C]2.3225[/C][C]0.021505[/C][C]0.010753[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0196534564877116[/C][C]0.044373[/C][C]-0.4429[/C][C]0.658446[/C][C]0.329223[/C][/ROW]
[ROW][C]Software[/C][C]0.534475689426862[/C][C]0.069055[/C][C]7.7398[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0608299746430892[/C][C]0.074729[/C][C]0.814[/C][C]0.416887[/C][C]0.208444[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0730079569894138[/C][C]0.055147[/C][C]-1.3239[/C][C]0.187492[/C][C]0.093746[/C][/ROW]
[ROW][C]t[/C][C]-0.00386059736518057[/C][C]0.003171[/C][C]-1.2174[/C][C]0.225317[/C][C]0.112659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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)6.34974192495052.4156612.62860.0094370.004719
Connected0.1087701278711140.0468332.32250.0215050.010753
Separate-0.01965345648771160.044373-0.44290.6584460.329223
Software0.5344756894268620.0690557.739800
Happiness0.06082997464308920.0747290.8140.4168870.208444
Depression-0.07300795698941380.055147-1.32390.1874920.093746
t-0.003860597365180570.003171-1.21740.2253170.112659






Multiple Linear Regression - Regression Statistics
Multiple R0.60000526141808
R-squared0.360006313729378
Adjusted R-squared0.335232364583418
F-TEST (value)14.5316482087028
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.02788913334007e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83960371912531
Sum Squared Residuals524.54198573005

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.60000526141808 \tabularnewline
R-squared & 0.360006313729378 \tabularnewline
Adjusted R-squared & 0.335232364583418 \tabularnewline
F-TEST (value) & 14.5316482087028 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 4.02788913334007e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83960371912531 \tabularnewline
Sum Squared Residuals & 524.54198573005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.60000526141808[/C][/ROW]
[ROW][C]R-squared[/C][C]0.360006313729378[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.335232364583418[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.5316482087028[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]4.02788913334007e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83960371912531[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]524.54198573005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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 R0.60000526141808
R-squared0.360006313729378
Adjusted R-squared0.335232364583418
F-TEST (value)14.5316482087028
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value4.02788913334007e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83960371912531
Sum Squared Residuals524.54198573005






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4478576580206-3.4478576580206
21616.1262297099743-0.126229709974346
31916.57754665654362.42245334345641
41512.11832778380512.8816722161949
51415.6897418557581-1.68974185575808
61315.127183532076-2.12718353207603
71915.61464844360713.38535155639289
81516.8407088301851-1.84070883018507
91416.1389284978286-2.13892849782861
101512.7589996513422.241000348658
111615.48006995522380.51993004477622
121616.536335974772-0.536335974771966
131615.67183670594760.328163294052397
141615.62129798241150.378702017588512
151717.5360646092586-0.536064609258616
161515.3632689151626-0.363268915162571
171514.74519090691580.254809093084166
182016.31141500011783.68858499988216
191815.77236687425712.22763312574294
201615.56214788846520.437852111534848
211615.56386583640810.436134163591941
221615.08815219214650.911847807853471
231916.67985994967122.32014005032876
241615.10217839795740.897821602042614
251714.90765461707842.09234538292162
261716.9186131499970.0813868500030126
271614.98830838405531.01169161594469
281516.343041470565-1.34304147056498
291615.48268125192940.517318748070574
301414.3483274619987-0.348327461998693
311515.7815568434856-0.781556843485553
321212.5055614851119-0.505561485111873
331415.2863666853187-1.28636668531869
341615.7125584379920.287441562007968
351415.6394261474189-1.63942614741892
36713.1733862119473-6.17338621194731
371011.0461411481428-1.04614114814276
381415.920262173235-1.92026217323497
391614.39304207815871.6069579218413
401614.75917543647241.24082456352758
411615.18717645761280.812823542387227
421415.6020935886963-1.60209358869628
432018.09444176952031.90555823047966
441414.390871244411-0.390871244410955
451415.0306121840659-1.03061218406592
461115.6425127536474-4.64251275364745
471416.7386358741594-2.73863587415941
481515.0742246270447-0.0742246270446635
491615.14768870867050.852311291329499
501416.062476724786-2.06247672478598
511616.6125238810403-0.612523881040249
521414.2142964006255-0.214296400625481
531214.745905060764-2.74590506076397
541615.74865492873010.251345071269938
55911.5883931701023-2.58839317010227
561412.77234086845941.22765913154062
571616.1413897680758-0.141389768075846
581615.28102974531410.718970254685909
591515.3180496875969-0.318049687596856
601614.23644027309091.76355972690906
611211.59486302349640.405136976503551
621615.9038346864390.0961653135610249
631616.3925315506692-0.392531550669172
641414.4954075113605-0.495407511360504
651615.56478474461890.435215255381124
661715.92811526638881.07188473361122
671816.22958963200361.77041036799639
681814.5527786106233.44722138937699
691215.9163118861243-3.91631188612432
701615.46332318974840.536676810251596
711013.6797016571232-3.67970165712316
721414.4542143552727-0.454214355272689
731816.44562369264951.55437630735054
741817.05656056476160.943439435238443
751615.62914856161060.370851438389434
761713.86185753529883.13814246470116
771616.3829828596368-0.382982859636822
781614.56642951220081.43357048779917
791314.8581156142797-1.85811561427966
801616.0821718723768-0.0821718723768332
811615.44199369044510.558006309554857
822015.96774462322154.0322553767785
831615.77817520894810.221824791051874
841515.9122747969509-0.912274796950867
851514.74960443967250.250395560327546
861614.24516071858221.75483928141784
871414.1542773938871-0.154277393887082
881615.32043849023180.679561509768201
891614.64095044874631.35904955125374
901514.17648135959340.823518640406613
911213.5580644280779-1.55806442807788
921716.99418662002910.00581337997090281
931615.36892358138780.631076418612182
941515.0874016461276-0.0874016461276298
951315.0221335624777-2.02213356247766
961615.02858409298350.971415907016516
971615.82190408112490.178095918875054
981614.02791403049421.97208596950581
991615.96855031161790.0314496883820499
1001414.2639287907713-0.263928790771288
1011617.1722516676264-1.17225166762642
1021614.73364971544531.26635028455468
1032017.25496656147112.74503343852894
1041514.62386188707740.37613811292265
1051614.39086350984371.60913649015626
1061315.2364399735904-2.23643997359043
1071715.96595222943681.03404777056318
1081615.7839184301440.216081569856024
1091614.54920341372121.45079658627882
1101212.3741851454276-0.374185145427642
1111614.82014842788361.17985157211643
1121615.69733493816920.302665061830836
1131714.45528152573672.54471847426335
1141314.3774467269586-1.3774467269586
1151214.825651392575-2.82565139257501
1161816.48888074115851.51111925884151
1171415.4790202031633-1.47902020316333
1181412.98455924352441.01544075647563
1191314.8656546969649-1.86565469696487
1201615.48241922995790.517580770042074
1211314.2589188980364-1.25891889803642
1221615.49767189493540.502328105064553
1231315.764738871-2.76473887099999
1241616.9597368520734-0.959736852073376
1251515.9048703782426-0.904870378242617
1261616.5914540417273-0.591454041727331
1271515.2673732462251-0.267373246225063
1281716.05824886824950.941751131750494
1291513.89657784720821.10342215279175
1301214.848833441351-2.84883344135097
1311614.00854273563821.99145726436177
1321013.2233392612261-3.22333926122609
1331613.91808526534782.08191473465219
1341213.8761744844056-1.87617448440564
1351415.556287074245-1.55628707424501
1361515.1002710065779-0.100271006577926
1371312.23425553874150.765744461258484
1381514.53603368995050.463966310049517
1391113.2341110537659-2.23411105376589
1401213.3331628569097-1.33316285690969
141813.2143760747236-5.21437607472362
1421613.07213923555542.9278607644446
1431513.15401221856031.84598778143971
1441716.36170826596120.638291734038782
1451614.62538649125531.37461350874467
1461013.9736402561804-3.9736402561804
1471815.77760295194212.22239704805795
1481315.1203082380402-2.1203082380402
1491614.9864803002511.01351969974896
1501312.91929598188630.0807040181136726
1511012.9720857152943-2.9720857152943
1521516.1019369700738-1.10193697007384
1531613.83691271538922.16308728461084
1541611.85639795158624.1436020484138
1551412.32621039265811.67378960734186
1561012.4800977645756-2.48009776457564
1571716.74324779129240.256752208707641
1581311.52959612679491.47040387320509
1591513.78075992625281.21924007374717
1601614.81427072768361.18572927231637
1611212.2125808492847-0.212580849284684
1621312.61345293979590.386547060204064

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.4478576580206 & -3.4478576580206 \tabularnewline
2 & 16 & 16.1262297099743 & -0.126229709974346 \tabularnewline
3 & 19 & 16.5775466565436 & 2.42245334345641 \tabularnewline
4 & 15 & 12.1183277838051 & 2.8816722161949 \tabularnewline
5 & 14 & 15.6897418557581 & -1.68974185575808 \tabularnewline
6 & 13 & 15.127183532076 & -2.12718353207603 \tabularnewline
7 & 19 & 15.6146484436071 & 3.38535155639289 \tabularnewline
8 & 15 & 16.8407088301851 & -1.84070883018507 \tabularnewline
9 & 14 & 16.1389284978286 & -2.13892849782861 \tabularnewline
10 & 15 & 12.758999651342 & 2.241000348658 \tabularnewline
11 & 16 & 15.4800699552238 & 0.51993004477622 \tabularnewline
12 & 16 & 16.536335974772 & -0.536335974771966 \tabularnewline
13 & 16 & 15.6718367059476 & 0.328163294052397 \tabularnewline
14 & 16 & 15.6212979824115 & 0.378702017588512 \tabularnewline
15 & 17 & 17.5360646092586 & -0.536064609258616 \tabularnewline
16 & 15 & 15.3632689151626 & -0.363268915162571 \tabularnewline
17 & 15 & 14.7451909069158 & 0.254809093084166 \tabularnewline
18 & 20 & 16.3114150001178 & 3.68858499988216 \tabularnewline
19 & 18 & 15.7723668742571 & 2.22763312574294 \tabularnewline
20 & 16 & 15.5621478884652 & 0.437852111534848 \tabularnewline
21 & 16 & 15.5638658364081 & 0.436134163591941 \tabularnewline
22 & 16 & 15.0881521921465 & 0.911847807853471 \tabularnewline
23 & 19 & 16.6798599496712 & 2.32014005032876 \tabularnewline
24 & 16 & 15.1021783979574 & 0.897821602042614 \tabularnewline
25 & 17 & 14.9076546170784 & 2.09234538292162 \tabularnewline
26 & 17 & 16.918613149997 & 0.0813868500030126 \tabularnewline
27 & 16 & 14.9883083840553 & 1.01169161594469 \tabularnewline
28 & 15 & 16.343041470565 & -1.34304147056498 \tabularnewline
29 & 16 & 15.4826812519294 & 0.517318748070574 \tabularnewline
30 & 14 & 14.3483274619987 & -0.348327461998693 \tabularnewline
31 & 15 & 15.7815568434856 & -0.781556843485553 \tabularnewline
32 & 12 & 12.5055614851119 & -0.505561485111873 \tabularnewline
33 & 14 & 15.2863666853187 & -1.28636668531869 \tabularnewline
34 & 16 & 15.712558437992 & 0.287441562007968 \tabularnewline
35 & 14 & 15.6394261474189 & -1.63942614741892 \tabularnewline
36 & 7 & 13.1733862119473 & -6.17338621194731 \tabularnewline
37 & 10 & 11.0461411481428 & -1.04614114814276 \tabularnewline
38 & 14 & 15.920262173235 & -1.92026217323497 \tabularnewline
39 & 16 & 14.3930420781587 & 1.6069579218413 \tabularnewline
40 & 16 & 14.7591754364724 & 1.24082456352758 \tabularnewline
41 & 16 & 15.1871764576128 & 0.812823542387227 \tabularnewline
42 & 14 & 15.6020935886963 & -1.60209358869628 \tabularnewline
43 & 20 & 18.0944417695203 & 1.90555823047966 \tabularnewline
44 & 14 & 14.390871244411 & -0.390871244410955 \tabularnewline
45 & 14 & 15.0306121840659 & -1.03061218406592 \tabularnewline
46 & 11 & 15.6425127536474 & -4.64251275364745 \tabularnewline
47 & 14 & 16.7386358741594 & -2.73863587415941 \tabularnewline
48 & 15 & 15.0742246270447 & -0.0742246270446635 \tabularnewline
49 & 16 & 15.1476887086705 & 0.852311291329499 \tabularnewline
50 & 14 & 16.062476724786 & -2.06247672478598 \tabularnewline
51 & 16 & 16.6125238810403 & -0.612523881040249 \tabularnewline
52 & 14 & 14.2142964006255 & -0.214296400625481 \tabularnewline
53 & 12 & 14.745905060764 & -2.74590506076397 \tabularnewline
54 & 16 & 15.7486549287301 & 0.251345071269938 \tabularnewline
55 & 9 & 11.5883931701023 & -2.58839317010227 \tabularnewline
56 & 14 & 12.7723408684594 & 1.22765913154062 \tabularnewline
57 & 16 & 16.1413897680758 & -0.141389768075846 \tabularnewline
58 & 16 & 15.2810297453141 & 0.718970254685909 \tabularnewline
59 & 15 & 15.3180496875969 & -0.318049687596856 \tabularnewline
60 & 16 & 14.2364402730909 & 1.76355972690906 \tabularnewline
61 & 12 & 11.5948630234964 & 0.405136976503551 \tabularnewline
62 & 16 & 15.903834686439 & 0.0961653135610249 \tabularnewline
63 & 16 & 16.3925315506692 & -0.392531550669172 \tabularnewline
64 & 14 & 14.4954075113605 & -0.495407511360504 \tabularnewline
65 & 16 & 15.5647847446189 & 0.435215255381124 \tabularnewline
66 & 17 & 15.9281152663888 & 1.07188473361122 \tabularnewline
67 & 18 & 16.2295896320036 & 1.77041036799639 \tabularnewline
68 & 18 & 14.552778610623 & 3.44722138937699 \tabularnewline
69 & 12 & 15.9163118861243 & -3.91631188612432 \tabularnewline
70 & 16 & 15.4633231897484 & 0.536676810251596 \tabularnewline
71 & 10 & 13.6797016571232 & -3.67970165712316 \tabularnewline
72 & 14 & 14.4542143552727 & -0.454214355272689 \tabularnewline
73 & 18 & 16.4456236926495 & 1.55437630735054 \tabularnewline
74 & 18 & 17.0565605647616 & 0.943439435238443 \tabularnewline
75 & 16 & 15.6291485616106 & 0.370851438389434 \tabularnewline
76 & 17 & 13.8618575352988 & 3.13814246470116 \tabularnewline
77 & 16 & 16.3829828596368 & -0.382982859636822 \tabularnewline
78 & 16 & 14.5664295122008 & 1.43357048779917 \tabularnewline
79 & 13 & 14.8581156142797 & -1.85811561427966 \tabularnewline
80 & 16 & 16.0821718723768 & -0.0821718723768332 \tabularnewline
81 & 16 & 15.4419936904451 & 0.558006309554857 \tabularnewline
82 & 20 & 15.9677446232215 & 4.0322553767785 \tabularnewline
83 & 16 & 15.7781752089481 & 0.221824791051874 \tabularnewline
84 & 15 & 15.9122747969509 & -0.912274796950867 \tabularnewline
85 & 15 & 14.7496044396725 & 0.250395560327546 \tabularnewline
86 & 16 & 14.2451607185822 & 1.75483928141784 \tabularnewline
87 & 14 & 14.1542773938871 & -0.154277393887082 \tabularnewline
88 & 16 & 15.3204384902318 & 0.679561509768201 \tabularnewline
89 & 16 & 14.6409504487463 & 1.35904955125374 \tabularnewline
90 & 15 & 14.1764813595934 & 0.823518640406613 \tabularnewline
91 & 12 & 13.5580644280779 & -1.55806442807788 \tabularnewline
92 & 17 & 16.9941866200291 & 0.00581337997090281 \tabularnewline
93 & 16 & 15.3689235813878 & 0.631076418612182 \tabularnewline
94 & 15 & 15.0874016461276 & -0.0874016461276298 \tabularnewline
95 & 13 & 15.0221335624777 & -2.02213356247766 \tabularnewline
96 & 16 & 15.0285840929835 & 0.971415907016516 \tabularnewline
97 & 16 & 15.8219040811249 & 0.178095918875054 \tabularnewline
98 & 16 & 14.0279140304942 & 1.97208596950581 \tabularnewline
99 & 16 & 15.9685503116179 & 0.0314496883820499 \tabularnewline
100 & 14 & 14.2639287907713 & -0.263928790771288 \tabularnewline
101 & 16 & 17.1722516676264 & -1.17225166762642 \tabularnewline
102 & 16 & 14.7336497154453 & 1.26635028455468 \tabularnewline
103 & 20 & 17.2549665614711 & 2.74503343852894 \tabularnewline
104 & 15 & 14.6238618870774 & 0.37613811292265 \tabularnewline
105 & 16 & 14.3908635098437 & 1.60913649015626 \tabularnewline
106 & 13 & 15.2364399735904 & -2.23643997359043 \tabularnewline
107 & 17 & 15.9659522294368 & 1.03404777056318 \tabularnewline
108 & 16 & 15.783918430144 & 0.216081569856024 \tabularnewline
109 & 16 & 14.5492034137212 & 1.45079658627882 \tabularnewline
110 & 12 & 12.3741851454276 & -0.374185145427642 \tabularnewline
111 & 16 & 14.8201484278836 & 1.17985157211643 \tabularnewline
112 & 16 & 15.6973349381692 & 0.302665061830836 \tabularnewline
113 & 17 & 14.4552815257367 & 2.54471847426335 \tabularnewline
114 & 13 & 14.3774467269586 & -1.3774467269586 \tabularnewline
115 & 12 & 14.825651392575 & -2.82565139257501 \tabularnewline
116 & 18 & 16.4888807411585 & 1.51111925884151 \tabularnewline
117 & 14 & 15.4790202031633 & -1.47902020316333 \tabularnewline
118 & 14 & 12.9845592435244 & 1.01544075647563 \tabularnewline
119 & 13 & 14.8656546969649 & -1.86565469696487 \tabularnewline
120 & 16 & 15.4824192299579 & 0.517580770042074 \tabularnewline
121 & 13 & 14.2589188980364 & -1.25891889803642 \tabularnewline
122 & 16 & 15.4976718949354 & 0.502328105064553 \tabularnewline
123 & 13 & 15.764738871 & -2.76473887099999 \tabularnewline
124 & 16 & 16.9597368520734 & -0.959736852073376 \tabularnewline
125 & 15 & 15.9048703782426 & -0.904870378242617 \tabularnewline
126 & 16 & 16.5914540417273 & -0.591454041727331 \tabularnewline
127 & 15 & 15.2673732462251 & -0.267373246225063 \tabularnewline
128 & 17 & 16.0582488682495 & 0.941751131750494 \tabularnewline
129 & 15 & 13.8965778472082 & 1.10342215279175 \tabularnewline
130 & 12 & 14.848833441351 & -2.84883344135097 \tabularnewline
131 & 16 & 14.0085427356382 & 1.99145726436177 \tabularnewline
132 & 10 & 13.2233392612261 & -3.22333926122609 \tabularnewline
133 & 16 & 13.9180852653478 & 2.08191473465219 \tabularnewline
134 & 12 & 13.8761744844056 & -1.87617448440564 \tabularnewline
135 & 14 & 15.556287074245 & -1.55628707424501 \tabularnewline
136 & 15 & 15.1002710065779 & -0.100271006577926 \tabularnewline
137 & 13 & 12.2342555387415 & 0.765744461258484 \tabularnewline
138 & 15 & 14.5360336899505 & 0.463966310049517 \tabularnewline
139 & 11 & 13.2341110537659 & -2.23411105376589 \tabularnewline
140 & 12 & 13.3331628569097 & -1.33316285690969 \tabularnewline
141 & 8 & 13.2143760747236 & -5.21437607472362 \tabularnewline
142 & 16 & 13.0721392355554 & 2.9278607644446 \tabularnewline
143 & 15 & 13.1540122185603 & 1.84598778143971 \tabularnewline
144 & 17 & 16.3617082659612 & 0.638291734038782 \tabularnewline
145 & 16 & 14.6253864912553 & 1.37461350874467 \tabularnewline
146 & 10 & 13.9736402561804 & -3.9736402561804 \tabularnewline
147 & 18 & 15.7776029519421 & 2.22239704805795 \tabularnewline
148 & 13 & 15.1203082380402 & -2.1203082380402 \tabularnewline
149 & 16 & 14.986480300251 & 1.01351969974896 \tabularnewline
150 & 13 & 12.9192959818863 & 0.0807040181136726 \tabularnewline
151 & 10 & 12.9720857152943 & -2.9720857152943 \tabularnewline
152 & 15 & 16.1019369700738 & -1.10193697007384 \tabularnewline
153 & 16 & 13.8369127153892 & 2.16308728461084 \tabularnewline
154 & 16 & 11.8563979515862 & 4.1436020484138 \tabularnewline
155 & 14 & 12.3262103926581 & 1.67378960734186 \tabularnewline
156 & 10 & 12.4800977645756 & -2.48009776457564 \tabularnewline
157 & 17 & 16.7432477912924 & 0.256752208707641 \tabularnewline
158 & 13 & 11.5295961267949 & 1.47040387320509 \tabularnewline
159 & 15 & 13.7807599262528 & 1.21924007374717 \tabularnewline
160 & 16 & 14.8142707276836 & 1.18572927231637 \tabularnewline
161 & 12 & 12.2125808492847 & -0.212580849284684 \tabularnewline
162 & 13 & 12.6134529397959 & 0.386547060204064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.4478576580206[/C][C]-3.4478576580206[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1262297099743[/C][C]-0.126229709974346[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5775466565436[/C][C]2.42245334345641[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1183277838051[/C][C]2.8816722161949[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6897418557581[/C][C]-1.68974185575808[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.127183532076[/C][C]-2.12718353207603[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.6146484436071[/C][C]3.38535155639289[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.8407088301851[/C][C]-1.84070883018507[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1389284978286[/C][C]-2.13892849782861[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.758999651342[/C][C]2.241000348658[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4800699552238[/C][C]0.51993004477622[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.536335974772[/C][C]-0.536335974771966[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6718367059476[/C][C]0.328163294052397[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.6212979824115[/C][C]0.378702017588512[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5360646092586[/C][C]-0.536064609258616[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3632689151626[/C][C]-0.363268915162571[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7451909069158[/C][C]0.254809093084166[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3114150001178[/C][C]3.68858499988216[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7723668742571[/C][C]2.22763312574294[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.5621478884652[/C][C]0.437852111534848[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5638658364081[/C][C]0.436134163591941[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0881521921465[/C][C]0.911847807853471[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6798599496712[/C][C]2.32014005032876[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1021783979574[/C][C]0.897821602042614[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9076546170784[/C][C]2.09234538292162[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.918613149997[/C][C]0.0813868500030126[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9883083840553[/C][C]1.01169161594469[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.343041470565[/C][C]-1.34304147056498[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4826812519294[/C][C]0.517318748070574[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3483274619987[/C][C]-0.348327461998693[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7815568434856[/C][C]-0.781556843485553[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.5055614851119[/C][C]-0.505561485111873[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2863666853187[/C][C]-1.28636668531869[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.712558437992[/C][C]0.287441562007968[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6394261474189[/C][C]-1.63942614741892[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1733862119473[/C][C]-6.17338621194731[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0461411481428[/C][C]-1.04614114814276[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.920262173235[/C][C]-1.92026217323497[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3930420781587[/C][C]1.6069579218413[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7591754364724[/C][C]1.24082456352758[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1871764576128[/C][C]0.812823542387227[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6020935886963[/C][C]-1.60209358869628[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0944417695203[/C][C]1.90555823047966[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.390871244411[/C][C]-0.390871244410955[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0306121840659[/C][C]-1.03061218406592[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6425127536474[/C][C]-4.64251275364745[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7386358741594[/C][C]-2.73863587415941[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0742246270447[/C][C]-0.0742246270446635[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1476887086705[/C][C]0.852311291329499[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.062476724786[/C][C]-2.06247672478598[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6125238810403[/C][C]-0.612523881040249[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2142964006255[/C][C]-0.214296400625481[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.745905060764[/C][C]-2.74590506076397[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7486549287301[/C][C]0.251345071269938[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5883931701023[/C][C]-2.58839317010227[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7723408684594[/C][C]1.22765913154062[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1413897680758[/C][C]-0.141389768075846[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2810297453141[/C][C]0.718970254685909[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3180496875969[/C][C]-0.318049687596856[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2364402730909[/C][C]1.76355972690906[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5948630234964[/C][C]0.405136976503551[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.903834686439[/C][C]0.0961653135610249[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3925315506692[/C][C]-0.392531550669172[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4954075113605[/C][C]-0.495407511360504[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5647847446189[/C][C]0.435215255381124[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9281152663888[/C][C]1.07188473361122[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2295896320036[/C][C]1.77041036799639[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.552778610623[/C][C]3.44722138937699[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9163118861243[/C][C]-3.91631188612432[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4633231897484[/C][C]0.536676810251596[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6797016571232[/C][C]-3.67970165712316[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.4542143552727[/C][C]-0.454214355272689[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4456236926495[/C][C]1.55437630735054[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0565605647616[/C][C]0.943439435238443[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6291485616106[/C][C]0.370851438389434[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8618575352988[/C][C]3.13814246470116[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3829828596368[/C][C]-0.382982859636822[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5664295122008[/C][C]1.43357048779917[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8581156142797[/C][C]-1.85811561427966[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0821718723768[/C][C]-0.0821718723768332[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4419936904451[/C][C]0.558006309554857[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.9677446232215[/C][C]4.0322553767785[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7781752089481[/C][C]0.221824791051874[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9122747969509[/C][C]-0.912274796950867[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7496044396725[/C][C]0.250395560327546[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2451607185822[/C][C]1.75483928141784[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1542773938871[/C][C]-0.154277393887082[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3204384902318[/C][C]0.679561509768201[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6409504487463[/C][C]1.35904955125374[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1764813595934[/C][C]0.823518640406613[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5580644280779[/C][C]-1.55806442807788[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9941866200291[/C][C]0.00581337997090281[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3689235813878[/C][C]0.631076418612182[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.0874016461276[/C][C]-0.0874016461276298[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0221335624777[/C][C]-2.02213356247766[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0285840929835[/C][C]0.971415907016516[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8219040811249[/C][C]0.178095918875054[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0279140304942[/C][C]1.97208596950581[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9685503116179[/C][C]0.0314496883820499[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2639287907713[/C][C]-0.263928790771288[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1722516676264[/C][C]-1.17225166762642[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7336497154453[/C][C]1.26635028455468[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.2549665614711[/C][C]2.74503343852894[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6238618870774[/C][C]0.37613811292265[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.3908635098437[/C][C]1.60913649015626[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2364399735904[/C][C]-2.23643997359043[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9659522294368[/C][C]1.03404777056318[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.783918430144[/C][C]0.216081569856024[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5492034137212[/C][C]1.45079658627882[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3741851454276[/C][C]-0.374185145427642[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8201484278836[/C][C]1.17985157211643[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.6973349381692[/C][C]0.302665061830836[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4552815257367[/C][C]2.54471847426335[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3774467269586[/C][C]-1.3774467269586[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.825651392575[/C][C]-2.82565139257501[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4888807411585[/C][C]1.51111925884151[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4790202031633[/C][C]-1.47902020316333[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9845592435244[/C][C]1.01544075647563[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8656546969649[/C][C]-1.86565469696487[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.4824192299579[/C][C]0.517580770042074[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2589188980364[/C][C]-1.25891889803642[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.4976718949354[/C][C]0.502328105064553[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.764738871[/C][C]-2.76473887099999[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9597368520734[/C][C]-0.959736852073376[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.9048703782426[/C][C]-0.904870378242617[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.5914540417273[/C][C]-0.591454041727331[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2673732462251[/C][C]-0.267373246225063[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0582488682495[/C][C]0.941751131750494[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8965778472082[/C][C]1.10342215279175[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.848833441351[/C][C]-2.84883344135097[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0085427356382[/C][C]1.99145726436177[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2233392612261[/C][C]-3.22333926122609[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9180852653478[/C][C]2.08191473465219[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8761744844056[/C][C]-1.87617448440564[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.556287074245[/C][C]-1.55628707424501[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1002710065779[/C][C]-0.100271006577926[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2342555387415[/C][C]0.765744461258484[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5360336899505[/C][C]0.463966310049517[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2341110537659[/C][C]-2.23411105376589[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3331628569097[/C][C]-1.33316285690969[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2143760747236[/C][C]-5.21437607472362[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.0721392355554[/C][C]2.9278607644446[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1540122185603[/C][C]1.84598778143971[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.3617082659612[/C][C]0.638291734038782[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6253864912553[/C][C]1.37461350874467[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9736402561804[/C][C]-3.9736402561804[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7776029519421[/C][C]2.22239704805795[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1203082380402[/C][C]-2.1203082380402[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.986480300251[/C][C]1.01351969974896[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9192959818863[/C][C]0.0807040181136726[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9720857152943[/C][C]-2.9720857152943[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1019369700738[/C][C]-1.10193697007384[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8369127153892[/C][C]2.16308728461084[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8563979515862[/C][C]4.1436020484138[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3262103926581[/C][C]1.67378960734186[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4800977645756[/C][C]-2.48009776457564[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.7432477912924[/C][C]0.256752208707641[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5295961267949[/C][C]1.47040387320509[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.7807599262528[/C][C]1.21924007374717[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8142707276836[/C][C]1.18572927231637[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2125808492847[/C][C]-0.212580849284684[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6134529397959[/C][C]0.386547060204064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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
11316.4478576580206-3.4478576580206
21616.1262297099743-0.126229709974346
31916.57754665654362.42245334345641
41512.11832778380512.8816722161949
51415.6897418557581-1.68974185575808
61315.127183532076-2.12718353207603
71915.61464844360713.38535155639289
81516.8407088301851-1.84070883018507
91416.1389284978286-2.13892849782861
101512.7589996513422.241000348658
111615.48006995522380.51993004477622
121616.536335974772-0.536335974771966
131615.67183670594760.328163294052397
141615.62129798241150.378702017588512
151717.5360646092586-0.536064609258616
161515.3632689151626-0.363268915162571
171514.74519090691580.254809093084166
182016.31141500011783.68858499988216
191815.77236687425712.22763312574294
201615.56214788846520.437852111534848
211615.56386583640810.436134163591941
221615.08815219214650.911847807853471
231916.67985994967122.32014005032876
241615.10217839795740.897821602042614
251714.90765461707842.09234538292162
261716.9186131499970.0813868500030126
271614.98830838405531.01169161594469
281516.343041470565-1.34304147056498
291615.48268125192940.517318748070574
301414.3483274619987-0.348327461998693
311515.7815568434856-0.781556843485553
321212.5055614851119-0.505561485111873
331415.2863666853187-1.28636668531869
341615.7125584379920.287441562007968
351415.6394261474189-1.63942614741892
36713.1733862119473-6.17338621194731
371011.0461411481428-1.04614114814276
381415.920262173235-1.92026217323497
391614.39304207815871.6069579218413
401614.75917543647241.24082456352758
411615.18717645761280.812823542387227
421415.6020935886963-1.60209358869628
432018.09444176952031.90555823047966
441414.390871244411-0.390871244410955
451415.0306121840659-1.03061218406592
461115.6425127536474-4.64251275364745
471416.7386358741594-2.73863587415941
481515.0742246270447-0.0742246270446635
491615.14768870867050.852311291329499
501416.062476724786-2.06247672478598
511616.6125238810403-0.612523881040249
521414.2142964006255-0.214296400625481
531214.745905060764-2.74590506076397
541615.74865492873010.251345071269938
55911.5883931701023-2.58839317010227
561412.77234086845941.22765913154062
571616.1413897680758-0.141389768075846
581615.28102974531410.718970254685909
591515.3180496875969-0.318049687596856
601614.23644027309091.76355972690906
611211.59486302349640.405136976503551
621615.9038346864390.0961653135610249
631616.3925315506692-0.392531550669172
641414.4954075113605-0.495407511360504
651615.56478474461890.435215255381124
661715.92811526638881.07188473361122
671816.22958963200361.77041036799639
681814.5527786106233.44722138937699
691215.9163118861243-3.91631188612432
701615.46332318974840.536676810251596
711013.6797016571232-3.67970165712316
721414.4542143552727-0.454214355272689
731816.44562369264951.55437630735054
741817.05656056476160.943439435238443
751615.62914856161060.370851438389434
761713.86185753529883.13814246470116
771616.3829828596368-0.382982859636822
781614.56642951220081.43357048779917
791314.8581156142797-1.85811561427966
801616.0821718723768-0.0821718723768332
811615.44199369044510.558006309554857
822015.96774462322154.0322553767785
831615.77817520894810.221824791051874
841515.9122747969509-0.912274796950867
851514.74960443967250.250395560327546
861614.24516071858221.75483928141784
871414.1542773938871-0.154277393887082
881615.32043849023180.679561509768201
891614.64095044874631.35904955125374
901514.17648135959340.823518640406613
911213.5580644280779-1.55806442807788
921716.99418662002910.00581337997090281
931615.36892358138780.631076418612182
941515.0874016461276-0.0874016461276298
951315.0221335624777-2.02213356247766
961615.02858409298350.971415907016516
971615.82190408112490.178095918875054
981614.02791403049421.97208596950581
991615.96855031161790.0314496883820499
1001414.2639287907713-0.263928790771288
1011617.1722516676264-1.17225166762642
1021614.73364971544531.26635028455468
1032017.25496656147112.74503343852894
1041514.62386188707740.37613811292265
1051614.39086350984371.60913649015626
1061315.2364399735904-2.23643997359043
1071715.96595222943681.03404777056318
1081615.7839184301440.216081569856024
1091614.54920341372121.45079658627882
1101212.3741851454276-0.374185145427642
1111614.82014842788361.17985157211643
1121615.69733493816920.302665061830836
1131714.45528152573672.54471847426335
1141314.3774467269586-1.3774467269586
1151214.825651392575-2.82565139257501
1161816.48888074115851.51111925884151
1171415.4790202031633-1.47902020316333
1181412.98455924352441.01544075647563
1191314.8656546969649-1.86565469696487
1201615.48241922995790.517580770042074
1211314.2589188980364-1.25891889803642
1221615.49767189493540.502328105064553
1231315.764738871-2.76473887099999
1241616.9597368520734-0.959736852073376
1251515.9048703782426-0.904870378242617
1261616.5914540417273-0.591454041727331
1271515.2673732462251-0.267373246225063
1281716.05824886824950.941751131750494
1291513.89657784720821.10342215279175
1301214.848833441351-2.84883344135097
1311614.00854273563821.99145726436177
1321013.2233392612261-3.22333926122609
1331613.91808526534782.08191473465219
1341213.8761744844056-1.87617448440564
1351415.556287074245-1.55628707424501
1361515.1002710065779-0.100271006577926
1371312.23425553874150.765744461258484
1381514.53603368995050.463966310049517
1391113.2341110537659-2.23411105376589
1401213.3331628569097-1.33316285690969
141813.2143760747236-5.21437607472362
1421613.07213923555542.9278607644446
1431513.15401221856031.84598778143971
1441716.36170826596120.638291734038782
1451614.62538649125531.37461350874467
1461013.9736402561804-3.9736402561804
1471815.77760295194212.22239704805795
1481315.1203082380402-2.1203082380402
1491614.9864803002511.01351969974896
1501312.91929598188630.0807040181136726
1511012.9720857152943-2.9720857152943
1521516.1019369700738-1.10193697007384
1531613.83691271538922.16308728461084
1541611.85639795158624.1436020484138
1551412.32621039265811.67378960734186
1561012.4800977645756-2.48009776457564
1571716.74324779129240.256752208707641
1581311.52959612679491.47040387320509
1591513.78075992625281.21924007374717
1601614.81427072768361.18572927231637
1611212.2125808492847-0.212580849284684
1621312.61345293979590.386547060204064






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8577475566872160.2845048866255680.142252443312784
110.7730217524300310.4539564951399380.226978247569969
120.7827050778820170.4345898442359660.217294922117983
130.7767780030237310.4464439939525390.223221996976269
140.7091046225766470.5817907548467070.290895377423353
150.6375450637980930.7249098724038150.362454936201907
160.658446584652710.683106830694580.34155341534729
170.6019668826563980.7960662346872040.398033117343602
180.8372315595626170.3255368808747660.162768440437383
190.8062729229441170.3874541541117660.193727077055883
200.7579864954722220.4840270090555570.242013504527779
210.6919998374523460.6160003250953080.308000162547654
220.6466543902677440.7066912194645120.353345609732256
230.6075949342344030.7848101315311940.392405065765597
240.5873875977551080.8252248044897840.412612402244892
250.5335870155474780.9328259689050440.466412984452522
260.4786872959253330.9573745918506660.521312704074667
270.4283980598121360.8567961196242710.571601940187864
280.4779008218234910.9558016436469820.522099178176509
290.4205721810591360.8411443621182730.579427818940864
300.436129764153210.8722595283064190.56387023584679
310.3843151756088890.7686303512177780.615684824391111
320.3893750193319540.7787500386639080.610624980668046
330.3801740308377790.7603480616755580.619825969162221
340.3237846448336260.6475692896672530.676215355166374
350.3095583249280770.6191166498561550.690441675071923
360.8236534220236860.3526931559526290.176346577976314
370.8046862129882670.3906275740234650.195313787011733
380.7888485890220810.4223028219558380.211151410977919
390.8241972105610240.3516055788779520.175802789438976
400.8099499883807360.3801000232385290.190050011619264
410.7828980811814450.434203837637110.217101918818555
420.760202689694880.4795946206102410.23979731030512
430.7753814633322440.4492370733355130.224618536667756
440.7358366391320520.5283267217358950.264163360867948
450.7025635588984930.5948728822030140.297436441101507
460.8708302213478150.2583395573043690.129169778652185
470.8808493630376450.238301273924710.119150636962355
480.8584330897929420.2831338204141160.141566910207058
490.8435187853884670.3129624292230650.156481214611533
500.8364530362498570.3270939275002850.163546963750143
510.8070054845424810.3859890309150380.192994515457519
520.774326878323830.4513462433523410.22567312167617
530.7906749262684590.4186501474630820.209325073731541
540.7635783846135180.4728432307729640.236421615386482
550.7859383166043120.4281233667913760.214061683395688
560.7766871444772780.4466257110454440.223312855522722
570.7404525859040650.5190948281918710.259547414095935
580.7288902201673660.5422195596652670.271109779832634
590.6934438308347390.6131123383305230.306556169165261
600.7022033204590390.5955933590819210.297796679540961
610.6671669839281650.665666032143670.332833016071835
620.6261116821980740.7477766356038520.373888317801926
630.585912691603050.8281746167939010.41408730839695
640.5433462929621340.9133074140757310.456653707037866
650.5052401898200320.9895196203599350.494759810179968
660.4815006106299710.9630012212599420.518499389370029
670.4772406056969260.9544812113938510.522759394303074
680.6007681888866430.7984636222267140.399231811113357
690.7322133764789920.5355732470420160.267786623521008
700.6990032172435090.6019935655129810.300996782756491
710.8041587538715950.391682492256810.195841246128405
720.7745437176328470.4509125647343070.225456282367153
730.7703168265153840.4593663469692330.229683173484616
740.7480923451574110.5038153096851790.25190765484259
750.7109974135956320.5780051728087370.289002586404368
760.7663604517670160.4672790964659690.233639548232984
770.7306734204321820.5386531591356360.269326579567818
780.7100997176612410.5798005646775170.289900282338759
790.7148829810483650.570234037903270.285117018951635
800.6756817598251320.6486364803497360.324318240174868
810.6392935488102250.721412902379550.360706451189775
820.7809075926997770.4381848146004460.219092407300223
830.7460296166793440.5079407666413130.253970383320656
840.7194435894617050.561112821076590.280556410538295
850.6796374982816590.6407250034366820.320362501718341
860.6670076906483420.6659846187033150.332992309351658
870.6244991174098170.7510017651803650.375500882590183
880.5829210599385420.8341578801229160.417078940061458
890.5562916419923130.8874167160153740.443708358007687
900.5228224693418730.9543550613162530.477177530658127
910.5144905911127160.9710188177745670.485509408887284
920.4713977194370360.9427954388740720.528602280562964
930.4291444496807970.8582888993615930.570855550319203
940.3846952422097710.7693904844195420.615304757790229
950.4057015903415690.8114031806831390.594298409658431
960.3685850110085920.7371700220171850.631414988991408
970.3272752724011770.6545505448023550.672724727598823
980.3224467603660990.6448935207321990.677553239633901
990.2809250803774520.5618501607549030.719074919622549
1000.2460736584713550.4921473169427090.753926341528645
1010.224776503003790.4495530060075810.77522349699621
1020.2071019225178080.4142038450356160.792898077482192
1030.2546362975220270.5092725950440530.745363702477973
1040.2176774833178410.4353549666356830.782322516682159
1050.2139335587458730.4278671174917450.786066441254127
1060.2250370730201490.4500741460402980.774962926979851
1070.203355431424850.40671086284970.79664456857515
1080.1818844093195670.3637688186391350.818115590680432
1090.175509663584740.3510193271694810.82449033641526
1100.1679979978139960.3359959956279930.832002002186004
1110.1585318640495940.3170637280991880.841468135950406
1120.1387671151867410.2775342303734810.861232884813259
1130.1862054337397510.3724108674795020.813794566260249
1140.1629662398064160.3259324796128320.837033760193584
1150.1832079739528190.3664159479056370.816792026047181
1160.1858885945110160.3717771890220310.814111405488984
1170.161872095986140.323744191972280.83812790401386
1180.1597352490726180.3194704981452350.840264750927382
1190.1492028033505350.2984056067010690.850797196649465
1200.1537188943652680.3074377887305350.846281105634732
1210.1300788456220920.2601576912441840.869921154377908
1220.1141021128957610.2282042257915220.885897887104239
1230.112968721110180.225937442220360.88703127888982
1240.09007812337306640.1801562467461330.909921876626934
1250.07102602911919170.1420520582383830.928973970880808
1260.05420680158336080.1084136031667220.945793198416639
1270.04047158971555350.0809431794311070.959528410284446
1280.04281570853578890.08563141707157790.957184291464211
1290.04076072940831410.08152145881662830.959239270591686
1300.04088670955059140.08177341910118280.959113290449409
1310.04492420730172920.08984841460345850.955075792698271
1320.0495975743611230.09919514872224610.950402425638877
1330.1018239024120930.2036478048241850.898176097587907
1340.1019736214302260.2039472428604530.898026378569774
1350.0802878314055180.1605756628110360.919712168594482
1360.05960236602197050.1192047320439410.94039763397803
1370.04996498854168330.09992997708336660.950035011458317
1380.04869841250396960.09739682500793920.95130158749603
1390.0432690193537920.0865380387075840.956730980646208
1400.02987984466788010.05975968933576030.97012015533212
1410.3993837876928470.7987675753856940.600616212307153
1420.3969857305857580.7939714611715160.603014269414242
1430.3555806320419360.7111612640838730.644419367958064
1440.3253516038412350.6507032076824710.674648396158765
1450.283062533633190.5661250672663790.71693746636681
1460.2643588540380810.5287177080761620.735641145961919
1470.4028071131439490.8056142262878980.597192886856051
1480.7343332271410660.5313335457178680.265666772858934
1490.690942444187440.6181151116251190.30905755581256
1500.5634593224019310.8730813551961380.436540677598069
1510.789096926291150.42180614741770.21090307370885
1520.8382675106060320.3234649787879360.161732489393968

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.857747556687216 & 0.284504886625568 & 0.142252443312784 \tabularnewline
11 & 0.773021752430031 & 0.453956495139938 & 0.226978247569969 \tabularnewline
12 & 0.782705077882017 & 0.434589844235966 & 0.217294922117983 \tabularnewline
13 & 0.776778003023731 & 0.446443993952539 & 0.223221996976269 \tabularnewline
14 & 0.709104622576647 & 0.581790754846707 & 0.290895377423353 \tabularnewline
15 & 0.637545063798093 & 0.724909872403815 & 0.362454936201907 \tabularnewline
16 & 0.65844658465271 & 0.68310683069458 & 0.34155341534729 \tabularnewline
17 & 0.601966882656398 & 0.796066234687204 & 0.398033117343602 \tabularnewline
18 & 0.837231559562617 & 0.325536880874766 & 0.162768440437383 \tabularnewline
19 & 0.806272922944117 & 0.387454154111766 & 0.193727077055883 \tabularnewline
20 & 0.757986495472222 & 0.484027009055557 & 0.242013504527779 \tabularnewline
21 & 0.691999837452346 & 0.616000325095308 & 0.308000162547654 \tabularnewline
22 & 0.646654390267744 & 0.706691219464512 & 0.353345609732256 \tabularnewline
23 & 0.607594934234403 & 0.784810131531194 & 0.392405065765597 \tabularnewline
24 & 0.587387597755108 & 0.825224804489784 & 0.412612402244892 \tabularnewline
25 & 0.533587015547478 & 0.932825968905044 & 0.466412984452522 \tabularnewline
26 & 0.478687295925333 & 0.957374591850666 & 0.521312704074667 \tabularnewline
27 & 0.428398059812136 & 0.856796119624271 & 0.571601940187864 \tabularnewline
28 & 0.477900821823491 & 0.955801643646982 & 0.522099178176509 \tabularnewline
29 & 0.420572181059136 & 0.841144362118273 & 0.579427818940864 \tabularnewline
30 & 0.43612976415321 & 0.872259528306419 & 0.56387023584679 \tabularnewline
31 & 0.384315175608889 & 0.768630351217778 & 0.615684824391111 \tabularnewline
32 & 0.389375019331954 & 0.778750038663908 & 0.610624980668046 \tabularnewline
33 & 0.380174030837779 & 0.760348061675558 & 0.619825969162221 \tabularnewline
34 & 0.323784644833626 & 0.647569289667253 & 0.676215355166374 \tabularnewline
35 & 0.309558324928077 & 0.619116649856155 & 0.690441675071923 \tabularnewline
36 & 0.823653422023686 & 0.352693155952629 & 0.176346577976314 \tabularnewline
37 & 0.804686212988267 & 0.390627574023465 & 0.195313787011733 \tabularnewline
38 & 0.788848589022081 & 0.422302821955838 & 0.211151410977919 \tabularnewline
39 & 0.824197210561024 & 0.351605578877952 & 0.175802789438976 \tabularnewline
40 & 0.809949988380736 & 0.380100023238529 & 0.190050011619264 \tabularnewline
41 & 0.782898081181445 & 0.43420383763711 & 0.217101918818555 \tabularnewline
42 & 0.76020268969488 & 0.479594620610241 & 0.23979731030512 \tabularnewline
43 & 0.775381463332244 & 0.449237073335513 & 0.224618536667756 \tabularnewline
44 & 0.735836639132052 & 0.528326721735895 & 0.264163360867948 \tabularnewline
45 & 0.702563558898493 & 0.594872882203014 & 0.297436441101507 \tabularnewline
46 & 0.870830221347815 & 0.258339557304369 & 0.129169778652185 \tabularnewline
47 & 0.880849363037645 & 0.23830127392471 & 0.119150636962355 \tabularnewline
48 & 0.858433089792942 & 0.283133820414116 & 0.141566910207058 \tabularnewline
49 & 0.843518785388467 & 0.312962429223065 & 0.156481214611533 \tabularnewline
50 & 0.836453036249857 & 0.327093927500285 & 0.163546963750143 \tabularnewline
51 & 0.807005484542481 & 0.385989030915038 & 0.192994515457519 \tabularnewline
52 & 0.77432687832383 & 0.451346243352341 & 0.22567312167617 \tabularnewline
53 & 0.790674926268459 & 0.418650147463082 & 0.209325073731541 \tabularnewline
54 & 0.763578384613518 & 0.472843230772964 & 0.236421615386482 \tabularnewline
55 & 0.785938316604312 & 0.428123366791376 & 0.214061683395688 \tabularnewline
56 & 0.776687144477278 & 0.446625711045444 & 0.223312855522722 \tabularnewline
57 & 0.740452585904065 & 0.519094828191871 & 0.259547414095935 \tabularnewline
58 & 0.728890220167366 & 0.542219559665267 & 0.271109779832634 \tabularnewline
59 & 0.693443830834739 & 0.613112338330523 & 0.306556169165261 \tabularnewline
60 & 0.702203320459039 & 0.595593359081921 & 0.297796679540961 \tabularnewline
61 & 0.667166983928165 & 0.66566603214367 & 0.332833016071835 \tabularnewline
62 & 0.626111682198074 & 0.747776635603852 & 0.373888317801926 \tabularnewline
63 & 0.58591269160305 & 0.828174616793901 & 0.41408730839695 \tabularnewline
64 & 0.543346292962134 & 0.913307414075731 & 0.456653707037866 \tabularnewline
65 & 0.505240189820032 & 0.989519620359935 & 0.494759810179968 \tabularnewline
66 & 0.481500610629971 & 0.963001221259942 & 0.518499389370029 \tabularnewline
67 & 0.477240605696926 & 0.954481211393851 & 0.522759394303074 \tabularnewline
68 & 0.600768188886643 & 0.798463622226714 & 0.399231811113357 \tabularnewline
69 & 0.732213376478992 & 0.535573247042016 & 0.267786623521008 \tabularnewline
70 & 0.699003217243509 & 0.601993565512981 & 0.300996782756491 \tabularnewline
71 & 0.804158753871595 & 0.39168249225681 & 0.195841246128405 \tabularnewline
72 & 0.774543717632847 & 0.450912564734307 & 0.225456282367153 \tabularnewline
73 & 0.770316826515384 & 0.459366346969233 & 0.229683173484616 \tabularnewline
74 & 0.748092345157411 & 0.503815309685179 & 0.25190765484259 \tabularnewline
75 & 0.710997413595632 & 0.578005172808737 & 0.289002586404368 \tabularnewline
76 & 0.766360451767016 & 0.467279096465969 & 0.233639548232984 \tabularnewline
77 & 0.730673420432182 & 0.538653159135636 & 0.269326579567818 \tabularnewline
78 & 0.710099717661241 & 0.579800564677517 & 0.289900282338759 \tabularnewline
79 & 0.714882981048365 & 0.57023403790327 & 0.285117018951635 \tabularnewline
80 & 0.675681759825132 & 0.648636480349736 & 0.324318240174868 \tabularnewline
81 & 0.639293548810225 & 0.72141290237955 & 0.360706451189775 \tabularnewline
82 & 0.780907592699777 & 0.438184814600446 & 0.219092407300223 \tabularnewline
83 & 0.746029616679344 & 0.507940766641313 & 0.253970383320656 \tabularnewline
84 & 0.719443589461705 & 0.56111282107659 & 0.280556410538295 \tabularnewline
85 & 0.679637498281659 & 0.640725003436682 & 0.320362501718341 \tabularnewline
86 & 0.667007690648342 & 0.665984618703315 & 0.332992309351658 \tabularnewline
87 & 0.624499117409817 & 0.751001765180365 & 0.375500882590183 \tabularnewline
88 & 0.582921059938542 & 0.834157880122916 & 0.417078940061458 \tabularnewline
89 & 0.556291641992313 & 0.887416716015374 & 0.443708358007687 \tabularnewline
90 & 0.522822469341873 & 0.954355061316253 & 0.477177530658127 \tabularnewline
91 & 0.514490591112716 & 0.971018817774567 & 0.485509408887284 \tabularnewline
92 & 0.471397719437036 & 0.942795438874072 & 0.528602280562964 \tabularnewline
93 & 0.429144449680797 & 0.858288899361593 & 0.570855550319203 \tabularnewline
94 & 0.384695242209771 & 0.769390484419542 & 0.615304757790229 \tabularnewline
95 & 0.405701590341569 & 0.811403180683139 & 0.594298409658431 \tabularnewline
96 & 0.368585011008592 & 0.737170022017185 & 0.631414988991408 \tabularnewline
97 & 0.327275272401177 & 0.654550544802355 & 0.672724727598823 \tabularnewline
98 & 0.322446760366099 & 0.644893520732199 & 0.677553239633901 \tabularnewline
99 & 0.280925080377452 & 0.561850160754903 & 0.719074919622549 \tabularnewline
100 & 0.246073658471355 & 0.492147316942709 & 0.753926341528645 \tabularnewline
101 & 0.22477650300379 & 0.449553006007581 & 0.77522349699621 \tabularnewline
102 & 0.207101922517808 & 0.414203845035616 & 0.792898077482192 \tabularnewline
103 & 0.254636297522027 & 0.509272595044053 & 0.745363702477973 \tabularnewline
104 & 0.217677483317841 & 0.435354966635683 & 0.782322516682159 \tabularnewline
105 & 0.213933558745873 & 0.427867117491745 & 0.786066441254127 \tabularnewline
106 & 0.225037073020149 & 0.450074146040298 & 0.774962926979851 \tabularnewline
107 & 0.20335543142485 & 0.4067108628497 & 0.79664456857515 \tabularnewline
108 & 0.181884409319567 & 0.363768818639135 & 0.818115590680432 \tabularnewline
109 & 0.17550966358474 & 0.351019327169481 & 0.82449033641526 \tabularnewline
110 & 0.167997997813996 & 0.335995995627993 & 0.832002002186004 \tabularnewline
111 & 0.158531864049594 & 0.317063728099188 & 0.841468135950406 \tabularnewline
112 & 0.138767115186741 & 0.277534230373481 & 0.861232884813259 \tabularnewline
113 & 0.186205433739751 & 0.372410867479502 & 0.813794566260249 \tabularnewline
114 & 0.162966239806416 & 0.325932479612832 & 0.837033760193584 \tabularnewline
115 & 0.183207973952819 & 0.366415947905637 & 0.816792026047181 \tabularnewline
116 & 0.185888594511016 & 0.371777189022031 & 0.814111405488984 \tabularnewline
117 & 0.16187209598614 & 0.32374419197228 & 0.83812790401386 \tabularnewline
118 & 0.159735249072618 & 0.319470498145235 & 0.840264750927382 \tabularnewline
119 & 0.149202803350535 & 0.298405606701069 & 0.850797196649465 \tabularnewline
120 & 0.153718894365268 & 0.307437788730535 & 0.846281105634732 \tabularnewline
121 & 0.130078845622092 & 0.260157691244184 & 0.869921154377908 \tabularnewline
122 & 0.114102112895761 & 0.228204225791522 & 0.885897887104239 \tabularnewline
123 & 0.11296872111018 & 0.22593744222036 & 0.88703127888982 \tabularnewline
124 & 0.0900781233730664 & 0.180156246746133 & 0.909921876626934 \tabularnewline
125 & 0.0710260291191917 & 0.142052058238383 & 0.928973970880808 \tabularnewline
126 & 0.0542068015833608 & 0.108413603166722 & 0.945793198416639 \tabularnewline
127 & 0.0404715897155535 & 0.080943179431107 & 0.959528410284446 \tabularnewline
128 & 0.0428157085357889 & 0.0856314170715779 & 0.957184291464211 \tabularnewline
129 & 0.0407607294083141 & 0.0815214588166283 & 0.959239270591686 \tabularnewline
130 & 0.0408867095505914 & 0.0817734191011828 & 0.959113290449409 \tabularnewline
131 & 0.0449242073017292 & 0.0898484146034585 & 0.955075792698271 \tabularnewline
132 & 0.049597574361123 & 0.0991951487222461 & 0.950402425638877 \tabularnewline
133 & 0.101823902412093 & 0.203647804824185 & 0.898176097587907 \tabularnewline
134 & 0.101973621430226 & 0.203947242860453 & 0.898026378569774 \tabularnewline
135 & 0.080287831405518 & 0.160575662811036 & 0.919712168594482 \tabularnewline
136 & 0.0596023660219705 & 0.119204732043941 & 0.94039763397803 \tabularnewline
137 & 0.0499649885416833 & 0.0999299770833666 & 0.950035011458317 \tabularnewline
138 & 0.0486984125039696 & 0.0973968250079392 & 0.95130158749603 \tabularnewline
139 & 0.043269019353792 & 0.086538038707584 & 0.956730980646208 \tabularnewline
140 & 0.0298798446678801 & 0.0597596893357603 & 0.97012015533212 \tabularnewline
141 & 0.399383787692847 & 0.798767575385694 & 0.600616212307153 \tabularnewline
142 & 0.396985730585758 & 0.793971461171516 & 0.603014269414242 \tabularnewline
143 & 0.355580632041936 & 0.711161264083873 & 0.644419367958064 \tabularnewline
144 & 0.325351603841235 & 0.650703207682471 & 0.674648396158765 \tabularnewline
145 & 0.28306253363319 & 0.566125067266379 & 0.71693746636681 \tabularnewline
146 & 0.264358854038081 & 0.528717708076162 & 0.735641145961919 \tabularnewline
147 & 0.402807113143949 & 0.805614226287898 & 0.597192886856051 \tabularnewline
148 & 0.734333227141066 & 0.531333545717868 & 0.265666772858934 \tabularnewline
149 & 0.69094244418744 & 0.618115111625119 & 0.30905755581256 \tabularnewline
150 & 0.563459322401931 & 0.873081355196138 & 0.436540677598069 \tabularnewline
151 & 0.78909692629115 & 0.4218061474177 & 0.21090307370885 \tabularnewline
152 & 0.838267510606032 & 0.323464978787936 & 0.161732489393968 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.857747556687216[/C][C]0.284504886625568[/C][C]0.142252443312784[/C][/ROW]
[ROW][C]11[/C][C]0.773021752430031[/C][C]0.453956495139938[/C][C]0.226978247569969[/C][/ROW]
[ROW][C]12[/C][C]0.782705077882017[/C][C]0.434589844235966[/C][C]0.217294922117983[/C][/ROW]
[ROW][C]13[/C][C]0.776778003023731[/C][C]0.446443993952539[/C][C]0.223221996976269[/C][/ROW]
[ROW][C]14[/C][C]0.709104622576647[/C][C]0.581790754846707[/C][C]0.290895377423353[/C][/ROW]
[ROW][C]15[/C][C]0.637545063798093[/C][C]0.724909872403815[/C][C]0.362454936201907[/C][/ROW]
[ROW][C]16[/C][C]0.65844658465271[/C][C]0.68310683069458[/C][C]0.34155341534729[/C][/ROW]
[ROW][C]17[/C][C]0.601966882656398[/C][C]0.796066234687204[/C][C]0.398033117343602[/C][/ROW]
[ROW][C]18[/C][C]0.837231559562617[/C][C]0.325536880874766[/C][C]0.162768440437383[/C][/ROW]
[ROW][C]19[/C][C]0.806272922944117[/C][C]0.387454154111766[/C][C]0.193727077055883[/C][/ROW]
[ROW][C]20[/C][C]0.757986495472222[/C][C]0.484027009055557[/C][C]0.242013504527779[/C][/ROW]
[ROW][C]21[/C][C]0.691999837452346[/C][C]0.616000325095308[/C][C]0.308000162547654[/C][/ROW]
[ROW][C]22[/C][C]0.646654390267744[/C][C]0.706691219464512[/C][C]0.353345609732256[/C][/ROW]
[ROW][C]23[/C][C]0.607594934234403[/C][C]0.784810131531194[/C][C]0.392405065765597[/C][/ROW]
[ROW][C]24[/C][C]0.587387597755108[/C][C]0.825224804489784[/C][C]0.412612402244892[/C][/ROW]
[ROW][C]25[/C][C]0.533587015547478[/C][C]0.932825968905044[/C][C]0.466412984452522[/C][/ROW]
[ROW][C]26[/C][C]0.478687295925333[/C][C]0.957374591850666[/C][C]0.521312704074667[/C][/ROW]
[ROW][C]27[/C][C]0.428398059812136[/C][C]0.856796119624271[/C][C]0.571601940187864[/C][/ROW]
[ROW][C]28[/C][C]0.477900821823491[/C][C]0.955801643646982[/C][C]0.522099178176509[/C][/ROW]
[ROW][C]29[/C][C]0.420572181059136[/C][C]0.841144362118273[/C][C]0.579427818940864[/C][/ROW]
[ROW][C]30[/C][C]0.43612976415321[/C][C]0.872259528306419[/C][C]0.56387023584679[/C][/ROW]
[ROW][C]31[/C][C]0.384315175608889[/C][C]0.768630351217778[/C][C]0.615684824391111[/C][/ROW]
[ROW][C]32[/C][C]0.389375019331954[/C][C]0.778750038663908[/C][C]0.610624980668046[/C][/ROW]
[ROW][C]33[/C][C]0.380174030837779[/C][C]0.760348061675558[/C][C]0.619825969162221[/C][/ROW]
[ROW][C]34[/C][C]0.323784644833626[/C][C]0.647569289667253[/C][C]0.676215355166374[/C][/ROW]
[ROW][C]35[/C][C]0.309558324928077[/C][C]0.619116649856155[/C][C]0.690441675071923[/C][/ROW]
[ROW][C]36[/C][C]0.823653422023686[/C][C]0.352693155952629[/C][C]0.176346577976314[/C][/ROW]
[ROW][C]37[/C][C]0.804686212988267[/C][C]0.390627574023465[/C][C]0.195313787011733[/C][/ROW]
[ROW][C]38[/C][C]0.788848589022081[/C][C]0.422302821955838[/C][C]0.211151410977919[/C][/ROW]
[ROW][C]39[/C][C]0.824197210561024[/C][C]0.351605578877952[/C][C]0.175802789438976[/C][/ROW]
[ROW][C]40[/C][C]0.809949988380736[/C][C]0.380100023238529[/C][C]0.190050011619264[/C][/ROW]
[ROW][C]41[/C][C]0.782898081181445[/C][C]0.43420383763711[/C][C]0.217101918818555[/C][/ROW]
[ROW][C]42[/C][C]0.76020268969488[/C][C]0.479594620610241[/C][C]0.23979731030512[/C][/ROW]
[ROW][C]43[/C][C]0.775381463332244[/C][C]0.449237073335513[/C][C]0.224618536667756[/C][/ROW]
[ROW][C]44[/C][C]0.735836639132052[/C][C]0.528326721735895[/C][C]0.264163360867948[/C][/ROW]
[ROW][C]45[/C][C]0.702563558898493[/C][C]0.594872882203014[/C][C]0.297436441101507[/C][/ROW]
[ROW][C]46[/C][C]0.870830221347815[/C][C]0.258339557304369[/C][C]0.129169778652185[/C][/ROW]
[ROW][C]47[/C][C]0.880849363037645[/C][C]0.23830127392471[/C][C]0.119150636962355[/C][/ROW]
[ROW][C]48[/C][C]0.858433089792942[/C][C]0.283133820414116[/C][C]0.141566910207058[/C][/ROW]
[ROW][C]49[/C][C]0.843518785388467[/C][C]0.312962429223065[/C][C]0.156481214611533[/C][/ROW]
[ROW][C]50[/C][C]0.836453036249857[/C][C]0.327093927500285[/C][C]0.163546963750143[/C][/ROW]
[ROW][C]51[/C][C]0.807005484542481[/C][C]0.385989030915038[/C][C]0.192994515457519[/C][/ROW]
[ROW][C]52[/C][C]0.77432687832383[/C][C]0.451346243352341[/C][C]0.22567312167617[/C][/ROW]
[ROW][C]53[/C][C]0.790674926268459[/C][C]0.418650147463082[/C][C]0.209325073731541[/C][/ROW]
[ROW][C]54[/C][C]0.763578384613518[/C][C]0.472843230772964[/C][C]0.236421615386482[/C][/ROW]
[ROW][C]55[/C][C]0.785938316604312[/C][C]0.428123366791376[/C][C]0.214061683395688[/C][/ROW]
[ROW][C]56[/C][C]0.776687144477278[/C][C]0.446625711045444[/C][C]0.223312855522722[/C][/ROW]
[ROW][C]57[/C][C]0.740452585904065[/C][C]0.519094828191871[/C][C]0.259547414095935[/C][/ROW]
[ROW][C]58[/C][C]0.728890220167366[/C][C]0.542219559665267[/C][C]0.271109779832634[/C][/ROW]
[ROW][C]59[/C][C]0.693443830834739[/C][C]0.613112338330523[/C][C]0.306556169165261[/C][/ROW]
[ROW][C]60[/C][C]0.702203320459039[/C][C]0.595593359081921[/C][C]0.297796679540961[/C][/ROW]
[ROW][C]61[/C][C]0.667166983928165[/C][C]0.66566603214367[/C][C]0.332833016071835[/C][/ROW]
[ROW][C]62[/C][C]0.626111682198074[/C][C]0.747776635603852[/C][C]0.373888317801926[/C][/ROW]
[ROW][C]63[/C][C]0.58591269160305[/C][C]0.828174616793901[/C][C]0.41408730839695[/C][/ROW]
[ROW][C]64[/C][C]0.543346292962134[/C][C]0.913307414075731[/C][C]0.456653707037866[/C][/ROW]
[ROW][C]65[/C][C]0.505240189820032[/C][C]0.989519620359935[/C][C]0.494759810179968[/C][/ROW]
[ROW][C]66[/C][C]0.481500610629971[/C][C]0.963001221259942[/C][C]0.518499389370029[/C][/ROW]
[ROW][C]67[/C][C]0.477240605696926[/C][C]0.954481211393851[/C][C]0.522759394303074[/C][/ROW]
[ROW][C]68[/C][C]0.600768188886643[/C][C]0.798463622226714[/C][C]0.399231811113357[/C][/ROW]
[ROW][C]69[/C][C]0.732213376478992[/C][C]0.535573247042016[/C][C]0.267786623521008[/C][/ROW]
[ROW][C]70[/C][C]0.699003217243509[/C][C]0.601993565512981[/C][C]0.300996782756491[/C][/ROW]
[ROW][C]71[/C][C]0.804158753871595[/C][C]0.39168249225681[/C][C]0.195841246128405[/C][/ROW]
[ROW][C]72[/C][C]0.774543717632847[/C][C]0.450912564734307[/C][C]0.225456282367153[/C][/ROW]
[ROW][C]73[/C][C]0.770316826515384[/C][C]0.459366346969233[/C][C]0.229683173484616[/C][/ROW]
[ROW][C]74[/C][C]0.748092345157411[/C][C]0.503815309685179[/C][C]0.25190765484259[/C][/ROW]
[ROW][C]75[/C][C]0.710997413595632[/C][C]0.578005172808737[/C][C]0.289002586404368[/C][/ROW]
[ROW][C]76[/C][C]0.766360451767016[/C][C]0.467279096465969[/C][C]0.233639548232984[/C][/ROW]
[ROW][C]77[/C][C]0.730673420432182[/C][C]0.538653159135636[/C][C]0.269326579567818[/C][/ROW]
[ROW][C]78[/C][C]0.710099717661241[/C][C]0.579800564677517[/C][C]0.289900282338759[/C][/ROW]
[ROW][C]79[/C][C]0.714882981048365[/C][C]0.57023403790327[/C][C]0.285117018951635[/C][/ROW]
[ROW][C]80[/C][C]0.675681759825132[/C][C]0.648636480349736[/C][C]0.324318240174868[/C][/ROW]
[ROW][C]81[/C][C]0.639293548810225[/C][C]0.72141290237955[/C][C]0.360706451189775[/C][/ROW]
[ROW][C]82[/C][C]0.780907592699777[/C][C]0.438184814600446[/C][C]0.219092407300223[/C][/ROW]
[ROW][C]83[/C][C]0.746029616679344[/C][C]0.507940766641313[/C][C]0.253970383320656[/C][/ROW]
[ROW][C]84[/C][C]0.719443589461705[/C][C]0.56111282107659[/C][C]0.280556410538295[/C][/ROW]
[ROW][C]85[/C][C]0.679637498281659[/C][C]0.640725003436682[/C][C]0.320362501718341[/C][/ROW]
[ROW][C]86[/C][C]0.667007690648342[/C][C]0.665984618703315[/C][C]0.332992309351658[/C][/ROW]
[ROW][C]87[/C][C]0.624499117409817[/C][C]0.751001765180365[/C][C]0.375500882590183[/C][/ROW]
[ROW][C]88[/C][C]0.582921059938542[/C][C]0.834157880122916[/C][C]0.417078940061458[/C][/ROW]
[ROW][C]89[/C][C]0.556291641992313[/C][C]0.887416716015374[/C][C]0.443708358007687[/C][/ROW]
[ROW][C]90[/C][C]0.522822469341873[/C][C]0.954355061316253[/C][C]0.477177530658127[/C][/ROW]
[ROW][C]91[/C][C]0.514490591112716[/C][C]0.971018817774567[/C][C]0.485509408887284[/C][/ROW]
[ROW][C]92[/C][C]0.471397719437036[/C][C]0.942795438874072[/C][C]0.528602280562964[/C][/ROW]
[ROW][C]93[/C][C]0.429144449680797[/C][C]0.858288899361593[/C][C]0.570855550319203[/C][/ROW]
[ROW][C]94[/C][C]0.384695242209771[/C][C]0.769390484419542[/C][C]0.615304757790229[/C][/ROW]
[ROW][C]95[/C][C]0.405701590341569[/C][C]0.811403180683139[/C][C]0.594298409658431[/C][/ROW]
[ROW][C]96[/C][C]0.368585011008592[/C][C]0.737170022017185[/C][C]0.631414988991408[/C][/ROW]
[ROW][C]97[/C][C]0.327275272401177[/C][C]0.654550544802355[/C][C]0.672724727598823[/C][/ROW]
[ROW][C]98[/C][C]0.322446760366099[/C][C]0.644893520732199[/C][C]0.677553239633901[/C][/ROW]
[ROW][C]99[/C][C]0.280925080377452[/C][C]0.561850160754903[/C][C]0.719074919622549[/C][/ROW]
[ROW][C]100[/C][C]0.246073658471355[/C][C]0.492147316942709[/C][C]0.753926341528645[/C][/ROW]
[ROW][C]101[/C][C]0.22477650300379[/C][C]0.449553006007581[/C][C]0.77522349699621[/C][/ROW]
[ROW][C]102[/C][C]0.207101922517808[/C][C]0.414203845035616[/C][C]0.792898077482192[/C][/ROW]
[ROW][C]103[/C][C]0.254636297522027[/C][C]0.509272595044053[/C][C]0.745363702477973[/C][/ROW]
[ROW][C]104[/C][C]0.217677483317841[/C][C]0.435354966635683[/C][C]0.782322516682159[/C][/ROW]
[ROW][C]105[/C][C]0.213933558745873[/C][C]0.427867117491745[/C][C]0.786066441254127[/C][/ROW]
[ROW][C]106[/C][C]0.225037073020149[/C][C]0.450074146040298[/C][C]0.774962926979851[/C][/ROW]
[ROW][C]107[/C][C]0.20335543142485[/C][C]0.4067108628497[/C][C]0.79664456857515[/C][/ROW]
[ROW][C]108[/C][C]0.181884409319567[/C][C]0.363768818639135[/C][C]0.818115590680432[/C][/ROW]
[ROW][C]109[/C][C]0.17550966358474[/C][C]0.351019327169481[/C][C]0.82449033641526[/C][/ROW]
[ROW][C]110[/C][C]0.167997997813996[/C][C]0.335995995627993[/C][C]0.832002002186004[/C][/ROW]
[ROW][C]111[/C][C]0.158531864049594[/C][C]0.317063728099188[/C][C]0.841468135950406[/C][/ROW]
[ROW][C]112[/C][C]0.138767115186741[/C][C]0.277534230373481[/C][C]0.861232884813259[/C][/ROW]
[ROW][C]113[/C][C]0.186205433739751[/C][C]0.372410867479502[/C][C]0.813794566260249[/C][/ROW]
[ROW][C]114[/C][C]0.162966239806416[/C][C]0.325932479612832[/C][C]0.837033760193584[/C][/ROW]
[ROW][C]115[/C][C]0.183207973952819[/C][C]0.366415947905637[/C][C]0.816792026047181[/C][/ROW]
[ROW][C]116[/C][C]0.185888594511016[/C][C]0.371777189022031[/C][C]0.814111405488984[/C][/ROW]
[ROW][C]117[/C][C]0.16187209598614[/C][C]0.32374419197228[/C][C]0.83812790401386[/C][/ROW]
[ROW][C]118[/C][C]0.159735249072618[/C][C]0.319470498145235[/C][C]0.840264750927382[/C][/ROW]
[ROW][C]119[/C][C]0.149202803350535[/C][C]0.298405606701069[/C][C]0.850797196649465[/C][/ROW]
[ROW][C]120[/C][C]0.153718894365268[/C][C]0.307437788730535[/C][C]0.846281105634732[/C][/ROW]
[ROW][C]121[/C][C]0.130078845622092[/C][C]0.260157691244184[/C][C]0.869921154377908[/C][/ROW]
[ROW][C]122[/C][C]0.114102112895761[/C][C]0.228204225791522[/C][C]0.885897887104239[/C][/ROW]
[ROW][C]123[/C][C]0.11296872111018[/C][C]0.22593744222036[/C][C]0.88703127888982[/C][/ROW]
[ROW][C]124[/C][C]0.0900781233730664[/C][C]0.180156246746133[/C][C]0.909921876626934[/C][/ROW]
[ROW][C]125[/C][C]0.0710260291191917[/C][C]0.142052058238383[/C][C]0.928973970880808[/C][/ROW]
[ROW][C]126[/C][C]0.0542068015833608[/C][C]0.108413603166722[/C][C]0.945793198416639[/C][/ROW]
[ROW][C]127[/C][C]0.0404715897155535[/C][C]0.080943179431107[/C][C]0.959528410284446[/C][/ROW]
[ROW][C]128[/C][C]0.0428157085357889[/C][C]0.0856314170715779[/C][C]0.957184291464211[/C][/ROW]
[ROW][C]129[/C][C]0.0407607294083141[/C][C]0.0815214588166283[/C][C]0.959239270591686[/C][/ROW]
[ROW][C]130[/C][C]0.0408867095505914[/C][C]0.0817734191011828[/C][C]0.959113290449409[/C][/ROW]
[ROW][C]131[/C][C]0.0449242073017292[/C][C]0.0898484146034585[/C][C]0.955075792698271[/C][/ROW]
[ROW][C]132[/C][C]0.049597574361123[/C][C]0.0991951487222461[/C][C]0.950402425638877[/C][/ROW]
[ROW][C]133[/C][C]0.101823902412093[/C][C]0.203647804824185[/C][C]0.898176097587907[/C][/ROW]
[ROW][C]134[/C][C]0.101973621430226[/C][C]0.203947242860453[/C][C]0.898026378569774[/C][/ROW]
[ROW][C]135[/C][C]0.080287831405518[/C][C]0.160575662811036[/C][C]0.919712168594482[/C][/ROW]
[ROW][C]136[/C][C]0.0596023660219705[/C][C]0.119204732043941[/C][C]0.94039763397803[/C][/ROW]
[ROW][C]137[/C][C]0.0499649885416833[/C][C]0.0999299770833666[/C][C]0.950035011458317[/C][/ROW]
[ROW][C]138[/C][C]0.0486984125039696[/C][C]0.0973968250079392[/C][C]0.95130158749603[/C][/ROW]
[ROW][C]139[/C][C]0.043269019353792[/C][C]0.086538038707584[/C][C]0.956730980646208[/C][/ROW]
[ROW][C]140[/C][C]0.0298798446678801[/C][C]0.0597596893357603[/C][C]0.97012015533212[/C][/ROW]
[ROW][C]141[/C][C]0.399383787692847[/C][C]0.798767575385694[/C][C]0.600616212307153[/C][/ROW]
[ROW][C]142[/C][C]0.396985730585758[/C][C]0.793971461171516[/C][C]0.603014269414242[/C][/ROW]
[ROW][C]143[/C][C]0.355580632041936[/C][C]0.711161264083873[/C][C]0.644419367958064[/C][/ROW]
[ROW][C]144[/C][C]0.325351603841235[/C][C]0.650703207682471[/C][C]0.674648396158765[/C][/ROW]
[ROW][C]145[/C][C]0.28306253363319[/C][C]0.566125067266379[/C][C]0.71693746636681[/C][/ROW]
[ROW][C]146[/C][C]0.264358854038081[/C][C]0.528717708076162[/C][C]0.735641145961919[/C][/ROW]
[ROW][C]147[/C][C]0.402807113143949[/C][C]0.805614226287898[/C][C]0.597192886856051[/C][/ROW]
[ROW][C]148[/C][C]0.734333227141066[/C][C]0.531333545717868[/C][C]0.265666772858934[/C][/ROW]
[ROW][C]149[/C][C]0.69094244418744[/C][C]0.618115111625119[/C][C]0.30905755581256[/C][/ROW]
[ROW][C]150[/C][C]0.563459322401931[/C][C]0.873081355196138[/C][C]0.436540677598069[/C][/ROW]
[ROW][C]151[/C][C]0.78909692629115[/C][C]0.4218061474177[/C][C]0.21090307370885[/C][/ROW]
[ROW][C]152[/C][C]0.838267510606032[/C][C]0.323464978787936[/C][C]0.161732489393968[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8577475566872160.2845048866255680.142252443312784
110.7730217524300310.4539564951399380.226978247569969
120.7827050778820170.4345898442359660.217294922117983
130.7767780030237310.4464439939525390.223221996976269
140.7091046225766470.5817907548467070.290895377423353
150.6375450637980930.7249098724038150.362454936201907
160.658446584652710.683106830694580.34155341534729
170.6019668826563980.7960662346872040.398033117343602
180.8372315595626170.3255368808747660.162768440437383
190.8062729229441170.3874541541117660.193727077055883
200.7579864954722220.4840270090555570.242013504527779
210.6919998374523460.6160003250953080.308000162547654
220.6466543902677440.7066912194645120.353345609732256
230.6075949342344030.7848101315311940.392405065765597
240.5873875977551080.8252248044897840.412612402244892
250.5335870155474780.9328259689050440.466412984452522
260.4786872959253330.9573745918506660.521312704074667
270.4283980598121360.8567961196242710.571601940187864
280.4779008218234910.9558016436469820.522099178176509
290.4205721810591360.8411443621182730.579427818940864
300.436129764153210.8722595283064190.56387023584679
310.3843151756088890.7686303512177780.615684824391111
320.3893750193319540.7787500386639080.610624980668046
330.3801740308377790.7603480616755580.619825969162221
340.3237846448336260.6475692896672530.676215355166374
350.3095583249280770.6191166498561550.690441675071923
360.8236534220236860.3526931559526290.176346577976314
370.8046862129882670.3906275740234650.195313787011733
380.7888485890220810.4223028219558380.211151410977919
390.8241972105610240.3516055788779520.175802789438976
400.8099499883807360.3801000232385290.190050011619264
410.7828980811814450.434203837637110.217101918818555
420.760202689694880.4795946206102410.23979731030512
430.7753814633322440.4492370733355130.224618536667756
440.7358366391320520.5283267217358950.264163360867948
450.7025635588984930.5948728822030140.297436441101507
460.8708302213478150.2583395573043690.129169778652185
470.8808493630376450.238301273924710.119150636962355
480.8584330897929420.2831338204141160.141566910207058
490.8435187853884670.3129624292230650.156481214611533
500.8364530362498570.3270939275002850.163546963750143
510.8070054845424810.3859890309150380.192994515457519
520.774326878323830.4513462433523410.22567312167617
530.7906749262684590.4186501474630820.209325073731541
540.7635783846135180.4728432307729640.236421615386482
550.7859383166043120.4281233667913760.214061683395688
560.7766871444772780.4466257110454440.223312855522722
570.7404525859040650.5190948281918710.259547414095935
580.7288902201673660.5422195596652670.271109779832634
590.6934438308347390.6131123383305230.306556169165261
600.7022033204590390.5955933590819210.297796679540961
610.6671669839281650.665666032143670.332833016071835
620.6261116821980740.7477766356038520.373888317801926
630.585912691603050.8281746167939010.41408730839695
640.5433462929621340.9133074140757310.456653707037866
650.5052401898200320.9895196203599350.494759810179968
660.4815006106299710.9630012212599420.518499389370029
670.4772406056969260.9544812113938510.522759394303074
680.6007681888866430.7984636222267140.399231811113357
690.7322133764789920.5355732470420160.267786623521008
700.6990032172435090.6019935655129810.300996782756491
710.8041587538715950.391682492256810.195841246128405
720.7745437176328470.4509125647343070.225456282367153
730.7703168265153840.4593663469692330.229683173484616
740.7480923451574110.5038153096851790.25190765484259
750.7109974135956320.5780051728087370.289002586404368
760.7663604517670160.4672790964659690.233639548232984
770.7306734204321820.5386531591356360.269326579567818
780.7100997176612410.5798005646775170.289900282338759
790.7148829810483650.570234037903270.285117018951635
800.6756817598251320.6486364803497360.324318240174868
810.6392935488102250.721412902379550.360706451189775
820.7809075926997770.4381848146004460.219092407300223
830.7460296166793440.5079407666413130.253970383320656
840.7194435894617050.561112821076590.280556410538295
850.6796374982816590.6407250034366820.320362501718341
860.6670076906483420.6659846187033150.332992309351658
870.6244991174098170.7510017651803650.375500882590183
880.5829210599385420.8341578801229160.417078940061458
890.5562916419923130.8874167160153740.443708358007687
900.5228224693418730.9543550613162530.477177530658127
910.5144905911127160.9710188177745670.485509408887284
920.4713977194370360.9427954388740720.528602280562964
930.4291444496807970.8582888993615930.570855550319203
940.3846952422097710.7693904844195420.615304757790229
950.4057015903415690.8114031806831390.594298409658431
960.3685850110085920.7371700220171850.631414988991408
970.3272752724011770.6545505448023550.672724727598823
980.3224467603660990.6448935207321990.677553239633901
990.2809250803774520.5618501607549030.719074919622549
1000.2460736584713550.4921473169427090.753926341528645
1010.224776503003790.4495530060075810.77522349699621
1020.2071019225178080.4142038450356160.792898077482192
1030.2546362975220270.5092725950440530.745363702477973
1040.2176774833178410.4353549666356830.782322516682159
1050.2139335587458730.4278671174917450.786066441254127
1060.2250370730201490.4500741460402980.774962926979851
1070.203355431424850.40671086284970.79664456857515
1080.1818844093195670.3637688186391350.818115590680432
1090.175509663584740.3510193271694810.82449033641526
1100.1679979978139960.3359959956279930.832002002186004
1110.1585318640495940.3170637280991880.841468135950406
1120.1387671151867410.2775342303734810.861232884813259
1130.1862054337397510.3724108674795020.813794566260249
1140.1629662398064160.3259324796128320.837033760193584
1150.1832079739528190.3664159479056370.816792026047181
1160.1858885945110160.3717771890220310.814111405488984
1170.161872095986140.323744191972280.83812790401386
1180.1597352490726180.3194704981452350.840264750927382
1190.1492028033505350.2984056067010690.850797196649465
1200.1537188943652680.3074377887305350.846281105634732
1210.1300788456220920.2601576912441840.869921154377908
1220.1141021128957610.2282042257915220.885897887104239
1230.112968721110180.225937442220360.88703127888982
1240.09007812337306640.1801562467461330.909921876626934
1250.07102602911919170.1420520582383830.928973970880808
1260.05420680158336080.1084136031667220.945793198416639
1270.04047158971555350.0809431794311070.959528410284446
1280.04281570853578890.08563141707157790.957184291464211
1290.04076072940831410.08152145881662830.959239270591686
1300.04088670955059140.08177341910118280.959113290449409
1310.04492420730172920.08984841460345850.955075792698271
1320.0495975743611230.09919514872224610.950402425638877
1330.1018239024120930.2036478048241850.898176097587907
1340.1019736214302260.2039472428604530.898026378569774
1350.0802878314055180.1605756628110360.919712168594482
1360.05960236602197050.1192047320439410.94039763397803
1370.04996498854168330.09992997708336660.950035011458317
1380.04869841250396960.09739682500793920.95130158749603
1390.0432690193537920.0865380387075840.956730980646208
1400.02987984466788010.05975968933576030.97012015533212
1410.3993837876928470.7987675753856940.600616212307153
1420.3969857305857580.7939714611715160.603014269414242
1430.3555806320419360.7111612640838730.644419367958064
1440.3253516038412350.6507032076824710.674648396158765
1450.283062533633190.5661250672663790.71693746636681
1460.2643588540380810.5287177080761620.735641145961919
1470.4028071131439490.8056142262878980.597192886856051
1480.7343332271410660.5313335457178680.265666772858934
1490.690942444187440.6181151116251190.30905755581256
1500.5634593224019310.8730813551961380.436540677598069
1510.789096926291150.42180614741770.21090307370885
1520.8382675106060320.3234649787879360.161732489393968






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level100.0699300699300699OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 10 & 0.0699300699300699 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147195&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0699300699300699[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147195&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147195&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 level00OK
5% type I error level00OK
10% type I error level100.0699300699300699OK


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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}